Hydrodynamic model calibration from pattern recognition of non-orthorectified terrestrial photographs

This paper presents a remote sensing technique for calibrating hydrodynamics models, which is particularly useful when access to the riverbed for a direct measure of flow variables may be precluded. The proposed technique uses terrestrial photography and automatic pattern recognition analysis together with digital mapping and does not require image ortho-rectification. Compared to others invasive or remote sensing calibration, this method is relatively cheap and can be repeated over time, thus allowing calibration over multiple flow rates . We applied this technique to a sequence of high-resolution photographs of the restored reach of the river Thur, near Niederneunforn, Switzerland.In order to calibrate the roughness coefficient, the actual exposed areas of the gravel bar are first computed using the pattern recognition algorithm, and then compared to the ones obtained from numerical hydrodynamic simulations over the entire range of observed flows. Analysis of the minimum error between the observed and the computed exposed areas show that the optimum roughness coefficient is discharge dependent; particularly it decreases as flow rate increases, as expected. The study is completed with an analysis of the root mean square error (RMSE) and mean absolute error (MEA), which allow finding the best fitting roughness coefficient that can be used over a wide range of flow rates, including large floods. This work presents a remote sensing technique for calibrating hydrodynamics models.We used terrestrial photography and automatic pattern recognition analysis.The hydrodynamic model is calibrated at different flow rates.Results show that the optimum roughness coefficient is discharge dependent.The study is completed with a statistical analysis of the errors.