Climate change impacts on flood risk essay

Climate change impacts on flood risk and asset damages within
mapped 100-year floodplains of the contiguous United States
Cameron Wobus1
, Ethan Gutmann2
, Russell Jones1
, Matthew Rissing1
, Naoki Mizukami2
, Mark Lorie1
Hardee Mahoney1
, Andrew W. Wood2
, David Mills1
, and Jeremy Martinich3
1Abt Associates, 1881 Ninth Street, Suite 201, Boulder, CO 80302, USA
2National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO 80301, USA
3US Environmental Protection Agency, Climate Change Division, 1200 Pennsylvania Ave NW,
Washington, DC 20460, USA
Correspondence to: Cameron Wobus ([email protected])
Received: 20 April 2017 – Discussion started: 24 April 2017
Revised: 27 October 2017 – Accepted: 27 October 2017 – Published: 8 December 2017
Abstract. A growing body of work suggests that the extreme weather events that drive inland flooding are likely
to increase in frequency and magnitude in a warming climate, thus potentially increasing flood damages in the future. We use hydrologic projections based on the Coupled
Model Intercomparison Project Phase 5 (CMIP5) to estimate changes in the frequency of modeled 1 % annual exceedance probability (1 % AEP, or 100-year) flood events at
57 116 stream reaches across the contiguous United States
(CONUS). We link these flood projections to a database of
assets within mapped flood hazard zones to model changes
in inland flooding damages throughout the CONUS over the
remainder of the 21st century. Our model generates early
21st century flood damages that reasonably approximate the
range of historical observations and trajectories of future
damages that vary substantially depending on the greenhouse
gas (GHG) emissions pathway. The difference in modeled
flood damages between higher and lower emissions pathways
approaches USD 4 billion per year by 2100 (in undiscounted
2014 dollars), suggesting that aggressive GHG emissions reductions could generate significant monetary benefits over
the long term in terms of reduced flood damages. Although
the downscaled hydrologic data we used have been applied
to flood impacts studies elsewhere, this research expands on
earlier work to quantify changes in flood risk by linking future flood exposure to assets and damages on a national scale.
Our approach relies on a series of simplifications that could
ultimately affect damage estimates (e.g., use of statistical
downscaling, reliance on a nationwide hydrologic model, and
linking damage estimates only to 1 % AEP floods). Although
future work is needed to test the sensitivity of our results to
these methodological choices, our results indicate that monetary damages from inland flooding could be significantly
reduced through substantial GHG mitigation.
1 Introduction
Inland floods are among the most costly natural disasters in
the United States (e.g., Pielke Jr. and Downton, 2000), with
annual damages ranging from hundreds of millions to many
tens of billions of dollars over the past century (Downton et
al., 2005; NOAA, 2016). In 2016, inland flooding events in
Louisiana and North Carolina alone caused over USD 10 billion of physical damages to homes, businesses, and other assets (Fortune, 2016; LED, 2016). This follows on other recent years with extreme flooding in Michigan (2014) and
Colorado (2013) and the mid-Atlantic floods caused by Superstorm Sandy (Hurricane Sandy) in 2012 (NOAA, 2016).
With each occurrence of these damaging flood events, there
is renewed interest in determining whether climate change
may be partially responsible for changes in the magnitude
or frequency of these events (e.g., IPCC, 2012; Trenberth
et al., 2015). Although the science linking changes in climate extremes to human-caused warming is advancing (e.g.,
Trenberth et al., 2015; National Academies of Sciences, Engineering, and Medicine, 2016), there are still many challenges to attributing observed historical trends in flooding
Published by Copernicus Publications on behalf of the European Geosciences Union.
2200 C. Wobus et al.: Climate change impacts on flood risk and asset damages
to human-caused climate change (e.g., Kundzewicz et al.,
2014; Berghuijs et al., 2016). As a complement to these attribution studies, forward-modeling approaches using linked
climate-hydrologic models could help to characterize future
changes in flood risk and vulnerability (e.g., Das et al., 2013;
Hirabayashi et al., 2013; Arnell and Gosling, 2016).
