Development of a Flood Risk Modeling System for Enhanced Resilience of Rural Regions
Abstract
Accurate estimation of flood extent and associated damages and economic losses is one of the critical issues of floodplain management. Since the majority of the floodplain management systems require data that can be difficult to obtain and higher computational power for 2D hydraulic/hydrodynamic model simulations and risk estimation; formulation of floodplain management strategies is challenging, particularly in data-scarce rural areas across the world. The height above nearest drainage (HAND) is a widely used, less complex, terrain-based, flood inundation approach for delineating the aerial extent of flood-susceptible areas. The Flood Assessment Structure Tool (FAST) is capable of estimating the flood impacts with building data and flood inundation depth. The FAST uses the HAZUS flood modeling methodology to assign depth damage functions depending on various building characteristics. We utilized the capabilities of HAND and the flood risk estimation methodology of FAST to estimate the extent of flood inundation and associated flood risk for improving disaster preparedness and response. We demonstrated the applicability of this integrated approach to develop a rural hazard resilience tool, based on Google Earth Engine (GEE), to delineate the flood inundation areas and associated flood risk in the City of Houghton, located in Houghton County, Michigan. The GEE-based tool is used to create a geospatial web application that satisfies the fundamental need of the user to understand the extent and cost of damages brought on by flooding in any given location. The visualization of the results is facilitated using a graphic user interface (GUI) to the GEE tool. This platform is extremely beneficial for decision-making authorities as well as community members with limited technical knowledge of flood modeling and geospatial platforms for implementing decisions, long-term planning purposes and understanding the flood risk with reasonable accuracy.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFMNH12D0305T