Improving Land Surface Hydrological Simulations in China Using CLDAS Meteorological Forcing Data

The accuracy of land surface hydrological simulations using an offline land surface model (LSM) depends largely on the quality of the atmospheric forcing data. In this study, Global Land Data Assimilation System (GLDAS) forcing data and the newly developed China Meteorological Administration Land Data Assimilation System (CLDAS) forcing data are used to drive the Noah LSM with multiple parameterizations (Noah-MP) and to explore how the newly developed CLDAS forcing data improve land surface hydrological simulations over mainland China. The monthly soil moisture (SM) and evapotranspiration (ET) simulations are then compared and evaluated against observations. The results show that the Noah-MP driven by the CLDAS forcing data (referred to as CLDAS_Noah-MP) significantly improves the simulations in most cases over mainland China and its eight river basins. CLDAS_Noah-MP increases the correlation coefficient (R) values from 0.451 to 0.534 for the SM simulations at a depth range of 0–10 cm in mainland China, especially in the eastern monsoon area such as the Huang–Huai–Hai Plain, the southern Yangtze River basin, and the Zhujiang River basin. Moreover, the root-mean-square error is reduced from 0.078 to 0.068 m³ m⁻³ for the SM simulations, and from 12.9 to 11.4 mm month⁻¹ for the ET simulations over mainland China, especially in the southern Yangtze River basin and Zhujiang River basin. This study demonstrates that, by merging more in situ and remote sensing observations in regional atmospheric forcing data, offline LSM simulations can better simulate regional-scale land surface hydrological processes.