A Comprehensive Assessment of Floodwater Depth Estimation Models in Semiarid Regions

Simple models continue to be important for continental-scale floodwater depth mapping due to the prohibitively expensive cost of calibrating and applying hydrodynamic models. This paper investigates the accuracy of three simple models for floodwater depth estimation from remote sensing derived water extent and/or Digital Elevation Models (DEMs) in semiarid regions. The three models are Height Above Nearest Drainage (HAND; Nobre et al., 2011, https://doi.org/10.1016/j.jhydrol.2011.03.051), Teng Vaze Dutta (TVD; Teng et al., 2013, http://hdl.handle.net/102.100.100/97033?index=1), and Floodwater Depth Estimation Tool (FwDET; Cohen, Brakenridge, et al., 2018, https://doi.org/10.1111/1752-1688.12609). The model accuracy and nature of errors are established using industry's best practice hydrodynamic models as benchmarks in three regions in eastern Australia. The overall results show that FwDET tends to underestimate (by 0.32 m at 50th percentile) while HAND and TVD overestimate floodwater depth for almost all floods (by 0.97 and 0.98 m, respectively). We quantify how switching DEM from 5 m LiDAR to national or global data sets DEM-H (Gallant et al., 2011, https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/72759), MERIT (Yamazaki et al., 2019, https://doi.org/10.1029/2019WR024873), or FABDEM (Hawker et al., 2022, https://doi.org/10.1088/17489326/ac4d4f) can affect different models differently; and we evaluate model performance against reach geomorphology and magnitude of flood events. The findings emphasize the importance of choosing a model that is fit for the intended application. By describing the applicability, advantages, and limitations of these models, this paper assists practitioners to choose the most appropriate model based on characteristics of their study area, type of problems they try to solve, and data availability. We have assessed the performance of three simple models for estimating floodwater depth by evaluating their accuracy against different Digital Elevation Model inputs, characteristics of study area, and magnitude of flood events. Our results emphasize that selection of an appropriate model depends on the circumstances. This study provides recommendations for future studies that enhance remote sensing-based flood maps with water depth, making use of the growing number of publicly available remote sensing water extent and digital elevation data sets globally. All the methods have been coded in Python and are freely available at https://github.com/csiro-hydroinformatics/water-depth-estimation.