A National Assessment of Tmdl Effectiveness Monitoring Approaches
Authors: Crumpacker, Andrea L.; Schollée, Jennifer E.; Butkus, Steve
Source: Proceedings of the Water Environment Federation, TMDL 2007 , pp. 238-249(12)
Publisher: Water Environment Federation
Abstract:One of the most important aspects of a Total Maximum Daily Load (TMDL) is monitoring the effectiveness of the implementation plan for achieving water quality standards. We evaluated the different TMDL effectiveness monitoring approaches used across the United States. By statute, EPA cannot approve or disapprove the TMDL implementation plans developed by the States and relevant stakeholders. As such, there are no federal requirements directing how the TMDL goals are achieved, including effectiveness monitoring. This situation has resulted in a wide variety of different approaches used across the United States to monitor TMDL effectiveness. Effectiveness monitoring during implementation of TMDLs is important for a number of reasons. Monitoring can help determine if the implementation plan is successful in reaching the allocations set by the TMDL. Monitoring can also demonstrate the reasonable assurance required by EPA for non-point source reduction strategies and may help to protect stakeholder interests. Effectiveness monitoring includes the assessment of both output and outcome performance measures. For this study, we compiled a list of different TMDL effectiveness assessment approaches used nationwide. Approaches were categorized based on similarities, and evaluated for statistical methodology (if any). We evaluated monitoring approaches used for TMDLs developed by both the states and EPA. We found that a wide variety of assessment approaches are being used due to the numerous types and complexities of TMDLs nationwide. Most states determine TMDL success as delisting from the §303(d) List. We reviewed each State's §303(d) Listing policy to assess what statistical methods are used to list or delist water segments. The results indicate that most states do not incorporate statistical methods in their data evaluations.
Document Type: Research Article
Publication date: October 1, 2007
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