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Managing New Orleans
Flood Risk in an Uncertain
Future Using Non-Structural
Risk Mitigation
Jordan R. Fischbach
This document was submitted as a dissertation in March 2010 in partial
fulfillment of the requirements of the doctoral degree in public policy analysis
at the Pardee RAND Graduate School. The faculty committee that supervised
and approved the dissertation consisted of Steven W. Popper (Chair), David G.
Groves, and Richard Hillestad.
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Abstract
Over four years after Hurricane Katrina devastated the Gulf Coast, the long-term future of the City of New
Orleans remains uncertain. This dissertation addresses one of New Orleans’ most critical challenges: how
to make the city more resilient and less vulnerable to future flood damages. Despite recent upgrades to
the protection system surrounding the city designed to protect against floods with a 1-in-100 (1%) annual
chance of occurrence, New Orleans remains vulnerable to lower-frequency, high-damage events. In addition,
uncertain factors that influence flood risk, including coastal land loss and subsidence, rising sea levels, and
population recovery and growth, may lead to increasing risk over time. Current proposals for risk reduction
in New Orleans and South Louisiana, however, have not fully accounted for these key uncertain drivers.
Rather than focus on additional large-scale structural infrastructure investments, this dissertation considers proposals to augment the existing protection system with “non-structural" risk mitigation programs.
Non-structural risk mitigation includes incentives for elevating existing or new structures, revised building codes, incentives for relocation to lower risk areas, and land use restrictions designed to curtail future
growth in the floodplain. This research estimates the risk reduction benefits and implementation costs of nonstructural risk mitigation strategies focused on single-family homes in New Orleans. I draw from existing
risk models to develop a low-resolution scenario generator, NOLArisk, designed to produce first-order estimates of property risk from 2011-2060 across a range of uncertain future scenarios. I then apply exploratory
modeling and Robust Decision Making (RDM) methods to a) suggest strategies that balance risk reduction
and implementation costs across many or most plausible futures, and b) identify scenarios in which current
alternatives yield negative net economic benefits or excessive levels of residual risk.
Analysis results reveal that flood risk estimates vary considerably by scenario and across different locations within the city. Non-structural risk mitigation strategies appear to provide cost-effective risk reduction
iii
in high-risk neighborhoods and help to hedge against futures in which damages from 1% annual recurrence
(or more frequent) events are greater than expected. However, substantial residual risk remains from lower
frequency (1-in-400 or 1-in-1,000 annual chance) events even with large investments in non-structural risk
mitigation. Scenarios that yield excessive residual damage typically include high rates of coastal land loss
outside of the protection system and levees that degrade over time, suggesting that wetlands restoration and
continued investments in structural protection remain critical to successful New Orleans flood risk management.
iv
Acknowledgements
I would first like to thank my committee—Steven Popper, David Groves, and Dick Hillestad—for their insights, advice, and faith in my ability to complete what turned out to be a substantial undertaking. Mark
Davis, Director of the Tulane Institute on Water Resources Law and Policy, provided helpful comments that
greatly improved the narrative. I conducted a series of informal interviews at the beginning of the research
process, and would like to thank those individuals I met with who took the time to help me understand the
complex and rapidly changing post-Katrina planning environment in New Orleans.
My sincere thanks to Keven Lovetro at the U.S. Army Corps of Engineers New Orleans District for sharing a version of the LACPR economics database as soon as the research was finalized. Allison Plyer at the
Greater New Orleans Community Data Center also shared post-Katrina repopulation data that was critical to
R
system
this research. My thanks also to Evolving Logic for the use of the Computer Assisted Reasoning
software (CARs) and the Analytica software harness. I would also like to acknowledge the assistance provided by the staff of the RAND Gulf States Policy Institute, and am especially grateful for the help and support
I received from Sally Sleeper and Melissa Flournoy. My PRGS classmates also helped me greatly through
this process, and I would like to thank Ryan Keefe for many productive (and stress-relieving) whiteboard
sessions.
This dissertation was supported by a generous gift from the Rothenberg Family. I also received an award
from the RGSPI Seminars and Scholars Program, supported by a grant from the Henry Luce Foundation,
and received financial support during the writing process from a Pew Foundation grant provided to RGSPI
and the NOAA Climate Program Office Sector Application Research Program.
Finally, I would like to thank my entire family for their unwavering support, advice and comments, and
the helping hand they lent when challenges arose. In particular, this dissertation would not have been possible
v
without my wife Yael’s guidance, patience, and willingness to put in the extra effort to keep our lives running
while I toiled away on many weekends and late nights. This research is her accomplishment as well, and I
am endlessly grateful for her love and support.
This dissertation is dedicated to my son Jacob, who has never known a daddy not writing a dissertation,
but will soon.
vi
Contents
1
Introduction: Coastal Flooding Continues to Threaten New Orleans
1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Non-structural risk mitigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Challenges to successful risk mitigation planning in New Orleans . . . . . . . . . . . . . . .
1.3.1 Historical cycles of induced development . . . . . . . . . . . . . . . . . . . . . . .
1.3.2 Substantial uncertainty regarding future flood risk . . . . . . . . . . . . . . . . . . .
1.4 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.5 New methods for addressing deep uncertainty can reduce vulnerability of risk reduction plans
1.6 Organization of this dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
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2
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4
5
5
2
Past and Present Flood Risk Management in New Orleans and Along the Lower Mississippi
River
2.1 Introduction: the Katrina paradigm shift . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Historical New Orleans flood protection and development patterns . . . . . . . . . . . . . .
2.2.1 An impossible, inevitable location . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.2 Mississippi River flooding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.3 Storm surge flooding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.4 Floodplain management and moral hazard . . . . . . . . . . . . . . . . . . . . . . .
2.3 Planning for New Orleans post-Katrina . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.1 Katrina investigations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.2 USACE planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.3 State of Louisiana planning efforts . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.4 New Orleans planning efforts . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.5 Independent proposals for New Orleans flood protection . . . . . . . . . . . . . . .
2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
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Addressing Deep Uncertainty in Flood Risk Assessments
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Addressing uncertainty in flood risk analysis . . . . . . . . . . . . .
3.2.1 Flood risk framework . . . . . . . . . . . . . . . . . . . . .
3.2.2 Stationarity . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.3 Deep uncertainty . . . . . . . . . . . . . . . . . . . . . . .
3.3 Deep uncertainty complicates flood risk projections for New Orleans
3.3.1 Climate-related uncertainty . . . . . . . . . . . . . . . . . .
3.3.2 Coastal change and protection system resilience . . . . . . .
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3.4
3.5
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4
3.3.3 Population growth and development patterns . . . . . . . . . . . . . . . . . . . .
Current planning approaches may not fully account for deep uncertainty . . . . . . . . . .
3.4.1 Optimization and the Dutch approach . . . . . . . . . . . . . . . . . . . . . . . .
3.4.2 IPET Risk and Reliability analysis . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.3 LACPR risk analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Robust Decision Making can help to account for deep uncertainty . . . . . . . . . . . . .
3.5.1 Methodology developed to support effective long-term decision making and address
“surprises” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5.2 RDM analysis proceeds through an iterative analysis in coordination with decisionmakers and stakeholders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Applying Robust Decision Making to non-structural risk reduction in New Orleans . . . .
3.6.1 Initial effort from master planner’s perspective . . . . . . . . . . . . . . . . . . .
3.6.2 Scoping the initial policy problem . . . . . . . . . . . . . . . . . . . . . . . . . .
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A Low-Resolution Flood Risk Scenario Generator for Orleans Parish
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.1 A scenario generator supports exploratory modeling and RDM
4.1.2 Organization of this chapter . . . . . . . . . . . . . . . . . .
4.2 Overview of NOLArisk . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.1 Model structure . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.2 Geographic scope . . . . . . . . . . . . . . . . . . . . . . . .
4.2.3 NOLArisk simplifying assumptions . . . . . . . . . . . . . .
4.3 Flood Hazards module . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.2 Input data . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.3 Uncertain drivers . . . . . . . . . . . . . . . . . . . . . . . .
4.3.4 Module relationships . . . . . . . . . . . . . . . . . . . . . .
4.4 Flood Consequences Module . . . . . . . . . . . . . . . . . . . . . .
4.4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4.2 Input data . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4.3 Uncertain drivers . . . . . . . . . . . . . . . . . . . . . . . .
4.4.4 Module relationships . . . . . . . . . . . . . . . . . . . . . .
4.5 Flood Depths and Damage Results modules . . . . . . . . . . . . . .
4.5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.2 Flood Depths . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.3 Damage Results . . . . . . . . . . . . . . . . . . . . . . . . .
4.6 Damage output calculations . . . . . . . . . . . . . . . . . . . . . . .
4.6.1 Discounting approach . . . . . . . . . . . . . . . . . . . . .
4.6.2 Equivalent annual damage at each recurrence interval . . . . .
4.6.3 Expected annual damages . . . . . . . . . . . . . . . . . . .
4.7 Example NOLArisk damage results from selected scenarios . . . . . .
4.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
viii
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5
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7
Non-Structural Risk Mitigation: Tools and Strategies
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.1 From flood “protection” to floodplain management . . . .
5.1.2 Organization of this chapter . . . . . . . . . . . . . . . .
5.2 Non-structural mitigation for single-family homes in New Orleans
5.2.1 Structure enhancements or modifications . . . . . . . . .
5.2.2 Buyouts and land use management . . . . . . . . . . . . .
5.3 Federal flood risk management assistance . . . . . . . . . . . . .
5.3.1 National Flood Insurance Program . . . . . . . . . . . . .
5.3.2 FEMA grant and incentives programs . . . . . . . . . . .
5.4 Hazard mitigation planning in the New Orleans Master Plan . . .
5.5 Quantifying non-structural mitigation strategies . . . . . . . . . .
5.5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . .
5.5.2 Identifying mitigation strategies . . . . . . . . . . . . . .
5.5.3 Mitigation implementation assumptions . . . . . . . . . .
5.5.4 Mitigation uncertainty . . . . . . . . . . . . . . . . . . .
5.5.5 Mitigation implementation relationships in NOLArisk . .
5.5.6 Mitigation costs . . . . . . . . . . . . . . . . . . . . . . .
5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Assessing Risk Mitigation Strategies in an Uncertain Future
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2 Generating plausible future scenarios for strategy comparison . . . . . . . . . . . .
6.2.1 Experimental design . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2.2 Results generation and data management . . . . . . . . . . . . . . . . . .
6.3 New Orleans flood risk with no additional mitigation . . . . . . . . . . . . . . . .
6.3.1 Citywide results project a higher range of future risk than current estimates
6.3.2 Projected damages vary substantially by location . . . . . . . . . . . . . .
6.4 Identifying high performing strategies for each neighborhood . . . . . . . . . . . .
6.4.1 Measuring strategy performance . . . . . . . . . . . . . . . . . . . . . . .
6.4.2 Results for one low-elevation neighborhood . . . . . . . . . . . . . . . . .
6.5 Identifying high-performing “citywide” strategies . . . . . . . . . . . . . . . . . .
6.5.1 Simplifying criteria used to develop citywide strategies . . . . . . . . . . .
6.5.2 Results from citywide strategy screening . . . . . . . . . . . . . . . . . .
6.5.3 Citywide strategy comparison across uncertain scenarios . . . . . . . . . .
6.5.4 Promising citywide strategies . . . . . . . . . . . . . . . . . . . . . . . .
6.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Developing More Robust Strategies for New Orleans
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.1 Organization of this chapter . . . . . . . . . . . . . . . .
7.2 Methods for identifying policy relevant scenarios . . . . . . . . .
7.3 When are elevation-only mitigation approaches vulnerable? . . . .
7.3.1 “Buyout enforcement and increasing risk” scenario . . . .
7.3.2 “Buyout enforcement and higher elevation costs” scenario
7.3.3 Improving strategy robustness . . . . . . . . . . . . . . .
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149
149
150
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157
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7.4
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159
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162
163
164
164
165
165
Conclusions and Recommendations
8.1 Key insights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2 Implications for New Orleans risk mitigation planning . . . . . . . . . . . . . . . . . . . .
8.2.1 Elevation targets exceeding 100-year BFEs can provide cost-effective risk reduction
8.2.2 Buyouts or growth restrictions can be cost-effective, but substantial hurdles remain
8.2.3 Location matters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2.4 Local risk mitigation is not necessarily effective in isolation . . . . . . . . . . . .
8.3 Suggestions for future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.3.1 Detailed flood hazards model . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.3.2 Expanded levers and performance metrics . . . . . . . . . . . . . . . . . . . . . .
8.3.3 Improved and adaptive strategies . . . . . . . . . . . . . . . . . . . . . . . . . . .
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166
166
168
168
168
169
169
169
170
171
171
7.5
7.6
8
What conditions lead to high residual damage with mitigation in place?
7.4.1 “Degrading protection” scenario . . . . . . . . . . . . . . . . .
7.4.2 Improving robustness when facing high-damage scenarios . . .
7.4.3 Discussion and caveats . . . . . . . . . . . . . . . . . . . . . .
Next steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.5.1 Adaptive strategies . . . . . . . . . . . . . . . . . . . . . . . .
7.5.2 Additional RDM iterations . . . . . . . . . . . . . . . . . . . .
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Appendices
A-1
A Strategy results for each neighborhood
A-1
B Additional citywide strategy comparisons
B-1
C Vulnerability analysis using the government discount rate
C-1
x
List of Figures
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
Flood depths in New Orleans on September 3, 2005 . . . . . . . . . . . . . . . . .
Map of New Orleans from the 1720 de la Tour Survey . . . . . . . . . . . . . . . .
Map of the Lower Mississippi River . . . . . . . . . . . . . . . . . . . . . . . . .
Historical (1932-2000) and projected (2000-2050) Louisiana coastal land loss . . .
Hurricanes that made landfall within a 50 mile radius of Orleans Parish, 1851-2008
Growth of New Orleans, by Neighborhood, from 1708-2000 . . . . . . . . . . . .
Flooding caused by storm surge in New Orleans in the 20th century . . . . . . . . .
Location of floodwall and levee breaches caused by Hurricane Katrina . . . . . . .
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10
11
15
17
19
20
21
28
3.1
3.2
3.3
3.4
3.5
Illustration of aleatory versus epistemic uncertainty
New Orleans subsidence map . . . . . . . . . . . .
Economic optimization of flood risk defenses . . .
LACPR planning scenarios . . . . . . . . . . . . .
RDM flowchart . . . . . . . . . . . . . . . . . . .
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42
48
51
55
58
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
NOLArisk model structure . . . . . . . . . . . . . . . . . .
Map of LACPR basins addressed in this study . . . . . . . .
Neighborhoods in Orleans Parish . . . . . . . . . . . . . . .
Mean elevation in Orleans Parish, by census block . . . . . .
NOLArisk and LACPR growth scenarios . . . . . . . . . . .
Example stage-damage curve . . . . . . . . . . . . . . . . .
Damage over time from two scenarios . . . . . . . . . . . .
Map of 100-year damages under “LACPR low/low scenario”
Map of 100-year damages in a higher risk scenario . . . . .
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68
70
71
82
84
88
92
93
94
5.1
5.2
5.3
5.4
5.5
5.6
RDM flowchart . . . . . . . . . . . . . . . . . . . . . . . . .
Risk mitigation strategy threshold schematic . . . . . . . . . .
Histogram of average elevation by census block, Orleans Parish
Strategy thresholds map . . . . . . . . . . . . . . . . . . . . .
NFIP Advisory Base Flood Elevations for New Orleans . . . .
Existing home elevation costs . . . . . . . . . . . . . . . . . .
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100
108
109
111
112
119
6.1
6.2
6.3
6.4
6.5
Annual damages with no new mitigation . . . . . . . . . . . . . . . . . .
Annual damages with no new mitigation, by basin . . . . . . . . . . . . .
Equiv. annual average damage per home with no new mitigation, by basin
Equiv. annual expected damage with no new mitigation, by neighborhood
Equiv. annual expected damage in Milneburg with no new mitigation . . .
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126
127
128
130
133
xi
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6.6
6.7
6.8
6.9
6.10
6.11
6.12
6.13
Expected damages versus implementation costs for (1, NA) strategy, Milneburg neighborhood134
Expected damages versus implementation costs for all strategies, Milneburg neighborhood . 135
Discounted net economic benefit from all strategies, Milneburg neighborhood . . . . . . . . 136
Map of citywide strategy E-NB-50 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
Citywide strategy net benefit (1.0% discount rate) . . . . . . . . . . . . . . . . . . . . . . . 142
Citywide strategy regret (1.0% discount rate) . . . . . . . . . . . . . . . . . . . . . . . . . 144
Residual damage by recurrence interval (1.0% discount rate) . . . . . . . . . . . . . . . . . 145
Map of citywide strategy C-NB-50 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
7.1
7.2
7.3
7.4
7.5
7.6
7.7
7.8
Citywide strategy E-NB-50 regret (1.0% discount rate) . . . . . . . . . . . . . . . .
Citywide strategy E-NB-50 PRIM peeling diagrams . . . . . . . . . . . . . . . . . .
Dimension restrictions for “buyout enforcement and increasing risk” scenario . . . .
Dimension restrictions for “buyout enforcement and reduced participation” scenario .
Strategy performance in/outside of constructed policy-relevant scenarios . . . . . . .
Citywide strategy C-NB-50 equivalent annual 100-year damage (1.0% discount rate)
Dimension restrictions for the “degrading protection” scenario . . . . . . . . . . . .
Strategy performance in/outside of “degrading protection” scenario . . . . . . . . . .
xii
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153
154
155
157
158
160
161
163
List of Tables
2.1
Planning alternatives recommended by LACPR for Planning Unit 1 . . . . . . . . . . . . . . 32
3.1
3.2
Projected mean sea level rise at Grand Isle, LA . . . . . . . . . . . . . . . . . . . . . . . . 46
XLRM framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.1
4.2
4.3
4.4
4.5
4.6
Orleans Parish spatial units . . . . . . . . . .
Orleans Parish neighborhoods . . . . . . . .
Flood Hazards model uncertain drivers . . . .
Flood Consequences module input data . . .
Flood Consequences module uncertain drivers
Growth dispersion in Orleans Parish . . . . .
5.1
5.2
5.3
Weighted census block elevation quantiles, Orleans Parish . . . . . . . . . . . . . . . . . . 110
Risk mitigation strategies tested in NOLArisk . . . . . . . . . . . . . . . . . . . . . . . . . 113
Buyout and easement cost estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
6.1
6.2
NOLArisk exogenous inputs and ranges . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
Citywide strategy summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
7.1
7.2
7.3
PRIM results summary for the “buyout enforcement and increasing risk” scenario . . . . . . 155
PRIM results summary for the “buyout enforcement and higher elevation costs” scenario . . 156
PRIM results summary for the “degrading protection” scenario . . . . . . . . . . . . . . . . 161
xiii
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70
72
76
80
83
85
Acronyms
ABFE
ADCIRC
ASCE
ASFPM
BFE
BNOB
CARs
CZMA
CPRA
DFIRM
ERP
FEMA
FIRM
FMA
GCM
GIWW
GNOCDC
HMGP
HSDRRS
IHNC
ILIT
IPET
IPCC
JPM
JPM-OS
LACPR
LP&VHPP
LPBF
LRA
MLOD
MR-GO
NAVD88
NFIP
NGVD29
NOAA
Advisory Base Flood Elevation
Coastal Circulation and Storm Surge Model
American Society of Civil Engineers
Association of State Floodplain Managers
Base Flood Elevation
Bring New Orleans Back Commission
R
Computer Assisted Reasoning
system software
Coastal Zone Management Act
Coastal Protection and Restoration Authority of Louisiana
Digital flood insurance rate map
External Review Panel
Federal Emergency Management Agency
Flood insurance rate map
Flood Mitigation Assistance program
Coupled atmosphere-ocean general circulation model
Gulf Interstate Waterway
Greater New Orleans Community Data Center
Hazard Mitigation Grant Program
Greater New Orleans Hurricane and Storm Damage Risk Reduction System
Inner Harbor Navigation Canal
Independent Levee Investigation Team
Interagency Performance Evaluation Team
Intergovernmental Panel on Climate Change
Joint Probability Method
Joint Probability Method with Optimum Sampling
Louisiana Coastal Protection and Restoration study
Lake Pontchartrain and Vicinity Hurricane Protection Project
Lake Pontchartrain Basin Foundation
Louisiana Recovery Authority
Multiple Lines of Defense strategy
Mississippi River Gulf Outlet
North American Vertical Datum of 1988
National Flood Insurance Program
National Geodetic Vertical Datum of 1929
National Oceanic and Atmospheric Administration
xiv
NOE
O&M
OCPR
OM
OW1
OW2
PDF
PDI
PRIM
RDM
RFC
RIDF
RGSPI
RMS
RSLR
S&WB
SB
SFHA
SLCRMA
SRES
SRL
SST
UNOP
USACE
New Orleans East basin
Operations and maintenance
Louisiana Office of Coastal Protection and Restoration
Orleans Main basin
Orleans West Bank 1 basin (English Turn)
Orleans West Bank 2 basin (Algiers)
Probability density function
Power dissipation index
Patient rule induction method
Robust Decision Making
Repetitive Flood Claims program
Risk Informed Decision Framework
RAND Gulf States Policy Institute
Risk Management Solutions
Relative sea level rise
Sewerage and Water Board of New Orleans
St. Bernard basin (developed)
Special Flood Hazard Area
Louisiana State and Local Coastal Resources Management Act
Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios
Severe Repetitive Loss program
Sea surface temperature
Unified New Orleans Plan
U.S. Army Corps of Engineers
xv
Chapter 1
Introduction: Coastal Flooding Continues
to Threaten New Orleans
1.1
Overview
Hurricane Katrina was the costliest natural disaster in United States history, causing over $80 billion in
damages and 1,800 deaths coastwide (Blake et al., 2007). The majority of the damage was in New Orleans,
and the devastation Katrina inflicted on the city has been well documented: 80% of the city flooded and $8$10 billion in direct damages to New Orleans’ residences alone, with 200,000 homes and 15,000 apartment
units in the city destroyed (Brinkley, 2006; Grossi and Muir-Wood, 2006). Despite substantial recovery
and reconstruction progress over the last four years, the City of New Orleans remains at a crossroads—
although the city has made notable recovery and rebuilding progress in many sectors, its long-term future
remains uncertain. Despite ongoing repairs and improvements to the hurricane protection system by the
U.S. Army Corps of Engineers (USACE) designed to provide protection from the “100-year” (1 in 100, or
1% annual chance of occurrence) flood, New Orleans remains at risk from storm surge inundation caused
by future hurricanes. As Katrina made clear, “structural" protection alone—levees, floodwalls, pumps, and
floodgates—is unlikely to protect the city from all possible storms, and the planning challenge is complicated
by a variety of uncertain factors that could increase flood risk to the city in the coming decades.
1
Managing New Orleans Flood Risk
1.2
Chapter 1
Non-structural risk mitigation
This dissertation is designed to address one of the most critical long-term challenges still facing New Orleans
after Katrina’s devastation: how to make the city more resilient to future coastal flood events. Rather than
focus on additional large-scale structural investments, however, this research considers proposals to augment
the existing protection system with “non-structural" risk mitigation measures. Non-structural risk mitigation is a term typically used by U.S. Army Corps of Engineers, and can include any action taken to reduce
exposure to the consequences of flooding aside from levees, floodwalls, gates, canals, or other “structural”
protection system investments (USACE, 2009c). Non-structural measures include incentives for elevating or
floodproofing existing structures, revised building codes to encourage or mandate elevation of new structures
in the floodplain, and buyouts—voluntary incentive-based relocation of households to lower risk areas.
Non-structural mitigation approaches may provide several advantages over structural protection systems.
First, they reduce the consequences of flooding itself, and thus reduce vulnerability if the structural system
fails. Second, they can be implemented at a local scale and targeted towards particularly high-risk areas,
providing incremental risk reduction when funding or political will for additional large-scale infrastructure
investments is unavailable. Finally, non-structural risk mitigation provides a mechanism to share responsibility for risk reduction with local residents which may in turn help to reduce additional risk-prone development
of the coastal floodplain.
1.3
Challenges to successful risk mitigation planning in New Orleans
Non-structural risk mitigation also presents challenges. Because the residual risk to the city with the protection system in place remains uncertain and is likely to be changing over time, the potential benefits of these
proposed risk mitigation efforts are unclear. In addition, non-structural risk mitigation presents implementation challenges. Risk mitigation frequently involves partnerships between private and public entities. Further,
systematic implementation of broad-scale strategies will require close coordination between federal, state,
and local authorities, businesses and non-profit organizations active in the city, and individual homeowners.
Post-Katrina risk mitigation efforts in New Orleans to date, however, have been mostly uncoordinated.
2
Managing New Orleans Flood Risk
1.3.1
Chapter 1
Historical cycles of induced development
The implementation of successful risk mitigation is complicated by past flood risk planning failures in New
Orleans. Because of its low elevation and proximity to the Mississippi River and Gulf Coast, New Orleans has
experienced numerous riverine and coastal floods over the last three centuries. In the twentieth century alone,
for example, the greater New Orleans area was damaged by significant hurricane-induced floods in 1915,
1947, and 1965. After each of these floods, new levees and protection structures were built in order to make
the city safer. However, each incremental improvement to the protection system also brought development of
previously unprotected and uninhabited areas of greater New Orleans, a process of induced development that
likely led to a net increase in New Orleans’ long-term flood risk despite the new defenses present (Bourne Jr.,
2007). Providing reasonable, long-term risk reduction in the face of the catastrophic flooding threat without
repeating this pattern once more presents a substantial challenge to local policymakers.
1.3.2
Substantial uncertainty regarding future flood risk
Effective planning for future flood risk in New Orleans is further challenged by natural and anthropogenic
changes expected to occur in the coming decades. Flood risk to the city from hurricane-induced storm surge
is influenced by a variety of environmental factors, including ongoing coastal land loss and subsidence (Dixon
et al., 2006b; Barras et al., 2003), the effects of global climate change on rising sea levels (Meehl et al., 2005),
and the debated effects on the intensity of future Atlantic hurricanes (Emanuel, 2005b; Landsea et al., 2006).
Overall risk will also depend on the state of the future protection system, both in terms of the design level
and how the system is maintained in the face of sea level rise and landscape subsidence. Future patterns of
population and economic growth will also influence the city’s vulnerability, though these factors are closely
connected to current and future risk reduction policies.
The likely effect of these uncertain drivers is to increase flood risk to city residents over time. Many
of these key influences, however, fall into a class of decisionmaking challenges that Lempert et al. (2003)
refer to as deep uncertainty. According to the authors, deep uncertainty is present in policy problems when
the relationships or models that drive the system, probability distributions used to characterize uncertainty
for key drivers, or value systems to compare different outcomes, are either unknown or disagreed upon by
decisionmakers and key stakeholders. Recent estimates of risk by USACE currently in use to support local
3
Managing New Orleans Flood Risk
Chapter 1
planning, however, do not fully address this uncertainty (USACE, 2009c; IPET, 2009b). As a result, the
protection system augmentation currently underway, as well as local risk mitigation efforts, could remain
vulnerable to a future in which risk is much greater than currently predicted.
1.4
Research questions
This dissertation seeks to address this substantial planning challenge by providing new estimates of economic
flood risk across a broad range of future uncertainty and considering the value of non-structural risk mitigation to reduce exposure to higher-than-expected risk. Specifically, the research addresses three primary sets
of interrelated research questions:
1. What are the historical methods for reducing riverine and coastal flood risk in New Orleans and along
the Lower Mississippi? What are the past and present planning conditions that contributed to protection system failures during Hurricane Katrina, and what steps have been taken since to improve flood
defenses? Which Federal, state, or city non-structural risk mitigation options for New Orleans are
currently in progress or under consideration?
2. What is the annual risk of hurricane-induced flooding to single-family homes in the City of New Orleans from 2011-2060, measured at the 100-, 400-, and 1,000-year storm recurrence frequencies? How
does this risk vary under different scenarios of population growth and dispersion, protection-system
maintenance, subsidence and coastal land loss, and climate-change induced effects on sea levels?
3. Which non-structural risk mitigation strategies reduce risk under many or most plausible future scenarios of flood risk? Under what conditions do promising strategies provide poor or inadequate risk
mitigation compared with implementation costs? Can these strategies be modified to better hedge
against poor outcomes, both in terms of risk reduction and net economic benefit?
4
Managing New Orleans Flood Risk
1.5
Chapter 1
New methods for addressing deep uncertainty can reduce vulnerability
of risk reduction plans
This research represents a first step towards addressing deep uncertainty in flood risk planning for New Orleans and across the Louisiana coast. The report is intended to help government, non-profit, and private sector planners and stakeholders participating in the rebuilding of New Orleans better understand the potential
benefits and costs of non-structural risk mitigation given substantial long-term scientific and planning uncertainties. The dissertation builds on recently-produced, detailed models of flood risk to the city developed by
federal agencies in order to develop a planning tool that generates estimates of risk under different mitigation
plans. In addition, the analysis utilizes an innovative approach to decisionmaking under uncertainty called
Robust Decision Making (RDM). RDM is a quantitative, scenario-based approach for identifying solutions
that are relatively insensitive to deep uncertainty. Rather than seek a policy designed to perform optimally
in the most likely prediction of the future, RDM instead seeks “robust” policies that perform reasonably well
across a wide range of plausible future scenarios. Using the RDM framework, the analysis tests different
mitigation strategies, with a focus on direct property damages to single family homes in Orleans Parish, to
find those that provide cost-effective risk reduction under a wide range of possible futures. Furthermore,
it highlights scenarios in which current strategies perform poorly, describes tradeoffs between different approaches under alternate assumptions about the future, and seeks adaptive solutions using non-structural risk
mitigation to improve the robustness of candidate strategies.
1.6
Organization of this dissertation
To answer the research questions above, the dissertation proceeds in seven substantive chapters. Chapter 2
documents historical approaches to flood risk reduction in New Orleans from the eighteenth century through
Hurricane Katrina. It describes a pattern in which historical floods were addressed via incremental improvement to the city’s protective levees and drainage system, which in turn led to an expansion of the city’s
footprint and increased the assets and population at risk from the next major storm event. The chapter also describes long-term planning efforts post-Katrina at the Federal, state, and local levels, the role of non-structural
mitigation in these plans, and potential challenges to plan implementation.
5
Managing New Orleans Flood Risk
Chapter 1
Chapter 3 introduces the risk modeling and uncertainty analysis approach to be applied in the analysis.
The chapter begins with a review of standard flood risk analysis methods, and describes recent applications
of these methods to post-Katrina risk reduction plans for New Orleans and southern Louisiana. I describe
a series of long-term, “deep" uncertainties not necessarily included in recent risk estimates but nevertheless
critical to successful future planning efforts. Finally, I describe an alternate framework for estimating risk
and selecting mitigation strategies using the RDM methodology. This approach tests strategies against a large
ensemble of plausible future scenarios and seeks a robust, rather than optimal, risk mitigation strategy.
To support the analysis, Chapter 4 describes the development of a scenario generator—a computer simulation model designed to generate a large number of quantitative scenarios from different input conditions—to
estimate future flood risk in New Orleans. The model described here, termed NOLArisk, generates estimates
of risk (in terms of direct damages) for single family homes in Orleans Parish from 2011-2060. Given the
inherent complexity of estimating risk in this context, NOLArisk is designed as a low-resolution simulation
model capable of generating thousands of cases on a desktop computer, and builds from detailed models produced by the U.S. Army Corps of Engineers (USACE, 2009c) and Federal Emergency Management Agency
(FEMA) (FEMA, 2007).
Chapter 5 discusses possible non-structural risk mitigation approaches for New Orleans. The chapter
first introduces non-structural mitigation within the broader context of floodplain management and describes
how these options have been incorporated into Federal, state, and local post-Katrina planning efforts. Using
the current suite of non-structural plans, I then develop and quantify a discrete set of strategies as candidate
options to test in the NOLArisk model using RDM.
The analysis results are presented in Chapters 6 and 7. Chapter 6 begins with base-case estimates of flood
risk across the quantitative scenario ensemble assuming no additional non-structural risk mitigation efforts.
The chapter then documents the experimental design and testing of the strategies described in Chapter 5
under alternate scenarios, and describes a process used to identify promising approaches. Next, Chapter 7
documents a vulnerability analysis of these candidate strategies designed to identify scenarios in which they
perform poorly. Chapter 7 also describes tradeoffs between different types of approaches in different future
states of the world, and discusses possible options to improve the adaptivity of the initial strategies.
Finally, in Chapter 8 I discuss the implications of this analysis for ongoing flood risk planning efforts in
6
Managing New Orleans Flood Risk
Chapter 1
New Orleans. Preliminary recommendations are provided, as well as a discussion of suggested next steps to
expand and improve upon this initial research effort.
7
Chapter 2
Past and Present Flood Risk Management in
New Orleans and Along the Lower
Mississippi River
2.1
Introduction: the Katrina paradigm shift
Hurricane Katrina was the costliest natural disaster in United States history, causing approximately $81 billion in damages to the Gulf Coast after it made landfall on August 29, 2005. Katrina also caused over 1,800
deaths to residents of coastal communities, with most fatalities occurring due to the massive floodwater
inundation that covered 80% of metropolitan New Orleans (Blake et al., 2007). With a minimum central
pressure of 920 millibars (26.35 inches), Katrina ranks as the third most intense storm to make landfall in the
mainland United States in the modern record. The storm weakened considerably before striking the coast,
however, and only rated as a Category 3 on the Saffir-Simpson scale (111-130 MPH) when it made landfall
in Louisiana and Mississippi. Katrina’s devastation was primarily caused by the wall of storm surge pushed
onto low-lying coastal areas rather than via wind damage as with other damaging storms such as Hurricane
Andrew in 1992. Peak storm surges in Mississippi were recorded at close to 30 feet, while Louisiana suffered
surge elevations over 15 feet above sea level (Kent, 2006).
Katrina morphed from a natural disaster into a humanitarian crisis when the Lake Pontchartrain and
8
Managing New Orleans Flood Risk
Chapter 2
Vicinity Hurricane Protection Project (LP&VHPP) failed catastrophically in dozens of locations around New
Orleans, including the floodwalls along the 17th Street and London Avenue Canals in the central part of the
city and along the levees on the Mississippi River Gulf Outlet (MR-GO) and Industrial Canal to the east
(IPET, 2009a; Seed et al., 2006; van Heerden et al., 2006). Water pouring through these breaches flooded
80% of the city, leading to up to 8-10 feet of standing water in some neighborhoods (Fig. 2.1). Residents
who were unable to evacuate prior to the storm, or chose not to, were trapped by the rising floodwaters in or
near their homes or in the Superdome (the “shelter of last resort”) and later at the city Convention Center.
As documented extensively via television, print, and internet news coverage, authorities at the Federal, state,
and city level were unable to gain control over the crisis for close to a full week, and trapped residents were
left without adequate food, water, shelter, or medical care for multiple days until large scale evacuations to
Houston, Baton Rouge, and other locales began on Thursday, September 1st (Brinkley, 2006). Evacuees from
the city spread across the country in the subsequent weeks and months, and the city population has not fully
recovered (72% of the pre-storm population had returned as of August 2008) (Brookings, 2008).
Over the last four years, much has been written regarding the immediate response to the Katrina disaster,
effects of storm on all aspects of life in New Orleans and along the central Gulf Coast, and long-term planning
for the future of New Orleans and other devastated areas. Underpinning nearly all of these planning documents, investigations, and reports, however, is fundamental agreement that the risk of future storm-induced
flooding—and another Katrina-scale disaster—to the City of New Orleans remains unacceptably high, and
that successful long-term recovery hinges on making the city safer and more resilient to future storms. The
degree of risk and appropriate risk mitigation policy solutions, however, are still being actively debated by
local residents, businesses and stakeholders, and policymakers at all levels of government.
This chapter describes the challenges of providing long-term, resilient risk mitigation for New Orleans.
First, I describe the historical cycle of flooding, recovery, and incremental improvements to the protection
system that led to the vulnerable pre-Katrina protection system. Next, I discuss the investigations into the
protection system performance that followed the storm, and the near-term decisions made based on these
investigations. Finally, I describe the long-term planning efforts (completed or underway) for New Orleans
and the Louisiana Gulf Coast, and the role of non-structural risk mitigation within these efforts.
9
Managing New Orleans Flood Risk
Chapter 2
Figure 2.1: Flood depths in New Orleans on September 3, 2005.
http://www.katrina.noaa.gov.
2.2
2.2.1
Source: NOAA, available from
Historical New Orleans flood protection and development patterns
An impossible, inevitable location
Founded in 1718 by French settlers under Jean-Baptiste Le Moyne de Bienville, the City of New Orleans
was originally situated on a natural, crescent-shaped levee carved out by past Mississippi floods. The site of
the city provided several important advantages to early Western European settlers, including close proximity
to the mouth of the Mississippi River, available high ground on the natural levee adjacent to the river, and
several natural waterways (such as Bayou St. John) that provided shortcut portages between the river, Gulf of
Mexico, and Lake Pontchartrain (Colten, 2005). Because the mouth of the river was difficult to access and
suffered from silting and sandbars, these portages were critical shortcuts for establishing the flow of goods
10
Managing New Orleans Flood Risk
Chapter 2
down the Mississippi into the Gulf (Campanella, 2002).
The advantages of the location, however, were balanced by considerable challenges to permanent urban
settlement. Most notably, the Mississippi River overran its banks and flooded annually, so that the natural
levee provided only some defense against flooding. The settlement was also surrounded by standing water
in brackish wetland areas, leading to extreme humidity and problems with mosquitos and disease. Despite
these challenges and competition from other potential locations, however, the location was a natural choice
for a major port city, and in the subsequent century New Orleans grew into the capital of French Louisiana.
Figure 2.2 shows a map of the new city, river mouth, and surrounding area from an early 18th century survey.
Figure 2.2: Map of New Orleans from the 1720 de la Tour Survey, with a view of the mouth of the river and
Lake Pontchartrain Basin. Source: commons.wikimedia.org.
2.2.2
Mississippi River flooding
From the city’s founding, New Orleans has faced the risk of regular floods from two sources: the Mississippi River, and tropical storms or hurricanes that developed in the Atlantic and moved into the Gulf Coast
11
Managing New Orleans Flood Risk
Chapter 2
region. For the first two-and-a-half centuries of life in New Orleans, the threat of flooding from the river was
considered the more immediate concern due to the frequency of the events. As a result, the settlement of the
city was quickly followed by the first construction of manmade levees along the river to protect against these
seasonal floods. Construction began on the first such levee in 1727, designed to flank the city by eighteen
miles on each side (Campbell, 1920). Although manmade levees adjacent to the city and extending upriver
provided some protection against direct river inundation, because the city lies in an alluvial floodplain—
where the ground slopes upwards towards the natural levee along the river with low-lying “backswamps”
behind—manmade levees would often just reroute the floodwaters into the backswamps and inundate the
city from behind. Additional levee construction further rerouted the floodwaters to other low-lying areas,
and as a result private levees of varying quality continued to be built along the river adjacent to and above the
city into the 19th century as the city passed from French to Spanish to American jurisdiction (Colten, 2005).
The early levee building began a centuries-long race against river floods. As levee heights increased, the
confined water levels during flood stage increased as well, leading to yet more devastating flooding when a
breach would develop. In turn, this would encourage additional construction to raise levee heights, and the
cycle would continue (Colten, 2005). Despite the growing levee system, major Mississippi floods occurred
throughout the 18th and 19th centuries. Monette (1903) describes “extraordinary” floods of the Mississippi
which either threatened or overwhelmed the levee system in 1782, 1797, 1811, 1815, 1823, 1828, 1832,
1840, 1844, and 1849, for example.
Early housing construction styles in New Orleans reflected the constant threat from high-frequency flooding events, with most styles incorporating pier foundations to elevate the home above floodwaters. Classic
New Orleans architectural styles such as the shotgun house—the most common style still evident in New
Orleans, and typically built from the latter half of the nineteenth century until about 1910—were typically
built on pier foundations.1 Despite the constant levee building, floodwaters were a regular part of life in New
Orleans in its first two centuries.
1
Preservation Resource Center of New Orleans, http://prcno.org/.
12
Managing New Orleans Flood Risk
2.2.2.1
Chapter 2
The Great Mississippi Flood of 1927
River floods remained a substantial threat to New Orleans throughout much of the twentieth century, though
recent investments in protection structures have considerably reduced the risk. Perhaps the best remembered
Mississippi flood that threatened the city within the last century was the Great Mississippi Flood of 1927. This
devastating flood inundated over 26,000 square miles throughout much of the Mississippi basin and displaced
700,000 people (USACE, 2002). As recounted by author John Barry in Rising Tide, in the preceding fifty
years the Mississippi River Commission and local levee boards had adopted a “levees only” flood control
policy designed to prevent water from flowing down side-channels or otherwise out of the river, with the
goal of harnessing the increased water velocity to cut a deeper main channel and hold more water during
a flood stage (Barry, 1997). This policy failed spectacularly during the 1927 flood, however, as a lack of
distributaries or other flooding “safety values” led to numerous levee breaches and massive flooding along
the river. After this failure and that of earlier piecemeal approaches were made evident in 1927, Congress
passed the Flood Control Act of 1928. This legislation placed flood control among the highest national
priorities and led to systematic management of Mississippi River floods through a comprehensive system
designed to protect against a “project flood” and spillways and other off-stream storage to divert and capture
excess water during flood stages (Pearcy, 2002).
New Orleans itself was spared the brunt of the 1927 floods, but the disaster nevertheless had a profound
effect on the city. As the flooding reached its apex upriver and the city grew increasingly concerned about
a breach near New Orleans, prominent local leaders, businessmen, and bankers came up with a plan to
purposely breach the levee system downriver of the city. This would provide an outlet for the floodwaters and
relieve the pressure on the city levees, but would also cause massive flooding in St. Bernard and Plaquemines
parishes and wipe out the homes and livelihoods of 10,000 residents. These powerful individuals successfully
lobbied the Governor of Louisiana and Mississippi River Commission to approve of the plan, and on April
29, 1927 the east bank levee was dynamited near Caernarvon (Barry, 1997).
This decision, made by the New Orleans elite at the expense of the trappers, fishermen, and other rural
residents of the deliberately flooded parishes, has been the subject of intense criticism because it may not
have been necessary to prevent flooding in New Orleans. Leading up to the decision, many argued that the
enormous breaches upriver would alleviate enough pressure (Barry, 1997). The decision also brought out
13
Managing New Orleans Flood Risk
Chapter 2
strong class resentment against the powerful business elite in New Orleans, and that mistrust has lingered
through the decades—some residents believed, for example, that the levee adjacent to the Lower Ninth Ward
was deliberately dynamited during Katrina in 2005 (Young, 2005).
2.2.2.2
River management in the twentieth century
Flood control on the Lower Mississippi River has improved since the passage of the Flood Control Act and
subsequent large-scale engineering undertaken by the U.S. Army Corps of Engineers, although floods in
1973 and 1983 nevertheless yielded $117 million and $15.7 million in damages (1983 dollars), respectively,
to areas along the Lower Mississippi (Trotter et al., 1998). River flooding threats to New Orleans have also
declined substantially with the introduction of massive engineered solutions in the twentieth century. The
linchpin of flood control on the Lower Mississippi is the Old River Control Structure, built by USACE in
1963 approximately 150 miles north of New Orleans at the point where the Mississippi was once connected
to its primary distributary, the Atchafalaya River, via a small bridging waterway (Fig. 2.3).
By the mid-twentieth century it had become apparent that, because the Atchafalaya provides a shorter
path to the Gulf of Mexico at a steeper pitch than the current waterway, the course of the Lower Mississippi
to the Gulf would eventually switch to the Atchafalaya at Old River as it has done countless times in previous
millenia (McPhee, 1989). Though a natural course of events, a change in the Mississippi’s path would have
dramatic repercussions for Southeast Louisiana, rendering inaccessible much of the oil-producing and other
industrial infrastructure in and above New Orleans, overwhelming the community of Morgan City in the
Atchafalaya floodplain, and fundamentally altering the environmental and economic drivers in southeast
Louisiana (Giegengack and Foster, 2006).
As a result, in mid-century Congress authorized construction of a massive control structure at the Old
River bridging site. The system of dams, gates, and locks currently constrains the flow of Mississippi water
to the Atchafalaya to no more than 30% of overall flow in normal operating conditions, provides navigation
locks for ship traffic to move between the rivers, and operate as a safety valve during Mississippi flood events
(America’s Wetland, 2009). Despite the scale of the engineered system, however, the structure nearly failed
only a decade after its completion during the Mississippi flood of 1973 after one of the control structures
was partially undermined. Had the structure failed, control over the flow to the Atchafalaya might have been
14
Managing New Orleans Flood Risk
Chapter 2
Figure 2.3: Map of the Lower Mississippi River, showing flood control levees, spillways, and the Old River
Control Structure circa June 1986. Source: U.S. Army Corps of Engineers, New Orleans District. Adapted
from www.mvn.usace.army.mil/pao/bro/misstrib.htm.
permanently lost, leading to a change in the Mississippi’s course (McPhee, 1989). Of course, this turn of
events would greatly reduce the threat of river flooding to the City of New Orleans, but at a steep, permanent
economic cost.
New Orleans is also protected from river floods by three spillways designed to divert potential floodwaters
upriver of the city. The Morganza and West Atchafalaya Floodways, together with the Old River Control
Structure, are designed to reroute up to 1.5 million cfs (cubic feet per second) from the Mississippi to the
Atchafalaya during a major flood event (USACE, 2010). The Bonnet Carré spillway, constructed immediately
after the 1927 floods about 12 miles north of the city, represents another line of defense. When opened, the
spillway allows Mississippi River water to flow into Lake Pontchartrain rather than along the river through
the city, in most cases preventing high waters from reaching the city’s protective levees. The spillway has
been used about once every ten years, most recently in 2008 (USACE, 2009a). Taken together, the multiple
15
Managing New Orleans Flood Risk
Chapter 2
floodways provide redundancy and a factor of safety to hedge against catastrophic riverine flooding.
2.2.2.3
River management and wetlands loss
Control of the river has brought obvious benefits to New Orleans, but the full environmental costs of these
policies to the Southeast Louisiana coast have only been fully appreciated in the last several decades.2 In the
twentieth century, flood control and land use policies, coupled with oil and gas extraction and the construction of over 9,000 miles of navigation canals, have contributed to the loss of approximately 1,525 sq. miles
of wetlands by the year 2000 (Reed and Wilson, 2004). Of particular concern is the closure of most natural
pathways for the Mississippi to deposit sediment along the deltaic coast; nearly all sediment carried down
the Mississippi is instead deposited in the deep waters of the Gulf via the current birds-foot delta terminus.
Barras et al. (2003) estimate recent land loss rates at nearly 30 sq. miles/year and project additional losses of
513 sq. miles by 2050 with no additional restoration in place (Fig. 2.4), while a controversial new study suggests massive losses of up to 3,800-5,200 sq. miles by 2100 (Blum and Roberts, 2009). Natural phenomena,
including geologic subsidence (Dokka, 2006), sea level rise (Penland and Ramsey, 1990), and major hurricanes also contribute to land loss, with Hurricanes Katrina and Rita alone accounting for the destruction of
217 sq. miles of coastal wetlands in September 2005 (USACE, 2009c).
These coastal wetlands are key contributors to the economy and quality of life in Southern Louisiana
and represent an important natural resource for the country. For example, these areas provide key habitat for
economically valuable fish species and millions of migratory waterfowl, and contribute substantially to the
fishing and tourism industries (USACE, 2004). In addition, the coastal wetlands provide a home to a wide
range of cultural groups, ranging from Acadians (Cajuns) to Native American tribes such as the Chitimacha
(CPRA, 2007).
The wetlands also play an important role in dissipating the energy from surge produced by hurricanes.
As discussed in detail below, coastal landscape features can help to reduce the velocity and elevation of storm
surge. The wetlands to the east and south of New Orleans in the delta region provide a first line of protection
against storm surge for the city, reducing the velocity and elevation of surge from before it reaches the flood
protection defenses (USACE, 2009c). As further land is lost, then, these defenses are also eroded, increasing
2
Some observers in the early twentieth century noted the environmental and economic benefits from unaltered Louisiana wetlands
(see Viosca, 1927, for example), but these views did substantially influence flood control planning for the Lower Mississippi.
16
Managing New Orleans Flood Risk
Chapter 2
Figure 2.4: Map showing historical (1932-2000) and projected (2000-2050) land loss along the Louisiana
coastline, assuming no additional coastal restoration. Source: National Wetlands Research Center, U.S.
Geological Survey. Available from www.nwrc.usgs.gov.
the risk of levee overtopping or failure. Post-Katrina coastal planning efforts identify the risk reduction
provided by coastal wetlands as an important reason to augment future restoration efforts (Sec. 2.3).
2.2.3
Storm surge flooding
Given the frequency of historical events and destructive potential of an unconstrained Mississippi, the threat
of flooding from the river tended to dominate flood planning and levee construction efforts during New
Orleans’ early centuries. Coastal flooding threats from tropical storm and hurricane storm surge, however,
have also been a part of the fabric of New Orleans’ life since the city’s founding. The anecdotal record of
landfalling storms near New Orleans begins in the sixteenth century, but quantitative data on such storms
is limited, extending only back to the mid-nineteenth century and with much of the pre-World War II data
biased or of limited quality due to the lack of standardized observation procedures and or technology for
measuring storm characteristics (see Landsea et al. (2007) and Landsea et al. (2004) for examples of recent
efforts to improve and standardize historical storm data).
Nevertheless, the recent record reveals numerous tropical storms and hurricanes making landfall near
New Orleans, with multiple direct hits recorded. Since 1851, 22 storms of hurricane strength have passed
within 50 miles of Orleans Parish (Fig. 2.5), 14 of which passed within 25 miles. Eight of these storms
17
Managing New Orleans Flood Risk
Chapter 2
(36%) were considered Category 3 or higher on the Saffir-Simpson scale, generating sustained winds above
110 MPH, and many yielded destructive flooding in New Orleans. Recent statistical estimates using the
historical record, in fact, suggest that the central Gulf Coast near New Orleans is twice as likely as the rest
of the Gulf to experience storms of Category 2 intensity or higher (USACE, 2009c).
As recent research and experience have revealed, even storms with lower sustained winds can be very dangerous to New Orleans in terms of storm surge due to the unique characteristics of the surrounding landscape
(USACE, 2009c). The “peninsula” created by the Mississippi extends southeast and tropical depressions
rotate counter-clockwise, meaning that a lower-intensity or farther-distant storm with the right mix of size
(area), track, and forward speed can pile up storm surge to the east of New Orleans in Lake Borgne or to the
north in Lake Pontchartrain even at lower wind speeds. Thus, Hurricane Camille, a Category 5 storm that
made landfall just to the east of New Orleans on the Mississippi coastline in 1969, caused only minor damages in New Orleans (Emanuel, 2005a), while Hurricane Gustav, making landfall in 2008 as a less-intense
Category 2 storm farther west of the city, nearly overtopped the Industrial Canal floodwalls in New Orleans.
Of course, this makes the current categorizations of storm intensity using the Saffir-Simpson scale problematic when applied to storm surge—a recent USACE report on Lousiana Coastal Protection and Restoration
(LACPR), for example, suggests using a combination of central pressure deficit (Δp, closely related to wind
speeds) and a measure of storm size (Rmax , radius to maximum wind speeds) to instead determine the surge
potential of storms (USACE, 2009c).
2.2.3.1
Technological advances allowed the city to grow into low-lying areas
In its first two centuries, New Orleans was spared total destruction via inundation by developing only along
the crescent-shaped strip of natural levee bordering the Mississippi River. The surrounding wetlands, frequent flood events, and lack of technological capacity to prevent floods from occurring or remove standing
water naturally constrained the city and prevented further development into the “backswamp” areas towards
Lake Pontchartrain. Nevertheless, the port city grew in population and wealth into the 1890s despite the topographic limits, poor drainage, and piecemeal land reclamation efforts, becoming the second-largest deepwater
port in the United States by 1879 with 410,000 tons of shipment annually (Rogers, 2008; Colten, 2005).
