Water-Quality Modeling for Decision Making

Today, mathematical modeling has become an accepted part of the process of establishing and evaluating alternative scenarios for water-quality management. The proliferation of microcomputers, the general availability of tested hydrodynamic and water-quality models, and accessibility to user-oriented...

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Veröffentlicht in:Journal of water resources planning and management 1992-05, Vol.118 (3), p.295-307
1. Verfasser: Orlob, G. T
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container_title Journal of water resources planning and management
container_volume 118
creator Orlob, G. T
description Today, mathematical modeling has become an accepted part of the process of establishing and evaluating alternative scenarios for water-quality management. The proliferation of microcomputers, the general availability of tested hydrodynamic and water-quality models, and accessibility to user-oriented graphics software have placed such decision-support tools at the disposal of the resource manager. They have also raised concerns about uncertainties in model structure and predictive capabilities, as well as the quality of data and information required for reliable decisions. Even though modeling has enhanced the decision process, there still are questions concerning the proper role of such tools and how they can be most effectively used in water-quality decision making. This paper traces the historical development of water-quality modeling of natural surface-water systems, from the 1960s to the present, citing some of the major advances and pitfalls in bringing computer-aided decision support to its present state. It seeks to highlight capabilities now available to analysts and decision makers in the form of integrated packages of models, data bases, and interpretive graphics software. It also offers some caveats, lest the user-decision maker become enamored with these newly discovered tools without realizing their limitations.
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source American Society of Civil Engineers:NESLI2:Journals:2014
subjects TECHNICAL PAPERS
title Water-Quality Modeling for Decision Making
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