An Uncertainty Estimation Framework to Quantify the Water Balance of Ethiopian Rift Valley Lake basin

In Ethiopia, more than 80% of big freshwater lakes are located in the Rift Valley Lake Basin, which is serving for multipurpose water use of over 30 million people. The basin is one of the most densely populated regions in Ethiopia and it covers an area of 53,035 km². However, most of the catchments recharging these lakes are ungauged and their water balance is not well quantified, and hence, limiting the development of appropriate water resource management strategies. Prediction for ungauged catchments has demonstrated its effectiveness in hydro-climatic data-rich regions. However, these approaches are not well evaluated in the climatic data-limited condition and the consecutive uncertainty emerging in the small catchments is not adequately quantified. In this study, we use the HBV model to simulate streamflow using global precipitation and potential evapotranspiration products as forcings. We develop and apply a Monte-Carlo scheme to calibrate the model and quantify uncertainty at 16 catchments in the basin where gauging stations are available. Out of these, we use 14 best catchments to derive the best regional regression model by correlating the best calibration parameters, the best validation parameters, and parameters that give the most stable predictions with catchment attributes that are available throughout the basin. A weighting scheme in the regional regression accounts for parameter uncertainty in the calibration. A spatial cross-valuation that is applied 14 times always leaving out one of the gauged catchments provides 14 regional regression functions that express uncertainty regionalization. It also shows that the regionalization procedure that uses the best validation parameters for regionalization provides the most robust results. We then subsequently apply the 14 spatial regression functions of the cross-validation to the remaining 35 ungauged catchments in the Rift Valley Lake Basin to provide regional water balance estimations including quantification of regionalization uncertainty. With these results, our study provides a new procedure to use global precipitation and evapotranspiration products to predict and evaluate streamflow simulation for hydro-climatically data scares regions considering uncertainty. It, therefore, enhances the confidence in the understanding of water balance in those regions and will support the planning and development of appropriate water resource management strategies.

 

Keywords: Parameters Estimation, Uncertainties, Ungauged Catchment, Weighted Regression, Water Balance