This study evaluates 21st century flood risk and floodrelated damages across the contiguous United States
(CONUS) using downscaled hydrologic projections from 29
global climate models (GCMs) and two representative concentration pathways (RCPs) for greenhouse gas (GHG) forcing. We cross-referenced spatially explicit hydrologic projections with a database of built assets within each of the
mapped 100-year floodplains in the CONUS. Using this
combined dataset, we generate regional estimates of how
cumulative damages from what are currently 1 % AEP (annual exceedance probability) events might change through
the 21st century, due to changes in the frequency of these
events through time. We then compare how flood damages
might differ under a higher GHG emissions scenario (RCP
8.5) vs. a lower emissions scenario (RCP 4.5). We focused
on these two RCPs both for consistency with the forthcoming Fourth National Climate Assessment (USGCRP, 2015)
and to help quantify changes in flood risk in response to reduced GHG emissions globally.
Because available hydrologic records tend to be short relative to the return interval of extreme flood events, simply detecting trends in historical flooding can be challenging (e.g.,
Hirsch and Ryberg, 2012; Mallakpour and Villarini, 2015;
Archfield et al., 2016). Furthermore, even where hydrologic
changes can be detected, concurrent changes in land use and
population make it difficult to attribute changing flood damages to climate change (e.g., Pielke Jr. and Downton, 2000;
Kundzewicz et al., 2014; Liu et al., 2015). Thus, there may
be some advantages to using forward-modeling approaches
where the effects of climate change can be modeled in isolation. Unfortunately, this is expensive computationally: at
present the most widely used strategy for assessing changes
in future flood risk requires downscaling GCM outputs to
hydrologically relevant spatial scales; estimating precipitation, infiltration, and runoff within a hydrologic-modeling
framework; and routing the resulting flows through a model
river network (e.g., Reclamation, 2014). Although less computationally demanding, studies attempting to link projected
changes in extreme precipitation directly to changes in flooding (i.e., without a spatially explicit hydrologic model) tend
to have high uncertainties (e.g., Kundzewicz et al., 2014;
Wobus et al., 2014).
Recently, computational power has increased to the point
that studies using downscaled and routed GCM-derived precipitation have become more common (e.g., Gosling et al.,
2010; Hirabayashi et al., 2008; Reclamation, 2014). These
outputs have been used to project future flood risk on
scales ranging from local (e.g., Das et al., 2013) to global
(e.g., Hirabayashi et al., 2013; Arnell and Gosling, 2016).
However, to our knowledge there has not yet been a CONUSscale assessment of how changing inland flood hydrology
could translate into changing monetary damages.
2 Methods
We used simulated daily hydrographs at 57 116 stream
reaches across the CONUS between 2000 and 2100 to calculate a CMIP5 modeled baseline (“current climate”) 1 %
AEP event and changes in the frequency of flows exceeding this magnitude through the 21st century. We quantified
asset exposure and expected flood damage within mapped
floodplains using a combination of Federal Emergency Management Agency (FEMA) flood maps, US census block data,
and land cover data. Because only the 100-year floodplains
are consistently mapped and available on a national scale,
our model of flood damages is driven only by changes in
the frequency of what are currently 1 % AEP events through
the 21st century. We also do not project changes in population growth, floodplain development, or flood protection
through time, since (1) such projections would require assumptions that would be difficult to apply on a national
scale across multi-decadal timeframes (e.g., Elmer et al.,
2012) and (2) the impacts of those assumptions might obscure the climate change signal we seek to characterize. Our
model projections should therefore be considered order-ofmagnitude estimates of how differences in emissions scenarios might propagate into changes in flood damages throughout the United States.
2.1 Hydrologic-modeling inputs
We used spatially and temporally disaggregated precipitation
and temperature at 1/8

resolution from 29 GCMs and two
emissions scenarios (RCP 4.5 and RCP 8.5), generated using the bias correction and spatial disaggregation (BCSD)
method (e.g., Wood et al., 2004). The BCSD method uses
a quantile mapping approach to match the distribution of
GCM-derived monthly outputs to the observed monthly data
at a 1◦
resolution in a historical period (1950–2000). It then
uses the spatial pattern of daily observations from an analog
month as a proxy for sub-grid-scale daily (temporal) variability and it scales or shifts these daily observations to ensure
that the analog monthly average values match the rescaled
GCM output. During the bias correction process (which applies to monthly precipitation and temperature values at the
GCM scale), projected precipitation values exceeding the upper end of the climatological range are extrapolated following an extreme value Type I distribution. Additional details
of the BCSD weather generation are given in Harding et
al. (2012) and Wood and Mizukami (2014).
