Irish pork
Columbia Water Center, IRI
Upmanu Lall
Global Flood Initiative

Hirschboeck, 1988

• What is a flood? : river out of banks and inundates area for some duration
• Design/Insurance: Estimate T-year flood using at site runoff or rainfall-runoff data
• Annual Max data or Partial duration series
• Regional Flood – use multiple locations to improve at site T-year estimates
• Loss estimates – typically direct physical loss for flood impacted area
• Operation/Warning : Map QPF into event flood peak, volume, duration prediction using hydro-models
• Hydraulic analyses to map flood plain for zoning
• Retrospective analysis of Synoptic Meteorology/Climate state associated with floods
_______________________________________________________________________
Mixtures? Climate Mechanisms? Duration, intensity, recurrence attributes?
Hirschboeck, Paleo-floods work, ENSO/interannual variability in flood incidence

Flooding affects more people worldwide than any other form of natural disaster. And yet insurance cover against the risk of flooding is not widespread (locally correlated risk).
- Swissre

Climate
Characteristics
Characteristics of Catchment/
Stream/
Floodplain
System
FLOOD FLOW
ESTIMATES
FLOOD LEVEL
ESTIMATES
Other Flood
Characteristics
Flood
Probabilities
Water Resource
Development + Use
Agricultural
Land Use
Urban / Industrial
Development
Socio-Economic
Values,
Environmental
Values,
Regulatory
Provisions,
Community
Attitudes
Flood Vulnerability FPM Goals
Flood
Hazard
FLOOD
RISK
Floodplain
Management
Strategies,
Flood Design

Flood: Atmospheric and terrestrial concentration of water flux into certain regions, that leads to multiple locations with inundation over a period of time?
• How do specific climate mechanisms lead to floods at different space-time scales across the world – conditional quantification using local, regional and global factors?
• IID : Fat tails or identifiable nonstationary, mixtures?
• Dynamics : Persistent climate state  high frequency space-time precipitation dynamics  with river basin topology and hydrologic dynamics: linked spatio-temporal stochastic models
• A dynamic risk rather than static risk paradigm, including its spatial implications
• Dynamic  time scales, lead times, space scales
• Shift from purely watershed/river basin perspective to ocean-atmosphere pathways: Local correlation structure vs global or far field correlation structure – inferred from dynamical models?
Global Impacts and Decisions :
• Persistent and delayed socio-economic and health impacts in addition to direct physical loss
• Global Supply Chains
• Insurance, and infrastructure design/operation considering cumulative impacts and risk layering
• Disaster response

AUSTRALIAN FLOODS IMPACT GLOBAL SUPPLY CHAINS
• The impact of the devastating floods in Queensland will be felt through global supply chains for many months to come. Almost 70% of global steel production depends on metallurgical or coking coal. Australia produces two-thirds of global exports of coking coal, of which Queensland accounts for 35%.
• Fears over coal supplies as Australia floods worsen
• More heavy rainfall causes exports of coal, wheat and sugar to significantly decline as country left underwater
• Coal supply
Australia is the world's largest exporter of coking coal, supplying half the global market. used to produce steel, and operators of around 40 mines have been affected by the floods.
• The supply of wheat, of which Australia is the world's fourth biggest exporter, has also been hit.
• Australia floods to squeeze India steel cost margins - CRISIL Reuters

• The floods have had a significant impact on Pakistan's nascent textile industry. Local business associations have estimated that the destruction has destroyed three million bales of cotton. As a consequence, the cost of clothes production within the country will rise by 20%. With apparel buyers seeking to stock inventories for the Christmas sales, companies are concerned over the viability of the Pakistan supply chain to deliver sufficient volume on time and on budget. Indeed, many orders have been re-directed to suppliers in Bangladesh and Sri Lanka. Already, export orders have declined by 7-10%, and this could fall by a further 30%.
• The FT reports that clothing companies such as Levi Strauss and UK-based Next have warned of inflating clothing prices .

