Inferring reservoir filling strategies under limited-data-availability conditions using hydrological modeling and Earth observations: the case of the Grand Ethiopian Renaissance Dam (GERD)

Abstract

The filling of the Grand Ethiopian Renaissance Dam (GERD) started in 2020, posing additional challenges for downstream water management in the Blue Nile River in the Republic of the Sudan, which is already struggling to cope with the effects of climate change. This is also the case for many transboundary rivers that are affected by a lack of cooperation and transparency during the filling and operation of new dams. Without information about water supply from neighboring countries, it is risky to manage downstream dams as usual, but operational information is needed to apply modifications. This study aims to develop a novel approach/framework that utilizes hydrological modeling in conjunction with remote-sensing data to retrieve reservoir filling strategies under limited-data-availability conditions. Firstly, five rainfall products (i.e., ARC2, CHIRPS, ERA5, GPCC, and PERSIANN-CDR; see Sect. 2.3 for more information) were evaluated against historical measured rainfall at 10 stations. Secondly, to account for input uncertainty, the three best-performing rainfall products were forced in the conceptual hydrological model HBV-light with potential evapotranspiration and temperature data from ERA5. The model was calibrated during the period from 2006 to 2019 and validated during the period from 1991 to 1996. Thirdly, the parameter sets that obtained very good performance (Nash–Sutcliffe efficiency, NSE, greater than 0.75) were utilized to predict the inflow of GERD during the operation period (2020–2022). Then, from the water balance of GERD, the daily storage was estimated and compared with the storage derived from Landsat and Sentinel imageries to evaluate the performance of the selected rainfall products and the reliability of the framework. Finally, 3 years of GERD filling strategies was retrieved using the best-performing simulation of CHIRPS with an RMSE of 1.7 ×109 and 1.52 ×109m3 and an NSE of 0.77 and 0.86 when compared with Landsat- and Sentinel-derived reservoir storage, respectively. It was found that GERD stored 14 % of the monthly inflow of July 2020; 41 % of July 2021; and 37 % and 32 % of July and August 2022, respectively. Annually, GERD retained 5.2 % and 7.4 % of the annual inflow in the first two filling phases and between 12.9 % and 13.7 % in the third phase. The results also revealed that the retrieval of filling strategies is more influenced by input uncertainty than parameter uncertainty. The retrieved daily change in GERD storage with the measured outflow to the Republic of the Sudan allowed further interpretation of the downstream impacts of GERD. The findings of this study provide systematic steps to retrieve filling strategies, which can serve as a base for future development in the field, especially for data-scarce regions. Locally, the analysis contributes significantly to the future water management of the Roseires and Sennar dams in the Republic of the Sudan.