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https://eos.org/opinions/millions-more-americans-face-flood-risks-than-previously-thought

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Millions More Americans Face Flood Risks Than Previously Thought

A different modeling approach fills large gaps in the U.S. governmentâ??s
flood risk estimates, revealing previously overlooked at-risk areas often
surrounding small flood-prone streams.
A Spring Lake, N.C., resident is carried from her flooded home on 17
September 2018, in the aftermath of Hurricane Florence.
An 84-year-old resident of Spring Lake, N.C., is carried from her flooded
home on 17 September 2018. The home flooded when the Little River, a small
tributary to the Cape Fear River, crested its banks after Hurricane
Florence made landfall. Credit: Joe Raedle/Staff/Getty Images News/Getty
Images

By Oliver Wing, Paul Bates, Christopher Sampson, Andrew Smith, Joseph
Fargione, and Kris Johnson 15 hours ago

Over the past week, the United States saw floodwaters rise near the coast
of North Carolina in the aftermath of Hurricane Florence. Swollen rivers
have effectively cut off Wilmington, a city of some 119,000 residents, and
residents in surrounding regions are being ordered to evacuate as rivers
continue to rise and test the strength of dams. Thus far, the storm has
claimed 36 lives.

The cost in lives and property damage from Florence will take years to
assess; initial estimates suggest that Florenceâ??s damage could reach $30
billion. Add this to last yearâ??s triumvirate of devastating U.S.
hurricanesâ??Harvey, Irma, and Mariaâ??which saw a combined death toll of
3,100 and damages estimated to be $275 billion. Not surprisingly given
these events, decision-makers and the American public are focusing on
issues related to flooding from hurricanes and other sources.

FEMA mapping currently covers only about 60% of the conterminous United
States and may not represent headwater areas and smaller floodplains.
To mitigate potential losses from floods yet to come, we first need an idea
of where these damaging floods can occur. In the United States, this
information is provided by the Federal Emergency Management Agency (FEMA),
which produces maps of flood zones to help enforce regulations under the
National Flood Insurance Program.

The approach FEMA takes involves developing separate hydraulic models for
individual river reaches and then stitching the model outputs together to
generate a nationwide view. This traditional â??bottom-upâ?? approach is
the gold standard in flood inundation modeling.

But although the approach allows important local details to be captured, it
also requires significant resources and time to accomplish. Also, there is
no efficient way to rerun a given simulation to incorporate new data or
test new scenarios. As a result, FEMA mapping currently covers only about
60% of the conterminous United States (CONUS), and the maps may not
represent headwater areas and smaller floodplains.

You may ask, Surely thereâ??s a better way to evaluate flood risks? The
short answer is yes. It involves a â??top-downâ?? approach, harnessing big
data to automatically create flood inundation models from local to global
scales.

A New Approach to Calculating the 100-Year Floodplain

Over the past 5 years, researchers across the globe have started to develop
a set of these alternative top-down approaches to flood inundation modeling
over vast areas, taking advantage of increasingly available large data sets
and high-performance computing resources. These methods take available
digital elevation models (DEMs), river hydrography, and gauging station
data and use them to automatically create flood inundation models of whole
regions, countries, or even the world.

Such approaches do not currently outperform local bespoke modeling, but
many flood management questions can be answered only by consistent flood
maps with the type of complete coverage that these top-down methods
produce. For many flood management questions, it may also be acceptable to
sacrifice a small amount of local accuracy to achieve a national-scale view.

We recently conducted a study [Wing et al., 2018] to quantify the number of
people exposed to flooding in the CONUS using just such a top-down
approach. Our team spanned both sides of the Atlantic, from the University
of Bristol and Fathom (a flood analytics company) in the United Kingdom and
The Nature Conservancy and the Environmental Protection Agency in the
United States.

(top) A new approach shows that 3 times more Americans are exposed to a
1-in-100-year flood than (bottom) FEMAâ??s estimate.
Fig. 1. (top) A large-scale modeling approach [Wing et al., 2018] estimates
the nationwide distribution of people exposed to a 1-in-100-year flood to
be about 3 times the estimate arrived at by (bottom) a local FEMA model
approach.
The regulatory flood zone mapped by FEMA captures the areas of land that
have a 1% or greater chance of being inundated in any given year, or to put
it another way, areas that would be expected to experience at least one
flood every 100 years. Using bottom-up approaches to calculating population
exposure (i.e., within these FEMA flood zones), we estimated that 13
million people are at risk of this so-called 100-year flood (Figure 1).

