Dynamics of Soil Moisture and Its Response to Meteorological Factors at Different Depths in an Arid Land, Northwest China

Soil moisture is a critical variable in the eco-hydrological processes of arid regions; however, the vertical stratified mechanisms of soil moisture response to meteorological factors in artificial grassland remain inadequately quantified. Based on 10-min interval monitoring data from 2015 to 2024 in the middle reaches of the Heihe River, this study investigated the dynamics of soil moisture within a 0–160 cm depth profile in an arid artificial grassland. By integrating the Mann–Kendall trend test, Pearson correlation, time-lagged cross-correlation, multiple regression analysis and redundancy analysis, we systematically investigated the changing relationships between meteorological factors and soil moisture. The results revealed the following: (1) main meteorological factors driving surface processes (e.g., net radiation, air temperature, vapor pressure deficit) showed significant increasing trends with strong variability, while relative humidity decreased significantly, and these findings collectively point to a general trend of warming and drying in the region; (2) WS, Ta, rainfall, and RH are the principal factors explaining soil moisture variations, wherein temperature and humidity exhibit positive correlations with soil moisture; (3) RDA results showed that shallow soil moisture (0–20 cm) was primarily governed by air temperature and rainfall, whereas deep soil moisture was increasingly regulated by vapor pressure deficit; (4) time-lagged cross-correlation analysis showed that the response time of soil moisture to rainfall almost increased with soil depth, while the correlation coefficient gradually weakened from 0.43 to 0.06. This study quantitatively elucidates the stratified control mechanism of meteorological factors on the vertical pattern of soil moisture, contributing to a deeper understanding of the response of eco-hydrological processes under climate change and providing a scientific basis for water resource management, agricultural planning, and climate prediction.