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Mechanistic Modeling of Water Quality in Onondaga Lake

  • Steven W. Effler
  • Susan M. Doerr
  • Martin T. Auer
  • Raymond P. Canale
  • Rakesh K. Gelda
  • Emmet M. Owens
  • Thomas M. Heidtke
Part of the Springer Series on Environmental Management book series (SSEM)

Abstract

Water quality modeling serves two important, and at times disparate, purposes: (1) to support basic research, by providing a quantitative framework for the synthesis of scientific data, and (2) to support effective management of water resources by providing reliable predictive frameworks. These research and management purposes are mutually consistent on a long-term basis, as research advancements have led to improved capabilities and credibility of mechanistic management models (Chapra and Reckhow 1983; Thoman and Mueller 1987). Empirical modeling (e.g., Reckhow and Chapra 1983) can be valuable in certain instances, but is often inadequate for more complex water quality problems. Further it provides only very limited theoretical insight. Models developed for Onondaga Lake and the adjoining Seneca River, as documented here, are mechanistic, as they explicitly accommodate key mechanisms underlying the dynamics of various aspects of the water quality of these systems. Model testing, to establish the credibility of each model, and application for selected management scenarios are also documented in this chapter. The models presented here are intended to meet both the research and management purposes stated above. The models will serve to identify and test hypotheses for ongoing research and guide related programs. They will undoubtedly evolve as research on the lake continues. In the shorter term, the models are expected to support management decisions related to the reclamation of this highly polluted lake and the protection of the adjoining river system.

Keywords

Dissolve Oxygen Fecal Coliform Zebra Mussel Total Phosphorus Concentration Sediment Oxygen Demand 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York, Inc. 1996

Authors and Affiliations

  • Steven W. Effler
  • Susan M. Doerr
  • Martin T. Auer
  • Raymond P. Canale
  • Rakesh K. Gelda
  • Emmet M. Owens
  • Thomas M. Heidtke

There are no affiliations available

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