Spatio-temporal surface water quality assessment for an alluvial plain: insight from combined water quality index and partial least squares structural equation modeling.

Surface water quality underpins sustainable development and public health. However, rapid urbanization and fertilizer-intensive agriculture have increasingly degraded surface waters worldwide. To rigorously evaluate surface water suitability for drinking and irrigation and to identify the dominant driving factors, 112 surface water samples were collected from four representative hydrological periods (wet, average-flow, dry, and pre-flood) in an alluvial plain. Temporally, hydrochemical characteristics exhibited a pronounced dilution-flushing-enrichment pattern, with dominant dilution during the wet season and marked pollutant accumulation during the dry season, while pollution signals spread from upstream point sources and gradually propagated throughout the basin. Hydrochemical analysis identified Ca-HCO3 as the dominant water type. Evaluation based on the comprehensive drinking water quality index (CDWQI) indicated that more than 90% of surface water samples met national drinking water quality regulations. Similarly, the comprehensive irrigation water quality index (CIWQI) indicated that most sites met the criteria for direct agricultural irrigation. However, localized contamination, attributable primarily to untreated urban wastewater discharge and fertilizer leaching from intensive croplands, was observed at several sampling locations. Structural equation modeling based on partial least squares (PLS-SEM), integrated with sensitivity analysis, confirmed that anthropogenic inputs directly drive the suitability of surface water for drinking and irrigation, while natural drivers (precipitation and temperature) indirectly influence water quality via hydrochemical parameters. These achievements offer empirically grounded, transferable insights to advance surface water quality assessment and management, not only in alluvial plains but also in analogous peri-urban agricultural regions globally.