Présentations Colloques

    Oral Presentation
    Session 8.07: Hydrogeophysics: innovative non-invasive technologies for groundwater resources exploitation and management
    Moreau Magali
    Predictive uncertainty and data worth analysis to determine cost effectiveness of airborne EM data for defining hydraulic properties in a groundwater flow model used for predicting long term groundwater level drawdowns.
    Traditional techniques used to define aquifer hydraulic properties combine field testing (e.g., slug tests and pumping tests) and data analysis. Although these techniques provide direct measurements of groundwater level response to a system stress, they are invasive and time cost intensive, and each test is only locally representative. In comparison, the acquisition of geophysical data is rapid and yields information at various depths and over large spatial scales. This study investigates whether airborne electromagnetic (AEM) data are a cost-effective tool for refining the spatial variability of hydraulic conductivity and vertical aquifer boundaries within a groundwater flow model when compared to more traditional techniques. The cost effectiveness assessment is made in the context of a groundwater impact assessment examining the long term pumping effects on groundwater levels and the ability of existing groundwater users to abstract at their desired yields. **In this project, a linear Bayesian uncertainty analysis method is used to assess predictive uncertainty (e.g. Christensen and Doherty). The methodology has previously been successfully applied to several synthetic numerical groundwater model problems. In this example, we examine the worth of various analyses of AEM data by the extent to which they improve the reliability of groundwater level drawdown impact assessments within an 8 x 8 km groundwater flow model. For instance, constructing a spatially distributed hydraulic conductivity field, defined using estimates from pump tests in conjunction with variograms constructed using a resistivity model from AEM measurements. A cost-benefit analysis is also performed considering the added value of drilling and testing at new locations versus that of using the AEM data. This is followed by an uncertainty targeted Pareto analysis, which allows the interdependencies of cost and predictive reliability achieved for alternative data sets to be compared directly.**The potential for a reduction in predictive uncertainty using AEM data in pumping effect scenarios on a small-scale groundwater flow model are currently being explored, as well as a cost-benefit comparison to traditional techniques.**
    New Zealand


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