A coupled stochastic/deterministic model to estimate the evolution of the risk of water contamination by pesticides across Canada.
Gagnon, P., Sheedy, C., Farenhorst, A., McQueen, R.D.A., Cessna, A.J., and Newlands, N.K. (2014). "A coupled stochastic/deterministic model to estimate the evolution of the risk of water contamination by pesticides across Canada.", Integrated Environmental Assessment and Management, 10(3), pp. 429-436. doi : 10.1002/ieam.1533 Access to full text
Periodic assessments of the risk of water contamination by pesticides help decision makers to improve the sustainability of agricultural management practices. In Canada, when evaluating the risk of water contamination by pesticides, there are two main constraints. 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 two aforementioned issues, we constructed a stochastic model and coupled it to the one-dimensional pesticide fate 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 (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 due to the increased area treated by pesticides and/or an increase in the number of days with runoff. More specifically, this work identifies the areas more at 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.
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