A coupled stochastic/deterministic model to estimate the evolution of the risk of water contamination by pesticides across Canada

Periodic assessments of the risk of water contamination by pesticides help decision makers improve the sustainability of agricultural management practices. In Canada, when evaluating the risk of water contamination by pesticides, 2 main constraints arise. First, because the area of interest is large, a pesticide transport model with low computational running time is mandatory. Second, some relevant input data for simulations are not known, and most are known only at coarse scale. This study aims to develop a robust methodology to estimate the evolution of the risk of water contamination by pesticides across Canada. To circumvent the 2 aforementioned issues, we constructed a stochastic model and coupled it to the 1-dimensional pesticide fate model Pesticide Root Zone Model (PRZM). To account for input data uncertainty, the stochastic model uses a Monte Carlo approach to generate several pesticide application scenarios and to randomly select PRZM parameter values. One hundred different scenarios were simulated for each of over 2000 regions (Soil Landscapes of Canada [SLC] polygons) for the years 1981 and 2006. Overall, the results indicated that in those regions in which the risk increased from 1981 to 2006, the increase in risk was mainly attributable to the increased area treated by pesticides or an increase in the number of days with runoff. More specifically, this work identifies the areas at higher risk, where further analyses with finer-scale input data should be performed. The model is specific for Canadian data, but the framework could be adapted for other large countries. Integr Environ Assess Manag 2014;10:429–436. © 2014 SETAC Key Points A stochastic model is developed and coupled to a pesticide fate model in order to estimate the evolution of the risk of water contamination by pesticides for over 2000 region across Canada for the years 1981 and 2006. Mapping and statistical analyses of the results help to identify the most-at-risk areas were further attention would be needed. Risk level is generally higher for regions where the proportion of the land treated by pesticides and where the number of days with surface runoff are high. The stochastic model developed is specific for Canadian data, but the framework could be adapted for other large countries.