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\begin{document}

\title{Rapid inundation mapping using the US National Water Model, satellite
observations, and a convolutional neural network}



\author[1]{Jonathan M Frame}%
\author[2]{Tanya Nair}%
\author[2]{Veda Sunkara}%
\author[2]{Philip Popien}%
\author[2]{Subit Chakrabarti}%
\author[2]{Tyler Anderson}%
\author[2]{Nicholas R Leach}%
\author[2]{Colin Doyle}%
\author[2]{Mitchell Thomas}%
\author[2]{Beth Tellman}%
\affil[1]{Floodbase, now at Lynker}%
\affil[2]{Floodbase}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
Rapid and accurate maps of floods across large domains, with high
temporal resolution capturing event peaks, have applications for flood
forecasting and resilience, damage assessment, and parametric insurance.
Satellite imagery produces incomplete observations spatially and
temporally, and hydrodynamic models require tradeoffs between
computational efficiency and accuracy. We address these challenges with
a novel flood model which predicts surface water area from the U.S.
National Water Model using a convolutional neural network (NWM-CNN). We
trained NWM-CNN on 780 flood events, at a 250m resolution with an RMSE
of 4.58\% on held out validation geographies. We demonstrate NWM-CNN
across California during the 2023 atmospheric rivers, comparing
predictions against Sentinel-1 mapped flood observations. Historically,
we compared the data from 1979-2023 to flood damage reports in
Sacramento County, California. Results show that NWM-CNN captures
inundation extent better than the Height Above Nearest Drainage (HAND)
approach (25\% to 36\% RMSE, respectively).%
\end{abstract}%



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\includegraphics[width=0.70\columnwidth]{figures/anomalous-surface-water-time-series-with-cal/anomalous-surface-water-time-series-with-cal}
\caption{{A: California statewide snapshot of surface water predicted by NWM-CNN
from a January 2023 AR event. B: Summarized surface water areas across
all of California at the HUC 6 scale. C: Summarized surface water area
at the HUC 8 scale across the Lower Sacramento catchment area, the HUC 6
catchment with the highest anomalous surface water response.
{\label{985666}}%
}}
\end{center}
\end{figure}

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