GWSDAT is an open source, user-friendly, software application for the visualisation and interpretation of groundwater monitoring data.
Key Features Include:
- Visualisation of site wide trends in solute concentrations, NAPL thickness and groundwater flow velocity for conceptual site model development.
- Spatiotemporal analysis: variation in groundwater solute concentration is modelled as a function of X,Y and time.
- Automatic generation of concentration contour plots at user specified time intervals, with the option to overlay groundwater elevation contours and NAPL thickness/ footprint data..
- Automatic report generation tools.
- Improved data transparency helps design and optimise groundwater monitoring or remediation programmes (i.e. avoid the collection of redundant data).
- Early identification of new releases, migration pathways, need for corrective action and stable/ declining trends that may aid in closure determinations.
- Rapid interpretation of complex data sets from large monitoring networks (e.g. refineries, terminals).
- More efficient evaluation and reporting of groundwater monitoring trends via simple, standardised plots and tables created at the ‘click of a mouse’.
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.
The application is supported for Windows XP, Vista, Windows 7 and 8 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, 2013, in prep.) The fit of the spatiotemporal algorithm to the monitoring data can be evaluated in either graphical or numerical format (export to MS Excel).
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 as a whole. Left-clicking on any of the user interface plots generates an identical but expanded plot in a separate window that can be saved to a variety of different formats including “jpeg”, “postscript”, “pdf”, “metafile”. Plots can also be automatically exported to a Microsoft PowerPoint or Word.
GWSDAT includes the following tools for trend visualization and detection:
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.
New in version 2.12, GWSDAT calculates plume metrics quantifying and reporting temporal plume evolution. The quantities of plume mass, average concentration, area, and location of the centre of mass are calculated by spatial integration of the plume concentrations above a predefined concentration threshold. Further details can be found in the GWSDAT user manual and references therein.
Useful Links & Presentations
- Open-access article: Evers et al. (2015). Efficient and aut omatic methods for flexible regression on spatiotemporal data, with applications to groundwater monitoring. Environmetrics , doi: 10.1002/env.2347.
- Open-access article: Jones et al. (2014). A softw are tool for the spatiotemporal analysis and reporting of groundwater monitoring data. Environmental Modelling & Softw are, doi:10.1016/j.envsoft.2014.01.020
- The API poster, “Introducing GWSDAT (GroundWater Spatiotemporal Data Analysis Tool)” can be downloaded below.
- Groundwater Open Access Technology Spotlight article
The authors gratefully acknowledge those people who have contributed their knowledge and time to the development of GWSDAT. The au thors wish to express their gratitude to Adrian Bowman, Ludger Evers and Daniel Molinari 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.
- W. R. Jones, M. J. Spence; A. W. Bowman, L. Evers, D. A. Molinari, 2014. A software tool for the spatiotemporal analysis and reporting of groundwater monitoring data. Envi ronmental Modelling & Software 55, 242-249.
- Evers, L., Molinari, D. A., Bowman, A. W., Jones, W. R., Spence, M. J., 2013. Efficient and automatic methods for flexible regression on spatiotemporal data, with applications to groundwate r monitoring. Environmetrics (2013).
- Adrian W. Bowman and Adelchi Azzalini. sm: Smoothing methods for nonparametric regression and density estimation. R package, w ww.stats.gla.ac.uk/~adrian/sm
- Adrian W. Bowman and A. Azzalini. Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustration s. Oxford University Press, Oxford, 1997.
- 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, P. H. C., Marx, B. D., 1996. Flexible smoothing with b-splines and penalties. Statistical Science 11, 89–121.
- R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vien na, Austria, 2008. ISBN 3-900051-07-0, http://www.r-project.org
- W. R. Jones, M. J. Spence, Matthijs Bonte. Analyzing Groundwat er Quality Data and Contamination Plumes with GWSDAT. Groundwater. doi:10.1111/gwat.12340.
System Requirements: Windows XP, Vista, 7 or 8. Microsoft Office versions: 2003, XP, 2007, 2010.
- Download and install the latest version of the open source statistical application “R” available from: http://cran.r-project.org/bin/windows/base/ . Please accept all default settings during installation. (Users must have administrator access rights to install R).
- Download the GWSDAT zip file from below and unzip to somewhere on your C: Drive, e.g. C:\Apps.
- Open up Excel and install the GWSDAT add-in by choosing: “Office button“->”Excel Options” ->”Add-Ins“->”Go“->”Browse” and then select “GWSDAT V2.12.xla< /strong>” located in the “C:\Apps\GWSDAT_v2.12” folder.
- A menu called “GWSDAT v2.12” will appear “Add-Ins” tab of Excel.
- To get started with a basic example select “GWSDAT v2.12->Insert Data File->Basic Example” and then “< strong>GWSDAT v2.12->GWSDAT Analysis”.
- To get started with a more complex example select “GWSDAT v2.12->Insert Data File->Comprehensive Example</stron g>” and then “GWSDAT v2.12->GWSDAT Analysis”.
- You can view the user manual by selecting “GWSDAT v2.12->User Manual“.
- GroundWater Spatiotemporal Data Analysis Tool (GWSD AT), vers. 2.12File Type: zip | File Size: 16904052 | Downloaded: Times
- API Poster – “Introducing GWSDAT (G roundWater Spatiotemporal Data Analysis Tool)”File Type: pdf | File Size: 747704
- GWSDAT User Manual</ a>File Type: pdf | File Size: 1836494
- E xample Plume Diagnostic GWSDAT OutputFile Type: pdf | File Size: 269776
- Analyzing Groundwater Quali ty Data and Contamination Plumes: GWSDATFile Type: pdf | File Size: 536455
FAQs and Support
- GWSDAT Tool FAQsFile Type: pdf | File Size: 68988