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Easier Installation: GWSDAT's Excel deployment has been redesigned for easier and more robust installation which no longer requires administrative rights. Please see installation Instructions section below here.
Faster Spatiotemporal Modelling: The GWSDAT add-in now comes bundled with a portable Windows R version optimized for linear algebra which enables the spatiotemporal model to run quicker for higher resolution model settings. This enables users to more readily increase the modelling resolution which can be useful for fixing occurrences of the statistical anomaly referred to as “ballooning” – where predictions can be high in areas where there are no data. For full details article here.
GWSDAT Tutorials – YouTube: YouTube channel which gives a structured comprehensive series of videos for GWSDAT training. Please like and subscribe!
Upgraded Well Time Series Plot:
Functionality to specify time range of data in Well Time‑Series plot.
Functionality to select multiple wells and solutes in matrix‑style plots.
For GWSDAT R Developers: New R package released on CRAN here and on GitHub here. Full list of changes here.
Functionality to export spatial plot predictions to ascii file format.
Functionality to aggregate temporal plotting resolution to Semi Annual.
CSS styling for Trends and Thresholds plot which includes scrolling to better cope with many wells and/or solutes.
Functionality to Select/Deselect all wells in Well Report and Spatiotemporal predictions plots.
GroundWater Spatiotemporal Data Analysis Tool (GWSDAT)
GWSDAT is an open source, user-friendly, software application for the visualisation and interpretation of groundwater monitoring data. It also enables to work with other types of monitoring data collected over time and space (e.g. soil gas concentrations).
Key functionalities of tool:
Trend analyses
Data smoothing
Spatiotemporal smoothing
Determination of contamination plume characteristics
Well redundancy analysis
Automatic report generation tools based on user input.
Business Benefits
Early identification of increasing trends or off-site migration.
Evaluation of groundwater monitoring trends over time and space (i.e., holistic plume evaluation).
Nonparametric statistical and uncertainty analyses to assess highly variable groundwater data.
Reduction in the number of sites in long-term monitoring or active remediation through simple, visual demonstrations of groundwater data and trends.
More efficient evaluation and reporting of groundwater monitoring trends via simple, standardised plots and tables created at the ‘click of a mouse.’
Well Redundancy Analysis functionality to identify potential optimization measures with regards to monitoring well network sampling locations.
Background
Introduction
The GroundWater Spatiotemporal Data Analysis Tool (GWSDAT) has been developed by Shell Global Solutions for the analysis of groundwater monitoring data. It is designed to work with simple time-series data for solute concentration and ground water elevation but can also plot non-aqueous phase liquid (NAPL) thickness if required. Spatial data is input in the form of well coordinates, and wells can be grouped to separate data from different aquifer units. The software also allows the import of a site basemap in GIS shapefile format. Concentration trend and 2D contour plots generated using GWSDAT can be exported directly to Microsoft PowerPoint and Word to expedite reporting.
Software Architecture
The application is supported for Windows 8,10 & 11 and the corresponding version of Microsoft Office (including 64-bit operating systems). Data input to GWSDAT is via a standardized Excel spreadsheet and the data analysis and plot functions are accessed through an Excel Add-in application. The statistical engine used to perform geo-statistical modelling and display graphical output is the open-source statistical programming language R (www.r-project.org). A user manual and two example datasets are provided with the software for training and demonstration purposes.
Spatiotemporal Data Analysis
The modelling of solute distribution in groundwater is typically restricted to either the analysis of trends in individual wells or independent fitting of spatial concentration distributions (e.g. by Kriging) to data from monitoring events. Neither of these techniques satisfactorily elucidate the interaction between spatial and temporal components of the data. GWSDAT applies a spatiotemporal model smoother for a more coherent and smooth interpretation of the interaction in spatial and time-series components of groundwater solute concentrations. A spatiotemporal concentration smoother is fitted for each analyte using a non-parametric regression technique known as Penalised Splines (Eilers and Marx, 1992, 1996). A Bayesian methodology is used to select the appropriate degree of model smoothness (Evers et al, 2015). The fit of the spatiotemporal algorithm to the monitoring data can be evaluated.
Graphical User Interface
The GWSDAT graphical user interface (GUI) allows the user to navigate through a groundwater dataset and explore concentration/ groundwater elevation trends in individual wells and across the site. Several options are available to customize the display and data analysis. Note that plots can also be automatically exported.
Data Visualisation
GWSDAT includes the following tools for trend visualization and detection:
Spatial plot: For the analysis of spatial trends in solute concentrations, groundwater flow and, if present, NAPL thickness. Overlaid on this plot are the predictions of the spatiotemporal solute concentration smoother which is a function that simultaneously estimates both the spatial and time series trend in site solute concentrations. GIS shapefiles can also be overlaid on this plot.
