Hydrology and fish population dynamics in the Okavango Basin: Managing for uncertainty in a data poor environment

The Okavango Basin is a large and exceptionally pristine transboundary catchment in southern Africa that empties into the Okavango Delta, an inland delta that spreads onto the Kalahari sands of Botswana and is evapotranspirated before ever reaching the sea. Because of its remote location and large size, the Okavango is a data scarce area which leads to concerns about model reliability. Though the uncertainty in models cannot be taken away, models can be given pedigrees to evaluate their 'goodness of fit' or reliability. This allows managers to make informed decisions based on a determination of a model.s usefulness. Two tools that can be used to develop this pedigree are global sensitivity and uncertainty analysis (GSA/UA).;Uncertainty analysis is useful for measuring model reliability and sensitivity analysis apportions the total model uncertainty to individual inputs and processes. In this work a methodology is used to optimize computational resources, conduct a robust GSA/UA, and potentially reduce input/output uncertainty using Monte Carlo Filtering. This work is threefold involving a GSA/UA on two existing hydrologic models in the area (the Pitman basin model and the Okavango Research Institute (ORI) delta model) as well as the development and analysis of a fish population model for the Okavango Delta based on the flood pulse concept.;Results show that important areas and processes of the Pitman model include the headwaters as well as infiltration, temporal rainfall, and groundwater inputs. Important aspects of the ORI model include a keystone reservoir at the head of the Delta where water is apportioned between downstream reservoirs as well as the volume threshold inputs. In the fish model, the flood pulse is shown to be an important driver. The input that relates the flood pulse to fish recruitment is not highly sensitive but is very important for achieving the best fit model simulations. Monte Carlo Filtering is successfully used to reduce and refine input/output uncertainty in the modeling applications. These results will aid ongoing efforts in the Basin and the Delta which are currently using hydrologic and ecological models to develop environmental flows, explore development and climate change scenarios, and apportion water.