Evaluating the Sensitivity of Multi‐Dimensional Model Predictions of Salmon Habitat to the Source of Remotely Sensed River Bathymetry

Abstract

Multi‐dimensional numerical models are fundamental tools for investigating biophysical processes in aquatic ecosystems. Remote sensing techniques increase the feasibility of applying such models at riverscape scales, but tests of model performance on large rivers have been limited. We evaluated the potential to develop two‐dimensional (2D) and three‐dimensional (3D) hydrodynamic models for a 1.6‐km reach of a large gravel‐bed river using three sources of remotely sensed river bathymetry. We estimated depth from hyperspectral image data acquired from conventional and uncrewed aircraft and multispectral satellite imagery. Our results indicated that modeled water depth errors were similar between 2D and 3D models, with depth residuals that were comparable to the uncertainty associated with the bathymetry used as input. We found good agreement between measured and modeled depth‐averaged velocities generated by 2D and 3D models, while 3D models provided superior predictions of near‐bed velocities. We found that optimal model performance occurred for lower flow resistance values than previously reported in the literature, possibly as a consequence of the high‐resolution bathymetry used as model input. Model predictions of winter‐run Chinook salmon (Oncorhynchus tshawytscha) spawning and rearing habitat were not sensitive to the source of bathymetric information, but bioenergetic predictions related to adult holding costs were influenced by the input bathymetry. Our results suggest that hyperspectral imagery acquired from piloted and/or uncrewed aircraft can be used to map the bathymetry of clear‐flowing, relatively shallow large rivers with sufficient accuracy to support multi‐dimensional flow model development; models developed from multispectral satellite imagery had more limited predictive capability.

Evaluating the potential for efficient, UAS-based reach-scale mapping of river channel bathymetry from multispectral images

“…These decisions are based in part on a series of coupled physical-biological models developed for evaluating the impacts of water operations, fisheries management, and environmental variability on salmon population dynamics (Daniels and Danner, 2020;Dudley et al, 2022). Currently, a key limitation of these decision support tools is the lack of continuous, high-quality river bathymetry data that could serve as input to multidimensional hydrodynamic models (Harrison et al, 2022).…”

Depth Mapping in Turbid and Deep Waters Using AVIRIS-NG Imagery: A Study in Wax Lake Delta, Louisiana, USA

“…Remote sensing of water depth has been gaining attention across various fields, including fluvial geomorphology, stream ecology, and hydrodynamics owing to its capability of mapping spatially distributed information (Harrison et al, 2022;Niroumand-Jadidi et al, 2022;Moramarco et al, 2019;Hossain et al, 2022). The conventional approach to water depth measurement using acoustic Doppler current profilers (ADCPs) or real-time kinematic global positioning systems (RTK-GPS) depends on point measurements, requiring a substantial investment of time and labor for frequent and spatial measurements (Zinger et al, 2013).…”