Multi-factor modeling of chlorophyll-a in South China's subtropical reservoirs using long-term monitoring data for quantitative analysis

TL;DR

This study uses long-term monitoring data from South China's reservoirs to develop a multi-factor model that elucidates the complex interactions among nutrients, temperature, and chlorophyll-a, aiding eutrophication management.

Contribution

It introduces a dynamic multi-factor hydro-ecological model validated with multi-year field data, highlighting the dominant role of total nitrogen and temperature interactions in Chl-a dynamics.

Findings

TN is a more influential driver than TP for Chl-a proliferation.

Chl-a increases by 4.2 ug/L per unit increase in TN.

Synergistic effects between temperature and nutrients enhance Chl-a growth.

Abstract

Eutrophication and harmful algal blooms, driven by complex interactions among nutrients and climate, threaten freshwater ecosystems globally, particularly in densely populated Asian regions where rapid urbanization and agricultural intensification exacerbate nutrient pollution. Understanding the non-linear interactions among water temperature, nutrient levels, and chlorophyll-a (Chl-a) dynamics is crucial for addressing eutrophication in freshwater ecosystems. Many existing studies, however, tend to oversimplify these relationships and lack validation with long-term field data. Here, we conducted multi-year field monitoring (2020-2024) of key environmental factors, including total nitrogen (TN), total phosphorus (TP), water temperature, and Chl-a, across three reservoirs in Guangdong Province, China: Tiantangshan (S1), Baisha River (S2), and Meizhou (S3). Strong positive correlations were found between Chl-a and TN, TP, and temperature. Numerical analysis of the long-term data revealed TN as a more influential driver than TP for Chl-a proliferation in these systems, with Chl-a increasing by an average of 4.2 ug/L per unit increase in TN, compared to 2.8 ug/L per unit increase in TP. Based on the collected data, we developed and calibrated a dynamic multi-factor hydro-ecological model. The model accurately reproduced the observed Chl-a patterns, identifying synergistic effects between temperature and nutrients, particularly a 15% enhancement in Chl-a growth rate when temperature exceeded 25 concurrent with high TN.