A method for balancing the terrestrial water budget and improving the estimation of individual budget components

Enforcing water budget closure is critical for providing consistent estimates of budget components to understand water movement between the atmosphere and the terrestrial land surface. However, existing water budget closure correction (BCC) methods do not consider improving the performance of budget-corrected datasets when closing the water budget. This study proposes a method for enforcing terrestrial water budget closures and improving the estimation of budget components by combining budget component measurements and introducing an error adjustment factor of ET. The proposed method first corrects raw budget component datasets based on their measurements and then enforces the water budget closure of the pre-corrected datasets using existing BCC methods. The proposed method was verified by comparing it with existing BCC methods in nine major basins in mainland China. Nine precipitation (P), four evapotranspiration (ET), two simulated runoff (R), and four terrestrial water storage change datasets were selected to comprehensively evaluate the performance of the proposed method. The results showed that the water budget closure constraint based on existing BCC methods improved the performance of datasets with low accuracy. However, the performance is not significantly improved or even reduced for datasets with high accuracy. Compared with existing BCC methods, the perfor-mance of budget-corrected datasets using the proposed method improved by approximately 8, 86, 8, 5, and 14% according to the statistical metrics root mean square error, percent bias, mean absolute error, Kling-Gupta ef-ficiency, and probability of detection, respectively.

Automated summary

Enforcing water budget closure is important for understanding water movement, but existing methods do not consider improving the performance of budget-corrected datasets. This study proposes a method for enforcing terrestrial water budget closure and improving estimation by combining measurements and error adjustment factors. The proposed method was verified in nine major basins in mainland China, showing significant improvement in performance compared to existing methods.