Improved accuracy and efficiency of flood inundation mapping of low-, medium-, and high-flow events using the AutoRoute model

Abstract

Abstract. 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.

Performance of automated methods for flash flood inundation mapping: a comparison of a digital terrain model (DTM) filling and two hydrodynamic methods

“…Several examples of flood mapping applications at finer resolutions (< 10 m) have also been recently presented, based on a large variety of models, including DHD-Iber (Cea and Bladé, 2015), Floodos (Davy et al, 2017), LISFLOOD-FP (Neal et al, 2018), PRIMo (Sanders and Schubert, 2019), and SRM (Xia et al, 2017). Specific applications for flash floods have been proposed using Iber (García-Feal et al, 2018), BreZo (Nguyen et al, 2016), and B-flood (Kirstetter et al, 2021). Finally, in addition to high resolution 2D models, 1D SWE models may also be applied based on cross sections extracted from high-resolution DTMs (Choi and Mantilla, 2015;Pons et al, 2014;Le Bihan et al, 2017;Lamichhane and Sharma, 2018); these also show interesting results in terms of accuracy and offer lower computation times.…”

Comparison of Flood Inundation Modeling Frameworks within a Small Coastal Watershed during a Compound Flood Event

“…For topography, we acquire 1/3 arc second (~9 m) horizontal resolution National Elevation Dataset (NED) digital elevation model (DEM) data (Gesch et al, 2002(Gesch et al, , 2010 for the study area. The 2016 collection of the National Land Cover Dataset (NLCD, Yang et al, 2018) and literature-derived roughness coefficients as described in Follum et al (2017Follum et al ( , 2020 provide estimates of surface roughness. Because the chosen DEM does not contain bathymetry, we implement the simple bathymetric estimation methodology within AutoRoute (Follum et al, 2020) by using the gage adjusted, Enhanced Runoff Method (EROM) mean annual flows (USEPA, 2020b).…”

Direct Integration of Numerous Dams and Reservoirs Outflow in Continental Scale Hydrologic Modeling

“…In total, 10.1029/2020WR029544 8 of 26 412 1° × 1° tiles with spatial resolution of 1/3 arc second (∼9 m) were utilized. For surface roughness, the 2016 collection of the National Land Cover Data set (NLCD, Yang et al, 2018) is used in combination with literature-derived roughness coefficients as described in Follum et al (2017Follum et al ( , 2020.…”

“…Instead, the NED represents the elevation at streams as a flat water surface. Follum et al (2020) introduced a simple mechanism within AutoRoute that accounts for bathymetry in each sampled cross-section. AutoRoute accounts for bathymetry within each sampled cross-section by first determining the top width of the water surface, assumed to be equal to the width of the flat surface in the sampled cross-section.…”