D.7 Groundwater Spatio-Temporal Data Analysis Tool (GWSDAT)

Approximate Cost: Free

Source:  http://www.api.org/GWSDAT

Current Version: v2.0

Operating System Needs: Windows XP, Vista or Windows 7, also requires Microsoft Office, XP, 2007 or 2010

Input Structure: Excel standardized data input template sheet

Overview

The Groundwater Spatiotemporal Data Analysis Tool (GWSDAT), was developed by Shell Global Solutions to help visualize trends in groundwater monitoring data. This program is designed to work with simple time-series data for contaminant concentration and ground water elevation, but can also plot non-aqueous phase liquid (NAPL) thickness if required. Spatial data are 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 base map in GIS shapefile format. Trend and contour plots generated using GWSDAT can be exported directly to Microsoft PowerPoint and Word to expedite reporting.

Add-Ins Available

None

Ease of Use and Data Import

GWSDATGroundwater Spatiotemporal Data Analysis Tool has been designed to be as user-friendly as possible. The application is supported for Windows XP, Vista, Windows 7, and the corresponding version of Microsoft Office. The user entry point and data input platform to GWSDAT is a custom built Excel Add-in application. The statistical engine used to perform geo-statistical modeling and display graphical output is the open source statistical programming language R (www.r-project.org).

Enter groundwater monitoring data into GWSDATGroundwater Spatiotemporal Data Analysis Tool by populating three tables in a standardized Excel input sheet. Include historical monitoring data, well coordinates, and GIS shape files. Two example data files are provided with the program for training and demonstration purposes. After entering the data, select “GWSDAT Analysis” from the Excel add-in menu to initiate a GWSDAT analysis.

The GWSDATGroundwater Spatiotemporal Data Analysis Tool operates with a graphical, stand-alone point-and-click interface that allows you to analyze time-series, spatial, and (uniquely) spatiotemporal trends. By left-clicking on any of the user interface plots, an identical but expanded plot is generated in a separate window. You can save plots to a variety of different formats including JPEG, postscript, PDF, metafile, and Microsoft Power Point slide.

Types of Distributions

GWSDATGroundwater Spatiotemporal Data Analysis Tool uses a wide variety of different nonparametricStatistical test that does not depend on knowledge of the distribution of the sampled population (Unified Guidance). statistical methods for the analysis of trends in temporal, spatial and spatiotemporal components of the groundwater monitoring data set.

Visualization

GWSDATGroundwater Spatiotemporal Data Analysis Tool includes sophisticated graphical visualization for trend detection:

Benefits

Limitations and Data Requirements

References

Ahmadi, S. H., and A. Sedghamiz. 2007. “Geostatistical Analysis of Spatial and Temporal Variations of Groundwater Level”, Environmental Monitoring and Assessment, Vol. 129, No. 1-3: 277-294.

Bowman, A.W., and A. Azzalini. 2012. The "sm" package for R. Smoothing methods for nonparametric regression and density estimation. 2012. www.stats.gla.ac.uk/~adrian/sm.

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

Jones, W.R., and M. Spence. 2012. “GroundWater Spatio-Temporal Data Analysis Tool (GWSDAT Version 2.0) User Manual.” Shell Global Solutions, UK.

Jones, W.R., M.J. Spence, A.W. Bowman, L. Evers, and D. A. Molinari. 2014. "A software tool for the spatiotemporal analysis and reporting of groundwater monitoring data." Environmental Modelling & Software. Vol. 55: 242-249. http://www.sciencedirect.com/science/article/pii/S1364815214000309

Paul H. C. Eilers, Dcmr Milieudienst Rijnmond, and Brian D. Marx. 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, Vienna, Austria, 2008. ISBN 3-900051-07-0, http://www.r-project.org.

Thyne, G., C. Guler, and E. Poeter. 2004. “Sequential Analysis of Hydrochemical Data for Watershed Characterization”, Ground Water, V. 42, Issue 42: 711-723.

 

Publication Date: December 2013

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