Articles | Volume 26, issue 5
Hydrol. Earth Syst. Sci., 26, 1261–1293, 2022

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

Hydrol. Earth Syst. Sci., 26, 1261–1293, 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.

Related authors

Developing a Bayesian network model for understanding river catchment resilience under future change scenarios
Kerr J. Adams, Christopher A. J. Macleod, Marc J. Metzger, Nicola Melville, Rachel C. Helliwell, Jim Pritchard, and Miriam Glendell
EGUsphere,,, 2022
Short summary
Assessing branched tetraether lipids as tracers of soil organic carbon transport through the Carminowe Creek catchment (southwest England)
Jingjing Guo, Miriam Glendell, Jeroen Meersmans, Frédérique Kirkels, Jack J. Middelburg, and Francien Peterse
Biogeosciences, 17, 3183–3201,,, 2020
Short summary
Interacting effects of climate and agriculture on fluvial DOM in temperate and subtropical catchments
D. Graeber, G. Goyenola, M. Meerhoff, E. Zwirnmann, N. B. Ovesen, M. Glendell, J. Gelbrecht, F. Teixeira de Mello, I. González-Bergonzoni, E. Jeppesen, and B. Kronvang
Hydrol. Earth Syst. Sci., 19, 2377–2394,,, 2015

Related subject area

Subject: Water Resources Management | Techniques and Approaches: Modelling approaches
A system dynamic model to quantify the impacts of water resources allocation on water–energy–food–society (WEFS) nexus
Yujie Zeng, Dedi Liu, Shenglian Guo, Lihua Xiong, Pan Liu, Jiabo Yin, and Zhenhui Wu
Hydrol. Earth Syst. Sci., 26, 3965–3988,,, 2022
Short summary
Net irrigation requirement under different climate scenarios using AquaCrop over Europe
Louise Busschaert, Shannon de Roos, Wim Thiery, Dirk Raes, and Gabriëlle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 26, 3731–3752,,, 2022
Short summary
The role of multi-criteria decision analysis in a transdisciplinary process: co-developing a flood forecasting system in western Africa
Judit Lienert, Jafet C. M. Andersson, Daniel Hofmann, Francisco Silva Pinto, and Martijn Kuller
Hydrol. Earth Syst. Sci., 26, 2899–2922,,, 2022
Short summary
Unfolding the relationship between seasonal forecast skill and value in hydropower production: a global analysis
Donghoon Lee, Jia Yi Ng, Stefano Galelli, and Paul Block
Hydrol. Earth Syst. Sci., 26, 2431–2448,,, 2022
Short summary
Drought impact links to meteorological drought indicators and predictability in Spain
Herminia Torelló-Sentelles and Christian L. E. Franzke
Hydrol. Earth Syst. Sci., 26, 1821–1844,,, 2022
Short summary

Cited articles

Aguilera, P. A., Fernandez, A., Fernandez, R., Rumi, R., and Salmeron, A.: Bayesian networks in environmental modelling, Environ. Modell. Softw., 26, 1376–1388,, 2011. 
Aller, L., Bennet, T., Leher, J. H., Petty, R. J., and Hackett, G.: DRASTIC: a standardized system for evaluating ground water pollution potential using hydrogeological settings, EPA, 641 pp., 1987. 
Babaei, H., Nazari-Sharabian, M., Karakouzian, M., and Ahmad, S.: Identification of critical source areas (CSAs) and evaluation of best management practices (BMPs) in controlling eutrophication in the Dez River basin, Environments, 6, 1–15,, 2019. 
Bereswill, R., Streloke, M., and Schulz, R.: Risk mitigation measures for diffuse pesticide entry into aquatic ecosystems: proposal of a guide to identify appropriate measures on a catchment scale, Integr. Environ. Assess., 10, 286–298,, 2014. 
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,, 2019. 
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.