Singh, Konthoujam KhelchandraSingh, Kshetrimayum KrishnakantaUsha, KhuraijamDas, SubhasishSingh, Salam Shantikumar2024-01-212024-08-142024-01-212024-08-142022-05-13944134410.1007/s11356-022-20631-1http://10.2.3.109/handle/32116/4134The present work elucidates the effective application of multivariate statistics in understanding the probable relations between surface water hydrochemistry and aquatic macrophyte productivity and their underlying seasonal dynamics in a remote mountainous lake of northeast India. The result of hierarchical cluster analysis revealed three distinct clusters corresponding to the pre-monsoon (35.42%), post-monsoon (52.08%), and monsoon (12.50%) seasons. The factor analysis yielded three principal components suggesting the sediment flux, farming discharge, domestic waste, bacterial oxidation of sulfur compounds, and dissolution of plant matters associated with dissolved feldspar minerals as the influential factors. The lake hydrochemistry also varied significantly, both spatially and temporally implying geogenic weathering processes from rock-soil-water interactions. Overall, sixteen aquatic macrophytes were identified, and their monthly and daily net primary productivity varied considerably in different seasons. Regression analysis highlighted the effect of temperature, total dissolved solids, electrical conductivity, and turbidity on the seasonal fluctuations in macrophyte productivity. Overall, the study provides insights into seasonal variation in the lake water chemistry and highlights the role of statistical tools in understanding the fragile aquatic ecosystems over cost-, labor-, and time-intensive inventory studies. � 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.en-USHydrochemistryMacrophyteMultivariate statistical analysisNortheast IndiaPrimary productivitySurface waterTamdil LakeArticlehttps://link.springer.com/10.1007/s11356-022-20631-1