Prediction at Ungauged Catchments through Parameter Optimization and Uncertainty Estimation to Quantify the Regional Water Balance of the Ethiopian Rift Valley Lake Basin

Abstract

Quantifying uncertainties in water resource prediction in data-scarce regions is essential for resource development. We use globally available datasets of precipitation and potential evapotranspiration for the regionalization of model parameters in the data-scarce regions of Ethiopia. A regional model was developed based on 14 gauged catchments. Three possible parameter sets were tested for regionalization: (1) the best calibration parameters, (2) the best validation parameter set derived from behavioral parameters during the validation period, and (3) the stable parameter sets. Weighted multiple linear regression was applied by assigning more weight to identifiable parameters, using a novel leave-one-out cross-validation technique for evaluation and uncertainty quantification. The regionalized parameter sets were applied to the remaining 35 ungauged catchments in the Ethiopian Rift Valley Lake Basin (RVLB) to provide regional water balance estimations. The monthly calibration of the gauged catchments resulted in Nash Sutcliffe Efficiencies (NSE) ranging from 0.53 to 0.86. The regionalization approach provides acceptable regional model performances with a median NSE of 0.63. The results showed that, other than the commonly used best-calibrated parameters, the stable parameter sets provide the most robust estimates of regionalized parameters. As this approach is model-independent and the input data used are available globally, it can be applied to any other data-scarce region.

Simulation of Gauged and Ungauged Streamflow of Coastal Catchments across Australia

“…The donor catchment is generally selected based on (i) physical features, similarities, and/or (ii) spatial proximity to the targeted ungauged catchment. It has been demonstrated that the geographically closest catchment (or spatial proximity) to the target ungauged catchment is often the best donor catchment [6,43,[49][50][51][52]. The parameter regression method has also been used to transfer parameters to ungauged catchments, with the presumption that the calibrated parameters represent catchment attributes (e.g., slope, elevation, drainage density, land use, soil type).…”

Integrated Modeling of Land Degradation Dynamics and Insights on the Possible Future Management Alternatives in the Gidabo River Basin, Ethiopian Rift Valley

“…The iteration was run for the simulation period of 1997-2014 and the first 3 years were used for warm-up period. A split sample test was employed to split the remaining years into calibration period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009) and validation period (2010-2014) on a monthly basis [56].…”

Simulation of Gauged and Ungauged Streamflow of Coastal Catchments Across Australia

“…The donor catchment is generally selected based on: (i) physical features, similarities and or (ii) spatial proximity to the targeted ungauged catchment. It has been demonstrated that the geographically closest catchment (or spatial proximity) to the target ungauged catchment is often the best donor catchment [6,43,[49][50][51][52]. The parameter regression method has also been used to transfer parameters to ungauged catchments, with the presumption that the calibrated parameters represent catchment attributes (e.g., slope, elevation, drainage density, land use, soil type).…”