Abstract
Until recently the subjects of model uncertainty and prediction accuracy were largely ignored by water-quality modelers. There were many reasons for this, including a widespread conviction that model predictions could be made as accurate as desired simply by increasing the detail and complexity of the governing equations. Enthusiasm for complex model structures led to a proliferation of sophisticated ecosystem models, which grew larger and larger and included more and more biological compartments, chemical interactions, etc. Unfortunately, increases in model size and complexity did not necessarily provide the expected improvements in prediction accuracy. If anything, they made the models more difficult to use and the results harder to interpret. It became apparent that the primary factor limiting model performance in many applications was not lack of detail but rather insufficiently accurate model inputs.
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References
Beck, M.B. and Young, P.C. (1976). Systematic identification of a DO-BOD model structure. Journal of the Environmental Engineering Division, American Society of Civil Engineers, 102 (EE5): 909–927.
Delhomme, J.P. (1976). Kriging in Hydrosciences. Condensed version of Ph.D. Thesis, University of Paris, Ecole Nationale Supérieure, Des Mines de Paris.
Dettinger, M.D. and Wilson, J.L. (1979). Numerical Modeling of Aquifer Systems under Uncertainty: A Second Moment Analysis. MIT/Cairo University Technological Planning Program Working Paper No. 1. Department of Civil Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts.
Kendall, M.G. and Stuart, A. (1973). The Advanced Theory of Statistics. Vol. 2, Inference and Relationship. Hafner Publishing Co., New York.
Lettenmaier, D.P. and Burges, S.J. (1976). Use of state estimation techniques in water resources system modeling. Water Resources Bulletin, 12(1): 88–99.
McLaughlin, D.B. (1979). Hanford Groundwater Modeling — A Numerical Comparison of Bayesian and Fisher Parameter Estimation Techniques. Rockwell Hanford Operations Consultants Report RHO-C-24 (available from Resource Management Associates, 3738 Mt. Diablo Blvd., Suite 200, Lafayette, California 94549, USA).
Moore, S.F. (1978). Applications of Kaiman filter to water quality studies. In C.L. Chiu (Editor), Applications of Kaiman Filter to Hydrology, Hydraulics and Water Resources. Stochastic Hydraulics Program, Department of Civil Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, pp. 485–499.
Moore, S.F. and McLaughlin, D.B. (1980). Computer Mapping of Contaminant Plumes in an Arid Site Vadose Zone. Final report for Rockwell Hanford Operations, Richland, Washington (available from Resource Management Associates, 3738 Mt. Diablo Blvd., Suite 200, Lafayette, California 94549, USA).
Zienkiewicz, O.C. (1977). The Finite Element Method. McGraw-Hill, London.
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© 1983 International Institute for Applied Systems Analysis, Laxenburg/Austria
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McLaughlin, D.B. (1983). Statistical Analysis of Uncertainty Propagation and Model Accuracy. In: Beck, M.B., van Straten, G. (eds) Uncertainty and Forecasting of Water Quality. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-82054-0_14
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DOI: https://doi.org/10.1007/978-3-642-82054-0_14
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