Spatio-temporal modeling of groundwater quality deterioration and resource depletion

In Hydrogeology, the analysis of groundwater features is based on multiple data related to correlated variables recorded over a spatio-temporal domain. Thus, multivariate geostatistical tools are fundamental for assessment of the data variability in space and time, as well as for parametric and nonparametric modeling. In this work, three key hydrological indicators of the quality of groundwater-sodium adsorption ratio, chloride and electrical conductivity-as well as the phreatic level, in the unconfined aquifer of the central area of Veneto Region (Italy) are investigated and modeled for prediction purposes. By using a new geostatistical approach, probability maps of groundwater resource deterioration are computed, and some areas where the aquifer needs strong attention are identified in the north-east part of the study region. The proposed analytical methodology and the findings can support policy makers in planning actions aimed at sustainable water management, which should enable better monitoring of groundwater used for drinking and also ensure high quality of water for irrigation purposes

Towards an automatic procedure for modeling multivariate space-time data

In many environmental sciences, several correlated variables are observed at some locations of the domain of interest and over a certain period of time. In this context, appropriate modeling and prediction techniques for multivariate space–time data as well as interactive software packages are necessary. In this paper, a new automatic procedure for fitting the space–time linear coregionalization model (ST-LCM) using the product–sum variogram model is discussed. This procedure, based on the simultaneous diagonalization of the sample matrix variograms, allows the identification of the ST-LCM parameters in a very flexible way. The fitting process is analytically described by a main flow chart and all steps are specified by four subprocedures. An application of this procedure is illustrated through a case study concerning the daily concentrations of three air pollutants measured in an urban area. Then the fitted space–time coregionalization model is applied to predict the variable of interest using a recent GSLib routine, named “COK2ST.