Understanding the spatiotemporal variations of soil moisture (SM) is crucial for the timely identification of drought and the advancement of regional water resource management. This study utilized ERA5-Land reanalysis data and employed the Mann-Kendall test, correlation analysis, wavelet transform, and Generalized Additive Models (GAM) to analyze the spatiotemporal patterns of SM from 1951 to 2024 in Shandong Province, China. The study also examined the factors influencing these patterns. The results showed that: 1.) The mean annual soil moisture exhibited a significant decreasing trend at a rate of −0.00055 mm/year, and a significant seasonal decreasing trend has been detected. Precipitation emerged as the dominant driver of soil moisture spatiotemporal variability. 2.) Spatially, soil moisture exhibited a distribution pattern consistent with the spatial configurations of precipitation and evaporation. Relationships between soil moisture and climatic variables varied significantly among regions and seasons. 3.) Wavelet analysis revealed 8-year, 16-year, and 32-year periodicities in soil moisture that aligned with the dominant oscillations observed in precipitation and evaporation. 4.) Time-lagged cross-correlation analysis revealed that soil moisture exhibited the strongest instantaneous correlation with precipitation, while this response delay became more pronounced in deeper soil layers. These findings not only provide a basis for enhanced drought monitoring and region-specific management, but also inform the development of sustainable water resource policies aligned with identified multi-year cycles.
Wu et al. (Thu,) studied this question.