Modern engineering and technological innovation changed the equation in New Orleans between the late
18
Managing New Orleans Flood Risk
Chapter 2
Figure 2.5: Hurricane-strength storms that made landfall within a 50 mile radius of Orleans Parish, 18512008. Red tracks indicate when the storm was at hurricane strength, with darker reds signifying more intense
storms. Map generated using NOAA Coastal Services Center interactive tool, accessed June 28, 2009. Available from http://csc-s-maps-q.csc.noaa.gov/hurricanes/index.jsp.
1800s and World War II, leading to substantial growth in previously uninhabitable areas. Several key factors contributed to the expansion. The city’s growing population—8,000 residents in 1800, approximately
300,000 by 1900—led to growing demand for additional land, exacerbated by a lack of sewage treatment
capacity and ongoing problems with tropical diseases such as yellow fever. The pent-up demand for additional land led to substantial investment in a comprehensive drainage system developed and managed by
the Sewerage and Water Board of New Orleans (S&WB) and relying on innovative new wood-screw water
pumps invented by S&WB engineer A. Baldwin Wood (Rogers, 2008). The new system allowed city officials to drain much of the wetlands between the existing city limits and Lake Pontchartrain, leading to a
700% increase in available dry land by 1920 (Campanella, 2002). Contemporaneously, the shoreline of Lake
Pontchartrain was beginning to be built up with low (6 foot) levees in response to the threat of storm surge
from the lake, and the federal government continued to invest in the aforementioned improvements to the
19
Managing New Orleans Flood Risk
Chapter 2
Mississippi River levees and other upstream defenses before and after the 1927 flood (Rogers, 2008; Colten,
2005).
Paradoxically, the improved Mississippi levees and new defenses along the lakefront prevented water
from naturally flowing into the lake and solidified the city’s bowl-like topography, ensuring that all water
that entered the city—rain, surge, or river flood—would have to be pumped out via the drainage system.
Nevertheless, the newly-drained swamps and fledgling hurricane defenses allowed residents to begin expanding northward after 1920, although most of the growth did not occur until after the Second World War
(Fig. 2.6).
Figure 2.6: Growth of New Orleans, by Neighborhood, from 1708-2000. Note that pre-twentieth century
growth tends to cluster on the above-sea-level high ground of the natural levee, while twentieth-century
growth expands into low-lying areas. Source: Campanella (2002).
2.2.3.2
Twentieth century storms initiated a cycle of increased defenses and induced development
In the early- to mid-20th century, the greater New Orleans area was damaged by significant hurricane-induced
floods in 1915, 1947, and 1965, with one powerful Category 5 near-miss (Hurricane Camille in 1969). Fig.
20
Managing New Orleans Flood Risk
Chapter 2
2.7 shows the pattern of flooding from 20th century storm surge flood events, including Hurricane Katrina.
Figure 2.7: Flooding caused by storm surge in New Orleans in the 20th century. Source: RMS, 2006.
After each of the pre-Katrina floods, the weaknesses of the hurricane protection and pumping system
in place at the time were identified, and improvements were made to improve the system relative to the
storm that was just experienced. However, each incremental improvement to the protection system also
continued to spur development of previously unprotected and uninhabited areas of greater New Orleans
(induced development), a process that contributed to a net increase in New Orleans’ long-term flood risk
despite the new defenses present (Bourne Jr., 2007).
For example, a Category 4 storm struck the Louisiana coast south of New Orleans on September 29,
1915, passing just west of the city (Fig. 2.5). The newly-developing areas near Lake Pontchartrain were inundated with 12 feet of storm surge from the lake, killing 275 people, while the Mid-City area received 3
feet of water from overtopped drainage canals (Rogers, 2008; Fitzpatrick, 1999). This storm was arguably
the most destructive pre-Katrina event in New Orleans, and spurred the S&WB to raise the Lake Pontchartrain levees as well as those along the drainage canals to 3 feet above sea level (Rogers, 2008). The drainage
canal levees were raised again after the 1947 hurricane, and growing pressure for federal investment in New
Orleans flood protection coincided with an explosion in the geographic dispersion of the city—both within
Orleans Parish and to neighboring Jefferson Parish to the west—after this event (Fig. 2.6) (Burby, 2006).
Home building styles also changed in New Orleans at this time, with most homes built in the new neighborhoods to the north and east built on slab foundations with little to no elevation to prevent flooding damage
21
Managing New Orleans Flood Risk
Chapter 2
(Colten and Sumpter, 2009). Although flood protection improved marginally after each event, Kates et al.
(2006) argue that these efforts fell into “...a long-term pattern of societal response to hazard events—reducing
consequences to relatively frequent events, and increasing vulnerability to very large and rare events.”
2.2.3.3
Federal involvement with the New Orleans hurricane protection system
Hurricane flood protection up to mid-century had been a locally-funded priority, but subsequent to the 1947
storm the Louisiana congressional delegation worked to make New Orleans protection a national priority.
Congress initially approved the LP&VHPP in 1955, and USACE was the agency designated to work with
the S&WB and local levee boards and design an improved protection system (Rogers, 2008). After several
plan iterations, the USACE New Orleans District finally proposed an ambitious “Barrier Plan” in 1962. This
plan consisted of a series of gates and large levees at the Rigolets and Chef Menteur entrance passes to
Lake Pontchartrain. In theory, the gates could be closed during a hurricane, preventing storm surge from
entering the lake. Secondary protection would be provided by raised levee heights along the lakefront and
drainage canals, but the mouths of the outfall canals would remain open to the lake (in contrast to previous
proposals) (Woolley and Shabman, 2008). The Barrier Plan faced a series of hurdles from its inception—
among them very high construction and operations and maintenance (O&M) costs, lack of political support,
and opposition by environmental groups, local government, residents of the north shore of the lake, and
other constituencies—and was never completed as initially proposed. Furthermore, Hurricane Betsy struck
in 1965, causing multiple levee failures, flooding over 164,000 homes (RMS, 2006), and revealing flaws
in the initial Barrier Plan that required an increase to the proposed lakefront levee heights (Rogers, 2008;
Woolley and Shabman, 2008).
Nevertheless, federally-sponsored protection moved forward and USACE would spend the next fortyplus years constructing the LP&VHPP. The original cost projection was $85 Million in 1961 dollars, with
a projected completion date of 1978. By 1981 the project was considered only 50% complete and the cost
had grown to $248 Million (1961$) with a new projected completion date of 2008 (Mittal, 2005). As a
further setback, environmental and other local citizens’ groups sued USACE in 1975 to stop construction of
the barrier across the lake entrance, arguing that the full adverse impacts from barrier construction had not
been taken into account by federal planners. This lawsuit eventually led to a 1977 federal court injunction
22
Managing New Orleans Flood Risk
Chapter 2
preventing construction of the Lake Pontchartrain barrier pending a new environmental impact statement,3
In light of the injunction, USACE re-evaluated the risk reduction benefits and costs of the Barrier Plan
and eventually dropped it altogether, replacing it with a “High-Level Plan” that instead called for further
increases in lakefront and drainage canal protection heights. By this point, however, neighborhoods abutting
the three primary drainage canals were built out, and acquiring rights-of-way for wider earthen levee became
a substantial bureaucratic challenge. As a result, the High-Level Plan instead called for concrete flood walls
along the canals to provide additional protection—parallel protection—without expanding the footprint of
the structures (Rogers, 2008; Mittal, 2005). By 2005, most areas of the system were considered over 90%
complete (Woolley and Shabman, 2008), but as discussed below the combination of an incomplete system,
design flaws, and serious errors in system construction and maintenance left New Orleans highly vulnerable
to catastrophic flooding from Katrina.
Substantial federal investments in the protection system once again contributed to induced development
after 1965. Coupled with interstate highway construction through Greater New Orleans and additional investment in marsh drainage, the promise of new protection led to the development of low-lying areas such
as Eastern New Orleans (Fig. 2.6) (Colten, 2005; Campanella, 2002). These recently-developed areas were
among the hardest hit by flooding during Katrina—many Eastern New Orleans neighborhoods, for example,
were inundated with over six feet of flood waters (Fig. 2.1).
2.2.4
Floodplain management and moral hazard
In light of the Katrina disaster and New Orleans’ complex water management history, there is widespread
agreement among planners and stakeholders that successful long-term rebuilding hinges on making the city
safer and more resilient to future storms. Rather than provide yet another marginal increment of physical
protection via raised levees or new structural enhancements to the system, many in the planning community have instead argued for a comprehensive reevaluation of government-provided flood protection (Burby,
2006; Berke and Campanella, 2006; Olshansky, 2006; Kahan et al., 2006). As past experience has revealed,
more structural protection can exacerbate the moral hazard problem by spurring additional growth in the
riskiest areas and providing a false sense of security to residents. In addition, recovery assistance provided
3
Save Our Wetlands v. Rush, Civ. A. No. 75-3710 (E.D. La. Dec. 30, 1977).
23
Managing New Orleans Flood Risk
Chapter 2
by the federal government after disasters such as Katrina can itself lead to excessive risk-taking by providing an implicit guarantee of reimbursement without purchasing insurance or taking other mitigating actions
(Kunreuther, 2006).
An alternative approach suggested by many policymakers, stakeholders, and urban planners is comprehensive floodplain management designed to incorporate structural protection from the LP&VHPP into a
broader toolkit of risk mitigation actions. In addition to structural protection, floodplain management includes hazard mitigation alternatives such as zoning and land use restrictions, risk mitigation for individual
homes or businesses via floodproofing and/or elevation, insurance incentives and requirements, public education campaigns, and comprehensive evacuation plans. Advocacy groups such as the Lake Pontchartrain
Basin Foundation (LPBF) have argued that coastal restoration should be another key element of comprehensive risk planning across the Louisiana coast because of the reduction in storm surge height and velocity
that coastal wetlands can provide. LPBF has proposed a “multiple-barriers” approach to risk mitigation
in coastal Louisiana, for example, that includes hazard mitigation in addition to natural ridges, marsh, and
barrier islands (Lopez, 2006). Hazard mitigation is discussed in detail in Chapter 5 of this document.
Advocates of a more comprehensive, multi-faceted approach to flood risk argue that the right combination of mitigation policies will make New Orleans more resilient to catastrophe when the next major hurricane
occurs. However, such approaches can be very difficult to implement because they require coordinated action at nearly every level of planning—from individual residents to the federal government—and necessitate
partnerships among public and non-profit organizations, businesses and insurance companies, and private
citizens (Burby, 2006; Burby and Dalton, 1994). A successful plan will require consistent implementation
and coordination by the City of New Orleans, and as discussed in Sec. 2.3.4 below, many factors could prevent
the city from achieving systematic reform post-Katrina.
2.3
Planning for New Orleans post-Katrina
Since Katrina, New Orleans has proceeded through the recovery phase and is well into what Kates et al.
(2006) refer to as “betterment” or long-term reconstruction. The reconstruction and planning process has
evolved in the almost five years since the storm and has included decisionmakers at all levels of government
and within the private sector. Federal, state, and local agencies all play critical roles in laying the groundwork
24
Managing New Orleans Flood Risk
Chapter 2
for New Orleans’ future, and residents and business owners make individual choices—stay or go, invest here
or elsewhere—that collectively determine the shape of the city to come. The population of urban New
Orleans was shrinking in the decades prior to Katrina: the city’s population peaked at 627,525 in the 1960
Census and declined by 31% to 437,136 by July 2005 (Kates et al., 2006; Frey and Singer, 2006). In contrast,
neighboring parishes such as Jefferson and St. Tammany grew substantially during the same period, reflecting
“white flight” from the urban core to suburban areas (Brookings, 2005). This gradual decline, coupled with
pervasive problems related to poverty, racism, lack of educational or economic opportunities, and violent
crime, have made it evident that a successful New Orleans in the future requires a substantially new vision
across many aspects of society.
No amount of successful urban renewal will help New Orleans to thrive in the long-term without additional resiliency to future flooding threats. Gains made on economic, social, criminal justice, or educational
fronts will all be built on the assumption that the city can survive future hurricanes and storm surge. This
reality is reflected in the planning efforts that have occurred to date at various levels, as well as in the views
of residents: for example, a 2008 survey of residents found that “repairing the levees, pumps, and floodwalls”
remained the most frequently cited (64%) post-Katrina reconstruction and recovery priority (Kaiser Family
Foundation, 2008).
This section describes the federal, state, local, and non-profit/academic investigations, studies, and planning efforts related to flood risk reduction performed since September 2005. Although near-term recovery
efforts have been completed and much of the groundwork for the future rebuilding process already exists, as
described below, long-term planning related to flood risk is still underway at all planning levels. Given that
many post-Katrina risk reduction proposals have not yet been implemented, there remains an opportunity to
test these proposed approaches in a quantitative framework in order to determine their efficacy in different
possible realizations of the future. These analytic results, in turn, can help to inform the ongoing debate
among decisionmakers, stakeholders, and residents regarding the future of New Orleans.
2.3.1
Katrina investigations
Numerous questions related to the performance of the protection system emerged in the weeks and months
after Katrina: How had the flooding occurred? Did the LP&VHPS perform as anticipated and face a storm
25
Managing New Orleans Flood Risk
Chapter 2
that exceeded the design criteria, or did errors in design, construction or maintenance contribute to the catastrophic flooding? To answer these questions regarding the system’s performance during Katrina, a number
of investigations of the LP&VHPP were undertaken in 2006 by key government agencies and independent
scientists. These investigations included careful reconstructions of the timeline of events, forensic examination of levee and floodwall breaches, and high-resolution computer simulations to better understand how the
destructive storm surge was formed and how it overwhelmed the system.
The primary federal investigation of the levee system was conducted by the Interagency Performance
Evaluation Team (IPET), a consortium of government, academic and private sector scientists and engineers
formed by USACE (IPET, 2009a). Separate investigations were also performed by researchers from the
Louisiana State University Hurricane Center (Team Louisiana) (van Heerden et al., 2006) and a National
Science Foundation “Independent Levee Investigation Team” (ILIT) led by scientists at the University of
California, Berkeley (Seed et al., 2006). The American Society of Civil Engineers (ASCE) also participated
in the investigation by forming an External Review Panel (ERP) for IPET and issuing a separate review report
(Andersen et al., 2007).
Although the investigations reached similar conclusions about many aspects of the systemic failure, the
process was hampered by allegations from the independent teams and outside stakeholders that the IPET and
ASCE teams were not working in an open and independent manner. Critics cited concerns about inherent
conflicts-of-interest at IPET (most IPET team members were also USACE employees), withheld evidence
and general lack of cooperation from USACE, and potentially inappropriate linkages between IPET and the
external ASCE peer review panel. In turn, this led some critics to request a new, wholly independent government investigation of USACE—an “8/29 Investigation”—patterned on the 9/11 Commission (van Heerden
and Bryan, 2006; Schleifstein, 2008, 2007a,b).
The investigations generally agreed on the following conclusions, many related to the long and complex
history of the protection system dating to 1965 (IPET, 2009a; Seed et al., 2006; van Heerden et al., 2006;
Andersen et al., 2007):
• The hurricane protection system was incomplete at the time of the storm and did not perform as a system, with gaps in several places and radically different design heights for adjacent structures in others.
In addition, 1965-era designs were not revisited despite many system elements being constructed over
twenty years later.
26
Managing New Orleans Flood Risk
Chapter 2
• Major contributing factors were errors made concerning the elevation datum used by USACE during
construction, leading to levee heights built 1-2 feet below the intended elevation relative to mean sea
level.
• Local levee boards, USACE, and S&WB did not effectively work together to inspect or maintain the
system, failing to heed the effects of ongoing subsidence and missing problems with the fill types used
on many levees as well as weak soils below the outfall canal floodwalls.
• The vast majority of flooding in central New Orleans (80%) was caused by breaches in the floodwalls
along the outfall canals within the city: two on the London Ave. Canal and one on the 17th St. Canal
(Fig. 2.8). These breaches were catastrophic, meaning that the floodwalls failed without overtopping
and before storm surge elevations reached the top of the structures. The teams differed on the precise
cause of the failures, but agree that weak underlying soils and inadequate “I-wall” type structures were
at fault.
• Flooding in the Ninth Ward, Eastern New Orleans and St. Bernard Parish was caused by another I-wall
breach along the Inner Harbor Navigation Canal (IHNC) caused by overtopping and scour, as well as
numerous breaches in earthen levees along the Gulf Interstate Waterway (GIWW), Mississippi River
Gulf Outlet (MR-GO), and levees along Lake Borgne (Fig. 2.8). As the ILIT team points out, the surge
heights in these areas were not substantially greater than design levels, but the combination of unarmored levees not designed to be overtopped and poor quality fill material (from local dredging) used
for major sections of the levees led many levees in this area to erode and fail rapidly when overtopped
by Katrina’s storm surge and wave action.
There were also several notable disagreements between the investigations as well, however. In terms
of what caused the IHNC breaches, Team Louisiana strongly asserts (and ILIT concurs) that the MR-GO
navigation channel leading from the Lake Borgne area into the city strongly contributed to the levee and
floodwall failures by providing a direct surge pathway into the heart of the city (the infamous “hurricane
highway”), while IPET disputes this claim.4 Nevertheless, USACE has decided to decommission and close
MR-GO, and has begun work on filling in the channel mouth (U.S. Army Corps of Engineers, 2008).
Team Louisiana also lays the blame for the majority of the engineering and design failures squarely at the
feet of USACE, and points out various points along the 40-year LP&VHPP timeline in which the Corps had
actionable information to modify existing designs or plans that the New Orleans District chose to disregard
(van Heerden et al., 2006). The ILIT team instead states that “no one group or organization had a monopoly
on responsibility for the catastrophic failure” and instead places responsibility on systematic failures across
4
In November 2009, a federal judge ruled that USACE mismanagement of MR-GO led directly to flood damages in the Lower
Ninth Ward and St. Bernard Parish during Hurricane Katrina. Initial damage awards to the four plaintiffs were only $700k, but the
decision could lead to billions of dollars in awards to nearly 100,000 residents potentially affected by flooding from MR-GO levee
failures (Schleifstein, 2009a). Another lawsuit regarding USACE’s liability for the drainage canal floodwall failures was dismissed
because the government organization was shielded from liability under the 1928 Flood Control Act, but the judge allowed the MR-GO
lawsuit to proceed because it was constructed as a navigation rather than a flood control project (Schwartz, 2009).
27
Managing New Orleans Flood Risk
28
Figure 2.8: Location of floodwall and levee breaches caused by Hurricane Katrina. Source: Seed et al. (2006), derived from USACE.
Chapter 2
Managing New Orleans Flood Risk
Chapter 2
“[m]any groups, organizations and even individuals” (Seed et al., 2006), while ASCE and IPET mostly avoid
discussions of liability and blame (IPET, 2009a; Andersen et al., 2007).
Ultimately, all investigations recommended substantial changes to the status quo in order to avoid future
flooding disasters. Many such recommendations were folded into the next planning cycle for the LP&VHPP
and are discussed in the following section, but there remains substantial concern that the next cycle of planning and reconstruction will inevitably lead to the same outcome New Orleans has experienced countless
times: incremental improvements, promises of safety unfulfilled, and another flooding disaster. Although
many promising steps have been taken subsequent to the Katrina investigations, as of 2009 the LP&VHPP
remains a patchwork system and the future of hurricane flood protection in New Orleans is unclear.
2.3.2
USACE planning
Alongside and emerging from the IPET Katrina investigation, USACE’s post-Katrina planning further branched
into two parallel efforts: one focused on near-term improvements to the LP&VHPP, and the other focused on
long-term protection or risk mitigation in response to a charge from Congress to study a “Category 5” flood
protection system.
2.3.2.1
Emergency repairs and the 100-year system
Immediately following the “dewatering” of New Orleans after Katrina in September 2005, USACE began
emergency repairs on the system to restore some level of protection prior to the 2006 hurricane season. The
actions taken included floodwall reinforcement and armoring in some locations, replacement of I-wall type
floodwalls with strong “T-walls” at breaching sites, interior pump station repairs, and, critically, temporary
flood gates and pumps installed at the end of the three outfall canals along Lake Pontchartrain. According to
USACE, this entailed repair/replacement of 220 miles of levees and floodwalls within a nine month period
(USACE, 2009b).
Subsequent to the immediate repairs, USACE also began the design and construction of a “100-year”
system for New Orleans. The 100-year flood is defined as the storm surge with a 1 in 100 (or 1%) chance
of occurring each year, and is a critical threshold used to delineate which floodplains are addressed by the
National Flood Insurance Program (NFIP). This approach differs from the previous design approach, which
29
Managing New Orleans Flood Risk
Chapter 2
pegged the system to a “standard project hurricane” roughly equivalent to a 1 in 200 (0.5%) annual chance
storm (Woolley and Shabman, 2008). Although the system will be substantially improved compared with
the incomplete pre-Katrina system, USACE has not increased the design criteria to address more extreme
events. USACE has characterized Hurricane Katrina as a 400-year (0.25%) event in terms of storm surge,
for example (USACE, 2009c), so by definition the 100-year system is not designed to prevent all flooding
during another storm with characteristics similar to the 2005 event. To make the concept of residual risk
more explicit, USACE also changed the name of the system to “Greater New Orleans Hurricane and Storm
Damage Risk Reduction System” (HSDRRS).
Nevertheless, the 100-year system is clearly designed with an eye towards the lessons learned from Hurricane Katrina, particularly concerning the easily eroded levees in the eastern portion of the system and parallel
protection floodwall failures that caused the majority of the flooding. New elements in the Orleans Parish
vicinity include:
• raised levee elevations and armoring along the eastern flank exposed to Lake Borgne, the levee ring
surrounding Eastern New Orleans, GIWW, and lakefront levees in certain locations;
• replacement of I-walls with T-walls in some vulnerable areas;
• new surge barrier at the intersection of IHNC and Lake Pontchartrain;
• new surge reduction barrier at the intersection of the GIWW and MR-GO; and
• permanent gates and pumps at the mouths of the 17th St., Orleans, and London Ave. outfall canals,
with the gates to be closed during high water events and outfall pumps to work in parallel with existing
pump stations to remove water from the city (USACE, 2009b).
These upgrades, though marginal when compared to total risk, represent a substantial federal and state
investment. The 100-year upgrades to the system are currently projected to cost $14.45 billion—$12.8 billion
from Congress, and $1.5 billion from the State of Louisiana—with an estimated completion date of 2011
(USACE, 2009b).5 Compared to the initial cost estimates for the federal system—$85 million in 1961 dollars,
or $605 million in 2008 dollars—this represents cost growth of 2,388% to complete and upgrade the system,
not counting the additional funds spent on top of the initial allocation prior to 2005.
5
The cost estimates for this work have already increased relative to the initial projections, and the completion dates have moved
from 2009 to 2011. Note also that some currently temporary elements of the systems, such as the interim enclosures and pumps on
the outfall canals, may not be completed until 2014 or later (CPRA, 2010; Schwartz, 2007).
30
Managing New Orleans Flood Risk
2.3.2.2
Chapter 2
Long-term risk reduction
Despite the high cost and effort required for the 100-year system, however, both IPET and their external
National Research Council review panel agree that a 100-year risk reduction system is inadequate for a
dense urban area such as New Orleans (IPET, 2009a; National Research Council, 2009a). To address the
long-term need across south Louisiana, Congress appropriated $8 million in 2006 for a study of “Category
5 protection” to be conducted by USACE, with a final report requested by December 2007.6 In response,
USACE established a study team at the New Orleans District Office to develop a report on Louisiana Coastal
Protection and Restoration. The LACPR team had a broad mandate to explore risk reduction across the
coast, taking into account structural, non-structural, and restoration approaches, and employed sophisticated
high-resolution storm surge models to support comparison of different coastwide plans (see Chapter 3).7
However, the detailed analysis, which considered thousands of different individual alternatives and required
numerous modeling runs on a supercomputer, ran well past the initial deadlines set by Congress. The cost of
the study eventually grew to $23 million (Schleifstein, 2009b),and the Final Technical Report was released
in June 2009, eighteen months past the initial deadline (USACE, 2009c).
The LACPR Report represents a substantial contribution to existing knowledge regarding the recurrence
of damaging storms, behavior of storm surge along the coast, and potential future risk from an eroding coastline. For each of the five coastal Planning Units (a geographic delineation adopted by USACE and the State
of Louisiana—New Orleans is located primarily in Planning Unit 1) USACE recommends 5-6 possible risk
reduction plans incorporating varying levels of coastal restoration, structural and non-structural protection.
The recommended options presented for Planning Unit 1 include a restoration-only alternative, restoration
plus non-structural protection to the 100-, 400-, and 1,000-year design level, and two variants of restoration plus a new barrier-weir across the Lake Pontchartrain passes and other structural features (one with and
one without additional 100-year non-structural measures). As it relates to metropolitan New Orleans, the
restoration-only option could provide the city with risk reduction benefits by slowing or preventing additional wetlands loss in front of the levees. The 100-year non-structural plan suggests a moderate number of
additional structure elevations but would not substantially affect the city because HSDRRS, when complete,
6
Public Law 109-148.
RAND participated in the LACPR effort in late 2006, but the LACPR team instead elected to use different methods for considering long-term uncertainty, a decision discussed in detail in Chapter 3 of this document.
7
31
Managing New Orleans Flood Risk
Plan
Coastal only
NS-100
NS-400
NS-1000
LP-a-100-1
C-LP-a-100-1
Includes
non-struct.
No
Yes
Yes
Yes
No
Yes
Chapter 2
Includes
barrier weir
No
No
No
No
Yes
Yes
Effect on
New Orleans
Restoration only
Few elevations
Some elevations
Many elevations
New structural
New structural
Total cost
($ billion)
$10.7
$17.1
$34.5
$49.7
$17.7
$21.6
Table 2.1: Planning alternatives recommended in the LACPR Final Technical Report for Planning Unit 1,
including Greater New Orleans. Note that each alternative includes a common set of coastal restoration
actions (USACE, 2009c).
would already theoretically provide 100-year risk reduction to most areas within the system. The 400- or
1000-year non-structural protection plans, alternately, would entail raising many more structures in vulnerable areas of the city. The structural alternatives would add substantial new features to the system, both in
terms of preventing storm surge from entering Lake Pontchartrain and further bolstering the eastern flank of
levee defenses (USACE, 2009c). Table 2.1 summarizes the alternatives proposed by LACPR.
The LACPR report and decision process have received substantial criticism. The eighteen-month delay,
for one, pushed the report’s release well outside of the initial recovery period post-Katrina, so that many
decisions were already made by state and local planners, stakeholders, and residents without additional information about USACE’s long-term plans. State officials have also criticized LACPR for not recommending
a specific, actionable plan to Congress—likely moving it out of the window for recent economic stimulus
spending—and failure to coordinate with recent state efforts (CPRA, 2009a). In addition, the risk analysis does not fully address concerns about the substantial long-term uncertainty associated with this policy
challenge, an issue treated in detail in Chapter 3 of this document (National Research Council, 2009b).
A novel feature of the LACPR report is the focus on different possible non-structural risk mitigation
plans as viable alternatives to further structural additions to the system. Although USACE has worked with
non-structural mitigation in other localities and contexts, this represents the first substantial attempt to apply
systematic land use planning to reduce risk in greater New Orleans or across south Louisiana. USACE
considers non-structural mitigation within the New Orleans system as a form of redundant protection, fitting
within the multiple-barriers scheme, and specifically includes a discussion of non-structural risk reduction
and a hypothetical maximum non-structural plan within the system that would raise all structures to +1 foot
above sea level (see Chapter 5) (USACE, 2009c). However, implementating a systematic, coast-wide non32
Managing New Orleans Flood Risk
Chapter 2
structural plan is far outside of USACE’s traditional domain, and were Congress to fund such a plan, USACE
and partnering organizations would face new and complex challenges in meeting the ambitious goals outlined
in the LACPR report. In addition, the implementation timeline remains unclear, and it could be years or
decades before such an effort begins.
2.3.3
2.3.3.1
State of Louisiana planning efforts
Coastal Master Plan
After Katrina, Louisiana began large-scale coastal redevelopment master planning, in parallel to LACPR,
designed to provide a clearly defined and maintained level of protection against future catastrophic storms
while also addressing coastal restoration and cultural preservation objectives. The initial Master Plan, completed in 2007, draws directly from the multiple-barriers framework developed by the Lake Pontchartrain
Basin Foundation and argues for land use planning and non-structural risk mitigation as key components of
any coastal risk reduction plan (CPRA, 2007). The state has since developed annual implementation plans
for 2008, 2009, and 2010 derived from the Master Plan and presented to the Louisiana State Legislature for
funding (CPRA, 2008a; CPRA, 2008b; CPRA, 2009b). It also created a new interdisciplinary organization,
the Office of Coastal Protection and Restoration (OCPR), to oversee and administer these efforts. Most of
OCPR’s efforts to date, however, have been focused on coastal restoration rather than risk reduction plans,
and the new office is currently working to integrate the recent LACPR output and address hurricane risk more
systematically in the 2011 and 2012 plans.8
2.3.3.2
Post-Katrina reconstruction
Another key organization created in Louisiana subsequent to the 2005 hurricane season is the Louisiana
Recovery Authority (LRA). Governor Blanco created the LRA to help oversee coastwide rebuilding efforts
from Hurricanes Katrina and Rita, cooperate with federal organizations providing rebuilding funds such as
FEMA, and develop land-use policies for the state. LRA has also administered the federally-funded Road
Home program since 2006, providing rebuilding, mitigation, and other assistance to homeowners affected by
8
RAND developed a draft prioritization and scheduling software tool in support of the 2010 Annual Plan, and is currently
working with OCPR as a contractor to help develop an integrated decision process to support future OCPR planning. The author is
participating as a member of the RAND contracting team.
33
Managing New Orleans Flood Risk
Chapter 2
the two storms.9 The Road Home program has faced criticism over the last several years primarily because
the disbursement of funds has been slow and homeowners have faced substantial administrative challenges
during the application process, leading to the replacement of the primary contractor earlier this year (Eden
and Boren, 2008).
2.3.4
New Orleans planning efforts
2.3.4.1
Initial post-Katrina planning
The City of New Orleans has also undertaken a series of post-Katrina planning efforts with limited success during the past four years. The first effort was conducted by the Bring New Orleans Back Commission
(BNOB) established by Mayor Nagin in November 2005. The Commission drew many of its recommendations from a report by the Urban Land Institute that suggested the city invest in neighborhoods mostly spared
by the flooding and place a moratorium on development in the hardest hit neighborhoods, with some slated
to revert to green space (York, 2006). When the New Orleans Times-Picayune published a map in January
2006 showing areas slated for a building moratorium as well as green dots on neighborhoods considered
for conversion to green space, however, there was an immediate and hostile reaction to the plan from city
residents—particularly because the areas targeted were disproportionately African-American (Barnett and
Beckman, 2006). These recommendations also were seen as top-down and lacking input from displaced
residents, and were mostly abandoned by FEMA and the Mayor’s Office in the face of growing opposition
(Nelson et al., 2007).
This initial failure was followed by two subsequent rounds of planning focused more closely on individual
neighborhoods: the 2006 New Orleans Neighborhood Rebuilding Plan, also know as the Lambert Plans,
which involved separate planning efforts in 49 different neighborhoods with extensive public meetings; and
the 2007 Unified New Orleans Plan (UNOP) (UNOP, 2007), a more centralized effort that divided the city into
thirteen planning districts, suggested different types of investment for each district depending on flood levels
and other characteristics (with no areas abandoned), and incorporated many of the existing Lambert Plans
(Nelson et al., 2007; Horne and Nee, 2006). Though the latter efforts involved substantial public involvement
and were better-received than BNOB, to date most recommendations regarding flood risk mitigation have not
9
See http://road2la.org/.
34
Managing New Orleans Flood Risk
Chapter 2
been implemented. At present, New Orleans has not yet revised zoning laws to address flood risk, adopted
systematic land use regulations, or developed other policies to encourage non-structural risk mitigation.
The lack of city efforts towards a systematic rebuilding process, coupled with a rapid infusion of funding
over the first two years of recovery, have led neighborhood and non-profit organizations to play a critical role
in the rebuilding process with little regard to comprehensive risk mitigation (Bourne Jr., 2007). Numerous
neighborhood organizations, for example, have worked to assist returning residents in applying for compensation funding and rebuilding or repairing their homes (Nelson et al., 2007). Other non-profit organizations
have sought outside funding to build new developments in devastated neighborhoods, including, for example,
Habitat for Humanity’s Musician’s Village,10 Brad Pitt’s Make It Right Foundation,11 and Global Green’s Rebuilding New Orleans Project.12 These non-profit efforts are often targeted at sustainable redevelopment or
green-building, and sustainability in the context of New Orleans necessitates elevating and/or floodproofing
structures. Nevertheless, the rebuilding efforts by these organizations have been mostly uncoordinated, and
as a result the rebuilding process has thus far yielded a patchwork of protected and unprotected homes.
The City of New Orleans has been working to develop the planning infrastructure for future hazard
mitigation actions. In February 2007, the city started a new hazard mitigation unit, which was folded into
the city’s Office of Homeland Security and Emergency Preparedness in August 2008. The unit’s goal is
to make the best possible use of existing federal hazard mitigation incentives made available by FEMA to
provide additional risk reduction to areas of the city that have frequently flooded in the past (high-frequency,
low-damage events) or are exposed to substantial storm surge flooding risk (low frequency, high-damage
events). The unit is also developing a new hazard mitigation plan—a requirement for localities to participate
in certain FEMA-funded programs—scheduled for adoption in 2010 (Nance, 2009).
2.3.4.2
Long-term master plan
In 2008, the city began its fourth cycle of post-Katrina planning, with the goal of developing a 20-year Master
Plan and Comprehensive Zoning Ordinance.13 This planning effort, ongoing throughout 2009, has folded in
many concepts and recommendations from the Lambert Plans and UNOP while seeking a broader long-term
10
http://www.nolamusiciansvillage.com/
http://www.makeitrightnola.org/
12
http://www.globalgreen.org/neworleans/about/
13
See http://www.nolamasterplan.org.
11
35
Managing New Orleans Flood Risk
Chapter 2
strategic vision. Once again, substantial input from residents was sought through frequent public meetings.
The Master Plan is intended to have the “force of law” behind land use recommendations, meaning that future
planning and land use decisions would have to be consistent with Master Plan “goals, policies, and strategies”
(City of New Orleans, 2009a). The plan was approved by the City Planning Commission in January 2010
(Eggler, 2010) and at the time of writing was under consideration by the City Council for final approval. The
Master Plan includes a chapter describing possible long-term flood risk mitigation initiatives, discussed in
further detail in Sec. 5.4. However, at present the city has no internal or external dedicated funding source
for broad-scale hazard mitigation actions, and New Orleans will therefore depend primarily on federal funds
for future mitigation projects.
2.3.5
Independent proposals for New Orleans flood protection
In addition to the officially-sanctioned post-Katrina investigations and master planning efforts, there have
been a series of proposals from independent researchers and research organizations intended to guide future
flood risk planning for New Orleans or across the Louisiana coast. Some of these independent proposals
present complete alternative plans for coastal protection and restoration. For example, the Lake Pontchartrain
Basin Foundation’s Comprehensive Recommendations Supporting the Use of the Multiple Lines of Defense
Strategy to Sustain Coastal Louisiana (MLOD) report recommends elevation for structures within the 1,000year floodplain (including all of New Orleans), as well as upgrades to achieve approximately a 400-year
design level for the HSDRRS and extensive restoration and shoreline protection for wetland areas seaward of
the levee system (Lopez et al., 2008). Similarly, Dutch researchers and planners published a comprehensive
plan calling for 1,000-year protection for New Orleans (5,000-year for the center of the city) via a combination
of upgraded levees, closure of the mouth of Lake Pontchartrain via a new barrier, marsh restoration and
landscape stabilization, and the partial in-filling and conversion of Lake Borgne to a fresh water cypress
swamp (Dijkman, 2007).
Other researchers have performed smaller-scale efforts to consider the economic benefits and costs of
additional New Orleans protection. Risk Management Solutions (RMS) evaluates risk in New Orleans using a proprietary hurricane and storm surge model and suggests that risk planning and protection system
improvements are needed, but refrains from specific suggestions (RMS, 2006). Hallegatte (2006) performs
36
Managing New Orleans Flood Risk
Chapter 2
a preliminary benefit-cost analysis of “Category 5” protection for New Orleans, and suggests it could yield
positive net benefits when taking into account second order impacts, intergenerational discounting, risk aversion, and damage heterogeneity—although results are highly sensitive to input assumptions. Other studies
relying on economic optimization or benefit-cost analysis similarly suggest much higher structural protection design standards under specific assumptions about future storm recurrence, population growth, and the
state of the coastal environment (von Winterfeldt, 2006; Jonkman et al., 2009). Overall, the majority of
these independent proposals and research efforts suggest providing a higher level of structural protection to
New Orleans, but do not necessarily account for potential challenges to implementation—including delayed
construction timelines, high overall cost plus potential cost overruns, and induced development—that may
limit the effectiveness of these large-scale investments. Moreover, only one external proposal—the MLOD
report—considers using systematic non-structural mitigation within New Orleans to augment the risk reduction provided by the levees.
2.4
Summary
Given the historical cycle of limited risk reduction, disaster, and rebuilding, there is a strong need for risk
analysis that considers the long-term implications of current choices and seeks sustainable risk reduction
for New Orleans. In the next five chapters, I describe and perform an analysis focused on non-structural
risk mitigation aimed towards this long-term resiliency objective. This analysis provides estimates of the
risk reduction from these efforts and informs the choice and prioritization of the mitigation alternatives that
remain available to local planners as they move forward with long-term planning. This analysis can also
provide an important source of information about flood risk uncertainty and risk mitigation to residents and
other organizations actively involved in the rebuilding process.
37
Chapter 3
Addressing Deep Uncertainty in Flood Risk
Assessments
3.1
Introduction
The river- and hurricane flood protection system that surrounds greater New Orleans dates from the eighteenth century, but the risk analysis methods to inform large and long-term infrastructure investment decisions
such as these were developed only in recent decades. Despite a large and growing literature, however, standard risk analysis approaches can lead to suboptimal or poor long-term decisions when applied to natural
disasters or other infrequent large hazards if there is limited information available to characterize the probability of a rare event or of its consequences (Milly et al., 2008). Planning efforts designed to determine the
appropriate level of flood protection in New Orleans prior to Hurricane Katrina, for example, likely underestimated the true risk and helped lead to a vulnerable and limited system (Gordon and Little, 2009). After
Katrina, USACE and other researchers have developed new numeric models to improve estimates of flood
risk in the region, but these analyses rely on standard approaches to uncertainty and their recommendations
are vulnerable if key assumptions about the effects of climate change or other coastal conditions are violated.
This chapter addresses the challenge of estimating and planning for future hurricane flood risk for the
City of New Orleans, and lays the foundation for the analytic methods applied through the remainder of the
dissertation. The discussion proceeds in several steps. First, I describe a conceptual framework for flood
38
Managing New Orleans Flood Risk
Chapter 3
risk analysis, and discuss the shortcomings of this framework as it relates to New Orleans flood risk due to
the substantial long-term uncertainties present. To illustrate these challenges, I next discuss and evaluate
the methods applied in the recent IPET and LACPR analyses. I then describe a new analytic framework for
making long-term decisions, Robust Decision Making, which can be applied in instances where traditional
tools for addressing uncertainty fall short. Finally, I describe an application of RDM to New Orleans flood
risk, with a focus on the risk posed to single-family residences, designed to improve on existing methods
3.2
3.2.1
Addressing uncertainty in flood risk analysis
Flood risk framework
Risk is a term defined in a variety of ways, both qualitatively and quantitatively, to describe uncertainty
surrounding future outcomes. A recent United Nations report, for example, defines risk as “the probability
of harmful consequences, or expected losses (deaths, injuries, property, livelihoods, economic activity disrupted or environment damaged) resulting from interactions between natural or human-induced hazards and
vulnerable conditions,” (United Nations, 2004) while a Clinton-era Presidential Commission simply defines
it as “the probability that a substance or situation will produce harm under specified conditions.” (Omenn
et al., 1997). The International Risk Governance Council reviewed the terminology used by different organizations and disciplines to describe risk and its related fields (risk analysis, risk assessment, risk management,
etc.) and determined that “risk terminology is neither consistent nor always coherent,” (Renn and Graham,
2006) a problem which can lead to confusion or ambiguity regarding exactly how risk is defined or what is
being addressed when using risk analysis to inform decisionmaking.
Quantitative formulations for risk, however, are more generally agreed upon. Mathematically, risk is
commonly described as the product of the likelihood of an event coming to pass with the adverse effects
from the event. When evaluating risk from hurricanes or other natural hazards, the likelihood is related to
the underlying threat or hazard probability, while the effects are generally derived from a combination of the
vulnerability of manmade systems to the event and the consequences or losses that could occur. When examining risk in a complex engineered system such as the Greater New Orleans Hurricane and Storm Damage
Risk Reduction System, the components can be described as follows: (Morgan and Henrion, 1990; IPET,
39
Managing New Orleans Flood Risk
Chapter 3
2009b)
1. Threat can be defined as the underlying probability of a surge-producing storm striking New Orleans,
P r(storm occurs). Threat is typically measured on an annual basis. This occurrence probability cannot
be directly controlled through policy efforts.
2. Vulnerability concerns the reliability of the engineered system. For the HSDRRS, vulnerability can
be defined as P r(flooding|storm occurs), the probability that storm surge will produce flooding by
overwhelming the physical protection system. The system vulnerability can be reduced for some events
by varying design parameters for the system (e.g., levee heights and alignments, floodwall designs,
construction materials), but remain nonzero even for storms at or below the “design level” of the
system due to the overall complexity of the engineered system and limitations of numeric modeling in
projecting system performance.
3. Consequences are the anticipated effects of a natural hazard, here the effects on the city if a flood
occurs: E(damage|storm & flooding occurs). The consequences of flooding can be measured in a
variety of ways, including direct and indirect economic losses, structures destroyed, lives lost, persons
displaced, etc. (This dissertation focuses on direct economic losses.)
Overall risk can then be described as:
Flood Risk = Threat × Vulnerability × Consequences
In order to quantitatively estimate risk within this framework, planners must be able to characterize the
probability distributions associated with storm surge threat and system vulnerability as well as project the
potential losses from different types of floods. These estimates themselves are uncertain, however, and it is
important to distinguish this type of uncertainty from the estimated risk itself. USACE (and others) refer to
irreducible uncertainty reflecting “the randomness of nature”—i.e., trying to predict whether it will be rainy
or sunny one month from today—as aleatory uncertainty, while uncertainty arising from a lack of knowledge
of a given system is instead referred to as epistemic uncertainty (Budnitz et al., 1997; IPET, 2009b). Risk
analysis seeks to address aleatory uncertainty through probabilistic characterizations—i.e., the chance of
40
Managing New Orleans Flood Risk
Chapter 3
rain one month from today is 20%—but the fundamental variation will always remain. In contrast, improved
historical data, a better understanding of the physical processes that lead to hurricane formation or storm
surge behavior, or other scientific insights could reduce the epistemic uncertainty surrounding flood risk
estimates.
3.2.2
Stationarity
Epistemic uncertainty is a substantial challenge to effectively estimating flood risk for planning purposes.
Decisions that rely on flood risk estimates—from the federal government considering billion-dollar investments in the HSDRRS down to the individual resident deciding whether or not to purchase a home within
the protection system—require an informed perspective on both current levels of hazard risk as well as how
risk will evolve over multiple decades. Traditionally, flood risk estimates have relied on an assumption of
stationarity in order to estimate current and future hazard probabilities. Stationarity implies that hazard
probabilities are at long-term equilibrium, and thus with sufficient historical data the probability distribution describing these hazards can be accurately characterized (even if many possible storms have never been
observed) (Milly et al., 2008).
The stationarity assumption, then, leads to the use of probabilistic characterizations for both the irreducible aleatory uncertainty inherent to flood risk and the remaining epistemic uncertainty surrounding its
estimation. This can lead to confusion, because the former is the “true” risk while the latter represents uncertainty about the true risk, but both are represented using the same language and similar methods. Fig. 3.1
illustrates this point with a hypothetical example. Here, the aleatory component (true risk) is described as
the annual probability of exceeding a certain flood elevation (exceedance), while the epistemic uncertainty
is represented using confidence intervals around the risk estimate. In traditional risk analysis, all epistemic
uncertainties are fully characterized in this manner using probability distributions, with the resulting calculations also described in terms of confidence intervals. When used in support of decision analysis, these
characterizations lead to a policy choice optimized for the most likely realization of flood risk.
41
Managing New Orleans Flood Risk
Chapter 3
Figure 3.1: Notional example of flood risk estimation designed to illustrate the difference between aleatory
and epistemic uncertainty. Aleatory uncertainty (true risk) is represented as the annual probability (y-axis)
of exceeding a certain flood elevation (x-axis), while the epistemic uncertainty surrounding this estimate is
shown with 5th , 50th and 95th percentiles from the cumulative probability distribution shown in the cut-out.
Source: IPET, 2009b, Appendix 11, p. 4.
3.2.3
Deep uncertainty
The standard model for addressing epistemic uncertainty may fall short, however, when trying to estimate
future flood risk in New Orleans. A variety of key contributing factors may be difficult to predict or characterize probabilistically, and scientists, planners, and stakeholders may disagree on the underlying probability
characterizations for these key factors. In particular, the stationarity assumption is threatened by ongoing
natural and anthropogenic changes to the Louisiana coastline (see Sec. 2.2.2.3) as well as the potential effects of global climate change on rising sea levels and hurricane recurrence or intensity (Milly et al., 2008).
Lempert et al. (2003) refer to important drivers that cannot be reasonably characterized or weighted using
the likelihood of different outcomes to support decisionmaking as “deep” uncertainty.
3.3
Deep uncertainty complicates flood risk projections for New Orleans
In this section, I describe the deep uncertainty inherent to flood risk planning for New Orleans that emerges
from ongoing environmental change, long-term economic and population drivers, and lack of knowledge
42
Managing New Orleans Flood Risk
Chapter 3
regarding implementation of risk reduction policies. These uncertainties complicate the planning and rebuilding process and have led to disagreements about the appropriate level of protection or risk reduction for
the city.
3.3.1
3.3.1.1
Climate-related uncertainty
Storm recurrence
Powerful hurricanes forming over the Atlantic and striking the Gulf Coast are a concern each year during
the summer months, but truly devastating storms remain relatively rare events. As discussed in Sec. 2.2.3,
the historical record of these storms only extends for approximately 150 years, and accurate measurements
of storm characteristics extend back just to the Second World War (Landsea et al., 2007, 2004). This limited
dataset is drawn on to support long-term planning for future extreme events, and even setting aside the threat
to stationarity posed by anthropogenic climate change there remains substantial concern that the recent record
is too limited to account for hurricanes with return intervals orders of magnitude greater (i.e., 1,000-year,
10,000-year) than the length of the existing record. This directly challenges the ability to apply likelihoods
to different types of storms, as any bias in the tail of the distribution could have a substantial effect on the
predicted return probability.
Recent efforts have been made to more effectively mine the current dataset to efficiently span the range
of possible storms in support of high-resolution hydrodynamic simulations of hurricanes and storm surge.
Methods to improve upon sole reliance on the historical dataset include: a) nonparametric resampling (Empirical Simulation Technique) (Borgman et al., 1992); b) Monte Carlo sampling and simulation over an
extended time range (Empirical Track Model) (Vickery et al., 2000); and c) estimation of a joint-probability
distribution across five key hurricane characteristics using data from a broad geographic area, which allows
for the simulation of combinations of parameters not previously seen in the historical record (Joint Probability Method; JPM) (Myers, 1975; Ho and Myers, 1975; Resio, 2007). USACE used an update of the JPM
termed “Joint Probability Method with Optimum Sampling” (JPM-OS) in support of the IPET and LACPR
hydrodynamic modeling runs (Toro et al., 2009; Resio et al., 2009). Resio (2007) argues that the revised
method should “...provide a fairly accurate description of the general characteristics of hurricane surges at
least up to the 500-year return period,” but these methods still rely on historical data to derive the likeli43
Managing New Orleans Flood Risk
Chapter 3
hood distributions of different storm characteristics and thus remain susceptible to small-sample bias when
estimating return frequency (particularly for more extreme events).
Another recent innovation has been the construction of a much longer record of hurricane landfalls via
the emerging field of paleotempestology. Similar to the paleohydrology reconstructions used to investigate
historical streamflow tracing back thousands of years (e.g., Woodhouse et al. (2006); Meko et al. (2007)),
paleotempestology uses tree rings, sediment deposits, chemical markers in coral, and other physical evidence
in order to reconstruct a record of the frequency and (in certain cases) magnitude of landfalling hurricanes
much farther back than the current instrumental record (Nott, 2004). These reconstructions have the potential
to extend the record by thousands of years and greatly improve our understanding of long-term variation in
tropical storm activity. For example, one recent study using several reconstructions detected notable variation
in Atlantic hurricane frequency over the last 1,500 years, including a period of high activity during the
medieval period (approxmiately 1000 CE) correlating with La-Niña-like climate conditions and warm seasurface temperatures (SST) in the tropical Atlantic (Mann et al., 2009). However, the literature does not yet
address the central Gulf Coast in detail, and the stationarity assumption may remain vulnerable in coming
decades even with a longer historical record.
3.3.1.2
Hurricanes and climate change
Over the last several decades, climate researchers have actively debated a potential link between climate
change and the intensity of landfalling Atlantic hurricanes (Mooney, 2007). In theory, the intensity of storms
increases with sea surface temperature (Emanuel, 2005b; Knutson et al., 1998), so that projected increases
in sea surface temperature due to climate forcing (Barnett et al., 2005) would lead to an increase in observed
intensity. Many studies have identified such correlations for the Atlantic basin in the recent (post-1970)
historical record (Emanuel, 2005b; Webster et al., 2005; Hoyos et al., 2006; Trenberth, 2005; Holland and
Webster, 2007), while others have used numeric modeling results taking into account the future climate to
suggest a relationship (Emanuel et al., 2008; Knutson and Tuleya, 2004). A recently-published study using
eighteen different coupled atmosphere-ocean general circulation model (GCM) projections, for example,
predicts almost double the number of Category 4 or 5 storms with future sea surface warming despite a decline
in overall storm formation (Bender et al., 2010). Other researchers, however, have described this causal link
44
Managing New Orleans Flood Risk
Chapter 3
as premature, citing a lack of reliable data in the historical record (Landsea et al., 2006), ascribing recent
destructive years to other climate anomalies such as La-Niña (Bell et al., 2005), or otherwise describing any
such effect as too small or too inconsistent to be useful for future policymaking (Pielke Jr. et al., 2005, 2006).
Nevertheless, the scientific consensus currently supports a relationship between atmospheric forcing and
an increase in hurricane intensity. The Intergovernmental Panel on Climate Change (IPCC) AR4 synthesis
report describes an increase in future hurricane intensity as “likely” (IPCC, 2007b), while a recent summary
in the United States suggests that sustained hurricane wind speeds will increase between 1-8% for each 1◦ C
increase in tropical sea surface temperature (USGCRR, 2007). Nevertheless, uncertainty about the amount
of SST increase, magnitude of the relationship with intensity, and other intervening factors make it difficult to
predict how such an effect will affect storm surge along the Louisiana Coast over the next 50 years. Changes
in overall intensity could have dramatic effects in the tail of the risk distribution: as Hallegatte (2006) points
out, an increase in the hurricane power dissipation index (PDI) of 65% by the end of the century (postulated
by Emanuel (2006)) would multiply the frequency of the 1% most powerful hurricanes by a factor of 3.8.
There is no similar consensus on the frequency of future storms. The frequency in any given year (or
decade) is dependent on numerous factors varying on annual or other cycles, and studies have projected both
increases and decreases in future Atlantic storm frequency (USGCRR, 2007, Emanuel et al. (2008)). IPCC
AR4 finds that “there is no clear trend in the annual numbers of tropical cyclones (IPCC, 2007b).” That
said, even if the total number of tropical depressions remains unchanged on average, if intensity continues to
increase the Gulf Coast will nevertheless experience more landfalling hurricanes (in place of tropical stormstrength storms). One study projects up to two additional hurricanes making landfall on the Gulf Coast by
2050 and up to four by 2100, depending on the rate of SST increase (Keim et al., 2008).
