Performance assessment and uncertainty prediction of a daily time-step HBV-Light rainfall-runoff model for the Upper Benue River Basin, Northern Cameroon

Abstract

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.