Articles | Volume 26, issue 5
Hydrol. Earth Syst. Sci., 26, 1261–1293, 2022
https://doi.org/10.5194/hess-26-1261-2022

Special issue: Frontiers in the application of Bayesian approaches in water...

Hydrol. Earth Syst. Sci., 26, 1261–1293, 2022
https://doi.org/10.5194/hess-26-1261-2022
Research article
09 Mar 2022
Research article | 09 Mar 2022

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

Mads Troldborg et al.

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Cited articles

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Beven, K., Asadullah, A., Bates, P., Blyth, E., Chappell, N., Child, S., Cloke, H., Dadson, S., Everard, N., Fowler, H. J., Freer, J., Hannah, D. M., Heppell, K., Holden, J., Lamb, R., Lewis, H., Morgan, G., Parry, L., and Wagener, T.: Developing observational methods to drive future hydrological science: Can we make a start as a community?, Hydrol. Process., 34, 868–873, https://doi.org/10.1002/hyp.13622, 2019. 
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Short summary
Pesticides continue to pose a threat to surface water quality worldwide. Here, we present a spatial Bayesian belief network (BBN) for assessing inherent pesticide risk to water quality. The BBN was applied in a small catchment with limited data to simulate the risk of five pesticides and evaluate the likely effectiveness of mitigation measures. The probabilistic graphical model combines diverse data and explicitly accounts for uncertainties, which are often ignored in pesticide risk assessments.