Issues of replicability in Monte Carlo modeling: A case study with a pesticide leaching model

• Published in: Environmental toxicology and chemistry Vol. 22; no. 12; pp. 3081 - 3087
• Main Authors: Dubus, Igor G., Janssen, Peter H. M.
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Periodicals, Inc 01.12.2003; SETAC
• Subjects: Animal, plant and microbial ecology; Applied ecology; Applied sciences; Biological and medical sciences; Ecotoxicology, biological effects of pollution; Environmental Monitoring; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; Latin hypercube sampling; Models, Statistical; Monte Carlo; Monte Carlo Method; Pesticides - analysis; Pollution; Random Allocation; Reproducibility of Results; Risk Assessment; Sensitivity; Soil and sediments pollution; Soil Pollutants - analysis; Techniques; Uncertainty; Water Pollutants, Chemical - analysis; Monte Carlo method; Monte Carlo Sensitivity Uncertainty Latin hypercube sampling Model; Uncertainty; Pollutant behavior; Pesticides; Models; Soil pollution; Risk analysis; Leaching
• ISSN: 0730-7268; 1552-8618
• DOI: 10.1897/02-470

Sensitivity and uncertainty analyses based on Monte Carlo sampling were undertaken for various numbers of runs of the pesticide leaching model (PELMO). Analyses were repeated 10 times with different seed numbers. The ranking of PELMO input parameters according to their influence on predictions for leaching was stable for the most influential parameters. For less influential parameters, the sensitivity ranking was severely influenced by the seed number used. For uncertainty analyses, probabilities of exceeding a particular concentration were significantly influenced by the seed number used in the random sampling of values for the two parameters considered, even for those cases in which 5,000 model runs were undertaken (coefficient of variation of 10 replicated analyses, 5%). A decrease in the variability of exceedance probabilities could be achieved by further increasing the number of model runs. However, this may prove to be impractical when complex deterministic models with a relatively long running time are used. Attention should be paid to replicability aspects by modelers when devising their approach to assessing the uncertainty associated with the modeling and by decision makers when examining the results of probabilistic approaches.