Effects of meteorological forcings and land surface model on soil moisture simulation over China

Land surface model (LSM) simulations forced by observed meteorological data provide spatially continuous and temporally complete soil moisture estimates, but the influences of models and meteorological forcings are yet to be validated over a large area due to the lack of in situ measurements. In this study, the Conjunctive Surface-Subsurface Process version 2 (CSSPv2) model was driven by three meteorological forcings, namely, CLDASv2.0, ERA5 and GLDASv2.1, to provide soil moisture simulations over China. The validations over 2090 in situ stations during 2012–2017 showed that CLDASv2.0/CSSPv2 soil moisture simulation performed better than ERA5 and GLDASv2.1 reanalysis products, with an increased correlation of 26%–68% and reduced errors of 14%–24% at the daily time scale. The improvements mostly originate from the use of an advanced LSM because CLDASv2.0/CSSPv2 only increased the correlation by 5%–35% and decreased the errors by up to 9% when compared with ERA5/CSSPv2 and GLDASv2.1/CSSPv2. In contrast, ERA5/CSSPv2 and GLDASv2.1/CSSPv2 soil moisture simulations increased the correlations from their alternative reanalysis LSMs by 17%–63%, and decreased the errors by up to 18%. The results are similar when using the SMAP satellite product as the validation data. The influence of the LSM was more obvious over semiarid regions, such as northern China. The influence of meteorological forcing was more significant for soil moisture simulations at the surface layer, while the LSMs played a more critical role for the middle and deep layers, especially during the cold season due to freeze-thaw processes. This study demonstrates the possibility to further improve soil moisture estimates at a large scale with advanced LSMs, even with the emergence of modern reanalyses.