Online Resource 2: Methodology for Estimating Flooding
Damages
Climatic Change article:
Benefits of Greenhouse Gas Mitigation on the Supply, Management,
and Use of Water Resources in the United States
K. Strzepek, J. Neumann, J. Smith, J. Martinich, B. Boehlert, M. Hejazi, J. Henderson, C.
Wobus, R. Jones, K. Calvin, D. Johnson, E. Monier, J. Strzepek, J.-H. Yoon
Corresponding author:
Kenneth Strzepek
Joint Program on the Science and Policy of Global Change
Massachusetts Institute of Technology, Cambridge, MA
strzepek@mit.edu
Methodology for Estimating Flooding Damages
The following section provides additional detail on the methodologies used to model impacts in
analyses described in the paper. This information is provided to supplement information
included in the paper.
Changes in flood damages are particularly difficult to estimate at a national scale, since flood
damages are often localized and driven by extreme events. As a scoping-level assessment
consistent with the other analyses in this paper, we estimated changes in flood damages
following the approach outlined by Wobus et al. (2013). This approach leverages the historical
record of damaging floods and measured precipitation in the U.S. to establish statistical
correlations between the probability of a damaging flood occurring and the antecedent
precipitation conditions for that location. The method involves four steps:
1. Gather available empirical data on inland floods for the 1993-2008 period (from the National
Climatic Data Center). The historical data reflects 17,000 floods with total damages of $26.3
billion.
2. Gather precipitation data from the same time period and the same locations.
3. Formulate logistic regression model of flood damages as a function of antecedent
precipitation conditions.
4. Estimate changes in precipitation using Bias Corrected IGSM-CAM results and revise the
damage model using perturbed precipitation values.
The database compiled for this report includes a total of 17,000 flood events between 1993-2008,
and daily precipitation data from 100-200 meteorological stations in each assessment sub-region
(ASR). Based on the historical record, we found that nearly all of the monetary damages from
flooding (98% of total damages nationwide) resulted from the uppermost quartile of damaging
events. As a result, we developed a logistic regression model to estimate the probability of a
>75th percentile event occurring in each ASR in the U.S., and aggregated these results up to the
U.S. Geological Survey (USGS) Water Resource Region (WRR) level. In general, we found that
the season and the total measured precipitation falling in the ASR in a given day were the most
significant factors in explaining the probability of a damaging flood occurring (see Wobus et al.,
2013).
Using the region-specific logistic regression parameters established from the historical record,
we then asked how the historical record of damages in each WRR would have changed if the
observed time series of precipitation events had been perturbed by a series of monthly deltas
determined from future GCM output. Average total damages for both historical and future
climates were then calculated using a Monte Carlo approach, in which damages from each
modeled flood were picked from the uppermost quartile of observed events for that WRR in
10,000 model-year simulations. Damages were updated to future years by estimating changes in
per capita real income, following the approach of Neumann et al. (2010).
This approach inherently assumes that average and extreme precipitation will be equally affected
by climate change; thus if average monthly precipitation increases (decreases) by 10%, the
extremes in that month are also assumed to increase (decrease) by 10%. This assumption is
almost certain to underestimate changes in extreme precipitation, since the literature suggests
that the extreme ends of the precipitation distribution are likely to change more than the averages
(e.g., Katz and Brown, 1992; Easterling et al., 2000; Meehl et al., 2000; Das et al. 2011). Our
approach also assumes that the distribution of nominal flood damages, and the deployment of
infrastructure that can affect flood damages (in both negative and positive directions), is
stationary in time, which could either overestimate or underestimate damages depending on what
is assumed about future mitigation and flood defense measures (e.g., Pielke and Downton, 2000;
Choi and Fisher, 2003). For these reasons, our national-scale estimates of changes in flood
damages should be considered order-of-magnitude estimates, pending further work.
Based on the core IGSM-CAM model, the regional patterns of changes in flood damages were
similar for the policy and reference scenarios, and for the 2050 and 2100 time periods (Figure 1
a-c). For example, statistically significant increases in flood damages occur in all scenarios in the
Ohio River valley and Texas-Oklahoma regions, and significant decreases in projected damages
happen in all scenarios in California.1 The fraction of the country projected to have increases in
flood damages also increases with time. Policy scenarios decrease the spatial extent of damages
in both time periods, with particularly pronounced differences between policy and reference
scenarios in 2100. For example, while the reference scenario projects statistically significant
increases in damaging flooding (at the 90% confidence level) for 10 of the 18 water resource
regions in the United States in 2100, the Policy 3.7 scenario reduces this number to 6.