3.3.1.3
Sea level rise
There is substantially greater consensus on the linkage between climate change and rising sea levels. A
warming atmosphere contributes to sea level rise in two ways: melting of land-based glaciers and ice sheets,
and thermal expansion as more heat is retained in ocean water. Observational evidence shows that the rate
of eustatic sea level rise increased substantially in the 20th century: annual rates averaged 1.8 ± 0.5 mm/year
from 1961-2003, but were roughly double this rate (3.1 ± 0.7 mm/year) when looking just at the most recent
45
Managing New Orleans Flood Risk
Chapter 3
decade in the dataset (1993-2003) (IPCC, 2007a). The GCMs developed in support of IPCC AR4 project a
worldwide average of 0.18-0.59 meters of sea level rise from 1990-1999 to 2090-2099, an annual average of
1.8-5.9 mm/year depending on SRES emissions scenario (IPCC, 2007b).1
Another study that downscaled the global trends from seven GCMs to a grid focused on the Gulf Coast
projects up to 74 cm ( > 12 mm/year) of SLR at Grand Isle, LA from 1990-2050 (Table 3.1). These long-term,
GCM-derived trends are of concern because they are substantially higher than those previously observed, and
are projected to have substantial cascading effects on coastal populations including New Orleans. Another
concern not considered quantitatively in the recent IPCC effort, however, is the possibility of catastrophic sea
level rise due to rapid melting of the Greenland or Antarctic ice sheets. Overpeck et al. (2006) suggest that
by there may be a “triggering threshold” of warming reached by the end of the century that could lead to a
catastrophic scenario of up to 4-6 meters of eustatic sea level rise, similar to conditions that occurred between
13,800 and 7,000 years ago. A more recent investigation looking at the last interglacial stage (∼ 125k years
ago) estimated that, with polar temperatures 3-5◦ degrees warmer, sea levels were upwards of 6.6 meters
(95% confidence) higher than today (Kopp et al., 2009). Rapid deglaciation is called into question by other
studies (e.g., Bamber et al. (2009), one of which argues that physical constraints on melting velocities make
a range of 0.8-2.0 meters by 2100 more likely (with the upper bound still a more remote scenario) (Pfeffer
et al., 2008). Nevertheless, scenarios of rapid sea level rise prior to 2100 remain plausible, and of particular
concern for New Orleans.
SRES Emissions Scenario
Low
B1
A1B
A1FI
A2
59.3
60.3
60.6
59.8
Scenario
Mid High
64.4
66.0
66.4
64.9
71.7
73.9
74.5
72.3
Table 3.1: Projected mean sea level rise (cm by 2050) at Grand Isle, LA from CoastClim model under high,
mid, and low scenarios. The model combines and downscales output from seven GCMs for four SRES global
emissions scenarios. Source: Keim et al. (2008).
1
In 2000, the IPCC Special Report on Emissions Scenarios (SRES) team developed four narrative scenario “families” (labeled
A1, A2, B1, and B2) describing different possible emissions pathways depending on demographic, social, economic, technological,
and environmental conditions. Up to 40 individual scenarios were derived from these divergent scenario families, though a subset
of six scenarios—A2, B1, B2, A1B, A1FI, and A1T—are typically used in GCM simulations. See Nakicenovic et al. (2000).
46
Managing New Orleans Flood Risk
Chapter 3
Sea level rise affects flood risk in coastal Louisiana through multiple pathways. First, an increase in mean
sea level increases the elevation of storm surges relative to ground level and protective barrier elevations
at least linearly. In areas fronted by wetlands (including the critical “Golden Triangle” area east of New
Orleans), however, rising seas could instead have a multiplicative effect: one recent hydrodynamic modeling
study, for example, found increases in storm surge elevation two to three times greater than the amount of sea
level rise in broad areas of southeast Louisiana, with isolated areas showing a multiplier of up to five (Smith
et al., 2009). Sea level rise also indirectly increases storm surge threat by increasing the rate of wetlands
loss, eroding the natural defenses outside the protection system (Nicholls et al., 1999; Penland and Ramsey,
1990).
3.3.2
3.3.2.1
Coastal change and protection system resilience
Coastal land loss and restoration efforts
The effects of eustatic sea level rise do not occur in isolation, but are instead additive with natural and
anthropogenic coastal land loss and subsidence. The projected rate of wetlands loss is uncertain, as discussed
in Sec. 2.2.2.3, but adding to these concerns is considerable uncertainty regarding both a) the type, location,
and level of investment in future restoration efforts conducted by OCPR and federal authorities and b) the
net effects of these planned restoration effort. The only certainty is that the coastal landscape will have
changed—perhaps substantially—by 2060. This presents an especially difficult challenge when estimating
future risk, because the storm surge threat may vary greatly under different coastal configurations (USACE,
2009c, Wamsley et al., 2009). It also complicates local planning efforts, because New Orleans officials do not
directly control state restoration efforts and lack sufficient information to fully address these uncertainties.
3.3.2.2
Local subsidence
Localized land subsidence—a sinking landscape—is the final uncertain environmental driver that influences
future flood risk. In southeast Louisiana, subsidence is thought to be caused by numerous factors including coastal land loss, soil drainage and compaction Penland and Ramsey (1990), groundwater withdrawals,
oil and gas extraction, and tectonic activity (Shinkle and Dokka, 2004; Dokka, 2005; Burkett et al., 2003;
González and Törnqvist, 2006). The subsidence rate may vary substantially across the Gulf Coast or over
47
Managing New Orleans Flood Risk
Chapter 3
time, however (Shinkle and Dokka, 2004), and there is still considerable disagreement about the relative
contributions of different causal effects and expected future rates in the New Orleans vicinity (González and
Törnqvist, 2006). For New Orleans, Dixon et al. (2006b) calculate an average annual subsidence rate of
−5.6 ± 2.5 mm/year using a high-resolution synthetic aperture radar estimation approach, but note substantial variation by geographic location (Fig. 3.2). In some areas, particularly Eastern New Orleans and along
the MR-GO, subsidence rates are 10-29 mm/year.
Figure 3.2: Subsidence across the Greater New Orleans region in mm/year. Source: Dixon et al. (2006b).
The absolute values of subsidence and sea level rise can be summed to calculate relative sea level rise
(RSLR), the net change in land elevation relative to sea elevation. Increasing subsidence and RSLR can
increase risk locally in New Orleans by increasing the vulnerability of the city and its defenses. Once again,
flood defenses built to a certain fixed standard will subside over time if not regularly maintained and upgraded,
reducing the effective protection elevation (see Sec. 2.3.1). In addition, subsidence within inhabited city
areas serves to “deepen the bowl” relative to sea level, potentially increasing flood depths and requiring
further pumping capacity to keep low-lying areas dry as the defensive ring grows higher. Outside of the city
defenses, natural subsidence is an important contributor to wetlands loss (Dokka, 2006).
48
Managing New Orleans Flood Risk
3.3.2.3
Chapter 3
Protection system performance
One of the primary lessons drawn from Hurricane Katrina is that predicting the performance of an engineered
system as complex and wide-ranging as the New Orleans HSDRRS under all possible surge and flooding conditions is difficult or impossible, particularly when the hurricane conditions faced exceed the design criteria.
As I discuss below, a portion of the IPET investigation attempted to probabilistically characterize all possible
sources of epistemic uncertainty related to the performance of the system (i.e., ground elevation estimates,
levee fragility, etc.). Though a substantial contribution to an improved understanding of system vulnerability,
these efforts nevertheless may miss key sources of uncertainty given the level of complexity. They are also
directed at a single “snapshot” estimate of epistemic uncertainty rather than a dynamic projection over time,
which would entail an additional set of assumptions regarding system evolution and other key factors. As
a result, current projections of protection system performance could understate the uncertainty surrounding
future system performance, leading to unexpected surprise when faced with the next major surge-producing
storm.
3.3.3
Population growth and development patterns
Finally, the consequences of future flooding are currently uncertain because future growth rates and patterns
within the city remain unknown, and future population growth will depend heavily on how the city emerges
from post-Katrina reconstruction and current planning efforts. The population of New Orleans was shrinking prior to Katrina, and the lack of economic opportunities for residents suggested a city in slow decline
(Kates et al., 2006; Frey and Singer, 2006). The frenzy of planning activities and investment after Katrina,
however, has provided an opportunity to reassess and change this trajectory. Depending on future economic
opportunities, perceptions about risk and safety, and other factors, for example, the City of New Orleans
projects to have a population of between 365,000-500,000 residents by 2030 (City of New Orleans, 2009b).
Depending on where new residents choose to live and risk reduction actions they might take, however, future
population gains may or may not substantially increase overall flood risk to the city. In turn, the level of
structural risk reduction provided, coupled with incentives available for non-structural protection, are important contributing factors to future growth itself. Given this inherent endogeneity, both the future population
and corresponding level of economic risk remain highly uncertain.
49
Managing New Orleans Flood Risk
3.4
Chapter 3
Current planning approaches may not fully account for deep uncertainty
The deep uncertainties above complicate efforts to plan for a more resilient New Orleans and help avoid
another Katrina-scale disaster. Since 2005, a series of analyses have been completed with the goal of reassessing the level of flood risk to the city and/or determining the appropriate level of flood protection or
risk reduction (e.g., Grossi and Muir-Wood (2006); Hallegatte (2006), von Winterfeldt (2006)), but existing
efforts have not yet been able to address in full the deep uncertainty previously described. In this section, I
describe three recent approaches to estimating flood risk in New Orleans designed to support future decision
making, and where these approaches may contain assumptions or that threaten their conclusions.
3.4.1
Optimization and the Dutch approach
One model for flood risk decisionmaking relies on economic optimization. This approach was famously
applied in the Netherlands to help design the massive Delta Works, a series of new flood defenses throughout
the country that stands as one of the most complex engineering systems ever built, after a devastating North
Sea flood in 1953 (Vrijling, 2001). The decision analysis leading to this system was based on the work
of Dutch mathematician David van Dantzig, who described the basic optimization approach in 1956 (van
Dantzig, 1956). Van Dantzig treats investment in flood protection as a cost minimization problem, where the
goal is to select a “design level”—the height of the levee system so that storms of that return frequency or
smaller yield zero flooding—that minimizes the sum of residual risk (expected damage) and the investment
cost in additional infrastructure. This point is also where the marginal benefits (in terms of reduction in
expected risk) equals the next marginal cost increment of improved flood protection (Fig. 3.3).
This work has been applied recently to studies of New Orleans flood protection (e.g., Jonkman et al.
(2009); Dijkman (2007)), and recent efforts have been made to improve upon the initial probabilistic framework (Eijgenraam, 2006). The Dutch optimization approach accounts for epistemic uncertainty in risk estimates by assuming that the protective value of flood defenses erodes over time due to a series of factors,
including economic growth (which increases the consequences term of the risk calculation) and sea level
rise (which increases the probability of flooding by reducing the effective design height of the defenses), and
calls for a new safety assessment every five years as well as a new risk assessment every 25-50 years (Dijkman, 2007). If the flood defenses are improved every five years—for example, by increasing levee heights
50
Managing New Orleans Flood Risk
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5000
4500
Totalcost
4000
Optimumiswheretotal
costsareminimized
MB=MC
Costs($Millions)
3500
3000
2500
2000
1500
1000
Investment
Risk
500
0
10
100
1000
10000
Designlevel(returnyear)
Figure 3.3: Example of the economic optimization approach to flood risk analysis developed by van Dantzig
(1956). The y-axis represents costs, both in terms of residual flood damages (yellow line) and potential
flood investments (dark blue line). The x-axis shows the return interval of different storms on the log scale,
increasing to the right as the storm size increases. Under this approach, investments should be made up to
the point that minimizes the sum of residual damage and investment cost (yellow line). Chart adapted from
Jonkman et al. (2009).
where necessary in order to keep up with sea level rise—a constant level of acceptable residual risk could
be maintained for the area protected. With sufficient resources, this adaptive approach could be considered
insensitive or robust to some deep uncertainties.
I note, however, that an intensive management and maintenance program continued in perpetuity may
not be feasible for New Orleans. Because a large proportion of the country is at risk from coastal flooding,
flood protection in the Netherlands is considered critical to the national welfare and continued flood protection
investments are prioritized accordingly (Jonkman et al., 2009). Conversely, flood protection for New Orleans
has not been similarly prioritized in the United States, and the pre-Katrina protection system performed
substantially worse than expected due to extended construction delays and a lack of regular maintenance
(see Sec. 2.3.1). Even with the renewed focus on flood risk in New Orleans post-Katrina, such an approach
seems unlikely without additional federal resources committed to protecting New Orleans in the long-term.
Furthermore, the Dutch approach also implies that flood risk is changing sufficiently slowly that incremental
research and improvements can detect and keep up with the rate of change, but the potential for threshold
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Managing New Orleans Flood Risk
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effects or other surprises threatens this assumption. In addition, the historical record in New Orleans shows
that risk reduction has been consistently behind the adaptation curve in this respect.
3.4.2
IPET Risk and Reliability analysis
The IPET investigation of Hurricane Katrina described in Chapter 3 also included an extensive investigation
of post-Katrina risk within the HSDRRS system. Central to this effort was the development of a “Risk and
Reliability" model that draws from the results of several other branches of the investigation to generate a
detailed flood risk model for the City of New Orleans (IPET, 2009b). The Risk and Reliability model is designed to estimate the risks to property and human life posed by storm-induced flooding, as well as examine
the reliability of the system under a variety of different conditions—including how the currently-upgraded
protection system would have fared during Katrina. The IPET risk analysis was not initially designed as
a decision analysis, but rather as a new baseline in support of other analysis efforts. Flood maps and other
summary results for the current system have therefore been made publicly available to support local and individual decision making,2 and the City of New Orleans is currently using these results to support development
of its Master Plan and Comprehensive Zoning Ordinance (City of New Orleans, 2009a).
The Risk and Reliability model is divided into three modules, corresponding to the three-part breakout
described in Sec. 3.2.1. The first module generates probability distributions (annual probability of occurrence) for storm surge, waves, and rain from a series of 76 hydrodynamically-simulated hurricanes for a
detailed geospatial grid surrounding New Orleans. The second module uses an event-tree risk model to calculate how these storm-produced hazards affect the system and yield flooding. The event-tree module models
how each element of the system might respond to storm effects of various magnitudes, and aggregates the
probabilistic information to generate probability distributions of flooding at the neighborhood level. The final
module calculates direct losses from different flood depths and combines this information with the flooding
probabilities produced by the event-tree model in order to generate probability distributions for economic
losses (IPET, 2009b).
The IPET risk analysis represents what Bankes (1993) terms a “consolidative" modeling approach to New
Orleans flood risk, where substantial detail regarding the protection system itself—particularly the complex
2
http://nolarisk.usace.army.mil
52
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Chapter 3
event-tree model that seeks to characterize every possible state of the system—is incorporated in order to
predict flooding and flood consequences from the range of storms considered. Epistemic uncertainty is
addressed either via assumption (using point estimates where uncertainty is expected to be low) or probability
distributions (parametric and non-parametric), with probabilistic uncertainty tracked and summarized via
confidence intervals (IPET, 2009b).
However, this approach is specifically intended to provide a single estimate of current flood risk rather
than an evolving picture over time. As a result, IPET in large part does not address most of the deep uncertainties described above, including RSLR,3 climate change effects on hurricane intensity or frequency,
coastal wetlands loss, population changes, or other long-term uncertainties. Even if the IPET model accurately captures flood risk in 2007 or 2011, then, it does not speak to risk over the useful life of expensive,
permanent infrastructure investments in the HSDRRS or over the time span involved with substantial land use
changes. The National Academies review panel, for one, expressed concerns with the quantification of uncertainty and lack of climate change discussion in a 2008 draft of the Risk and Reliability analysis (National
Research Council, 2008), and the summary IPET review noted “...little treatment of the approximations and
extrapolation of sparse geotechnical data" (National Research Council, 2009b).
3.4.3
LACPR risk analysis
The LACPR coastwide risk analysis differs in objective, geographic and temporal scope, and key assumptions from the IPET analysis, though both share a similar hurricane threat foundation: JPM-OS sampling and
a discrete set of storms run through a high-resolution ADCIRC model to calculate expected surge elevations.
As previously discussed (Sec. 2.3.2.2), LACPR’s goal was to develop a comprehensive plan for risk reduction and coastal restoration across the Louisiana coast, with a time horizon of 50 years (2011-2060). The
investigation team sought to improve on previous planning efforts by moving away from the standard economic decision framework for federal water resources projects set forth in the 1983 Principles and Guidelines
(WRC, 1983), and instead explicitly considering a) multiple objectives, some of which are non-economic,
and b) substantial systematic uncertainty. RAND developed a prototype analysis using exploratory model3
IPET states that “an allowance is made for 2 ft of combined sea-level rise and subsidence” in calculations performed for the
2011 system update and released in a supplementary report (IPET, 2009b; IPET, 2009c). However, the authors do not specify how
sea level rise was incorporated or how these effects alter the city’s risk profile.
53
Managing New Orleans Flood Risk
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ing and Robust Decision Making techniques to inform development of this new “Risk Informed Decision
Framework” (RIDF) (Groves et al., 2006), but LACPR ended up choosing a more standard approach relying
on multi-criteria decision analysis (MCDA) to address the former and a discrete scenario analysis aimed at
the latter (USACE, 2009c).
Given the coastwide scope and multiple protection system configurations considered, LACPR used a
simpler approach to calculate flood risk in areas across south Louisiana. Once again, a series of storms
were chosen using the JPM-OS method, though generally fewer were run through ADCIRC than in the IPET
analysis: 56 storms for the 2010 base condition (no additional barriers) in the eastern grid, 46 to consider
the effect of restored marshes in 2060, and as few as nine for a sea level rise sensitivity analysis (USACE,
2009c). Surge hydrographs—surge elevation over time as the storm makes landfall—were then constructed
for 274 representative points in the east grid (mostly surrounding the HSDRRS). These surge hydrographs
were then compared with levee heights to determine overtopping volumes, subsequently converted to interior
flood elevations using simple stage-storage curves for interior basins. LACPR also developed an economic
consequences database at the census block resolution using an approach adapted from the FEMA HAZUSMH flood risk model (FEMA, 2007), and used assumptions about percent damage at different flood stages to
construct stage-damage curves. Finally, the flood elevations were coupled with mean census block elevations
and the stage-damage curves to calculate economic damages (USACE, 2009c).
LACPR addressed epistemic uncertainty in its risk analysis both with probabilistic characterizations and
qualitative scenario analysis. The probabilistic characterizations covered most uncertainties considered, and
included both parametric and non-parametric estimation methods. Scenario analysis was performed for two
key drivers—RSLR and coastal development rate/patterns—and two levels of each driver were selected, for
a total of four qualitative futures. The scenarios were selected in order to bound the range of each input, with
the development/pattern scenario set to represent futures with low/high total assets at risk in the floodplain.
LACPR scenarios also allowed for either 40 cm (1.3 ft.) or 80 cm (2.6 ft.) in the Pontchartrain Basin by
2060, corresponding to an average annual RSLR rate of 8 or 16 mm/year.4 Fig. 3.4 shows the four LACPR
scenarios chosen.
Although LACPR’s discrete scenario analysis approach acknowledges that the long-term analysis nec4
The West Bank portion of Orleans Parish lies in the Delta Plain rather than Pontchartrain Basin, and potentially faces higher
rates of RSLR. RSLR scenarios for the Delta Plain in the LACPR analysis are 0.6 m (1.9 ft.; low) and 1.0 m (3.2 ft.; high).
54
Managing New Orleans Flood Risk
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High Employment,
Dispersed Population
“Low” Relative Sea
Level Rise
Scenario 1
Scenario 2
Scenario 3
Scenario 4
“High” Relative
Sea Level Rise
Business-as-usual,
Compact Population
Figure 3.4: Future scenarios selected by LACPR. Source: USACE, 2009c.
essarily includes “...key uncertainties for which no reliable or credible probabilities can be obtained," the
methods used to select these specific scenarios remain unclear. Why are these two key drivers incorporated,
for example, while others are addressed only via assumption? In addition, although the levels of each variable were selected “...to capture a range of worst-case to best-case damage estimates,” they do not necessarily
span the range of plausible future outcomes. RSLR could exceed 80 cm by 2060 under some extreme SLR
scenarios, for example, while levels below 40 cm also remain possible. As for population projections, a
current estimate of the number of households in Orleans Parish already substantially exceeds the 2075 projection from the “Business-as-usual, compact population” scenario (see Fig. 4.5), suggesting a wider range is
necessary. There could also be threshold or non-linear effects that occur within the ranges considered useful
for planning purposes that cannot be observed using only extreme values.
Other deep uncertainties are addressed via small-scale sensitivity analysis (for example, the effects of
climate change on hurricane intensity/frequency) or by assumption alone. The Final Technical Report includes a list of critical assumptions that undergird the analysis, and the consequences if these assumptions
prove invalid (USACE, 2009c, Table 4-2). Critical assumptions regarding deep uncertainties include:
• “Extensive coastal landscapes in Louisiana can be constructed and maintained at a pace sufficient to offset expected future landscape degradation.” Although LACPR considers the additional
risk from a fully degraded coastline in the final report, nearly all coastwide plans considered also
incorporate an extensive restoration package assumed to be sufficient to maintain the coastline in approximately its current configuration. This is a strong assumption, given that recently implemented
restoration has only mitigated a fraction of the land loss and there are substantial concerns that there are
simply insufficient sediment resources available to keep up with loss rates (National Research Council,
2009b).
• “Structural measures can be built to perform reliably to specified risk reduction levels. Therefore, hydrologic stages assume no failure or breaching of levees.” This assumption directly contradicts the IPET approach, which explicitly considers levee fragility as a function of exterior surge
55
Managing New Orleans Flood Risk
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height. In addition, floodwalls failed catastrophically (before surge reached design heights) in multiple
locations during Hurricane Katrina, violating this assumption (see Sec. 2.3.1).
• “Nonstructural plans are presented as voluntary participation; however, associated risk reduction for nonstructural alternatives is assumed for this stage of analysis to be based on 100 percent
participation.” LACPR acknowledges that this is a strong assumption, and later performs sensitivity
analysis on this specific input in order to identify a participation threshold below which plan rankings
would change. The program participation uncertainty is discussed in Chapter 5 of this document.
The final National Academies peer review highlighted these three assumptions as substantial concerns
in the final technical report (National Research Council, 2009b), and they could substantially reduce the
credibility or usefulness of the analysis results to federal, state, and local decisionmakers. Although discrete
scenario analysis is a useful and important step forward for USACE planning, in this instance the limited number of scenarios do not appear to sufficiently capture the range of plausible outcomes planners must consider
when designing long-term risk reduction policies for New Orleans and South Louisiana. This dissertation is
designed as a first step to help bridge this gap by explicitly planning for a large number of plausible future
scenarios.
3.5
Robust Decision Making can help to account for deep uncertainty
To address the deep uncertainty inherent to flood risk planning in New Orleans, this research applies the
Robust Decision Making decision analysis methodology. RDM is a quantitative, scenario-based approach
for identifying solutions that are relatively insensitive to deep uncertainty. Instead of developing a single
probabilistic forecast and associated optimal solution, the approach uses a scenario generator—a computer
simulation designed to generate a large number of quantitative scenarios from different input conditions,
without regard for the initial likelihood of any given scenario—to evaluate large sets of possible outcomes
and provide an analytic framework for identifying robust solutions. Groves et al. (2006) first applied RDM
to flood risk planning for New Orleans in a demonstration analysis for LACPR, and this work builds from
that initial application and the data produced by LACPR in order to formulate a full-scale analysis focused
on a specific set of risk mitigation tools. In this section, I provide a concise overview of the methodology
and key analytic steps, and subsequently describe how RDM can be applied to New Orleans risk mitigation
planning.
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Managing New Orleans Flood Risk
3.5.1
Chapter 3
Methodology developed to support effective long-term decision making and address
“surprises”
RDM was designed to address problems that contain uncertainty that is difficult or impossible to characterize
probabilistically, either due to a lack of scientific knowledge regarding key relationships that define a system, inability to place likelihoods on key drivers, or disagreement among decisionmakers and stakeholders
regarding these relationships or likelihoods. In the standard decision model, the goal is to find an optimal
policy for the single most-likely realization of the future. When considering the challenge of planning with
deep uncertainty about future outcomes, however, RDM instead suggests that the goal should be to identify
an approach that is robust against the range of plausible outcomes and relatively insensitive to adverse futures
rather than optimal in one realization.
To seek robustness, RDM inverts the “predict then act” paradigm and instead evaluates strategies across a
large ensemble of plausible future scenarios. Each of these scenarios is initially unweighted, and the methodology suggests first identifying in which scenarios a given strategy is vulnerable (i.e., the strategy performs
considerably worse than other options considered) and then determining likelihood thresholds for vulnerable
scenarios which would lead the decisionmaker to switch strategies. In this way, RDM provides a quantitative,
guided approach to identifying key narrative scenarios of concern, and through several iterative loops can
help improve the robustness of strategy options against these poor outcomes.
The RDM approach emerges from decision literature on qualitative scenario planning methods (Schwartz,
1996), assumption-based planning (Dewar, 2002; Dewar et al., 1993), and exploratory modeling (Bankes,
1993). The general approach has been described extensively in the literature (Lempert et al., 2003, 2004,
2006; Groves and Lempert, 2007; Lempert and Collins, 2007), and has been applied to planning challenges
in multiple fields. Previous application areas include long-term global development and sustainability (Lempert et al., 2003), U.S. Social Security policy (Popper, 2007), Korean science and technology investments
(Seong et al., 2005), Israel’s energy future (Popper et al., 2009), and adaptive water resources management
(Groves, 2006; Groves et al., 2008b,a,c).
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3.5.2
Chapter 3
RDM analysis proceeds through an iterative analysis in coordination with decisionmakers and stakeholders
RDM analyses vary depending on the topic area and scope of the policy problem, but typically involve four
analysis steps (Fig. 3.5). This process is designed to be performed interactively with decisionmakers and
stakeholders, and as a result there are multiple points where input to shape the analysis can be elicited. In
addition, RDM is intended to be iterative, and the series of steps can be repeated two or more times in order
to test revised adaptive strategies for improved robustness against adverse scenarios.
X
L
R
M
Evaluate strategies
over many scenarios
Develop
hedges
Describe vulnerabilities
Summarize key tradeoffs
among strategies
“Robust, adaptive strategies”
Figure 3.5: Typical steps in an RDM analysis. Adapted from Groves et al., 2008b.
3.5.2.1
XLRM scoping exercise
The first step in an RDM analysis is to structure the decision problem. Lempert et al. (2003) propose developing the appropriate scope with a four-part “XLRM" chart, where each letter corresponds to a key portion
of the problem definition. The four components include:
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• Exogenous uncertainties (X): key inputs to the decision problem that are outside the control of the
decisionmaker. These uncertainties can include deep uncertainty about the future as well as decisions
made by external organizations that influence policy outcomes. This dissertation, for example, specifically focuses on the local planning perspective for the City of New Orleans. Federal investments in
additional levee-building for the New Orleans flood protection system are outside the direct control of
local planners, and are therefore treated as exogenous in this analysis.
• Policy levers/strategies (L): actions taken by the decisionmaker to be considered in the analysis. RDM
tends to group sets of actions into broader strategies, as plans developed for the long-term will typically
entail a variety of actions enacted at different times.
• Modeling relationships (R): the systematic combination of input factors designed to generate a plausible description of how the future might unfold. Typically, such relationships are represented using a
numeric model.
• Performance metrics (M): outcome measurements used to determine the success of strategies under
different scenarios.
The XLRM chart provides a convenient visual representation of what can otherwise be a complicated and
difficult-to-explain analysis approach. In a typical RDM analysis, the scoping exercise would be performed
together with the decisionmaker and potentially interested stakeholders (if applicable). This can help to
ensure that key dimensions of the decision problem are captured in the analysis, as well as obtaining early
buy-in from the participating parties.
3.5.2.2
Evaluate strategies over many scenarios
The next step—typically the most time-consuming and resource-intensive portion of the analysis—builds
from the XLRM framework with the goal of producing a dataset describing the performance of each strategy
in a common set of quantitative scenarios (a scenario ensemble). The scenario ensemble is made up of
different combinations of exogenous uncertainties selected by sampling from the multi-dimensional range of
possible future conditions. Critically, each scenario in the ensemble remains unweighted at this stage, and
weighted summaries across the ensemble (i.e., expected values) are not reported.
To generate this dataset, a suitable scenario generator is required. The scenario generator, simply, is the
collected set of relationships that bring together the exogenous uncertainties and policy levers to produce an
ensemble of plausible futures, with outcomes measured using the selected performance metrics. Lempert
et al. (2003) make clear that the scenario generator should not be developed as a consolidative model designed
to fully predict the dynamics of the policy problem. Instead, it is built with the goal of producing the widest
59
Managing New Orleans Flood Risk
Chapter 3
possible range of plausible quantitative scenarios against which to test strategies. For convenience I use the
terms “scenario generator" and model interchangeably in this discussion, but note that the term model does
not imply a strictly predictive or consolidative model as interpreted in other research contexts. The scenario
generator could be an existing numeric model of the system of interest previously utilized by the decisionmaker or in prior research, or a new tool developed in support of the analysis. Working with an existing
model may be preferable if the tool has already received buy-in from the decisionmaker and stakeholders;
once again, this is a decision made in coordination with parties involved.
Strategy performance is then compared across scenarios, with two broad goals: 1) identifying strategies
that are dominated in all cases and thus unlikely to be candidates for a robust strategy; and 2) selecting one
or more strategies that perform well in many or most scenarios as candidate robust strategies for further
comparison and analysis. Selecting one or more candidate strategies is another opportunity for input from
the decisionmaker and stakeholders.
3.5.2.3
Describe strategy vulnerabilities and identify hedges
Next, the analyst examines the candidate robust strategies and seeks to identify scenarios in which the strategies are vulnerable or perform poorly—what Groves and Lempert (2007) refer to as “policy-relevant” scenarios. Given the size of the scenario ensemble, this process entails using statistical search algorithms to identify
clusters of “bad” cases for a given strategy within the ensemble (Bryant and Lempert, 2009). The goal of
this process is both to identify the exogenous uncertainties that most often contribute to poor outcomes as
well as the problematic ranges of those inputs. Using this information, the analyst can then construct narrative descriptions of these clusters that are useful and relevant to the decisionmaker and stakeholders. These
narrative scenarios are superficially similar to scenarios selected via a strictly qualitative process, but are
identified through a rigorous, quantitative process rather than a qualitative exercise and relate directly to the
performance of the strategy/strategies under consideration.
At this stage, the analyst can also determine if there are other policy options that the decisionmaker
would prefer to switch to when faced with vulnerable scenarios, and if such options could be incorporated
into the candidate strategy as an adaptive hedge against these poor outcomes. This idea draws directly from
assumption-based planning, which suggests identifying both “signposts” that would indicate a vulnerable
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Managing New Orleans Flood Risk
Chapter 3
future is becoming extant and hedging actions against these vulnerable futures that could be incorporated into
current plans (Dewar, 2002). At this point, the initial policy levers/strategies considered could be revisited
and improved with the adaptive hedging actions, leading to another iterative loop through the RDM process
(Fig. 3.5).
3.5.2.4
Summarize key tradeoffs
At the end of the initial or subsequent RDM iterations, the final step is to summarize the results of the vulnerability analysis and identify for the decisionmaker key tradeoffs between different strategies within and outside
of policy-relevant scenarios. In the case where the decisionmaker and various stakeholders differ on their
expectations about the future, such tradeoffs can be presented with visualizations of the likelihood thresholds
for policy-relevant scenarios at which switching to another strategy would be preferred. This brings potential
disagreements about the probabilities different parties place on different futures to the fore, and provides the
opportunity to identify alternate approaches (i.e., incorporating hedging actions) that would satisfy different expectations by performing reasonably well regardless of the prior probabilities each participant would
assign. The discussion of tradeoffs could be performed multiple times in an iterative RDM analysis, with
interim results presented and discussed to help identify additional adaptive strategies to be tested.
3.6
Applying Robust Decision Making to non-structural risk reduction in
New Orleans
The remainder of this dissertation describes an initial application of RDM to risk planning in New Orleans.
Given time and resource limitations, this initial effort is constrained in scope compared with a full application
of RDM in this context, and the simplifications and assumptions entailed are described below. An expanded
effort based on this initial work, funded by the National Oceanic and Atmospheric Administration (NOAA),
is currently being performed by a research team at the RAND Gulf States Policy Institute (RGSPI) to support
ongoing planning efforts by the New Orleans Office of Homeland Security and Emergency Preparedness.
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3.6.1
Chapter 3
Initial effort from master planner’s perspective
The most substantial simplification for this effort is that decisionmaker interaction did not occur during this
RDM analysis. At the outset of the research effort, I conducted a series of informal interviews with a variety
of local stakeholders as well as planners at the City of New Orleans, FEMA Transitional Recovery Office,
and the USACE New Orleans District Office. These interviews helped indirectly inform the risk mitigation
actions to be considered and quantitative modeling scope, as well as providing recent historical context and
suggesting potential data resources. These interviews were completed in Spring 2008, and from that point
forward I adopted the perspective of a “master planner” for New Orleans and conducted the remainder of the
RDM analysis without additional decisionmaker or stakeholder input.
A consequence of this choice is that the results do not directly feed into New Orleans’ current planning
efforts, including the Master Plan that is nearing completion (see City of New Orleans, 2009a). The RDM
analysis is also limited by the lack of input at the points previously noted. However, the results of this analysis
can provide illustrative initial results and guidance for long-term risk mitigation planning in New Orleans,
and the scenario visualizations, highlighted tradeoffs, and broader conclusions should provide important
information to support decision making by both planners and residents. This initial analysis can be considered the first iteration of a broader RDM effort to support local flood risk mitigation planning. Subsequent
RDM iterations will be conducted via the multi-year NOAA-funded follow-on project and will involve direct
interaction with New Orleans’ planners.
3.6.2
Scoping the initial policy problem
This analysis considers the effects of new non-structural risk mitigation strategies in New Orleans over the
50-year span 2011-2060. The period of analysis follows the recently-completed LACPR analysis (USACE,
2009c), both because I use data produced by LACPR over this time range and for comparability between
the results. To structure the initial RDM analysis, I developed an XLRM chart from the local master planner’s perspective. Table 3.2 summarizes the results of this scoping exercise. The specific components are
described in detail in subsequent chapters: relationships comprising the scenario generator, implementation
of exogenous uncertainties, and performance metrics in Chapter 4 and risk mitigation levers in Chapter 5.
The list of exogenous uncertainties includes a subset of the deep uncertainty (environmental, population
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Managing New Orleans Flood Risk
Chapter 3
E(x)ogenous Uncertainties
Coastal degradation by 2060
Sea level rise (mm/year)
Protection system maintenance
Residential growth rate (thou. homes/yr.)
Growth dispersion (in/out of OM basin)
Discount rate
Program participation rate
Program length/uptake rate
Buyout/easement enforcement parameter
Induced development multiplier
Elevation cost multiplier
Buyout/easement cost multiplier
(R)elationships
Flood hazards module
Flood depths module
Flood consequences module
Mitigation strategies module
(L)evers
Elevation incentives (existing homes)
Elevation incentives (new homes)
Buyout incentives (existing homes)
Growth restrictions/easements (new homes)
(M)easures
Discounted net benefits (2011/2036 base year)
Expected annual damages
Implementation costs
100-, 400-, and 1,000-year damages
Table 3.2: RDM Analysis XLRM Framework.
growth/patterns, and system maintenance) previously described in this chapter as well as additional uncertain drivers related to the implementation of non-structural risk mitigation programs in New Orleans (also
described in Chapter 5).
Note that several key uncertain drivers described above, including uncertainty related to storm recurrence,
climate change effects on hurricane frequency/intensity, and protection system reliability, are not listed on the
XLRM chart. These drivers were removed from the analysis because the data required to develop a scenario
generator of sufficient resolution to capture these dynamics was not available at the time of writing (further
discussed in Sec. 4.2.3).
3.7
Summary
In this chapter, I have established a framework for distinguishing between aleatory and epistemic uncertainty
inherent to the flood risk problem, and argued that in the case of New Orleans probabilistic characterization
of epistemic uncertainty is challenged by a series of deep uncertainties. I next described how several current
approaches to estimating future flood risk in the area, including analyses performed by IPET and LACPR,
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Chapter 3
may not fully address these uncertainties, and as a result plans based on these approaches may be vulnerable
if current assumptions prove false. To improve upon these efforts, I then propose a new planning approach
for New Orleans based on the Robust Decision Making methodology designed to identify risk mitigation
strategies that are more robust to the inherent deep uncertainty, and perform an initial scoping exercise in
support of this analysis approach. In the next four chapters, this RDM approach is fully applied. Chapter
4 describes a scenario generator to support New Orleans flood risk planning, documenting the necessary
relationship and exogenous uncertainty quantification in detail. Chapter 5 describes the non-structural risk
mitigation levers considered in this initial effort, while Chapters 6 and 7 document the strategy comparison,
vulnerability analysis, and key tradeoffs.
64
Chapter 4
A Low-Resolution Flood Risk Scenario
Generator for Orleans Parish
4.1
4.1.1
Introduction
A scenario generator supports exploratory modeling and RDM
In order to perform an RDM analysis considering non-structural risk mitigation in New Orleans, a model
designed to evaluate strategies against scenarios—often called a scenario generator—must be developed
(Lempert et al., 2003). A scenario generator is a numerical simulator designed to support exploratory modeling. In contrast to a “consolidative" model designed to fully capture the behavior of a system and support
development of an optimal strategy, a scenario generator is purposely designed to allow both the input conditions and relationships within the model to vary (Bankes, 1993; Lempert et al., 2006). This create a range of
plausible future conditions, rather than a single or most-likely prediction of the future, against which strategies are tested in order to evaluate robustness. Lempert et al. (2006) argue that this approach is preferable
to consolidative modeling when the optimum strategy is sensitive to how uncertainty is characterized. As
discussed in Chapter 3, developing long-term risk reduction strategies for New Orleans involves substantial
deep uncertainty, suggesting that exploratory modeling supported by a scenario generator is appropriate in
this context.
Developing a model at the appropriate resolution (spatial and temporal), however, presents a considerable
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Chapter 4
challenge. The model must account for complex physical phenomena, including the behavior of different
types of hurricanes that could affect the city as well as the generation of surge and waves from these storms,
the impact of these storm effects on the protection system, and the projected flood levels if the system fails.
Furthermore, understanding how the city would fare under different scenarios and policy regimes requires a
level of resolution sufficiently detailed to capture differential flood effects on areas in the city facing different
levels of risk (i.e., higher versus lower elevation neighborhoods) efficiently. In turn, the model must be
compact enough to run an experimental design of thousands to millions of futures. It must also be simple
enough in structure to vary key input parameters without requiring extensive data modifications for each run.
4.1.2
Organization of this chapter
This chapter describes the development of NOLArisk, a low-resolution simulation model developed as a
scenario generator for the RDM analysis of non-structural risk mitigation in New Orleans. The chapter first
describes the overall structure of NOLArisk, including basic relationships, geographic scope, and simplifying
assumptions. It also describes how the high-resolution hydrodynamic, system reliability, and economic data
produced in the recently-published LACPR investigation (USACE, 2009c) were incorporated and modified
into a low-resolution simulator. I provide detailed descriptions of the two key modules where the majority
of the calculations are performed—Flood Hazards and Flood Consequences—and summarize the input data
and uncertain exogenous drivers that feed into each module. Next, the chapter discusses how the outputs
from each module are combined into estimates of future flood risk for each New Orleans neighborhood from
2011-2060. (Note that the non-structural risk mitigation strategies to be considered are described separately
in Chapter 5.) Finally, I present damage results from selected scenarios with no additional mitigation in place
to provide an illustration of the output produced by the model.
4.2
Overview of NOLArisk
NOLArisk is based on the recent LACPR coastwide risk analysis and IPET Risk and Reliability model,
both described in Chapters 2 and 3 (USACE, 2009c; IPET, 2009b). The LACPR and IPET efforts represent
the state-of-the-art regarding flood risk in New Orleans and South Louisiana, and the scenario generator is
designed to build on this solid foundation. NOLArisk is designed to maximize the user’s ability to perform
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Managing New Orleans Flood Risk
Chapter 4
exploratory modeling across the range of uncertain drivers critical to long-term flood risk planning in New
Orleans while drawing upon the limited published data from the LACPR analysis. The model therefore is
sufficiently low-resolution to generate thousands of cases in a short timeframe while simultaneously including
enough detail to address the key uncertainties and allow for nuanced consideration of non-structural policies.
In addition, because the focus of this analysis is on difficult-to-characterize “deep” uncertainty, probabilistic
uncertainty (treated in-depth in both LACPR and IPET Risk and Reliability) is not considered in this model,
and except where noted all values should be treated as mean outcomes with no corresponding confidence
intervals.1
R
The scenario generator was developed in Analytica 4.2
, a modeling-platform that provides a straightfor-
ward visual flowchart interface and automatically tracks and calculates across a variety of atomic, vector, or
R
R
and the Analytica Decision Engine
,
array objects.2 The version of Analytica used, Analytica Enterprise
also provide two critical benefits: first, the ability to draw arrays of input data from an outside database
based on SQL, Microsoft Access, or other common database platforms, and second, the use of a “harness"
R
system software (CARs)
that allows the modeling platform to connect to the Computer Assisted Reasoning
to generate, manage, and visualize large ensembles of model runs.3 As a result, data elements gathered for
model construction have been collected into an external database using Microsoft Access, and the model
queries the database directly in order to derive the necessary data for calculations. This keeps the size of the
model itself compact and, given the volume of data required, dramatically improves processing speed.
4.2.1
Model structure
The structure of NOLArisk follows the basic risk calculation framework described in Chapter 3.2.1. Threat
and vulnerability are treated jointly in the Flood Hazards module, while flood consequences are calculated
separately. Exogenous drivers modify each module to generate different future scenarios. For each scenario,
the Flood Hazards module produces flood elevations, while the Flood Consequences module generates depthdamage curves that connect flood depths to different damage values. Flood depths are calculated based on
flood and local elevations (Flood Depths module), and flood depths and consequences are then combined to
1
Flood elevation input from LACPR, which are derived from storm surge estimates at the 90% confidence level, are key exceptions
to this assumption. See Sec. 4.3.2.
2
Available from Lumina Decision Systems, Inc., http://www.lumina.com.
3
Available from Evolving Logic, http://www.evolvinglogic.com/.
67
Managing New Orleans Flood Risk
Chapter 4
calculate damages at each recurrence interval and construct a posterior damage distribution (Damage Results
module). Fig. 4.1 shows a basic influence diagram of the scenario generator, including exogenous inputs
(yellow), calculation modules (blue), and outputs (red). The calculations within each module are described
in detail in subsequent sections.
Environment
and System
Drivers
Economic
Drivers
Flood Hazards
Flood Depths
Mitigation
Drivers
Mitigation
Strategies
Flood
Consequences
Damage
Results
Figure 4.1: NOLArisk flow diagram showing exogenous uncertainties (yellow), calculation modules (blue),
and outputs (red). The Mitigation Strategies module is also shown (green), but this module is described
separately in Chapter 5.
4.2.2
Geographic scope
This analysis focuses on non-structural mitigation strategies for the City of New Orleans, which is restricted
to Orleans Parish. The Greater New Orleans Metropolitan Statistical Area as defined by the U.S. Census,
alternately, includes parts of seven parishes surrounding the city, while the Greater New Orleans Hurricane
and Storm Damage Risk Reduction System encompasses the parishes of Orleans, Jefferson, St. Bernard, and
parts of Plaquemines and St. Charles. LACPR examined risk across the Louisiana Gulf Coast, whereas the
IPET Risk and Reliability analysis focuses in detail on areas within HSDRRS alone. Local non-structural
mitigation plans, if and when enacted, will emerge from separate local authorities in New Orleans as well as
the cities that comprise the greater New Orleans area (e.g., Metairie, Kenner). I therefore elected to focus
on a single decision making entity for the city proper rather than consider multiple local authorities for this
initial analysis.
68
Managing New Orleans Flood Risk
Chapter 4
LACPR and IPET each use a separate approach for dividing Orleans Parish and other areas within the
HSDRRS into different geographic subunits in order to analyze the incidence of flooding. IPET defines
polders—hydrologically-distinct “bowls" defined by natural (ridges) and man-made (levees) borders—and
smaller sub-polders within these units. LACPR, alternately, uses a more coarse formulation defining Planning
Subunits. To determine the economic consequences, alternately, both analyses use census blocks, the smallest
units identified and tracked by the U.S. Census. There are approximately 10,000 census blocks within Orleans
Parish, and 73,000 across the Louisiana Coast (USACE, 2009c; IPET, 2009b).4
Despite the apparent disparity in geographic specificity between polders/basins and census blocks, transitioning between a geographically-coarse flood elevation and specific asset damages within a polder requires
a single assumption and simple calculations. If flood elevations and census block elevations are both determined relative to a common vertical datum—in this case, the reference standard NAVD88 2004.65 (hereafter
“NAVD88”)—and interior flood elevations are assumed to “fill up the bowl" consistently in terms of surface
elevations, then the flooding depth for each census block is simply the difference between these two elevations.
Because much of the data used in this preliminary analysis is derived from LACPR, I adopt the LACPR
subunits (hereafter “basins") for the flood elevation module and census blocks for the consequences module.
Orleans Parish includes all or parts of the following basins: Orleans Main (OM), New Orleans East (NOE),
St. Bernard (SB; only the northwest tip including Holy Cross and the Lower Ninth Ward), and Orleans West
Bank (OW1: English Turn; OW2: Algiers). Fig. 4.2 shows a map of the basins of interest.
For risk mitigation strategy comparisons, however, the basin-level is too geographically broad to consider
different types of approaches within the city, and greater geospatial resolution is required for more nuanced
comparisons across different areas. Technical constraints—insufficient computer memory to capture a 10,000
unit matrix across thousands of scenarios and dozens of strategies—prevented the use of census blocks, and
led me to instead choose to output results and compare strategies at the neighborhood level. To support
this analysis, I identified 72 separate neighborhoods across Orleans Parish using data from the City Planning
4
NAVD88 is the North American Vertical Datum of 1988, a vertical control standard used in surveying and topographic analyses
(see http://www.ngs.noaa.gov/faq.shtml). The 2004.65 update was used by IPET and LACPR in their respective investigations, and
is adopted here to maintain consistency with the input data (although newer updates, such as 2006.81, are now available). As of
2006, NAVD88 2004.65 was considered “...within approximately 3-inches of Mean Sea Level in the New Orleans area” (Seed et al.,
2006).
69
Managing New Orleans Flood Risk
±
0
0.5
1
2
3
Chapter 4
4
Miles
Lake Pontchartrain
New Orleans East (NOE)
Lower Ninth Ward
Orleans Main (OM)
Mi
ss
St. Bernard developed (SB)
i ss
ipp
iR
ive
r
Algiers (OW2)
English Turn (OW1)
Jefferson Parish
Figure 4.2: Map of LACPR basins (subunits) addressed in this study. Map created by author, with basin
definitions drawn from data provided by USACE, 2009c.
Commission of New Orleans (with minor changes to ensure that each neighborhood fell into only one LACPR
basin). All results were then aggregated from the census block to the neighborhood level when sent to the
CARs software for collection and analysis. Table 4.1 shows a summary of different spatial units and their use
in the NOLArisk model. Fig. 4.3 and Table 4.2 identify the New Orleans neighborhoods used as the primary
geographic index for the analysis.
Unit
NOLArisk application
Count in Orleans Parish
Census block
Census block group
Census tract
Neighborhood
Basin
Flood depth and consequences calculations
NA
NA
Location-specific strategy comparisons (output)
Flood elevation calculations
10,181
485
181
72
5
Table 4.1: Levels of spatial aggregation in Orleans Parish. Elements are sorted by level of aggregation (least
to most).
70
Managing New Orleans Flood Risk
±
0
0.5
1
2
3
Chapter 4
4
Miles
Village De L'Est
Lake Pontchartrain
Edgelake/Little Woods
Lake Terrace/Lake Oaks
Lakeshore/Lake Vista
Read Boulevard East
West Lake Forest
Pontchartrain Park
Pines Village
Milneburg
Read Boulevard West
Plum Orchard
St. Anthony
Lakewood/West End
Gentilly Woods
Fillmore
Viavant/Venetian Isles
Lakeview
Gentilly Terrace
Desire Area
Dillard
City Park
St. Bernard Area/Project
Desire Dev
Navarre
Fairgrounds/Broad
Lakewood
St. Roch
Florida Project
Florida Area
Seventh Ward
Bayou St. John
St. Claude
Dixon
Lower Ninth Ward
Mid-City
Sixth Ward/Treme/Lafitte
Hollygrove
Marigny
Bywater
Gerttown/Zion City Tulane/Gravier
Iberville Project
Vieux Carre
Leonidas/West Carrollton
Calliope Project
Marlyville/Fontainbleau
Central Business District
East Carrollton
Algiers WhitneyAlgiers Naval Station Mi
s si
ss i
McDonogh
Broadmoor/Freret
Central City/Magnolia
Black Pearl
Audubon/University
Uptown
St. Thomas Area
Milan
Holy Cross
Algiers Point
Fischer Project
Behrman
Garden District
St. Thomas Project
Touro
St.Bernard Parish
pp
iR
ive
r
Aurora/Walnut Bend/Huntlee Village
East Riverside Irish Channel
River Park/Cut Off/Lower Coast1
West Riverside
Jefferson Parish
Tall Timbers/Brechtel
River Park/Cut Off/Lower Coast2
Figure 4.3: Neighborhoods in Orleans Parish. Map created by author; data source: New Orleans City Planning Commission.
4.2.3
NOLArisk simplifying assumptions
NOLArisk draws from existing, detailed studies of flood risk in New Orleans and across southern Louisiana
performed since 2005. Because the model requirements cross numerous scientific disciplines and are well
beyond the scope of a single study, NOLArisk relies on a series of simplifying assumptions. Some assumptions cannot be relaxed given the current state of the science, while others are related to available data and
could be improved upon in subsequent efforts.
The strongest assumptions relate to the limited hydrodynamic data available at the time of writing. Detailed storm surge simulations generated by USACE for the IPET and LACPR analyses were not made publicly available as of mid-2009. Lacking sufficient data to develop a detailed system fragility and interior
71
Managing New Orleans Flood Risk
Chapter 4
ID
Name
ID
Name
ID
Name
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Algiers Naval Station
Algiers Point
Algiers Whitney
Audubon/University
Aurora/Walnut Bend/Huntlee Village
Bayou St. John
Behrman
Black Pearl
Broadmoor/Freret
Bywater
Calliope Project
Central Business District
Central City/Magnolia
City Park
Desire Area
Desire Dev
Dillard
Dixon
East Carrollton
East Riverside
Edgelake/Little Woods
Fairgrounds/Broad
Fillmore
Fischer Project
Florida Area
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
Florida Project
Garden District
Gentilly Terrace
Gentilly Woods
Gerttown/Zion City
Hollygrove
Holy Cross
Iberville Project
Irish Channel
Lake Terrace/Lake Oaks
Lakeshore/Lake Vista
Lakeview
Lakewood
Lakewood/West End
Leonidas/West Carrollton
Lower Ninth Ward
McDonogh
Marigny
Marlyville/Fontainbleau
Mid-City
Milan
Milneburg
Navarre
Pines Village
Plum Orchard
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
Pontchartrain Park
Read Boulevard East
Read Boulevard West
River Park/Cut Off/Lower Coast1
Seventh Ward
Sixth Ward/Treme/Lafitte
St. Anthony
St. Bernard Area/Project
St. Claude
St. Roch
St. Thomas Area
St. Thomas Project
Tall Timbers/Brechtel
Touro
Tulane/Gravier
Uptown
Viavant/Venetian Isles
Vieux Carre
Village De L’Est
West Lake Forest
West Riverside
River Park/Cut Off/Lower Coast2
Table 4.2: Orleans Parish neighborhoods names and NOLArisk IDs. Neighborhood names adapted from
New Orleans City Planning Commission data.
drainage module, I instead constructed a much simpler Flood Hazards module that draws from summary
output data published in the final LACPR report. Using the summary data, however, required a series of
simplifying assumptions, and some portions of this module use notional relationships. Future versions of
NOLArisk will incorporate a more sophisticated and fully realized Flood Hazards module. However, because of the simplifying assumptions required, analysis results from this initial version should be treated as
preliminary and interpreted with caution.
