A New Global Hypsometric Data Set Derived From Satellite and Geometric Methods for Reservoir Storage Estimation

Abstract Reservoir hypsometric relationships are crucial in determining water storage changes and modeling hydrological processes. Previous studies have demonstrated that uncertainties in reservoir surface area observations are a primary source of error in satellite‐derived storage estimates. However, little research has examined how variability in hyposometric data sets influences storage estimation accuracy. In addition to evaluating the impacts of hypsometric data set variability on storage estimation, this study introduced a new, simple hypsometric satellite and geometry‐based (SAGE) approach. SAGE combines remote sensing and geometry‐based methods to generate a global hypsometric data set for 7,250 reservoirs worldwide using the Global Reservoir and Dam (GRanD) database, and its performance is compared with existing data sets. The SAGE‐based results were validated against in situ Elevation‐Volume (E‐V), Area‐Volume (A‐V) relationships and storage dynamics from 132 reservoirs in the United States and Vietnam. Validation results showed strong performance, with median Normalized Root Mean Square Error (NRMSE) of 9.2% for A‐V curves and 12.4% for E‐V curves, compared to higher median NRMSE values from other global data sets, exceeding 20% for E‐V curves and ranging from 12.5% to 32.3% for A‐V curves. Reservoir storage dynamics were then estimated by integrating satellite‐derived surface areas from the Global Reservoir Surface Area Data set (GRSAD) with the SAGE‐derived AEV curves. The SAGE‐based approach significantly improved storage estimates, reducing errors by over 20% compared to other global AEV products, based on in situ storage data from 109 reservoirs in the United States and Vietnam. Overall, this study improved the accuracy of hypsometric relationships and storage dynamics, producing a more robust product for global hydrological applications.