This article presents improvements and the development of a postprocessing module for the regional-scale flood mapping tool, AutoRoute. The accuracy of this model to simulate low-, medium-, and high-flow-rate scenarios is demonstrated at seven test sites within the US. AutoRoute is one of the tools used to create high-resolution flood inundation maps at regional to continental scales, but it has previously only been tested using extreme flood events. Modifications to the AutoRoute model and postprocessing scripts are shown to improve accuracy (e.g., average F value increase of 17.5 % for low-flow events) and computational efficiency (simulation time reduced by over 40 %) when compared to previous versions. Although flood inundation results for low-flow events are shown to be comparable with published values (average F value of 63.3 %), the model results tend to be overestimated, especially in flatter terrain. Higher-flow scenarios tend to be more accurately simulated (average F value of 77.5 %). With improved computational efficiency and the enhanced ability to simulate both low- and high-flow scenarios, the AutoRoute model may be well suited to provide first-order estimates of flooding within an operational, regional- to continental-scale hydrologic modeling framework.

This study assesses the accuracy of flood extent predictions in five U.S. watersheds. We generated flood maps for four return periods using various digital elevation models (DEMs)—FABDEM, SRTM, ALOS, and USGS 3DEP—and two versions of the GEOGLOWS River Forecast System (RFS) hydrography. These comparisons are notable because they build on operational global hydrology models so subsequent work can develop global modeled flood products. Models were made using the Automated Rating Curve (ARC) and Curve2Flood tools. Accuracy was measured against USGS reference maps using the F-statistic. Our results show that flood map accuracy generally increased with higher return periods. The most consistent and reliable improvements in accuracy occurred when both the DEM and hydrography datasets were upgraded to higher-resolution sources. While DEM improvements generally had a greater impact, hydrography refinements were more important for lower return periods when flood extents were the smallest. Generally, DEM resolution improved accuracy metrics more as the return period increased and hydrography and bare earth DEMs mattered more as the return period decreased. There was a 38.9% increase in the mean F-statistic between the two principal pairings of interest (FABDEM-RFS2 and SRTM 30 m DEM-RFS1). FABDEM’s bare-earth representation combined with RFS2 sometimes outperformed higher-resolution non-bare-earth DEMs, suggesting that there remains a need for site-specific investigation. Using ARC and Curve2Flood with FABDEM and RFS2 is a suitable baseline combination for general flood extent application.