Spatio-Temporal Statistical Assessment of Water Quality Dynamics in the Kelani River, Sri Lanka: A Data-Driven Framework for Sustainable River Basin Management

The Kelani River is one of the most important freshwater resources in Sri Lanka, supplying drinking water, supporting aquatic ecosystems, and sustaining industrial and agricultural activities. However, increasing anthropogenic pressures have intensified the need for comprehensive long-term assessments of river water quality. This study evaluated the spatio-temporal variability of six physicochemical water-quality parameters (pH, temperature, turbidity, chemical oxygen demand, dissolved oxygen, and chloride) using monthly observations from twelve monitoring locations covering January 2007 to May 2022, representing the most up-to-date long-term CEA monitoring record available to the authors at the time of data acquisition. An integrated statistical framework comprising correlation analysis, Granger causality testing, vector autoregressive (VAR) modelling, ARIMA and seasonal ARIMA (SARIMA) forecasting, K-means time-series clustering, Pettitt temporal homogeneity testing, changepoint detection, and regression kriging was applied to investigate temporal dynamics, predictive relationships, temporal stability, and spatial variability. VAR, ARIMA, and SARIMA were selected as interpretable baseline models for assessing lagged dependence and seasonal temporal structure in the monthly water-quality series; comparison with machine-learning and hybrid models was beyond the scope of this study. The best-performing imputation method varied among parameter–location series; for pH, the minimum RMSE values of the selected methods ranged from 0.07 to 0.27. Temporal forecasting performance was also strongly parameter- and location-dependent. For DO, test-set RMSE ranged from 0.69 to 1.33 mgL−1, whereas chloride forecast RMSE ranged from 5.60 to 838.56 mgL−1, with the largest error observed at Victoria Bridge, reflecting the pronounced variability of the downstream chloride series. After Bonferroni correction across the 360 directional parameter–site comparisons, only two Granger-predictive relationships remained statistically significant: COD → chloride at Maha Oya and temperature → pH at Kaduwela Bridge. The corresponding VAR models had R2 values of 0.20 and 0.25, respectively, indicating modest explanatory power, while ARIMA and SARIMA models provided satisfactory forecasting performance for several parameters. The Pettitt homogeneity assessment identified nine candidate shifts at the unadjusted 5% significance level, but none remained statistically significant after Bonferroni correction across the 72 parameter–location tests. Regression kriging provided an exploratory representation of spatial variability in water-quality characteristics along the river. Overall, the proposed framework provides a robust statistical approach for long-term river water-quality assessment and supports evidence-based monitoring, pollution management, and sustainable river basin management, contributing to Sustainable Development Goal 6.