EFFECTS OF KRIGING AND INVERSE MODELING ON CONDITIONAL SIMULATION OF THE AVRA VALLEY AQUIFER IN SOUTHERN ARIZONA.

Peter M. Clifton, Shlomo P Neuman

Research output: Contribution to journalArticle

177 Citations (Scopus)

Abstract

The Avra Valley aquifer in southern Arizona is modeled stochastically at three levels of uncertainty. The highest level of uncertainty occurs when log transmissivity estimates are based on measured values of this parameter but without regard to the geographic location of each measurement point. The resulting steady state hydraulic heads in the aquifer, computed by unconditional simulation with the aid of a multivariate normal random number generator coupled with a finite element model, have a relatively large variance. This variance can be reduced by conditioning the log transmissivity estimates on the spatial arrangement of the data by means of kriging.

Original languageEnglish (US)
Pages (from-to)1215-1234
Number of pages20
JournalWater Resources Research
Volume18
Issue number4
StatePublished - Aug 1982
Externally publishedYes

Fingerprint

kriging
transmissivity
Aquifers
aquifers
uncertainty
valleys
aquifer
valley
hydraulic head
conditioning
modeling
simulation
fluid mechanics
Hydraulics
effect
Uncertainty
parameter

ASJC Scopus subject areas

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

Cite this

EFFECTS OF KRIGING AND INVERSE MODELING ON CONDITIONAL SIMULATION OF THE AVRA VALLEY AQUIFER IN SOUTHERN ARIZONA. / Clifton, Peter M.; Neuman, Shlomo P.

In: Water Resources Research, Vol. 18, No. 4, 08.1982, p. 1215-1234.

Research output: Contribution to journalArticle

@article{37f30cb77c4443ae8b88c00c115978e3,
title = "EFFECTS OF KRIGING AND INVERSE MODELING ON CONDITIONAL SIMULATION OF THE AVRA VALLEY AQUIFER IN SOUTHERN ARIZONA.",
abstract = "The Avra Valley aquifer in southern Arizona is modeled stochastically at three levels of uncertainty. The highest level of uncertainty occurs when log transmissivity estimates are based on measured values of this parameter but without regard to the geographic location of each measurement point. The resulting steady state hydraulic heads in the aquifer, computed by unconditional simulation with the aid of a multivariate normal random number generator coupled with a finite element model, have a relatively large variance. This variance can be reduced by conditioning the log transmissivity estimates on the spatial arrangement of the data by means of kriging.",
author = "Clifton, {Peter M.} and Neuman, {Shlomo P}",
year = "1982",
month = "8",
language = "English (US)",
volume = "18",
pages = "1215--1234",
journal = "Water Resources Research",
issn = "0043-1397",
publisher = "American Geophysical Union",
number = "4",

}

TY - JOUR

T1 - EFFECTS OF KRIGING AND INVERSE MODELING ON CONDITIONAL SIMULATION OF THE AVRA VALLEY AQUIFER IN SOUTHERN ARIZONA.

AU - Clifton, Peter M.

AU - Neuman, Shlomo P

PY - 1982/8

Y1 - 1982/8

N2 - The Avra Valley aquifer in southern Arizona is modeled stochastically at three levels of uncertainty. The highest level of uncertainty occurs when log transmissivity estimates are based on measured values of this parameter but without regard to the geographic location of each measurement point. The resulting steady state hydraulic heads in the aquifer, computed by unconditional simulation with the aid of a multivariate normal random number generator coupled with a finite element model, have a relatively large variance. This variance can be reduced by conditioning the log transmissivity estimates on the spatial arrangement of the data by means of kriging.

AB - The Avra Valley aquifer in southern Arizona is modeled stochastically at three levels of uncertainty. The highest level of uncertainty occurs when log transmissivity estimates are based on measured values of this parameter but without regard to the geographic location of each measurement point. The resulting steady state hydraulic heads in the aquifer, computed by unconditional simulation with the aid of a multivariate normal random number generator coupled with a finite element model, have a relatively large variance. This variance can be reduced by conditioning the log transmissivity estimates on the spatial arrangement of the data by means of kriging.

UR - http://www.scopus.com/inward/record.url?scp=0020332331&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0020332331&partnerID=8YFLogxK

M3 - Article

VL - 18

SP - 1215

EP - 1234

JO - Water Resources Research

JF - Water Resources Research

SN - 0043-1397

IS - 4

ER -