Rapid Inundation Mapping Using the US National Water Model, Satellite Observations, and a Convolutional Neural Network

Abstract

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 250 m 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. We compared historical predictions from 1979 to 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%–36% RMSE, respectively).

Keywords

• flood
• insurance
• inundation
• machine learning
• national water model
• atmospheric river