Statistical Modelling of Groundwater Contamination Monitoring Data using GWSDAT: a Comparison of Spatial and Spatiotemporal Methods

Field monitoring of groundwater contamination plumes is an important component of managing risks for downgradient receptors and remedial strategies that rely on monitored natural attenuation. Collection of groundwater quality data can however take a considerable effort and be associated with high cost. Here, we investigated the relative merits of analyzing groundwater quality data using spatiotemporal statistical modelling as implemented in GWSDAT compared to a spatial method (e.g. kriging data for specific time steps) (Jones e.a., 2015) We assessed the accuracy of both methods and implications for data collection requirements. The aim of this work was to determine whether the quantity of data collected can be reduced, while retaining the same level of estimation accuracy, by analyzing groundwater contamination data using a spatiotemporal model which “borrows strength” across time, rather than a spatial model for individual sampling events