References

GWSDAT: further information

• GWSDAT User Manual: http://gwsdat.net/gwsdat_manual/
• Wayne Jones (2026). Beneath the surface: The statistical story of groundwater monitoring, Significance 23, Issue 2, 18–21.
• Building Trust Through Open Science at Shell. 2026: How Shell Builds Regulatory Trust Through Open Science and GWSDAT, Posit.
• Jones, W.R., Rock, L., Wesch, A., Marzusch, E., Low, M. (2022). Groundwater Spatiotemporal Data Analysis Tool: Case Studies, New Features and Future Developments. Groundwater Monit R, 42: 14-22.
• Jones, W.R., Spence, M.J., Bonte, M. (2015). Analyzing Groundwater Quality Data and Contamination Plumes with GWSDAT, Groundwater 53, 513-514.
• Jones, W.R., Spence, M.J., Bowman, A.W., Evers, L., Molinari, D.A. (2014). A software tool for the spatiotemporal analysis and reporting of groundwater monitoring data. Environmental Modelling & Software 55, 242-249.
• Introducing GWSDAT (GroundWater Spatiotemporal Data Analysis Tool). API poster.

Statistical methods

• Radvanyi, P., Miller, C., Alexander, C., Low, M., Rock, L. and Jones, W. (2026). Computationally Efficient Ranking of Sampling Locations by Redundancy in Groundwater Monitoring Networks. Groundwater Monit R.
• Siciliano S.D., Jones, W.R. (2026). Beyond Mann-Kendall: Three Statistical Approaches for Groundwater Trend Analysis in a Single Well. Environmental Services Association of Alberta (ESAA) technical report.
• Radvanyi, P., 2025. Sampling designs for the spatiotemporal modelling of groundwater quality monitoring data, PhD Thesis (Glasgow).
• Low, M. I., Bowman, A. W., Jones, W., & Bonte, M. (2024). Exact optimisation of spatiotemporal monitoring networks by p-splines with applications in groundwater assessment. Environmetrics, 35(6), e2874. 

• Radvanyi, P., Miller, C., Alexander, C., Low, M., Jones, W. R. and Rock, L. (2023). Computationally Efficient Ranking of Groundwater Monitoring Locations. In: 37th International Workshop on Statistical Modelling (IWSM), Dortmund, Germany, 16-21 Jul 2023, pp. 332-338. ISBN 9783947323425.

• Mclean, M.I., Evers, L., Bowman, A.W., Bonte, M., Jones, W.R. (2018). Statistical modelling of groundwater contamination monitoring data: A comparison of spatial and spatiotemporal methods. Science of the Total Environment 652, 1339-1346.

• Mclean, M.  I., 2018. Spatiotemporal models for the analysis and optimisation of groundwater quality monitoring networks, PhD Thesis (Glasgow).
• Evers L., Molinari, D.A., Bowman, A.W., Jones, W.R., Spence, M.J. (2015). Efficient and automatic methods for flexible regression on spatiotemporal data, with applications to groundwater monitoring. Environmetrics 26, 431-441.
• Molinari, D. A., 2014. Spatiotemporal modelling of groundwater contaminants, PhD Thesis (Glasgow).
• Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford, 1997.
• Eilers, P. H. C., Marx, B. D. (1996). Flexible smoothing with b-splines and penalties. Statistical Science 11, 89–121.

Software tools

• Bowman, A.W., Crawford, E., Alexander, G., Bowman, R.W. (2018). R package panel: Simple Interactive Controls for R using the ‘tcltk’ Package (version 1.1-4).  CRAN archive.
• Chang, W., Cheng, J., Allaire, JJ, Xie, J., McPherson, J. (2017). shiny: Web Application Framework for R. R package (version 1.0.4).  CRAN archive.
• Bowman, A.W. and Azzalini. A. (2014). R package sm: nonparametric smoothing methods (version 2.2-5.5).  CRAN archive.
• R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2008. ISBN 3-900051-07-0, http://www.r-project.org
• Bowman, A.W., Crawford, E., Alexander, G., Bowman, R.W. (2007). rpanel: Simple Interactive Controls for R Functions Using the tcltk Package. Journal of Statistical Software, 17(9), 1-18.