Multi-Scale and Multi-Depth Validation of Soil Moisture From the China Land Data Assimilation System
Yangxiaoyue Liu, Wenlong Jing, Shuai Sun, Chongyang Wang
Abstract
Soil moisture (SM) plays a critical role in vegetation growth, terrestrial hydrological cycle, and regional climate change. In this study, the China Meteorological Administration Land Data Assimilation System (CLDAS) SM was validated at multi-scale and multi-depth against in-situ measurements over the Tibetan Plateau, and the Global Land Data Assimilation System (GLDAS) SM was also used as a reference for the validation process. The results revealed the following: (1) In validations using the large, medium, and small scale networks, the small network achieved comparatively superior accuracy with a higher fitting degree and smaller statistical error, proving the reliability and robustness of densely clustered in-situ stations. (2) The CLDAS SM can correctly present the promotion effect from precipitation. The most significant response of the CLDAS SM to precipitation lagged from 1 to 3 days in terms of growing depth, whereas promotion to SM tended to decrease as the soil depth increased. (3) CLDAS and GLDAS reasonably fitted in-situ measurements in the frozen and unfrozen seasons, while their bias gradually increased with increasing depth. The SM errors in the snow-covered region were relatively stable. Diurnal land surface temperature exhibited a positive correlation with the bias of top-layer SM. In summary, this study shows that CLDAS SM retrievals favorably reproduced values and dynamics of in-situ measurements and can be expected to act as a valuable reference for water cycle studies and climate evolution analysis.