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Monitoring data are particularly interesting when analysing processes characteristics because can reveal not only temporal patterns but also spatial distributions and variation over time if collected in a geospatial network. Character and causes of variability can be explored from spatial and temporal correlations and allow to predict values at points from neighbouring observations. Spatio-temporal (ST) interpolation allows making predictions in between observation times and can potentially provide more accurate predictions than spatial interpolation because observations taken at other times can be included. This procedure requires covariance model capable to join spatial, temporal and spatiotemporal dependence structures, for instance represented as variograms. Recent developments in the implementation of these methods allowed that a whole family of theoretical covariance models was available to several natural phenomena applications. The aim of this project is to verify the applicability of ST geostatistics methods based on covariance models to water table depths monitoring data. Data from Bauru Aquifer System collected at 65 locations in the Santa Barbara Ecological Station/SP with a monthly frequency from September 2014 until the present date will be tested for this target. ST variograms can reveal information about spatial and temporal dependence, based on groundwater oscillation process. These results are important information for groundwater management and planning, monitoring strategies and remediation plans. (AU)

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Scientific publications (8)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
FERREIRA SILVA, CESAR DE OLIVEIRA; MANZIONE, RODRIGO LILLA; ALBUQUERQUE FILHO, JOSE LUIZ. Combining remotely sensed actual evapotranspiration and GIS analysis for groundwater level modeling. ENVIRONMENTAL EARTH SCIENCES, v. 78, n. 15, . (14/04524-7, 16/09737-4)
MANZIONE, RODRIGO LILLA; CASTRIGNANO, ANNAMARIA. A geostatistical approach for multi-source data fusion to predict water table depth. Science of The Total Environment, v. 696, . (16/09737-4)
MANZIONE, RODRIGO LILLA; FERREIRA SILVA, CESAR DE OLIVEIRA; CASTRIGNANO, ANNAMARIA. A combined geostatistical approach of data fusion and stochastic simulation for probabilistic assessment of shallow water table depth risk. Science of The Total Environment, v. 765, . (14/04524-7, 16/09737-4)
MANZIONE, RODRIGO LILLA. Physical-based time series model applied on water table depths dynamics characteristics simulation. RBRH-REVISTA BRASILEIRA DE RECURSOS HIDRICOS, v. 23, . (16/09737-4, 14/04524-7)
SANTAROSA, LUCAS VITURI; MANZIONE, RODRIGO LILLA. Soil variables as auxiliary information in spatial prediction of shallow water table levels for estimating recovered water volume. RBRH-REVISTA BRASILEIRA DE RECURSOS HIDRICOS, v. 23, . (15/05171-3, 14/04524-7, 16/09737-4)
DE MORAES TAKAFUJI, EDUARDO HENRIQUE; DA ROCHA, MARCELO MONTEIRO; MANZIONE, RODRIGO LILLA. Groundwater Level Prediction/Forecasting and Assessment of Uncertainty Using SGS and ARIMA Models: A Case Study in the Bauru Aquifer System (Brazil). NATURAL RESOURCES RESEARCH, v. 28, n. 2, p. 487-503, . (16/09737-4)
DE OLIVEIRA FERREIRA SILVA, CESAR; MATULOVIC, MARIANA; LILLA MANZIONE, RODRIGO. New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management. SN APPLIED SCIENCES, v. 3, n. 6, . (14/04524-7, 16/09737-4)

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