Uncertainty and Forecasting of Water Quality

1. Front Matter

   Pages i-xii

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2. Introduction

   1. Front Matter

      Pages 1-1

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   2. Uncertainty, System Identification, and the Prediction of Water Quality

      • M. B. Beck

      Pages 3-68

   3. The Validity and Credibility of Models for Badly Defined Systems

      • Peter Young

      Pages 69-98

3. Uncertainty and Model Identification

   1. Front Matter

      Pages 99-99

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   2. An Approach to the Analysis of Behavior and Sensitivity in Environmental Systems

      • G. M. Hornberger, R. C. Spear

      Pages 101-116

   3. Distribution and Transformation of Fenitrothion Sprayed on a Pond: Modeling Under Uncertainty

      • Efraim Halfon, R. James Maguire

      Pages 117-128

   4. Input Data Uncertainty and Parameter Sensitivity in a Lake Hydrodynamic Model

      • L. Somlyódy

      Pages 129-155

   5. Maximum Likelihood Estimation of Parameters and Uncertainty in Phytoplankton Models

      • G. van Straten

      Pages 157-171

   6. Identification Methods Applied to Two Danish Lakes

      • Henning Mejer, Leif Jørgensen

      Pages 173-182

   7. Analysis of Prediction Uncertainty: Monte Carlo Simulation and Nonlinear Least-Squares Estimation of a Vertical Transport Submodel for Lake Nantua

      • F. Chahuneau, S. des Clers, J. A. Meyer

      Pages 183-202

   8. Multidimensional Scaling Approach to Clustering Multivariate Data for Water-Quality Modeling

      • Saburo Ikeda, Hidekiyo Itakura

      Pages 205-223

   9. Nonlinear Steady-State Modeling of River Quality by a Revised Group Method of Data Handling

      • Hiroyuki Tamura, Tadashi Kondo

      Pages 225-241

4. Uncertainty, Forecasting, and Control

   1. Front Matter

      Pages 243-243

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   2. Parameter Uncertainty and Model Predictions: A Review of Monte Carlo Results

      • R. H. Gardner, R. V. O’Neill

      Pages 245-257

   3. A Monte Carlo Approach to Estimation and Prediction

      • Kurt Fedra

      Pages 259-291

   4. The Need for Simple Approaches for the Estimation of Lake Model Prediction Uncertainty

      • Kenneth H. Reckhow, Steven C. Chapra

      Pages 293-303

   5. Statistical Analysis of Uncertainty Propagation and Model Accuracy

      • Dennis B. McLaughlin

      Pages 305-319

   6. Modeling and Forecasting Water Quality in Nontidal Rivers: The Bedford Ouse Study

      • P. G. Whitehead

      Pages 321-337

   7. Adaptive Prediction of Water Quality in the River Cam

      • H. N. Koivo, J. T. Tanttu

      Pages 339-356

Since the International Institute for Applied Systems Analysis began its study of water quality modeling and management in 1977, it has been interested in the relations between uncertainty and the problems of model calibration and prediction. The work has focused on the theme of modeling poorly defined environmental systems, a principal topic of the effort devoted to environmental quality control and management. Accounting for the effects of uncertainty was also of central concern to our two case studies of lake eutrophication management, one dealing with Lake Balaton in Hungary and the other with several Austrian lake systems. Thus, in November 1979 we held a meeting at Laxenburg to discuss recent method­ ological developments in addressing problems associated with uncertainty and forecasting of water quality. This book is based on the proceedings of that meeting. The last few years have seen an increase in awareness of the issue of uncertainty in water quality and ecological modeling. This book is relevant not only to contemporary issues but also to those of the future. A lack of field data will not always be the dominant problem for water quality modeling and management; more sophisticated measuring techniques and more comprehensive monitoring networks will come to be more widely applied. Rather, the important problems of the future are much more likely to emerge from the enhanced facility of data processing and to concern the meaningful interpretation, assimilation., and use of the information thus obtained.

• Seen
• Wassergüte
• environment
• quality control
• simulation
• uncertainty
• water
• water quality and water pollution

• Department of Civil Engineering, Imperial College of Science and Technology, London, England

  M. B. Beck

• Department of Chemical Technology, Twente University of Technology, Enschede, The Netherlands

  G. Straten

• Laxenburg, Austria

  IIASA International Institute for Applied Systems Analysis

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