Development of a HAND-based flood risk assessment tool in Google Earth Engine for a data-scarce region in the US

Abstract

Despite the decreased disaster resilience of rural communities in the Great Lakes region to flooding, flood mitigation efforts have been impeded by inadequate data and lack of appropriate tools for understanding flood risk. Development of such resources often requires data and computationally intensive approaches, which are challenging in data-scarce conditions. This study presents the development of a web application in Google Earth Engine (GEE) for flood risk assessment. The application utilizes the Height Above the Nearest Drainage (HAND) model and synthetic rating curve (SRC) for fluvial flood inundation modeling, the Simulating WAves Nearshore (SWAN) model for coastal flood inundation modeling, the United States Geological Survey (USGS) regional regression equations for estimating peak discharge, and depth-damage functions of the HAZUS-MH flood model for estimating losses due to building-level impacts. The GEE-based geospatial web application, which is operational across five counties in the Western Upper Peninsula (WUP) of Michigan, fulfills the requirement of the community and decision-makers to assess the risks caused by flooding in the region. We demonstrated the applicability of the tool in the Ontonagon River, Michigan, and the results indicate the suitability of the platform for implementing decisions, long-term planning, and understanding flood risk with a reasonable degree of accuracy.