Spatiotemporal variation of physicochemical parameters in Aripal and Watalara streams of Kashmir Himalaya using multivariate statistical techniques.

The sustainability and management of ecological services provided by the stream ecosystem requires regular assessment of its physicochemical parameters. The anthropogenic pressure in terms of deforestation, urbanization, use of fertilizers and pesticides, land use, and climate change are the major factors responsible for the deterioration of water quality. In the present study, we monitored 14 physicochemical parameters at three different sites from June 2018 to May 2020 in both the Aripal and Watalara streams of Kashmir Himalaya. The data was analyzed through one-way ANOVA, Duncan's multiple range test, two-tailed Pearson's correlation, and multivariate statistical techniques like principal component analysis (PCA) and cluster analysis (CA). A significant variation (p < 0.05) was observed in all the physicochemical parameters on both spatial (except AT, WT, and DO) and seasonal (except TP and NO-N) scales. Pearson's correlation revealed a significantly strong positive correlation for AT, WT, EC, Alk, TDS, TP, NO-N, and NO-N. PCA's first four principal components were considered significant as they represented the highest cumulative percent variances of 76.49% and 74.72% in Aripal and Watalara streams, respectively. The loading and scatter plots revealed that AT, WT, TP, NO-N, and NO-N influence water quality. The strong loading of these parameters indicates the anthropogenic activities in the streams. CA depicted two well-defined clusters, wherein cluster-I is comprised of sites A3 and W3 which indicate poor water quality. In contrast, cluster-II is comprised of sites A1, W1, A2, and W2 which indicate good water quality. The present study could be helpful to ecologists, limnologists, policymakers, and other stakeholders in developing long-term management programs and conservation strategies for water resources.