Increased climate sensitivity increases the fraction of the country where flood damages are
projected to increase. Under the IGSM-CAM reference scenario with a 6ºC climate sensitivity,
nearly the entire country is projected to have statistically significant increases in damages by
2100 (not pictured). The only exceptions to this result are New England, the northwest, and the
uppermost Midwest (where there was no significant difference between current and projected
damages) and California (where damages were projected to decrease by 2100).
In addition to projecting locations where damages are projected to increase or decrease, the
model also compiles changes in total monetary damages from flooding by region. Figure 2
shows results by WRR, and Table 1 shows the changes in monetary damages from flooding in
each WRR.
1
The details of the statistical tests conducted are included in Wobus et al. (2013).
References
Choi, O. and A. Fisher (2003) The impacts of socioeconomic development and climate change
on severe weather catastrophe losses: Mid-Atlantic Region and the U.S. Climatic Change 58(1–
2):149–170.
Das, T., Dettinger, M.D., Cayan, D.R., Hidalgo, H.G., 2011. Potential increase in floods in
California's Sierra Nevada under future climate projections. Climatic Change, 109 (SUPPL. 1):
71-94.
Easterling, D.R., Meehl, G.A., Parmesan, C., Changnon, S.A., Karl, T.R., and Mearns, L.O.
(2000). Climate extremes: observations, modeling, and impacts. Science, 289(5487), 2068-2074.
Katz, R.W., and Brown, B.G. (1992). Extreme events in a changing climate: variability is more
important than averages. Climatic Change, 21(3), 289-302.
Meehl, G.A., Zwiers, F., Evans, J., Knutson, T., Mearns, L., and Whetton, P. (2000). Trends in
extreme weather and climate events: issues related to modeling extremes in projections of future
climate change. Bulletin of the American Meteorological Society, 81(3), 427-436.
Neumann, J.E., D.E. Hudgens, J. Herter, and J. Martinich (2010) Assessing sea-level rise
impacts: a GIS-based framework and application to coastal New Jersey. Coastal Management,
38:4, 433-455.
Pielke Jr., R.A. and M.W. Downton (2000) Precipitation and damaging floods: Trends in the
United States, 1932–97. Journal of Climate 13:3625–3637.
Wobus, C., Lawson, M., Jones, R., Smith, J., and Martinich, J., (2013). Estimating monetary
damages from flooding in the United States under a changing climate. Journal of Flood Risk
Management. DOI: 10.1111/jfr3.12043
Figure 1 Changes in Flood Damages for Climate Policy
Figure 2: Distribution of Benefits of Policy 3.7 by WRR for CAM,
CCSM, and MIROC Climate Models
Table 1: Flood model results, annual estimates in 2100 of the
benefits of Policy 3.7 (Reference minus Policy 3.7, million 2005$)
Region
New England
North Atlantic
Southeast
Great Lakes
Ohio River
Tennesee Valley
Upper MS
Lower MS
Upper Upper MS
N Great Plains
S Great Plains
Texas
New Mexico
Colorado
Arizona
Great Basin
Northwest
California
CAM
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
TOTAL
$
Without Property Value Adjustment (nominal)
CCSM
MIROC
3.4 $
6.0 $
3.7
186.6 $
178.7 $
104.6
67.0 $
24.4 $
(10.6)
87.1 $
75.6 $
18.1
184.8 $
140.2 $
53.9
36.1 $
28.3 $
20.9
30.8 $
15.1 $
2.2
37.7 $
11.4 $
(0.6)
5.3 $
1.8 $
(0.0)
62.5 $
30.6 $
(4.8)
63.6 $
25.7 $
(13.4)
422.4 $
14.6 $
(78.3)
38.3 $
0.1 $
(4.2)
(29.9) $
25.8 $
(8.8)
(129.6) $
6.5 $
(89.5)
8.1 $
23.4 $
(2.9)
(0.3) $
1.2 $
0.3
(3.5) $
(0.9) $
(4.6)
1,070.3 $
608.7 $
CAM
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
(14.1) $
With Property Value Adjustment (nominal)
CCSM
MIROC
7.9 $
13.8 $
427.3 $
409.4 $
153.6 $
55.8 $
199.5 $
173.3 $
423.2 $
321.1 $
82.7 $
64.9 $
70.6 $
34.6 $
86.2 $
26.1 $
12.1 $
4.2 $
143.1 $
70.1 $
145.7 $
58.9 $
967.4 $
33.5 $
87.6 $
0.3 $
(68.6) $
59.1 $
(296.8) $
15.0 $
18.5 $
53.5 $
(0.6) $
2.8 $
(8.1) $
(1.9) $
2,451.4 $
1,394.3 $
8.5
239.5
(24.2)
41.4
123.4
47.8
5.1
(1.3)
(0.1)
(10.9)
(30.8)
(179.4)
(9.7)
(20.1)
(205.1)
(6.6)
0.8
(10.6)
(32.3)