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Review of the validation of models used in Federal Insecticide, Fungicide, and Rodenticide Act Environmental exposure assessments
Author(s) -
Jmones Russell L.,
Mangels Gary
Publication year - 2002
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1002/etc.5620210802
Subject(s) - environmental science , computer science , surface runoff , simulation modeling , model validation , calibration , statistics , data science , mathematics , ecology , mathematical economics , biology
The first activity of the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) Environmental Model Validation Task Force, established to increase confidence in the use of environmental models used in regulatory assessments, was to review the literature information on validation of the pesticide root zone model (PRZM) and the groundwater loading effects of agricultural management systems (GLEAMS). This literature information indicates that these models generally predict the same or greater leaching than observed in actual field measurements, suggesting that these models are suitable for use in regulatory assessments. However, additional validation research conducted using the newest versions of the models would help improve confidence in runoff and leaching predictions because significant revisions have been made in models over the years, few of the literature studies focused on runoff losses, the number of studies having quantitative validation results is minimal, and modelers were aware of the field results in most of the literature studies. Areas for special consideration in conducting model validation research include improving the process for selecting input parameters, developing recommendations for performing calibration simulations, devising appropriate procedures for keeping results of field studies from modelers performing simulations to validate model predictions while providing access for calibration simulations, and developing quantitative statistical procedures for comparing model predictions with experimental results.