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

Low, M.I. , Bonte, M., Evers, L. , Bowman, A.W. and Jones, W.R. (2019) Statistical Modelling of Groundwater Contamination Monitoring Data using GWSDAT: a Comparison of Spatial and Spatiotemporal Methods. AquaConSoil 2019, Antwerp, Belgium, 20-24 May 2019. pp. 5-6.

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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 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.

Item Type:Conference or Workshop Item
Status:Published
Refereed:No
Glasgow Author(s) Enlighten ID:Bowman, Professor Adrian and Evers, Dr Ludger and Low, Dr Marnie
Authors: Low, M.I., Bonte, M., Evers, L., Bowman, A.W., and Jones, W.R.
Subjects:H Social Sciences > HA Statistics
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
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