Auteur:  Djamel Kechnit

Co-Auteur: Tshimanga, R.M., Abdelhadi Ammari, Mark A. Trigg, Andrew B. Carr, Farhad Bahmanpouri, Silvia Barbetta & Tommaso Moramarco

Review: Hydrological Sciences Journal

Lien:  https://www.tandfonline.com/doi/full/10.1080/02626667.2024.2402933

Résumé:

This study implements an entropy theory-based approach to infer bathymetry for 29 selected cross-sections along a 1740 km reach of the Congo River. A genetic algorithm optimization approach is used based on an analysis of near-surface velocity measurements to generate a random sample of 1000 bathymetry profiles from which the analysis is carried out. The resulting simulated bathymetry shows good agreement compared to the measurements obtained via Accoustic Doppler Current Profiler (ADCP), with a correlation that varies from 0.49 to 0.88. The bathymetry results are subsequently used to estimate the two-dimensional cross-sectional flow velocity distribution and, consequently, to calculate the river discharge. The mean errors observed for flow area, discharge, and mean velocity are found to be 2.7%, 1.3%, and 1%, respectively. This study demonstrates, for the first time, the successful application of an entropy-based approach to estimate bathymetry and discharge in large rivers and has significant implications for remote sensing applications.

Keywords: bathymetry, discharge, entropy approach, large rivers, near-surface velocity