J Environ Manage. 2026 Jun 19;412:130228. doi: 10.1016/j.jenvman.2026.130228. Online ahead of print.
ABSTRACT
Arctic lakes are exceptionally vulnerable to climate change, yet the depth-specific responses of their water quality remain largely unexplored, primarily due to the scarcity of long-term, depth-resolved water quality data. This study investigates how climatic and ice phenology conditions influence water quality in the surface and deep layers of Lake Inari, a large, oligotrophic arctic lake with minimal human-induced disturbance. Leveraging a 44-year dataset of in-situ measurements (1980-2023), we utilize canonical correlation analysis (CCA) to assess the potential interactions among key water quality parameters in the surface and deep layers of this stratified lake and climatic-ice factors. The results show a relatively robust statistical relationship between surface water quality and climatic-ice factors (R2 = 0.67, p < 0.05). These climatic-ice factors account for approximately 53% of the variance in the lake’s surface water quality. In contrast, deep-water quality exhibits a relatively moderate but statistically non-significant correlation with climatic-ice factors (R2 = 0.42, p > 0.05). The absence of statistical significance, likely a false negative due to limited power of our CCA analysis, suggests that deep-water quality responses may be delayed or indirect, potentially mediated by thermal stratification and reduced mixing. Both surface water quality and deep-water quality display exceptionally strong multivariate interactions (R2 = 0.94, p < 0.05), driven by shared variability in turbidity, chemical oxygen demand, and electrical conductivity. The overall statistically significant redundancy between surface water quality and deep-water quality is around 60% for the latter. These findings suggest that surface-layer observations, including satellite and automated-sensor data, may help inform assessments of deep-water quality in large, oligotrophic, dimictic Arctic lakes. If further validated, this approach could support more cost-effective monitoring and strengthen climate-resilient lake-management planning.
PMID:42320202 | DOI:10.1016/j.jenvman.2026.130228
