Skip to main content
ARS Home » Southeast Area » Oxford, Mississippi » National Sedimentation Laboratory » Watershed Physical Processes Research » Research » Publications at this Location » Publication #427452
Research Project: Computational Tools and Decision Support System Technologies for Agricultural Watershed Physical Processes, Water Quality and Ground Water Management

Location: Watershed Physical Processes Research

Title: Advancing surface water quality modeling for TMDL implementation: enhancing sediment processes, atmospheric reaeration, and bed layer interactions

Author
item CHAO, XIAOBO - University Of Mississippi
item ZHANG, ZHONGLONG - Portland State University
item ZHANG, HARRY - The Water Research Foundation

Submitted to: Journal of Environmental Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/24/2025
Publication Date: 5/8/2025
Citation: Chao, X., Zhang, Z., Zhang, H. 2025. Advancing surface water quality modeling for TMDL implementation: enhancing sediment processes, atmospheric reaeration, and bed layer interactions. Journal of Environmental Engineering. 151(7). https://doi.org/10.1061/JOEEDU.EEENG-7888.
DOI: https://doi.org/10.1061/JOEEDU.EEENG-7888

Interpretive Summary: This paper provides a comprehensive review of advanced methodologies for simulating sediment-associated water quality processes, atmospheric reaeration, and interactions within the bed sediment layer in process-based water quality (PB-WQ) Models. Sediment-associated processes play a crucial role in water quality dynamics when sediment concentration is significant. For instance, suspended sediment (SS) obstructs light penetration, limiting phytoplankton growth. Nutrients interact with SS through adsorption-desorption phenomena. Furthermore, sediment deposition and erosion influence nutrient concentrations both adsorbed on the sediment particles and within porous water. Integrating these sediment-associated processes with other processes is essential for water quality modeling. This review provides guidance on selecting empirical formulas for calculating reaeration coefficients induced by flow, wind. and combined flow-wind interactions. Additionally, formulas based on surface turbulence dissipation rates are included, offering promising alternatives, although their complexity requires further improvement in these studies. Integrating PB-WQ models with remote sensing and machine learning enhances the accuracy and efficiency of water quality simulations, greatly improving the development, validation, and optimization of strategies for effective water quality management.

Technical Abstract: Process-based water quality (PB-WQ) models have been extensively reviewed in the ASCE Manuals and Reports on Engineering Practice (MOP) No. 150 (Chapter 3). However, 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 vital 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, posing 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 datasets 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 PB-WQ model accuracy by supplying initial conditions and validation datasets. 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.