A Novel Two‐Step Method for Enforcing Water Budget Closure and an Intercomparison of Budget Closure Correction Methods Based on Satellite Hydrological Products

Abstract

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.

Assessing the Water Budget Closure Accuracy of Satellite/Reanalysis-Based Hydrological Data Products over Mainland China

“…Precipitation (P) is the main driver of the terrestrial hydrological cycle [1][2][3]. A great number of satellite and reanalysis precipitation (SRP) products, characterized by global coverage and high temporal and spatial resolution, have been produced and widely applied in large-scale meteorological and hydrological analyses [4][5][6][7]. This is because, despite the high accuracy of in situ rainfall observations, they are sparse, and the record length in many basins around the world is insufficient, limiting the accuracy of hydrological research in these basins greatly [8].…”