HBV Performance under complex and poorly gauged context

In North African countries where water scarcity and limited data prevail, employing predictive hydrological modeling is crucial to gain accurate insights into current and future water reserves. Hence, these models parameters exhibit instability in this context due to the climate variability observed through basins. Therefore, our efforts focus on using a combination of measured data, remote sensing information, and reanalysis data for calibration and validation, to check the improvement in the result accuracy. Through this study, we simultaneously investigate the spatiotemporal stability of the HBV model in several sub-catchments of Oum Er-Rbia Basin, by improving the performance of a bucket-type conceptual model. We created a Nested Cross-Validation (NCV) framework to assess spatiotemporal stability. The framework uses optimal parameters from a donor catchment of the Hydrologiska Byråns Vattenbalansavdelning (HBV) model as inputs for target catchment parameter ranges. In particular, we evaluated HBV's capacity for prediction over time and space, as well as its impact on model parametrization throughout the regionalization process in the setting of sparse data catchments. As results, the HBV model is spatially transferable from one basin to another, with NSE ranging from  0.5 to 0.8 and KGE values between 0.1 to 0.9, meaning a moderate to high performance. The HBV optimum parameter sets exhibit unpredictable behavior over space. On the contrary, their inter-annual behavior is nearly identical. It also detected a decrease in the model's predictive skills over time, which can be explained by the research area's tendency to dry out year after year. Furthermore, employing KGE for calibration rather than NSE improves model predictive performance significantly. The model  calibration process with the KGE outperformed those with the NSE metric, especially when simulating high flows. Furthermore, the findings demonstrate a significant relationship between high model performance and high values of several optimal parameter sets throughout the calibration and validation periods.