Preprints
https://doi.org/10.5194/egusphere-2024-1563
https://doi.org/10.5194/egusphere-2024-1563
08 Jul 2024
 | 08 Jul 2024
Status: this preprint is open for discussion.

Technical Note: Operational calibration and performance improvement for hydrodynamic models in data-scarce coastal areas

Francisco Rodrigues do Amaral, Benoît Camenen, Tin Nguyen Trung, Tran Anh Tu, Thierry Pellarin, and Nicolas Gratiot

Abstract. In this study, we address the challenges posed by data scarcity in hydrodynamic modeling within one of the most vulnerable coastal zones in the world—the Saigon-Dongnai tidal river system in South Vietnam. We investigate calibration strategies for a 1D hydrodynamic model using minimal in-situ data obtained from an existing local monitoring program, which provides 48 hours of measurements per month. To further improve discharge estimation from the 1D model, the coupling of a modified Manning-Strickler (MS) equation is explored. Calibration efforts reveal distinct trends in friction coefficients along the river. The introduction of indirectly measured discharge data significantly improves model performance, particularly for the Saigon River branch. Validation against independent measurements demonstrates promising results, with the coupling of the modified MS equation providing improved discharge estimates. The study underscores the complexities of calibrating hydrodynamic models in data-scarce regions, with recommendations for future modeling endeavors including incorporating more accurate upstream boundary conditions. The long time-series of estimated water level and discharge provided by this study have practical implications for water resource management and decision-making in data-scarce estuarine systems and are provided in open-access for operational use.

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Francisco Rodrigues do Amaral, Benoît Camenen, Tin Nguyen Trung, Tran Anh Tu, Thierry Pellarin, and Nicolas Gratiot

Status: open (until 02 Sep 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Francisco Rodrigues do Amaral, Benoît Camenen, Tin Nguyen Trung, Tran Anh Tu, Thierry Pellarin, and Nicolas Gratiot

Data sets

Water discharge and water level output from the MAGE 1D hydrodynamic model for the Saigon and Dongnai rivers, Vietnam, 2016-2022 Francisco Rodrigues Do Amaral et al. https://doi.org/10.23708/KLQMSR

Model code and software

Mage Jean-Baptiste Faure and Theophile Terraz https://gitlab.irstea.fr/jean-baptiste.faure/mage

Francisco Rodrigues do Amaral, Benoît Camenen, Tin Nguyen Trung, Tran Anh Tu, Thierry Pellarin, and Nicolas Gratiot

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Short summary
This study explores how to improve models predicting water flow in South Vietnam's Saigon and Dongnai rivers, where data is scarce. By testing three different methods to adjust the river model using river water level and river discharge measurements, we found ways to better predict river behavior. These findings can help manage water resources more effectively and aid decision-making for flood protection and environmental conservation.