Improved Science and Decision Support for Managing Watershed Nutrient LoadsEPA Grant Number: R830654
Title: Improved Science and Decision Support for Managing Watershed Nutrient Loads
Investigators: Chapra, Steve
Current Investigators: Chapra, Steve , Durant, John , Hemond, Harold F. , Kirshen, Paul , Vogel, Richard
Institution: Tufts University
Current Institution: Tufts University , Massachusetts Institute of Technology
EPA Project Officer: Hiscock, Michael
Project Period: January 20, 2003 through January 19, 2006 (Extended to January 19, 2007)
Project Amount: $749,179
RFA: Nutrient Science for Improved Watershed Management (2002) RFA Text | Recipients Lists
Research Category: Water , Water and Watersheds
The proposed research addresses two critical gaps in the TMDL process: (1) the inadequacy of presently existing receiving water models to accurately simulate nutrient-sediment-water interactions and fixed plants; and (2) the lack of decision-oriented optimization frameworks for managing nutrient loads to achieve multiple water quality objectives.
To advance understanding of sediment-water nutrient releases, we will test the hypothesis that rates of phosphorus release are predictable from the dynamics of iron release and iron speciation in the bottom waters, and that these in turn are governed first by oxygen and then by nitrate concentrations. We will also determine the stoichiometry and final products of nitrogen reduction/iron oxidation. These new results, along with existing scientific research on attached plants, will be integrated into a watershed/receiving water model that will be applied and tested on the Aberjona River/Upper Mystic Lake watershed. The watershed/receiving water model will then serve as a part of a decision-support system (DSS) that represents the second component of this research. The decision-support component will allow managers and stakeholders to rapidly develop different management scenarios, explore the decision space to identify least-cost solutions and integrate uncertainty into their considerations.
Seasonal measurements of surface water chemistry and biology and whole lake input-output fluxes of phosphorus and nitrogen will be made for the Upper Mystic Lake. In addition, detailed vertical profiles will be sampled for the lake's water column. The profiles will include the nitrogen/argon ratio, nitrous oxide, and carbonate species (an approximation of total respiration, in the absence of methane fermentation). In addition to particulate and dissolved phosphorus we will measure iron (II) and total iron oxyhydroxides, reduced sulfur species and methane. These measurements will be used to calculate electron balances, to confirm that nitrogen redox and iron redox reactions are quantitatively coupled, and to identify and quantify specific key mechanisms governing nutrient dynamics. The results will then be used to parameterize and test a submodel of sediment-water exchange and phosphorus speciation in the water column, including surface complexation on iron oxyhydroxides. The submodel, along with an attached plant model will be integrated with a seasonal, eutrophication model of the lake. This model will be tested using the seasonal data.
Genetic algorithms (GA) using multiple objectives will serve as the optimization tool for the decision support component of this research. The DSS will consist of a series of linked simulation models (a GIS-based watershed model and the improved eutrophication model) that will be interfaced with the GA solver and a graphical user interface (GUI). Based on collaboration with a group of stakeholders, environmental, social, and economic indicators and criteria will be developed for use as objective functions within the DSS. We will also assemble data on the options and costs for watershed nutrient management including data on the costs and effectiveness of Best Management Practices, sediment management (e.g., dredging, capping, and re-aeration) and lake and river management (e.g., seasonal lake level manipulation to control light to macrophytes in forebays). The DSS will be tested by developing a nutrient TMDL and implementation plan for the study watershed.
The study's science component will result in improved algorithms to account for two critical gaps in presently existing models: attached plants and sediment-water nutrient cycling. The decision-support system component will provide a tool and methodology to empower stakeholders to define and prioritorize nutrient management actions to implement nutrient TMDLs.