Comparison Between Dynamic and Static Sensitivity Analysis Approaches for Impact Assessment of Different Potential Evapotranspiration Methods on Hydrological Models Performance

Potential evapotranspiration (PET) plays a crucial role in water management, including irrigation system design and management. It is an essential input to hydrological models. Direct measurement of PET is difficult, time-consuming, and costly; therefore, a number of different methods are used to compute this variable. This study compares the two sensitivity analysis approaches generally used for PET impact assessment on hydrological model performance. We conducted the study in the upper Benue River basin (UBRB) located in northern Cameroon using two lumped-conceptual rainfall-runoff models and 19 PET estimation methods. A Monte Carlo procedure was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. Although there were notable differences between PET estimation methods, the hydrological model's performance was satisfactory for each PET input in the calibration and validation periods. The optimized model parameters were significantly affected by the PET inputs, especially the parameter responsible for transforming PET into actual ET. The hydrological model's performance was insensitive to the PET input using a dynamic sensitivity approach, while it was significantly affected using a static sensitivity approach. This means that the over- or underestimation of PET is compensated by the model parameters during the model recalibration. The model performance was insensitive to the rescaling PET input for both dynamic and static sensitivities approaches. These results demonstrate that the effect of PET input to model performance is necessarily dependent on the sensitivity analysis approach used and suggest that the dynamic approach is more effective for hydrological modeling perspectives.