Soil water is a key factor limiting ecosystem sustainability in arid and semi-arid areas of the Hexi Corridor of China, which is characterized by an ecological environment that is vulnerable to climate change. However, there is a knowledge gap regarding the large-scale spatial distribution of soil water in this region. The specific objectives of this study were to determine the spatial distribution patterns of soil water content (SWC) across the entire Hexi Corridor and identify the factors responsible for spatial variation of SWC at a regional scale. This study collected and analyzed SWC in the 0–100 cm soil profile from 109 field sampling sites (farmland, grassland and forestland) across the Hexi Corridor in 2017. We selected 17 factors, including land use, topography (latitude, longitude, elevation, slope gradient, and slope aspect), soil properties (soil clay content, soil silt content, soil bulk density, saturated hydraulic conductivity, field capacity, and soil organic carbon content), climate factors (mean annual precipitation, potential evaporation, and aridity index), plant characteristic (vegetation coverage) and planting pattern (irrigation or rain-fed), as possible environmental variables to analyze their effects on SWC. The results showed that SWC was 0.083 (±0.067) g/g in the 0–100 cm soil profile and decreased in the order of farmland, grassland and forestland. The SWC in the upper soil layers (0–20, 20–40 and 40–60 cm) had obvious difference when the mean annual precipitation differed by 200 mm. The SWC decreased from southeast to northwest following the same pattern as precipitation, and had a moderate to strong spatial dependence in a large effective range (75–378 km). The SWC showed a similar distribution and had no significant difference between soil layers in the 0–100 cm soil profile. The principal component analysis showed that the mean annual precipitation, geographical position (longitude and latitude) and soil properties (soil bulk density and soil clay content) were the main factors dominating the variance of environmental variables. A stepwise linear regression equation showed that plant characteristic (vegetation coverage) and soil properties (soil organic carbon content, field capacity and soil clay content) were the optimal factors to predict the variation of SWC. Soil clay content could better explain SWC variation in the deeper soil layers compared with other factors.</p
