Paul J. Kinzel
Carl J. Legleiter
2025
<p><span>Although established techniques for remote sensing of river bathymetry perform poorly in turbid water, image velocimetry can be effective under these conditions. This study describes a framework for mapping both of these attributes: Depths Inferred from Velocities Estimated by Remote Sensing, or DIVERS. The workflow involves linking image-derived velocities to depth via a flow resistance equation and invoking an optimization algorithm. We generalized an earlier formulation of DIVERS by: (1) using moving aircraft river velocimetry (MARV) to obtain a continuous, spatially extensive velocity field; (2) working within a channel-centered coordinate system; (3) allowing for local optimization of multiple parameters on a per-cross section basis; and (4) introducing a second objective function that can be used when discharge is not known. We also quantified the sensitivity of depth estimates to each parameter and input variable. MARV-based velocity estimates agreed closely with field measurements (</span><span class="math"><span id="MathJax-Element-1-Frame" class="MathJax_SVG" data-mathml="<math xmlns="http://www.w3.org/1998/Math/MathML"><msup is="true"><mi is="true">R</mi><mn is="true">2</mn></msup><mo is="true">=</mo><mn is="true">0.81</mn></math>"><span class="MJX_Assistive_MathML"><i>R</i><sup>2</sup>=0.81</span></span></span><span>) and the use of DIVERS led to cross-sectional mean depths that were correlated with in situ observations (</span><span class="math"><span id="MathJax-Element-2-Frame" class="MathJax_SVG" data-mathml="<math xmlns="http://www.w3.org/1998/Math/MathML"><msup is="true"><mi is="true">R</mi><mn is="true">2</mn></msup><mo is="true">=</mo><mn is="true">0.75</mn></math>"><span class="MJX_Assistive_MathML"><i>R</i><sup>2</sup>=0.75</span></span></span><span>). Errors in the input velocity field had the greatest impact on depth estimates, but the algorithm was not highly sensitive to initial parameter estimates when a known discharge was available to constrain the optimization. The DIVERS framework is predicated upon a number of simplifying assumptions — steady, uniform, one-dimensional flow and a strict, purely local proportionality between depth and velocity — that impose important limitations, but our results suggest that the approach can provide plausible, first-order estimates of river depths.</span></p>
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10.1016/j.geomorph.2025.109732
en
Elsevier
A generalized framework for inferring river bathymetry from image-derived velocity fields
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