Spatio-temporally varying Strickler coefficient: A calibration approach applied to a Danish river using in-situ water surface elevation and UAS altimetry

Hydraulic roughness (e.g., expressed in terms of Manning’s or Strickler’s coefficient) is an essential input to numerical hydrodynamic models. One way estimate parameters by hydraulic inversion, using observed water surface elevation (WSE) collected from gauging stations, satellite platforms, Unmanned Aerial System (UAS) altimeters. Specifically, UAS altimetry provides close instantaneous observations longitudinal profiles and seasonal variations WSE for various river types, which are helpful calibrating parameters. However, it computationally expensive run high-resolution models thousands millions times as required the global optimization distributed parameter sets spatio-temporally varying roughness). This study presents efficient calibration approach a simplified steady-state solver, datasets, in-situ observations. The minimized weighted sum misfit term, spatial smoothness penalty, a-priori sinusoidal temporal variation constraint. was first demonstrated several synthetic experiments, results indicated that search algorithm accurately recovered Strickler coefficient (Ks) short reaches (due growth cycle aquatic vegetation) due spatially variable density submerged scales. Subsequently, real dataset at Danish test site (Vejle Å). In this river, friction dominated vegetation growing stream shows strong plant species composition. Results spatio-temporal Ks fit accurately. illustrates how modeling can be combined achieve improved understanding better parameterization small medium-sized rivers, where channel conveyance controlled other factors.