article · Journal of Hydrology Regional Studies
The Upper Benue River Basin serves as a critical water resource for water supply and hydroelectric power generation in northern Cameroon. To address its specific hydro-climate characteristics, a daily conceptual lumped rainfall-runoff model known as HBV-Light was calibrated and assessed. Using a one-factor-at-time sensitivity analysis alongside Monte-Carlo methods, influential and optimal parameters were identified across five performance measures. The sensitivity analysis enabled a reduction from nine to five key parameters, decreasing parameter interactions, calibration time, and overall prediction uncertainty. Despite inherent uncertainties related to parameter identifiability and calibration datasets, the evaluated model achieved good to very good performance. Best streamflow simulations fell within a narrow 95 percent uncertainty band, confirming the reliability of the calibrated model for supporting regional water resources management initiatives.
Northern Cameroon depends heavily on the Upper Benue River Basin for municipal water supplies and hydropower generation. Accurately simulating river discharge while managing mathematical uncertainties gives water resource planners reliable data. By streamlining the modelling process to five key parameters, managers gain a practical, efficient tool to guide ongoing water management initiatives and reservoir operations across the catchment.
The calibrated hydrological model represents applied and tested research suitable for implementation by water authorities, basin managers, and hydropower operators in the Upper Benue River Basin. It can directly support operational planning for water supply schemes and hydroelectric installations. While the model is ready to support regional water management initiatives, wider commercial or operational deployment would require integration into daily decision-support software used by regional utility bodies.
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The Upper Benue River Basin (UBRB), the second-largest river in Cameroon and one of the most important water resources in northern Cameroon from both a water supply and hydro-power generation perspective. the aim of the study is to establish a rainfall-runoff model that is fitted in the context of hydro-climate characteristics of the basin. The study applies a One-factor-At-Time (OAT) method for sensitivity analysis (SA) and a Monte-Carlo method for model calibration and parameter identifiability analysis to identify influential, well-identified and optimal parameters, and to predict uncertainties of a conceptual-lumped rainfall-runoff model -the daily HBV-Light model, for the basin using five performance measures. Based on the individual SA, the model parameters were reduced from nine to five parameters. This can reduce the interaction between model parameters, time-consuming for model calibration and therefore limiting model prediction uncertainty. Despite the uncertainties arising from both the model parameters identifiability and calibrated parameter sets themselves, the results reveal that the model performance varies from good to very good, while the model prediction uncertainty for the behavioural parameter sets reveals that the best simulation with regard to the measured streamflow lies within the narrow 95 % uncertainty band. Therefore, this model can be used to support various water resources management initiatives in the basin.
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DOI: 10.1016/j.ejrh.2021.100849
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