Literature DB >> 22107357

Interpolations of groundwater table elevation in dissected uplands.

Jae-won Chung1, J David Rogers.   

Abstract

The variable elevation of the groundwater table in the St. Louis area was estimated using multiple linear regression (MLR), ordinary kriging, and cokriging as part of a regional program seeking to assess liquefaction potential. Surface water features were used to determine the minimum water table for MLR and supplement the principal variables for ordinary kriging and cokriging. By evaluating the known depth to the water and the minimum water table elevation, the MLR analysis approximates the groundwater elevation for a contiguous hydrologic system. Ordinary kriging and cokriging estimate values in unsampled areas by calculating the spatial relationships between the unsampled and sampled locations. In this study, ordinary kriging did not incorporate topographic variations as an independent variable, while cokriging included topography as a supporting covariable. Cross validation suggests that cokriging provides a more reliable estimate at known data points with less uncertainty than the other methods. Profiles extending through the dissected uplands terrain suggest that: (1) the groundwater table generated by MLR mimics the ground surface and elicits a exaggerated interpolation of groundwater elevation; (2) the groundwater table estimated by ordinary kriging tends to ignore local topography and exhibits oversmoothing of the actual undulations in the water table; and (3) cokriging appears to give the realistic water surface, which rises and falls in proportion to the overlying topography. The authors concluded that cokriging provided the most realistic estimate of the groundwater surface, which is the key variable in assessing soil liquefaction potential in unconsolidated sediments.
© 2011, The Author(s). Ground Water © 2011, National Ground Water Association.

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Year:  2011        PMID: 22107357     DOI: 10.1111/j.1745-6584.2011.00889.x

Source DB:  PubMed          Journal:  Ground Water        ISSN: 0017-467X            Impact factor:   2.671


  1 in total

1.  GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran.

Authors:  Seyed Amir Naghibi; Hamid Reza Pourghasemi; Barnali Dixon
Journal:  Environ Monit Assess       Date:  2015-12-19       Impact factor: 2.513

  1 in total

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