Key assumptions are listed below. Each of the assumptions is also discussed in further detail as model
calculations are described through the remainder of the chapter.
• NOLArisk assumes that no additional structural protection (levees) will be built in the New Orleans vicinity beyond the 2011 system. USACE and the State of Louisiana are considering additional
upgrades to the New Orleans HSDRRS, but at present it is not clear whether or not additional protection will be constructed to provide risk reduction beyond the 100-year design level. In addition,
additional upgrades could take years or decades to complete once authorized. However, if the system
is improved during the period of analysis (2011-2060), NOLArisk will overestimate risk once upgrades
72
Managing New Orleans Flood Risk
Chapter 4
are completed.
• Statistically-derived flood elevations at the 90th percentile are drawn from the LACPR analysis
rather than estimating these results using separate hydrodynamic and fragility/interior drainage
analyses. As a result of this simplification, NOLArisk also relies on many or most of the assumptions
made during the LACPR hydrodynamic and interior drainage analysis. In addition, interior elevations
reflect the upper range of the statistical estimates generated from the LACPR probabilistic uncertainty
analysis.
• The Flood Hazards module directly applies LACPR flood elevation results by basin with firstorder modifications based on environmental drivers. This is a substantial simplification necessitated by data availability at the time of writing. NOLArisk does not capture how storm surge is
modified by alternate landscape configurations outside of the levee system or how overtopping volume
would change with different exterior surge patterns. Interior flood elevations could be biased upwards
or downwards due to this simplifying assumption.
• The Flood Hazards module assumes that sea level rise and subsidence (RSLR) linearly alter
interior flood elevations. This assumption again derives from the direct application of LACPR interior
flood elevation estimates. Additional RSLR almost certainly will not affect interior flood elevations
linearly, and NOLArisk results could be biased in either direction as a result.
• Given LACPR assumptions and available data, NOLArisk follows the LACPR analysis and assumes zero probability of levee or system failure. As discussed in Chapter 3, this is a strong assumption and fails to address the additional risk posed by system fragility. This assumption likely leads to
an underestimate of New Orleans flood risk.
• The model considers only the direct economic consequences of flooding for single-family residences in Orleans Parish. Because the model excludes damages to other asset classes (e.g., multifamily structures, commercial buildings) as well as indirect flooding costs (e.g., regional economic
effects), it does not provide a complete estimate of flood risk for New Orleans.
• Existing structure replacement values for single family homes are depreciated according to the
average age of homes by block, while new structures use a 10-year effective age. NOLArisk depreciates existing structure values without considering upgrades over time that would reduce the effective
age or increase structure value. In addition, NOLArisk does not take into account upgrades or retrofits
made to a substantial fraction of single family homes after Hurricane Katrina. As a result, the model
could underestimate home values as well as potential flood damages.
4.3
4.3.1
Flood Hazards module
Overview
The Flood Hazards module generates flood elevations for the 10-, 100-, 400-, and 1,000- year recurrence
intervals, based on input data and exogenous uncertainties, for the fifty-year span 2011-2060 (in decade increments). The module is a meta-model that interpolates between (or extrapolates from) discrete scenario
73
Managing New Orleans Flood Risk
Chapter 4
flood elevation projections generated by LACPR by recurrence interval. Interpolation is linear, and the module also uses linear projections for inputs that are changing over time. Environment and fragility drivers
directly modify the flood elevation calculations by determining the level of interpolation or extrapolating
outside of the LACPR projections using simplifying assumptions.
4.3.2
4.3.2.1
Input data
Interior flood elevations
LACPR published estimates of interior flood elevations (e), by basin, for the 100-, 400-, and 1,000-year
recurrence intervals (1%, 0.25%, and 0.1% annual probabilities of occurrence). These projections are in
terms of exceedances, which are defined as the probability of exceeding a certain level (in this case, flood
elevation) in a given year—also referred to as the complementary cumulative distribution. Exceedances are
useful when considering rare events because they provide a full summary of all possible events in the upper
tail of the distribution. A “100-year” flood elevation exceedance value of 10 ft., for example, implies that
there is a 1% total probability of seeing greater than a 10 ft. flood elevation in each year. Exceedances can
be calculated as:
EP (e > E) = 1 − P r(e ≤ E)
= 1−
N
(1 − pk );
(4.1)
(4.2)
k=1
where E is the interior flood elevation threshold exceeded, pk is the annual recurrence probability for the
flood elevation ek , and N is the number of discrete flood events considered (Grossi et al., 2005).
Exceedance distributions, however, do not necessarily provide information about the underlying frequency distribution from which they are drawn, and cannot be interpreted the same way as a simple frequency. For example, although LACPR published exceedance estimates for storm surge, flood elevations,
and damages at these three intervals, these estimates do not refer to the same underlying event—that is, the
100-year storm and 100-year flood elevation are not necessarily equivalent. Each is a separate statistical construction with a distinct interpretation, and summary data from these intervals cannot be readily combined
74
Managing New Orleans Flood Risk
Chapter 4
for further analysis. For this analysis, flood elevation exceedances are thus maintained as distinct values until
the final calculation stages. All subsequent calculations—including damages—are performed at these three
elevation exceedance intervals.
LACPR produced flood elevations for each exceedance interval (i) and basin (j).5 Flood elevations
published in the final report are derived from storm surge estimates at the 90% statistical confidence level.6
LACPR presents the 90% confidence level rather than the mean or another summary interval in order to
show a “high confidence, low uncertainty” estimate unlikely to be exceeded (USACE, 2009c). NOLArisk
therefore also relies on the 90% confidence level assumption, meaning that the results from this analysis
reflect the upper range of the statistical estimates generated by LACPR. However, this does not suggest that
uncertainty is “double-counted” in this dissertation, because the uncertain drivers considered in NOLArisk
were not included in LACPR’s statistical analysis.
LACPR also calculated interior flood elevations for two distinct cases regarding the future coastal landscape. In the baseline assumption, sufficient restoration is performed coast-wide to maintain roughly the
current landscape configuration, so that the landscape effect on storm surge is identical in 2011 and 2060.
The alternate scenario is a “degraded” landscape in which no new restoration is performed between 20112060, leading to substantial coastwide land loss. These scenarios represent two possible extremes depending
on the efficacy of planned restoration efforts, and NOLArisk interpolates within this range in order to provide
a more complete picture of partial restoration effects (or an alternate landscape altogether). Of course, the
interaction of storm surge with wetlands is a complex phenomenon with a variety of non-linear effects, and
simple interpolation is not sufficient for capturing the full range of possible outcomes. Nevertheless, without additional physical modeling results to determine alternate coastal configurations and the interaction of
surge with these possible coastlines, interpolation is a reasonable first order approximation to support current
planning efforts.
5
The indices refer to Eq. 4.4, discussed below.
The study team incorporated probabilistic uncertainty into surge elevation calculations to represent epistemic uncertainty (see
Sec. 3.2.1).
6
75
Managing New Orleans Flood Risk
4.3.2.2
Chapter 4
10-year rainfall
In addition to the three low-probability intervals described above, NOLArisk also considers flooding from the
10-year rainfall event in order to capture mitigation benefits from high-frequency, low-damage flood events.
The 10-year rainfall calculation in NOLArisk again draws from the LACPR approach, and assumes 6.5" of
rainfall over a 6-hour span. LACPR assumes that pumping in New Orleans can remove approximately 1" of
rainfall in the first hour and 0.5" in each hour following, yielding 3.5" of water removed and 3" remaining
(USACE, 2009c).7
The remaining 3" was multiplied by the area of each basin (in sq. ft., provided to the author by LACPR)
and converted to determine flood volume in acre-feet. Finally, I used stage-storage curves published by
LACPR, which relate a volume of water within each basin to the flood elevation that volume would produce,
to convert from flood volume in acre-feet to flood elevation in terms of ft. above NAVD88.8 As with the
low-frequency events, damages are separately calculated for the 10-year event.
4.3.3
Uncertain drivers
The uncertain drivers in NOLArisk that influence the recurrence of flood elevations over time surrounding
New Orleans include ongoing natural and anthropogenic changes to the coastal environment, climate-change
influenced sea level rise, and the possibility that the 2011 protection system may not be consistently maintained over the next 50 years (see Sec. 3.3 for a detailed discussion). The drivers are summarized in Table
4.3 below, and the quantification of each element is subsequently described.
Id.
Driver
Units
Range
CD
SLR
ν
Coastal degradation by 2060
Sea level rise
Protection system maintained
% of LACPR proj.
mm/year
yes/no
0 – 100
2 – 14
0–1
Table 4.3: Uncertain drivers for the Flood Hazards module.
7
LACPR rainfall and flood elevation data are presented in British units, and this discussion follows that convention in order to
connect to the recent literature.
8
In the Orleans Main (OM) basin, this calculation led to a elevation that slightly exceeded the 100-year storm elevation in 2011
(-4.8 ft. versus -5.1 ft.), and in this case the 10-year event was assumed to equal the initial 100-year event (i.e., the lower of the two
values was used for the rainfall event).
76
Managing New Orleans Flood Risk
4.3.3.1
Chapter 4
Coastal degradation
Uncertainty concerning the coastal landscape is addressed through a degradation parameter CD that controls
the ultimate percentage of degradation by 2060 relative to the two discrete LACPR scenarios. The driver
essentially interpolates between the “maintained” and “degraded” coast scenarios for 2060, and specifies
a linear rate of degradation (d) for the years 2011-2060 in order to reach the specified uncertain value by
the end of the simulation (i.e., 60d = CD). Furthermore, this simple abstraction also assumes that the
relationship between coastal degradation and interior flood elevations is linear, so that, for example, 50%
degradation in 2060 would lead to a flood elevation halfway between the “maintained” and “degraded” cases
in that year. This quantification makes strong assumptions, does not capture nonlinear or threshold effects
that are likely emerge from landscape conditions between the two extremes, and is deliberately simplified for
this preliminary exploratory modeling investigation. Future iterations, however, could incorporate additional
information to better reflect a) changing rates of coastal land loss (or land building) over time and b) more
sophisticated relationships between coastal land area, storm surge, and eventual interior flood elevations.
4.3.3.2
Sea level rise and subsidence
NOLArisk considers uncertainty about future eustatic SLR, and adds this uncertain value to a fixed assumption for local subsidence in order to calculate relative sea level rise. RSLR for year t is defined as:
RSLRt = −SLRt + Lt ,
(4.3)
where SLR is eustatic sea level rise and L is net subsidence. Because SLR is a positive value and
subsidence a negative one, relative sea level rise describes the combined effect of both trends in reducing the
effective land elevation relative to NAVD88. In the NOLArisk model, the sea level reference standard is kept
constant throughout the simulation period, and RSLR simply reduces the land elevation within the basins
and levee elevations relative to this standard over time.
As discussed in Sec. 3.3.1.3, there is ongoing disagreement in the scientific literature regarding future
rates of SLR, with IPCC AR4 projecting a consensus estimate of 1.8-5.9 mm/year while other studies taking
possible rapid deglaciation into account project much higher rates or threshold effects (Kopp et al., 2009;
77
Managing New Orleans Flood Risk
Chapter 4
Bindoff et al., 2007; Overpeck et al., 2006). LACPR considers two SLR scenarios of approximately 2 and 10
mm/year to reflect this uncertainty, but their high SLR scenario remains below more pessimistic projections
(USACE, 2009c). As a result, NOLArisk considers an expanded uncertainty range of 2-14 mm/year. Once
again, NOLArisk treats this rate as linear and, once specified, it remains fixed throughout the period of
analysis.
Local subsidence is not treated as uncertain in the initial analysis, and is fixed at 6 mm/year for basins east
of the Mississippi River (NOE, OM, and SB) (Dixon et al., 2006b). For basins west of the river, alternately,
an additional 4 mm/year is added to reflect the more rapid changes occurring along the Delta Plain (USACE,
2009c), yielding 10 mm/year subsidence for OW1 and OW2. Although subsidence is initially fixed, the total
range of RSLR considered is nevertheless broad and represents a wide range of plausible future conditions:
8-20 mm/year for NOE, OM, and SB, and 12-24 mm/year for OW1 and OW2.
4.3.3.3
System maintenance
The final flood hazard exogenous driver relates to the performance of the protection system. In the postKatrina investigation, it was discovered that in many locations actual levee or floodwall elevations were
much lower than the design elevation due to ongoing RSLR and a failure to appropriately rebuild or maintain
these levees at the design elevations (IPET, 2009a). Although current plans for the 100-year system include
regular maintenance and retrofitting, given past failures it seems appropriate to investigate how risk might
increase over time if levee heights are not maintained. As a result, I include a binary parameter ν for future
system maintenance. When ν = 1, the levees and other structures that define the system remain at a constant
elevation relative to NAVD88, whereas when ν = 0 the effective levee reach heights diminish with RSLR. In
turn, ν = 1 also leads to an increase in interior flood volumes within the system. For a rough approximation
of the additional volume when levees are not maintained, I assume that the additional interior flood height
equals the change in RSLR up to that point in time.
4.3.4
Module relationships
The input data and exogenous parameters described above are combined to generate flood elevations (e) for
each recurrence interval (i), basin (j) and year (t) using the following relationship:
78
Managing New Orleans Flood Risk
ei,j,t =
Chapter 4
⎧
0
t
t
⎪
⎪
0
0
⎨
t=1 RSLRt + (ei,j,2011 +
t=1 dt (ei,j,2060 − ei,j,2011 ))
if ν = 1;
0
t
⎪
⎪
0
⎩(e0i,j,2011 +
t=1 dt (ei,j,2060 ) − ei,j,2011 ))
if ν = 0,
(4.4)
where e0 are the initial flood elevations derived from (USACE, 2009c), less LACPR’s initial RSLR
estimates. This equation reflects how RSLR increases flood elevations if the system is not upgraded, describes
the linear increase in relative sea level and landscape degradation over time, and shows how the degradation
parameter determines interpolation between discrete LACPR scenarios. Once specified, flood elevation is
passed to the Flood Depths module.
4.4
4.4.1
Flood Consequences Module
Overview
The NOLArisk Flood Consequences model determines the asset values at risk by census block (c) over the
period of analysis 2011-2060. The module structure closely mirrors the calculations performed by the FEMA
HAZUS-MH MR3 multi-hazard flood risk model (FEMA, 2007). Inventory data are drawn from both the
FEMA model and a separate economic consequences database acquired from the LACPR Economic Analysis
team in June 2009. However, this analysis uses a subset of the full assets database compiled by LACPR. Their
goal was to determine all possible types of economic damages in the event of storm-surge or rainfall-induced
flooding, whereas this analysis focuses instead on direct damages to single-family residences and possible
damage mitigation via non-structural mitigation. As a result, many asset classes are not included in the
data described below, and calculations related to assets outside the scope of this analysis (e.g., commercial
buildings, government facilities, vehicles) are excluded from the model.
The consequences module uses an inventory of single-family homes and a series of assumptions regarding
the valuation of homes in each census block in order to determine the total value of homes in Orleans Parish.
Next, it combines these values with depth-damage curves—which describe the percent damage incurred
with each additional foot of flood depth—in order to develop a relationship between flood depth and damages
(stage-damage curves). Finally, stage-damage curves for each census block and year are passed to the Damage
Results module to determine damages at difference recurrence intervals.
79
Managing New Orleans Flood Risk
4.4.2
Chapter 4
Input data
Key data inputs for the consequences inventory are described in Table 4.4 below. Data elements were extracted from the HAZUS-MH and LACPR databases in Microsoft Access format and compiled into a common Microsoft Access database subsequently linked to NOLArisk. Except where noted, data elements are
indexed by 2000 Orleans Parish census block. Note that information from the FEMA HAZUS-MH model is
specific to New Orleans and/or Louisiana when possible.
Id.
Data element
c
M HIc
RESc0
GEc0
SDDC
CDDC
CSV R
ST ORc
0
F N Dc,l
0
SEc,l
RC
SAc
DEP
Source
Orleans Parish census blocks
2000 Median Household Income
Orleans Parish single-family residences (Aug. 2008)
Average elevation of each census block
Structure depth-damage curves
Contents depth-damage curves
Contents-to-structure value ratios
Proportion 1 or 2 story structures
Proportion by foundation type (pier vs. slab)
Initial structure elevation by foundation type
Replacement costs by sq. ft.
Average structure age (year built)
Depreciation curves by structure quality
U.S. Census
U.S. Census
GNOCDC
LACPR
LACPR
LACPR
LACPR
LACPR
LACPR
LACPR
HAZUS-MH
HAZUS-MH
HAZUS-MH
Table 4.4: Input data elements for the Flood Consequences module.
4.4.2.1
Residential structures inventory
One key shortcoming of both the LACPR and IPET analyses is the starting inventory for single-family residences applied to New Orleans. Both efforts start with a pre-Katrina inventory of structures by census
block derived from the 2006 version of the HAZUS-MH application. IPET uses the pre-Katrina values as is,
whereas LACPR assumes that household growth in the high-employment scenario will reach the 2005 values
by 2050, and adjusts these values uniformly by census block with percentages by parish that reflect growth
scenarios developed by an outside contractor (IPET, 2009b; USACE, 2009c). Katrina, however, caused major
changes in the makeup and distribution of population in New Orleans, and even though a substantial fraction
of the pre-Katrina population has returned as of 2009 the assumption that both the amount and spatial distribution of assets at risk pre- and post-Katrina are comparable is likely invalid and potentially biases flood
80
Managing New Orleans Flood Risk
Chapter 4
damages.
As a result, this analysis seeks to improve upon the LACPR and IPET estimates by using recentlygenerated data about population return developed by the Greater New Orleans Community Data Center
(GNOCDC) in cooperation with Valassis Lists. This database was developed by compiling active residential
postal addresses, by census block, for Orleans Parish at various time intervals post-Katrina. To support the
consequences module, data as of August 2008 was received from GNOCDC in November 2008.9
The GNOCDC data includes counts of all residential addresses, including single- (HAZUS Occupancy
Class RES1) and multi-family (RES3A-F) residences as well as manufactured housing (RES2), dormitories
(RES5), and nursing homes (RES6) (FEMA, 2007). Because this analysis focuses on single-family structure
mitigation and the data provided does not distinguish between residence type, I use the pre-Katrina counts of
residences by Occupancy Class for each census block and develop a ratio of RES1/ RES by block. This
ratio is then applied to the residential address counts from GNOCDC in order to develop an approximation of
the number of RES1 structures as of August 2008. Of course, this approach may introduce bias, in particular
where large multi-family structure or other facility have not been rebuilt post-2005, but it is a reasonable firstorder approach lacking more specific data from the U.S. Census or other official sources. The initial count
of single-family residences by census block used in the module, RES10c , is the result of this approximation.
Across the entire parish, the initial estimate is approximately 120,000 single-family homes.
4.4.2.2
Census block elevation
Another key data element to determine the consequences of flooding is the elevation of each census block
relative to the reference standard NAVD88. Ground elevation in New Orleans, as previously discussed, is a
key factor in determining relative flood risk by location, and differences of even 1-2 ft. can have a substantial
effect on levels of damage. To address this need, LACPR started with topographic estimates derived from a
LIDAR (Light Detection and Ranging) digital elevation model (DEM), also used in IPET, and then examined
aerial photography to identify the variation in elevation for individual structures as a means of determining
variation in elevation across each block. Mean ground elevations were then calculated for each block, and
LACPR characterized the uncertainty assuming a normal distribution with a standard deviation of 1.43 ft.
9
Data driven by Valassis Lists. From a compilation by the Greater New Orleans Community Data Center, http://www.gnocdc.org,
August 2008.
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Managing New Orleans Flood Risk
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(USACE, 2009c).
This analysis does not treat ground elevation probabilistically, but instead simply adopts the mean ground
elevations calculated by LACPR, GEc0 as baseline values (Fig. 4.4). The effects of subsidence on block
elevation—which could alter the interior stage-storage relationships or lead to a higher gradient to pump
water out of the city—are not considered in the initial NOLArisk model due to the limited input data available.
Legend
Mean block elevation
-15 - -10 ft.
-10 - -5 ft.
-5 - 0 ft.
0 - 5 ft.
5 - 10 ft.
10 - 20 ft.
³
Figure 4.4: Mean elevation in ft. above NAVD88 (2004.65), Orleans Parish census blocks, 2009. Map
developed by author; data source: USACE, 2009c.
4.4.2.3
Residential structure characteristics: USACE survey
Data for the remaining characteristics of single-family residences used in the module calculations, including
proportion of structures 1 or 2 stories (ST ORc ), proportion with slab versus pier foundations (F N Dc ), and
average elevation of structure by foundation type (SEc0 ) were derived from the LACPR economics database.
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The original sources for these data elements are prior USACE surveys of the New Orleans region and estimates made by local emergency management personnel (USACE, 2009c).
4.4.3
Uncertain drivers
The exogenous drivers that contribute to the consequences module produce different scenarios of population
growth and dispersion across Orleans Parish, thus modifying the growth of assets at risk over the study period
2011-2060. The drivers are listed in Table 4.5 below.
Id.
Driver
Units
α
π
Residential growth rate
Growth dispersion (in/out of OM basin)
thou. homes/year
%
Range
-0.8 – 1.6
50 – 75
Table 4.5: Uncertain drivers for the Flood Consequences module.
4.4.3.1
Single family residential growth
The future population of New Orleans is uncertain, and depends on numerous factors including regional
economic development, post-Katrina “betterment” reconstruction (Kates et al., 2006), and perceived future
flood risk (see Sec. 3.3.3). To represent this uncertainty, NOLArisk includes an exogenous driver for annual
growth in single family homes (α). Growth is assumed to proceed linearly from 2011-2060, starting from
the 120,000 baseline previously discussed, and is again designed to consider scenarios beyond the discrete
bounds used in the LACPR analysis. Specifically, future growth scenarios are allowed to vary from substantial
new growth (1,600 new homes per year, leading to 200,000 single-family homes in 2060) to an overall decline
(a loss of 800 homes/year, leading to 80,000 single-family homes at the end of the period of analysis). Fig.
4.5 shows the range of growth scenarios in NOLArisk compared with the discrete LACPR growth scenarios.
Note that the assumed 2010 inventory for LACPR is substantially below the initial estimate in NOLArisk
derived from GNOCDC estimates. This difference means that nearly all NOLArisk scenarios will show
greater assets at risk in the first half of the simulation (2011-2035).
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Managing New Orleans Flood Risk
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225,000
200,000
Single-family homes
175,000
LACPR: high/dispersed
scenario
150,000
LACPR: business-asusual/compact scenario
125,000
NOLArisk highest case
NOLArisk lowest case
100,000
75,000
50,000
2010
2020
2030
2040
2050
2060
2070
Figure 4.5: Future growth scenarios for single-family homes in Orleans Parish from NOLArisk (solid lines)
and LACPR (dashed lines). Note that NOLArisk can specify linear growth paths anywhere between the
low and high scenarios shown. LAPCR values were adjusted from households to single-family homes with
a factor of 0.86, drawn from the ratio of households to single-family homes in pre-Katrina New Orleans.
Source: USACE, 2009c.
4.4.3.2
Growth dispersion
In addition to uncertainty regarding the overall population growth rate in metropolitan New Orleans, another
question is how settlement patterns will evolve in the wake of the Katrina devastation. In Chapter 2, I describe
how the city grew northwards and eastwards, spurred by improved drainage and a growing protection system,
and how these newly-developed areas were among the hardest hit by the Katrina flooding. Although postKatrina return has slowed and many residents have returned to their old neighborhoods, in the long-term it
remains to be seen how growth will occur in different areas of the city.
To represent this uncertainty, NOLArisk includes a separate uncertain driver (π) that determines the
percentage of new growth that occurs in the center of the city (OM basin) or peripheral basins (1 − π).
The nominal dispersion defaults to the pre-Katrina OM basin value (64.57% of homes in OM, 35.43% in
the remaining areas), and the range of π for OM can vary from 50-75% of new growth. In turn, each of
the remaining four basins is weighted by pre-Katrina proportions to determine how much of the remaining
growth it is assigned. Although there are many OM neighborhoods at low elevations, in general a greater
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concentration of growth in the peripheral areas should tend to increase risk over time. Table 4.6 shows the
range of dispersion values for each basin.
ID
Basin name
OM
NOE
SB
OW1
OW2
Orleans Main
New Orleans East
St. Bernard (Lower Ninth Ward)
English Turn
Algiers
Range
Nominal value
50-75%
13-26%
3-7%
0-1%
8-16%
64.57%
18.74%
4.62%
0.85%
11.12%
Table 4.6: Range of scenarios for growth dispersion in Orleans Parish, by basin. Note that the ranges are not
independent: in each scenario, the percentages always sum 100% based on the weighting.
Collectively, a scenario realization of the residential growth parameter α and dispersion parameter π
determine the increase (or decrease) in structural inventory in each census block c and year t, according to:
⎧
⎪
⎪
⎨RESc0 + (π ∗ τc0 ∗ α)
if j = OM;
RESc =
⎪
⎪
⎩RESc0 + (1 − π)(μ0j ∗ τc0 ∗ α)
if j = OM,
(4.5)
where τc0 is the weighting of census blocks within each basin and μ0 is the conditional weighting of nonOM basins, both based on the pre-Katrina LACPR inventory. Note that this equation describes growth under
the “no new mitigation” case. Mitigation strategies can alter growth rates and patterns, and these effects are
described in Chapter 5.
4.4.4
Module relationships
Below, I summarize the relationships and calculation steps used in the Flood Consequences module. These
relationships are drawn directly from the HAZUS-MH model, and a more detailed description can be found
in (FEMA, 2007).
4.4.4.1
Depreciated structure replacement cost
The first calculation step in the Flood Consequences module is to determine the replacement value of structures at risk by census block. Following the HAZUS-MH approach, the model performs the following sequential calculations:
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1. Using median household income estimated in the 2000 Census, the module determines an income ratio
(M HIc /32500) by census block.
2. An average area per home (in sq. ft.) value10 is selected based on income ratio and multiplied by the
structure count (RES1c,t ) to calculate total residential area by census block.
3. Based on the income ratio and HAZUS-MH weights, the proportion of residences in each “construct
class" (Economy, Average, Custom, Luxury) is determined by census block.
4. Total residential area by block is multiplied by a replacement cost, subdivided by the construct class
and number of stories and weighted according to step #3, yielding a base replacement cost per block.
5. Garage replacement costs are added to the base replacement cost, incorporating HAZUS-MH assumptions about distribution of garage types in Orleans Parish, yielding total structure replacement cost by
census block.
Existing structure values are next depreciated by age, using data on average age of residence by census
block and depreciation schedules from the HAZUS-MH model. Structures currently in place are depreciated
to 2009 and their values are left in place at that time, with the assumption that home upgrades over time will
keep effective age constant through the period of analysis. New structures, alternately, are assigned a 10-year
effective age and left constant throughout the simulation, following similar assumptions made by the LACPR
economics team (Maestri, 2009).
Although residential structure depreciation is used in both the HAZUS-MH and LACPR approaches, the
assumptions regarding depreciation in NOLArisk have a substantial effect on overall damage calculations.
An alternate approach using full replacement values or a lower effective age for existing or future residences
would likely estimate much higher flood damages and thus greater economic benefits from damage reduction
via non-structural risk mitigation. Although the depreciation approach is fixed in NOLArisk and not tested
as part of the uncertainty analysis, future research building from this work should re-evaluate this assumption
and consider alternate approaches to account for structure valuation uncertainty.
4.4.4.2
Contents valuation
Once the depreciated structural replacement cost is determined, the consequences module uses a simple
contents-to-structure value ratio (CSV R) to calculate the value of assets within the single-family household.
The ratios vary by the number of stories: contents are assumed to be valued at 69% of the depreciated
10
Based on data from the Energy Information Administration, Housing Characteristics, 1993.
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structure value for a 1-story structure, or 59% of the structure value if 2 stories tall (USACE, 2009c). The
CSV R is multiplied by depreciated replacement cost for each block, and weighted by proportion of 1 or 2
story structures to determine the contents replacement value (FEMA, 2007).
4.4.4.3
Stage-damage curves
The last consequences sub-module calculation determines damages from floods of different depths. This
calculation relies on depth-damage relationships—denoting the percentage of the total value that is damaged
with each additional foot of water depth—for structures and contents developed by a panel of building experts
for a USACE Jefferson and Orleans Parishes Feasibility study in 1996. The depth-damage curves are for
saltwater, long-duration (one-week) exposure (USACE, 2009c). Separate curves were provided for structures
and contents, and further divided by number of stories and foundation type, yielding 4 structure (by stories
and foundation) and 2 content (by stories) curves.
These curves are then multiplied by the total structure and total content replacement values previously
calculated and summed by census block in order to generate stage-damage curves (Fig. 4.6). Each curve
describes the damages incurred by a certain depth of flooding for each block, with the depth of flooding
referring to the depth faced by the structure (and thus reduced if the structure is elevated). Stage-damage
curves shift over time depending on the economic growth scenario specified, and are further modified by
the non-structural mitigation strategies described in the next chapter. Finally, the stage-damage curves are
passed to the Damage Results module for the final set of calculations.
4.5
4.5.1
Flood Depths and Damage Results modules
Overview
The final calculation modules—Flood Depths and Damage Results (see Fig. 4.1)—calculate damage results
from the outputs generated by the Flood Hazards and Flood Consequences modules. There are three basic
steps: first, flood depths are calculated for each census block by comparing the flood elevation from each
discrete interval to the average elevation of each census block and the structures within; second, the flood
depths are compared to the stage-damage curves to determine specific damage values for each case; and
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Figure 4.6: Example stage-damage curve for one census block from NOLArisk (in dollars).
finally, an expected value is calculated for each recurrence interval and year using the damage values and
probabilities of failure by basin.
4.5.2
Flood Depths
NOLArisk determines effective flood depth by census block according to:
F Di,c,t,l = (ei,c,t − (GEc,t + SEc,t,l )) ,
(4.6)
where F D is the effective flood depth for each recurrence interval (i), census block (c), year (t), and
foundation type (l), ec is the flood elevation for each census block (ej → ec if c ∈ j), GEc,t is the mean ground
elevation by census block and year, and SEc,t,l is the average elevation above ground level for foundation type
l. This equation essentially determines the flood depth faced by structures with different assumed foundation
and elevation amounts in each census block. As SE increases, effective flood depth decreases. Note that this
calculation is directly modified by non-structural mitigation strategies; this quantification is discussed in the
next chapter.
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Managing New Orleans Flood Risk
4.5.3
Chapter 4
Damage Results
Final damages are calculated by comparing the depth-damage curves from the Flood Consequences module
to the effective flood depths for each census block, foundation type, and year. The comparison is made using
linear interpolation across depth damage curves for structure and content values. Once damage values are
selected for each foundation type (DM Gi,c,t,l ), NOLArisk calculates a weighted sum of these values and also
adds the contents damage estimate (CDM Gi,c,t ), so that total damages for each census block are calculated
as:
DM Gi,c,t = CDM Gi,c,t +
2
(DM Gi,c,t,l )(F N Dc,l ),
(4.7)
l=1
where F N Dc,l is the proportion of structures with foundation type l (1 = slab, 2 = pier) in census block c,
l
F N Dc,l = 1. Because the high dimensionality of these steps led to slow calculation times and excessive
memory use on a desktop computer, results were calculated for 5-year intervals (2011, 2015, 2020, etc.) to
reduce the number of dimensions. Cubic spline interpolation was then used after the intensive calculation
step in order to produce an annual curve.
4.6
Damage output calculations
The final calculation step produces damage values at each recurrence interval, census block, and year in the
analysis. This information is then discounted, summed across years, and amortized to estimate equivalent annual 100-, 400- and 1,000-year exceedance values. Discounted annual damages are also separately modified
to develop expected annual damage estimates.
4.6.1
Discounting approach
There is a substantial literature describing alternate approaches to setting the social discount rate or addressing intergenerational discounting when considering infrastructure or other long-term investments that will
affect multiple generations (e.g., Arrow et al., 1996). This is of particular concern for risk mitigation investments, where the costs are borne up front and benefits can accrue for decades (i.e., an elevated home remains
elevated through its useful life) and to multiple generations of owners. Rather than specify a single discount
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rate assumption, NOLArisk instead allows the decisionmaker to consider multiple discount rates or choose a
discounting assumption consistent with the city’s values and priorities (discussed further in Sec. 6.4.1). The
nominal value in NOLArisk is set to the fiscal year 2008 federal discount rate of 4.875% (USACE, 2009c),
4.6.2
Equivalent annual damage at each recurrence interval
Results from 2011-2060 are discounted, summed (net present value), and amortized to generate annual values
according to:
ADM Gi,c =
1
a50
r
50
t=1
DM Gi,c,t
,
(1 + r)t
(4.8)
where ADM Gi,c is the equivalent annual value for each recurrence interval and census block, r is the
selected discount rate, and a50
r is the annuity factor corresponding to the 50-year period of analysis and
selected discount rate.11 Rather than consider damage in specific future years (e.g., 2025, 2050), discounted
equivalent annual values provide convenient annualized summaries of residual damage across the 50-year
span.
4.6.3
Expected annual damages
The discounted equivalent annual exceedance values are useful summaries of residual risk for different areas
of New Orleans over the next fifty years. However, they do not directly support calculation of economic
risk reduction benefits, because they only consider a portion of the damage-frequency distribution. Benefitcost analysis with probabilistic uncertainty instead requires that the expected value of future benefits—the
probability-weighted sum across the entire frequency distribution—be calculated and compared with cost
in order to judge the efficiency of possible policy interventions. Although the expected value calculation
downweights the consequences from low-probability, high-damage events, it nevertheless incorporates damages from high-frequency, low-damage events that might otherwise be missed. This dissertation seeks to
understand both the risk reduction and overall economic efficacy of different mitigation incentives, and an
expected damage estimate is important to achieving the latter objective.
11
An annuity factor is defined as “the present value of an annuity of $1 per year for the life of a project,” and is used to convert
(amortize) a net present value calculation into an annual stream of equal amounts (Boardman et al., 2006).
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Managing New Orleans Flood Risk
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As previously discussed, frequency information for the entire range of possible floods was not available
to support an expected value calculation. Instead, I generated a rough empirical estimate of the damage
probability density function (PDF) using four discrete points from the complementary cumulative distribution
function (0.1, 0.01, 0.0025, 0.001). The approach relies on non-parametric sampling, and proceeds through
the following steps:
1. NOLArisk treats damage estimates at the four discrete intervals above as points along a cumulative
distribution, with probabilities equal to 1 − pi for the four complementary cumulative intervals;
2. Below the 10-year storm (F (x) < 0.9), damages are assumed to be zero;
3. Above the 1,000-year storm (F (x) > 0.999), damages are assumed to remain constant at the 1,000year value;
4. Using these assumptions, NOLArisk performs nonparametric sampling from this discrete set in order
to develop an empirical PDF. Analytica uses linear interpolation to generate this distribution, and
normalizes the area under the curve to equal unity. Median Latin hypercube sampling with a sample
size of 1,000 is used to ensure all ranges of the distribution are uniformly covered;
5. Finally, the model calculates expected value from the empirical PDF for each neighborhood.
This calculation is repeated at 5-year intervals across the period of analysis. Interpolation is used to
develop annual values that are then discounted, summed, and amortized to develop equivalent annual expected damages by neighborhood. This is the final damage output from NOLArisk, and (as discussed in the
proceeding chapter) is used to calculate mitigation benefits in terms of avoided damages.
4.7
Example NOLArisk damage results from selected scenarios
Using the risk measurements described above, I next present selected NOLArisk results to provide example
outputs for the reader and develop a baseline for comparison. This discussion first shows annual damages over
time for all of Orleans Parish from selected scenarios assuming no new risk mitigation is put into place (base
case). I then show equivalent annual damages from the 100-year recurrence interval for each neighborhood
under one scenario assumption.
I start by examining single scenarios with assumptions deliberately similar to those made in the discrete
LACPR scenarios. The first such case, which I term the “LACPR low/low” scenario, assumes 8 mm/year of
RSLR, a maintained (no degradation) coastline, and a somewhat declining number of future single family
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Managing New Orleans Flood Risk
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homes (see Fig. 4.5). The “modified LACPR high/high” scenario, alternately, assumes 16 mm/year of RSLR,
a growing city, and a maintained coastline. In contrast to the LACPR analysis, however, this scenario assumes
that levee heights degrade with RSLR, which will tend to yield increasing flood risk over time. Fig. 4.7 shows
residual damage, by recurrence interval, for the two LACPR-derived scenarios.
Figure 4.7: Annual damage over time, by recurrence interval, in the LACPR “low/low” (top) and modified
“high/high” (bottom) scenarios (2009 $ billions). The latter scenario reflects the LACPR “high/high” RLSR
and population level assumptions, but allows levee heights to degrade over time with RSLR.
In the LACPR low/low scenario, damages are flat or declining at all intervals, reflecting somewhat stable
risk. In the modified high/high scenario, alternately, the effects from additional RSLR and a growing population lead to increasing damages at the 100, 400, and 1,000-year recurrence intervals. Damages approximately
double at the 400- or 1,000-year levels from 2011-2060, while 100-year damages increase from $400 million
to greater than $5 billion over the time period.
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Managing New Orleans Flood Risk
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Risk varies substantially across different areas of the city, however, and the change in risk over time
also varies by neighborhood. For example, examining equivalent annual 100-year damages by neighborhood
under the LACPR low/low scenario (Fig. 4.8), we see that damages are spread fairly evenly across most
neighborhoods in New Orleans East (NOE). In Algiers (OW2), alternately, damages are higher in Tall Timbers/Brechtel than in surrounding neighborhoods. Damages in OM generally correlate with lower elevations,
with high 100-year damages noted in the low-lying neighborhoods adjacent to Lake Pontchartrain.
±
0
0.5
1
2
3
4
Miles
Village De L'Est
Lake Pontchartrain
Edgelake/Little Woods
Lake Terrace/Lake Oaks
Lakeshore/Lake Vista
Read Boulevard East
West Lake Forest
Pontchartrain Park
Pines Village
Milneburg
Read Boulevard West
Plum Orchard
St. Anthony
Lakewood/West End
Gentilly Woods
Fillmore
Viavant/Venetian Isles
Lakeview
Gentilly Terrace
Desire Area
Dillard
City Park
St. Bernard Area/Project
Legend
Desire Dev
Navarre
Fairgrounds/Broad
Lakewood
St. Roch
Florida Project
Florida Area
100-year equiv. ann. damage (2009 $ 1000s)
0 - 500
Seventh Ward
Bayou St. John
St. Claude
Dixon
Mid-City
Sixth Ward/Treme/Lafitte
Hollygrove
501 - 1000
Lower Ninth Ward
1001 - 5000
Marigny
Bywater
Iberville Project
Gerttown/Zion City Tulane/Gravier
Vieux Carre
5001 - 10000
Holy Cross
10001 - 25000
Leonidas/West Carrollton
Calliope Project
Marlyville/Fontainbleau
Central Business District
East Carrollton
Audubon/University
Uptown
Milan
25001 - 50000
Algiers WhitneyAlgiers Naval Station Mi
s si
ss i
McDonogh
Broadmoor/Freret
Central City/Magnolia
Black Pearl
Algiers Point
St. Thomas Area
Fischer Project
Garden District
St. Thomas Project
Touro
Behrman
St.Bernard Parish
50001 - 100000
pp
iR
ive
100001 - 200000
r
200001 - 300000
>300000
Aurora/Walnut Bend/Huntlee Village
East Riverside Irish Channel
River Park/Cut Off/Lower Coast1
West Riverside
Jefferson Parish
Tall Timbers/Brechtel
River Park/Cut Off/Lower Coast2
Figure 4.8: Equivalent annual 100-year damages (2009 $ billions, 4.875% discount rate), by neighborhood,
in the “LACPR low/low” scenario.
Using alternate plausible assumptions, however—a levee system not maintained over time, RSLR averaging 6.3 mm/year, a coastline degrading by approximately 71% of the maximum LACPR projection, and
a growth rate of 470 homes/year—100-year risk increases dramatically. In this scenario, the environmental drivers are more adverse than those in the LACPR low/low scenario, while the household growth rate
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Managing New Orleans Flood Risk
Chapter 4
is positive but smaller than the LACPR high/high growth assumption. In this scenario, the environmental
drivers lead to much greater 100-year damages in NOE neighborhoods (e.g., Edgelake/Little Woods) as well
as neighborhoods on the West Bank (e.g., Tall Timbers/Brechtel), while risk in other areas increases more
slowly (Fig. 4.9). The variation across neighborhoods suggests that non-structural risk mitigation effects may
also vary substantially by location, a hypothesis discussed in depth in Chapter 6.
±
0
0.5
1
2
3
4
Miles
Village De L'Est
Lake Pontchartrain
Edgelake/Little Woods
Lake Terrace/Lake Oaks
Lakeshore/Lake Vista
Read Boulevard East
West Lake Forest
Pontchartrain Park
Pines Village
Milneburg
Read Boulevard West
Plum Orchard
St. Anthony
Lakewood/West End
Gentilly Woods
Fillmore
Viavant/Venetian Isles
Lakeview
Gentilly Terrace
Desire Area
Dillard
City Park
St. Bernard Area/Project
Legend
Desire Dev
Navarre
Fairgrounds/Broad
Lakewood
St. Roch
Florida Project
Florida Area
100-year equiv. ann. damage (2009 $ 1000s)
0 - 500
Seventh Ward
Bayou St. John
St. Claude
Dixon
501 - 1000
Lower Ninth Ward
Mid-City
Sixth Ward/Treme/Lafitte
Hollygrove
1001 - 5000
Marigny
Bywater
Gerttown/Zion City Tulane/Gravier
Iberville Project
Vieux Carre
5001 - 10000
Holy Cross
10001 - 25000
Leonidas/West Carrollton
Calliope Project
Marlyville/Fontainbleau
Central Business District
East Carrollton
Audubon/University
Milan
St. Thomas Area
Fischer Project
Garden District
St. Thomas Project
Touro
Uptown
25001 - 50000
Algiers WhitneyAlgiers Naval Station Mi
s si
ss i
McDonogh
Broadmoor/Freret
Central City/Magnolia
Black Pearl
Algiers Point
Behrman
St.Bernard Parish
50001 - 100000
pp
iR
ive
100001 - 200000
r
200001 - 300000
>300000
Aurora/Walnut Bend/Huntlee Village
East Riverside Irish Channel
River Park/Cut Off/Lower Coast1
West Riverside
Jefferson Parish
Tall Timbers/Brechtel
River Park/Cut Off/Lower Coast2
Figure 4.9: Equivalent annual 100-year damages (2009 $ billions, 4.875% discount rate), by neighborhood,
in a higher-risk example scenario.
4.8
Summary
In this chapter, I have discussed the challenges of developing a suitable scenario generator to investigate
future flood risk in New Orleans in the face of substantial long-term uncertainty. I next described a lowresolution model, NOLArisk, an initial attempt to address these challenges while relying on recently-produced
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Managing New Orleans Flood Risk
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hazard and consequences data from LACPR as well as relationships derived from the FEMA HAZUS-MH
flood risk model. Input data, key uncertainties, and relationships for different modules within the NOLArisk
model were described in detail, with caveats noted where the model requires additional assumptions. Finally,
I presented risk results from the NOLArisk scenario generator in selected future scenarios and described
substantial variation in flood risk in different geographic locations. In the next chapter, I will describe a set
of possible non-structural mitigation strategies for New Orleans that feed into the model, and in the remaining
chapters will conduct a Robust Decision Making analysis of risk mitigation strategies using the NOLArisk
platform.
95
Chapter 5
Non-Structural Risk Mitigation: Tools and
Strategies
5.1
Introduction
In Chapter 2, I describe how engineered solutions came to dominate local and federal response to river and
coastal flood risk, including a levees-only policy along much of the Lower Mississippi River leading up to the
great flood of 1927 (Barry, 1997). Although in its early history, New Orleans residents avoided flood damages
primarily by elevating their homes and building only on the natural levee along the Mississippi River (Colten,
2005), in the 20th century the city grew increasingly reliant on the growing hurricane protection system to
keep out storm surge and prevent flood damages. Recently developed areas, particularly those developed
after World War II, combined below-sea-level elevations with slab-on-grade structure foundations providing
little elevation, further exacerbating potential exposure to flood damages when the system eventually failed
(Campanella, 2002; Gordon and Little, 2009). The heavy damage to local residences caused by Hurricane
Katrina can be traced in part to these historical land use choices and overconfidence in the safety and risk
reduction provided by the levee system.
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Managing New Orleans Flood Risk
5.1.1
Chapter 5
From flood “protection” to floodplain management
Increasing vulnerability to catastrophic damage from hurricanes and storm surge flooding, however, is a trend
not isolated to New Orleans. Recent research has suggested that the increasing trend in observed hurricane
and flood losses in the United States over the twentieth century has been caused primarily by the concentration
of wealth and new development along the coast (Pielke Jr. et al., 2008; Pielke Jr., 2007). Over half of the U.S.
population lives within 50 miles of the coast, for example, and the coastal population increased by 33 million
from 1980-2003.1 Adjusted for inflation, average annual flood damages increased along with the population
and wealth concentration, from $2.2 billion in the first half of the twentieth century to $3.9 billion in the
latter half (Larson and Plasencia, 2001).
Researchers have also connected these increasing flood losses to federal policy encouraging coastal development, pointing to the availability of federally-subsidized flood insurance, post-disaster relief funds, and
federal levee building as primary drivers of increasing flood losses (Burby, 2006; Berke and Campanella,
2006; Kunreuther, 2006). Critics of these policies argue for a substantial change in course in floodplain management, particularly with regard to additional levee building. The Association of State Floodplain Managers
(ASFPM), for example, recommend that “levees should be used as a structure of last resort and only after
other measures, especially nonstructural ones, have been fully considered” (ASFPM, 2007). In this view,
a systematic re-evaluation of land use aimed at “no adverse impact” in the floodplain is needed (Larson
and Plasencia, 2001), with non-structural risk mitigation tools used to mitigate possible encroachments on
community risk standards (Wilkins et al., 2008; Burby, 2006; Burby and Dalton, 1994). Advocates of new
approaches further cite the anticipated effects of climate change on flood risk as an argument for proscriptive
planning in the floodplain (Törnqvist and Meffert, 2009; Wilkins and Emmer, 2008).
These arguments have gained traction in federal and state planning since 2005. The Obama Administration, for example, is currently considering revisions to the “Principles and Guidelines” for water resources
studies called for in the Water Resources Planning Act of 19652 that would require “full and equal treatment to nonstructural approaches that avoid and minimize actions and changes that are incompatible with or
adversely impact floodplain functions” (Office of the President, 2009).
1
2
National Ocean Service, Ocean Facts, oceanservice.noaa.gov/facts/population.html.
Public Law 89-8.
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Non-structural approaches and floodplain management are also considered in most long-term plans for
the Louisiana coast (USACE, 2009c, Lopez et al., 2008, CPRA, 2007). However, implementation of land
use management changes in Louisiana has not kept pace with these calls to action. In general, state or local
changes have been in response to federal requirements, and usually targeted only to meet minimum standards.
Emmer et al. (2007), for example, document a general lack of local hazard planning across 19 Louisiana
parishes and conclude that “comprehensive planning and consideration of natural hazards is virtually nonexistent except in reaction to federal laws.” As noted in Chapter 2, moreover, efforts to change long-term land
use practices in New Orleans after Hurricane Katrina were unsuccessful and have been reduced to a lower
priority in the face of the city’s more immediate needs. The lack of observed implementation speaks to
the difficulty of implementing policies designed to restrict growth for local governments, and suggests that
improved understanding and communication of the potential benefits of such approaches remains a priority.
5.1.2
Organization of this chapter
This chapter describes non-structural risk mitigation approaches designed to reduce flood damages for single family homes in New Orleans. First, I introduce the basic policy tools that can be used to encourage or
mandate additional defensive actions, including home elevation or floodproofing, buyouts, and easement purchases to prevent future development. Next, I describe representative federal programs designed to encourage
adoption of such approaches, including the incentives provided through the National Flood Insurance Program. I also briefly describe the non-structural risk mitigation planning currently underway in Louisiana and
in the City of New Orleans. Finally, I describe how such approaches were quantified and incorporated into the
NOLArisk scenario generator, including the key uncertainties associated with the systematic implementation
of such programs.
5.2
Non-structural mitigation for single-family homes in New Orleans
Non-structural risk mitigation is a term typically used by USACE, and is generally synonymous with hazard
mitigation or other terms describing land use management actions. Broadly defined, non-structural risk mitigation can include any action taken to reduce exposure to the consequences of flooding aside from levees,
floodwalls, gates, canals, or other “structural” protection system investments (USACE, 2009c). This defini98
Managing New Orleans Flood Risk
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tion includes actions taken to protect critical infrastructure (hospitals, pump stations, police/fire facilities),
improved evacuation routes and evacuation planning, and other local planning actions. This dissertation focuses on possible non-structural risk mitigation efforts focused on single-family homes, however, and below
I describe the actions can be taken to reduce inundation risk for individual residences.
5.2.1
Structure enhancements or modifications
5.2.1.1
Elevation
Elevating above ground level, and above potential floodwaters, is the most common method for reducing
flood exposure to single-family homes. As previously discussed, elevation has historical local antecedents,
and classical New Orleans architecture typically incorporates elevated foundations (see Sec. 2.2.2). Elevation
can be accomplished in two ways: retrofitting an existing home or incorporating an elevated design into a
new structure. Retrofitting typically entails lifting the entire structure and erecting a new foundation below,
or building a second-story onto the home and converting the first floor into a flood-compliant, uninhabited
structure (i.e., garage, building access, or storage) (FEMA, 2009). For homes built on a slab foundation
(including many postwar structures in Orleans Parish), the slab and house can be either separated or left
attached and elevated as a unit, with a new foundation erected below (CHART, 2008a).
Elevated foundations can be either enclosed (first floor conversion and wall extension) or open. Open
foundations can be built on piers (masonry/concrete), posts (wood/steel), or piles (wood, steel, or concrete
embedded deep into the ground). Piles are more stable and less susceptible to erosion and velocity effects, and
are therefore used in areas outside of the levee system or where there is otherwise concern about water velocity
and waves (FEMA, 2006a; CHART, 2008a; CHART, 2008b). New homes have similar options and can
incorporate open or closed foundations, but with greater flexibility in terms of the design and attractiveness
of the finished structure. Fig. 5.1 shows an example of a retrofitted home elevated one story (approximately
9-10 ft.).
The target elevation for the first-floor of the structure is typically determined by the Base Flood Elevation
(BFE) required for NFIP participation, where the BFE is the predicted flood elevation for the 100-year flood.
Elevation methods will vary depending on the eventual target: FEMA provides closed foundation designs
up to 8 ft. and open foundation styles up to 15 ft. above ground level, for example, and recommends that
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Figure 5.1: Example retrofitted home with style elements incorporated. Source: FEMA, 2009.
a professional engineer custom-design homes above these limits (FEMA, 2006a). Importantly, as overall
elevation increases, the number of other factors to consider also multiply. At higher elevations (above 3-4
ft.), for example, the house may be further exposed to hurricane wind damage and may need further reinforcement. In addition, new means of access must be constructed for high-elevation homes (stairs, ramp, or
elevator), a particular concern for New Orleans due to a larger elderly and disabled population (FEMA, 2009;
LSUAgCenter, 2008). Elevation costs generally increase with home size, target elevation, and construction
type (masonry versus wood frame) (FEMA, 2009).
5.2.1.2
Floodproofing
Floodproofing can be performed in conjunction with, or in some circumstances in place of elevation, and is
designed to make a structure resistant to flood damages when waters are above the first floor of the residence.
FEMA identifies two types of floodproofing: wet floodproofing, which allows water to enter enclosed areas
of a structure, and dry floodproofing, which is designed to maintain a sealed home and keep floodwaters out.
