A generalized framework for inferring river bathymetry from image-derived velocity fields
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Abstract
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 (R2=0.81) and the use of DIVERS led to cross-sectional mean depths that were correlated with in situ observations (R2=0.75). 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.
Suggested Citation
Legleiter, C.J., and Kinzel, P.J., 2025, A generalized framework for inferring river bathymetry from image-derived velocity fields: Geomorphology, v. 479, 109732, 18 p., https://doi.org/10.1016/j.geomorph.2025.109732.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | A generalized framework for inferring river bathymetry from image-derived velocity fields |
| Series title | Geomorphology |
| DOI | 10.1016/j.geomorph.2025.109732 |
| Volume | 479 |
| Year Published | 2025 |
| Language | English |
| Publisher | Elsevier |
| Contributing office(s) | WMA - Observing Systems Division |
| Description | 109732, 18 p. |
| Country | United States |
| State | Alaska |
| City | Nenana |
| Other Geospatial | Tanana River |