Validation of pesticide root zone model 3.12: Employing uncertainty analysis

• Published in: Environmental toxicology and chemistry 2002-08, Vol.21 (8), p.1578-1590
• Main Authors: Carbone, John P., Havens, Patrick L., Warren-Hicks, William
• Format: Article
• Language: English
• Subjects: Applied sciences; Atmospheric pollution; Decision Making; Environment; Exact sciences and technology; Exposure modeling; Forecasting; Global environmental pollution; Model validation; Models, Theoretical; Pesticides - analysis; Plant Roots; Pollutants physicochemistry study: properties, effects, reactions, transport and distribution; Pollution; Risk Assessment; Soil Pollutants - analysis; Uncertainty; Uncertainty analysis
• ISSN: 0730-7268; 1552-8618
• DOI: 10.1002/etc.5620210808

Computer models are being increasingly used to provide an efficient cost‐effective means of evaluating the fate and behavior of chemicals in the environment. These models can be used in lieu of or in conjunction with field studies. Because of the increasing reliance on models for critical regulatory decision making, the need arose to assess the validity of regulatory models via an analysis of the correlation of model response estimates with measured data. In conjunction with the evaluation of the correlation of model response estimates and measured field data, a rigorous statistically based validation was also warranted. Because of the unique nature of the correlative exercise using modeled and measured data, standard statistical analyses, while informative, failed to encompass factors associated with the uncertainty of measured environmental fate data and potential model inputs. In an effort to evaluate this uncertainty, an initial sensitivity analysis was performed where key model input parameters for runoff and leaching simulations were identified. Once the sensitive input parameters were identified, a Monte Carlo‐based preprocessor was developed whereby the sampling distributions of these parameters were used to propagate uncertainty in the input parameters into error in model predictions. Importantly, assumptions about parameter distributions for input into the Monte Carlo tool were made only after a formal detailed site‐specific analysis of measured field data. Employing the functionality of the Crystal Ball® Pro development environment, the pesticide root zone model (PRZM) 3.12 was run iteratively for 500 trials, and model output was collated and analyzed. The model predictions were considered reasonably accurate for most regulatory requirements, and the model prediction error was considered acceptable.