Due to the hydrostatic pressure exerted by external floodwaters, dry floodproofing is only appropriate for low
(2-3 ft.) floodwaters (FEMA, 2009), and as a result may not be appropriate for many areas of Orleans Parish.
Wet floodproofing, alternately, allows water to enter, flow through, and equalize pressure in- and outside of
the structure. This approach is typically used to limit damages to enclosures (e.g., garages) that are otherwise
not inhabited, and could therefore be performed in conjunction with a 1-story elevation retrofit and first-floor
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conversion. Wet floodproofing requires openings for water to flood into and out of the enclosure as well as
flood-resistent construction materials (FEMA, 2009).
5.2.2
5.2.2.1
Buyouts and land use management
Existing property buyouts
LACPR defines a property buyout as “...selling the structure to the non-Federal sponsor for demolition or
salvage, evacuating the property, and relocating the property owner to another site outside the 100-year floodplain." (USACE, 2009c). Technically, residents can relocate from the floodplain by selling the property and
moving the structure itself to a new location (FEMA, 2009), but given practical considerations—particularly
with flood-damaged homes—selling the entire structure and using the proceeds to purchase a home in a less
flood-prone area may be a more cost-efficient approach. Buyouts are typically treated as a post-disaster option to address substantially damaged homes. The post-Katrina Road Home program, for example, offered
property buyouts as an option for homeowners with damaged or destroyed properties in parishes affected
by Hurricanes Katrina or Rita. “Option 2” allows the homeowner to sell their home and purchase another
home with the State of Louisiana, whereas “Options 3” necessitates selling and either moving out of state
or simply not purchasing another home (becoming a renter) (Road Home Program, 2009). As of the end of
2009, 3,404 Orleans Parish residents had completed the Road Home process using Option 2, and another
1,406 chose Option 3 and closed on their homes. Statewide, the totals are 7,935 and 2,160, respectively
(Road Home Program, 2010). Note that this is only a small fraction of overall disbursements: total closings
were 126,090 statewide as of this writing, meaning that only 8% of homeowners elected to use Road Home
incentives to relocate rather than use the assistance to rebuild and stay in place.
For future planning in at-risk coastal areas, however, subsidized buyouts could become an important
tool for long-term risk reduction. The non-structural plans considered by LACPR, for example, include
substantial buyouts outside of existing levee systems.3 As previously discussed, LACPR assumes that such
non-structural programs would be voluntary with 100% participation, but they do not preclude the possibility
of mandatory buyout programs (USACE, 2009c). ASFPM also considers acquisition a primary tool for
3
LACPR briefly discusses a conceptual “redundant” elevation program within the HSDRRS system that would elevate all structures up to +1 ft. above NAVD88, but did not consider the possible costs or benefits of buyouts within the levee system (USACE,
2009c).
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permanent risk reduction, particularly for homes that are difficult to relocate (e.g., slab foundations homes)
(ASFPM, 2003). Participation in large-scale, voluntary buyout programs remains uncertain, however, and
mandatory acquisitions face substantial legal and logistical challenges (see Emmer et al., 2007 for a detailed
discussion of government takings related to non-structural mitigation, for example).
5.2.2.2
Zoning and development restrictions
Rezoning an area to prevent future housing development is another, complementary non-structural approach
to consider. As with buyouts, reducing the number of structures in the floodplain provides guaranteed risk
reduction benefits regardless of uncertainty regarding the flooding threat. Wilkins et al. (2008) describe a
list of possible low- or no-density use zones to consider for comprehensive planning purposes, for example,
including Open, Conservation, Preservation, Hazard, or Parks. The authors note that such zoning can provide
multiple benefits: in addition to risk reduction, rezoning can provide parks or green space and/or provide
conservation value. As part of a community-wide plan, creating undeveloped low-lying areas can also provide
water catchment and storage in the event of a flood (Wilkins et al., 2008).
A rezoning plan that would take residential areas and convert them wholesale to green space is not
currently considered a viable option for the city. As discussed in Chapter 2, a plan of this type was proposed
by the Urban Land Institute in the months following Hurricane Katrina (York, 2006), but public backlash led
Mayor Nagin to reject this approach and instead ensure that residents could return and rebuild in all areas
(Nelson et al., 2007). That said, the City of New Orleans could use overlay zoning in order to add a natural
hazard component to existing residential zoning (Wilkins et al., 2008). In addition, an approach that took
currently undeveloped or city-held properties permanently out of commerce remains technically feasible and
could be considered in future years. As a result, I consider property easements as one potential policy lever
in this analysis.
5.3
Federal flood risk management assistance
State and local governments are technically responsible for coastal land use management, but federal policies often play a lead role in guiding development in South Louisiana (Wilkins and Emmer, 2008). Federal
laws and regulations provide different types of incentives for states, localities, and individuals who undertake
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hazard planning or mitigation actions through several statutes, including the Coastal Zone Management Act
(CZMA) of 1972 4 and National Flood Insurance Program, established in 1968.5 The CZMA provides incentives to states for establishing and maintaining coastal zone management programs, which led to Louisiana
adopting the State and Local Coastal Resources Management Act (SLCRMA) in 1978. The SLCRMA establishes guidelines for coastal natural resource management, permitting, and development, but plays a limited
role in hazard mitigation because the regulations exclude single-family homes (Emmer et al., 2007).
5.3.1
National Flood Insurance Program
The National Flood Insurance Program, alternately, has been central to risk management and planning in New
Orleans and South Louisiana since 1968. The NFIP provides federally-subsidized flood insurance to residents
in flood-prone communities who participate in the program (FEMA, 2002). Private insurers typically have
not offered flood insurance to floodplain residents due to excessive risk, and the NFIP is designed address
this market failure by simultaneously shifting the burden of risk from residents to the taxpayer while incentivizing communities and homeowners to reduce exposure to flood losses through risk mitigation actions.
The program is technically voluntary, but federal law prohibits federally-regulated lenders from providing
mortgages to non-participating floodplain residents, making it mandatory for most home buyers (Wilkins and
Emmer, 2008). Furthermore, the NFIP requires participating communities to enforce floodplain management
regulations to minimum federal standards (FEMA, 2002).
To identify communities vulnerable to flooding, the NFIP establishes “Special Flood Hazard Areas”
(SFHAs), defined as areas that are have at least a 1% chance of being flooded in each year (100-year flood).
SFHAs are delineated on flood insurance rate maps (FIRMs) produced at regular intervals by FEMA. In
SFHAs, new residential construction is required to be elevated at or above the predicted elevation of the 100year flood (BFE). Existing structures that have been substantially (more than 50%) damaged by flood are
also required to elevate in order to receive federal insurance and post-disaster assistance (FEMA, 2002). As
of 2004, there were approximately 20,000 communities participating in the NFIP. However, a recent study
estimated that only 50% of single family households in SFHAs nationwide have flood insurance policies
(63% in areas subject to coastal flooding) (Dixon et al., 2006a).
4
5
16 U.S.C. §§1454-1455.
National Flood Insurance Act, Public Law 09-448
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5.3.2
Chapter 5
FEMA grant and incentives programs
Via the NFIP and other programs, FEMA is the primary provider of flood risk hazard mitigation funding to
states and communities. FEMA provides incentives through a variety of programs, some regularly offered
and others activated for targeted communities after a disaster declaration. FEMA incentives are described
briefly below (FEMA, 2006b).6
• Flood Mitigation Assistance (FMA): Provides funds to states and communities participating in the
NFIP to provide non-structural risk mitigation to NFIP-eligible structures. Eligible activies include
elevations, buyouts (acquistion/relocation/demolition), and dry floodproofing of non-residential structures. Includes a cost-share requirement.
• Repetitive Flood Claims (RFC): Similar to FMA, but in areas unable to meet a cost-share requirement.
• Severe Repetitive Loss (SRL): Provides incentives directed at properties with four or more flood
claims of at least $2,000, or two or more claims that sum to a value greater than the market value of
the structure itself. Actions and eligibility similar to FMA.
• Pre-Disaster Mitigation: Competitive grant program for mitigation funds targeted at elevation, buyouts, structure relocation, or easement purchases and open space conversion. Grants awarded to states
or communities annually.
• Hazard Mitigation Grant Program (HMGP): This program is activated after a disaster declaration,
and is generally administered at the state level. HMGP funds can be targeted at a variety of mitigation
actions (not restricted to flood disasters), but eligible flood mitigation activities include elevations,
buyouts, and structure relocations.
HMGP funds have been made available to residents rebuilding in New Orleans and South Louisiana
through the Louisiana Recovery Authority, the same entity responsible for administering the Road Home
Program. HMGP grants are available for either elevation of an existing structure or demolishing and building a new elevated structure (within certain limits). Awards were initially capped at $30,000, but in mid-2009
the cap was increased to $100,000 per home.7 Disputes between the FEMA and LRA and program administration problems, however, delayed grant availability for over two years after Hurricanes Katrina and Rita.
As of December 2009, the state had only paid $3.4 million of an initial $1.2 billion of available funds (later
reduced to $750 million), and many New Orleans residents have dropped out of program consideration due
6
7
See also http://www.fema.gov/government/grant/hma/index.shtm for an overview of FEMA programs.
LRA, HMGP Q&A, http://lra.louisiana.gov/assets/docs/searchable/Other/HMGPElevationQA.doc.
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to the delays. HMGP funds must be spent by October 2010, adding to concerns about ongoing program
administration and oversight (Hammer, 2009).
5.4
Hazard mitigation planning in the New Orleans Master Plan
As discussed in Chapter 2, the 20-year Master Plan recently completed by the City of New Orleans considers
a variety of non-structural risk mitigation actions. Some initiatives involve specifically-targeted activities,
while others are more goal-oriented and lack an implementation mechanism. Specifically, the draft master
plan calls for the city to:
• Develop standards to achieve the 500-year risk reduction standard (note that this is a general aim
and no specific land use restrictions are described yet, although they could be incorporated into the
Comprehensive Zoning Ordinance);
• “Design new public facilities and retrofit existing public facilities to make them resilient to wind and
flooding”;
• Consider additional freeboard elevation requirements (beyond the 100-year flood insurance requirements) for certain neighborhoods, including Algiers, the Lower Ninth Ward, Mid-City, Broadmoor,
Hollygrove, Lakeview, and New Orleans East; and
• “Secure additional funding to assist property owners with the cost of elevating and storm proofing
structures” (City of New Orleans, 2009b).
In addition, the Master Plan notes that the city is pursuing “funds to develop a non-structural program for
areas of highest risk with incentives to promote land use and building practices that provide more protection,”
and are planning demonstration programs focused on 17 Target Recovery Areas. These programs—designed
in collaboration with USACE—would include elevation of structures and property buyouts, with residents
relocated to new elevated structures in the same area. The pilot efforts are at a small scale, however (150
homes at present), and no funding source for a large-scale non-structural program has been identified to date
(City of New Orleans, 2009b). Future non-structural mitigation programs, as a result, will most likely be
funded through the FEMA incentives programs previously described.
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5.5
5.5.1
Chapter 5
Quantifying non-structural mitigation strategies
Overview
As described above, policies designed to encourage additional non-structural risk mitigation or alter land use
and development patterns in New Orleans have not yet been clearly defined by the city. Given the current
lack of specific plans to test quantitatively, this analysis instead considers a series of hypothetical mitigation
strategies for Orleans Parish that could be implemented over the next decade to reduce risk to single-family
homes. The strategies tested include either incentives for home elevation alone or a combination of elevation
incentives, existing home buyouts, and permanent easements purchased on vacant properties. Except where
noted, I assume that the City of New Orleans—using funds from federal hazard mitigation programs—would
be responsible for 100% of the implementation costs for these risk reduction efforts, and I assume the role
of master planner to make decisions from the city’s perspective.
In this section, I first describe the process used to identify an initial set of non-structural risk mitigation
strategies. The strategies use elevation thresholds (compared to the sea level reference standard NAVD88) in
order to select census blocks for additional mitigation, where the census block is selected if its average ground
elevation is below the threshold level. One threshold determines if additional home elevation incentives
will be provided in that block, as well as the new target elevation for those homes. The other determines
whether a buyout and permanent easement program will be enacted in the area. A strategy is defined as the
combination of these two thresholds, and using this approach sixteen strategies made up of different elevation
and buyout/easement thresholds are identified for comparison in NOLArisk. In Chapter 6, the performance
of these strategies is compared separately for each neighborhood in New Orleans to identify potentially robust
mitigation approaches.
Next, I discuss how these strategies are quantified and incorporated into the NOLArisk model. This quantification addresses several key implementation challenges that could threaten the efficacy of such plans—
including, for example, the level of participation in voluntary mitigation programs—but does not systematically address all possible implementation issues. Implementation uncertainty is addressed by incorporating
additional exogenous drivers into NOLArisk that are considered in the scenario analysis. Based on the strategy enacted and the values of these exogenous drivers, NOLArisk determines how many homes are modified
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annually in each census block, and the supporting calculations are described in detail below.
Finally, I describe how NOLArisk calculates strategy implementation costs. The model tallies the total
cost of each strategy over the period of analysis, taking into account uncertainty about the per-unit cost of
the policy levers considered. To provide a suitable comparison with the flood damage outputs described in
Chapter 4, these costs are discounted to present value and summed in order to calculate equivalent annual
cost and net benefit over time.
5.5.2
Identifying mitigation strategies
The hypothetical mitigation strategies developed for the initial NOLArisk analysis use elevation thresholds
(relative to the sea level reference standard NAVD88) in order to select census blocks for additional mitigation. Census blocks with an mean ground elevation below the threshold are selected for additional mitigation,
whereas no action is taken if the average elevation is above the line. To simplify the analysis, the mean ground
elevation for each census block in 2012—the assumed project start year—is used as a proxy for the ground
elevation of all structures within that block.
Two different thresholds are identified for each strategy. The first threshold (x in Fig. 5.2 below) determines whether incentives to elevate existing or new homes will be offered for that location. In addition, this
threshold is also used as a home elevation target, similar to the base flood elevation utilized by the NFIP.
Similarly, if mean block elevation falls below the second threshold (y in Fig. 5.2 below), a buyout or permanent easement program will be active in the census block. These two thresholds are implemented jointly in
a single strategy, with the buyout/easement threshold superseding the home elevation threshold for census
blocks that are below y.
Fig. 5.2 shows a stylized schematic describing these decision rules. If the mean block elevation falls
below the buyout/easement threshold (y, red line), existing homes are selected for buyout incentives and new
growth is restricted. If the block elevation falls below the elevation threshold (x, green line) but above the
buyout/easement threshold, alternately, both existing and new homes are selected for elevation incentives.
No additional action is taken in census blocks above the green line. The analysis then considers a series
of different possible combinations of elevation and buyout thresholds, forming an experimental design of
strategies to test in the scenario ensemble.
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Below x and above y,
homes are elevated up
to x ft. above sea level
No action
Elevation
threshold (x)
Sea level
(NAVD88 2004.65)
Buyout/easement
threshold (y)
Elevations
Buyouts &
easements
Below y, homes are
bought out and
easements purchased
Figure 5.2: Schematic describing how elevation and buyout/easement thresholds are applied depending on
mean census block elevation.
This approach contrasts with the approach taken by FEMA in the NFIP. Although the NFIP also uses
target elevations in the form of base flood elevations, FEMA’s thresholds are derived from predicted 100-year
flood elevations. Here, strategies chosen for testing are instead independent of flood risk predictions because
the long-term flood elevation predictions are treated as highly uncertain. Instead, I test strategies reflecting a
wide range of mitigation intensity and use the RDM analysis to identify strategies that cost-effectively reduce
damage across many different flood risk scenarios.
Specific strategies are developed using an experimental design that reflects different combinations of
home elevation and buyout/easement thresholds (see Sec. 5.5.2.2 below). To ensure that the thresholds selected for the experimental design adequately span the range of ground elevations in Orleans Parish, I examined the distribution of mean census block elevations in Orleans Parish (Fig. 5.3). This distribution is
weighted by the pre-Katrina household count in each block in order to down-weight unpopulated blocks.
Based on this distribution, I selected six thresholds at 3 ft. intervals for home elevations (-5, -2, +1, +4,
+7, and +10 ft. relative to NAVD88). Buyout/easement thresholds, alternately, were set at two intervals,
-5 ft. and -4 ft. These were selected so that no more than 25% of populated (or potentially redeveloped)
census blocks would be bought out even under the most intensive buyout strategy considered (Table 5.1).
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This limit is somewhat arbitrary, but was selected to reflect that extensive buyouts across Orleans Parish are
likely infeasible in the near future. Fig. 5.3 shows the selected thresholds in green (home elevations) or red
(buyout/easements).
0.05
Weighted density
0.04
0.03
0.02
0.01
0.00
−10
−5
0
5
10
15
Block elevation (ft. above NAVD88 2004.65)
Figure 5.3: Orleans Parish census block elevations. The histogram is weighted by a pre-Katrina count of
residential households in order to focus on developed areas. The red lines show the block elevation thresholds below which buyouts/easements are implemented, while the green lines show the thresholds for home
elevations. Mean block elevations and pre-Katrina household counts were provided by LACPR.
The effects of these strategies vary considerably by location within the city given the observed elevation
variation. Fig. 5.4 shows which census blocks would be affected using different thresholds. Each color on
the map represents one threshold, and a census block with that color would be affected by that threshold
or any other threshold set at a higher elevation. Lighter colors reflect higher elevation thresholds (and
therefore strategies with a broader geographic effect). Green-colored census blocks are above -4 ft. and
would thus only be affected by home elevation strategies, while red census blocks are affected by either
buyout strategies, if applied, or elevation strategies if no buyouts are incorporated. The map clearly denotes
the areas where buyouts could be considered under this approach: New Orleans East, neighborhoods along
Lake Pontchartrain (West End, Lakeview, Fillmore, St. Anthony, Milneburg, and Pontchartrain Park, from
west to east), parts of Tall Timbers/Brechtel on the West Bank of the Mississippi, and other selected areas in
the center of the Orleans Main “bowl” (including Broadmoor). Note that only about 10 Census blocks in the
Lower Ninth Ward would be affected by a buyouts strategy if implemented—the vast majority of the Lower
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Quantile
Block elev (ft.)
0%
5%
10%
15%
20%
25%
30%
40%
50%
75%
90%
95%
100%
-10.4
-7.0
-6.5
-5.9
-4.8
-4.1
-3.7
-2.7
-1.7
1.4
4.8
6.3
18.0
Table 5.1: Weighted mean census block elevation quantiles, Orleans Parish. The distribution shown is
weighted by pre-Katrina household count. Mean elevation and pre-Katrina household counts provided by
LACPR
Ninth and Broadmoor otherwise lie above the -4 ft. threshold.
5.5.2.1
Home elevation targets
For home elevations, the threshold set for each strategy also serves as the target elevation to achieve for that
census block. For example, if the strategy specifies a +1 ft. elevation (relative to NAVD88) and the mean
ground elevation of a given census block is -2 ft., then homes in that block would need to be raised 3 ft. to
achieve the +1 ft. target. NOLArisk assumes that homes cannot be raised more than 15 ft. above ground level
due to concerns about structural soundness and feasibility (USACE, 2009c; FEMA, 2006a), so all elevation
thresholds should be read as “elevate to target x or 15 ft. above ground level, whichever is lower.”
This process is very similar to the procedure FEMA uses to convert from BFEs on a flood map to target
elevations for specific homes. However, the thresholds considered here are at much higher elevations when
compared with the FEMA Advisory Base Flood Elevations (ABFEs) currently in effect in Orleans Parish.
Fig. 5.5 shows the FEMA target elevations, again relative to sea level. For some areas, the FEMA analysis suggests targets of greater than 7 ft. above NGVD29 to meet the interim standard, but note that this is
only in areas with generally higher (above sea level) initial elevations. For the lowest-lying areas, including
Eastern New Orleans, neighborhoods near the Lake Pontchartrain lakefront, and parts of the West Bank,
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Managing New Orleans Flood Risk
±
0
0.5
1
2
3
Chapter 5
4
Miles
Lake Pontchartrain
Legend
Mi
ss
-5
-4
-2
+1
+4
0
+7
+1
NA
Strategy thresholds (ft. above NAVD88 2004.65)
St.Bernard Parish
i ss
ipp
iR
ive
r
Jefferson Parish
Figure 5.4: Effect of strategy thresholds across Orleans Parish. Green-colored Census blocks would be
included under elevation strategies that used that threshold or higher, while red-colored blocks would be
included in either buyout or elevation strategies depending on whether buyouts are incorporated. Lighter
colors shown higher-threshold (and therefore more geographically-intensive) approaches. Map created by
author; elevation data provided by LACPR.
recommended ABFEs are at or below sea level.
5.5.2.2
Experimental design of strategies
Using the elevation and buyout/easement thresholds identified above, I defined 16 non-structural risk mitigation strategies for consideration in the RDM analysis (Table 5.2). Six strategies use only structure elevations,
while the remaining ten combine elevation and buyout/easement thresholds. The simulation is also run under
a “no new mitigation” approach, designated Strategy 0. For the remainder of the discussion, these strategies
are referred to with the shorthand (x, y), where x is the home elevation threshold and y the buyout/easement
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Managing New Orleans Flood Risk
±
0
0.5
1
2
3
Chapter 5
4
Miles
Lake Pontchartrain
Legend
Neighborhood outlines
Advisory Base Flood Elevations (ABFEs)
ABFE EL 7.5 or 3 ft Above HEAG
ABFE EL 6 or 3 ft Above HEAG
ABFE EL 5 or 3 ft Above HEAG
ABFE EL 4.5 or 3 ft Above HEAG
ABFE EL 4 or 3 ft Above HEAG
ABFE EL 3.5 or 3 ft Above HEAG
ABFE EL 2.5 or 3 ft Above HEAG
ABFE EL 2 or 3 ft Above HEAG
ABFE EL 1.5 or 3 ft Above HEAG
Mi
ss
St.Bernard Parish
i ss
ipp
ABFE EL 0.5 or 3 ft Above HEAG
ABFE EL 0 or 3 ft Above HEAG
iR
ive
r
ABFE EL -0.5 or 3 ft Above HEAG
ABFE EL -1 or 3 ft Above HEAG
ABFE EL -1.5 or 3 ft Above HEAG
ABFE EL -2 or 3 ft Above HEAG
ABFE EL -2.5 or 3 ft Above HEAG
ABFE EL -4 or 3 ft Above HEAG
Jefferson Parish
3 ft Above HEAG
Figure 5.5: NFIP Advisory Base Flood Elevations (ABFEs) for New Orleans adopted after Katrina. Homes in
each colored section should be elevated to either the designated elevation (relative to the sea level standard)
or 3 ft. above the highest existing adjacent grade (HEAG), whichever is higher. Note that the elevation
datum referenced here is the National Geodetic Vertical Datum of 1929 (NGVD29), an older standard that is
generally more optimistic than NAVD88 because it does not take into account recent subsidence or sea level
increases. Map developed by author; data source: FEMA National Flood Hazard Layer database.
threshold. “NA” is used when no threshold is applied, so that, for example, the strategy raising all homes to
at least +1 ft. above sea level would be designed (1, NA).
5.5.3
Mitigation implementation assumptions
The strategies described above are implemented within the “Mitigation Strategies” module in NOLArisk.
Once a strategy is specified, the module calculates the number of structures affected and cost in each census
block and year. Strategy implementation is also tracked separately for existing (pre-2011) and new (post2011) homes, with different assumptions for each category. The calculations within NOLArisk are deliberately simplified for this initial exploratory modeling approach, and to reduce complexity I make the following
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No.
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Chapter 5
Strategy
Abbreviation
Elevation
Threshold
Buyout/easement
(NA, NA)
(-5, NA)
(-2, NA)
(1, NA)
(4, NA)
(7, NA)
(10, NA)
(-2, -5)
(1, -5)
(4, -5)
(7, -5)
(10, -5)
(-2, -4)
(1, -4)
(4, -4)
(7, -4)
(10, -4)
NA
-5
-2
1
4
7
10
-2
1
4
7
10
-2
1
4
7
10
NA
NA
NA
NA
NA
NA
NA
-5
-5
-5
-5
-5
-4
-4
-4
-4
-4
Table 5.2: Non-structural risk mitigation strategies tested in NOLArisk. Census block elevation thresholds
are in ft. above NAVD88 (2004.65).
assumptions:
• Elevation incentives for existing (pre-2011) homes are considered voluntary, with an uncertain participation rate;
• All new homes are assumed to be elevated to comply with NFIP requirements. If a strategy is active
in a given census block homes will be elevated to the NOLArisk target, otherwise they are assumed to
be elevated to +3 ft. above ground level as a simplified proxy for the ABFE;
• Buyouts of existing (pre-2011) homes are mandatory if the buyout/easement enforcement parameter
is switched on, or otherwise follow the participation rate;
• Permanent easements for new (post-2011) homes are mandatory (no new growth) if the buyout/easement
enforcement parameter is switched on; otherwise, easements are purchased according to the participation rate and growth proceeds at a lower rate;
• All mitigation actions started in a given year are completed within that year;
• Mitigation occurs uniformly in each year of the project; and
• All program costs are borne 100% by the government entity, and there is no cost share requirement.
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Chapter 5
These assumptions allow for a basic set of implementation uncertainties to be considered while relying
on a relative simple set of calculations. In a full treatment, of course, participation in mitigation incentives
programs would vary with the type and amount of the incentive as well as with a series of other program
considerations. Such an investigation would need to delve into economic incentives literature and would
be worthy of a separate econometric research effort. Thus, voluntary program participation is treated exogenously in NOLArisk, and behavioral response to different types of voluntary mitigation incentives is
considered outside the scope of the analysis.
5.5.4
5.5.4.1
Mitigation uncertainty
Program participation and length
NOLArisk also considers several other implementation uncertainties within the RDM framework. In addition to the program participation rate (P ART ), which governs the overall number of structures modified, the
model also considers an uncertain program length (LEN G). Using a 2011 start year, the program length uncertainty allows for different implementation (uptake) rates for mitigation actions directed at existing homes.
Actions directed at new homes, alternately, are assumed to be in force throughout the analysis period once
mitigation projects begin (2012-2060). The program length for existing homes is allowed to vary between
1-10 years, and a longer program length implies slower implementation across the city. For example, if the
program length is set exogenously at 2 years, then 50% of the homes affected are modified in each year. Alternately, if program length is 10 years, then the uniform uptake rate is only 10% in each year. The program
length uncertainty allows for different linear incentive uptake rates, which in turn affects the assets at risk in
the interim years and the amount of discounting applied to program implementation costs.
5.5.4.2
Buyout/easement enforcement
As previously discussed, an important challenge to the effective implementation of systematic non-structural
risk mitigation in New Orleans or along the Louisiana coast will be the enforcement of new land use restrictions or revised building codes. If enforcement is lax or varies with location, available funding, or other local
considerations, actual risk reduction will lag behind program targets. To capture this uncertain dynamic,
NOLArisk includes a binary enforcement uncertainty (EN F ).
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This parameter affects mitigation implementation in several ways. When enforcement is active, buyouts
of existing (pre-2011) homes are mandatory but still governed by the program length uncertainty, so that at
the end of the program 100% of the homes in the census block have been bought out. If enforcement is not
active, the number of buyouts is governed by the voluntary participation rate as with elevations. Similarly, if
enforcement is active then no new homes are built in a given census block, and any growth that would have
occurred there (per the new home growth rate, α), is instead converted to permanent easements purchased
on those properties by the government entity. If enforcement is not active, the program participation rate
then governs what percentage of new homes are instead converted to easement purchases. In this case, the
remaining percentage (1 − P ART ) continues as new home growth alongside the easement purchases. For
example, with an inactive enforcement parameter and P ART = 0.50, the new home growth rate in a census
block with an active buyout/easement program would be reduced by half, but would still continue.
5.5.4.3
Induced development
The final implementation uncertainty derives from concerns about induced development. Previously, I discussed the phenomenon of induced development within a protection system: the construction of new or
improved levees can provide an incentive for more people to move into a flood-prone area, eventually leading to an increase in overall risk despite improved flood defenses. This process is not necessarily limited,
however, to levee construction. Other research has addressed, for example, the moral hazard associated with
providing government-subsidized flood insurance to residents in a floodplain or from post-disaster government rebuilding funds (Burby, 2006). Similarly, NOLArisk considers the possibility that offering elevation
incentives for homes in Orleans Parish might lead to a growth rate increase in targeted areas, another kind
of induced development. This would not be a concern if incentives were only offered for existing rather
than new homes—for example, if existing homeowners were provided incentives, while new construction
was governed by building codes—but given the initial assumptions in the NOLArisk Mitigation Strategies
module it seemed reasonable to consider potential adverse effects from an elevation incentives program.
NOLArisk therefore also includes an induced development uncertainty (IN DEV ) that takes effect when
an elevation program is active. I was not able to locate quantitative estimates of such an effect, and as a result
elected to use a simple notional multiplier on the growth rate within each block. IN DEV therefore multiplies
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Chapter 5
the prior growth rate in affected Census blocks by a factor of 1-2, depending on the specific scenario chosen,
to allow for an exploration of the effect within the RDM analysis.
5.5.5
Mitigation implementation relationships in NOLArisk
Non-structural risk mitigation modifies and builds on the calculations described in Chapter 4. Depending
on the strategy implemented, NOLArisk tracks the proportion of existing (pre-2011) and new (post-2011)
homes elevated as well as the new target elevation, by census block. The model also tracks buyouts of existing
homes over time and easements in place of new growth. Given a base year of 2011, strategies are assumed
to go into effect in 2012 and remain active for existing structures for 1-10 years, depending on the realization
of LEN G by scenario. Strategies remain active for new structures or growth, alternately, throughout the
remaining period of analysis (2012-2060).
5.5.5.1
Existing home elevations
If the elevation of a census block c falls below the elevation threshold and above the buyout threshold (if
applicable) for the active strategy, the number of homes elevated in each census block c and year t is calculated
as:
x
ELEVc,t
=
⎧
⎪
⎪
⎨RESc,2011 ∗ P ART ∗
1
LEN G
if 2 ≤ t ≤ LEN G and α ≥ 0;
⎪
⎪
⎩max RESc,t , RESc,2011 ∗ P ART ∗
1
LEN G
(5.1)
if 2 ≤ t ≤ LEN G and α < 0,
where x denotes existing home elevations, and the second equation ensures that the number of elevations
cannot exceed the total number of homes in the census block in the event that the residential growth rate α
is negative.
The new elevations are introduced into the damage calculations by introducing a new foundation type
l = new that denotes just new elevations in each location. All elevations are assumed to be on piers or piles
(as opposed to fill), so that the water storage below the home is maintained. The revised flood depths for this
category are calculated as:
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F Di,c,t,new = (ei,c,t − (GEc,t + SEc,t,new )) ,
(5.2)
where F D is the effective flood depth for each recurrence interval (i), census block (c), year (t), ec is the
flood elevation for each census block (ej → ec if c ∈ j), GEc,t is the mean ground elevation by census block
and year, and SEc,t,new is the average elevation above ground level for the new elevation specified in the strategy. NOLArisk then keeps track of the proportion of structures in this category over time F N Dc,t,new and
rebalances the remaining proportions of pier and slab foundation accordingly, assuming that equal numbers
of each type are being retrofitted (calculation not shown).
5.5.5.2
New home elevations
For new homes, alternately, the number of elevations is equal to α in each year if easements (unless no growth
is allowed per the strategy). If a strategy is active, new homes are elevated to the specified threshold and the
incremental costs (above 3 ft.) are calculated accordingly. If an elevation or buyout/easement strategy is not
active, alternately, all new homes are assumed to be elevated to 3 ft. above ground level as a rough proxy of
current NFIP ABFE guidelines, and the costs are assumed to be borne by the new resident.8
5.5.5.3
Existing home buyouts
If a buyout/easement strategy is active, the number of buyouts depends on the enforcement parameter EN F ,
participation rate P ART , and program length LEN G:
BU Yc,t =
⎧
⎪
⎪
⎨RESc,2011 ∗ P ART ∗
⎪
⎪
⎩RESc,2011 ∗
1
LEN G
1
LEN G
if 2 ≤ t ≤ LEN G and EN F = 0;
(5.3)
if 2 ≤ t ≤ LEN G and EN F = 1.
When enforcement is active, all single-family homes will be bought out in the census block when the
program is complete.
8
FEMA has released preliminary DFIRMs (Digital Flood Elevation Rate Maps) designed to replace the post-Katrina ABFEs in
Orleans Parish, but the new maps have not yet been adopted by the city.
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Managing New Orleans Flood Risk
5.5.5.4
Chapter 5
Permanent easements
Finally, easement purchases (EASE) displace new home growth (π ∗ τc0 ∗ α) within each census block:
EASEc,t =
⎧
⎪
⎪
⎨P ART (π ∗ τc0 ∗ α)
if t ≥ 2 and EN F = 0;
⎪
⎪
⎩(π ∗ τc0 ∗ α)
if t ≥ 2 and EN F = 1.
(5.4)
Note that this equation shows results for the OM basin; the calculation is slightly different for non-OM
basins (see Sec. 4.4.3.2). The effective growth rate with easements is then (π ∗ τc0 ∗ α) − EASEc,t , which
equals zero when enforcement is active.
5.5.6
Mitigation costs
Once NOLArisk has determined the number of existing and new structures augmented in each census block
and year, the final calculations in the Mitigation Strategies module determine the implementation costs of
these strategies. Initial cost estimates for home elevations were adopted from the LACPR report (USACE,
2009c) and recent FEMA guidelines (FEMA, 2009), while buyout and permanent easement costs are drawn
solely from LACPR’s Nonstructural Plan Component Appendix. LACPR cost estimates were in 2007 dollars,
and I subsequently inflated these values to 2009 dollars using a consumer price index (CPI) calculator.
5.5.6.1
Existing home elevation costs
Elevation retrofit costs for existing homes depend on both the amount of elevation from ground level and
average home size per census block, in sq. ft. LACPR and FEMA both provided similar estimates of this unit
cost, with LACPR using a simple threshold (up to 6 ft. and 6-15 ft.) and FEMA providing costs at +2 ft., +4
ft., and +8 ft. intervals. Following LACPR, I assume that all elevations will raise slab-on-grade foundation
homes with the slab attached.9 I then combined both sets of costs assumptions to develop a simple linear
cost model designed to vary with each foot of elevation (Fig. 5.6)
Using the linear cost model 1.41+81x, where x is the amount of elevation required, costs per home at max
elevation (15 ft.) range from approximately $153k to $255k for homes ranging in size from 1500-2500 sq. ft.
9
FEMA also provided costs for continuous foundation walls. The continuous foundation costs are somewhat lower than for
slab-on-grade, and as a simplifying assumption (and considering the technical limits for continuous foundation homes) I chose the
higher-cost assumptions.
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Chapter 5
110
105
y = 1.4023x + 81.003
R² = 0.7383
Cost per sq. ft. (2009 $)
100
95
90
FEMA
USACE
85
Linear (Joint)
80
75
70
0
2
4
6
8
10
12
14
16
Elevation above ground level (ft.)
Figure 5.6: Cost per sq. ft. for elevating an existing home. The line shows the fitted linear cost model adopted
in NOLArisk. Data sources: USACE, 2009c; FEMA, 2009.
NOLArisk also introduces an uncertainty for elevation costs. This exogenous input multiplies the intercept of
the linear cost model by a factor of 0.75-1.25, so that the entire model shifts up or down in different scenarios.
Scenarios with lower elevation costs may help to capture economies of scale if a large-scale elevation program
is implemented, while higher-cost scenarios allow for additional costs and considerations (e.g., home access
ramps) that may not receive full treatment in the initial cost estimate. Nevertheless, the range of ±25% is
deliberately narrow to reflect greater certainty regarding structural elevation costs compared with buyouts.
The uncertainty value also matches the contingency added to LACPR estimates (USACE, 2009c).
5.5.6.2
New home elevation costs
To determine strategy costs for new home elevations, I follow the assumption made in the LACPR Nonstructural Plan Component Appendix that all new homes built in Orleans Parish will already be elevated to at least
the required BFE, although as a simplifying assumption NOLArisk assumes that this value is always 3 ft.
above ground level (USACE, 2009c).10 Thus, all new homes are assumed to be elevated, and only elevations
10
The 3 ft. threshold above ground level is the minimum elevation specified by the FEMA ABFEs for New Orleans.
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Chapter 5
specified at greater than 3 ft. above ground level incur costs within the NOLArisk model. Once again, I follow
LACPR and assume a cost of $2,500 per ft. of elevation above 3 ft., with costs ranging from $2,500-$30,000
for each new home elevated 4-15 ft. The elevation cost uncertainty is applied to the per ft. cost, resulting in
a range of $1,875-$3,125 per ft. elevation across different scenarios.
5.5.6.3
Buyout/easement costs
Costs for buyouts and easements were similarly derived from LACPR estimates. USACE assumes that a
full buyout incentive would include a) the value of the property and structure (and subsequent improvements/conversion), b) relocation assistance to the resident, and c) other real estate transaction costs, with
averages for Orleans Parish of $150k, $100k, and $20k respectively. This yields a total buyout cost per unit
of $270k in 2007 dollars (USACE, 2009c). Similarly, easement costs are assumed to consist of the value of
the vacant lot ($20k for Orleans Parish) and transaction costs ($20k), yielding a total of $40k in 2007 dollars.
These were inflated to 2009 values, and a separate cost uncertainty multiplier with the range 0.5-1.5 was
applied. Table 5.3 shows the initial cost estimates in 2009 dollars and cost range derived from the uncertain
multiplier.
Type
Existing property buyout
Vacant lot easement
USACE estimate
Scenario range
$278,100
$41,200
$139,050 - $417,150
$20,600 - $61,800
Table 5.3: Buyout and easement cost estimates applied in NOLArisk model. Initial estimates from USACE,
2009c for Orleans Parish and inflated to 2009 dollars using a CPI calculator.
5.5.6.4
Discounted total costs
Using the specific scenario realization of unit cost as described above, the unit cost for elevations, buyouts,
and easements are multiplied by the total number of modified structures or properties per year to generate an
annual implementation cost. This cost is then discounted and summed across the period of analysis to yield
a net present cost, and is also amortized to calculate an equivalent annual implementation cost (see Sec. 4.6).
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Managing New Orleans Flood Risk
5.6
Chapter 5
Summary
In this chapter, I have reviewed the non-structural risk mitigation options for single-family homes in Orleans
Parish, described programs at the federal and state level designed to incentivize mitigation for homeowners,
and discussed how mitigation may fit into future city planning. I then described a framework to consider
future broad-scale home elevation and buyout mitigation strategies, and discussed how these strategies were
implemented in the NOLArisk model. Finally, I described how strategies are compared in this framework.
Results from the RDM framework discussed in this and the preceding two chapters are described in the next
two chapters. Chapter 6 compares the performance of risk mitigation strategies and describes a series of steps
designed to identify candidate citywide strategies, while Chapter 7 examines the vulnerabilities of selected
candidate strategies in order to determine in what conditions the planner might prefer to switch to a different
approach.
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Chapter 6
Assessing Risk Mitigation Strategies in an
Uncertain Future
6.1
Introduction
In Chapters 3, 4, and 5, I developed an analytical framework designed to address the challenge of reducing
flood risk in New Orleans given substantial uncertainty regarding future conditions. In this chapter, I use
this framework to develop an analysis that addresses several of the primary research questions set forth in
Chapter 1. Specifically, I provide estimates of annual flood damages to single-family homes in New Orleans
from 2011-2060, and evaluate the damage reduction and economic benefit provided by different risk mitigation strategies under a wide range of plausible future conditions. The overall goal is to identify one or
more approaches that are robust to the key uncertain drivers identified, meaning that the strategy performs
reasonably well across most or all plausible future outcomes.
The chapter proceeds in several steps. First, I describe how 255 scenarios were systematically selected
from the wide range of future conditions in order to consider a broad range of uncertainty across eleven
different drivers. I then show how future flood damages in New Orleans vary across the 255 scenarios chosen
and describe substantial geographic variation within these results. Next, the non-structural risk mitigation
strategies described in Chapter 5 are evaluated on a neighborhood-by-neighborhood basis across the range
of scenarios, with results from one example neighborhood discussed in depth. In addition, I use a series
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Managing New Orleans Flood Risk
Chapter 6
of simplifying criteria to identify high performing strategies for each neighborhood, and then combine the
strategies identified with each criterion into “citywide” strategies that reflect varying levels of non-structural
risk mitigation in different areas of the city. Finally, citywide strategies are compared for all of Orleans Parish
with the goal of identifying approaches that perform well despite substantial deep uncertainty.
6.2
6.2.1
Generating plausible future scenarios for strategy comparison
Experimental design
In Chapters 4 and 5, I postulated eleven uncertain drivers that might yield very different outcomes in terms
of the baseline risk and performance of risk mitigation strategies. Five of these drivers directly contribute
to the calculation of baseline economic risk, while the remaining six are related to the costs and effects of
home elevations, buyouts, and easement purchases. The goal of this analysis is to consider a wide range
of uncertainty, not necessarily constrained by the likelihood of any given scenario. As a result, I follow
Lempert et al. (2003) and use Latin hypercube sampling in order to efficiently generate an ensemble of
scenarios from the ranges of these uncertain inputs.1 The Latin hypercube approach helps to ensure that
the sample is consistent across the sampling space, whereas simple random (or Monte Carlo) sampling may
oversample points in some regions and miss other regions entirely. Balancing the need for a sufficiently large
ensemble for strategy comparisons, model runtime, and efficiencies introduced by Latin hypercube sampling,
I selected an initial sampling size of 250 points, and utilized the CARs software to generate the appropriate
Latin hypercube sample from the input parameter space.
In addition to the 250-point Latin hypercube sample, I also include five other scenarios designed to
represent current baseline projections for future New Orleans flood risk. The first of these additional scenarios
sets each of the parameters to their nominal values, which tend to be optimistic about future risk relative to
the entire parameter ranges considered. The remaining four use nominal values for all variables except for
future single-family household growth and sea level rise. These scenarios are designed to approximate the
1
Latin hypercube sampling draws from the concept of a two-dimensional Latin square, where each parameter dimension is
divided into a series of segments with sizes inversely proportional to the number of samples from each segment. In this process,
each parameter range is divided into segments, and then random sampling is performed within each of the “hypercubes" formed
by factorial combinations of the segments. Random Latin hypercube sampling in CARs also ensures that the end-points of each
parameter range are included in the sample.
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Managing New Orleans Flood Risk
Chapter 6
four discrete scenarios selected by USACE for the LACPR study, with levels of home growth and sea level
rise growth set so that the final 2060 values coincide with the LACPR results.2 As discussed in Chapter 3,
the discrete scenarios selected by LACPR for economic/population growth and sea level rise were designed
to represent the end-points (low and high) of a range of possible values. Note, however, that the ranges used
in NOLArisk extend beyond the LACPR values for both parameters of interest. Table 6.1 summarizes the
input parameter ranges, nominal values, and approximations of LACPR growth rates for new homes and sea
level rise.
Exogenous input
Coastal degradation by 2060
Sea level rise
Protection system maintained
Residential growth rate
Growth dispersion (in/out of OM basin)
Program participation rate
Program length
Buyout/easement enforcement
Induced development multiplier
Elevation cost multiplier
Buyout/easement cost multiplier
∗
Units
Range
% of LACPR proj.
mm/year
yes/no
thou. homes/year
%
%
years
yes/no
n/a
n/a
n/a
0 - 100
2 - 14
0-1
-0.8 - 1.6
50 - 75
10 - 100
1 - 10
0-1
1-2
0.75 - 1.25
0.5 - 1.5
Nominal value(s)
0
{10, 2∗ , 10∗∗ }
1
{0, −0.38∗ , 0.94∗∗ }
64.57
100
5
1
1
1
1
LACPR “low" scenario
∗∗
LACPR “high" scenario
Table 6.1: Ranges and nominal values for exogenous inputs used to generate future scenarios with NOLArisk.
Nominal assumptions are also shown, as well as LACPR scenario approximations.
6.2.2
Results generation and data management
Results were generated for the 255 scenarios described above for each of sixteen initial mitigation strategies
described in Chapter 5, as well as an additional “No new mitigation" strategy for comparison purposes. The
same scenarios were run for each of the strategies to ensure “apples-to-apples" comparisons of strategies
across scenarios. Seventeen total strategies run in 255 scenarios yielded a total of 4,335 cases to be generated
2
The NOLArisk model uses linear growth rates for both household growth and sea level rise increase, whereas the LACPR
growth rates are not similarly constrained and use kinked or non-linear rates of increase. As a result, intermediate household growth
and sea level rise values during the years 2011-2059 do not match one another and are likely to produce somewhat divergent results.
In addition, other elements of the analysis diverge from the LACPR assumptions, making the results from NOLArisk and LACPR
not directly comparable. Nevertheless, the nominal assumptions seem to provide a reasonable first order approximation of LACPR
projections of future flood risk under the four different scenarios.
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Managing New Orleans Flood Risk
Chapter 6
using CARs and NOLArisk. Each output variable from NOLArisk was also recorded for each of the 72
identified Orleans Parish neighborhoods, leading to a total dataset size of 255∗17∗72 = 312, 120 records. An
individual case took approximately one minute to run, and the total processing time for the full experimental
design took approximately 72 hours (three days). Once the runs were complete, they were collected in
a Microsoft Access relational database, and queried datasets from the database were imported into the R
statistical software in order to perform the remaining analysis.
6.3
New Orleans flood risk with no additional mitigation
Using the quantitative scenario data generated according to the previous section, I first estimate flood risk
for the city if no new non-structural risk mitigation strategies are implemented. Results across 255 uncertain
future scenarios suggest the plausible range of future damages the city could face from coastal flooding and
provide a baseline against which mitigation strategies can be compared. In this section, I show selected risk
estimates with no new mitigation in place (“no action”) for the entire city in aggregate. In order to consider
how risk varies by geographic location, I also present results for each basin and each neighborhood within
the city.
Damage results shown below are summarized as discounted equivalent annual values across the 20112060 period of analysis (see Sec. 4.6.2 and 4.6.3 for a definition and descriptions of the calculations).
6.3.1
Citywide results project a higher range of future risk than current estimates
Fig. 6.1 shows a summary of equivalent annual damages at the 100-, 400-, and 1,000-year recurrence intervals
across Orleans Parish using a 4.875% discount rate. The colored triangles in the plot show the damage
results from each of the four LACPR-derived scenarios, while the boxplots summarize results across the
entire range of 255 scenarios. At the 100-year recurrence interval, the LACPR scenario results are clustered
between $360-$500 million, while the range across all scenarios extends up to much higher damage values
($360 million-$4.6 billion). Note that the range of 100-year damage scenarios does not cross zero, meaning
that NOLArisk projects damages at the 100-year recurrence interval in all futures even with the “100-year”
system in place.
At the 400- and 1,000-year intervals, the LACPR “high-growth” scenarios show notably higher damages
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Managing New Orleans Flood Risk
Chapter 6
12
10
LACPR scenario
8
Low RSLR, low growth
High RSLR, low growth
Low RSLR, high growth
High RSLR, high growth
6
Boxplot legend
Outlier
4
High or 1.5 x IQR
75th percentile
IQR
Equiv. annual damage ($B, 4.875% discount rate)
14
2
Median
25th percentile
0
Low or 1.5 x IQR
Outlier
100
400
1000
Recurrence interval (yrs.)
Figure 6.1: Equivalent annual 100-, 400- and 1,000-year damages for four LACPR-derived scenarios (triangles) and across all uncertain scenarios (boxplots) with no new mitigation actions. Each boxplot summarizes
damages in 255 different scenarios. The “box” shows the interquartile range (IQR, 25th to 75th percentile),
with the interior line representing the median. The “whiskers” show the extremes of the dataset (Either the
lowest/highest value or 1.5 times the IQR, whichever is closer to the median), while specific points outside
of the whiskers represent outliers.
than the LACPR “low-growth” scenarios. Nevertheless, in all three intervals the LACPR-derived scenarios
are in the lowest two quartiles (bottom 50%) of the scenario range. The introduction of additional uncertainties and wider ranges for RSLR and population growth notably increases the upper range of plausible future
damages, suggesting that the LACPR scenarios may be overly optimistic and do not necessarily capture
higher-risk scenarios relevant to local planning efforts.
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6.3.2
6.3.2.1
Chapter 6
Projected damages vary substantially by location
Damages by basin
The parish-wide summary, however, does not consider geographic variation in risk. Damage results separated
by basin at the three recurrence intervals show that residual damages vary substantially by location (Fig.
6.2).3 The variation is driven by two factors. First, the concentration of assets is much higher in some basins
(e.g., OM) than others (e.g., OW1, SB), leading to much higher observed damages. Second, the distinct flood
elevations calculated by NOLArisk for each basin can vary by the location of the basin and level of protection
afforded by the system. Fig. 6.2 shows that 100-year damages are highest in New Orleans East (NOE) and
the Algiers vicinity (OW2), suggesting that even with an upgraded protection system in place, the range of
Equiv. annual damage ($B, 4.875% discount rate)
plausible 100-year flood elevations would be highest in these peripheral basins.
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Figure 6.2: Equivalent annual 100-, 400- and 1,000-year damages across 255 uncertain scenarios with no
additional mitigation, by basin (2009 $ billions, 4.875% discount rate).
The range of 400- and 1,000-year damages for NOE and OW2 are also high—above $2-3 billion in
many scenarios. In addition, the damage range from these intervals for OM is very wide, ranging from $185
million to $5 billion (400-year) or $540 million to $5.7 billion (1,000-year). This pattern is likely due to
the concentration of assets and comparably higher level of protection afforded to the main basin—that is, a
3
See Chapter 4, Fig. 4.2 for a map identifying each basin.
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Chapter 6
flood greater than the 100-year design level that led to extensive flooding would lead to dramatically higher
damages in the OM basin. NOLArisk also assumes that 50-75% of new home growth would occur in the
OM basin, so that high growth scenarios would lead to high damages in this area as flood elevations increase.
In contrast, the SB and OW1 basins have a very small number of single family homes relative to the others
and, thus, show much lower total damages for each interval.
Given the population disparity across basins, another way to consider risk is on a per-home basis. Fig.
6.3 shows the same basin-specific breakdown as Fig. 6.2, but looking at damages per household rather than
total damages. These results show a clear pattern of higher risk for NOE and both basins on the West Bank of
the Mississippi (OW1 and OW2) across all three recurrence intervals, with OW1 residual 100-year damages
resembling those in OW2 when correcting for population differences. The OM basin, alternately, shows a
general pattern of lower risk per household across scenarios and recurrence intervals. The 400- and 1,000year per-home damages in OM are notably lower than the other basins on a per-home basis, confirming that
the high concentration of homes in OM was the main driver for the high damages observed at these intervals
in Fig. 6.2. In general, both per-basin plots show that damages at all three recurrence intervals are particularly
high for the low elevation, more recently developed areas of NOE, OW1, and OW2.
Equiv. annual damage per home ($thou., 4.875% discount rate)
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Figure 6.3: Equivalent annual 100-, 400- and 1,000-year average damage per home across 255 uncertain
scenarios with no additional mitigation, by basin (2009 $ thousands, 4.875% discount rate).
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6.3.2.2
Chapter 6
Damages by neighborhood
Risk variation is also evident when comparing neighborhood-by-neighborhood results. Fig. 6.4 shows the
equivalent annual expected damages—a summary across the damage distribution, rather than from selected
exceedance intervals—for each of the 72 neighborhoods in Orleans Parish. In this figure, boxplots summarizing results across 255 uncertain scenarios are sorted by expected damages at the median scenario for each
neighborhood.
This graph shows a wide disparity across neighborhoods: expected damages in approximately 19 neighborhoods (left side of the plot) are notably higher than in other locations across the scenario range, while
many neighborhoods (right side of the plot) show zero or near-zero expected damages. In particular, Tall Timbers/Brechtel has the highest median damage of any location, followed by Edgelake/Little Woods (which also
shows the widest damage range across scenarios) and Aurora/Walnut Bend/Huntlee Village. These neighborhoods are in either the NOE or OW2 basins. Neighborhoods in OM, including Lakeview, Milneburg, and
Behrman, also appear among the highest risk areas. In general, the neighborhoods showing the highest range
of damages are among the lowest elevation areas of the city, and generally cluster in NOE, OW2, and along
the lakefront in OM (see Chapter 4, Fig. 4.4). These results suggest that risk mitigation efforts designed to
make the city more resilient to future adverse risk scenarios may have substantially varying effects by neighborhood. Given the observed variation, strategies designed to improve robustness may need to be focused
on those areas with the highest ranges of possible future damages.
