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Global inland surface water bodies such as lakes and reservoirs, important components of the hydrosphere and ecosphere, are increasingly affected by climate change. Generating bathymetric volume-area-height (BVAH) curves for global inland surface water bodies can enhance our understanding of their topography and climate impacts. However, accurately quantifying the topographic patterns of these water bodies remains challenging due to the difficulties in collecting comprehensive bathymetric data. Therefore, we collected and processed over 2000 bathymetric maps of global water bodies from over 50 different data sources and then developed the BVAH model. Finally, the BVAH hydrological curves of 16671 global inland surface water bodies (larger than 10 km2) were generated. The results include but are not limited to (1) For most targeted water bodies, area (A) and volume (V) exhibit significant power function relationships with surface heights (H), with optimal power values quantified as 1.42 for Aand 2.42 for V. (2) The BVAH model outperforms GLOBathy in estimating area and volume changes, achieving higher correlation coefficients (CC) of approximately 0.962 for the area and 0.991 for volume, and demonstrating lower percentages of root mean squared errors (PRMSE) around 10.9% for the area and 4.8% for volume. (3) In the case study of the Tibetan Plateau and various large global reservoirs, the BVAH curve database can capture dynamic volume changes. As a unified simulation of the bathymetric topographical patterns, our bathymetric dataset and corresponding BVAH curve database have great potential to contribute to effective water resource management and ecological conservation efforts worldwide.

Enforcing terrestrial water budget closure is critical for obtaining consistent data sets of budget components to understand the changes and availability of water resources over time. However, most existing budget closure correction methods (BCCs) are significantly affected by errors in the budget‐component products. Moreover, these existing BCCs do not fully account for the preselection of high‐precision input data sets before enforcing the water budget closure, resulting in uncertainties in the budget‐corrected data sets. In this study, a two‐step method was proposed to enforce the water budget closure of satellite‐based hydrological products. First, high‐precision budget‐component data sets were selected and second, the water budget closure of the selected high‐precision data sets was then enforced by proposing an improved BCC strategy, that is, the Minimized Series Deviation method (MSD). The performance of the proposed two‐step method was verified in 24 global basins by comparing it to three existing BCCs of varying complexity, that is, Proportional Redistribution (PR), Constrained Kalman Filter (CKF), and Multiple Collocation (MCL). The results showed that compared to the existing BCCs, the proposed two‐step method significantly improved the accuracy of budget‐corrected data sets in the range of 2%–19% (statistical analysis was based root mean square error (RMSE) and mean absolute error (MAE) with estimated observation (EO) as a reference). This study also summarized the main factors influencing the performance of the existing BCCs and their further development prospects based on the results. This provides insight into the expansion of theories and methods related to closing the terrestrial water budget. A two‐step method was proposed for enforcing terrestrial water budget closureThe method estimated errors of budget components using a portion of the time series instead of data at a single point in timeThe improvement of three existing budget closure correction methods increased by the range of 2%–19% based on verification in 24 global basins A two‐step method was proposed for enforcing terrestrial water budget closure The method estimated errors of budget components using a portion of the time series instead of data at a single point in time The improvement of three existing budget closure correction methods increased by the range of 2%–19% based on verification in 24 global basins