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

Approximate Cost: Free

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.

Statistical Functions

Statistical Method	Capability As Is	Capability with Scripts/Add-Ins
Handling of NDs
Simple Substitution	●	N/A
Kaplan-Meier		N/A
ROS		N/A
Cohen/MLE		N/A
Exploratory/Diagnostic Tools
Summary Statistics	◒	N/A
Distributional tests		N/A
Outlier tests	●	N/A
Data transformations	◒	N/A
Statistical Design
Statistical Power		N/A
SWFPR		N/A
Contaminant ranking		N/A
Monitoring network optimization		N/A
Statistical Limits
Confidence Limits	●	N/A
Tolerance Limits		N/A
Prediction Limits		N/A
Testing Compliance Limits	●	N/A
Graphics
Plots/Charts	●	N/A
Batch plots	●	N/A
Tweaking of graphics	●	N/A
Statistical Comparisons
t-tests		N/A
ANOVA		N/A
Spatial Analysis
Geostatistics/Mapping	●	N/A
Kriging/Interpolation	●	N/A
Spatial smoothing	●	N/A
Regression/Time Series
Trend Tests	●	N/A
Mann-Kendall	●	N/A
Linear regression	●	N/A
Nonlinear regression	●	N/A
Theil-Sen line		N/A
Time Series analysis	●	N/A
Multivariate Analysis
Multiple regression		N/A
Factor/Discriminant analysis		N/A
Bootstrapping		N/A

Capability Ratings:

N/A = Not applicable or not available

● = Full capability

◒ = Some capability

(blank cell) = No capability

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:

Spatial plot: This is for the analysis of spatial trends in solute concentrations, groundwater flow and, if present, nonaqueous phase liquid (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. User specified shape files can also be overlaid on this plot. The spatial plot can be automatically plotted in time series order to provide a movie depicting the changing trends in spatial solute concentrations.
Well Trend plot: This is 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 provides a summary of the level and time series trend in solute concentrations at a particular model output interval.

Benefits

Early identification of increasing trends or off-site migration

Evaluation of groundwater monitoring trends over time and space (holistic plume evaluation)

Nonparametric statistical and uncertainty analyses to assess highly variable groundwater monitoring 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, standardized plots and tables.

Limitations and Data Requirements

Spatiotemporal solute concentration predictions do not necessarily lie on observed data points because the program smoothes rather than interpolates.

The quality of the spatiotemporal smoothing is directly influenced by the quality of the underlying data.

The analysis may be skewed if data are input from monitoring wells with disparate construction or screened in different aquifers.

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

Permission is granted to refer to or quote from this publication with the customary acknowledgment of the source (see suggested citation and disclaimer).