Well Trend plot: For the investigation of historical time-series trends in solute concentrations, groundwater elevation and, if present, NAPL thickness for individual wells. Users can overlay a nonparametric smoother which estimates the time-series trend in solute concentration. The advantage of this nonparametric method is that the trend estimate is not constrained to be monotonic, i.e. the trend can change direction.
Trend and Threshold Indicator Matrix: This feature provides a summary of the level and time series trend in solute concentrations at a particular model output interval.
Plume diagnostics plots: This feature enables to calculate and display plume diagnostic quantities (area, mass, concentration) for a delineated plume displayed with a solid red contour line.
Well redundancy: This feature enables users to very conveniently drop a well or a combination of wells from the analysis and investigate the resultant impact.
Well Influence Analysis:
Building on the existing well redundancy analysis feature, GWSDAT now provides an ordered well omission list such that the wells estimated to have the least influence are presented first. This offers users more assistance in assessing which monitoring wells may be the most suitable for future omission and eventual decommissioning. This well influence order is established via a procedure fully documented here and here.
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 Adrian Bowman, Ludger Evers, Daniel Molinari, Claire Miller, Marnie Low, Peter Radvanyi, and Craig Alexander 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.
ITRC (Interstate Technology & Regulatory Council). 2013. Groundwater Statistics and Monitoring Compliance, Statistical Tools for the Project Life Cycle. GSMC-1. Washington, D.C.: Interstate Technology & Regulatory Council, Groundwater Statistics and Monitoring Compliance Team. https://projects.itrcweb.org/gsmc-1/
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
Adrian W. Bowman and Adelchi Azzalini. sm: Smoothing methods for nonparametric regression and density estimation. R package, www.stats.gla.ac.uk/~adrian/sm
Adrian W. Bowman and A. Azzalini. Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford, 1997.
Eilers and Marx, 1996. Flexible smoothing with b-splines and penalties. Statistical Science 11, 89–121.
Eilers, P. H. C., Marx, B. D., 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, 1992. Generalized Linear Models with P-Splines in Advances in GLIM and Statistical Modelling (L.Fahrmeir et al.eds.). Springer, New York.
Installation Instructions
The GWSDAT V3.3 Excel Add-in has a new simpler installation procedure where all files are bundled together, and you don’t need admin rights to install the software. Before attempting installation, it is recommended to read the article here for more information on Excel Trusted Locations and add-in troubleshooting. A video demonstrating end to end installation can be found here.
Download https://gwsdat.net/r-portable-win/ and unzip to an Excel Trusted Location on your C drive (not a network drive). e.g. C:\Users\User.Name\AppData\Local\GWSDAT\R-Portable-Win\R-Portable-Win\library. This may take a few minutes.
Open Excel and install the GWSDAT add-in by choosing: "File" > "Options" > "Add-Ins" > "Go" > "Browse" and then select "GWSDAT V3.3.xlam" located in the "C:\Users\User.Name\AppData\Local\GWSDAT\R-Portable-Win\R-Portable-Win\library\GWSDAT\extdata" folder. To avoid any Excel add-in security issues please ensure that this is a trusted location.
A menu called "GWSDAT v3.3" will appear in the EXCEL ribbon (on right side) along the top of the EXCEL window.
Troubleshooting:
If the “GWSDAT v3.3” menu does not appear in Excel or an add-in error occurs, then follow the steps in this article here. Most commonly, the solution here is to unblock the add-in file.
Navigate to “C:\Users\User.Name\AppData\Local\GWSDAT\R-Portable-Win\R-Portable-Win\library\GWSDAT\extdata” folder and check the file “GWSDAT V3.3.xlam” isn’t blocked by doubling click on it. If it is blocked then right click the file, select properties and check the unblock check as seen in the screenshot below.
If the “GWSDAT v3.3” menu is visible, but nothing happens when you run the example then please navigate to “C:\Users\User.Name\AppData\Local\GWSDAT\R-Portable-Win\R-Portable-Win\bin\x64” and check the file “Rterm.exe” isn’t blocked. You can check it isn’t blocked by double clicking on the file. If it is blocked then right click the file, select properties and check the unblock check as seen in below screenshot
You may see a security warning – as screenshot below – click run to proceed and uncheck “Always ask before opening this file” to stop this message appearing in the future.
Getting started
To get started with a basic example activate menu by clicking on “GWSDAT v3.30” and then select “Open Example” → select “Basic Example” and then select “Analyse”.
Note – The first time you run this it may take a while as it needs to download and install some additional R packages.
To get started with a more complex example activate menu by clicking on “GWSDAT v3.30” and then select “Open Example” → select “Comprehensive Example”, and then select “Analyse”.
