Enhancing harmful algal bloom predictions through integrated modeling of turbidity and nutrient dynamics in monsoon...

Effective management of harmful algal blooms (HABs) in dam reservoirs is crucial for water resource sustainability, particularly in monsoon climates, such as South Korea, where approximately 55 % of the domestic water supply depends on these reservoirs. To address the critical need for accurate HABs prediction, an innovative three-dimensional hydrodynamic and water quality model that integrates turbidity with traditional nutrient-based approaches was developed. This study focused on the Daecheong Reservoir in the Geum River Basin, simulating the density currents of turbid water during rainfall events, and quantifying their impact on algal (Chl-a) concentrations. The results revealed that turbidity-induced light limitation significantly reduced Chl-a concentrations by 44-58 % across the reservoir, with the effects intensifying from the riverine and transitioning to lacustrine zones. The model improved light extinction coefficient estimations by incorporating multiple water quality parameters, including suspended solids, and outperformed the conventional single-parameter Secchi depth approach. The integrated modeling approach improved the prediction accuracy by 51-70 % (RMSE-ERR, Root Mean Square Error-Error Reduction Rate) and 52-67 % (MAPE-ERR, Mean Absolute Percentage Error-Error Reduction Rate) when compared to conventional nutrient-only models, while also quantifying seasonal variations in turbidity-nutrient interactions during monsoon events. These findings demonstrate that traditional nutrient-focused HABs modeling and management strategies are insufficient, as they overlook the critical role of turbidity in algal growth in monsoon-affected reservoirs. This novel modeling approach advocates comprehensive HABs management that considers turbidity-nutrient interactions, thereby contributing to more effective and sustainable water quality practices in monsoon-affected reservoirs under changing climatic conditions.