GWSDAT - Useful Links and Presentations

Useful Links and Presentations

GWSDAT is listed in the following ITRC guidance document: Groundwater statistics for Monitoring and Compliance

Case studies: http://gwsdat.net/case-studies/

Article: Groundwater Spatiotemporal Data Analysis Tool: Case Studies, New Features and Future Developments  

Article: on benefits of spatiotemporal modelling GWSDAT in Science of Total Environment.

Article: on GWSDAT in Science Direct

Article: "Analyzing Groundwater Quality Data and Contamination Plumes with GWSDAT" in Groundwater

Paper: "Efficient and automatic methods for flexible regression on spatiotemporal data, with applications to groundwater monitoring" in Environmetrics

Supporting information for the above Groundwater article:

Acknowledgements

The authors gratefully acknowledge those people who have contributed their knowledge and time to the development of GWSDAT.

The authors wish to express their gratitude to Craig Alexander, Adrian Bowman, Ludger Evers, Marnie Low, Claire Miller, Daniel Molinari and Peter Radvanyi from the department of Statistics, University of Glasgow, for their invaluable contributions to the development of the spatiotemporal algorithm.

Thanks also to Ewan Mercer from the University of Glasgow for his assistance in the development of the GWSDAT user interface.

We acknowledge and thank the R project for Statistical Computing and all its contributors without which this project would not have been possible.

A big thank you to Shell's worldwide environmental consultants for assistance in evaluating and testing the earlier versions of GWSDAT.

Thanks also to the Shell Year in Industry students who spent a great deal of time testing GWSDAT and making suggestions for improvements.

We thank both current and former colleagues including Matthew Lahvis, Jonathan Smith, George Devaull, Dan Walsh, Curtis Stanley, Marco Giannitrapani and Philip Jonathan for their support, vision and advocacy of GWSDAT.

References

• Bowman and Azzalini, 1997. Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford, 1997.

• Bowman and Azzalini. sm: Smoothing methods for nonparametric regression and density estimation. R package, www.stats.gla.ac.uk/~adrian/sm

• Eilers and Marx, 1992. Generalized Linear Models with P-Splines in Advances in GLIM and Statistical Modelling (L.Fahrmeir et al.eds.). Springer, New York.

• Eilers and Marx, 1996. Flexible smoothing with b-splines and penalties. Statistical Science 11, 89–121.

• Evers et al., 2015. Efficient and automatic methods for flexible regression on spatiotemporal data, with applications to groundwater monitoring, Environmetrics (open access), 26(6), 431-441.

• Jones, et al., 2014. A software tool for the spatiotemporal analysis and reporting of groundwater monitoring data (open access), Environmental Modelling & Software, 55, 242-249.

• Jones, et al., 2015. Analyzing Groundwater Quality Data and Contamination Plumes with GWSDAT (open access), Groundwater, 53 (4), 513-514.

• Jones, W.R., Rock, L., Wesch, A., Marzusch, E. and Low, M. (2022), Groundwater Spatiotemporal Data Analysis Tool: Case Studies, New Features and Future Developments. Groundwater Monit R, 42: 14-22. https://doi.org/10.1111/gwmr.12522

• McLean et al., 2019, Statistical modelling of groundwater contamination monitoring data: A comparison of spatial and spatiotemporal methods, Science of The Total Environment (open access), 652, 1339-1346.

• R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2-008. ISBN 3-900051-07-0, http://www.r-project.org