6.4
Identifying high performing strategies for each neighborhood
The next step in the analysis is to consider the performance of the strategies described in Chapter 5 against
the range of uncertain futures. In the previous section, I noted substantial variation in risk across different
locations, related both to basin characteristics and average ground elevation. This variation suggests that
different levels of mitigation may be appropriate for different neighborhoods depending on the underlying
level of risk, so that scarce mitigation funds can be targeted at high-risk areas. As a result, rather than
apply the mitigation strategies uniformly across the city, I first consider strategy performance separately on
a neighborhood-by-neighborhood basis, seeking high-performing approaches for each location.
129
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St. Thomas Project
East Riverside
West Riverside
Black Pearl
Irish Channel
St. Thomas Area
Florida Project
Garden District
Marigny
Touro
Vieux Carre
Iberville Project
Calliope Project
Desire Dev
Bywater
East Carrollton
Uptown
Central Business District
Dixon
St. Bernard Area/Project
City Park
Florida Area
Desire Area
Tulane/Gravier
Bayou St. John
Sixth Ward/Treme/Lafitte
Lake Terrace/Lake Oaks
Gerttown/Zion City
St. Claude
Milan
Leonidas/West Carrollton
Central City/Magnolia
Viavant/Venetian Isles
Navarre
Fairgrounds/Broad
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Fischer Project
Lakeshore/Lake Vista
Hollygrove
Lakewood
Seventh Ward
Dillard
Holy Cross
Marlyville/Fontainbleau
McDonogh
Mid−City
Broadmoor/Freret
Algiers Point
Gentilly Woods
Algiers Whitney
St. Roch
Algiers Naval Station
Gentilly Terrace
Village De L'Est
Fillmore
Pontchartrain Park
Lower Ninth Ward
Lakewood/West End
River Park/Cut Off/Lower Coast2
Plum Orchard
Read Boulevard West
Pines Village
West Lake Forest
St. Anthony
River Park/Cut Off/Lower Coast1
Behrman
Lakeview
Milneburg
Read Boulevard East
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Edgelake/Little Woods
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Managing New Orleans Flood Risk
50
Chapter 6
Figure 6.4: Equivalent annual expected damage, by neighborhood, with no new mitigation actions (2009 $ millions, 4.875% discount rate).
Managing New Orleans Flood Risk
6.4.1
Chapter 6
Measuring strategy performance
Non-structural risk mitigation strategies can be evaluated by comparing the benefits from risk mitigation
with the anticipated implementation costs. However, risk mitigation benefits and costs can be assessed in
different ways depending on the policymaker’s priorities and goals. I address the potential divergence in
planning goals by considering multiple perspectives and performance metrics when evaluating mitigation
strategies below.
6.4.1.1
Discounting future benefits and costs
In Sec. 4.6.1, I discuss the challenge of comparing discounted benefits and costs when benefits accrue over
many decades and across generations. To better address this challenge and consider tradeoffs between the
current and future generations, this analysis compares benefits and costs over time using two different discount rates. In contrast to uncertainties about the future or about elements of the modeled system, however,
the discount rate is a planning assumption that reflects the values and priorities of the planner. As a result, this
analysis treats results calculated at different discount rates as separate performance metrics. Except where
noted, the reader can assume that if only one discount rate is shown in the narrative below, results using the
other rate lead to similar conclusions and have been omitted for clarity.
I selected discount rates that reflect two planning assumptions. One is set at 4.875% and reflects the
baseline USACE/federal assumption used in the LACPR analysis (hereafter referred to as the “government
discount rate”) (USACE, 2009c). The other, which I term the “long-term discount rate,” is set at a much lower
rate, 1%, and emerges from discussions of appropriate long-term discount rates when considering climate
change mitigation policies (e.g., Davidson, 2006).
6.4.1.2
Residual damage
A natural question when considering citywide strategies of varying geographic scope and investment level
is how much residual risk remains once mitigation is in place. Flood protection or risk reduction is often
described in terms of an absolute standard for residual risk—e.g., the “100-year” future HSDRRS system
designed to prevent flooding from storm surge with a 1% annual chance of occurring—and non-structural
risk mitigation might be used to maintain such a standard. For example, the New Orleans Master Plan calls
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Chapter 6
for a 500-year risk reduction standard for the city, implying that the combination of structural and nonstructural risk reduction should yield near-zero residual damage from storm surge or flooding with a 0.2%
annual chance of occurring (City of New Orleans, 2009b). Residual annual damages at the 100, 400, and
1,000-year exceedances, as well expected annual damages, are therefore used as performance metrics in this
analysis.
6.4.1.3
Net economic benefit
I also evaluated the performance of non-structural risk mitigation strategies by comparing direct economic
benefits and implementation costs. In contrast to a residual damage standard, this metric addresses the economic efficiency of the strategies considered. To perform this comparison over time, NOLArisk calculates
discounted net benefit—the net present value of the implemented strategy—from 2011-2060. Benefits are
calculated as avoided damages—the difference between damages under a “no new mitigation” approach and
damages with a strategy in place—with the expected value calculation described in Sec. 4.6.3 used to summarize damages across the damage distribution for the benefit calculation. Discounted net costs are calculated as
described in Chap. 5, and discounted net benefit is simply discounted benefit minus discounted costs summed
over the period of analysis.
6.4.2
Results for one low-elevation neighborhood
To demonstrate strategy performance in terms of both residual damage and net benefit across the uncertain
scenarios, I next describe detailed results in Milneburg, an example of one high-risk neighborhood. Similar
visualizations of strategy performance for each of the 72 neighborhoods can be found in Appendix A. Milneburg is located in the OM basin, near the shore of Lake Pontchartrain (Fig. 4.3), with an average elevation
of -6.95 ft. below the sea level reference standard NAVD88. With no additional mitigation, NOLArisk estimates that expected flood damages in Milneburg will be approximately $5.5-13.4 million annually (4.875%
discount rate) from 2011-2060 across the scenario range (Fig. 6.5).
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Chapter 6
50
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Figure 6.5: Histogram showing equivalent annual expected damage in Milneburg across 255 scenarios with
no additional mitigation in place (2009 $ millions, 4.875% discount rate).
6.4.2.1
Residual damage and strategy costs
Analysis results suggest that non-structural risk mitigation strategies reduce expected annual damages in
Milneburg, but with uncertain effects and costs. For example, Fig. 6.6 shows a comparison of expected
annual damages (y-axis) and annual implementation costs (x-axis) with the (1, NA) strategy—which would
provide incentives for all homes to be raised to +1 ft. above NAVD88, or approximately 8 ft. above average
ground level—implemented in the Milneburg neighborhood. Each point in the plot represents one scenario,
and the colored triangles show strategy performance under the four LACPR-derived cases.
Fig. 6.6 shows that the (1, NA) reduces expected damages to $1-11 million annually under different scenarios. This comparison shows a curved, downward-sloping relationship between damages and implementation costs across the scenario range. The curved pattern, illustrated with a smoothing line fit to the underlying
data, indicates that damages decrease only with increased investment costs. This can be attributed primarily
to the voluntary participation uncertainty, which varies from 10-100% across the range of scenarios. As
participation rate increases, the risk mitigation strategy grows more effective in reducing total expected damages, while implementation costs continue to grow. The four LACPR-derived scenarios (colored triangles)
assume 100% program participation and mid-range per-unit elevation costs, leading to a projection of low
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Chapter 6
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Figure 6.6: Equivalent annual expected damages (y-axis) versus equivalent annual implementation costs
(x-axis) across 255 uncertain scenarios for the Milneburg neighborhood with the (1, NA) strategy in place
(2009 $ millions, 4.875% discount rate). Each point in the plot represents one scenario, and the LACPR
scenarios are marked separately as colored rectangles (some LACPR scenarios overlap). A locally weighted
polynomial regression (LOESS) line has also been added to clarify the underlying pattern.
residual risk and comparatively high total implementation costs under this strategy.
Expanding the risk reduction/implementation cost comparison to all 16 strategies for Milneburg (Fig.
6.7), we see a similar pattern at different elevation thresholds and when incorporating buyouts and easements.
The distribution of residual damages generally shifts downwards across the scenario range as the elevation
target increases, and the range of implementation costs expands dramatically when buyouts or easements
are incorporated. Nevertheless, regardless of strategy implemented, uncertain program participation leads
to plausible futures in which expected annual damages remain higher than $9 million in Milneburg. These
results suggest a vulnerability common to all initial strategies considered: if risk mitigation participation
is voluntary, program success will depend heavily on currently unknown participation levels. In turn, this
suggests that boosting participation levels (via public education efforts, for example) may be critical to new
mitigation efforts.
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Managing New Orleans Flood Risk
Chapter 6
(−5, NA)
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Figure 6.7: Equivalent annual expected damages (y-axis) versus equivalent annual implementation costs (xaxis) across 255 uncertain scenarios for the Milneburg neighborhood with each risk mitigation strategy in
place (2009 $ millions, 4.875% discount rate). Each point in the plot represents one scenario, and the LACPR
scenarios are marked separately as colored rectangles. A locally weighted polynomial regression (LOESS)
line has also been added to clarify the underlying pattern.
6.4.2.2
Net economic benefit
Non-structural risk mitigation strategy performance in the Milneburg neighborhood can also be considered in
terms of of net economic benefit. Fig. 6.8 shows net benefit results for sixteen strategies across 255 uncertain
scenarios in Milneburg using the government discount rate (white) or the long-term rate (blue) (net benefit
results for all neighborhoods are shown in Appendix A). Unsurprisingly, the long-term discount rate yields
much higher total net benefits than the government rate across all strategies and scenarios. The long-term
rate places greater weight on benefits from long-term risk reduction, while the government rate places greater
weight on near-term implementation costs. As a result, at the median scenario all of the strategies yield
positive benefits for Milneburg using the long-term rate, while most yield zero or negative net benefits using
the more conventional discounting approach.
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Strategy ID
Figure 6.8: Discounted net economic benefit across 255 uncertain scenarios for the Milneburg neighborhood with each risk mitigation strategy in place (2009 $millions). Both discounting assumptions are shown:
4.875% (green) and 1.0% (purple).
Comparing among strategies at the 4.875% discount rate, we see that net benefits from elevation-only
strategies are slightly negative to slightly positive at the median scenario, with nearly equal numbers of scenarios yielding positive versus negative benefits across the range. Including buyout or easement approaches
(strategies (-2, -5) through (10, -4) in Fig. 6.8), alternately, yields negative net benefit of approximately -$50
million at the median with a much wider distribution of results depending on the scenario. In this neighborhood, however, note that introducing buyout/easement programs essentially crowds out elevation incentives
because all census blocks in Milneburg are below either the -5 ft. or -4 ft. buyout/easement thresholds used
(see Fig. 5.4). As a result, nearly all strategies including buyouts yield nearly identical risk reduction and net
benefit across the range of scenarios.
When comparing with the long-term (1%) discount rate, nearly all strategies yield similar, positive results
at the median. Across the scenario range, the distribution of net benefits from elevation-only strategies
for Milneburg increases with higher elevation targets up to -2 ft., at which point the results level off and
slightly decline with higher thresholds. Elevation-only strategies with targets ranging from -2 to 10 ft. yield
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Chapter 6
positive net benefit across most scenarios considered. Introducing buyouts/easements, additionally, leads to
some scenarios with high benefits as well as others in which net benefit turns negative. Because it offers
little downside risk and good performance across the range of scenarios, using the long-term discount rate
perspective the (-2, NA) elevation-only approach appears to be a promising choice for Milneburg according
to the net benefit results.
6.5
Identifying high-performing “citywide” strategies
The previous section describes a detailed strategy comparison for one neighborhood, Milneburg, using both
residual damages and net economic benefit as metrics to evaluate the performance of each strategy. A similar analysis could be performed in order to identify high-performing strategies for all 72 Orleans Parish
neighborhoods. This approach would be complex and very time-intensive, however, and is outside the scope
of this initial research effort. Instead of performing a neighborhood-by-neighborhood analysis at this level
of detail, I next developed a series of plausible screening criteria that are intended to expediently identify
promising strategies for each neighborhood. The individual, neighborhood-specific strategies selected with
each criterion, in turn, are used to construct citywide strategies that reflect the implementation of different
levels of non-structural risk mitigation in different areas of the city. Below, I describe the screening criteria
used to develop these citywide strategies, and then compare the performance of these strategies for all of
Orleans Parish. The vulnerability analysis described in Chapter 7, furthermore, builds from these citywide
strategy comparisons.
6.5.1
Simplifying criteria used to develop citywide strategies
The screening criteria used to develop citywide strategies reflect alternate planning considerations for local
planners, including different implementation constraints, planning objectives, and level of concern about
adverse futures. First, I divide citywide strategies into those that use elevation incentives alone (elevationonly, coded “E”) and those that allow for combinations of both elevation incentives and buyouts/permanent
easements (elevation+buyout, coded “C”). This is a key distinction, because while home elevations are a
commonly-used form of risk mitigation in New Orleans, buyouts and easements have not yet been employed
and remain both controversial and very difficult to implement (see Emmer et al., 2007). When developing
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Chapter 6
long-term risk mitigation plans for New Orleans, then, local planners should be prepared to consider futures
in which buyouts and easements are not feasible policy levers, and I therefore incorporated this possibility
into the citywide strategy analysis.
Next, I consider four different planning objectives from the master planner’s perspective: 1) maximizing net economic benefit without regard for residual damage; 2) balancing 100-year residual damage and
implementation costs, but allowing implementation costs to exceed expected risk reduction; 3) maximizing
100-year damage reduction without regard for implementation cost (for comparison); and 4) maximizing total
damage reduction without regard for implementation cost (for comparison). Citywide strategies developed
from these approaches are coded “NB,” “100-BAL,” “100-MAX,” or “MAX,” respectively.
These alternate approaches reflect that a tradeoff may exist between the cost-effectiveness of a risk mitigation strategy, which compares the reduction in direct damages with implementation costs, and the residual
risk once a strategy is in place. Excessive residual risk may lead to negative economic outcomes—a lack
of economic growth or job opportunities because businesses have relocated to safer areas, for example—not
incorporated into the direct net benefit calculation used in this analysis. Furthermore, difficult-to-quantify
benefits from risk reduction (e.g., community resilience) are also not considered in the direct benefit-cost
comparison. As a result, I consider residual damage reduction as an alternate objective when constructing
citywide strategies, and use the residual damage calculation as a proxy for these unquantified risk reduction
benefits. Because the goal of the current protection system is to bring 100-year flood damages close to zero in
the near-term, I adopt the 100-year standard in measuring future residual damages, while also incorporating
approaches that maximize overall damage reduction to provide an upper-bound for possible risk reduction.
Lastly, because this analysis is designed to identify strategies that are robust against a wide range of
plausible future conditions, I develop screening criteria designed to identify strategies that perform well
in adverse conditions. Specifically, for each neighborhood I select strategies that maximize net benefit at
the 25th , 50th , and 75th percentiles, reflecting a pessimistic, mid-range, and optimistic net benefit estimate
emerging from different parts of the scenario ensemble. These are coded “25,” “50,” and “75,” respectively.
Furthermore, I use the 75th percentile estimate—a more-adverse case—for both residual 100-year damage
and implementation costs when considering the damage reduction objective.
The screening criteria can be summarized as follows:
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Chapter 6
• For each neighborhood, select the strategy that either:
– Maximizes net benefit at the 25th /50th /75th percentile;
– Balances 100-year risk reduction and implementation costs in a more-adverse case (75th percentile);
– Maximizes 100-year risk reduction in a more-adverse case (75th percentile); or
– Maximizes total risk reduction (for comparison).
• Repeat for different planning constraints:
– Only elevation incentives permitted; or
– Both elevation and buyout/easements approaches permitted.
6.5.2
Results from citywide strategy screening
Applying these criteria yielded 12 citywide strategies, and adding a “No Action” (no new mitigation) citywide
strategy for comparison purposes led to a total of 13 citywide strategies to compare. This process was also
repeated under each discount rate assumption, but the results from each approach are kept separate through
the remainder of the discussion. A summary description of each citywide strategy is shown in Table 6.2.
Detailed tables describing which strategy was selected for each neighborhood by citywide strategy, as well
as summary plots describing the screening criteria used for each neighborhood, are shown in Appendix A.
ID
Type
Selection criteria
No Action
E-NB-25
E-NB-50∗
E-NB-75
C-NB-25
C-NB-50∗∗
C-NB-75
E-100-BAL
E-100-MAX
C-100-BAL
C-100-MAX
E-MAX
C-MAX
NA
Elev. only
Elev. only
Elev. only
Elev. + buyouts
Elev. + buyouts
Elev. + buyouts
Elev. only
Elev. only
Elev. + buyouts
Elev. + buyouts
Elev. only
Elev. + buyouts
NA
Maximize net benefit
Maximize net benefit
Maximize net benefit
Maximize net benefit
Maximize net benefit
Maximize net benefit
Balance 100-year dmg. and strategy cost
Maximize 100-year damage reduction
Balance 100-year dmg. and strategy cost
Maximize 100-year damage reduction
Maximize damage reduction
Maximize damage reduction
∗
∗∗
Percentile
NA
25th
50th
75th
25th
50th
75th
75th
75th
75th
75th
75th
75th
No. neighborhoods
4.875%
1.0%
0
1
3
5
1
3
8
29
46
29
46
64
64
0
6
12
15
6
13
17
29
46
29
46
64
64
Candidate elevation-only strategy
Candidate combined elevation+buyouts strategy
Table 6.2: Summary of citywide strategy attributes and summary results from screening. Includes identifer, type, selection criteria, scenario ensemble percentile used for screening, and number of neighborhoods
selected for additional mitigation actions under alternate discount rate assumptions.
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Table 6.2 shows that citywide strategies focused on residual damage suggest providing mitigation incentives in a greater number of neighborhoods than using the net benefit criterion. When seeking to maximize
net benefit, furthermore, using the long-term discount rate perspective also suggests programs in a greater
number of locations, while using the government rate leads to a more limited approach because mitigation
leads to negative net benefits in many locations. Note that, even under the “E-MAX” or “C-MAX” citywide
strategies, not all neighborhoods are targeted for additional mitigation. These areas are excluded because
additional mitigation shows essentially no risk reduction benefits under any strategy.
One elevation-only citywide strategy (E-NB-50), developed from the long-term discounting perspective,
is shown in Fig. 6.9. Using this screening criteria, the map shows that the 12 neighborhoods targeted for
additional mitigation are located in the lowest elevation areas of the city. This pattern is unsurprising, and
holds for all of the citywide strategies developed using the net benefit criteria (not shown, see Appendix A).
Fig. 6.9 also shows that the selected strategies tend to cluster for neighborhoods in the same basin. Citywide
strategy E-NB-50, for example, suggests lower elevation targets (-5 ft., -2 ft., or +1 ft. above NAVD88) for
low-elevation neighborhoods along the lakefront in the OM basin, +1 ft. or +4 ft. targets for most NOE
neighborhoods, +4 ft. for the Lower Ninth Ward (SB basin), and the maximum possible elevation target (+10
ft.) for selected areas of OW1 and OW2.
6.5.3
Citywide strategy comparison across uncertain scenarios
Next, I compare the performance of different citywide strategies across the scenario set using the net economic benefit and residual damage performance metrics. These comparisons were made separately for citywide strategies constructed from the government and long-term discount rate analyses. As previously discussed, using the long-term discount rate generally leads to higher net benefits from risk mitigation because
future benefits diminish at a much lower rate. As a result, citywide strategies constructed by maximizing net
benefit in each neighborhood using the long-term discount rate suggest risk mitigation investment in approximately ten additional neighborhoods compared with the government discount rate. Because this dissertation
is designed to consider the long-term planning perspective for New Orleans, from this point forward in the
discussion I use the long-term (1.0%) discounting results to compare citywide strategy performance and consider potential plan vulnerabilities. Selected results using the government discount rate (4.875%) are also
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0
0.45
0.9
1.8
2.7
Chapter 6
3.6
Miles
Village
Village De
De L'Est
L'Est
Lake Pontchartrain
Edgelake/Little
Edgelake/Little Woods
Woods
West
West Lake
Lake Forest
Forest
Lake
Lake Terrace/Lake
Terrace/Lake Oaks
Oaks
Lakeshore/Lake
Lakeshore/Lake Vista
Vista
Read
Read Boulevard
Boulevard East
East
Pontchartrain
Pontchartrain Park
Park
Pines
Pines Village
Village
Milneburg
Milneburg
Read
Boulevard West
West
Read Boulevard
Plum
Plum Orchard
Orchard
St.
St. Anthony
Anthony
Lakewood/West
Lakewood/West End
End
Gentilly
Gentilly Woods
Woods
Fillmore
Fillmore
Viavant/Venetian
Viavant/Venetian Isles
Isles
Lakeview
Lakeview
Gentilly
Gentilly Terrace
Terrace
Desire
Desire Area
Area
Dillard
Dillard
City
City Park
Park
St.
St. Bernard
Bernard Area/Project
Area/Project
Desire
Desire Dev
Dev
Navarre
Navarre
Fairgrounds/Broad
Fairgrounds/Broad
Lakewood
Lakewood
Bayou
Bayou St.
St. John
John
St.
St. Roch
Roch
Citywide strategy E-NB-50
Seventh
Seventh Ward
Ward
St.
St. Claude
Claude
Dixon
Dixon
Sixth
Sixth Ward/Treme/Lafitte
Ward/Treme/Lafitte
Gerttown/Zion
Tulane/Gravier
Gerttown/Zion City
City Tulane/Gravier
Bywater
Bywater
Iberville
Iberville Project
Project
Vieux
Vieux Carre
Carre
Uptown
Uptown
(1, NA)
Algiers
Algiers Point
Point
Algiers Naval
Naval Station
Station M
Algiers
Algiers Whitney
WhitneyAlgiers
is s
i ss
McDonogh
McDonogh
ip
pi
St.
St. Thomas
Thomas Area
Area
Garden
Garden District
District
(-2, NA)
Holy
Holy Cross
Cross
Broadmoor/Freret
Broadmoor/Freret
Central
Central City/Magnolia
City/Magnolia
Milan
Milan
(-5, NA)
Marigny
Marigny
Leonidas/West
Leonidas/West Carrollton
Carrollton
Calliope
Calliope Project
Project
Marlyville/Fontainbleau
Marlyville/Fontainbleau
Central
Central Business
Business District
District
Black
Black Pearl
Pearl
Audubon/University
Audubon/University
(NA, NA)
Lower
Lower Ninth
Ninth Ward
Ward
Mid-City
Mid-City
Hollygrove
Hollygrove
East
East Carrollton
Carrollton
Legend
Florida
Florida Project
Project
Florida
Florida Area
Area
Fischer
Fischer Project
Project
St.
St. Thomas
Thomas Project
Project
Touro
Touro
Behrman
Behrman
St.Bernard Parish
Riv
(4, NA)
(7, NA)
er
(10, NA)
Aurora/Walnut
Aurora/Walnut Bend/Huntlee
Bend/Huntlee Village
Village
Irish Channel
Channel
East
East Riverside
Riverside Irish
River
River Park/Cut
Park/Cut Off/Lower
Off/Lower Coast1
Coast1
West
West Riverside
Riverside
Jefferson Parish
Tall
Tall Timbers/Brechtel
Timbers/Brechtel
River
River Park/Cut
Park/Cut Off/Lower
Off/Lower Coast2
Coast2
Figure 6.9: Map showing citywide strategy E-NB-50. Neighborhoods selected for additional mitigation
are shaded in blue, with different target elevations set for each neighborhood depending on which strategy
maximizes median net benefit.
presented in Appendix B, but are not discussed below.
6.5.3.1
Net economic benefit
Net benefit by citywide strategy for all of Orleans Parish across the 255 uncertain scenarios is shown in Fig.
6.10. Elevation-only citywide strategies are shown in blue, while combined elevation and buyout/easement
strategies are colored red. In the plot, the level of investment in non-structural risk mitigation generally
increases from left to right. Fig. 6.10 shows that citywide net benefit results are similar across the scenario
set for all strategies constructed from the net benefit criterion (E-NB-25 through C-NB-75), though with a
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generally narrower range observed for the strategies constructed using more pessimistic projections (E-NB25 and C-NB-25). Performance at the median and in the distribution extremes is similar, with each citywide
strategy yielding positive net benefit across most scenarios and a small number of cases in which net benefit
is negative. Citywide strategies constructed using 100-year residual risk, alternately, yield overall lower
net benefit across the range of scenarios, with higher-cost approaches (e.g., E-100-MAX) leading to lower
returns. As expected, net benefit from the E-MAX and C-MAX comparison strategies is strongly negative
across most of the scenario range.
●
●
●
●
●
●
●
●
Net benefit (2009 $B, 1.0% discount rate)
0
●
●
●
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●
−5
●
●
●
●
●
−10
●
●
●
−15
No action
E−NB−25
E−NB−50
E−NB−75
C−NB−25
C−NB−50
C−NB−75
E−100−BAL E−100−MAX C−100−BAL C−100−MAX
E−MAX
C−MAX
Citywide strategy
Figure 6.10: Net benefit from citywide strategies across 255 uncertain scenarios (2009 $ billions, 1.0%
discount rate). Blue boxes show elevation-only citywide strategies, while red boxes show combined elevation
and buyout/easement approaches.
Strategies E-NB-25 through C-NB-75 all appear promising according to this performance metric, but the
similarity of the results makes it difficult to distinguish a preferred approach. To address this challenge, I next
convert net benefit outcomes into an alternate performance metric, regret, designed specifically to consider
the robustness of different strategies. Regret is formally defined as the difference between the performance of
strategy s in a given scenario f and the performance of the best possible strategy s for that scenario, where s
searches through all strategies to determine the top-ranked strategy for each scenario (Lempert et al., 2003;
Savage, 1950):
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Regretnb (s, f ) = max
(s
,
f
)
− Performancenb (s, f )
Performance
nb
s
(6.1)
This calculation works by examining each scenario individually, identifying the optimal strategy under
that scenario, and then calculating the difference between the optimal outcome and what a given strategy
produced in that case (the best strategy for each scenario always yields zero regret). This calculation is then
repeated across all strategies and scenarios. Strategies that yield low regret across the scenario set may not
produce the optimal result in all cases, but are near optimal in many or most, providing a useful way to
measure comparative robustness. Lempert et al. (2006) argue that “a regret-based definition of robustness is
often preferable to one based directly on the absolute performance of each strategy because regret focuses
attention on those states of the world most relevant to the choice among alternative strategies—those in which
alternative policies have significantly different outcomes."
In this instance, net benefit outcomes are converted to regret using the calculation above, with the unit
of analysis remaining billions of 2009 dollars. A comparison of citywide strategy regret derived from net
benefit is shown in Fig. 6.11. Using regret as a measure of citywide strategy robustness, a distinction between the elevation-only and combined elevation+buyout strategies emerges from this plot. Combined elevation+buyout strategies including C-NB-50 and C-NB-75 yield the lowest regret at the median and across
the scenario range, consistently outperforming the elevation-only approaches on a scenario-by-scenario basis. These results suggest that incorporating buyouts and easements may help to provide more consistent
economic benefits from risk mitigation regardless of future scenario. Elevation-only citywide strategies derived from the net-benefit criterion (E-NB-50, E-NB-75) perform next best, while the remaining citywide
strategies yield higher regret overall (E-MAX and C-MAX regret not shown).
6.5.3.2
Residual damage reduction
Next, I consider the overall level of residual damage in the city under the constructed citywide strategies.
Fig. 6.12 shows the reduction in annual damages provided by each citywide strategy, broken out by discrete
recurrence interval. At the 100-year (1% exceedance) level, the citywide strategy that yields the lowest
residual damages, C-100-MAX, reduces equivalent annual damages at the median scenario from $2.9 billion
(under the No Action approach, colored white) to approximately $620 million—greater than 75% damage
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10
Regret (2009 $B, 1.0% discount rate)
8
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●
●
●
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●
●
6
●
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●
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●
4
●
●
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●
●
2
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
C−NB−50
C−NB−75
0
No action
E−NB−25
E−NB−50
E−NB−75
C−NB−25
E−100−BAL E−100−MAX C−100−BAL C−100−MAX
Citywide strategy
Figure 6.11: Regret from citywide strategies, derived from the net benefit metric, across 255 uncertain scenarios (2009 $ billions, 1.0% discount rate). Blue boxes show elevation-only citywide strategies, while red
boxes show combined elevation and buyout/easement approaches. Regret from the “E-MAX” and “C-MAX”
strategies is omitted from this plot to better show differences between low-regret strategies.
reduction. Most citywide strategies reduce 100-year damages compared with the No Action baseline when
considering results across the scenario range, with greater investments generally yielding lower damages
overall. Nevertheless, all strategies—including strategies such as C-100-MAX designed to minimize 100year residual damage—also show scenarios in which 100-year damages remain greater than $4 billion. The
100-year results suggest that all strategies remain vulnerable to high damage futures, and the initial nonstructural mitigation approaches do not necessarily provide an effective hedge against these outcomes.
Mitigation also reduces damages at the 400- and 1,000-year recurrence intervals. As the surge elevation becomes more extreme, strategies that include buyouts more obviously outperform elevation-only
strategies—note, for example, that elevation-only strategies E-NB-50 and E-NB-75 provide no damage reduction at the 1,000-year recurrence interval, while C-NB-50 and C-NB-75 show damage reduction for this
metric across most scenarios. Furthermore, the graph shows that greater investment in non-structural risk
mitigation reduces damage from more extreme events, with the C-MAX strategy substantially reducing 400and 1,000-year damage in most scenarios relative to the No Action approach. However, Fig. 6.12 also indicates that substantial vulnerability remains regardless of the risk mitigation strategy enacted. Many high
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●
Residual damage (equivalent $B, 1.0% discount rate)
●
●
20
●
●
●
●
●
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●
●
●
●
●
●
15
●
●
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●
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●
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●
●
Citywide strategy
●
No action
E−NB−25
●
●
E−NB−50
●
E−NB−75
●
●
C−NB−25
C−NB−50
●
●
C−NB−75
10
E−100−BAL
E−100−MAX
C−100−BAL
5
●
●
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●
●
●
●
C−100−MAX
●
●
●
100
●
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●
●
●
●
●
●
●
●
●
●
●
●
●
E−MAX
●
●
C−MAX
400
1000
Recurrence interval (yrs.)
Figure 6.12: Residual equivalent annual damage from citywide strategies, by recurrence interval (2009 $
billions, 1.0% discount rate). 10-year damage not shown.
damage scenarios remain across the mitigation strategies considered (long upper tails on every strategy), and
even the most intensive strategy considered (C-MAX) leaves nearly $2.5 billion in residual damages from
the 400-year flood and $3 billion from the 1,000-year event in the most optimistic scenario (lower tail).
There are two complementary explanations for this result. First, the LACPR interior drainage model
projects catastrophic flooding—up to 15 ft. above NAVD88—in nearly every area of the city when sea level
rise is high and the coastline is degraded. Even if enforced perfectly, an elevation standard of +10 ft. would
leave these areas vulnerable in similar scenarios. Moreover, because homes cannot be feasibly raised more
than fifteen feet from ground elevation, many areas could not even achieve the +10 ft. standard. Second, the
only other viable option given technical constraints on home elevations are buyouts, and reducing risk further would entail buyouts and growth restrictions in a large proportion of the city. The -4 ft. buyout/easement
threshold was initially set when constructing strategies so that no more than 25% of populated census blocks
could be bought out if implemented citywide (Sec. 5.5.2), and it seems highly unlikely that even this proportion of the city could be feasibly curtailed. These results suggest that there may be a limit to the total risk
reduction that non-structural risk mitigation can provide in New Orleans when faced with high damage, low
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probability events.
6.5.4
Promising citywide strategies
The comparisons shown above illustrate the potential tradeoff between maximizing direct economic benefit
and reducing overall damages faced by local planners when determining an appropriate level of investment in
non-structural risk mitigation. The balance between these objectives, ultimately, should reflect the priorities
of local decisionmakers and stakeholders, and suggests the need for a multi-criteria decision analysis (MCDA)
that is beyond the scope of this initial effort.
That said, the comparisons above suggest that most citywide strategies would result in similar levels of
100-year residual damage across the range of scenarios considered. In addition, all strategies leave the city
vulnerable to high damage scenarios at the 400- or 1,000-year recurrence levels. Because most citywide
strategies perform similarly according to the residual damage performance metrics, I set aside residual damage and use only the net benefit/regret comparison in order to select promising strategies for further exploration in Chapter 7. Based on the comparisons shown in Sec. 6.5.3.1, I selected one elevation-only strategy,
E-NB-50, and one combined elevation+buyout strategy, C-NB-50, as candidates for further analysis.
The candidate elevation-only citywide strategy E-NB-50 includes elevation incentives offered in 12
neighborhoods. Neighborhoods selected for additional mitigation, as well as location-specific elevation targets, are shown above in Fig. 6.9. Similarly, the combined elevation+buyout citywide strategy selected for
further investigation is shown in Fig. 6.13 below. The combined strategy targets nearly the same set of lowelevation neighborhoods as the elevation-only approach. However, in this approach several neighborhoods
on the West Bank are selected for a combination of elevation incentives and buyout/easements. Two other
neighborhoods in the NOE and SB basins are also targeted for buyouts, although in both cases only selected
census blocks would be affected (not shown) while the rest would receive elevation incentives.
In the next chapter, the elevation-only candidate strategy is investigated further to determine under what
future conditions New Orleans planners would clearly prefer to incorporate buyouts in addition to elevation
incentives. In addition, the combined elevation+buyout strategy is used as a candidate to identify which
uncertain drivers lead to high residual damages even when buyout approaches are incorporated.
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±
0
0.45
0.9
1.8
2.7
Chapter 6
3.6
Miles
Village
Village De
De L'Est
L'Est
Lake Pontchartrain
Edgelake/Little
Edgelake/Little Woods
Woods
West
West Lake
Lake Forest
Forest
Lake
Lake Terrace/Lake
Terrace/Lake Oaks
Oaks
Lakeshore/Lake
Lakeshore/Lake Vista
Vista
Read
Read Boulevard
Boulevard East
East
Pontchartrain
Pontchartrain Park
Park
Pines
Pines Village
Village
Milneburg
Milneburg
Read
Boulevard West
West
Read Boulevard
Plum
Plum Orchard
Orchard
St.
St. Anthony
Anthony
Lakewood/West
Lakewood/West End
End
Gentilly
Gentilly Woods
Woods
Fillmore
Fillmore
Viavant/Venetian
Viavant/Venetian Isles
Isles
Lakeview
Lakeview
Gentilly
Gentilly Terrace
Terrace
Legend
Desire
Desire Area
Area
Dillard
Dillard
City
City Park
Park
St.
St. Bernard
Bernard Area/Project
Area/Project
Citywide strategy C-NB-50
Desire
Desire Dev
Dev
Navarre
Navarre
Fairgrounds/Broad
Fairgrounds/Broad
Lakewood
Lakewood
Bayou
Bayou St.
St. John
John
St.
St. Roch
Roch
(-2, NA)
(1, NA)
Seventh
Seventh Ward
Ward
St.
St. Claude
Claude
Dixon
Dixon
Sixth
Sixth Ward/Treme/Lafitte
Ward/Treme/Lafitte
Leonidas/West
Leonidas/West Carrollton
Carrollton
Calliope
Calliope Project
Project
Marlyville/Fontainbleau
Marlyville/Fontainbleau
Central
Central Business
Business District
District
Bywater
Bywater
Uptown
Uptown
(7, -5)
Algiers Naval
Naval Station
Station M
Algiers
Algiers Whitney
WhitneyAlgiers
is s
i ss
McDonogh
McDonogh
ip
pi
St.
St. Thomas
Thomas Area
Area
Garden
Garden District
District
(-2, -5)
Holy
Holy Cross
Cross
Algiers
Algiers Point
Point
Broadmoor/Freret
Broadmoor/Freret
Central
Central City/Magnolia
City/Magnolia
Milan
Milan
(10, NA)
Marigny
Marigny
Iberville
Iberville Project
Project
Gerttown/Zion
Tulane/Gravier
Gerttown/Zion City
City Tulane/Gravier
Vieux
Vieux Carre
Carre
Black
Black Pearl
Pearl
Audubon/University
Audubon/University
(4, NA)
Lower
Lower Ninth
Ninth Ward
Ward
Mid-City
Mid-City
Hollygrove
Hollygrove
East
East Carrollton
Carrollton
(-5, NA)
Florida
Florida Project
Project
Florida
Florida Area
Area
Fischer
Fischer Project
Project
St.
St. Thomas
Thomas Project
Project
Touro
Touro
Behrman
Behrman
St.Bernard Parish
Riv
(-2, -4)
(4, -4)
er
(10, -4)
Aurora/Walnut
Aurora/Walnut Bend/Huntlee
Bend/Huntlee Village
Village
Irish Channel
Channel
East
East Riverside
Riverside Irish
River
River Park/Cut
Park/Cut Off/Lower
Off/Lower Coast1
Coast1
West
West Riverside
Riverside
Jefferson Parish
Tall
Tall Timbers/Brechtel
Timbers/Brechtel
River
River Park/Cut
Park/Cut Off/Lower
Off/Lower Coast2
Coast2
Figure 6.13: Map showing citywide strategy C-NB-50. Neighborhoods selected for additional mitigation
are shaded in either blue (elevation-only) or red (combined elevations and buyouts), with different target
elevations and buyout thresholds set for each neighborhood depending on which strategy maximizes median
net benefit.
6.6
Summary
In this chapter, I have described how the strategies described in Chapter 5 were tested and evaluated across
an ensemble of uncertain future scenarios using the NOLArisk model. First, I presented the range of plausible future risk for New Orleans with no additional mitigation in place for 255 uncertain future scenarios,
and described substantial variation in flood risk in different geographic locations and across scenarios. Nonstructural risk mitigation strategies were then evaluated separately for each neighborhood by net economic
benefit, residual damage, and implementation cost across these uncertain scenarios, and results from the
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Milneburg neighborhood were described in detail. Next, I used a series of simplifying criteria to select highperforming strategies for each neighborhood and merged these into composite “citywide” strategies, which
were then compared in detail. Several citywide strategies were shown to provide positive net benefits under
many or most scenarios. Most strategies reduce residual flood damage regardless of the scenario considered,
but the citywide strategy comparison also revealed that damages from low-probability flood events remain
high in many cases despite high risk mitigation investment. Finally, two promising citywide strategies, including an elevation-only and combined elevation+buyout approach, were selected for vulnerability analysis
in the next chapter.
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Chapter 7
Developing More Robust Strategies for New
Orleans
7.1
Introduction
In the previous chapter, I explored the performance of non-structural risk mitigation strategies in individual neighborhoods and citywide across a range of plausible future scenarios in order to identify approaches
that perform well, in terms of residual risk and economic net benefit, across many or most scenarios. A
comparison of the net economic benefit from citywide strategies suggested that including buyouts and easements improves the robustness of strategies when compared with elevation-only approaches. In addition,
the comparison showed that even the most expensive and wide-ranging mitigation strategies considered in
NOLArisk may nevertheless yield extensive damage when faced with low-probability flood events (e.g., the
400- or 1,000-year flood).
In Chapter 6, I also identified two citywide strategies–one using elevation incentives only, and one combining both elevation incentives and buyouts/easements—that appeared robust against future conditions. In
this chapter, these candidate robust strategies are investigated in further depth in order to identify potential
vulnerabilities that could lead to poor strategy performance as well as to address key questions that emerged
from the analysis in Chapter 6. Specifically, two research questions guide the discussion:
1. In which future scenarios do candidate mitigation strategies perform poorly according to net economic
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benefit? Are there alternative approaches that perform better in these adverse scenarios? Given future uncertainty, what tradeoffs exist between strategies utilizing different types of mitigation (e.g.,
elevation incentives versus buyouts)? What additional actions could help to hedge against these poor
outcomes?
2. Which future conditions lead to high-damage scenarios regardless of the non-structural mitigation
strategy implemented, as measured with 100-year residual damage? Are there additional policy levers
outside the scope of this analysis that could help to prevent these adverse futures from coming to pass?
To address these questions, I perform an investigation of the exogenous drivers that define each scenario
in order to identify a small number of input conditions that lead to the majority of poor or unacceptable outcomes. This process is referred to as “scenario discovery” in the RDM literature (Groves and Lempert, 2007;
Bryant and Lempert, 2009). Following other RDM studies, this investigation uses an interactive statistical
search algorithm to help identify these input conditions, discussed in depth below.
7.1.1
Organization of this chapter
The chapter proceeds in several steps. First, I describe the methods used to identify input conditions that
lead to poor strategy performance. Next, I use these methods to explore potential vulnerabilities in the
elevation-only candidate strategy E-NB-50, and seek conditions under which local planners might prefer to
switch to alternate mitigation methods (research question #1). I then perform a similar analysis focused on
a combined elevation+buyout citywide strategy, C-NB-50, designed to identify inputs that most often lead
to high residual damage even with substantial investment in non-structural risk mitigation (research question
#2). In both steps, I also discuss tradeoffs among citywide strategies considered in the analysis and identify
additional policy actions not included in this analysis that might help to hedge against the adverse conditions
identified. Finally, I summarize the analysis results and suggest how future analyses could build on this initial
effort.
7.2
Methods for identifying policy relevant scenarios
The analysis described in Chapter 6 considers future flood risk in New Orleans across 255 scenarios, representing uncertainty from eleven exogenous drivers. Although each scenario is treated as equal when initially
exploring strategy performance, this does not necessarily mean that all scenarios are equally relevant to the
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decisionmaker or useful for long-term planning. This is particularly true when the goal is a robust strategy
relatively insensitive to deep uncertainty.1 In order to improve the robustness of a given approach, a decisionmaker should focus on adverse scenarios in which the selected strategy continues to perform poorly—not
necessarily the worst-case, but rather futures in which a given approach would be considered a failure, and
the decisionmaker would have preferred a different approach with perfect information about the future. The
scenarios of interest—defined by Groves and Lempert (2007) as “policy-relevant” scenarios—are those in
which tradeoffs between different approaches are evident and regret from choosing a suboptimal approach is
high.
As described in the RDM literature, scenario discovery is designed to address this challenge by taking the
high-dimensional scenario set and distilling it into a small number of narrative scenarios, constructed using a
subset of the key inputs, that describe many or most of the quantitative policy-relevant cases (Lempert et al.,
2006; Groves and Lempert, 2007; Bryant and Lempert, 2009). These new narrative scenarios are designed
be readily understandable to the decisionmaker and resemble traditional narrative scenarios defined for a
handful of key states of the world (e.g., Schwartz, 1996). Once identified, policy-relevant scenarios can
be used to identify additional actions outside of the current set of policy levers, called hedging actions, that
could help to incorporate adaptivity and improve strategy performance if faced with these conditions (Dewar,
2002; Groves, 2006; Groves and Lempert, 2007; Lempert et al., 2006). These scenarios can also be used to
summarize key tradeoffs for the decisionmaker and stakeholders.
To identify policy-relevant scenarios in this analysis, I follow previous RDM studies and apply a statistical
data-mining algorithm called the patient rule induction method (PRIM). PRIM was originally developed by
Friedman and Fisher (1999) as a “bump-hunting” process designed to locate high-density regions across
multi-dimensional datasets. The algorithm works by iteratively peeling away different portions of an input
dataset in order to generate high-density “boxes” containing primarily cases of interest. Boxes are made up
of restrictions on some of the key input dimensions, with the general goal of capturing as many scenarios of
interest as possible using the fewest number of input restrictions. As Lempert et al. (2006) and Bryant and
Lempert (2009) describe, PRIM is particularly useful for scenario discovery because it is both interactive
and iterative, allowing the analyst to consider tradeoffs between different constructed boxes in real-time and
1
See Sec. 3.3 for a definition of deep uncertainty.
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select one or more boxes that balance competing analysis objectives.
For this study, I use a version of PRIM developed as a package for the R Statistical Software called
sdtoolkit.2 The algorithm proceeds in several iterative steps. First, it produces a “peeling diagram” that
describes a series of alternate boxes to consider, and then allows the analyst to interactively select and alter a
sequence of boxes in order to identify a suitable policy-relevant scenario. In general, selecting an appropriate
box involves trading off across three different goals (Bryant and Lempert, 2009):
• Density: The precision of the chosen box in selecting cases that fall below the adequacy threshold.
Using a binary metric where poor or unacceptable strategy outcomes are coded with a “1” and acceptable strategy outcomes a “0,” a perfect box containing only cases of interest would have a density of 1
(100%).
• Coverage: The proportion of unacceptable cases included in a given box. Once again, a box that
captured every below-threshold case would yield a coverage proportion of 1 (100%).
• Interpretability: The simplicity and understandability of a set of box restrictions, measured by the
number of constrained parameters used to construct a given box as well as the overall number of boxes
used. Bryant and Lempert (2009) suggest that “...a highly interpretable box set should consist of on
the order of three or four boxes, each with on the order of two or three constrained parameters.”
The peeling diagram visually describes tradeoffs between these objectives, and allows the analyst to
select one or more boxes from the diagram to consider in depth. Using information on box density, coverage,
and input dimensions, the analyst can select a box that best balances across these three objectives. In many
instances, policy-relevant scenarios will occur in multiple regions of the scenario set. In these cases, PRIM
will iteratively identify a new set of boxes and continue the search process until the analyst is satisfied that
sufficient coverage has been achieved.
7.3
When are elevation-only mitigation approaches vulnerable?
Using the methods described above, I first seek to identify the conditions under which a candidate robust
citywide strategy that uses elevation incentives only—E-NB-50, defined in Chapter 6—yields low net economic benefit when compared with other approaches. To facilitate direct comparisons with other citywide
strategies, I once again use the regret metric calculated from discounted net benefit to measure strategy per2
The sdtoolkit was developed by Pardee RAND Graduate School student Benjamin Bryant in 2008, and is available from
http://cran.r-project.org/web/packages/sdtoolkit/index.html.
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formance (see Sec. 6.5.3). For clarity, only results from the long-term discount rate analysis are shown.
Additional analysis using the government discount rate can be found in Appendix C.
The PRIM algorithm, as implemented in sdtoolkit, requires a clear, binary distinction between acceptable
and unacceptable policy outcomes in order to identify regions with poor performance. Because I assume the
role of master planner for this analysis and lack a policy-relevant threshold to clearly distinguish outcomes, I
define the threshold for success as the 65th percentile of regret, meaning that 35% of scenarios are assumed to
yield unacceptable performance under this strategy. Fig. 7.1 shows a histogram of regret across the 255-point
scenario ensemble for candidate strategy E-NB-50, with a red line identifying the 65th percentile threshold
for acceptability. The high regret cutoff is $373 million, and high regret cases range up to and exceed $1.5
billion in some cases.
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Figure 7.1: Histogram of regret for E-NB-50 across 255 uncertain scenarios, 1.0% discount rate. The vertical
red line shows the 65th percentile value used as the acceptability threshold in the PRIM analysis.
I identified two policy relevant scenarios using the PRIM algorithm, discussed below. I describe the full
PRIM analysis in this case in order to better illustrate the methodology for the reader. In the next section,
however, only summary PRIM results are presented in order to streamline the discussion.
The peeling diagrams for both scenarios identified through PRIM are shown in Fig. 7.2. Each point on
the diagram represents one candidate box, made up of a series of restrictions on some of uncertain inputs used
in the scenario analysis. Solid points indicate the initial set of boxes developed by PRIM, while the smaller
crosses show “new dominating points” that emerge from the original boxes when one or more dimension
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restrictions are removed. Colors changes indicate when the number of box dimension restrictions changes.3
The y-axis shows the density of each candidate box—the percentage of scenarios captured within the box
that are above the regret threshold and have been correctly flagged by the algorithm. The x-axis, alternately,
shows the corresponding box coverage, defined as the proportion of the total number of high-regret scenarios
captured by the box. A perfect box would yield 100% density and 100% coverage—moving as far into the
0.9
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Figure 7.2: PRIM peeling diagrams for citywide strategy E-NB-50 representing the first (left) and second
(right) box selection process. Each point represents one box identified by PRIM through dimension restrictions. The y-axis shows the proportion of cases within the box that yield unacceptable outcomes (density),
while the x-axis shows what proportion of the overall number of bad cases are included in the box (coverage).
Color changes indicate when the number of box dimension restrictions changes. Points circled in red show
the boxes chosen to represent policy-relevant scenarios.
The left pane of Fig. 7.2 shows the initial peeling diagram. Box density rapidly slopes downwards with
only small increases in coverage until density is below 0.8 (80%), where a large jump in coverage occurs.
When some dimensions are removed from the candidate boxes (represented by crosses on the plot), however, coverage seems to improve without a substantial drop off in density. To balance density and coverage
with a comparatively small number of input dimensions (to maintain interpretability), I selected a reduced3
Colors often appear more than once on the same peeling diagram using sdtoolkit, so a color legend is not meaningful in this
instance.
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dimension box derived from box #10 on the diagram. Once this box was selected, PRIM developed another
peeling diagram (right pane) reflecting the remaining high-regret cases not captured in the first box. The
selected box (circled in red) was again selected to balance density, coverage, and interpretability.
7.3.1
“Buyout enforcement and increasing risk” scenario
The first policy-relevant scenario identified through the PRIM analysis shown in Fig. 7.2 includes restrictions
along three of the eleven key input dimensions: a binary variable describing whether or not levee heights
are maintained to keep pace with RSLR (“system maintained”), the residential growth rate in New Orleans,
and a binary parameter determining whether future buyout or easement policies will be voluntary or strictly
enforced (“buyout/easement enforcement”). Table 7.1 summarizes the dimension restrictions and PRIM
performance statistics for this constructed scenario, while Fig. 7.3 shows the restrictions graphically (in blue)
to show how much of each parameter range is used to define the scenario.
Policy-relevant
scenario statistics
Performance metric
Parameter restrictions
Regret (from net benefit)
System maintained = 0
Residential growth rate > −116 homes/year
Buyout/easement enforcement = 1
Interest: 35% (89 of 255)
Coverage: 35% (31 of 89)
Density: 72% (31 of 43)
Table 7.1: PRIM results summary for the “buyout enforcement and increasing risk” scenario. The threshold
for poor performance is the 65th percentile of regret (derived from net benefit) for strategy E-NB-50.
System maintained
False
True
Residential growth rate (homes/year)
-800
1,600
-116
Buyout/easement enforcement
False
True
Figure 7.3: Dimension restrictions for the “buyout enforcement and increasing risk” scenario. The blue bar
indicates how much of the parameter range is included in the constructed scenario.
I term this the “buyout enforcement and increasing risk” scenario. It describes a future in which levees
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are degrading over time, the population of New Orleans is either somewhat stable (shrinking slowly or no
new growth, but not rapidly contracting) or growing, and New Orleans officials are fully enforcing buyouts
and future growth restrictions (by purchasing permanent easements) in selected areas. Risk is increasing in
this scenario both because of declining levee protection over time and (in many cases) population growth.
Approximately 72% of scenarios that fall into these ranges lead to high regret (much lower than optimal net
economic benefit), meaning that other strategies considered in the analysis—those incorporating buyouts—
would be strongly preferred in these futures (discussed further in Sec. 7.3.3).
7.3.2
“Buyout enforcement and higher elevation costs” scenario
The second policy-relevant scenario identified with PRIM includes only uncertainties related to the implementation of future non-structural risk mitigation. Termed the “buyout enforcement and higher elevation
costs” scenario, this case once again incorporates restrictions for three key drivers: buyout/easement enforcement (as with the first scenario) and multipliers for both future elevation and buyout/easement costs.
The dimension restrictions and statistics are summarized in Table 7.2 and Fig. 7.4.
