From Contamination to Forecast: Linking Anthropogenic Hydrological Change to Ecological Risk in Poyang Lake

Anthropogenic activities including industrial and agricultural discharges, sand mining, and water regulation have drastically altered the hydrological regime and water quality of Poyang Lake, China’s largest freshwater lake. These modifications lead to non-stationary inputs of heavy metals (e.g., As, Hg, Cr, Se) and nutrients, driving eutrophication and posing significant risks to aquatic ecosystems.

This study analyses multi-year (2018–2020) water quality data from key inflow sites to quantify human impacts on contaminant regimes. Results reveal strong seasonal patterns: heavy metal concentrations (As, Hg) peak during low-flow periods, whereas nutrient loads and algal blooms intensify following high-flow events linked to agricultural runoff. This dynamic hydrological contamination directly threatens the endangered Yangtze finless porpoise (Neophocaena asiaeorientalis), with tissue analyses showing high bioaccumulation of Hg and Cu in the liver, indicating significant ecological risk.

Building on these findings, we highlight the urgent need for forecasting frameworks tailored to human-influenced catchments. We propose integrating process-based hydrological models with water quality modules and machine learning techniques to simulate contaminant transport under non-stationary climatic and anthropogenic drivers. Furthermore, we demonstrate how remote sensing and continuous sensor data can improve the monitoring of pollutant sources and algal blooms. Finally, we outline a pathway towards ecological risk forecasting by coupling hydrological-water quality predictions with bioaccumulation models for vulnerable species.

This work underscores the critical gap in forecasting tools for heavily modified systems and provides a case for developing coupled human-natural models to support early warning systems and adaptive management strategies for biodiversity conservation.