Our transatlantic team performed the same calculation with an automated,
large-scale hydraulic model covering the entire CONUS at ~30-meter
resolution [Wing et al., 2017]. We found that almost 41 million
peopleâ??more than 3 times the FEMA estimateâ??are exposed to the
possibility of a 100-year flood [Wing et al., 2017; Smith et al., 2015;
Sampson et al., 2015].
Comparing the Flood Maps

Both flood maps are the output of hydraulic models that in their simplest
sense receive input in the form of an extreme river flow. The models then
work out how this translates to flood extent on the ground.

Two key pieces of information are required: the magnitude of the extreme
river flow and the elevation of the adjacent land. The input to both
models, the 1-in-100-year maximum discharge for a particular stretch of
river, is calculated on the basis of historic annual river gauge records.
The lay of the land is determined by a DEM. The quality of DEMs varies
across the United States; the majority of densely populated areas are
covered by highly accurate airborne laser scanning (lidar) data, with
correspondingly sparser coverage in less populated areas.
Flood map in Illinois shows FEMAâ??s tendency to focus on larger rivers,
missing flood risks associated with smaller streams.
Fig. 2. A typical snapshot of the flood maps produced by each model
approach for an area of Illinois demonstrates FEMAâ??s tendency to focus on
larger rivers, missing the flood risk associated with smaller streams. Our
modeling method, on the other hand, captures a finer level of detail.

Thus, although both FEMAâ??s bottom-up approach and our top-down approach
use the same input, the two methods produce different estimates of exposed
populations. Examining the simulation results obtained using both
approaches highlights why the discrepancy in flood-exposed population
estimates arises. Unlike the FEMA map, our map is able to estimate flood
hazard in all basins down to just a few square kilometers in area, thus
capturing the risk posed by smaller streams (Figure 2).

How does our top-down approach achieve a higher precision than FEMAâ??s
bottom-up approach? Our approachâ??s ability to represent smaller streams
is not by virtue of obtaining better or more localized data. Rather, our
spatially consistent, automated, and efficient model-building process,
coupled with our computational capacity, can produce flood maps in every
river basin. In contrast, FEMAâ??s highly detailed approaches take skilled
operators several months to perform for river reaches a few tens of
kilometers in length, and this is why spatial coverage is relatively low.
FEMA engineers triage the larger streams in a catchment, often to the
detriment of the headwaters, whereas our approach captures conditions in
the headwater areas as well.

A map comparing our large-scale model with FEMAâ??s model for an area of
Alabama.
Fig. 3. A map comparing our large-scale model with FEMAâ??s model for an
area of Alabama. The two approaches produce similar results here, where the
FEMA data exist, with our large-scale floodplain model capturing more than
90% of the FEMA floodplain with minimal genuine overprediction. From Wing
et al. [2017].
The caveat here is that our approach sacrifices some local accuracy to
achieve this broad view of flood hazard across the CONUS. In areas where
FEMA models do contain detailed surveys of flow-controlling structures
(e.g., levees, culverts, dikes, bridges), these models will likely
supersede the large-scale alternative. However, a study that performed a
point-by-point comparison between the FEMA model and large-scale models
suggests that where FEMA maps exist, the two approaches often produce
similar results (Figure 3).

Compared to high-quality FEMA flood maps, the large-scale model captured
86% of the specified floodplain [Wing et al., 2017]. The correspondence was
even better on larger rivers, in temperate climates, and in more rural
basins. This top-down model therefore fills in the areas that the bottom-up
FEMA maps miss, such as areas around small streams and across broad swaths
of the midwestern and northwestern United States.

We find that this data gap equates to an extra 28 million Americans living
in the 100-year flood zone. This difference is so large that even amid
likely model uncertainties, it is a significant result.
Implications for Flood Risk Management in the United States

Scientists and policy makers require comprehensive floodplain mapping to
manage U.S. flood risk in a coordinated way. Accurate, engineering-grade
flood models in every river basin in the CONUS would be ideal, but given
realistic financial constraints, it is clear that we need new, affordable
approaches to generating comprehensive national maps.

Through a large-scale modeling approach, nationwide flood maps can be
produced quickly and relatively cheaply.
An understanding of flood risk across the nation, rather than in patches,
could lead to fewer surprises akin to Hurricanes Harvey in Texas and Irma
in Florida. Such a comprehensive understanding could also assist opponents
of continued development in risky areas and ensure that the culture of
preparedness FEMA wishes to build becomes a reality across the country.

In addition, a large-scale modeling approach can answer a number of crucial
questions that cannot be addressed by a patchwork of local models. Because
nationwide flood maps can be produced quickly and relatively cheaply, a
wealth of new products could be generated. For example, in addition to the
100-year floodplain, other return periods can be mapped, which enables
improved risk management through a graded system. With this information,
community authorities could perhaps seek to impose more stringent
regulations on buildings in the 1-in-20-year floodplain while relaxing
those in the 1-in-500-year floodplain.

