Estimating River Channel Bathymetry in Large Scale Flood Inundation Models

Abstract

Flood inundation modeling across large data sparse areas has been increasing in recent years, driven by a desire to provide hazard information for a wider range of locations. The sophistication of these models has steadily advanced over the past decade due to improvements in remote sensing and modeling capability. There are now several global flood models (GFMs) that seek to simulate water surface dynamics across all rivers and floodplains regardless of data scarcity. However, flood models in data sparse areas lack river bathymetry because this cannot be observed remotely, meaning that a variety of methods for approximating river bathymetry have been developed from uniform flow or downstream hydraulic geometry theory. We argue that bathymetry estimation in these models should follow gradually varying flow theory to account for both uniform and nonuniform flows. We demonstrate that existing methods for bathymetry estimation in GFMs are only accurate for kinematic water surface profiles and are unable to simulate unbiased water surface profiles for reaches with diffusive or shallow water wave properties. The use of gradually varied flow theory to estimate bathymetry in a GFM reduced model error compared to a target water surface profile by 66% and eliminated bias due to backwater effects. For a large-scale test case in Mozambique this reduced flood extents by 40% and floodplain storage by 79% at the 5 years return period. The wet bias associated with uniform flow derived channels could have significant implications for modeling the role floodplains play in attenuating river discharges, potentially overstating their role. NEAL ET AL.

HydroMT: Automated and reproducible model building and analysis

“…However, sufficiently accurate datasets are often not readily available and need to be converted into model-specific, sometimes idiosyncratic, file formats. For instance, many Digital Elevation Models (DEMs) require ample preprocessing as these typically represent the elevation of the top of buildings, trees or water surface, while in the model the terrain and riverbed elevation are required (Hawker et al, 2022;Neal et al, 2021). Therefore, hydro models require various steps to process raw input data to model data which, if done manually, makes the process time consuming and hard to reproduce.…”

Integrating social vulnerability into global flood risk mapping

“…Fluvial flood hazard maps from Version 2 of the University of Bristol/Fathom Global Flood Model (GFM) 9 are used in this study. This version uses the same modelling framework as described in Sampson et al (2015), but with improved elevation data from MERIT DEM 35 , a more accurate river network from MERIT-Hydro 36 and an updated method to estimate river conveyance capacity 37 . Model boundary conditions (i.e.…”

Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting

“…The initial state of the system is affected by uncertainties stemming from topographic and bathymetric (topobathy) data including elevation errors in light detection and ranging (LiDAR) datasets and inadequate representation of flood-protection infrastructure in digital elevation models (DEMs) (e.g., levees, barriers, and seawalls) ( Bates et al., 2021 ; Gallien et al., 2018 ; Holmquist and Windham-Myers, 2022 ). Likewise, uncertainty from bathymetric data can affect velocity and current speed magnitude estimates, and thereby complex processes such as sedimentation, backwater effect, salinization, and mixing in tidal rivers ( Cea and French, 2012 ; Neal et al., 2021 ; Ye et al., 2018 ). Uncertainties from observational and forcing data are considered other major sources of errors that can propagate from ensemble-based meteorological predictions to boundary conditions and accordingly translate into flood inundation extent and WL errors ( Flowerdew et al., 2009 ; Jafarzadegan et al., 2021b ; Pappenberger et al., 2005 ; Saleh et al., 2017 ).…”

“…Recent advances in remote sensing and machine learning (ML) techniques have shown the benefits of using satellite, radar, and unmanned aerial imagery to correct elevations errors associated with building artifacts, flood defense structures, forests, and wetlands ( Cooper et al., 2019 ; Hawker et al., 2022 ; Liu et al., 2021 ; Zhao et al., 2022 ). In addition, these techniques have been used to estimate bathymetry in rivers, near shore, and intertidal zones for ungauged sites with satisfactory results ( Kasvi et al., 2019 ; Legleiter and Harrison, 2019 ; Ma et al., 2020 ; Moramarco et al., 2019 ; Neal et al., 2021 ). HD models are also subject to model parameter uncertainties such as channel bed and floodplain friction that are often represented via Manning's roughness (n) coefficients.…”