Application of a Large-Scale Terrain-Analysis-Based Flood Mapping System to Hurricane Harvey

Flood modeling provides inundation estimates and improves disaster preparedness and response. Recent development in hydrologic modeling and inundation mapping enables the creation of such estimates in near real time. To quantify their performance, these estimates need to be compared to measurements collected during historical events. We present an application of a flood mapping system based on the National Water Model and the Height Above Nearest Drainage method to Hurricane Harvey. The outputs are validated with high-water marks collected to record the highest water levels during the flood. We use these points to compute elevation-related variables and flood extents and measure the quality of the estimates. To improve the performance of the method, we calibrate the roughness coefficient based on stream order. We also use lidar data with a workflow named GeoFlood and we compare the modeled inundation to that recorded by the high-water marks and to the maximum inundation extent provided by the Dartmouth Flood Observatory based on remotely sensed data from multiple sources. The results show that our mapping system estimates local water depth with a mean error of about 0.5 m and that the inundation extent covers over 90% of that derived from high-water marks. Using a calibrated roughness coefficient and lidar data reduces the mean error in flood depth but does not affect as much the inundation extent estimation.

Automated summary

Flood modeling provides inundation estimates for disaster preparedness and response. This study presents an application of a flood mapping system and validates its outputs using high-water marks. The results show that the mapping system estimates water depth with a mean error of 0.5 m and covers over 90% of the inundation extent derived from high-water marks.