The objective of this study is to explore the spatio-temporal nature of groundwater monitoring data using space-time (ST) geostatistics in order to predict water table depths at Bauru Aquifer System (BAS) in a conservation area at São Paulo State, Brazil. The information about the groundwater oscillation process in space and time can be measured in terms of spatial and temporal correlation through the ST variogram. The targets was predict water table depths in a missing date inside the monitoring period and propose a validation of these predictions based on predicted and observed values distribution curves for that specific date. Before modelling the ST empirical variogram, separability between space and time structures was checked. Then, the ST kriging predictions for March 31, 2016 were compared with independent observed dataset. ST kriging was a robust interpolator, turning possible a reasonable reconstructions of a hypothetical missing scenario inside the monitoring period in the BAS study area. The results showed a strong dependence of the temporal mean in the predictions.
Spatio-temporal Kriging to Predict Water Table Depths from Monitoring Data in a Conservation Area at São Paulo State, Brazil
De Iaco Sandra;Cappello Claudia;
2019-01-01
Abstract
The objective of this study is to explore the spatio-temporal nature of groundwater monitoring data using space-time (ST) geostatistics in order to predict water table depths at Bauru Aquifer System (BAS) in a conservation area at São Paulo State, Brazil. The information about the groundwater oscillation process in space and time can be measured in terms of spatial and temporal correlation through the ST variogram. The targets was predict water table depths in a missing date inside the monitoring period and propose a validation of these predictions based on predicted and observed values distribution curves for that specific date. Before modelling the ST empirical variogram, separability between space and time structures was checked. Then, the ST kriging predictions for March 31, 2016 were compared with independent observed dataset. ST kriging was a robust interpolator, turning possible a reasonable reconstructions of a hypothetical missing scenario inside the monitoring period in the BAS study area. The results showed a strong dependence of the temporal mean in the predictions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
