A framework for error analysis of a long-range transport model with emphasis on parameter uncertainty

A comprehensive framework for model error analysis is applied to the EMEP-W model of longrange transport of sulfur in Europe. This framework includes a proposed taxonomy of model uncertainties. Parameter uncertainties were investigated by Monte Carlo simulation of two source-receptor combinations. A 20% input parameter uncertainty (expressed as a coefficient of variation = standard deviation/mean) yielded a 15–22% output error of total sulfur deposition. The relationship between output error and input uncertainty was approximately proportional. Covariance between parameters can have an important effect on computed model error, and can either exaggerate or reduce errors compared to the uncorrelated case. Of the model state variables, SO2 air concentration and wet deposition had the highest error, and total sulfur deposition the lowest. It was also found that it is more important to specify the dispersion of the input parameter frequency distributions than their shape. The results of the model error analysis were applied to routine calculations of deposition in Europe. An error (coefficient of variation) of 20% for transfer coefficients throughout Europe yielded spatial variations in the order of a few tens to a few hundreds of km in computed deposition isolines of 2 and 5 g sulfur m−2a−1.

[1]  L. Hordijk A model for evaluation of acid deposition in Europe , 1985 .

[2]  Robert V. O'Neill,et al.  Parameter constraints in a stream ecosystem model: Incorporation of a priori information in Monte Carlo error analysis☆ , 1982 .

[3]  D. Lettenmaier,et al.  PROBABILISTIC METHODS IN STREAM QUALITY MANAGEMENT , 1975 .

[4]  Jørgen Saltbones,et al.  Modelling of long-range transport of sulphur over Europe: A two-year model run and some model experiments , 1983 .

[5]  Thomas A. McMahon,et al.  Empirical atmospheric deposition parameters—A survey , 1979 .

[6]  Douglas G. Fox,et al.  Uncertainty in Air Quality Modeling , 1984 .

[7]  Anton Eliassen,et al.  The oecd study of long range transport of air pollutants: Long range transport modelling , 1978 .

[8]  Jerzy Bartnicki,et al.  An Approach to Uncertainty of a Long Range Air Pollutant Transport Model , 1985 .

[9]  Robert V. O'Neill,et al.  Application of error analysis to a Marsh Hydrology Model , 1980 .

[10]  M. Granger Morgan,et al.  Technical Uncertainty in Quantitative Policy Analysis — A Sulfur Air Pollution Example , 1984 .

[11]  Sverre Petterssen,et al.  Weather analysis and forecasting , 1940 .

[12]  John G. Watson,et al.  A Method for Propagating Measurement Uncertainties through Dispersion Models , 1986 .

[13]  L. Hordjl,et al.  Integrated Analysis of Acidification in Europe , 2022 .

[14]  John H. Seinfeld,et al.  Sensitivity analysis of a mathematical model for photochemical air pollution , 1982 .

[15]  J. Hobbie,et al.  Random differential equations as models of ecosystems— II. initial condition and parameter specifications in terms of maximum entropy distributions☆ , 1976 .

[16]  W. C. Clark Technical Uncertainty in Quantitative Policy Analysis1 , 1984 .

[17]  Jørgen Saltbones,et al.  Decay and transformation rates of SO2, as estimated from emission data, trajectories and measured air concentrations , 1975 .

[18]  M. B. Beck,et al.  Uncertainty and forecasting of water quality , 1983 .

[19]  M. B. Beck,et al.  Uncertainty and arbitrariness in ecosystems modelling: A lake modelling example , 1981 .