Statistical modelling of groundwater contamination monitoring data: A comparison of spatial and spatiotemporal methods

Abstract

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 spatial compared to spatiotemporal statistical modelling and assessed the accuracy of both methods and implications for data collection requirements. The aim of this 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. To capture the variability encountered under field conditions, we used three hypothetical groundwater contamination plumes with increasing complexity, and site data for a large groundwater gasoline additive plume. The results show that spatiotemporal methods can increase efficiency markedly so that, in comparison with repeated spatial analysis, spatiotemporal methods can achieve the same level of performance but with smaller sample sizes.