Interpolating and Extrapolating Contaminant Concentrations from Monitor Wells to Model Grids for Fate-and-Transport Calculations

PDF Version Also Available for Download.

Description

Geostatistical interpolation of groundwater characterization data to visualize contaminant distributions in three dimensions is often hindered by the sparse distribution of samples relative to the size of the plume and scale of heterogeneities. Typically, placement of expensive monitoring wells is guided by the conceptualized plume rather than geostatistical considerations, focusing on contaminated areas rather than thoroughly gridding the plume boundary. The resulting data sets require careful analysis in order to produce plausible plume shells. A purely geostatistical approach is usually impractical; kriging parameters based on the observed data structure can extrapolate contamination far beyond the demonstrated extent of the plume. ... continued below

Physical Description

13 pages

Creation Information

Ward, D. B.; Clement, P. & Bostick, K. February 26, 2002.

Context

This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

Who

People and organizations associated with either the creation of this article or its content.

Publishers

Provided By

UNT Libraries Government Documents Department

Serving as both a federal and a state depository library, the UNT Libraries Government Documents Department maintains millions of items in a variety of formats. The department is a member of the FDLP Content Partnerships Program and an Affiliated Archive of the National Archives.

Contact Us

What

Descriptive information to help identify this article. Follow the links below to find similar items on the Digital Library.

Description

Geostatistical interpolation of groundwater characterization data to visualize contaminant distributions in three dimensions is often hindered by the sparse distribution of samples relative to the size of the plume and scale of heterogeneities. Typically, placement of expensive monitoring wells is guided by the conceptualized plume rather than geostatistical considerations, focusing on contaminated areas rather than thoroughly gridding the plume boundary. The resulting data sets require careful analysis in order to produce plausible plume shells. A purely geostatistical approach is usually impractical; kriging parameters based on the observed data structure can extrapolate contamination far beyond the demonstrated extent of the plume. When more appropriate kriging parameters are selected, holes often occur in the interpolated distribution because realistic kriging ranges may not bridge large gaps between data points. Such artifacts obscure the probable location of the plume boundary and distort the contaminant distribution, obstructing quantitative modeling of remedial strategies. Two methods of constraining kriging can successfully eliminate these geostatistical artifacts. Laterally, the plume boundary may be controlled using a manually constructed mask that delineates the plan-view extent of the plume. After kriging, the mask is used to set all grid cells outside of the plume to a concentration of zero. Use of non-zero control points is a more refined but laborious approach that also bridges data gaps within the body of a plume and permits use of tighter kriging parameters. These can be obtained by manual linear interpolation between measured samples, or derived from historical data migrated along flow paths while accounting for all attenuative processes. Masking and use of non-zero control points result in a plume shell that reflects the intuition and professional judgment of the hydrologist, and can be interpolated automatically to any desired grid, providing initial conditions for fate-and-transport simulations. Error maps are a valuable aid in assessing data density, identifying areas that require additional sampling, or that must be filled by control points, if additional sampling is impractical.

Physical Description

13 pages

Source

  • Waste Management 2002 Symposium, Tucson, AZ (US), 02/24/2002--02/28/2002

Language

Item Type

Identifier

Unique identifying numbers for this article in the Digital Library or other systems.

  • Report No.: none
  • Office of Scientific & Technical Information Report Number: 828422
  • Archival Resource Key: ark:/67531/metadc780834

Collections

This article is part of the following collection of related materials.

Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

What responsibilities do I have when using this article?

When

Dates and time periods associated with this article.

Creation Date

  • February 26, 2002

Added to The UNT Digital Library

  • Dec. 3, 2015, 9:30 a.m.

Description Last Updated

  • April 26, 2016, 6:58 p.m.

Usage Statistics

When was this article last used?

Yesterday: 0
Past 30 days: 0
Total Uses: 5

Interact With This Article

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

International Image Interoperability Framework

IIF Logo

We support the IIIF Presentation API

Ward, D. B.; Clement, P. & Bostick, K. Interpolating and Extrapolating Contaminant Concentrations from Monitor Wells to Model Grids for Fate-and-Transport Calculations, article, February 26, 2002; Tucson, Arizona. (digital.library.unt.edu/ark:/67531/metadc780834/: accessed September 26, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.