Présentations Colloques

    Oral Presentation
    Session 8.10: Recent developments in groundwater modeling and mathematical tools in Hydrogeology
    Lilla Manzione Rodrigo
    Modelling spatio-temporal variability of water table depths monitoring data
    Groundwater monitoring data are particularly interesting when analysing aquifer characteristics because can reveal not only temporal patterns but also spatial distributions and variation over time if collected in a geospatial network. From spatial and temporal correlations, it is possible to predict values at points from neighbouring observations and make predictions in between observation times. Spatio-temporal interpolation can potentially provide more accurate predictions than spatial interpolation because observations taken at other times can be included. Recent advances in the implementation of spatial statistics methods allowed the join modelling of spatial and temporal structures into the geostatistical scope. However, adding the temporal domain implies that variability in space and time must be modelled. This procedure requires covariance models capable to join spatial, temporal and spatiotemporal dependence structures, for instance represented as variograms. This work presents a spatio-temporal modelling of water table depths monitoring data collected in a conservation area in Águas de Santa Barbara SP-Brazil. The Santa Barbara Ecological Station (EEcSB) is a 15 km2 area with natural Cerrado vegetation and some reforest with pine and eucalyptus trees, under the domains of Bauru Aquifer, a cenozoic sandstone sedimentary rock 60-m thick limited in the bottom with basaltic volcanic rock and in the top with more recent geological covers. This aquifer is one of the major groundwater sources of Médio Paranapanema hydrographical region (UGRHI-17), an important region for hydroelectricity generation and biofuel production in Sao Paulo state. The water table is monitored semi-monthly from September 2014 to March 2016 at 65 piezometers near the main drainage channels. Three covariance models were tested- separable, product sum and metric. The fitted variograms revealed information about spatial and temporal dependence, based on groundwater oscillation process in space and time. These results are important information for groundwater management and planning, monitoring strategies, managed aquifer recharge and remediation plans.


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