Mehdi Bahrami, Abdol Rassoul Zarei, Amir Reza Ahmadi
Groundwater is the primary source of irrigation water in semi-arid regions, where poor water quality can progressively degrade the physical and chemical properties of soil. This study investigated the influence of groundwater quality on soil salinization and degradation risk in the Eghlid agricultural valleys by integrating hydrochemical analysis, multivariate statistics, machine learning, and spatial assessment. Groundwater and soil samples were collected from irrigated fields across two contrasting hydrological units (HU-A and HU-B) separated by mountainous terrain. Major ions, salinity- and sodicity-related indices, and soil electrical conductivity (soil EC) were analyzed to evaluate irrigation suitability and soil response. Spearman's rank correlation, multiple linear regression (MLR), and random forest (RF) analyses were applied to identify the dominant groundwater parameters controlling soil salinity. The results indicated that groundwater electrical conductivity (EC) is the primary driver of soil EC in both units, while sodicity indicators play a secondary role. Cluster analysis further revealed distinct hydrochemical regimes with consistent soil salinity responses, particularly in HU-B, which exhibited greater spatial heterogeneity. Based on these findings, a framework for soil degradation risk zoning was developed by integrating groundwater quality indicators with observed soil EC conditions. The results showed that HU-A is predominantly characterized by low degradation risk due to favorable groundwater chemistry, whereas localized moderate risk zones occur in HU-B, associated with higher salinity inputs under intensive irrigation. Generally, the study demonstrated that groundwater salinity, rather than sodicity, governs soil degradation processes in the study area. The proposed integrated framework provides a robust decision-support tool for sustainable groundwater-based irrigation management in semi-arid agroecosystems.