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

Abstract

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. HCO3 remained broadly stable, whereas NO3 declined by 43.5%. Diagnostic plots and PCA indicate chloride-rich salinization governed by mixed geochemical and mixing processes rather than simple halite dissolution alone. Random holdout models performed well, with R2 values of 0.947 for SO4, 0.913 for Na, 0.912 for Ca, 0.877 for HCO3, 0.868 for TDS, 0.808 for NO3, 0.807 for Cl, and 0.673 for Mg. However, validation performance was analyte-specific: Cl declined mainly under temporal holdout, NO3 declined under grouped-well holdout, and Mg showed the lowest random-holdout skill and largest train-test gap despite higher grouped-well performance. Predictor importance showed that spatial coordinates, elevation, and lagged chemistry were more influential than meteorological predictors alone. The framework supports targeted monitoring of chloride-rich hotspots and validation-aware groundwater quality decision support in hyper arid freshwater reserves.