Rethinking TMDLs: Perspective Based on Community Survey

The study investigated the perspectives of professionals involved in total maximum daily load (TMDL) development. A survey instrument was developed to understand the challenges and advancements necessary to enhance water quality management. This survey explored various dimensions of TMDL development, including identifying impaired waterbodies, water quality modeling, implementation, postimplementation assessment, and stakeholder engagement. Thirty-seven professionals involved in TMDL development took the survey. The results indicated a consensus on the need to reassess existing methodologies, particularly in the postimplementation phase, with a strong emphasis on the importance of sufficient funding for data collection. Limited resources, computational challenges, and a lack of trust in advanced models were identified as barriers to advancing water quality modeling. The participants also recognized the urgency of incorporating more validation data, especially through conventional monitoring and remote sensing, to enhance water quality modeling efforts. Although including social systems in modeling was considered crucial, it was not universally prioritized. This study developed a survey instrument to capture the evolving perspectives of stakeholders involved in TMDL processes. The survey’s structure provides a framework that can be improved and adapted for ongoing assessment and improvement. A continuous feedback mechanism, such as the one provided by the survey, is essential for ensuring that TMDL practices remain responsive to emerging challenges and are better equipped to manage water quality in an increasingly complex landscape.

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Publication Details

Journal
Journal of Environmental Engineering
Published
2025-05-06
DOI
https://doi.org/10.1061/joeedu.eeeng-8104
Citations
1
Primary Topic
Semantic Web and Ontologies
Type
article
Field-Weighted Citation Impact
2.48
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article

Rethinking TMDLs: Perspective Based on Community Survey

Nigel W.T. Quinn, Ebrahim Ahmadisharaf, R. Plante, Saurav Kumar et al.
1 citations
Journal of Environmental Engineering
Semantic Web and Ontologies
2.48
article

Rethinking TMDLs: Perspective Based on Community Survey

Nigel W.T. Quinn, Ebrahim Ahmadisharaf, R. Plante, Saurav Kumar, Harry X. Zhang, John Jairo Ramirez-Avila, Vamsi K. Sridharan, Craig Lott, Sumon Hossain Rabby, Sanaz Imen, Raquel Neri Barranco
article en
1 citations

Abstract

The study investigated the perspectives of professionals involved in total maximum daily load (TMDL) development. A survey instrument was developed to understand the challenges and advancements necessary to enhance water quality management. This survey explored various dimensions of TMDL development, including identifying impaired waterbodies, water quality modeling, implementation, postimplementation assessment, and stakeholder engagement. Thirty-seven professionals involved in TMDL development took the survey. The results indicated a consensus on the need to reassess existing methodologies, particularly in the postimplementation phase, with a strong emphasis on the importance of sufficient funding for data collection. Limited resources, computational challenges, and a lack of trust in advanced models were identified as barriers to advancing water quality modeling. The participants also recognized the urgency of incorporating more validation data, especially through conventional monitoring and remote sensing, to enhance water quality modeling efforts. Although including social systems in modeling was considered crucial, it was not universally prioritized. This study developed a survey instrument to capture the evolving perspectives of stakeholders involved in TMDL processes. The survey’s structure provides a framework that can be improved and adapted for ongoing assessment and improvement. A continuous feedback mechanism, such as the one provided by the survey, is essential for ensuring that TMDL practices remain responsive to emerging challenges and are better equipped to manage water quality in an increasingly complex landscape.

Journal of Environmental EngineeringVol. 151(7)
Florida A&M University - Florida State University College of Engineering (US), Lawrence Berkeley National Laboratory (US), Water Research Foundation (US), Virginia Department of Environmental Quality (US), Stantec (Canada) (CA), Arizona State University (US), Tetra Tech (United States) (US), Mississippi State University (US)
Openalex Percentile: Top 11%
Semantic Web and Ontologies
2.48
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