Effective Representation of River Geometry in Hydraulic Flood Forecast Models

Abstract

Bathymetric data are a critical input to hydraulic models. However, river depth and shape cannot be systematically observed remotely, and field data are both scarce and expensive to collect. In flood modeling, river roughness and geometry compensate for each other, with different parameter sets often being able to map model predictions equally well to the observed data, commonly known as equifinality. This study presents a numerical experiment to investigate an effective yet parsimonious representation of channel geometry that can be used for operational flood forecasting. The LISFLOOD‐FP hydraulic model was used to simulate a hypothetical flood event in the Clarence catchment (Australia). A high‐resolution model simulation based on accurate bathymetric field data was used to benchmark coarser model simulations based on simplified river geometries. These simplified river geometries were derived from a combination of globally available empirical formulations, remote sensing data, and a limited number of measurements. Model predictive discrepancy between simulations with field data and simplified geometries allowed an assessment of the geometry impact on inundation dynamics. In this study site, the channel geometrical representation for a reliable inundation forecast could be achieved using remote sensing‐derived river width values combined with a few measurements of river depth sampled at strategic locations. Furthermore, this study showed that spatially distributed remote sensing‐derived inundation levels at the very early stages of a flood event have the potential to support the effective diagnosis of errors in model implementations.

The Value of Distributed High-Resolution UAV-Borne Observations of Water Surface Elevation for River Management and Hydrodynamic Modeling

“…Regarding the August case, calibrated K s varied from 1.3 to 6.5 m 1/3 /s, which was much smaller than the values usually used in the hydrodynamic modeling of large rivers [16,29,30]. However, this studied channel was extremely narrow (~2 m), flat (~30 cm/km), and highly vegetated.…”

Inundation Analysis of the Oda River Basin in Japan during the Flood Event of 6–7 July 2018 Utilizing Local and Global Hydrographic Data

“…HydroSHEDS has been processed and carefully presented for hydrological applications [20,35]. Therefore, it has been used in numerous hydrological modeling studies for specific river basins [15][16][17][21][22][23]36]. Information on river geometries, i.e., river width (R W ), river depth (R D ), and embankment height (E H ), are necessary parameters that must be correctly incorporated into the model [36]; these are extracted from DEM data.…”

“…Extraction of accurate river geometry information, especially for medium-to-small river basins, may yield more uncertainties due to such coarse resolution [22]. In many cases, empirical equations have been used to define river geometries [14][15][16][17]19,[21][22][23]33,[36][37][38]. However, obtaining the actual empirical equations of river geometries is a real challenge, particularly for inundation analysis.…”

Wavelet and index methods for the identification of pool–riffle sequences

“…Flood forecasting models are based on the description of river morphology (cross-sectional geometry), and this is their essential input despite its scarcity and price (Saleh et al, 2013;Grimaldi et al, 2018). In fact, the most important aspect to know is the river bathymetric data at the local scale, detailed and specific to the site and local conditions (Alfieri et al, 2016), for the accurate modeling of river hydraulics, which is essential for predicting floodplain flooding (Neal et al, 2015;Trigg et al, 2009).…”

“…Many researchers are working on determining the best simplified representation of channel geometry (Saleh et al, 2013;Grimaldi et al, 2018;Neal et al, 2015;Orlandini and Rosso, 1998), based on the variability of cross sections but without the knowledge of the bed elevation variability on a small scale. This longitudinal variability in the river geometry has greater impact on the simulation of the water level than the cross-sectional shapes (Saleh et al, 2013) and it must be taken into account in the hydraulic models designed to improve flood forecasting.…”