Identifying the key environmental drivers of chlorophyll-a in the Danjiangkou reservoir using interpretable machine...

Algal blooms have become a critical environmental issue worldwide, with chlorophyll-a (Chl-a) serving as a key biological indicator for assessing bloom intensity and providing essential insights into bloom dynamics. Based on four field investigations conducted in the Danjiangkou Reservoir between 2023 and 2024, this study utilized data on Chl-a, nutrients (total nitrogen, total phosphorus, ammonia nitrogen, etc.), water temperature, and light intensity to develop a Chl-a concentration prediction model using the XGBoost algorithm. The Shapley Additive Explanations method was further employed to quantitatively evaluate the relative contributions of environmental variables to Chl-a in the Danjiangkou Reservoir area and the Hanjiang Reservoir area. The results showed that the XGBoost model achieved high accuracy in modeling Chl-a concentrations (R² > 0.8). In the Hanjiang Reservoir, water temperature and light intensity were identified as the dominant factors influencing Chl-a, together accounting for over 58% of its variation, highlighting the leading role of physical conditions in algal growth. In contrast, in the Danjiang Reservoir, water temperature, total phosphorus, and ammonia nitrogen were the primary drivers, jointly contributing approximately 62.2%, indicating a stronger influence of nutrient availability. This study elucidates the major environmental drivers affecting Chl-a levels in the Danjiangkou Reservoir and provides important implications for safeguarding water quality in the water source area of the South-to-North Water Diversion Project.