TMDL ADVISORY PANEL TO THE STATE OF NEW JERSEY: ACADEMIC EXPERTISE SUPPORTING REGULATORY REQUIREMENTS

Decision-making in the face of scientific uncertainty is one issue inherent in the development and establishment of TMDLs (Total Maximum Daily Loads), the implementation of which is central to affecting watershed restoration. In support of this process, an advisory panel was formed under the auspices of Rutgers' Cook College, the Land Grant College of New Jersey, to provide technical assistance to the New Jersey Department of Environmental Protection (NJDEP). This multidisciplinary and multiinstitutional panel has included scientists and engineers from Rutgers University, Stevens Institute of Technology, New Jersey Institute of Technology, Rowan University and Richard Stockton College of New Jersey. The members are selected based on their background and experience in dealing with TMDL related processes, including lake, stream, and estuary modeling, water quality monitoring, and ecological systems studies. The panel is sponsored by the NJDEP Division of Watershed Management, as an integral part of a project whose objective is to ensure the availability of clean water and healthy aquatic ecosystems through system-level watershed analysis, planning and management efforts. The NJDEP, in cooperation with public and technical advisory committees, develops “technical approaches” to restore impaired waterbodies for each of New Jersey's watershed management areas. The panel reviews these technical approaches, as well as proposals solicited for related research and technical support, and offers formal recommendations to the Department. The outcome is TMDL development that is based on sound science. This model of an academic advisory panel that provides technical support to the State for its approaches to watershed restoration is unique, as far as we are aware. Its implementation by other states would be a flexible way to promote consistency in state approaches to the TMDL process, and in this and other applications, would generally enhance science-based decision making in the face of uncertainty.