Can conditioning to transmissivity data worsen model predictions?

Details

Serval ID
serval:BIB_1B0443AA4C8B
Type
A part of a book
Collection
Publications
Title
Can conditioning to transmissivity data worsen model predictions?
Title of the book
Calibration and Reliability in Groundwater Modelling: Credibility of Modeling
Author(s)
Kerrou J., Hendricks-Franssen H.J., Renard P., Lunati I.
Publisher
IAHS Press
ISBN
978-1-901502-49-7
Publication state
Published
Issued date
2006
Editor
Bierkens M.F.P., Gehrels J.C., Kovar K.
Volume
304
Pages
299-304
Language
english
Abstract
It is reasonable to think that spatially variable transmissivity fields
often follow non-multi-Gaussian statistics. Nevertheless, in groundwater
flow and mass transport studies multi-Gaussian models are very popular.
This paper investigates the consequences of adopting a wrong Random
Function (RF) model. Previous studies have shown that conditioning
to hydraulic head data, adopting a multi-Gaussian approach, only
very marginally detects connected structures typical for non-multi-Gaussian
fields. In addition, several numerical simulations performed have
given us a hint that conditioning on a large number of transmissivity
data might prevent head conditioning from being effective. We consider
non-multi-Gaussian T fields (with braided structures) and compare
the results obtained by using the T data only for computing the variogram
with those obtained by additionally conditioning to T data (erroneously,
a multi-Gaussian RF model is assumed). The preliminary results presented
here do not clearly show an improvement when only part of the data
is used for T conditioning (this confirms the primary role played
by the RF model in deteriorating field characterization). However,
evidence is found that conditioning to T data yields a systematic
loss of connectivity behind a distance of the order of the variogram
range. This fact prevents the inverse problem from identifying elongated
capture zones. Conditioning to h data, instead, generally yields
an increase in connectivity, which is more effective at distances
larger than the variogram range, and seems to allow a partial recovery
of non-Gaussian structures.
Keywords
Australasia, New Zealand, probability, maps, rivers, aerial photography
Create date
25/11/2013 19:19
Last modification date
20/08/2019 12:51
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