Operational uncertainty and sensitivity analyses of a model assessing water catchment vulnerability to pesticides
Abstract
Public authorities, in response to the Water Framework Directive, impose to take measures to protect catchments that provide drinking water when the levels of contamination exceed (or are susceptible to exceed) drinking water standards. Companies and public research institutes involved in the transformation of conventional agricultural practices are therefore engaged in the development of methods aiming at assessing the vulnerability of territories towards diffuse pollutions. This poster presents an uncertainty and sensitivity analysis on a model able to assess the vulnerability towards hydrological transfers of pesticides. This research work helps to enhance the reliability of the information that is given to public authorities regarding priority areas for reduction of pesticide use. The research is being conducted in an agricultural study area located in the center of France (30km2). The studied model makes calculations based on cartographic data (DTM, soil properties, hydrographic network, climate, land cover) to identify vulnerable plots and subcatchments, on the basis of institutional guidelines for pesticide risk transfer assessment. Considered uncertainties for the analysis are the accuracy and resolution of the input cartographic data, as well as the parameterization of the model. The results highlight that these uncertainties can influence, in some cases significantly, the outputs of the model and therefore information given to public authorities. This research collaboration between two French public research institutes (INRAE, AgroParisTech) and a private company (Envilys) on an operational case study allows to identify levers to enhance the reliability of outputs from vulnerability models and in turn the efficiency of measures for catchment protection. It also allows the determination of the resolution of vulnerability mapping outputs according to obtained uncertainties.
- Publication:
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EGU General Assembly Conference Abstracts
- Pub Date:
- May 2020
- DOI:
- Bibcode:
- 2020EGUGA..22.9489R