<p>Water balance closure using purely remote sensing products was difficult to achieve until the launch of Gravity Recovery and Climate Experiment (GRACE) satellites in 2002. The accurate quantification of<strong> </strong>water cycle components (precipitation, evapotranspiration, runoff, and terrestrial water storage) over a large-scale basin is an important step in improving the understanding of the water balance and the response of the basin to different hydrologic extremes. The Upper Blue Nile (UBN) basin contributes about 60% of the streamflow to the main Nile River annually, and hundreds of millions of people heavily rely on the Nile River. Thus, accurate quantification of the hydrological cycle fluxes will help manage the water resources in an effective, sustainable manner. Hydrometeorological data is lacking; nevertheless, remote sensing data provides an alternative approach to estimating the water cycle components. However, prior to incorporating these products into the water budget calculation, their performance over the studied basin should be assessed. In this study, we aim to estimate runoff from the water budget equation and diagnose the estimated runoff with the Eldiem gauge records at the outlet of the UBN basin for the 2003&#8211;2014 period. We evaluate the water cycle components for seven rainfall products (CHIRPSv2, CRU TS4.06, ERA5, TRMM 3B43 V7, GPM, CFSR, and SM2RAIN-CCI), three evapotranspiration products (GLEAM, MOD16, and PLM), and two terrestrial water storage solutions (GRACE JPL MASCON, and Spherical Harmonic (SH) products). The Overall Unified Metric (OUM) approach is adopted to choose the best performing combination among the 42 combination scenarios. The OUM is an approach based on summing up the rankings given for the error and linear fit metrics&#8212;namely, R<sup>2</sup>, slope, y-intercept, RMSE, MAE, and PBIAS. Among the 42 combinations, the best rainfall, TWS, and ET combination performance products to estimate runoff are SM2RAIN-CCI, GLEAM, and GRACE SH, respectively. The statistical results for the six chosen metrics are R<sup>2</sup> = 0.7, slope = 1.6, y-intercept = - 0.5 cm, RMSE = 3 cm, MAE = 2.8 cm, and PBIAS = 36%. The 95% confidence bound of the combination scenarios was found to be able to bracket the runoff during the dry season, but the runoff was overestimated during the&#160;rainy&#160;season. The uncertainty analysis revealed that all the combinations were able to estimate the seasonal trend variation, but closing the water balance equation was not achieved. This deviation in closing the water budget equation might be attributed to the uncertainty associated with satellites, the limitation of land surface models to account for anthropogenic activities, and the coarse resolution of GRACE. Additionally, the signal processing uncertainties and the different algorithm assumptions of the remote sensing products may also have an influence. Further studies are needed to improve the reliability of the remote sensing product for the water budget closure, especially for applications on ungauged basins. Moreover, advancement in satellites will lead to accurate estimates in the near future.</p>
<p><span class="fontstyle0">The filling of the Grand Ethiopian Renaissance Dam (GERD) started in 2020, posing additional challenges for downstream water management in Sudan, which is already struggling to cope with the effects of climate change. </span><span class="fontstyle0">This is also the case for many transboundary rivers that observe a lack of cooperation and transparency during the filling and operation of new dams. Without information about water supply from neighbouring countries, it is risky to manage downstream dams as usual and operation information is </span><span class="fontstyle0">needed to apply modifications. This study aims to test the applicability of using lumped hydrological modelling coupled with remote sensing data in retrieving reservoir filling strategies in regions with limited data availability. Firstly, five rainfall products (namely; ARC2, CHIRPS, ERA5, GPCC, and </span><span class="fontstyle0">PERSIANN-CDR) were evaluated against historical measured rainfall at ten stations. Secondly, to account for input uncertainty, the best three performing rainfall products were forced in the conceptual hydrological model HBV-light with potential evapotranspiration and temperature data</span><span class="fontstyle2">&#160;</span><span class="fontstyle0">from ERA5. The model was calibrated during the period 2006 - 2019 and validated during the period 1991 - 1996. Thirdly, the parameter sets that obtained very good performance (NSE > 0.75) were utilized to predict the inflow of GERD during the operation period (2020 - 2022). Then, from</span><span class="fontstyle2">&#160;</span><span class="fontstyle0">the water balance of GERD, the daily storage was estimated and compared with the storage derived from Landsat observations to evaluate the performance of the selected rainfall products. Finally, three years of GERD filling strategies were retrieved using the best-performing simulation of CHIRPS </span><span class="fontstyle0">with RMSE of 1.7 billion cubic meters (BCM) and NSE of 0.77 when compared with Landsat-derived reservoir storage. 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 </span><span class="fontstyle0">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 Sudan allowed further interpretation of the </span><span class="fontstyle0">downstream impacts of GERD. The findings of this study provide systematic steps to retrieve filling strategies for data-scarce regions, which can serve as a base for future development in the field. Locally, the analysis contributes significantly to the future water management of the Roseires and </span><span class="fontstyle0">Sennar dams in Sudan.</span>&#160;</p>
Abstract. The filling of the Grand Ethiopian Renaissance Dam (GERD) started in 2020, posing additional challenges for downstream water management in Sudan, which is already struggling to cope with the effects of climate change. This is also the case for many transboundary rivers that observe a lack of cooperation and transparency during the filling and operation of new dams. Without information about water supply from neighbouring countries, it is risky to manage downstream dams as usual and operation information is needed to apply modifications. This study aims to test the applicability of using lumped hydrological modelling coupled with remote sensing data in retrieving reservoir filling strategies in regions with limited data availability. Firstly, five rainfall products (namely; ARC2, CHIRPS, ERA5, GPCC, and PERSIANN-CDR) were evaluated against historical measured rainfall at ten stations. Secondly, to account for input uncertainty, the best three 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 2006–2019 and validated during the period 1991–1996. Thirdly, the parameter sets that obtained very good performance (NSE > 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 observations to evaluate the performance of the selected rainfall products. Finally, three years of GERD filling strategies were retrieved using the best-performing simulation of CHIRPS with RMSE of 1.7 billion cubic meters (BCM) and NSE of 0.77 when compared with Landsat-derived reservoir storage. 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 Sudan allowed further interpretation of the downstream impacts of GERD. The findings of this study provide systematic steps to retrieve filling strategies for data-scarce regions, which can serve as a base for future development in the field. Locally, the analysis contributes significantly to the future water management of the Roseires and Sennar dams in Sudan.
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