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. Pesticides are contaminants of priority concern thatcontinue to present a significant risk to drinking water quality. Whilepollution mitigation in catchment systems is considered a cost-effectivealternative to costly drinking water treatment, the effectiveness ofpollution mitigation measures is uncertain and needs to be able to considerlocal biophysical, agronomic, and social aspects. We developed aprobabilistic decision support tool (DST) based on spatial Bayesian beliefnetworks (BBNs) that simulates inherent pesticide leaching risk to ground-and surface water quality to inform field-level pesticide mitigationstrategies in a small (3.1 km2) drinking water catchment with limitedobservational data. The DST accounts for the spatial heterogeneity in soilproperties, topographic connectivity, and agronomic practices; the temporalvariability of climatic and hydrological processes; and uncertaintiesrelated to pesticide properties and the effectiveness of managementinterventions. The rate of pesticide loss via overland flow and leaching togroundwater and the resulting risk of exceeding a regulatory threshold fordrinking water was simulated for five active ingredients. Risk factorsincluded climate and hydrology (e.g. temperature, rainfall, evapotranspiration,and overland and subsurface flow), soil properties (e.g. texture, organic mattercontent, and hydrological properties), topography (e.g. slope and distance to surfacewater/depth to groundwater), land cover and agronomic practices, and pesticideproperties and usage. The effectiveness of mitigation measures such as thedelayed timing of pesticide application; a 10 %, 25 %, or 50 % reductionin the application rate; field buffers; and the presence/absence of soil pan on riskreduction were evaluated. Sensitivity analysis identified the month ofapplication, the land use, the presence of buffers, the field slope, and the distance as themost important risk factors, alongside several additional influentialvariables. The pesticide pollution risk from surface water runoff showed clearspatial variability across the study catchment, whereas the groundwater leachingrisk was uniformly low, with the exception of prosulfocarb. Combinedinterventions of a 50 % reduced pesticide application rate, management of theplough pan, delayed application timing, and field buffer installation notablyreduced the probability of a high risk of overland runoff and groundwaterleaching, with individual measures having a smaller impact. The graphicalnature of BBNs facilitated interactive model development and evaluationwith stakeholders to build model credibility, while the ability to integratediverse data sources allowed a dynamic field-scale assessment of “criticalsource areas” of pesticide pollution in time and space in a data-scarcecatchment, with explicit representation of uncertainties.

hydrology and earth system sciences

Probabilistic modelling of the inherent field-level pesticide pollution risk in a small drinking water catchment using spatial Bayesian belief networks