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Journal of Environmental Quality Abstract -

Uncertainty Assessment in Modelling Soil Acidification at the European Scale: A Case Study


This article in JEQ

  1. Vol. 28 No. 2, p. 366-377
    Received: Dec 17, 1997

    * Corresponding author(s): j.kros@sc.dlo.nl
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  1. J. Kros *,
  2. E. J. Pebesma,
  3. G. J. Reinds and
  4. P. A. Finke
  1. DLO Winand Staring Centre, P.O Box 125, 6700 AC Wageningen, the Netherlands;
    Univ. of Amsterdam, Faculty of Environmental Science.



When modelling soil acidification at the European scale, it is inevitable that both model and data have varying degrees of associated uncertainty. The present study attempted to quantify the uncertainty in long-term forecasts of soil solution concentrations of Al3+ and NO3 concentrations resulting from the uncertainty in small resolution European-scale maps and input data, using the Netherlands as a case study. Large-scale forecasts were made with a relatively simple dynamic process-oriented model, SMART2. Model outputs were considered as block median concentrations and the block areal fractions in which concentrations exceeded a critical level. Sources of uncertainty considered included (i) uncertainty in soil and vegetation maps (categorical data), and (ii) uncertainty in soil and vegetation-related parameters (continuous data). The uncertainty in model outputs was quantified by an efficient two-step Monte Carlo simulation approach, which takes spatial correlation into account. The uncertainty in the input data at the European scale led to major uncertainties in the predicted Al3+ concentration. Uncertainties in the areas where the Al3+ concentration exceeded the maximum allowable concentration were much smaller. The uncertainties in soil-related parameters contributed most to the uncertainty in the A3+ concentration, whereas the uncertainty contributed by the soil and vegetation maps was negligible. For the NO3 concentration, however, the uncertainty originated mainly from the soil and vegetation maps. Evaluation of the different error sources is of great practical significance, as it identifies which sources need further improvement. The present study shows that the uncertainty contribution of the different error sources depends greatly on the model output considered.

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