Krishnaraj, A.Honnasiddaiah, R.2026-02-042023Environmental Monitoring and Assessment, 2023, 195, 12, pp. -1676369https://doi.org/10.1007/s10661-023-12059-yhttps://idr.nitk.ac.in/handle/123456789/21564Studying spatiotemporal water quality characteristics and their correlation with land use/land cover (LULC) patterns is essential for discerning the origins of various pollution sources and for informing strategic land use planning, which, in turn, requires a comprehensive analysis of spatiotemporal water quality data to comprehend how surface water quality evolves across different time and space dimensions. In this study, we compared catchment, riparian, and reach scale models to assess the effect of LULC on WQ. Using various multivariate techniques, a 14-year dataset of 20 WQ variables from 20 monitoring stations (67,200 observations) is studied along the Middle Ganga Basin (MGB). Based on the similarity and dissimilarity of WQPs, the K-means clustering algorithm classified the 20 monitoring stations into four clusters. Seasonally, the three PCs chosen explained 75.69% and 75% of the variance in the data. With PCs > 0.70, the variables EC, pH, Temp, TDS, NO<inf>2</inf> + NO<inf>3</inf>, P-Tot, BOD, COD, and DO have been identified as dominant pollution sources. The applied RDA analysis revealed that LULC has a moderate to strong contribution to WQPs during the wet season but not during the dry season. Furthermore, dense vegetation is critical for keeping water clean, whereas agriculture, barren land, and built-up area degrade WQ. Besides that, the findings suggest that the relationship between WQPs and LULC differs at different scales. The stacked ensemble regression (SER) model is applied to understand the model’s predictive power across different clusters and scales. Overall, the results indicate that the riparian scale is more predictive than the watershed and reach scales. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.CatchmentsK-means clusteringLand useQuality controlRiver pollutionSurface watersData clusteringGanga basinLand use/land coverMonitoring stationsMulti scale analysisPollution sourcesRedundancy analysisSpatial scaleStacked ensembleWater quality characteristicsWater qualityhydrogennitratenitrogen dioxidephosphoruscluster analysisdata processingland coverland usepollutant sourceriver basinwater qualityagriculturealgorithmArticlebiochemical oxygen demandcatchment area (hydrology)chemical oxygen demandcontrolled studydry seasonelectric conductivityenvironmental factorenvironmental impact assessmentenvironmental temperaturegeographic distributionrainy seasonredundancy analysisriparian ecosystemseasonal variationspatiotemporal analysisurban areavegetationwater monitoringwater pollutantenvironmental monitoringproceduresriverGanges BasinAgricultureEnvironmental MonitoringRiversWater Quality