Catchment hydrology was simulated using the variable
infiltration capacity (VIC) hydrologic model (Liang et al.,
1994) forced by the BCSD precipitation and temperature
Nat. Hazards Earth Syst. Sci., 17, 2199–2211, 2017
C. Wobus et al.: Climate change impacts on flood risk and asset damages 2201
fields. The VIC model simulates the range of hydrologic
processes relevant to generating runoff, including interception on the forest canopy, evapotranspiration, water storage
and melt from snowpack, infiltration, and direct runoff. The
runoff component of each model grid cell was remapped to
the hydrologic response units (HRUs) defined in the United
States Geological Survey (USGS) Geospatial Fabric (GF;
Viger and Block, 2014) and then routed through the GF river
network using the MizuRoute routing tool, which incorporates both hillslope and river channel processes (Mizukami
et al., 2016a). The GF dataset contains ∼ 57 000 river segments and ∼ 108 000 HRUs (including the right and left bank
of most river segments), representing catchments approximately equivalent in area to 12-digit Hydrologic Unit Code
basins. The methods used for the downscaling and land surface hydrology were identical to those used in previous studies (e.g., Das et al., 2013; Reclamation, 2014). However, for
this effort we used a multi-scale parameter regionalization
approach (Samaniego et al., 2010) to improve the spatial coherence of VIC model parameters across basin boundaries
(Mizukami et al., 2017). Nash–Sutcliffe efficiency coefficients indicate that the model adequately captures the magnitude and variability of observed flows across most of the
CONUS, while the updated VIC parameters remove some of
the artifacts that were observed from the Reclamation (2014)
dataset (see Sect. S1). Full details of the downscaling, VIC
model parameters, and routing methodologies are described
in Reclamation (2014) and Mizukami et al. (2017) and are
summarized in Sect. S1.
2.2 Modeling flood probability
For each of the 58 GCM–RCP combinations in the hydrologic model output, we extracted the time series of annual maximum flow between 1950 and 2099 at each of the
∼ 57 000 GF stream locations in the CONUS. Average annual maximum flows in the modeled reaches range from < 5
to > 1000 m3
(Fig. 1). Prior to generating statistics of
peak flows from these events, we plotted the normalized annual maximum time series across all segments and all models (Fig. 2). This plot revealed a step in the annual maximum
flow time series in the year 2000, which corresponds to the
end of the hindcast period used in the BCSD method. This
step is even more pronounced in the BCSD precipitation inputs (Fig. 3) and most likely reflects the change in how the
BCSD method constrains the distribution of events in the historical period compared to in the future period.
In order to prevent this artifact from influencing our analysis of future flooding events, we used an early 21st century ensemble average (2001–2020) to represent baseline
hydrologic conditions, rather than the more traditional late
20th century baseline. We calculated the magnitude of the
baseline modeled 1 % AEP flood event at each stream segment by fitting a generalized extreme value (GEV) distribution to the full ensemble of annual maximum flow estimates for each RCP over the 2001–2020 period (29 models × 20 years = 580 values) and extracting the 99th percentile value from this model fit. Although the emissions
pathways for RCP 4.5 and RCP 8.5 begin to diverge in 2006,
there were no systematic differences between GEV fits for
the two RCPs, justifying our treatment of this early 21st century period as a baseline across the full ensemble.
Individual GCMs exhibit a degree of dependence due
to shared code, shared scientific literature, shared observations, etc. and as such are not statistically independent (Abramowitz, 2010; Knutti et al., 2010b; Bishop and
Abramowitz, 2013). However, the consensus of the community remains that it is best to average across many ensemble
members (Tebaldi and Knutti, 2007; Knutti et al., 2010a) as
we have done here. From this full ensemble, we evaluated
uncertainty in the 1 % annual probability event by bootstrapping (see Sect. S2). Based on these analyses, we expect the
sample uncertainty on our 1 % AEP flood event to be in the
range of 5–20 %. As shown later, the variability in the multimodel GCM ensemble is much larger than this uncertainty
in the GEV fits, so we did not propagate this source of uncertainty through all of our calculations.

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