Climate
Change
Anthropogenic
“Natural”
Abrupt
“Smooth”
Predictable
Dynamic
Risk
Unpredictable
Long Term
Statistics
Near Term
Evolution
Adaptive Operation &
Allocation
Early Warning Systems
Infrastructure Design
Allocation/Operation Rules
Pizarro, Lall and Atallah, Env Finance 10(10), 2009
Residual
Risk
Financial
Instruments:
Insurance
Cat Bonds
Relief

• Floods associated with large scale circulation patterns
• Meridional and Zonal Moisture Transport and Convergence
• Spatial Incidence of Floods  regions with high potential
• Identifiable low frequency forcing….ENSO etc
• Prediction? Hierarchical Bayesian Models of Floods
• Area Scaling
• Covariates
• Diagnosis of Large floods in a region
• Ohio River Basin

Columbia Water Center Global Flood Initiative

Hypothesis: Meridional water vapor transport changes drive latitudinal shifts in flood incidence
JFM

Columbia Water Center Global Flood Initiative

2002 JJA t u d e i t
L a
Year 2001 2002
Longitude
2003 2004 2005 2006 2007 2008 2009 2010
No. of floods 60 110 100 69 60 77 91 56 47 52

2003 JJA t u d e i t
L a
Year 2001 2002
Longitude
2003 2004 2005 2006 2007 2008 2009 2010
No. of floods 60 110 100 69 60 77 91 56 47 52

2004 JJA t u d e i t
L a
Year
Longitude
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
No. of floods 60 110 100 69 60 77 91 56 47 52

2009 JJA t u d e i t
L a
Year
Longitude
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
No. of floods 60 110 100 69 60 77 91 56 47 52

2010 JJA t u d e i t
L a
Year
Longitude
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
No. of floods 60 110 100 69 60 77 91 56 47 52

2001
2005
2008
2002
2004
2006
2010
2009
2003
2007
Latitude

Flood Magnitude depends on Area (Scaling law)
Flood magnitude may depend on a pre-season climate covariate
Can we predict conditional flood distribution at gaged/ungaged locations?
A Hierarchical Bayesian Model -- Lima and Lall, 2010

• Daily naturalized series of 37 sites (Parana basin)
• Provided by ONS – Period 1931-2001
• Homogeneous sub-basins re climate (ENSO and SACZ)
Location of Basin in Brazil
Location of streamflow sites (red dots are testing sites)

• Simple Scaling Law: log(flow moments) ~ log(drainage area)
E [ Y
 h
]
  hk
E [ Y
1 h
] log E [ Y
 h
]
 hk log
  log E [ Y
1 h
]
• Hierarchical Bayesian Model: event based scaling log( q ij
) ~ N (

0 i
 
1 i x ( j ),

2
) x ( j )
 log( A ( j ))
 log( A )
• Priors Climate index: NINO3 DEC(-1)



0 , i

1 , i

 ~ N




10
20




11 y ( i )
21 y ( i )
,


 , i

1 ,  , N p (

)

1 .
• Hyperpriors (uniform) p (

10
,

11
,

20
,

21
)

1 p
 

( d

1 ) / 2
, d

2 .

Drainage areas varying from 2588 to 823555 km 2
Testing sites

Slopes are statistically significant!

r=0.74
r=0.71
r=0.66

Q* such that P(Q(t) > Q*) = 0.01

Inverse Problem: I see a big flood….how did it get here
A very few selected examples out of many diagnostic ventures

Atmospheric Moisture Transport associated with one of the top 10 floods at different locations
Source: Hyun-Han Kwon
Columbia Water Center Global Flood Initiative

Nakamura et al, Dec 2010 AGU
Columbia Water Center Global Flood Initiative

Columbia Water Center Global Flood Initiative

Columbia Water Center Global Flood Project

Columbia Water Center Global Flood Project

Columbia Water Center Global Flood Initiative

Columbia Water Center Global Flood Project

Columbia Water Center Global Flood Initiative

Columbia Water Center Global Flood Initiative

Columbia Water Center Global Flood Initiative

Columbia Water Center Global Flood Project

Columbia Water Center Global Flood Initiative

Columbia Water Center Global Flood Initiative

• Invitation to develop global flood risk initiative
• An Open Source Risk Modeling & Mitigation Effort – Climate to
Impacts to Response
• The design and exploration of a statistical-dynamical approach for the short (-5 to 10 days) and long lead (> 1 month) prediction, and for the conditional simulation of such events using climate (model) states.
• Inverse/Forward Modeling and Prediction at various lead times appears possible enabling dynamic risk management
• Spatio-temporal causal structure at large and fine scales needs to be identified and modeled (joint flood/drought incidence/extent)
• Integrating storm track dynamics and drainage network response including infrastructure
• Loss dynamics – composite events, delayed and far field losses
• Mitigation: Risk Layering, Response and Recovery Design
Columbia Water Center Global Flood Initiative