Estimation of spatial covariance structures by adjoint state maximum likelihood cross validation, 3. Application to hydrochemical and isotopic data

F. J. Samper, Shlomo P Neuman

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Presents applications of the adjoint state maximum likelihood cross-validation (ASMLCV) method to aquifer data. The Madrid basin in Spain serves as the source of information about 11 hydrochemical variables (pH, electrical conductivity, silica content, and the concentration of major ions) and two isotopes (oxygen 18 and carbon 14). With the exception of oxygen 18 and silica, the variables appear to be free of a horizontal drift. No discernible directional effects are seen. All variables exhibit a large nugget effect which is indicative of background noise. Concludes that more detailed and careful sampling in three dimensions is required if groundwater quality information is to become less prone to such noise and thereby more useful in the context of quantitative hydrogeological analyses. -from Authors

Original languageEnglish (US)
Pages (from-to)373-384
Number of pages12
JournalWater Resources Research
Volume25
Issue number3
StatePublished - 1989
Externally publishedYes

Fingerprint

Silicon Dioxide
silica
Maximum likelihood
oxygen isotope
Oxygen Isotopes
oxygen
information sources
Aquifers
aquifers
electrical conductivity
carbon isotope
Groundwater
isotopes
Carbon
water quality
Spain
aquifer
isotope
basins
Ions

ASJC Scopus subject areas

  • Aquatic Science
  • Environmental Science(all)
  • Environmental Chemistry
  • Water Science and Technology

Cite this

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AU - Neuman, Shlomo P

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AB - Presents applications of the adjoint state maximum likelihood cross-validation (ASMLCV) method to aquifer data. The Madrid basin in Spain serves as the source of information about 11 hydrochemical variables (pH, electrical conductivity, silica content, and the concentration of major ions) and two isotopes (oxygen 18 and carbon 14). With the exception of oxygen 18 and silica, the variables appear to be free of a horizontal drift. No discernible directional effects are seen. All variables exhibit a large nugget effect which is indicative of background noise. Concludes that more detailed and careful sampling in three dimensions is required if groundwater quality information is to become less prone to such noise and thereby more useful in the context of quantitative hydrogeological analyses. -from Authors

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