Advancing Surface Water Quality Modeling for TMDL Application: Enhancing Sediment-Associated Processes, Atmospheric Reaeration, and Bed Layer Interactions

Process-based water quality (PB-WQ) models have been extensively studied, but critical processes such as sediment-associated processes, atmospheric reaeration, and bed layer interactions are often oversimplified or omitted due to their complexity. These processes play a crucial role in shaping water quality in receiving water bodies. This paper provides a comprehensive review of advanced methodologies for simulating these processes, with a focus on sediment influences on phytoplankton growth and nutrient cycles, atmospheric reaeration coefficients, and biogeochemical interactions within the bed sediment layer. Despite advancements in PB-WQ modeling, challenges remain due to gaps in understanding complex biochemical interactions and constraints in model inputs. Empirical coefficients in PB-WQ models require field measurements, which pose practical challenges. Recent advancements in remote sensing (RS) and machine learning (ML) offer promising alternatives for monitoring and predicting water quality. RS provides spatial and temporal distributions of key water quality parameters, while ML enables data-driven predictions. However, RS is limited by depth penetration, and ML models require large, high-quality data sets for reliable performance. Integrating PB-WQ models with RS and ML enhances the accuracy and efficiency of water quality simulations. PB-WQ models extend simulations beyond surface water, broadening RS applications, while RS data improve the accuracy of PB-WQ models by supplying initial conditions and validation data sets. Additionally, PB-WQ model outputs can be used to train and validate ML models, while ML techniques can estimate key parameters and coefficients within PB-WQ models. RS data can also assist in determining parameters within PB-WQ models and support ML training, further improving predictive capabilities. Finally, this paper reviews the applications of PB-WQ models, RS, and ML in the development and implementation of total maximum daily loads (TMDLs). Their integration significantly enhances TMDL strategies by improving model precision, optimizing pollutant load estimations, and supporting informed decision-making for effective water quality management.