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\begin{document}

\title{A global flood risk modeling framework built with climate models and
machine learning}



\author[1]{David A. Carozza}%
\author[1]{Mathieu Boudreault}%
\affil[1]{Université du Québec à Montréal}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\begin{abstract}
Large scale flood risk analyses are fundamental to many applications
requiring national or international overviews of flood risk. While
large-scale climate patterns such as teleconnections and climate change
become important at this scale, it remains a challenge to represent the
local hydrological cycle over various watersheds in a manner that is
physically consistent with climate. As a result, global models tend to
suffer from a lack of available scenarios and flexibility that are key
for planners, relief organizations, regulators, and the financial
services industry to analyze the socioeconomic, demographic, and
climatic factors affecting exposure. Here we introduce a data-driven,
global, fast, flexible, and climate-consistent flood risk modeling
framework for applications that do not necessarily require
high-resolution flood mapping. We first use statistical and machine
learning methods to examine the relationship between historical (from
the Dartmouth Flood Observatory) flood occurrence and impact, and
climatic, watershed, and socioeconomic factors at over 4700 watersheds
globally. Using bias-corrected output from the NCAR CESM Large Ensemble
from 1980 to 2020, and the fitted statistical relationships, we simulate
one million years of events worldwide along with the population
displaced. We discuss potential applications of the model and present
global flood hazard and risk maps. The main value of this global flood
model lies in its ability to quickly simulate realistic flood events at
a resolution that is useful for large-scale socioeconomic and financial
planning, yet we expect it to be useful to climate and natural hazard
scientists who are interested in socioeconomic impacts of climate.%
\end{abstract}%



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