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Roger Bannister Penn State MGIS Program Advisor: Pat Kennelly Geog 596A, Spring 2013

Improving Interpolation Tools for Groundwater Data

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Improving Interpolation Tools for Groundwater Data. Roger Bannister Penn State MGIS Program Advisor: Pat Kennelly Geog 596A, Spring 2013. Overview. How and why groundwater elevation is measured Basics of water table interpretation Challenges with automated contouring - PowerPoint PPT Presentation

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Page 1: Improving Interpolation Tools for Groundwater Data

Roger Bannister

Penn State MGIS Program

Advisor: Pat Kennelly

Geog 596A, Spring 2013

Page 2: Improving Interpolation Tools for Groundwater Data

OverviewHow and why groundwater elevation is measuredBasics of water table interpretationChallenges with automated contouringIdeas for new tools to improve workflowsProject schedule

Page 3: Improving Interpolation Tools for Groundwater Data

BackgroundHydrogeologists work on “clean-water” and “dirty-

water” projectsSubsurface investigations depend heavily on

discrete samplesContour maps help visualize what’s happening

between samplesAutomated contouring results are often unrealisticUsually resort to manually drawing (blunder prone)

or trying to “game” the algorithm with control points (tedious) – Both are inefficient!

Page 4: Improving Interpolation Tools for Groundwater Data

How Groundwater is MeasuredMonitoring wells intersect aquifer

of interestDepth to water measured at wells

with an interface probeData logged in field book and

later entered into site databaseWell reference elevations

surveyed to common datum

http://www.bodineservices.com/environmentalconsulting/groundwater-monitoring.php

http://www.in-situ.com/products/Water-Level/Pressure-Transducers/PXD-261-Pressure-Transducers

Page 5: Improving Interpolation Tools for Groundwater Data

Conceptual Site Models (CSM)A CSM is basically a working hypothesis of what is going

on overall at the siteWhat’s the geologic setting?Which direction is groundwater flowing and how fast?Are there preferential flow pathways?Where are the contaminants and what are they?Where’s the source of contamination and is it still present?Are there sensitive receptors down-gradient?Will the contaminants reach the receptor or will they

degrade before they get there?Contouring groundwater elevations play a large role in

developing the CSM

Page 6: Improving Interpolation Tools for Groundwater Data

What Hydrogeology Textbooks Have to Say… Fetter (4th ed. 2001, 1st ed. 1980) p.98-100 – Linear interpolation between sets

of three wells, influenced by topography and surface water. Hiscock (2005) p.146 – Linear interpolation. Also consider local topography,

springs, and streams. Kresic (1997) – Begin with linear triangulation and adjust based on

topography, springs, and streams. Mentions kriging. Kresic and Mikszewski (2013) – Linear interpolation a good start for manual

contouring. Several automated methods are available (e.g. IDW, Spline, Kriging) that can get close.

From Kresic and Mikscewski 2013

Page 7: Improving Interpolation Tools for Groundwater Data

Factors Considered During Interpretation (and Interpolation)Field measurementsRegional groundwater flowSite topographySurface water bodiesSubsurface composition (soil/geology)Preferential flow conduits (e.g. fractures, utility lines)

Page 8: Improving Interpolation Tools for Groundwater Data

Common Hydraulic FeaturesStreams and Lakes

Gaining vs LosingFracture Zones

High permeability, preferential flow zonesGeologic Boundary

Changes in transmissivityImpervious Barriers

Natural (e.g. dike or fault) or man-made (e.g. slurry wall)No flow, contours should be perpendicular

Interceptor TrenchesHigh permeability linear zone with pumping

Extraction WellLocalized cone of depression due to pumping

Page 9: Improving Interpolation Tools for Groundwater Data

From Heath 1983

Page 10: Improving Interpolation Tools for Groundwater Data

From Heath 1983

Page 11: Improving Interpolation Tools for Groundwater Data

From Kresic and Mikszewski 2013

From Kresic and Mikszewski 2013

From Heath 1983

Page 12: Improving Interpolation Tools for Groundwater Data

Typical Computer Contouring Workflow

It takes two steps to make contours with ArcGIS

Page 13: Improving Interpolation Tools for Groundwater Data

ChallengesDigitizing control points is not always intuitiveWorkflow is iterative and dynamic but current

outputs are staticGenerates a lot of intermediate datasets to manageTime consuming and frustrating

Page 14: Improving Interpolation Tools for Groundwater Data

Example Problem

Manual Solution (Kresic 1997)

Page 15: Improving Interpolation Tools for Groundwater Data

Triangulation (TIN)

Pro: quick and simple, exact interpolator, allows breaklinesCon: angular, limited to convex hull of points

Without Breakline With Breakline

Data from Kresic 1997 processed in ArcGIS 10.0 with 3D Analyst Extension

Page 16: Improving Interpolation Tools for Groundwater Data

Pro: nice organic curves, honors data points, allows breaklinesCon: limited to convex hull of points

Natural Neighbors

With Breakline

Data from Kresic 1997 processed in ArcGIS 10.0 with 3D Analyst Extension

Page 17: Improving Interpolation Tools for Groundwater Data

Spline

Pro: nice organic curves, can extrapolateCon: can’t use breaklines

Without Control Points With Control Points

Data from Kresic 1997 processed in ArcGIS 10.0 with 3D Analyst Extension

Page 18: Improving Interpolation Tools for Groundwater Data

Pro: statistical method incorporating anisotropy and trend removal Con: complex settings, requires large set of points, can’t use breaklines

Kriging

Without Control Points With Control Points

Data from Kresic 1997 processed in ArcGIS 10.0 with 3D Analyst Extension

Page 19: Improving Interpolation Tools for Groundwater Data

Goals and Objectives Determine how to incorporate information that is not

represented in point measurements into automated contouring

Develop intuitive interactive tools to capture interpretive information and help streamline contouring

Keep “drawing” to a minimum

NOT trying to develop a robust numerical flow model

Page 20: Improving Interpolation Tools for Groundwater Data

RequirementsKeep it simple: Target user is a hydrogeologist with

minimal training/experience in GISKeep additional inputs to a minimumMake processes as interactive as possibleDistinguish between real data and control featuresProduce repeatable resultsContours should honor all measurementsEntire workflow should take less than an hourKeep licensing costs down (ArcView with few

extensions)

Page 21: Improving Interpolation Tools for Groundwater Data

Proposed Methodology Research automated contouring algorithmsInterview geologists about their manual contouring

methodsIdentify information geologists use that is not

represented in the point dataDesign interfaces using mockupsDevelop as an ArcGIS Desktop Add-on programmed

in VB.net

Page 22: Improving Interpolation Tools for Groundwater Data

Iterative Design/Agile Development

Page 23: Improving Interpolation Tools for Groundwater Data

Interface Mockups

http://www.balsamiq.com/

Page 24: Improving Interpolation Tools for Groundwater Data

Project Timeline Work May-September

Monthly milestones for testing design iterationsConference abstract due August 6, 2013Present at Geological Society of America Annual

Meeting October 27-30

Page 25: Improving Interpolation Tools for Groundwater Data

ReferencesFetter, C.W., 2001. Applied Hydrogeology (4th ed.).Heath, R.C., 1983. Basic Ground-water Hydrology,

U.S. Geological Survey Water-Supply Paper 2220, 86p.

Hiscock, K.M., 2005. Hydrogeology Principles and Practice.

Kresic, N.,1997. Quantitative Solutions in Hydrogeology and Groundwater Modeling.

Kresic, N. and Mikszewski, A., 2013. Hydrogeological Conceptual Site Models: Data Analysis and Visualization.