<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study investigates the performance of two lumped hydrological models, BILAN and GR2M, in simulating runoff across six catchments in the Gambia River Basin (Senegal) over a 30-year period employing a 7-year sliding window under different climatic conditions. The results revealed differences in overall performance and variable sensitivity of the models to hydrological conditions and calibration period lengths, stemming from their different structure and complexity. In particular, the BILAN model, which is based on a more complex set of parameters, showed better overall results in simulating dry conditions, while the GR2M model had superior performance in wet conditions. The study emphasized the importance of the length of the calibration period on model performance and on the reduction of uncertainty in the results. Extended calibration periods for both models narrowed the range of the Kling-Gupta Efficiency (KGE) values and reduced the loss of performance during the parameter transfer from calibration to validation. For the BILAN model, a longer calibration period also significantly reduced the variability of performance metric values. Conversely, for the GR2M model, the variability rate did not decrease with the length of the calibration periods. Testing both models under variable conditions underscored the crucial role of comprehending model structure, hydrological sensitivity, and calibration strategy effects on simulation accuracy and uncertainty for reliable results.</p>
</abstract>

This study investigates the performance of two lumped hydrological models, BILAN and GR2M, in simulating runoff across six catchments in the Gambia River Basin (Senegal) over a 30-year period employing a 7-year sliding window under different climatic conditions. The results revealed differences in overall performance and variable sensitivity of the models to hydrological conditions and calibration period lengths, stemming from their different structure and complexity. In particular, the BILAN model, which is based on a more complex set of parameters, showed better overall results in simulating dry conditions, while the GR2M model had superior performance in wet conditions. The study emphasized the importance of the length of the calibration period on model performance and on the reduction of uncertainty in the results. Extended calibration periods for both models narrowed the range of the Kling-Gupta Efficiency (KGE) values and reduced the loss of performance during the parameter transfer from calibration to validation. For the BILAN model, a longer calibration period also significantly reduced the variability of performance metric values. Conversely, for the GR2M model, the variability rate did not decrease with the length of the calibration periods. Testing both models under variable conditions underscored the crucial role of comprehending model structure, hydrological sensitivity, and calibration strategy effects on simulation accuracy and uncertainty for reliable results.

Testing sensitivity of BILAN and GR2M models to climate conditions in the Gambia River Basin

Department of Physical Geography and Geoecology, Faculty of Science

Department of Water Resources and Environmental Modelling, Faculty of Environmental Sciences

Laboratoire Leïdi “Dynamique des Territoires et Développement”

Journal of Hydrology and Hydromechanics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This study investigates the performance of two lumped hydrological models, BILAN and GR2M, in simulating runoff across six catchments in the Gambia River Basin...

Testing sensitivity of BILAN and GR2M models to climate conditions in the Gambia River Basin

Pubblicato online: 08 feb 2024

Pagine: 131 - 147

Ricevuto: 01 lug 2023

Accettato: 11 dic 2023

DOI: https://doi.org/10.2478/johh-2023-0044

Parole chiave
Rainfall-runoff model, BILAN, GR2M, Lumped models, The Gambia River Basin, Crash test

© 2023 Doudou Ba et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Testing sensitivity of BILAN and GR2M models to climate conditions in the Gambia River Basin

Pubblicato online: 08 feb 2024

Pagine: 131 - 147

Ricevuto: 01 lug 2023

Accettato: 11 dic 2023

DOI: https://doi.org/10.2478/johh-2023-0044

Parole chiaveRainfall-runoff model, BILAN, GR2M, Lumped models, The Gambia River Basin, Crash test

© 2023 Doudou Ba et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Rainfall-runoff model, BILAN, GR2M, Lumped models, The Gambia River Basin, Crash test