Spatial heterogeneity of agricultural drought drivers in irrigation district: A causal inference framework bridging covariation and structural equation modeling

Analyzing the driving factors of agricultural drought is important for irrigation management. This study established a new causal framework integrating causal covariation and Structural Equation Modeling (SEM) to reveal the agricultural drought mechanisms in the Hetao Irrigation District of China. Using multi-source data (2001–2020), we quantified the drought patterns by Temperature Vegetation Dryness Index (TVDI) and identified the driving factors in 23 sub-regions using the causal covariation method. The findings are as follows: 1) Over the past two decades in the Hetao Irrigation District, 47.8 % of the region maintained a stable drought condition, 17.4 % experienced aggravated drought, and 34.8 % saw alleviated drought. The study area exhibited significant spatial heterogeneity in drought intensity: elevation explained 81 % of spatial variability (r = 0.904), with higher-elevation zones (>1035 m) facing more severe drought severity. Drainage density significantly reduced drought pressure (r = −0.76). 2) Among all sub-regions, temperature factors (LST and TEMP) consistently influenced the severity of drought, while PET, SM, and runoff exhibited significant spatial heterogeneity in their driving strength for agricultural drought in different sub-regions. The SEM, constrained by the causal covariation results, demonstrated excellent model fit (P > 0.05, CFI>0.95, GFI>0.95, RMSEA<0.05), confirming the reliability of the causal covariation results. 3) The conclusions were verified by ESI, and the non-stationarity analysis using TVDI revealed that some driving factors (such as SM and runoff) changed over time due to human interventions like water-saving techniques, but still affirmed the dominate role and spatial pattern of temperature. This study provides a replicable model for analyzing the drought mechanisms in irrigation districts, which is helpful for improving the ability of precise prediction and sustainable management of water resource.