Evaluation of the Hyper-Resolution Model-Derived Water Cycle Components Over the Upper Blue Nile Basin
Freshwater scarcity is a major global concern that is being exacerbated by the increasing population and changing climate. While close estimates of water cycle components and water budget analysis can help with planning for sustainable water resources management, limited hydroclimatic data have made such analysis challenging. The Upper Blue Nile basin in Ethiopia is such a hydroclimatic data-scarce region, with relatively arid and hot weather. Multiple studies have focused on water budget analysis in the UBN basin, but water cycle analyses are missing at high spatiotemporal resolution yield from a fully distributed hydrological model that closes water and energy budgets. In this paper, we describe the simulation of evapotranspiration (ET) and discharge at fine spatiotemporal resolution (500 m and 3 hourly) from 1979 to 2014, using the Coupled Routing and Excess STorage Soil-Vegetation-Atmosphere-Snow (CREST- SVAS) distributed hydrological model, which physically maintains water and energy balance. We also present a comparison of ET and discharge as derived from CREST with those simulated by other existing global models. Our results show that, where the global models perform well for specific hydrological components, ET, and discharge, CREST can simulate ET at 500 m resolution with 0.93 correlation coefficient, 0.81 Nash-Sutcliffe efficiency (NSE), and -4.75% bias (for daily scale) and discharge at different basin scales with NSE values ranging from 0.46 to 0.75. We performed cascade calibration for discharge and tested the optimized parameters on noncalibrated sites. The good model performance at the noncalibrated sites (NSE > 0.6; bias within 15%) indicated the calibrated parameters can be used for ungauged locations. Finally, the terrestrial water storage change (TWSC) from CREST was also consistent with reference data and the Gravity Recovery and Climate Experiment (GRACE). The hyper-resolution accurate hydrological components from CREST will enable effective prediction of water budget, helpful for initiating local and large-scale sustainable development plans that address social and policy requirements, as well as extreme climatic conditions.