Performance metric
Parameter restrictions
Regret (from net benefit)
Buyout/easement enforcement = 1
Elevation cost multiplier > 0.87
Buyout cost multiplier < 1.07
Policy-relevant
scenario statistics
Interest: 35% (89 of 255)
Coverage: 35% (27 of 77)
Density: 75% (27 of 36)
Table 7.2: PRIM results summary for the “buyout enforcement and higher elevation costs” scenario. The
threshold for poor performance is the 65th percentile of regret (derived from net benefit) for strategy E-NB-50.
This constructed scenario suggests a future in which implementation conditions would strongly favor
incorporating buyouts and permanent easements into citywide mitigation strategies: buyout/easement enforcement, home elevation costs at or above current projections, and buyout costs that are generally less than
or equal to current estimates generated by LACPR. High regret from the elevation-only candidate strategy
E-NB-50 is observed in 82% of scenarios where these three conditions occur together.
Taken together, both policy-relevant scenarios represent the majority of high-regret cases for citywide
strategy E-NB-50. Total coverage (across both boxes) is 71% with an overall density of 77%. Note that some
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Buyout/easement enforcement
False
True
Elevation cost multiplier
1.5
0.5
0.87
Buyout/easement cost multiplier
1.5
0.5
1.07
Figure 7.4: Dimension restrictions for the “buyout enforcement and reduced participation” scenario. The
blue bar indicates how much of the parameter range is included in the constructed scenario.
cases appear in both constructed scenarios, but these are grouped with the first scenario (and omitted from
the second) when calculating PRIM summary statistics.
7.3.3
7.3.3.1
Improving strategy robustness
Policy-relevant scenarios highlight tradeoffs between elevation-only and elevation+buyout approaches
Incorporating buyouts and easements would improve strategy performance in both policy-relevant scenarios
identified above. This result is illustrated in Fig. 7.5, which shows regret from the candidate strategy E-NB50 as well as from other selected citywide strategies for cases in and outside of each constructed scenario.
In both plots, the range of regret for citywide strategy E-NB-50 is notably higher within (blue boxes) versus
outside (white boxes) of the “buyout enforcement and increasing risk” and “buyout enforcement and reduced
participation” scenarios, confirming that the strategy is vulnerable in these instances. In contrast, citywide
strategies including buyouts (C-NB-50 and C-NB-75) yield low regret in the constructed scenarios and appear
to be the preferred choice if these conditions come to pass.
We might expect a more obvious tradeoff to emerge outside of the policy-relevant scenarios, reflecting
conditions under which elevation-only approaches would be clearly preferred. However, Fig. 7.5 shows that
combined elevation+buyout approaches perform similarly or better than the elevation-only approaches even
when the policy-relevant scenario conditions are not met. When considering net economic benefit from risk
mitigation strategies, the plot suggests that combined elevation+buyout citywide strategies are more robust
across the full range of future uncertainties than elevation-only approaches. This may be attributable to
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No action E−NB−25 E−NB−50 E−NB−75 C−NB−25 C−NB−50 C−NB−75
Citywide strategy
Citywide strategy
Figure 7.5: Performance of selected citywide strategies (regret in 2009 $B, 1.0% discount rate) in and outside
of the constructed “buyout enforcement and increasing risk” (left pane) and “buyout enforcement and higher
elevation costs” (right pane) scenarios across the ensemble. Scenarios within the policy-relevant constructed
scenarios are summarized separately from those outside to consider potential strategy tradeoffs.
diverging performance between approaches when faced with high damage, low-probability events. Home
elevation incentives help to reduce damages as long as flood elevations are below the first-floor of the home,
but extreme flood heights will damage even an elevated home. In contrast, buyouts or easements provide
guaranteed risk reduction benefits regardless of the flood elevation faced, assuming current residents do not
remain within the city.
7.3.3.2
Hedging actions could reduce strategy vulnerability
These results suggest that New Orleans planners should consider incorporating buyouts or growth restrictions
in selected low-elevation areas if feasibility constraints—both political and legal—can be overcome. The
city could plan for this contingency by developing a phased long-term mitigation strategy that first offered
elevation incentives only while simultaneously exploring the feasibility of buyout/easement implementation
and where such approaches would be most effective. City officials could also work towards improving public
acceptance of such programs via risk awareness or other public information campaigns. If public acceptance
and legal constraints could be addressed, buyouts and growth restrictions targeted at low-elevation, high risk
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areas would then be implemented in later phases of the risk mitigation strategy.
7.4
What conditions lead to high residual damage with mitigation in place?
Turning next to the second research question, an important result from the previous chapter is that even a citywide strategy designed to provide mitigation incentives in 60 neighborhoods, including buyouts or permanent
easements in up to 25% of populated census blocks in the city (C-MAX), nevertheless leaves single-family
homes in the city vulnerable to high flood damages from low-probability events in many scenarios (Fig. 6.12).
Although most non-structural risk mitigation strategies appear to produce reliable damage reduction insensitive to many external drivers, damages at the 400- or 1,000-year exceedance intervals remain consistently
high across the scenario ensemble. In addition, while risk mitigation leads to little to no residual 100-year
damage in some scenarios, in others, 100-year damage remains high irrespective of the strategy chosen. As
a result, one conclusion from this initial research effort is that non-structural risk mitigation alone, even
at very high investment levels, does not necessarily provide an effective hedge against scenarios that yield
particularly high damage from low-probability flood events.
This insight naturally leads us to consider which of the uncertain drivers considered in this analysis most
often lead to high damage states-of-the-world. In this section, I use scenario discovery to investigate the
city’s vulnerability to scenarios of high residual damage even with a mitigation strategy in place. To address this question, I test citywide strategy C-NB-50, which includes both home elevation incentives and
buyout/easements in selected locations. C-NB-50 was identified as potentially robust according to the net
benefit results in both Chapter 6 and in the previous section, and represents a substantial investment in additional risk mitigation.
To measure residual damage in alternate futures, I use the 100-year equivalent annual damage rather
than expected annual damage or the 400- or 1000-year exceedances. The 100-year metric was chosen for
two reasons. First, 100-year flood risk is a commonly-used threshold (e.g., floodplain mapping by the NFIP)
and current structural protection improvements are intended to bring this residual risk level close to zero.
Second, I previously found that damages at this recurrence interval are responsive to local risk mitigation
while still showing high damage levels in some futures (Fig. 6.12). Fig. 7.6 shows the distribution of 100-year
equivalent annual damage with the C-NB-50 strategy in place. The acceptability threshold is set at the 80th
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percentile of 100-year equivalent annual damage, shown on the plot with a vertical red line.
Number of scenario outcomes
25
20
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Equivalent annual 100−year damage (2009 $B, 1.0% discount rate)
Figure 7.6: Histogram of equivalent annual 100-year damage for citywide strategy C-NB-50 across 255
uncertain scenarios, 1.0% discount rate. The vertical red line shows the 80th percentile value used as the
vulnerability threshold in the PRIM analysis.
Here, the pattern differs from that observed in the regret analysis, with annual damage showing a somewhat bell-shaped (Gaussian) distribution across different scenarios rather than the extreme-value, long-tail
distribution previously observed. No probabilities are assigned to scenarios at this stage, however, and as a
result the concentration of scenarios in the center of the distribution does not suggest that these outcomes are
more likely.
Once again, I use the PRIM algorithm to help identify a narrative scenario reflecting the key input dimensions that define high-damage outcomes. For clarity, results described below reflect the long-term discount
rate assumption only. Results using the government discount rate are similar to those described in this narrative and are shown in Appendix C.
7.4.1
“Degrading protection” scenario
Using PRIM, I identified a single policy-relevant scenario made up of four constrained dimensions. This
scenario describes 77% of the high-damage cases with a box density of 75% (Table 7.3 and Fig. 7.7).
I call this the “degrading protection” scenario, and it describes a future in which the coastline continues to
degrade, effective levee heights are declining over time with RSLR, the residential growth rate is not strongly
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Policy-relevant
scenario statistics
Performance metric
Parameter restrictions
100-year equiv. annual damage
Coastal degradation by 2060 > 37%
System maintained = 0
Residential growth rate > −380 homes/year
Participation rate < 83%
Interest: 20% (51 of 255)
Coverage: 77% (39 of 51)
Density: 75% (39 of 52)
Table 7.3: PRIM vulnerability analysis parameter restrictions and summary statistics describing the “degrading protection” scenario. These were derived when testing the candidate strategy C-NB-50, and the threshold
for poor performance is the 80th percentile of 100-year equivalent annual damage.
Coastal degradation by 2060
0%
100%
37%
System maintained
False
True
Residential growth rate (homes/year)
1,600
-800
-380
Participation rate
100%
10%
83%
Figure 7.7: PRIM dimension restrictions for the “degrading protection” scenario.
negative, and participation in voluntary mitigation programs is lower than 83%. Interestingly, scenarios
with high RSLR do not factor into this policy-relevant scenario directly, although RSLR plays an important
indirect role by increasing flood elevations when the HSDRRS is not regularly upgraded. Nevertheless, the
combination of a degrading landscape and degrading levee heights in light of ongoing RSLR appears to yield
particularly high-damage outcomes when using the 100-year damage interval.
The contribution of the participation rate uncertainty—related to non-structural mitigation implementation, rather than environmental or other factors—to this policy-relevant scenario is also notable. When
comparing risk mitigation strategies in Chapter 6, I found that the participation rate is a key driver for both
the damage reduction and implementation costs associated with a given approach (see Sec. 6.4.2). The contribution of the participation rate to high-damage scenarios, however, suggests that mitigation with reasonably
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high levels of voluntary participation does reduce damage when a sufficient number of neighborhoods are
targeted, even in cases with greater flood elevations. In turn, this suggests that local planners may be able to
hedge against these outcomes to an extent by taking actions designed to increase program participation.
7.4.2
Improving robustness when facing high-damage scenarios
7.4.2.1
Current strategies do not address high-damage cases
Fig. 7.8 shows the performance of all citywide strategies in or outside of the “degrading protection” scenario.
Damages at the 100-year recurrence interval with no new mitigation (left-most strategy) are higher in the
adverse case, with a median of $4.5 billion within the scenario versus approximately $2.6 billion outside.
Progressively more intensive non-structural mitigation strategies reduce damages in the constructed scenario,
but no strategy—including strategies developed specifically to maximize damage reduction (e.g., C-100MAX, C-MAX)—effectively reduces damages in the policy-relevant scenario. For example, median annual
100-year damage under these adverse conditions when the C-100-MAX strategy is implemented is $1.3
billion, with selected outliers exceeding $4 billion. From these results, we can reasonably conclude that none
of the non-structural mitigation strategies considered adequately hedge against the “degrading protection”
scenario.
7.4.2.2
Additional hedging actions could involve actions at all levels of government
The remaining vulnerability to high-damage scenarios identified in this analysis suggests that the initial set
of non-structural risk mitigation strategies considered for New Orleans should be augmented with additional
hedging actions. Importantly, the adverse conditions identified in the “degrading protection” all reflect drivers
that could be directly or indirectly addressed with additional policy levers not considered in this initial analysis. The constructed scenario therefore provides guidance for the types of new hedging actions to pursue.
To improve outcomes and provide a hedge against this case, for instance, the scenario discovery results
suggest that New Orleans should develop policies designed to increase the participation rate in voluntary
programs into future mitigation strategies. Policy options could include public education and outreach campaigns, fast-tracked funding designed to reduce the up-front risk taken by a homeowner upgrading an existing
home, or increased incentive size to reduce the homeowner’s cost-share burden. Alternately, the city could
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E−MAX
C−MAX
Citywide strategy
Figure 7.8: Citywide strategy performance (equivalent annual 100-year damage in 2009 $B, 1.0% discount
rate) in and outside of the constructed “degrading protection” scenario across the scenario ensemble.
consider making elevation above the BFE mandatory for new homes in high risk areas using building codes or
revised residential zoning. Such hedging actions are not directly tested in this initial research effort, although
they could be incorporated into the follow-on research effort currently underway.
Other actions that could hedge against high-damage futures include regular operations and maintenance
to ensure that levees are maintained to a fixed elevation above sea level as well as additional investments
in restoration for the wetlands, barrier islands, and other natural defenses surrounding the New Orleans
protection system. New Orleans planners cannot directly implement either of these hedging actions, however,
as they are currently addressed by federal authorities or the State of Louisiana. Because initial analysis
results suggest that local actions alone are not sufficient to avoid high damage futures, robust risk mitigation
strategies for New Orleans will instead require coordination among local, state, and federal efforts.
7.4.3
Discussion and caveats
The adverse scenario identified with PRIM for the 100-year residual damage metric is suggestive, but should
be interpreted with caution. Two of the key drivers identified, landscape degradation and system maintenance,
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are treated simplistically in the NOLArisk scenario generator and do not take into account nonlinearities
or threshold effects that might be evident with a more sophisticated model. For example, interior flood
elevation is assumed to increase linearly as effective levee heights decline over time, a substantial simplifying
assumption that would be superseded using a separately-constructed interior drainage model. As a result,
the PRIM analysis is based on first-order approximations and results should be considered preliminary rather
than conclusive.
That said, the choice of landscape degradation and system maintenance as key exogenous drivers for
high-risk cases suggests that the LACPR scenario analysis did not, in fact, consider the range of future
scenarios necessary to fully address the deep uncertainty present. LACPR used scenario analysis to consider
uncertainty about future sea level rise and economic growth, but the final report simply assumes that a) any
restoration policy enacted will maintain the surge reduction provided by the current coastline and b) levees
will be raised at regular intervals to account for ongoing RSLR (USACE, 2009c). The NOLArisk analysis,
however, suggests that damages may be much higher than expected if these LACPR assumptions are violated.
Accounting for deep uncertainty related to the future coastline and state of the levee system, therefore, may
be as critical as accounting for RSLR or economic growth for successful long-term flood risk planning.
7.5
7.5.1
Next steps
Adaptive strategies
Based on the scenario discovery results, the next RDM step would be to develop adaptive or phased strategies
incorporating the hedging actions previously discussed, particularly those related to program participation.
Subsequent strategy designs could also be more carefully structured. For example, the elevation targets could
be tied directly to FEMA’s new digital flood insurance rate maps (DFIRMs), when adopted by the city, and
expressed in terms of freeboard above the BFE. Adaptivity could also be incorporated by phasing in either
additional levers (buyouts) or additional neighborhoods, depending on observed conditions.
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7.5.2
Chapter 7
Additional RDM iterations
The revised strategies could then be tested in future RDM analysis cycles, repeating the scenario generation
and vulnerability analysis steps, to test whether the adaptive strategies improve robustness to the scenarios of
concern. The next RDM iteration is beyond the scope of this dissertation, but is planned for a follow-on study
underway working directly with the New Orleans Office of Homeland Security and Emergency Preparedness
Hazard Mitigation Branch.
7.6
Summary
In this chapter, I completed the initial investigation of non-structural risk mitigation strategy performance
in New Orleans in the presence of substantial long-term uncertainty. The chapter addressed two research
questions that emerged from the strategy exploration in Chapter 6 and sought to identify future conditions
that either a) lead local planners to prefer combined elevation+buyout approaches to elevation-only citywide
strategies, or b) yield high residual damage regardless of the investment level in non-structural risk mitigation.
I first described the statistical methods used to perform the investigation, and then addressed each research
question in sequence. When comparing elevation-only and combined elevation+buyout approaches, the results suggested two key sets of future conditions under which buyout-inclusion would be preferred, mostly
related to uncertainty regarding future implementation of risk mitigation. Finally, I identified exogenous
drivers that lead to high damage scenarios irrespective of the non-structural risk mitigation in place. Using
discounted 100-year residual damages as a metric, the analysis revealed that a combination of moderate to
substantial coastal degradation, levees heights that decline with ongoing RSLR, a stable or increasing population, and moderate-to-low participation in voluntary risk mitigation programs (below 84%) most often
contribute to high residual damages. The first two of these uncertainties were not fully considered in the
recent LACPR coastwide analysis, but the analysis suggests that both are critical to understanding and addressing plausible future flood risk. In the next chapter, I discuss the implications of these analysis results
and provide preliminary recommendations for hazard mitigation planners in New Orleans.
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Chapter 8
Conclusions and Recommendations
8.1
Key insights
Local actions designed to reduce the consequences of future floods will be critical to developing a more
resilient New Orleans over the next 50 years. This dissertation marks an initial step towards understanding
how non-structural risk mitigation targeted at single-family homes can help to reduce property risk from
different types of flood events.
Several key insights can be drawn from the analysis results. First, even with the upgraded “100-year”
hurricane protection system in place, citywide residual damages at the 100-year exceedance level are greater
than zero and tend to increase from 2011-2060 under plausible future conditions. Particularly vulnerable
areas include the West Bank of Orleans Parish (Algiers/English Turn), New Orleans East, and low-elevation
neighborhoods south of Lake Pontchartrain in the OM basin. Damages from the 400- or 1,000-year recurrence intervals are much higher than 100-year levels, and vary with different assumptions regarding future
sea level rise, coastal degradation, ongoing system maintenance, or population growth.
This research tested non-structural risk mitigation strategies focused on damage reduction for single family homes in 72 Orleans Parish neighborhoods across 255 uncertain scenarios in order to assess damage reduction and net economic benefit. Mitigation approaches tested included incentives to elevate homes to different
elevation targets, buyouts of existing structures, and permanent easements purchased in lieu of new growth.
Non-structural risk mitigation was shown to reduce damage in many locations, but the amount of damage
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Chapter 8
reduction varied considerably with voluntary program participation as well as other uncertain factors. Risk
reduction strategies also showed positive net benefit across many or most scenarios in 1-17 neighborhoods
depending on the type of mitigation and discount rate assumption (4.875% versus 1.0%). Neighborhoods
yielding positive returns are generally located in the lowest elevation areas of the city, including New Orleans
East, portions of Algiers or English Turn on the West Bank, and low-elevation neighborhoods in the main
basin of the city adjacent to Lake Pontchartrain.
Promising strategies identified for selected neighborhoods were merged into composite citywide strategies using a series of simplifying criteria in order to compare risk reduction and economic performance
parish-wide. In terms of damage reduction, risk mitigation strategies implemented in vulnerable areas of the
city consistently reduce direct damages from 100-year (1% annual chance) or more frequent events. Damages
from low-frequency (400- or 1,000-year) events are also reduced under selected approaches, but remain high
even when substantial buyouts or growth restrictions are incorporated into tested strategies. In addition, the
range of plausible damages from low-probability events remains wide, suggesting continuing vulnerability
even with additional non-structural risk mitigation in place.
An investigation was next performed to determine under what conditions New Orleans planners would
prefer to include home buyouts or future growth restrictions rather than use home elevation incentives alone.
Although elevation-only and combined elevation and buyout approaches yield similar levels of risk reduction,
citywide strategies incorporating both elevations and buyouts yield more consistent and robust economic benefit across the range of uncertain scenarios, with two sets of conditions identified through the analysis where
elevation+buyout approaches provide notably higher net benefit. Though buyouts and growth restrictions
appear to be cost-effective in selected locations, however, these approaches remain difficult to implement
and they are not currently under consideration by city hazard mitigation planners.
Finally, because even the most intensive risk mitigation strategies considered in this initial analysis leave
substantial residual risk at the 100-, 400-, or 1,000-year exceedances under some conditions, this analysis
sought to identify key exogenous drivers most often connected to high damage scenarios. Equivalent annual
100-year damages were shown to be consistently high when HSDRRS levees are not maintained to keep
pace with RSLR, coastal land loss outside of the system is allowed to continue, residential growth is flat or
positive, and the participation rate in voluntary mitigation programs is below 84%. The recent LACPR study
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Managing New Orleans Flood Risk
Chapter 8
did not fully consider the performance of coastal risk reduction plans if the first two conditions were to occur,
and thus it may have missed scenarios important to ongoing coastal protection and restoration planning.
8.2
Implications for New Orleans risk mitigation planning
The results of this initial analysis suggest a path forward for reducing flood risk in New Orleans via nonstructural risk mitigation. However, because the study is preliminary and incorporates notional assumptions
when estimating the flood hazard, additional research is needed in order to develop robust and adaptive
risk mitigation plans for the city. Nevertheless, the results point to several useful insights for New Orleans
planning at this stage.
8.2.1
Elevation targets exceeding 100-year BFEs can provide cost-effective risk reduction
Currently, elevation incentives provided to New Orleans residents from FEMA programs typically mandate
that the homeowner elevate to the 100-year BFE. However, the NOLArisk analysis shows that 100-year flood
elevations increase over time in many plausible future scenarios, meaning that home elevations performed
today may no longer meet the 100-year standard one or more decades into the future. Particularly in the
most vulnerable areas of the city, then, New Orleans should consider augmenting current hazard mitigation
incentive programs to encourage residents to instead elevate to a standard exceeding the currently-adopted
BFEs. For new homes, the city could also consider incremental incentives (or requirements) to include
additional freeboard above the current 100-year flood elevation.
8.2.2
Buyouts or growth restrictions can be cost-effective, but substantial hurdles remain
This analysis shows that voluntary or mandatory buyouts can provide robust and cost-effective risk reduction
in low-elevation areas of the city, particularly in suburban areas such as the West Bank where the protection system is not yet substantially upgraded. However, successful implementation of a voluntary buyouts
program, mandatory buyouts, or mandatory growth restrictions will require the city to resolve legal barriers
and improve the level of public acceptance for these actions. One possible path forward for existing home
buyouts might be to identify vacant homes or parcels of land in nearby higher-elevation neighborhoods and
provide a mechanism for residents to move to lower-risk locations while remaining in the city.
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Managing New Orleans Flood Risk
8.2.3
Chapter 8
Location matters
NOLArisk shows that programs targeting mitigation funds to the lowest-elevation neighborhoods tend to
yield the greatest net social benefit. Providing mitigation incentives uniformly across the city, alternately,
may allocate scarce funding inefficiently. That said, there is an inherent tradeoff between equity and efficiency
when considering where to target incentives, and providing elevation incentives only in the riskiest areas
might inadvertently lead to induced development without a carefully structured approach. Nevertheless,
the analysis points to areas of particular concern with elevations below -4 to -5 ft. relative to sea level that
should be considered for any future mitigation programs. In addition, although post-Katrina rebuilding and
recovery has focused attention on areas heavily flooded during the storm, West Bank neighborhoods such
as Tall Timbers/Brechtel that were unaffected in 2005 nevertheless remain at substantial risk and should be
treated as high-priority for future mitigation.
8.2.4
Local risk mitigation is not necessarily effective in isolation
Non-structural risk mitigation is not necessarily effective against low-probability events in isolation. As
shown in the vulnerability analysis, high damages are observed in scenarios with continuing coastal land
loss and levees heights that decline with RSLR. This suggests that local risk mitigation must be combined
with additional wetlands restoration and ongoing levee upgrades in order to provide reliable risk reduction—
a finding consistent with the “multiple-barriers” approach proposed for coastal risk reduction. Furthermore,
the 400- and 1,000-year damage results suggest that the only plausible way to dramatically reduce risk from
extreme events may be to augment the protection system to a higher design standard, as suggested in the
Dutch approach and other recent research.
8.3
Suggestions for future research
This study represents an initial effort to apply exploratory modeling and RDM methods to the challenge of
flood risk reduction in New Orleans when faced with deep uncertainty. Additional research utilizing this
approach to could improve upon this dissertation in several key areas, which in turn would improve the
usefulness of the decision analysis methods for local hazard mitigation planners.
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Managing New Orleans Flood Risk
8.3.1
Chapter 8
Detailed flood hazards model
As described in Sec. 4.2.3, the initial version of NOLArisk interpolates across a small number of LACPR
summary outputs in order to estimate interior flood elevations for each of the five Orleans Parish basins. This
approach therefore relies on the assumptions made by LACPR when performing the storm surge and interior
drainage modeling and uses a coarse unit of spatial analysis when considering flood hazards. Critically, the
current flood hazards module in NOLArisk does not allow the user to consider deep uncertainty related to
a) changes in future hurricane intensity or frequency or b) the possibility of protection system failure given
a future flood event. Because the probability of failure is assumed to be zero, NOLArisk damage estimates
may be biased downwards relative to the true risk.
However, the low-resolution Flood Hazards module may also lead to upwardly-biased risk estimates.
Linear interpolation across the two LACPR landscape scenarios, for one, may miss key threshold effects
and overestimate interior flood elevations with moderate levels of land loss. In addition, linearly adding
exterior SLR to interior flood elevations fails to capture how additional overtopping elevations produce flood
volumes and may overstate SLR effects. Finally, the coarse unit of spatial analysis for flood elevations within
the system assumes that these flood elevations equalize across very large areas, which in turn may overstate
flood depths in some areas. Given the simplifying assumptions necessary, analysis results should be treated
as preliminary.
Given the limitations of the current Flood Hazards module, additional research is needed to consider
non-structural risk mitigation against the full range of deep uncertainty inherent to the problem. A substantial advancement would be to construct a new flood hazards module—similar in scope to the IPET Risk and
Reliability model—designed to estimate interior flood elevations based on external surge elevations, a system reliablity/fragility model, and pumping or drainage estimates at the sub-basin unit of analysis. Such a
study could draw on the detailed hydrodynamic surge data generated by USACE in support of the LACPR
analysis, and would allow for the consideration of uncertainty related to storm recurrence and intensity as
well as protection system fragility. In addition, greater interior spatial resolution and a drainage/pumping
model would better estimate flood damages from higher-frequency rainfall events, which in turn could reveal
additional benefits from home elevation investments.
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Managing New Orleans Flood Risk
8.3.2
Chapter 8
Expanded levers and performance metrics
Although this research focuses on non-structural risk mitigation for single-family homes in Orleans Parish,
the methodology could be expanded in future efforts to consider a broader set of research questions. For
example, risk mitigation focused on multi-family structures, commercial/industrial sites, and critical government facilities could be considered using an expanded inventory of assets. The model could also be expanded
to consider other areas within the HSDRRS, including portions of Jefferson and St. Bernard Parish. Future
efforts could also consider indirect flooding costs (e.g., displacement costs, foregone rental income) and
regional economic effects, although risk mitigation effects on these costs may be difficult to estimate.
8.3.3
Improved and adaptive strategies
This analysis describes one iteration of the RDM methodology, but future iterations should incorporate adaptive strategy elements not considered here. For example, a strategy that incorporates programs designed to
improve public understanding of flood risk and encourage additional household risk mitigation might help to
hedge against uncertainty regarding voluntary program participation. Improved or adaptive strategies could
also be identified by drawing from the recently completed New Orleans Master Plan or by working in direct
consultation with hazard mitigation planners in New Orleans. Finally, although buyouts and growth restrictions are closely connected in the first analysis iteration, considering each lever separately—for example,
voluntary buyouts coupled with mandatory growth restrictions—could yield more acceptable alternatives
that are not yet considered in NOLArisk.
171
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184
Appendix A
Strategy results for each neighborhood
This appendix provides selected additional results from the initial exploratory modeling analysis. First, tables
describing the strategy selected for each neighborhood under each citywide strategy are shown, for both the
government (4.875%) and long-term (1.0%) discount rate assumptions. Next, the appendix displays onepage exploratory modeling summaries for each neighborhood. The top-most figure shows a scatterplot of
expected annual damage (y-axis) versus annual implementation costs (x-axis) using the government discount
rate assumption. Each point the plot represents 1 of 255 uncertain scenarios, and the plot is repeated for each
strategy. The middle plot shows discounted net benefit from each strategy across 255 scenarios, repeated for
both discount rate assumptions. Both plots are discussed in detail for one neighborhood in Sec. 6.4.2.
The bottom-most figure, finally, shows 100-year damages (y-axis) versus implementation costs (x-axis)
for the points representing the 75th percentile across the scenario range for each metric. All strategies are
represented in the plot, and it is repeated for the long-term (left pane) and government (right pane) discount
rate assumptions. These final plots were used to develop citywide strategies based on 100-year damage reduction (e.g., E-100-BAL, E-100-MAX). For balanced (-BAL) citywide strategies, I used the plots to identify
the “knee in the curve” where residual damage drops substantially with the next most costly strategy. For
maximum risk reduction (-MAX), alternately, I simply selected the strategy that provided the greatest risk
reduction, choosing the least expensive approach when ties occurred. Note that the points are slightly perturbed so that overlapping points can be viewed. In instances where damages are zero or near-zero this may
lead to negative damage values appearing on the plot, but these should be considered zero values by the
reader.
A-1
Managing New Orleans Flood Risk
Appendix A
Citywide strategy development crosswalk: 4.875% discount rate
ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
Neighborhood name
Algiers Naval Station
Algiers Point
Algiers Whitney
Audubon/University
Aurora/Walnut Bend/Hunt. Village
Bayou St. John
Behrman
Black Pearl
Broadmoor/Freret
Bywater
Calliope Project
Central Business District
Central City/Magnolia
City Park
Desire Area
Desire Dev
Dillard
Dixon
East Carrollton
East Riverside
Edgelake/Little Woods
Fairgrounds/Broad
Fillmore
Fischer Project
Florida Area
Florida Project
Garden District
Gentilly Terrace
Gentilly Woods
Gerttown/Zion City
Hollygrove
Holy Cross
Iberville Project
Irish Channel
Lake Terrace/Lake Oaks
Lakeshore/Lake Vista
Lakeview
Lakewood
Lakewood/West End
Leonidas/West Carrollton
Lower Ninth Ward
McDonogh
Marigny
Marlyville/Fontainbleau
Mid-City
Milan
Milneburg
Navarre
Pines Village
Plum Orchard
Pontchartrain Park
Read Boulevard East
Read Boulevard West
River Park/Cut Off/Lower Coast1
Seventh Ward
Sixth Ward/Treme/Lafitte
St. Anthony
St. Bernard Area/Project
St. Claude
St. Roch
St. Thomas Area
St. Thomas Project
Tall Timbers/Brechtel
Touro
Tulane/Gravier
Uptown
Viavant/Venetian Isles
Vieux Carre
Village De L'Est
West Lake Forest
West Riverside
River Park/Cut Off/Lower Coast2
Basin
OW2
OW2
OW2
OM
OW2
OM
OW2
OM
OM
OM
OM
OM
OM
OM
OM
OM
OM
OM
OM
OM
NOE
OM
OM
OW2
OM
OM
OM
OM
OM
OM
OM
SB
OM
OM
OM
OM
OM
OM
OM
OM
SB
OW2
OM
OM
OM
OM
OM
OM
NOE
NOE
OM
NOE
NOE
OW1
OM
OM
OM
OM
OM
OM
OM
OM
OW2
OM
OM
OM
NOE
OM
NOE
NOE
OM
OW2
E-NB-25
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-5, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
E-NB-50
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-5, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-2, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(1, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
E-NB-75
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-5, NA)
(NA, NA)
(-5, NA)
(NA, NA)
(1, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-2, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(1, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
C-NB-25
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-5, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
C-NB-50
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-5, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-2, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(1, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
A-2
Citywide strategy ID
C-NB-75 E-100-BAL E-100-MAX C-100-BAL C-100-MAX E-MAX
(NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(10, NA)
(NA, NA)
(NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(10, NA)
(NA, NA)
(NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(10, NA)
(NA, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(-2, -4)
(10, NA)
(10, NA)
(10, -4)
(10, -4)
(NA, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(-2, -4)
(10, NA)
(10, NA)
(10, NA)
(10, -4)
(NA, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(1, NA)
(1, NA)
(1, -5)
(1, -4)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, -4)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, -4)
(10, NA)
(NA, NA)
(1, NA)
(1, NA)
(1, NA)
(1, NA)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, -4)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(NA, NA)
(1, NA)
(4, NA)
(1, -5)
(4, -5)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, NA)
(10, NA)
(NA, NA)
(-2, NA)
(1, NA)
(-2, -5)
(1, -4)
(10, NA)
(NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(10, NA)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, -4)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(-2, NA)
(1, NA)
(-2, -4)
(1, -4)
(10, NA)
(NA, NA)
(1, NA)
(1, NA)
(-2, -4)
(1, -4)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, NA)
(10, NA)
(NA, NA)
(1, NA)
(1, NA)
(-2, -4)
(1, -4)
(10, NA)
(NA, NA) (NA, NA)
(7, NA)
(NA, NA)
(7, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(-5, NA)
(-2, NA)
(1, NA)
(-2, -5)
(-2, -4)
(10, NA)
(NA, NA)
(-5, NA)
(1, NA)
(-2, -4)
(-2, -4)
(10, NA)
(-5, NA)
(-2, NA)
(1, NA)
(-2, -5)
(-2, -4)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(1, -4)
(4, NA)
(7, NA)
(4, -4)
(7, -4)
(10, NA)
(NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(10, NA)
(NA, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, -4)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, NA)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, NA)
(10, NA)
(-2, NA)
(-2, NA)
(1, NA)
(-2, -5)
(1, -5)
(10, NA)
(NA, NA)
(-2, NA)
(1, NA)
(-2, -5)
(1, -4)
(10, NA)
(NA, NA)
(1, NA)
(4, NA)
(1, -5)
(1, -5)
(10, NA)
(NA, NA)
(1, NA)
(4, NA)
(1, -5)
(4, -5)
(10, NA)
(NA, NA)
(-2, NA)
(1, NA)
(-2, -5)
(-2, -5)
(10, NA)
(NA, NA)
(1, NA)
(4, NA)
(1, -5)
(1, -5)
(10, NA)
(NA, NA)
(1, NA)
(4, NA)
(1, -5)
(1, -4)
(10, NA)
(1, NA)
(10, NA)
(10, NA)
(10, NA)
(10, NA)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(-2, NA)
(1, NA)
(-2, -5)
(-2, -5)
(10, NA)
(NA, NA)
(-2, NA)
(1, NA)
(-2, NA)
(1, -4)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, NA)
(10, NA)
(NA, NA)
(-2, NA)
(1, NA)
(-2, -5)
(-2, -4)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(-2, -4)
(10, NA)
(10, NA)
(10, -4)
(10, -4)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(1, NA)
(1, NA)
(1, NA)
(1, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(4, NA)
(4, NA)
(4, NA)
(4, -4)
(10, NA)
(NA, NA)
(1, NA)
(4, NA)
(1, NA)
(1, -5)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(10, NA)
(10, NA)
(10, -4)
(10, -4)
(10, NA)
C-MAX
(NA, NA)
(NA, NA)
(NA, NA)
(10, NA)
(NA, NA)
(10, NA)
(NA, NA)
(10, NA)
(10, -4)
(10, NA)
(10, NA)
(10, NA)
(10, -4)
(10, NA)
(10, -4)
(10, NA)
(10, -4)
(10, -4)
(10, NA)
(NA, NA)
(10, -4)
(10, NA)
(10, -4)
(10, NA)
(10, -4)
(10, NA)
(10, NA)
(10, -4)
(10, -4)
(10, NA)
(10, -4)
(10, NA)
(10, NA)
(10, NA)
(10, NA)
(10, NA)
(10, -4)
(10, -4)
(10, -4)
(10, NA)
(10, -4)
(NA, NA)
(10, NA)
(10, -4)
(10, -4)
(10, NA)
(10, -4)
(10, -4)
(10, -4)
(10, -4)
(10, -4)
(10, -4)
(10, -4)
(10, NA)
(10, -4)
(10, NA)
(10, -4)
(10, -4)
(10, NA)
(10, -4)
(NA, NA)
(10, NA)
(10, -4)
(10, NA)
(10, NA)
(10, NA)
(10, NA)
(10, NA)
(10, -4)
(10, -4)
(10, NA)
(10, -4)
Managing New Orleans Flood Risk
Appendix A
Citywide strategy development crosswalk: 1.0% discount rate
ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
Neighborhood name
Algiers Naval Station
Algiers Point
Algiers Whitney
Audubon/University
Aurora/Walnut Bend/Hunt. Village
Bayou St. John
Behrman
Black Pearl
Broadmoor/Freret
Bywater
Calliope Project
Central Business District
Central City/Magnolia
City Park
Desire Area
Desire Dev
Dillard
Dixon
East Carrollton
East Riverside
Edgelake/Little Woods
Fairgrounds/Broad
Fillmore
Fischer Project
Florida Area
Florida Project
Garden District
Gentilly Terrace
Gentilly Woods
Gerttown/Zion City
Hollygrove
Holy Cross
Iberville Project
Irish Channel
Lake Terrace/Lake Oaks
Lakeshore/Lake Vista
Lakeview
Lakewood
Lakewood/West End
Leonidas/West Carrollton
Lower Ninth Ward
McDonogh
Marigny
Marlyville/Fontainbleau
Mid-City
Milan
Milneburg
Navarre
Pines Village
Plum Orchard
Pontchartrain Park
Read Boulevard East
Read Boulevard West
River Park/Cut Off/Lower Coast1
Seventh Ward
Sixth Ward/Treme/Lafitte
St. Anthony
St. Bernard Area/Project
St. Claude
St. Roch
St. Thomas Area
St. Thomas Project
Tall Timbers/Brechtel
Touro
Tulane/Gravier
Uptown
Viavant/Venetian Isles
Vieux Carre
Village De L'Est
West Lake Forest
West Riverside
River Park/Cut Off/Lower Coast2
Basin
OW2
OW2
OW2
OM
OW2
OM
OW2
OM
OM
OM
OM
OM
OM
OM
OM
OM
OM
OM
OM
OM
NOE
OM
OM
OW2
OM
OM
OM
OM
OM
OM
OM
SB
OM
OM
OM
OM
OM
OM
OM
OM
SB
OW2
OM
OM
OM
OM
OM
OM
NOE
NOE
OM
NOE
NOE
OW1
OM
OM
OM
OM
OM
OM
OM
OM
OW2
OM
OM
OM
NOE
OM
NOE
NOE
OM
OW2
E-NB-25
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-5, NA)
(NA, NA)
(-5, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-2, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(1, NA)
(NA, NA)
(NA, NA)
(-2, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(10, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
E-NB-50
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-5, NA)
(NA, NA)
(-5, NA)
(NA, NA)
(4, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-2, NA)
(NA, NA)
(1, NA)
(NA, NA)
(1, NA)
(4, NA)
(NA, NA)
(1, NA)
(NA, NA)
(NA, NA)
(-2, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(10, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(1, NA)
(NA, NA)
(10, NA)
E-NB-75
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-5, NA)
(NA, NA)
(-5, NA)
(NA, NA)
(4, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-2, NA)
(NA, NA)
(1, NA)
(1, NA)
(1, NA)
(7, NA)
(1, NA)
(1, NA)
(NA, NA)
(NA, NA)
(-2, NA)
(NA, NA)
(NA, NA)
(-5, NA)
(NA, NA)
(NA, NA)
(10, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(1, NA)
(NA, NA)
(10, NA)
C-NB-25
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-5, NA)
(NA, NA)
(-5, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-2, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(1, NA)
(NA, NA)
(NA, NA)
(-2, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(10, -5)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
C-NB-50
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-2, -4)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-5, NA)
(NA, NA)
(-5, NA)
(NA, NA)
(4, -4)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(-2, NA)
(NA, NA)
(1, NA)
(NA, NA)
(1, NA)
(-2, -5)
(NA, NA)
(1, NA)
(NA, NA)
(NA, NA)
(-2, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(10, -4)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(NA, NA)
(1, NA)
(NA, NA)
(10, NA)
A-3
Citywide strategy ID
C-NB-75 E-100-BAL E-100-MAX C-100-BAL C-100-MAX E-MAX
(NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(10, NA)
(NA, NA)
(NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(10, NA)
(NA, NA)
(NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(10, NA)
(NA, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(-2, -4)
(10, NA)
(10, NA)
(10, -4)
(10, -4)
(NA, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(-2, -4)
(10, NA)
(10, NA)
(10, NA)
(10, -4)
(NA, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(1, NA)
(1, NA)
(1, -5)
(1, -4)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, -4)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, -4)
(10, NA)
(NA, NA)
(1, NA)
(1, NA)
(1, NA)
(1, NA)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, -4)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(NA, NA)
(1, NA)
(4, NA)
(1, -5)
(4, -5)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, NA)
(10, NA)
(NA, NA)
(-2, NA)
(1, NA)
(-2, -5)
(1, -4)
(10, NA)
(NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(10, NA)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, -4)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(-2, NA)
(1, NA)
(-2, -4)
(1, -4)
(10, NA)
(NA, NA)
(1, NA)
(1, NA)
(-2, -4)
(1, -4)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, NA)
(10, NA)
(NA, NA)
(1, NA)
(1, NA)
(-2, -4)
(1, -4)
(10, NA)
(NA, NA) (NA, NA)
(7, NA)
(NA, NA)
(7, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(-5, NA)
(-2, NA)
(1, NA)
(-2, -5)
(-2, -4)
(10, NA)
(NA, NA)
(-5, NA)
(1, NA)
(-2, -4)
(-2, -4)
(10, NA)
(-5, NA)
(-2, NA)
(1, NA)
(-2, -5)
(-2, -4)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(4, -4)
(4, NA)
(7, NA)
(4, -4)
(7, -4)
(10, NA)
(NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(10, NA)
(NA, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, -4)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, NA)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, NA)
(10, NA)
(-2, NA)
(-2, NA)
(1, NA)
(-2, -5)
(1, -5)
(10, NA)
(NA, NA)
(-2, NA)
(1, NA)
(-2, -5)
(1, -4)
(10, NA)
(1, NA)
(1, NA)
(4, NA)
(1, -5)
(1, -5)
(10, NA)
(1, NA)
(1, NA)
(4, NA)
(1, -5)
(4, -5)
(10, NA)
(1, NA)
(-2, NA)
(1, NA)
(-2, -5)
(-2, -5)
(10, NA)
(1, -5)
(1, NA)
(4, NA)
(1, -5)
(1, -5)
(10, NA)
(1, NA)
(1, NA)
(4, NA)
(1, -5)
(1, -4)
(10, NA)
(1, NA)
(10, NA)
(10, NA)
(10, NA)
(10, NA)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(-2, NA)
(-2, NA)
(1, NA)
(-2, -5)
(-2, -5)
(10, NA)
(NA, NA)
(-2, NA)
(1, NA)
(-2, NA)
(1, -4)
(10, NA)
(NA, NA) (NA, NA)
(1, NA)
(NA, NA)
(1, NA)
(10, NA)
(-5, NA)
(-2, NA)
(1, NA)
(-2, -5)
(-2, -4)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(10, -4)
(10, NA)
(10, NA)
(10, -4)
(10, -4)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(1, NA)
(1, NA)
(1, NA)
(1, NA)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(NA, NA)
(4, NA)
(4, NA)
(4, NA)
(4, -4)
(10, NA)
(1, NA)
(1, NA)
(4, NA)
(1, NA)
(1, -5)
(10, NA)
(NA, NA) (NA, NA) (NA, NA) (NA, NA) (NA, NA)
(10, NA)
(10, NA)
(10, NA)
(10, NA)
(10, -4)
(10, -4)
(10, NA)
C-MAX
(NA, NA)
(NA, NA)
(NA, NA)
(10, NA)
(NA, NA)
(10, NA)
(NA, NA)
(10, NA)
(10, -4)
(10, NA)
(10, NA)
(10, NA)
(10, -4)
(10, NA)
(10, -4)
(10, NA)
(10, -4)
(10, -4)
(10, NA)
(NA, NA)
(10, -4)
(10, NA)
(10, -4)
(10, NA)
(10, -4)
