US flood damage risk is underestimated

Date:
February 22, 2022
Source:
North Carolina State University
Summary:
Researchers found a high probability of flood damage -- including
monetary damage, human injury and loss of life -- for more than
a million square miles of land across the United States across a
14-year period.



FULL STORY ==========================================================================
In a new study, North Carolina State University researchers used
artificial intelligence to predict where flood damage is likely to happen
in the continental United States, suggesting that recent flood maps from
the Federal Emergency Management Agency do not capture the full extent
of flood risk.


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In the study, published in Environmental Research Letters, researchers
found a high probability of flood damage -- including monetary damage,
human injury and loss of life -- for more than a million square miles
of land across the United States across a 14-year period. That was more
than 790,000 square miles greater than flood risk zones identified by
FEMA's maps.

"We're seeing that there's a lot of flood damage being reported outside
of the 100-year floodplain," said the study's lead author Elyssa
Collins, a doctoral candidate in the NC State Center for Geospatial
Analytics. "There are a lot of places that are susceptible to flooding,
and because they're outside the floodplain, that means they do not have
to abide by insurance, building code and land-use requirements that
could help protect people and property." It can cost FEMA as much
as $11.8 billion to create national Flood Insurance Rate Maps, which
show whether an area has at least a 1% chance of flooding in a year,
according to a 2020 report from the Association of State Floodplain
Managers. Researchers say their method of using machine learning tools
to estimate flood risk offers a way of rapidly updating flood maps as conditions change or more information becomes available.

"This is the first spatially complete map of flood damage probability for
the United States; wall-to-wall information that can be used to learn
more about flood risk in vulnerable, underrepresented communities,"
said Ross Meentemeyer, Goodnight Distinguished Professor of Geospatial Analytics at NC State.

To create their computer models, researchers used reported data of
flood damage for the United States, along with other information such
as whether land is close to a river or stream, type of land cover,
soil type and precipitation.

The computer was able to "learn" from actual reports of damage to predict
areas of high flood damage likelihood for each pixel of mapped land. They created separate models for each watershed in the United States.



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"Our models are not based in physics or the mechanics of how water flows;
we're using machine learning methods to create predictions," Collins
said. "We developed models that relate predictors -- variables related
to flood damage such as extreme precipitation, topography, the relation
of your home to a river -- to a data set of flood damage reports from
the National Oceanic and Atmospheric Administration. It's very fast --
our models for the U.S.

watersheds ran on an average of five hours." The actual flood damage
reports they used to "train" the models were publicly available reports
from NOAA made between December 2006 and May of 2020.

Compared with recent FEMA maps downloaded in 2020, 84.5% of the damage
reports they evaluated were not within the agency's high-risk flood
areas. The majority, at 68.3%, were located outside of the high-risk floodplain, while 16.2% were in locations unmapped by FEMA.

When they ran their computer models to determine flood damage risk,
they found a high probability of flood damage for more than 1.01 million
square miles across the United States, while the mapped area in FEMA's
100-year flood plain is about 221,000 square miles. Researchers said there
are factors that could help explain why the differences were so large, including that their machine- learning-based model assessed damage from
floods of any frequency, while FEMA only includes flooding that would
occur from storms that have a 1% chance of happening in any given year.

"Potentially, FEMA is underestimating flood damage exposure," Collins
said.

One of the biggest drivers of flood damage risk was proximity to a stream, along with elevation and the average amount of extreme precipitation
per year.

The three Census regions with the highest probability were in the
Southeast.

Louisiana, Missouri, the District of Columbia, Florida and Mississippi
had the highest risk of any U.S. state or district in the continental
United States. Of the 30 most high-risk counties, North Carolina had
three: Dare, Hyde and Tyrrell.

In their model, researchers used historical climate data. In the future,
they plan to account for climate change.

In the meantime, researchers say their findings, which will be publicly accessible, could be useful for helping policymakers involved in land-use planning. They also represent a proof-of-concept method for efficiently updating flood maps in the future.

"There is still work to be done to make this model more dynamic,"
Collins said.

"But it's part of a shift in thinking about how we approach these problems
in a more cost-effective and computationally efficient manner. Inevitably,
with climate change, we're going to have to update these maps and models
as events occur. It would be helpful to have future estimates that we
can use to prepare for whatever is to come." The study, "Predicting
flood damage probability across the conterminous United States," was
published online Feb. 21, 2022, in Environmental Research Letters.

In addition to Collins and Meentemeyer, other authors included Georgina M.

Sanchez, Adam Terando, Charles C. Stillwell, Helena Mitasova and Antonia Sebastian. This project was supported by the U.S. Geological Survey
Southeast Climate Adaptation Science Center (G19AC00083) and the North
Carolina State University Sea Grant program (R/MG-2011).

special promotion Explore the latest scientific research on sleep and
dreams in this free online course from New Scientist -- Sign_up_now_>>> ========================================================================== Story Source: Materials provided
by North_Carolina_State_University. Original written by Laura
Oleniacz. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Elyssa L Collins, Georgina M Sanchez, Adam Terando, Charles
C Stillwell,
Helena Mitasova, Antonia Sebastian, Ross K Meentemeyer. Predicting
flood damage probability across the conterminous United
States. Environmental Research Letters, 2022; 17 (3): 034006 DOI:
10.1088/1748-9326/ac4f0f ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220222151855.htm

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