Assessing the spatio-temporal changes of groundwater parameters: a multivariate geostatistical approach
- 1DES-Sect. of Mathematics and Statistics, University of Salento (Italy); National Center of High Performance Computing, Big Data and Quantum Computing, (Italy) (monica.palma@unisalento.it)
- 2DES-Sect. of Mathematics and Statistics, University of Salento (Italy) (sabrina.maggio@unisalento.it)
- 3DES-Sect. of Mathematics and Statistics, University of Salento (Italy) (claudia.cappello@unisalento.it)
- 4DES-Sect. of Mathematics and Statistics, University of Salento (Italy) (antonella.congedi@unisalento.it)
- 5DES-Sect. of Mathematics and Statistics, University of Salento (Italy); National Center of High Performance Computing, Big Data and Quantum Computing, (Italy) (sandra.deiaco@unisalento.it)
Groundwater over-exploitation and environment pollution, together with rising temperatures and other climate changes, can cause a large imbalance in the soil physicochemical properties, with a negative impact on economic, social and human health conditions. Therefore, monitoring and assessing the evolution in space and time of groundwater qualitative parameters as well as quantitative status are crucial aspects for a sustainable water management.
Multivariate Geostatistics foresees dedicated tools for analyzing multivariate spatio-temporal data which are characterized by heterogeneous patterns in space-time, such as those concerning hydrogeological data. In the literature, few analyses (Jang et al., 2012; Yazdanpanah, 2016; Mastrocicco et al., 2021) have been developed on the main groundwater qualitative indicators through the use of spatio-temporal multivariate geostatistical methodologies.
This paper aims to propose a spatio-temporal multivariate analysis for some benchmark indicators describing the qualitative and quantitative status of an unconfined aquifer in Italy. By applying the fitting procedure proposed in De Iaco et al. (2019) and recalled in Cappello et al. (2022), a spatio-temporal multivariate correlation model is developed for forecasting purposes. Then, on the basis of a comparison among predicted values of the variables under study and values recorded for the same variables a decade before, hazard maps of groundwater degradation are produced by through a non-parametric approach, identifying those vulnerability areas where the aquifer system could be contamined. The empirical findings will help the policy makers to pursue effective actions aimed at safeguarding groundwater resources.
REFERENCES
- Cappello, C., De Iaco, S., Palma, M., 2022. Computational advances for spatio-temporal multivariate environmental models. Comput. Stat. 37, 651–670. https://doi.org/10.1007/s00180-021-01132-0
- De Iaco, S., Palma. M., Posa, D., 2019. Choosing suitable linear coregionalization models for spatio-temporal data. Stoch. Environ. Res. and Risk Assess. 33, 1419–1434.
- Jang, C.S., Chen, S.K., Kuo, Y.M., 2012. Establishing an irrigation management plan of sustainable groundwater based on spatial variability of water quality and quantity. Journal of Hydrology, 414-415, 201–210
- Mastrocicco, M., Gervasio, M.P., Busico, G., Colombani, N., 2021. Natural and anthro- pogenic factors driving groundwater resources salinization for agriculture use in the Campania plains (Southern Italy). Science of the Total Environment, 758, 144033.
- Yazdanpanah, N. 2016. Spatiotemporal mapping of groundwater quality for irrigation using geostatistical analysis combined with a linear regression method. Model. Earth Syst. Environ., 2, 1-18.
How to cite: Palma, M., Maggio, S., Cappello, C., Congedi, A., and De Iaco, S.: Assessing the spatio-temporal changes of groundwater parameters: a multivariate geostatistical approach, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14743, https://doi.org/10.5194/egusphere-egu23-14743, 2023.
