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Flood mapping is a vital component for sustainable land use in flood-prone areas. Due to the frequency of flood events, local authorities demand effective yet simple methods for the preliminary identification of flood-prone areas at large scales to subsequently define mitigation strategies. We focus here on the workflow GeoFlood, a parsimonious model which uses only high-resolution Digital Terrain Models (DTMs) to define the geomorphological and hydrological information necessary for flood inundation mapping, thus allowing for large-scale simulations at a reasonable computational cost. The purpose of the present study is to investigate the conditions under which GeoFlood is able to correctly reproduce inundation scenarios (with an assigned return period) and their flooding characteristics. Specifically, we analyze its performance over a highly urbanized area, the mid-lower portion of the Tiber River (Italy). We

We compare inundation estimates with high-water marks collected during Hurricane Harvey. Our system estimates depth with a 0.5-m mean error and extent covering 90% of that obtained from observations. ABSTRACT: 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.