(10, NA)
(10, NA)
(10, -4)
(10, -4)
(10, NA)
(10, -4)
(10, NA)
(10, NA)
(10, NA)
(10, NA)
(10, NA)
(10, -4)
(10, -4)
(10, -4)
(10, NA)
(10, -4)
(NA, NA)
(10, NA)
(10, -4)
(10, -4)
(10, NA)
(10, -4)
(10, -4)
(10, -4)
(10, -4)
(10, -4)
(10, -4)
(10, -4)
(10, NA)
(10, -4)
(10, NA)
(10, -4)
(10, -4)
(10, NA)
(10, -4)
(NA, NA)
(10, NA)
(10, -4)
(10, NA)
(10, NA)
(10, NA)
(10, NA)
(10, NA)
(10, -4)
(10, -4)
(10, NA)
(10, -4)
Managing New Orleans Flood Risk
Appendix A
Algiers Naval Station
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
●
−20
●
−40
●
●
Discount rate
−60
4.875%
−80
1.0%
−100
−120
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
14
●
●
●
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
● (−5, NA)
● (−2, NA)
● (1, NA)
12
● (4, NA)
● (7, NA)
● (10, NA)
10
● (−2, −5)
● (1, −5)
8
● (4, −5)
●
● (7, −5)
6
●
●
● (10, −5)
●
●
● (−2, −4)
● (1, −4)
4
● (4, −4)
●
0
1
2
3
4
0
1
Equiv. annual strategy cost ($M 2009)
A-4
2
3
4
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Algiers Point
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
−20
−40
Discount rate
−60
4.875%
−80
1.0%
−100
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
● (−5, NA)
●
●
● (−2, NA)
● (1, NA)
5
● (4, NA)
● (7, NA)
● (10, NA)
4
● (−2, −5)
● (1, −5)
●
●
3
● (4, −5)
● (7, −5)
● (10, −5)
●
●
●
●
2
● (−2, −4)
● (1, −4)
●
●
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0.0
0.5
Equiv. annual strategy cost ($M 2009)
A-5
1.0
1.5
2.0
2.5
3.0
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Algiers Whitney
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−20
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
−40
Discount rate
−60
4.875%
−80
1.0%
−100
−120
−140
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
●
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
● (−2, NA)
16
● (1, NA)
14
● (4, NA)
●
● (7, NA)
● (10, NA)
12
● (−2, −5)
● (1, −5)
10
● (4, −5)
8
●
●
●
● (7, −5)
●
● (10, −5)
6
● (−2, −4)
●
●
● (1, −4)
● (4, −4)
4
●
0
1
2
3
4
0
1
Equiv. annual strategy cost ($M 2009)
A-6
2
3
4
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Aurora/Walnut Bend/Huntlee Village
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
400
0
●
●
200
●
●
−200
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Discount rate
4.875%
−400
●
●
●
●
●
●
●
●
●
●
−600
●
●
●
●
●
●
●
●
−800
●
●
●
●
●
●
●
●
−1000
(−5, NA) (−2, NA) (1, NA)
(4, NA)
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
●
●
●
●
●
●
●
●
●
●
●
●
●
●
(7, −5)
1.0%
●
●
●
●
●
●
●
●
●
●
●
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
●
●
●
●
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
600
Strategy ID
4.875% discount rate
● (NA, NA)
●●
●
● (−5, NA)
● (−2, NA)
●
●
● (1, NA)
● (4, NA)
●●
500
● (7, NA)
● (10, NA)
●●●
400
● (−2, −5)
●
●
● (1, −5)
●
●●
● (4, −5)
● (7, −5)
●
●
300
● (10, −5)
●
●
●
● (−2, −4)
● (1, −4)
●
●●
●
●
0
10
20
30
0
Equiv. annual strategy cost ($M 2009)
A-7
10
20
30
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Bayou St. John
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−50
−100
Discount rate
4.875%
−150
1.0%
−200
−250
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
● (−2, NA)
●
0.005
●
●
● ●
●
● (1, NA)
● (4, NA)
●
●
●
●
●
●
●
● (1, −5)
●
●
●
●
●
●
● (10, −5)
● (−2, −4)
●
●
● ●
0
● (1, −4)
●
●
4
● (4, −4)
●
●●
●
2
● (4, −5)
● (7, −5)
●
−0.005
● (10, NA)
● (−2, −5)
●
0.000
● (7, NA)
6
8
0
Equiv. annual strategy cost ($M 2009)
A-8
2
4
●
6
8
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Behrman
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
100
●
●
●
●
●
●
●
●
0
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
−100
Discount rate
−200
4.875%
●
−300
●
●
●
●
●
1.0%
●
●
−400
●
●
−500
(−5, NA) (−2, NA) (1, NA)
(4, NA)
●
●
●
●
●
●
●
●
●
●
●
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
●
●
●
●
●
●
●
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●●
●
● (−5, NA)
●●
● (−2, NA)
200
● (1, NA)
●●
● (4, NA)
● (7, NA)
● (10, NA)
150
● (−2, −5)
●●
●
●
●
● (1, −5)
●●
● (4, −5)
● (7, −5)
●
●
● (10, −5)
●●
● (−2, −4)
100
● (1, −4)
●●
●●
0
5
10
15
0
Equiv. annual strategy cost ($M 2009)
A-9
5
10
15
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Black Pearl
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
●
−20
Discount rate
−40
4.875%
−60
1.0%
−80
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
●
●
●
●
●
●
0.005
●
●
●
●
● (1, NA)
● (4, NA)
● (7, NA)
●
●
● (10, NA)
●
●
●
●
●
●
●
0.0
1.0
1.5
2.0
2.5
0.0
● (10, −5)
● (4, −4)
● (7, −4)
0.5
Equiv. annual strategy cost ($M 2009)
A-10
● (7, −5)
● (1, −4)
●
●
0.5
● (1, −5)
● (−2, −4)
●
●
●
● (−2, −5)
● (4, −5)
●
−0.005
● (−5, NA)
● (−2, NA)
●
●
●
●
●
●
0.000
● (NA, NA)
1.0
1.5
2.0
2.5
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Broadmoor/Freret
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
−200
Discount rate
4.875%
−400
1.0%
−600
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
● (−5, NA)
0.5
● (−2, NA)
●
●
● (1, NA)
● (4, NA)
0.4
● (7, NA)
● (10, NA)
● (−2, −5)
0.3
● (1, −5)
0.2
●●●
● ●●●
●
0.1
●
●
●
● (4, −5)
●
●
● (7, −5)
● (10, −5)
● (−2, −4)
●
●
● ●●●
0
5
10
15
20
0
5
Equiv. annual strategy cost ($M 2009)
A-11
10
●
●●
●●
●
●
15
● (1, −4)
● ●●●
20
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Bywater
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−50
−100
Discount rate
4.875%
−150
1.0%
−200
−250
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
●
●
●
●
● ●
●
0.000
● (NA, NA)
●
●
●
●
0.005
Strategy ID
4.875% discount rate
●
●
● (−5, NA)
● (−2, NA)
● (1, NA)
● (4, NA)
● (7, NA)
●
●
●
●
●
●
●
● (10, NA)
●
●
−0.005
0
● (1, −5)
● (4, −5)
●
●
●
●
●
● (−2, −5)
●
2
4
●
●
●
●
6
8
0
Equiv. annual strategy cost ($M 2009)
A-12
●
● (7, −5)
● (10, −5)
● (−2, −4)
● (1, −4)
● (4, −4)
● (7, −4)
2
4
6
8
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Calliope Project
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
−5
Discount rate
−10
4.875%
−15
1.0%
−20
−25
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
● (−5, NA)
●
●
0.005
●
●
●
● (−2, NA)
● (1, NA)
●
●
● (4, NA)
●
●
●
●
0.000
●
●
●
−0.005
●
●
0.0
●
●
● ●
0.1
0.2
0.3
0.4
● (−2, −5)
● (4, −5)
● (7, −5)
● (10, −5)
●
●
● (10, NA)
● (1, −5)
● ●
●
● (7, NA)
● (−2, −4)
● (1, −4)
●●
●
0.5
0.6
0.7
0.0
●
●
0.1
Equiv. annual strategy cost ($M 2009)
A-13
● (4, −4)
●●
0.2
0.3
0.4
0.5
● (7, −4)
0.6
0.7
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Central Business District
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−50
Discount rate
4.875%
−100
1.0%
−150
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
●
● (−2, NA)
●
●
●
0.005
●
●
0.000
● (4, NA)
● (7, NA)
●
●
●
● (10, NA)
●
● (−2, −5)
●
●
●
●
●
−0.005
●
●
●
●
●
●
●
●
●
●
● (4, −5)
● (10, −5)
● (−2, −4)
●
●
● (1, −4)
●
1
● (1, −5)
● (7, −5)
●
●
●
0
● (1, NA)
●
2
3
4
0
●
1
Equiv. annual strategy cost ($M 2009)
A-14
2
3
4
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Central City/Magnolia
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
−100
−200
Discount rate
−300
4.875%
−400
1.0%
−500
−600
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
0.020
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
● (−2, NA)
●
● (1, NA)
0.015
●
●
0.010
●
●
●
● (7, NA)
●
● (−2, −5)
●
●
●
0.000
●
●
●
●
●
● (10, NA)
●●
●
●
●
0.005
● (4, NA)
●
●
●
● (10, −5)
● (−2, −4)
●
●
● (1, −4)
−0.005
●
0
5
10
20
0
Equiv. annual strategy cost ($M 2009)
A-15
●
●
●
15
● (4, −5)
● (7, −5)
●
●
● (1, −5)
5
● (4, −4)
● (7, −4)
10
15
20
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
City Park
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−50
Discount rate
−100
4.875%
1.0%
−150
−200
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
0.005
0.000
●
●
●
Strategy ID
4.875% discount rate
●
●
●
●●
●
●
● (NA, NA)
●
● (−5, NA)
● (−2, NA)
●
● (1, NA)
●
●
●
●
●●
●
●
●
●
●
1
2
●
●
3
● (−2, −5)
● (7, −5)
● (10, −5)
● (−2, −4)
●
● (1, −4)
● (4, −4)
●
●
0
● (10, NA)
● (4, −5)
●
●
●
● (7, NA)
● (1, −5)
●
−0.005
● (4, NA)
4
5
6
0
● (7, −4)
1
Equiv. annual strategy cost ($M 2009)
A-16
2
3
4
5
6
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Desire Area
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
●
●
●
●
−20
Discount rate
−40
4.875%
●
−60
●
●
1.0%
●
●
−80
●
●
●
●
●
●
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
0.05
●
0.04
●
● (−5, NA)
● (−2, NA)
● (1, NA)
● (4, NA)
● (7, NA)
● (10, NA)
●
0.03
● (−2, −5)
●
● (1, −5)
0.02
● ●
●
●
●
0.01
●
●
0.00
0.0
0.5
1.0
●
● (4, −5)
●
●
●
●
●
●
●
●
● (7, −5)
●
●
●●
●
1.5
●
●
●
2.0
2.5
0.0
0.5
Equiv. annual strategy cost ($M 2009)
A-17
1.0
1.5
● (10, −5)
●
●
● (−2, −4)
●
●
●
2.0
2.5
● (1, −4)
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Desire Dev
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−2
−4
Discount rate
−6
4.875%
−8
−10
1.0%
−12
−14
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
0.010
●
●
●
Strategy ID
4.875% discount rate
● (NA, NA)
● (−5, NA)
●
●
●
●
●
0.005
●●
●
● (1, NA)
● (4, NA)
●
●
●
● (−2, NA)
●
●
●●
● (7, NA)
● (10, NA)
●
● (−2, −5)
● (1, −5)
0.000
●
● (4, −5)
●
●
●
●
●●
−0.005
●●
●
●
0.0
0.1
0.2
●
0.3
0.4
0.0
0.1
Equiv. annual strategy cost ($M 2009)
A-18
0.2
0.3
0.4
● (7, −5)
● (10, −5)
● (−2, −4)
●
●
●
● (1, −4)
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Dillard
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
−50
−100
Discount rate
−150
4.875%
−200
1.0%
−250
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
● (−5, NA)
● (−2, NA)
0.25
● (1, NA)
● (4, NA)
0.20
● (7, NA)
●
●
● (10, NA)
●
0.15
● (−2, −5)
● (1, −5)
● (4, −5)
●
●
● (7, −5)
0.10
●
●
●
0.05
●
●
●
●
●
●
●
● ●
● (10, −5)
● (−2, −4)
●
●
●
●
●
●
●
0
2
4
6
8
0
2
Equiv. annual strategy cost ($M 2009)
A-19
4
6
● ●
● ●
● ●
8
● (1, −4)
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Dixon
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−10
−20
Discount rate
−30
4.875%
−40
1.0%
−50
−60
−70
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
0.04
0.03
● (−2, NA)
● (1, NA)
●●
●
● (4, NA)
● (7, NA)
● (10, NA)
●
●
0.02
●
●
0.01
●
● (−2, −5)
● (1, −5)
●
● ● ●
●
●
●
●
● (4, −5)
●
●
●
●
0.00
0.0
0.5
1.0
●
●
●
●
●
●
●
2.0
0.0
0.5
Equiv. annual strategy cost ($M 2009)
A-20
1.0
1.5
● (10, −5)
●
●
● ●
1.5
● (7, −5)
●
●
2.0
● (−2, −4)
● (1, −4)
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
East Carrollton
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−50
−100
Discount rate
−150
4.875%
−200
1.0%
−250
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
●
●
Strategy ID
4.875% discount rate
● (NA, NA)
● (−5, NA)
●
● (−2, NA)
●
●
0.005
●
●
0.000
●
● (7, NA)
●
●
●
−0.005
●
●
● (10, NA)
●
● (−2, −5)
● (1, −5)
●
●
●
● (4, NA)
●
●
●
●
●
● (1, NA)
●
●
● (4, −5)
●
●
●
● (10, −5)
●
● (−2, −4)
●
●
●
●
0
●
2
4
6
8
0
Equiv. annual strategy cost ($M 2009)
A-21
● (7, −5)
2
4
6
8
● (1, −4)
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
East Riverside
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
●
●
●
●
−20
−40
Discount rate
4.875%
−60
1.0%
−80
−100
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
●
●
●
●
● (−5, NA)
● (−2, NA)
●
●
●
●
●
●
● (1, NA)
●
● (7, NA)
● (4, NA)
● (−2, −5)
● (1, −5)
●
●
●
●
●
● (4, −5)
● (7, −5)
● (10, −5)
●
●
●
●
●
●
●
● (10, NA)
●
●
●
●
−0.005
● (NA, NA)
●
0.005
0.000
Strategy ID
4.875% discount rate
●
● (−2, −4)
●
●
●
● (1, −4)
● (4, −4)
●
0.0
● (7, −4)
0.5
1.0
1.5
2.0
2.5
3.0
0.0
0.5
Equiv. annual strategy cost ($M 2009)
A-22
1.0
1.5
2.0
2.5
3.0
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Edgelake/Little Woods
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
●
1000
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
0
Discount rate
4.875%
●
●
−1000
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
1.0%
−2000
●
●
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
●
●
●
(1, −5)
(4, −5)
●
●
●
●
(7, −5)
●
●
●
(10, −5) (−2, −4)
(1, −4)
(4, −4)
●
(7, −4)
●
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
●
700
● (−2, NA)
● (1, NA)
● (4, NA)
600
● (7, NA)
●
500
● (10, NA)
●
● (−2, −5)
●
● (1, −5)
400
● (4, −5)
●●●●
● (7, −5)
●
300
200
● (10, −5)
● ● ●●
●
● (−2, −4)
● (1, −4)
●
●
●
●
●
●●●
●
0
20
40
●
60
80
0
20
Equiv. annual strategy cost ($M 2009)
A-23
40
60
80
●●
●●● ●●●●
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Fairgrounds/Broad
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−100
Discount rate
−200
4.875%
1.0%
−300
−400
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
●●
0.010
● (NA, NA)
●
●
●
●
● (−5, NA)
● (−2, NA)
●
● (1, NA)
● (4, NA)
0.005
●
●
●
●
●
●
0.000
●
●
−0.005
●
2
4
6
● (−2, −5)
●
●
●
●
●
●
● (1, −5)
● (4, −5)
● (7, −5)
● (10, −5)
● (−2, −4)
●
●
●
0
● (10, NA)
●
●
●
●
● (7, NA)
●
8
10
12
0
2
Equiv. annual strategy cost ($M 2009)
A-24
●
●
4
6
8
10
●
12
● (1, −4)
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Fillmore
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
●
●
●
Discount rate
●
●
−100
●
●
4.875%
●
●
−200
●
●
●
●
1.0%
●
●
●
●
●
●
●
●
●
−300
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
●
●
●
●
●
●
(7, −5)
●
●
●
●
●
(10, −5) (−2, −4)
●
●
●
●
(1, −4)
●
●
(4, −4)
●
●
●
●
●
(7, −4)
●
●
●
●
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
40
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
● (−5, NA)
● (−2, NA)
35
● (1, NA)
● (4, NA)
30
● (7, NA)
●
●
● (10, NA)
25
● (−2, −5)
● (1, −5)
20
● (4, −5)
●
15
● (7, −5)
●● ●●
● (10, −5)
●
● ●
10
● ●
● (−2, −4)
● (1, −4)
●
5
0
2
●●●●
●●●●
●
4
6
●
8
10
0
2
Equiv. annual strategy cost ($M 2009)
A-25
4
6
●
8
●● ●● ●● ●●
10
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Fischer Project
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
−5
●
●
●
●
●
●
●
●
●
●
●
●
●
−10
Discount rate
−15
4.875%
−20
1.0%
−25
−30
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
10
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
●●
●
● (−5, NA)
● (−2, NA)
9
● (1, NA)
● (4, NA)
●●
8
● (7, NA)
● (10, NA)
7
● (−2, −5)
5
● (1, −5)
●
●
●
● ●
●●
●
●
6
● (4, −5)
● (7, −5)
●
●●
● (10, −5)
●
●
● (−2, −4)
4
● (1, −4)
●
●
3
●
●
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
Equiv. annual strategy cost ($M 2009)
A-26
0.4
0.6
0.8
1.0
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Florida Area
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
●
●
●
●
●
−20
●
●
Discount rate
−40
4.875%
−60
1.0%
●
●
−80
●
●
●
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
●
●
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
0.08
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
● (−5, NA)
● (−2, NA)
● (1, NA)
● (4, NA)
0.06
● (7, NA)
● (10, NA)
0.04
●
●
● (−2, −5)
● (1, −5)
●
●
●
●
● ● ●●
●● ●
●
0.02
●
● (4, −5)
●
● (7, −5)
●
●
●
●
●
●
●
●
●
0.00
0.0
0.5
1.0
● ●
1.5
2.0
0.0
0.5
Equiv. annual strategy cost ($M 2009)
A-27
1.0
1.5
2.0
● (10, −5)
●
●●
● (−2, −4)
● (1, −4)
● ●
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Florida Project
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0.0
●
●
●
●
●
−0.5
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
−1.0
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
−2.0
●
●
●
●
●
●
−1.5
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Discount rate
●
●
●
●
●
●
●
●
●
4.875%
●
●
●
●
●
1.0%
−2.5
●
●
●
●
●
●
●
●
●
−3.0
●
(−5, NA) (−2, NA) (1, NA)
(4, NA)
●
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
●
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
●
●
0.005
●
●
●
●
●
●
● (−2, NA)
●
●
● (1, NA)
●
● (4, NA)
●
● (7, NA)
●
0.000
●
●
●
●
●
●
−0.005
●
●
● (4, −5)
● (7, −5)
●
● (10, −5)
●
●
●
●
0.00
0.01
0.02
● (−2, −5)
● (1, −5)
●
●
● (10, NA)
0.03
0.04
0.00
Equiv. annual strategy cost ($M 2009)
A-28
●
●
●
0.01
● (−2, −4)
●
●
● (1, −4)
● (4, −4)
● (7, −4)
0.02
0.03
0.04
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Garden District
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−50
Discount rate
4.875%
−100
1.0%
−150
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
●
●
●
●
● (NA, NA)
●
●
●
●
● (−5, NA)
● (−2, NA)
● (1, NA)
●
0.005
● (4, NA)
●
0.000
Strategy ID
4.875% discount rate
●
●
●
●
●
●
●
●
●
● (−2, −5)
● (4, −5)
● (7, −5)
● (10, −5)
● (−2, −4)
● (1, −4)
●
●
●
●
0
● (10, NA)
● (1, −5)
●
●
●
●
−0.005
● (7, NA)
●
1
2
3
4
5
0
●
●
1
Equiv. annual strategy cost ($M 2009)
A-29
2
3
4
5
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Gentilly Terrace
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
−100
−200
Discount rate
−300
4.875%
−400
1.0%
−500
−600
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
3.5
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
● (−2, NA)
● (1, NA)
3.0
● (4, NA)
● (7, NA)
2.5
● (10, NA)
● (−2, −5)
●
●
2.0
● (1, −5)
● (4, −5)
●
1.5
1.0
● (7, −5)
● ● ●
●
●
● (10, −5)
● ●●
●
●
●
●
0.5
●
0
5
● ●●
10
●
●
●
15
20
0
5
10
15
● (−2, −4)
● ●
● ●
●
●
Equiv. annual strategy cost ($M 2009)
A-30
●
●
● (1, −4)
● ●
20
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Gentilly Woods
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
−50
Discount rate
●
●
4.875%
●
●
−100
●
1.0%
●
●
●
●
●
−150
●
●
●
●
●
●
●
●
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
●
●
●
(1, −4)
(4, −4)
●
●
●
●
●
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
10
● (−2, NA)
● (1, NA)
● (4, NA)
8
●
● (7, NA)
● (10, NA)
●
6
● (−2, −5)
● (1, −5)
●
4
● ●●●
●
● (4, −5)
●
● (7, −5)
● ●●●
2
● (10, −5)
●
● ● ● ●
●
●
0
1
2
●
●●●●
3
● (−2, −4)
5
0
1
Equiv. annual strategy cost ($M 2009)
A-31
2
3
4
● (4, −4)
● ● ●●
●
4
● (1, −4)
●● ●
5
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Gerttown/Zion City
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
●
●
●
−50
Discount rate
−100
4.875%
1.0%
−150
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
0.010
●
●
Strategy ID
4.875% discount rate
●
● (NA, NA)
●
●
●
●
●
●
●
●
● ●
●
● (1, NA)
● (4, NA)
● (10, NA)
● (−2, −5)
●
● (1, −5)
● (4, −5)
0.000
●
●
−0.005
●
●
●
●
●
●
●
●
●
●
●
●
1
2
3
5
6
0
1
Equiv. annual strategy cost ($M 2009)
A-32
● (10, −5)
● (1, −4)
● (4, −4)
●
4
● (7, −5)
● (−2, −4)
●
●
0
● (−2, NA)
● (7, NA)
●
0.005
●
● (−5, NA)
2
3
4
5
● (7, −4)
6
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Hollygrove
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
−50
−100
Discount rate
−150
4.875%
−200
1.0%
−250
−300
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
0.20
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
● (−5, NA)
● (−2, NA)
● (1, NA)
● (4, NA)
● (7, NA)
0.15
●
● (10, NA)
● (−2, −5)
● (1, −5)
●
●
0.10
0.05
●
0
2
4
●
● (4, −5)
● (7, −5)
●
●
● ● ● ●
●
●
●
●
●
●
● (10, −5)
●
●
●
●
●
●
●
6
8
0
2
Equiv. annual strategy cost ($M 2009)
A-33
4
6
8
●
●
●
●
● ● ●
● (−2, −4)
● (1, −4)
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Holy Cross
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−20
−40
Discount rate
−60
4.875%
−80
1.0%
−100
−120
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
0.40
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
●
●
●
● (−5, NA)
● (−2, NA)
0.35
● (1, NA)
● (4, NA)
0.30
● (7, NA)
●
●
●
0.25
● (10, NA)
● (−2, −5)
● (1, −5)
0.20
● (4, −5)
●
●
●
●
●
0.15
●
●
●
●
0.10
● (7, −5)
● (10, −5)
●
●
●
● (−2, −4)
● (1, −4)
●
●
0.05
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0.0
0.5
Equiv. annual strategy cost ($M 2009)
A-34
1.0
1.5
2.0
2.5
●
●
●
3.0
3.5
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Iberville Project
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−10
Discount rate
−20
4.875%
−30
1.0%
−40
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
●
●
0.005
●
●
●
● (NA, NA)
● (−5, NA)
● (−2, NA)
●
● (1, NA)
●
●
●
Strategy ID
4.875% discount rate
●
●
● (4, NA)
●
●
●
● (7, NA)
● (10, NA)
●
● (−2, −5)
0.000
●
●
●
●
●
●
−0.005
●
●
●
●
0.0
●
0.4
0.6
0.8
1.0
1.2
1.4
0.0
0.2
Equiv. annual strategy cost ($M 2009)
A-35
0.4
0.6
0.8
1.0
1.2
●
● (10, −5)
●
● (−2, −4)
● (1, −4)
● (4, −4)
●
●
0.2
● (4, −5)
● (7, −5)
●
●
● (1, −5)
●
● (7, −4)
1.4
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Irish Channel
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
●
−50
Discount rate
−100
4.875%
−150
1.0%
−200
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
●
●
●
●
●
●
●
●
●
● (1, NA)
●
● (4, NA)
● (7, NA)
● (10, NA)
● (−2, −5)
●
●
●
●
●
●
●
● (1, −5)
● (4, −5)
● (7, −5)
● (10, −5)
−0.005
● (−2, −4)
●
●
●
● (1, −4)
● (4, −4)
●
0
● (−5, NA)
●
●
●
●
●
●
●
●
● (NA, NA)
● (−2, NA)
●
0.005
0.000
Strategy ID
4.875% discount rate
1
2
3
4
● (7, −4)
5
6
7
0
1
Equiv. annual strategy cost ($M 2009)
A-36
2
3
4
5
6
7
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Lake Terrace/Lake Oaks
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
−50
Discount rate
4.875%
−100
1.0%
−150
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
●
●
●
●
●
●
●
●
●
●
●
−0.005
●
●
● (−5, NA)
●
●
0.005
0.000
● (NA, NA)
●
●
● (1, NA)
●
●
●
●
● (4, NA)
● (7, NA)
● (10, NA)
● (−2, −5)
●
●
● (1, −5)
●
●
●
●
● (4, −5)
● (7, −5)
● (10, −5)
● (−2, −4)
●
●
● (1, −4)
●
●
● (4, −4)
●
0
● (−2, NA)
1
2
● (7, −4)
3
4
5
0
1
Equiv. annual strategy cost ($M 2009)
A-37
2
3
4
5
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Lakeshore/Lake Vista
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
−50
−100
Discount rate
−150
4.875%
−200
1.0%
−250
−300
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
0.005
●
●
●
● (−5, NA)
●
●
●
●
●
●
●
●
●
●
●
●
●
● (1, −5)
●
● (4, −5)
●
● (7, −5)
● (10, −5)
●
●
●
●
0
● (10, NA)
● (−2, −5)
●
●
−0.005
● (7, NA)
●
0.000
● (1, NA)
● (4, NA)
●
●
●
● (−2, NA)
● (1, −4)
● (4, −4)
●
2
● (−2, −4)
●
4
6
8
10
0
2
Equiv. annual strategy cost ($M 2009)
A-38
4
6
● (7, −4)
8
10
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Lakeview
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
200
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
0
●
●
●
●
●
●
●
Discount rate
●
●
●
●
●
●
●
●
●
−200
●
●
●
4.875%
●
●
●
●
●
●
●
●
●
●
●
●
●
●
1.0%
●
−400
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
120
●
Strategy ID
4.875% discount rate
● (NA, NA)
● (−5, NA)
●
● (−2, NA)
● (1, NA)
100
● (4, NA)
●
● (7, NA)
●
● (10, NA)
80
● (−2, −5)
● (1, −5)
● (4, −5)
60
● (7, −5)
● (10, −5)
●●●●●
● ● ● ●●
● (−2, −4)
40
● (1, −4)
●
●
●
●
●
●
● ●
●
●
0
5
10
15
20
0
5
Equiv. annual strategy cost ($M 2009)
A-39
10
15
20
●
●
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Lakewood
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
−50
Discount rate
●
●
●
●
−100
●
●
4.875%
●
●
1.0%
−150
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
1.5
● (−2, NA)
● (1, NA)
● (4, NA)
● (7, NA)
● (10, NA)
1.0
● (−2, −5)
● (1, −5)
●
●
● (4, −5)
●
0.5
●
●
● ●
●●
0
1
● (7, −5)
●●
● (10, −5)
●
●
● ●●
● ●●
2
3
●
●
●
● ●●
4
5
0
1
Equiv. annual strategy cost ($M 2009)
A-40
2
3
●
●
●
4
●
● ●●
●
5
● (−2, −4)
● (1, −4)
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Lakewood/West End
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
100
●
●
●
●
●
●
●
0
●
●
●
●
●
Discount rate
●
−100
●
4.875%
1.0%
−200
−300
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
●
● (−2, NA)
50
● (1, NA)
● (4, NA)
●
● (7, NA)
●
40
● (10, NA)
● (−2, −5)
● (1, −5)
30
● (4, −5)
● (7, −5)
●● ● ●●
●●
20
● (10, −5)
● ●●
● (−2, −4)
● (1, −4)
● (4, −4)
●
●●
●●●
●
●●●
10
0
2
4
6
●● ●●
●
8
10
12
0
2
Equiv. annual strategy cost ($M 2009)
A-41
4
6
8
10
12
● ●●
●
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Leonidas/West Carrollton
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
−100
Discount rate
−200
4.875%
−300
1.0%
−400
−500
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
●
●
●
●
●
●
●
●
0.005
Strategy ID
4.875% discount rate
● (NA, NA)
● (−5, NA)
●
●
●
●
● (7, NA)
●
● (10, NA)
●
●
0.000
●
●
● (−2, −5)
●
●
●
●
0
● (−2, −4)
●
●
●
● (1, −4)
●
●
5
10
15
0
Equiv. annual strategy cost ($M 2009)
A-42
● (4, −5)
● (10, −5)
●
●
●
●
● (1, −5)
● (7, −5)
●
−0.005
● (1, NA)
● (4, NA)
●
●
● (−2, NA)
5
10
15
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Lower Ninth Ward
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
200
●
●
●
●
100
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
0
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
−100
●
●
●
●
●
●
●
●
●
●
−200
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Discount rate
4.875%
●
●
●
●
●
●
●
1.0%
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
●
80
● (−2, NA)
● (1, NA)
70
●
● (4, NA)
● (7, NA)
60
●
●
●
50
● (10, NA)
●
● (−2, −5)
●
● (1, −5)
● (4, −5)
40
●
30
● (7, −5)
● (10, −5)
●
● (−2, −4)
● (1, −4)
20
●
●●
0
1
2
●●
●
●●
●
3
4
0
1
Equiv. annual strategy cost ($M 2009)
A-43
2
3
●●
●●
4
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Marigny
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−50
Discount rate
−100
4.875%
1.0%
−150
−200
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
●
●
●
● (NA, NA)
● (−5, NA)
●
●
●
0.005
●
●
●
●
●
●
●
● (4, NA)
● (7, NA)
● (10, NA)
● (−2, −5)
0.000
● (1, −5)
●
●
●
●
−0.005
● (−2, NA)
● (1, NA)
●
● (4, −5)
●
●
●
●
●
●
●
●
●
0
● (10, −5)
● (−2, −4)
2
3
4
5
6
0
● (4, −4)
● (7, −4)
1
Equiv. annual strategy cost ($M 2009)
A-44
● (1, −4)
●
●
●
●
●
1
● (7, −5)
2
3
4
5
6
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Marlyville/Fontainbleau
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−100
Discount rate
−200
4.875%
−300
1.0%
−400
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
● (−5, NA)
● (−2, NA)
●
0.06
● (1, NA)
●
● (4, NA)
● (7, NA)
● (10, NA)
● (−2, −5)
0.04
●
●
●
0.02
● (4, −5)
● (7, −5)
● ●
●
●●
●
●
●
● (1, −5)
●
●
●
●
●
●
●
●
●
●●
●
0.00
●
0
2
4
6
8
10
12
14
0
2
Equiv. annual strategy cost ($M 2009)
A-45
4
●
●
●●
●
●
● (10, −5)
● (−2, −4)
● (1, −4)
● (4, −4)
● (7, −4)
6
8
10
12
14
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
McDonogh
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
●
●
●
●
−50
Discount rate
4.875%
−100
1.0%
−150
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
●
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
16
● (−2, NA)
● (1, NA)
14
● (4, NA)
●
● (7, NA)
12
● (10, NA)
● (−2, −5)
10
● (1, −5)
8
●
●
●
● (4, −5)
●
● (7, −5)
● (10, −5)
6
● (−2, −4)
●
●
● (1, −4)
● (4, −4)
4
●
0
1
2
3
4
0
1
Equiv. annual strategy cost ($M 2009)
A-46
2
3
4
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Mid-City
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
−100
−200
Discount rate
−300
4.875%
−400
1.0%
−500
−600
−700
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
●
0.10
● (−5, NA)
● (−2, NA)
●
● (1, NA)
● (4, NA)
0.08
● (7, NA)
● (10, NA)
● (−2, −5)
0.06
● (1, −5)
●
●
●
●
0.04
● (4, −5)
●
●
●●
●
●
●
● ● ●
●●
0.02
● (7, −5)
● (10, −5)
● (−2, −4)
●
●
●
●
0
5
10
15
20
0
5
Equiv. annual strategy cost ($M 2009)
A-47
10
15
●
●
● ● ●●
●
●
● (1, −4)
● (4, −4)
● (7, −4)
20
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Milan
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
●
−50
−100
Discount rate
−150
−200
4.875%
−250
1.0%
−300
−350
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
0.030
● (−5, NA)
●
● (−2, NA)
●
● (1, NA)
0.025
0.020
● (4, NA)
● (7, NA)
●
● (10, NA)
● (−2, −5)
0.015
0.010
●
●
●
0.005
●
●
●
0.000
●
●
●
●
●
●
● (1, −5)
●
●
●
●
●●
●
●
●●
●
●
● (4, −5)
● (7, −5)
●
●
● (10, −5)
●
● (1, −4)
● (−2, −4)
● (4, −4)
●
0
2
4
6
8
10
0
2
Equiv. annual strategy cost ($M 2009)
A-48
●
4
6
8
● (7, −4)
10
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Milneburg
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
300
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
200
●
●
●
●
●
100
●
●
0
●
●
●
●
●
−100
●
●
●
●
●
●
Discount rate
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
4.875%
1.0%
●
●
●
−200
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
90
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
● (−2, NA)
●
● (1, NA)
●
80
● (4, NA)
● (7, NA)
70
●
● (10, NA)
● (−2, −5)
60
● (1, −5)
● (4, −5)
50
● ●●●
●
● (7, −5)
● ● ● ●●
● (10, −5)
● (−2, −4)
40
● (1, −4)
30
●
●
●
●
0
2
4
6
8
10
12
0
2
Equiv. annual strategy cost ($M 2009)
A-49
4
6
8
10
12
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Navarre
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
−50
Discount rate
−100
4.875%
−150
1.0%
−200
−250
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
● (−5, NA)
● (−2, NA)
0.5
● (1, NA)
● (4, NA)
● (7, NA)
0.4
● (10, NA)
●
0.3
● (−2, −5)
●
●
●
● (1, −5)
● (4, −5)
● ● ●
● ●●
●●
0.2
● (7, −5)
● (10, −5)
●●
●●
0.1
●
0
1
2
3
● ●
●
●
4
● (−2, −4)
●
●●●● ●
● (1, −4)
●
5
6
7
0
1
Equiv. annual strategy cost ($M 2009)
A-50
2
3
4
5
●
6
7
●●●
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Pines Village
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
200
●
●
●
100
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Discount rate
●
0
4.875%
●
●
●
−100
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
1.0%
●
●
●
●
●
●
●
−200
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
● (−2, NA)
● (1, NA)
100
●
● (4, NA)
● (7, NA)
● (10, NA)
●
80
● (−2, −5)
● (1, −5)
●
60
● (4, −5)
● (7, −5)
●
●●●●
● (10, −5)
● ●●●
● (−2, −4)
● (1, −4)
40
●
● ●●●
● ●●
●
●
●
●
●
●
●●●
●
0
2
4
6
8
10
0
2
Equiv. annual strategy cost ($M 2009)
A-51
4
6
8
10
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Plum Orchard
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
200
100
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Discount rate
0
●
●
●
●
−100
−200
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
4.875%
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
1.0%
●
●
●
●
●
●
−300
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
●
● (−2, NA)
● (1, NA)
100
● (4, NA)
●
80
● (7, NA)
●
● (10, NA)
● (−2, −5)
● (1, −5)
●
60
● (4, −5)
● (7, −5)
●●●●
● (10, −5)
●
● (−2, −4)
● ● ●●
40
●
0
2
4
6
● (1, −4)
● ●
●
●
●
●
●
●
8
●
10
12
0
2
Equiv. annual strategy cost ($M 2009)
A-52
4
6
8
10
●●●
●●
12
● (4, −4)
●
●●
●
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Pontchartrain Park
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
100
50
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Discount rate
0
4.875%
●
●
●
●
−50
●
●
●
●
●
●
●
●
●
●
●
●
●
●
1.0%
●
−100
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
40
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
● (−2, NA)
35
● (1, NA)
●
● (4, NA)
● (7, NA)
30
● (10, NA)
● (−2, −5)
25
●
● (1, −5)
● (4, −5)
20
●
●
●●●●
15
● (7, −5)
● (10, −5)
● ● ● ●●
● (−2, −4)
● (1, −4)
10
●
●●
●
●
●
0
1
2
3
4
5
0
1
Equiv. annual strategy cost ($M 2009)
A-53
2
3
4
5
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Read Boulevard East
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
800
●
●
●
●
600
●
●
●
●
●
●
●
●
●
●
400
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Discount rate
●
●
●
200
4.875%
0
1.0%
−200
●
−400
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
●
●
●
(10, −5) (−2, −4)
(1, −4)
(4, −4)
●
(7, −4)
●
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
Strategy ID
4.875% discount rate
● (NA, NA)
● (−5, NA)
●
● (−2, NA)
300
● (1, NA)
● (4, NA)
250
● (7, NA)
●
● (10, NA)
●
●
200
● (−2, −5)
● (1, −5)
● (4, −5)
150
● ●●●
● (7, −5)
●
● (10, −5)
● (−2, −4)
● ● ●●
100
●
0
5
10
● (1, −4)
●●
●
●
●
●
●
●
15
● ●●●●● ●
●●
●
20
25
0
5
Equiv. annual strategy cost ($M 2009)
A-54
10
15
20
25
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Read Boulevard West
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
●
●
●
●
200
●
●
●
●
100
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Discount rate
0
4.875%
●
●
●
−100
●
●
●
●
●
●
●
●
●
●
●
●
−200
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
1.0%
●
●
●
●
●
●
●
●
●
●
●
●
●
●
−300
●
●
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
●
(7, −4)
●
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
120
●
Strategy ID
4.875% discount rate
● (NA, NA)
● (−5, NA)
●
● (−2, NA)
● (1, NA)
100
● (4, NA)
●
● (7, NA)
● (10, NA)
80
●
●
● (−2, −5)
● (1, −5)
60
● (4, −5)
● ●●●
● (7, −5)
●
● (10, −5)
● ● ●●
●
40
0
2
4
● (−2, −4)
● (1, −4)
●
●
●
●
●●
●
●
●
6
●
8
10
12
0
2
Equiv. annual strategy cost ($M 2009)
A-55
4
6
8
10
●
●●●●
12
● (4, −4)
●●
●●
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
River Park/Cut Off/Lower Coast1
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
●
100
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
50
●
●
●
●
0
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
−50
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
(4, NA)
Discount rate
4.875%
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
(−5, NA) (−2, NA) (1, NA)
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
−100
●
●
●
●
●
●
●
●
●
●
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
1.0%
●
●
●
●
●
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
120
●
●
Strategy ID
4.875% discount rate
● (NA, NA)
● (−5, NA)
●
● (−2, NA)
110
● (1, NA)
● (4, NA)
●
100
● (7, NA)
● (10, NA)
●
●
90
● (−2, −5)
●
● (1, −5)
●
80
● (4, −5)
● (7, −5)
●
70
● (10, −5)
●
● (−2, −4)
60
● (1, −4)
●
50
● (4, −4)
●
0
1
2
3
4
5
6
7
0
1
Equiv. annual strategy cost ($M 2009)
A-56
2
3
4
5
6
7
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
River Park/Cut Off/Lower Coast2
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
50
0
●
●
●
●
●
●
●
●
●
●
●
●
●
●
−50
−100
●
●
●
●
●
●
●
●
●
Discount rate
●
●
●
●
●
●
●
●
4.875%
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
(−5, NA) (−2, NA) (1, NA)
(4, NA)
●
●
●
●
●
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
●
●
●
●
●
●
●
(7, −5)
1.0%
●
●
●
●
●
●
●
●
●
●
●
●
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
●
●
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
● (−5, NA)
●
120
● (−2, NA)
● (1, NA)
● (4, NA)
●
100
● (7, NA)
● (10, NA)
●
●
80
● (−2, −5)
● (1, −5)
●
●
● (4, −5)
● (7, −5)
●
●
60
● (10, −5)
●
●
● (−2, −4)
● (1, −4)
●
40
● (4, −4)
●
●
0
1
2
3
4
0
1
Equiv. annual strategy cost ($M 2009)
A-57
2
3
4
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Seventh Ward
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−100
−200
Discount rate
−300
4.875%
−400
1.0%
−500
−600
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
0.010
●
●
● (−5, NA)
●
●
● (−2, NA)
●
●
●
0.005
●
●
●
●
● (1, NA)
●
●
●
●
● (4, NA)
● (7, NA)
● (10, NA)
●
● (−2, −5)
0.000
●
●
●
●
●
●
●
●
●
●
●
● (7, −5)
●
● (−2, −4)
−0.005
● (1, −4)
●
●
5
10
15
0
Equiv. annual strategy cost ($M 2009)
A-58
● (4, −5)
● (10, −5)
●
0
● (1, −5)
●
●
5
10
15
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Sixth Ward/Treme/Lafitte
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
−50
−100
Discount rate
−150
4.875%
−200
1.0%
−250
−300
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
●
Strategy ID
4.875% discount rate
●
●
●
●
● (1, NA)
● (4, NA)
●
●
●
●
● (7, NA)
●
● (10, NA)
●
● (−2, −5)
●
●
−0.005
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● (1, −5)
●
●
● (4, −5)
● (7, −5)
● (10, −5)
● (−2, −4)
● (1, −4)
● (4, −4)
●
0
● (−5, NA)
● (−2, NA)
●
●
0.005
0.000
● (NA, NA)
2
4
6
8
0
● (7, −4)
2
Equiv. annual strategy cost ($M 2009)
A-59
4
6
8
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
St. Anthony
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
100
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Discount rate
0
●
●
●
−100
●
●
●
●
●
●
●
●
●
●
4.875%
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
1.0%
●
−200
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
● (−2, NA)
60
● (1, NA)
●
● (4, NA)
●
50
● (7, NA)
● (10, NA)
● (−2, −5)
● (1, −5)
40
● (4, −5)
● (7, −5)
●●●●
●
30
● (10, −5)
● ● ● ●●
● (−2, −4)
● (1, −4)
20
● ●
●
●●
0
2
4
6
8
10
0
2
Equiv. annual strategy cost ($M 2009)
A-60
4
6
8
10
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
St. Bernard Area/Project
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
−10
Discount rate
−20
4.875%
−30
1.0%
−40
−50
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
0.20
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
● (−5, NA)
● (−2, NA)
● (1, NA)
0.15
● (4, NA)
●
● (7, NA)
●
● (10, NA)
● (−2, −5)
●
● (1, −5)
0.10
●
●
0.05
●
0.0
0.5
● (4, −5)
●
●
● ●
● ● ●
●
● (7, −5)
●
●
●
● ●
● (10, −5)
● (−2, −4)
●
1.0
1.5
0.0
Equiv. annual strategy cost ($M 2009)
A-61
●
●
0.5
1.0
●
●
●
●
●
●
●
●
●
1.5
● (1, −4)
●
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
St. Claude
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
−100
Discount rate
−200
4.875%
−300
1.0%
−400
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
●
●
●
●
●
0.005
●
● (NA, NA)
● (−5, NA)
●
●
0.010
Strategy ID
4.875% discount rate
●
●
●
●
●
●
●
● (1, NA)
● (4, NA)
●
●
●
● (−2, NA)
●
●
●
●
●
●
● (1, −5)
●
0.000
●
●
●
6
● (10, −5)
● (4, −4)
●
4
● (7, −5)
● (1, −4)
●
2
● (4, −5)
● (−2, −4)
●
●
0
● (10, NA)
● (−2, −5)
●
−0.005
● (7, NA)
8
10
12
14
0
● (7, −4)
2
Equiv. annual strategy cost ($M 2009)
A-62
4
6
8
10
12
14
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
St. Roch
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
●
●
●
−100
−200
Discount rate
●
●
●
●
4.875%
−300
1.0%
−400
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
7
● (−2, NA)
●
● (1, NA)
6
● (4, NA)
●
● (7, NA)
5
● (10, NA)
●
● (−2, −5)
4
● (1, −5)
● (4, −5)
● (7, −5)
●
3
●
● ●
●
● (10, −5)
●
2
●
●
●
● (−2, −4)
●
● (1, −4)
●
1
0
●
●●
2
4
●●
●●●●
6
8
●
10
12
14
0
2
Equiv. annual strategy cost ($M 2009)
A-63
●
● ●
●
4
6
8
10
● ●● ●
●
12
14
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
St. Thomas Area
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−50
−100
Discount rate
−150
4.875%
−200
1.0%
−250
−300
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
●
●
●
●
●
●
●
0.005
● (1, NA)
●
●
●
● (4, NA)
● (7, NA)
●
● (−2, −5)
0.000
● (1, −5)
●
●
●
●
●
●
●
●
● (4, −5)
● (7, −5)
●
●
● (10, −5)
● (−2, −4)
● (1, −4)
●
●
●
0
● (−2, NA)
● ●
● (10, NA)
●
●
−0.005
● (−5, NA)
2
4
6
8
●
0
● (4, −4)
● (7, −4)
2
Equiv. annual strategy cost ($M 2009)
A-64
4
6
8
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
St. Thomas Project
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
●
−5
Discount rate
−10
4.875%
−15
1.0%
−20
−25
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
● (NA, NA)
●
●
●
●
●
0.005
Strategy ID
4.875% discount rate
●
● (−5, NA)
● (−2, NA)
●
●●
● (1, NA)
● (4, NA)
●
●
● (7, NA)
● (10, NA)
0.000
●
●
●
−0.005
● (1, −5)
● (4, −5)
●
●
●
●
● (7, −5)
●
●
●
●
●
0.1
0.2
0.3
0.4
● (10, −5)
● (−2, −4)
●●
●
●●
●
●
0.0
● (−2, −5)
●
●
0.5
0.6
0.7
0.0
● (1, −4)
●
●
0.1
Equiv. annual strategy cost ($M 2009)
A-65
0.2
0.3
0.4
0.5
0.6
0.7
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Tall Timbers/Brechtel
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
1000
●
●
●
●
●
●
500
●
●
●
●
●
●
●
●
●
●
Discount rate
●
●
0
●
4.875%
●
1.0%
●
●
●
●
●
●
●
●
●
●
●
●
●
−500
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
●
●
●
●
●
●
(1, −4)
●
●
●
●
●
●
●
●
●
●
●
●
●
(4, −4)
●
●
●
(7, −4)
●
●
●
●
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
● (−5, NA)
● (−2, NA)
● (1, NA)
●
●
600
500
●
●
● (4, NA)
● (7, NA)
●
● (10, NA)
●
●
●
●
●
●
●
●
400
● (1, −5)
● (4, −5)
●
●
●●●●
●
●
●
300
● (−2, −5)
●
● (7, −5)
● (10, −5)
●
●
0
5
10
15
20
● (−2, −4)
●
● (1, −4)
●
●
●
25
30
0
5
Equiv. annual strategy cost ($M 2009)
A-66
10
15
20
25
30
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Touro
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
●
−50
Discount rate
−100
4.875%
1.0%
−150
−200
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
● (−5, NA)
0.005
● (−2, NA)
● (1, NA)
● (4, NA)
●
● (10, NA)
●
●
●
●
● (−2, −5)
● (1, −5)
●
●
●
●
●
●
●
●
●
●
●
●
●
1
●
●
● (4, −5)
● (7, −5)
● (10, −5)
● (−2, −4)
● (1, −4)
● (4, −4)
●
0
● (7, NA)
●
●
●
●
−0.005
● (NA, NA)
●
●
●
0.000
Strategy ID
4.875% discount rate
●
●
●
2
3
● (7, −4)
4
5
6
0
1
Equiv. annual strategy cost ($M 2009)
A-67
2
3
4
5
6
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Tulane/Gravier
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
●
●
●
−20
−40
Discount rate
−60
4.875%
−80
1.0%
−100
−120
−140
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
●
●
0.005
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
0
1
● (7, NA)
● (−2, −5)
● (1, −5)
●
● (4, −5)
●
● (7, −5)
●
●
2
● (4, NA)
● (10, −5)
●
−0.005
● (1, NA)
● (10, NA)
●
0.000
● (−2, NA)
3
4
0
Equiv. annual strategy cost ($M 2009)
A-68
● (−2, −4)
● (1, −4)
●
●
●
● (4, −4)
●
1
2
3
4
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Uptown
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−100
Discount rate
−200
4.875%
−300
1.0%
−400
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
●
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
●
●
●
0.005
●
●
● (7, NA)
●
0.000
●
●
●
●
● (1, NA)
● (4, NA)
●
●
● (−2, NA)
●
●
●
●
●
● (−2, −5)
● (4, −5)
● (7, −5)
● (10, −5)
●
●
●
●
●
●
● (1, −5)
●
●
●
●
−0.005
● (10, NA)
●
●
●
● (−2, −4)
● (1, −4)
● (4, −4)
●
0
2
4
6
8
● (7, −4)
10
12
14
0
2
Equiv. annual strategy cost ($M 2009)
A-69
4
6
8
10
12
14
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Viavant/Venetian Isles
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
−10
●
●
●
−20
Discount rate
−30
4.875%
−40
1.0%
−50
−60
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
0.20
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
● (−5, NA)
● (−2, NA)
● (1, NA)
● (4, NA)
0.15
● (7, NA)
●
●
●
● (10, NA)
● (−2, −5)
● (1, −5)
0.10
● (4, −5)
●
●
●
●
●
●
●
●
●
●
●
●
●
●
0.05
● (7, −5)
● (10, −5)
●
●
●
● (−2, −4)
●
●
●
●
●
●
0.0
0.5
1.0
1.5
0.0
Equiv. annual strategy cost ($M 2009)
A-70
0.5
1.0
● (1, −4)
●
●
●
●
●
●
1.5
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Vieux Carre
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
−50
−100
Discount rate
−150
4.875%
−200
1.0%
−250
−300
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
●
●
●
●
●
●
●
●●
●
●
●
●
● (10, NA)
●
● (−2, −5)
●●
● (1, −5)
●
● (4, −5)
● (7, −5)
●
●
●
●
●
●
●
●
●
2
4
6
8
0
2
Equiv. annual strategy cost ($M 2009)
A-71
● (10, −5)
● (−2, −4)
● (1, −4)
●
●
●
●
0
● (1, NA)
● (7, NA)
●
●
−0.005
● (−2, NA)
● (4, NA)
0.005
0.000
● (−5, NA)
4
● (4, −4)
● (7, −4)
6
8
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
Village De L’Est
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
−100
Discount rate
4.875%
−200
1.0%
−300
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
50
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
● (−2, NA)
● (1, NA)
● (4, NA)
40
● (7, NA)
● (10, NA)
● (−2, −5)
●
30
● (1, −5)
●
● (4, −5)
●
20
● (7, −5)
● (10, −5)
● ●
● ● ●
●●●
10
0
2
4
6
● (−2, −4)
●
●
● ● ●
● ●● ● ●
8
10
12
0
2
Equiv. annual strategy cost ($M 2009)
A-72
4
6
8
10
12
● (1, −4)
● (4, −4)
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
West Lake Forest
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
●
●
●
●
●
●
●
●
●
●
200
●
●
●
●
●
●
●
●
100
Discount rate
0
4.875%
−100
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
1.0%
●
−200
−300
●
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
●
(1, −5)
●
(4, −5)
●
(7, −5)
●
●
●
(10, −5) (−2, −4)
(1, −4)
●
(4, −4)
●
(7, −4)
●
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
Strategy ID
4.875% discount rate
● (NA, NA)
●
● (−5, NA)
● (−2, NA)
●
120
● (1, NA)
● (4, NA)
●
● (7, NA)
100
● (10, NA)
●
●
● (−2, −5)
● (1, −5)
80
● (4, −5)
●●●
●
● (7, −5)
●
60
● (10, −5)
●●●●
● (−2, −4)
● (1, −4)
40
●
● (4, −4)
●
0
2
4
6
8
10
12
0
2
Equiv. annual strategy cost ($M 2009)
A-73
4
6
8
10
12
● (7, −4)
● (10, −4)
Managing New Orleans Flood Risk
Appendix A
West Riverside
Discounted net benefit (2009 $M)
Net benefit by strategy and discount rate assumption
0
●
●
●
●
●
●
−20
Discount rate
−40
4.875%
1.0%
−60
−80
(−5, NA) (−2, NA) (1, NA)
(4, NA)
(7, NA) (10, NA) (−2, −5)
(1, −5)
(4, −5)
(7, −5)
(10, −5) (−2, −4)
(1, −4)
(4, −4)
(7, −4)
(10, −4)
Strategy ID
100−year annual damage versus strategy cost, 75th percentile
Equiv. annual 100−year damage ($M 2009)
1.0% discount rate
●
0.005
●
−0.005
● (NA, NA)
● (−5, NA)
● (−2, NA)
●
●
●
●
0.000
Strategy ID
4.875% discount rate
●
●
●
●
●
●
● (10, NA)
●
●
●
●
●
●
●
●
1.0
1.5
2.0
0.0
● (−2, −4)
● (4, −4)
● (7, −4)
0.5
Equiv. annual strategy cost ($M 2009)
A-74
● (10, −5)
● (1, −4)
●
●
0.5
● (1, −5)
● (7, −5)
●
0.0
● (−2, −5)
● (4, −5)
●
●
●
● (4, NA)
● (7, NA)
●
●
●
●
● (1, NA)
1.0
1.5
2.0
● (10, −4)
Appendix B
Additional citywide strategy comparisons
Introduction
This appendix shows additional comparisons for the citywide non-structural risk mitigation strategies described in Chapter 6. In particular, results from citywide comparisons using the government (4.875%) discount rate are presented for comparison with the long-term discounting results discussed in the chapter text.
The appendix also shows estimates of equivalent annual implementation costs—not specifically discussed in
Chapter 6—for both the government and long-term discount rate assumptions.
●
●
Residual damage (equivalent $B, 4.875% discount rate)
●
●
15
●
●
●
●
●
●
●
●
●
●
●
●
●
●
10
●
●
●
●
●
●
●
●
●
●
●
Citywide strategy
●
No action
●
E−NB−25
●
●
●
●
E−NB−50
E−NB−75
C−NB−25
●
C−NB−50
●
●
C−NB−75
E−100−BAL
E−100−MAX
C−100−BAL
5
C−100−MAX
●
●
●
100
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
E−MAX
●
●
C−MAX
●
●
●
●
400
1000
Recurrence interval (yrs.)
Figure B.1: Residual equivalent annual damage from citywide strategies, by recurrence interval (2009 $
billions, 4.875% discount rate). 10-year damage not shown.
B-1
Managing New Orleans Flood Risk
●
●
Net Benefit (2009 $B, 4.875% discount rate)
0
●
●
Appendix B
●
●
●
●
●
●
●
●
●
●
−5
−10
●
−15
No action
E−NB−25
E−NB−50
E−NB−75
C−NB−25
C−NB−50
C−NB−75
E−100−BAL E−100−MAX C−100−BAL C−100−MAX
E−MAX
C−MAX
Citywide strategy
Figure B.2: Net benefit from citywide strategies across 255 uncertain scenarios (2009 $ billions, 4.875%
discount rate).
Regret (2009 $B, 4.875% discount rate)
15
●
10
●
5
0
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
No action
E−NB−25
E−NB−50
E−NB−75
C−NB−25
C−NB−50
C−NB−75
E−100−BAL E−100−MAX C−100−BAL C−100−MAX
E−MAX
C−MAX
Citywide strategy
Figure B.3: Regret from citywide strategies, derived from the net benefit metric, across 255 uncertain scenarios (2009 $ billions, 4.875% discount rate).
B-2
Equiv. annual mitigation costs (2009 $M, 4.875% discount rate)
Managing New Orleans Flood Risk
Appendix B
1000
Citywide strategy
No action
E−NB−25
800
E−NB−50
E−NB−75
C−NB−25
600
C−NB−50
C−NB−75
E−100−BAL
400
E−100−MAX
C−100−BAL
C−100−MAX
E−MAX
200
C−MAX
0
No action E−NB−25 E−NB−50 E−NB−75 C−NB−25 C−NB−50 C−NB−75 E−100−BALE−100−MAXC−100−BALC−100−MAX E−MAX
C−MAX
Citywide strategy
Figure B.4: Equivalent annual citywide strategy costs (2009 $ billions, 4.875% discount rate).
Equiv. annual mitigation costs (2009 $M, 1.0% discount rate)
600
Citywide strategy
500
No action
E−NB−25
E−NB−50
400
E−NB−75
C−NB−25
C−NB−50
300
C−NB−75
E−100−BAL
E−100−MAX
200
C−100−BAL
C−100−MAX
E−MAX
100
C−MAX
0
No action E−NB−25 E−NB−50 E−NB−75 C−NB−25 C−NB−50 C−NB−75 E−100−BALE−100−MAXC−100−BALC−100−MAX E−MAX
C−MAX
Citywide strategy
Figure B.5: Equivalent annual citywide strategy costs (2009 $ billions, 1.0% discount rate).
B-3
Appendix C
Vulnerability analysis using the government
discount rate
Introduction
This appendix provides additional vulnerability analysis results not discussed in Chapter 7. Specifically, it
presents results from the same analysis steps described in the chapter, but using the government discount rate
(4.875%) rather than the long-term assumption (1.0%). As in Chapter 7, the candidate strategy E-NB-50 is
first tested to determine under what conditions other approaches would be preferred when considering net
economic benefit. Next, candidate strategy C-NB-50 is investigated in order to identify conditions that lead
to high residual 100-year damage with mitigation in place. The acceptability thresholds chosen are slightly
different than those used in the long-term discount rate investigation, but policy-relevant scenarios identified
are similar to those described in the Chapter 7 discussion.
C-1
Managing New Orleans Flood Risk
Appendix C
When are elevation-only mitigation approaches vulnerable?
Using the government discount rate, one policy-relevant scenario was identified. The single box generated
using the PRIM analysis captures 66% of the relevant cases (coverage) with a density of 80%.
Number of scenario outcomes
60
50
40
30
20
10
0
0
50
100
150
200
250
300
Regret (2009 $M, 4.875% discount rate)
Figure C.1: Histogram of regret for E-NB-50 across 255 uncertain scenarios, 4.875% discount rate. The
vertical red line shows the 70th percentile value used as the acceptability threshold in the PRIM analysis.
Performance metric
Parameter restrictions
Regret (from net benefit)
Coastal degradation by 2060 > 14%
Buyout cost multiplier < 1.07
Buyout/easement enforcement = 1
Policy-relevant
scenario statistics
Interest: 30% (77 of 255)
Coverage: 66% (51 of 77)
Density: 80% (51 of 64)
Table C.1: PRIM results summary for the high-regret policy-relevant scenario identified using the 4.875%
discount rate. The threshold for poor performance is the 70th percentile of regret for strategy E-NB-50.
C-2
Managing New Orleans Flood Risk
Appendix C
What conditions lead to high residual damage with mitigation in place?
The single high-damage policy relevant scenario identified using the government discount rate is very similar
to the scenario described in Chapter 7, with the exception that participation rate is not included as a key driver.
Final statistics are shown in the summary table.
Number of scenario outcomes
35
30
25
20
15
10
5
0
1
2
3
4
Equivalent annual 100−year damage (2009 $B, 4.875% discount rate)
Figure C.2: Histogram of equivalent annual 100-year damage for citywide strategy C-NB-50 across 255
uncertain scenarios, 4.875% discount rate. The vertical red line shows the 70th percentile value used as the
vulnerability threshold in the PRIM analysis.
Performance metric
Parameter restrictions
100-year equiv. annual damage
Coastal degradation by 2060 > 33%
System maintained = 0
Residential growth rate > −227 homes/year
Policy-relevant
scenario statistics
Interest: 30% (77 of 255)
Coverage: 74% (57 of 77)
Density: 92% (57 of 62)
Table C.2: PRIM results summary for the policy-relevant scenario identified using the government discount
rate. The threshold for poor performance is the 70th percentile of 100-year equivalent annual damage for
strategy C-NB-50.
C-3