Enhancing reservoir water quality simulation through machine learning-driven remote sensing integration with EFDC: a coupled framework for eutrophication management in data-scarce regions

Hydrological model accuracy is often constrained by limited in-situ data. This study develops a coupled framework integrating machine-learning-based remote sensing retrievals with the Environmental Fluid Dynamics Code (EFDC) to improve reservoir water quality simulations. Landsat imagery (2013-2023) and multiple algorithms (Random Forest, Gradient Boosting, AdaBoost, etc.) were used to derive spatiotemporal distributions of total nitrogen (TN), total phosphorus (TP), and chlorophyll-a (Chl-a), which were incorporated as dynamic boundary conditions in EFDC. The coupled model reduced simulation errors by 0.13-5.28% and increased mean R² from 0.70 to 0.81. Compared with standalone EFDC, retrieval-based estimates showed lower mean relative errors for TN (20.61%), TP (28.95%), and Chl-a (26.08%). Seasonal analysis revealed Chl-a peaks in June (14.6 µg/L) and TN/TP accumulation in summer. Scenario simulations indicated that external load reduction (5-30%) effectively decreased TN and TP but had limited influence on Chl-a due to threshold effects. The optimal integrated strategy (30% external load reduction + 1.0% outflow reduction, scenario f-1) achieved concurrent reductions of 27.4% (TN), 23.7% (TP), and 13.2% (Chl-a), averaging 21.4% across all indicators. Critically, scenario analysis revealed that reducing external inflow loads alone produced limited suppression of algal biomass due to threshold effects and internal nutrient buffering, whereas combined reductions in inflow loading and moderate adjustments to hydrodynamic outflow regulation were necessary to achieve meaningful Chl-a control. These findings demonstrate that alterations to both nutrient inflow and reservoir hydrodynamics are essential levers for eutrophication management, with implications for operational decision-making in data-scarce reservoir systems worldwide.