Hydrologic Modeling, Uncertainty, and Sensitivity in the Okavango Basin: Insights for Scenario Assessment

Abstract

The development of watershed models with minimal quantified uncertainty under nonstationary conditions is a major challenge in the field of hydrology. This is especially problematic in data-poor areas where values for model inputs are lacking or measured on temporally and/or spatially sparse scales. The objective of this work is to conduct a global sensitivity and uncertainty analysis (GSA/UA) of the Pitman semidistributed hydrologic model for the data-poor Okavango Basin in southern Africa under both stationary and climate change scenarios. The Morris GSA method allowed qualitative ranking of important model inputs whereas the variance-based Fourier amplitude sensitivity test (FAST) method quantitatively identified the parametric uncertainty and sensitivity to these inputs. Results showed that the most important model inputs determining mean annual flow and model fit to observed data were the infiltration rate and the temporal rainfall distribution. In addition, the wetter western headwaters region was shown to be the most important region in determining the flow at the outlet of the basin. Parameter equifinality was significant in this study, and hence the evaluation of the relationships between mechanisms was not straightforward. Analysis of model results under climate change scenarios showed that a hot and wet scenario introduced more change in mean annual flow than a hot and dry scenario. The climate change scenarios also altered model sensitivity. For example, the parameter that controls the rate of infiltration decreased in importance and the parameter that controls soil moisture storage gained importance under the dry scenario. These results are useful when determining the applicability of model predictions under stationary and nonstationary conditions and when focusing watershed monitoring efforts.