This nuanced approach could also lead to more realistic risk assessments
and flood preparation measures. Right now, risk zones in maps are treated
as binary, which can be problematic: People who live just outside the
currently mapped 100-year flood boundary do not face considerably less risk
than people who live just within it. Yet new development tends to cluster
around these boundaries, often still in risky areas. Maps that contour
risks in a more detailed way could prevent this effect through ensuring a
smoother gradient of development increase as you move farther outside of
the higher risk zones.
Flood waters from rising rivers inundate the town of Trenton, N.C. on 16
September 2018, following Hurricane Florence.
Floodwaters from rising rivers and streams inundate the town of Trenton,
N.C., on 16 September 2018, following Hurricane Florence. Credit:
Associated Press/Steve Helber

Large-scale and automated modeling approaches offer flexibility as well:
The ability to rerun multiple flood maps allows modelers to test the effect
of different scenarios by perturbing inputs to the model. For example, the
effects of climate change, population growth, land use change, and flood
risk management scenarios can be included for making decisions on a
national scale.

Large-scale models wonâ??t completely replace the bottom-up approach:
Certain decisions can only be made using accurate local modeling
techniques. There is unambiguous value to engineering-grade local models.
Such models are needed, for example, when exploring the effect of
installing or removing levees. Highly localized decisions will still
require a FEMA-style analysis.
What Next for Hydraulic Modelers?

Flood inundation models, two types of which are outlined here, are subject
to fundamental constraints with regard to their accuracy. The performance
of FEMA-style models is approaching a ceiling: The characterization of
extreme flows often imposes a fundamental limit on further model
improvements. Errors of 25% or more are common in gauge observations [Di
Baldassarre and Montanari, 2009], and there are gaps in the data required
for the statistical analysis used to calculate flow return periods [Smith
et al., 2015].

There is little value to a centimeter-scale hydraulic model if you donâ??t
actually know how big the 100-year flow is.
For modeling results to be meaningful, any improvements in calculations
must be accompanied by commensurate improvements in the measured data and
other factors in the modeling approach. For instance, there is little value
to a centimeter-scale hydraulic model if you donâ??t actually know how big
the 100-year flow is.

This need for better data, coupled with the need for many more decades or
even centuries of river flow observations and an expanded river gauge
network, means that the flow characterization gap is not likely to be
resolved any time soon.

There is, however, still plenty of room for improvement in large-scale
modeling. For example, scientists have yet to generate seamless,
hyperresolution DEMs of entire continents, and the remote detection of
local-scale, hydraulically important features is not yet feasible. With
solutions to these problems on the horizon, it may not be long before
large-scale models, too, hit the ceiling imposed by extreme flow
characterization.

Despite these limitations, scientists assessing flood risk could expand and
deepen their insights using a multimodel approach similar to the approach
that climate scientists use. Also, on an international scale, the next big
step is to run flood models with data from the high-accuracy DEM of the
world. Creating a high-resolution global flood hazard model [Sampson et
al., 2015] could help identify at-risk populations in areas like Africa,
South America, and Southeast Asia that donâ??t have FEMA-style local flood
maps.
Floods of Data Can Help Address Flood Risk

To help protect people from floods, we will continue to need a deluge of data.
Without freely available data, we could not have conducted our assessments,
which means that we could not have identified that millions of Americans
face flood risks beyond those charted in FEMA maps. One thing, then, is
clear: To help protect people from floods, we will continue to need a
deluge of data.

Ultimately, better predictions also require better data, which is why
itâ??s so important that the United States continues to fund U.S.
Geological Survey (USGS) efforts to maintain river gauges and expand lidar
data coverage to areas in the United States that lack this coverage. And it
is equally important that the USGS continue to make these data public.
References

Di Baldassarre, G., and A. Montanari (2009), Uncertainty in river discharge
observations: A quantitative analysis, Hydrol. Earth Syst. Sci., 13,
913â??921, https://doi.org/10.5194/hess-13-913-2009.

Sampson, C. C., et al. (2015), A high-resolution global flood hazard model,
Water Resour. Res., 51, 7358â??7381, https://doi.org/10.1002/2015WR016954.

Smith, A., C. Sampson, and P. Bates (2015), Regional flood frequency
analysis at the global scale, Water Resour. Res., 51, 539â??553,
https://doi.org/10.1002/2014WR015814.

Wing, O. E. J., et al. (2017), Validation of a 30 m resolution flood hazard
model of the conterminous United States, Water Resour. Res., 53, 7968-7986,
https://doi.org/10.1002/2017WR020917.

Wing, O. E. J., et al. (2018), Estimates of present and future flood risk
in the conterminous United States, Environ. Res. Lett., 13, 034023,
https://doi.org/10.1088/1748-9326/aaac65.

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