Statistical Modeling and Spatiotemporal Analysis of Water Quality Parameters in Alborz Dam Reservoir

Accurate prediction of water quality parameters is essential to sustainable management of reservoir systems. In this study, the spatiotemporal variability of water quality in the Alborz Dam reservoir was investigated using advanced statistical modeling techniques. Key physicochemical indicators, including turbidity, total suspended solids (TSS), total dissolved solids (TDS), electrical conductivity (EC), total hardness (TH), Ca and Mg, dissolved oxygen (DO), and pH, were measured across multiple seasons and depths. Field and laboratory datasets were analyzed in SPSS to characterize interdependencies among variables. Correlation analysis revealed strong relationships among major parameters, including TSS–turbidity (R= 0.985, p < 0.01), TDS–EC (R= 0.991, p < 0.01), and TH-Ca (R= 0.885, p < 0.01). Regression modeling showed that turbidity was primarily predicted by TSS (β= 0.926, p < 0.001), with TDS contributing marginally. Similarly, EC was strongly determined by TDS (β= 1.559, p < 0.001), while the contribution of TH was minimal. TH was significantly predicted by Ca (β= 2.696, p < 0.001), whereas Mg demonstrated negligible effects. For DO, pH displayed a borderline positive association (β= 6.051, p= 0.054), while phosphorus and chlorophyll-a were not significant predictors. The results demonstrate the capability of statistical approaches to model reservoir water quality and to elucidate complex physicochemical interactions. Although linear regression provided valuable insights, the potential integration of nonlinear and machine learning methods could enhance predictive accuracy. Overall, this study underscores the importance of predictive modeling in integrated water, energy, and environment management, supporting sustainable hydropower, cost reduction, and ecosystem protection.