Identification of the Driving Factors to Algal Biomass in Lake Dianchi: Implications for Eutrophication Control
2024
Jie Huang | Jing Zhang | Nenghan Wang | Sheng Hu | Youai Duan
Accurate analysis of spatiotemporal variations in algal biomass and their underlying causes is crucial for controlling algal blooms and enhancing aquatic ecological quality. The present study, spanning 2011 to 2020, was conducted across 10 sites in Lake Dianchi, where peak algal biomass levels occur from May to September, with higher concentrations in the lake&rsquo:s northern zones compared to other areas. Employing Spearman&rsquo:s correlation analysis, generalized additive models (GAMs), and random forest (RF) techniques, the relationships between algal biomass and water quality indicators were investigated. Spearman&rsquo:s correlation analysis revealed a positive relationship between chlorophyll a (Chla) and total phosphorus (TP) across various spatial scales. RF analysis identified TP as the most influential factor on a lake-wide scale: while in localized RF models, organic pollution-related indicators (COD, CODMn, and BOD5) and TP consistently emerged as the primary predictors of Chla at most sites. GAM results indicated spatially variable and nonlinear responses of algal biomass to predictors, reinforcing TP&rsquo:s significance lake-wide and at many localized scales. This comprehensive analytical approach provides valuable insights into the role of water quality factors and nonlinear dynamics, thereby advancing our understanding of the relationships between algal biomass and environmental conditions. These findings are pivotal for the development of scientifically informed strategies for lake management and conservation.
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