Hydrochemical Evolution and Predictive Modeling of Chloride-Dominated Groundwater Salinization in Northern Kuwait.

Groundwater quality assessment in hyper arid aquifers is often limited by sparse monitoring, incomplete hydraulic data, and difficulty separating prior hydrochemical conditions from short-term meteorological forcing. This study develops a hydrochemically grounded and machine learning framework with internal transferability testing to diagnose chloride-dominated salinization in the Rawdatain-Umm Al Aish freshwater aquifers of northern Kuwait. The framework combines four components: comparison of pre-1990 baseline chemistry with 2012-2015 observations, chloride-specific salinization diagnostics, separation of predictors into meteorological, space-time, prior-chemistry, and hybrid groups, and model evaluation using random, temporal, and grouped-well validation. The analysis also integrates spatial salinity mapping, Cl-TDS analysis, Na:Cl ratios, carbonate/sulfate balance, principal component analysis (PCA), and supervised learning models to evaluate both hydrochemical change and predictive structure. This integrated analysis shows substantial deterioration relative to the historical baseline. Median TDS increased from 905.5 to 1821.0 mg/L (+101.1%), whereas median Cl, Ca, Mg, and Na increased by 228.5%, 194.6%, 227.0%, and 75.3%, respectively. HCO