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Soil moisture is a vital factor affecting the hydrological cycle and the evolution of soil and geomorphology, determining the formation and development of the vegetation ecosystem. The previous studies mainly focused on the effects of different land use patterns and vegetation types on soil hydrological changes worldwide. However, the spatial heterogeneity and driving factors of soil gravimetric water content in alpine regions are seldom studied. On the basis of soil sample collection, combined with geostatistical analysis and the geographical detector method, this study examines the spatial heterogeneity and driving factors of soil gravimetric water content in the typical alpine valley desert of the Qinghai–Tibet Plateau. Results show that the average value of soil gravimetric water content at different depths ranges from 3.68% to 7.84%. The optimal theoretical models of soil gravimetric water content in 0–50 cm layers of the dune are different. The nugget coefficient shows that the soil gravimetric water content in the dune has a strong spatial correlation at different depths, and the range of the optimal theoretical model of semi-variance function is 31.23–63.38 m, which is much larger than the 15 m spacing used for sampling. The ranking of the influence of each evaluation factor on the alpine dune is elevation &gt; slope &gt; location &gt; vegetation &gt; aspect. The interaction detection of factors indicates that an interaction exists among evaluation factors, and no factors are independent of one another. In each soil layer of 0–50 cm, the interaction among evaluation factors has a two-factor enhancement and a nonlinear enhancement effect on soil gravimetric water content. This study contributes to the understanding of spatial heterogeneity and driving factors of soil moisture in alpine deserts, and guidance of artificial vegetation restoration and soil structure analysis of different desert types in alpine cold desert regions.

Spatial Heterogeneity and Driving Factors of Soil Moisture in Alpine Desert Using the Geographical Detector Method