Uncovering eutrophication dynamics using PCA-driven spatiotemporal assessment of lake water quality indicators

The degradation of freshwater ecosystems due to nutrient enrichment has underscored the imperative for robust water quality assessment. In this study, 11 monitoring stations were established in the critical water supply reservoir, Tappan Lake, Ohio, USA, to record the key physicochemical and biological parameters on a biweekly basis, especially in the summer. The parameters like temperature, pH, dissolved oxygen (DO), turbidity, conductivity, and biological parameters like chlorophyll-a, blue-green algae (BGA), were analyzed to identify spatiotemporal variations and latent patterns in water quality. More than twenty-three principal component analyses (PCA) were employed on high-resolution, multi-temporal datasets of the lake to investigate lake water quality dynamics. By and large, PCA results suggested that the first two principal components collectively represent nearly 70% of total variance, with a factor loading for temperature (0.818), Chl-a (0.740), and BGA (0.949) emerging as the most influential parameters, jointly accounting for a substantial proportion of variance across principal components. Temporally, the first principal component represented 63% in spring, 55% in summer, and 62% in fall, indicating the dynamic nature of the lake's ecosystem and biological activity. These indicators revealed a strong association of eutrophication dynamics with temporal period, underscoring them as primary ecological stressing patterns. Vertical stratification phenomena and distinct temporal clustering were detected, indicating the interplay between thermal structure and biogeochemical cycling. By isolating the dominant patterns of water quality depletion, this research offers insights into water quality nuances for informed decisions and the targeted management interventions for the lake restoration and management of mid-size reservoirs.