Code and dataset for the budget closure correction method Minimized Series Deviation method (MSD)

Enforcing terrestrial water budget closure is critical for obtaining consistent datasets 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 random errors and outliers in the budget-component products. Moreover, these existing BCCs do not fully account for the preselection of high-precision input datasets before enforcing the water budget closure, resulting in uncertainties in the budget-corrected datasets. 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 datasets were selected and second, the water budget closure of the selected high-precision datasets was then enforced by proposing an improved BCC strategy, i.e., 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, i.e., 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 datasets between 2 and 19% (statistical analysis was based root mean square error (RMSE) and mean absolute error (MAE). 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.