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Global sampling to assess the value of diverse observations in conditioning a real-world groundwater flow and transport model

Joost R Delsman, Pieter Winters, Alexander Vandenbohede, Gualbert HP Oude Essink and Luc Lebbe UGent (2016) WATER RESOURCES RESEARCH. 52(3). p.1652-1672
abstract
The use of additional types of observational data has often been suggested to alleviate the ill-posedness inherent to parameter estimation of groundwater models and constrain model uncertainty. Disinformation in observational data caused by errors in either the observations or the chosen model structure may, however, confound the value of adding observational data in model conditioning. This paper uses the global generalized likelihood uncertainty estimation methodology to investigate the value of different observational data types (heads, fluxes, salinity, and temperature) in conditioning a groundwater flow and transport model of an extensively monitored field site in the Netherlands. We compared model conditioning using the real observations to a synthetic model experiment, to demonstrate the possible influence of disinformation in observational data in model conditioning. Results showed that the value of different conditioning targets was less evident when conditioning to real measurements than in a measurement error-only synthetic model experiment. While in the synthetic experiment, all conditioning targets clearly improved model outcomes, minor improvements or even worsening of model outcomes was observed for the real measurements. This result was caused by errors in both the model structure and the observations, resulting in disinformation in the observational data. The observed impact of disinformation in the observational data reiterates the necessity of thorough data validation and the need for accounting for both model structural and observational errors in model conditioning. It further suggests caution when translating results of synthetic modeling examples to real-world applications. Still, applying diverse conditioning data types was found to be essential to constrain uncertainty, especially in the transport of solutes in the model.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
WATER MODELS, NETHERLANDS, NEW-ZEALAND, INVERSE METHODS, POROUS-MEDIA, numerical modeling, parameter estimation, GLUE, groundwater salinity, real world, PARAMETER-ESTIMATION, SENSITIVITY-ANALYSIS, SALINE GROUNDWATER, UNCERTAINTY ASSESSMENT, VIRUS TRANSPORT
journal title
WATER RESOURCES RESEARCH
Water Resour. Res.
volume
52
issue
3
pages
1652 - 1672
Web of Science type
Article
Web of Science id
000374706300006
JCR category
WATER RESOURCES
JCR impact factor
4.397 (2016)
JCR rank
4/88 (2016)
JCR quartile
1 (2016)
ISSN
0043-1397
DOI
10.1002/2014WR016476
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
8203474
handle
http://hdl.handle.net/1854/LU-8203474
date created
2016-12-04 10:26:52
date last changed
2016-12-19 15:48:22
@article{8203474,
  abstract     = {The use of additional types of observational data has often been suggested to alleviate the ill-posedness inherent to parameter estimation of groundwater models and constrain model uncertainty. Disinformation in observational data caused by errors in either the observations or the chosen model structure may, however, confound the value of adding observational data in model conditioning. This paper uses the global generalized likelihood uncertainty estimation methodology to investigate the value of different observational data types (heads, fluxes, salinity, and temperature) in conditioning a groundwater flow and transport model of an extensively monitored field site in the Netherlands. We compared model conditioning using the real observations to a synthetic model experiment, to demonstrate the possible influence of disinformation in observational data in model conditioning. Results showed that the value of different conditioning targets was less evident when conditioning to real measurements than in a measurement error-only synthetic model experiment. While in the synthetic experiment, all conditioning targets clearly improved model outcomes, minor improvements or even worsening of model outcomes was observed for the real measurements. This result was caused by errors in both the model structure and the observations, resulting in disinformation in the observational data. The observed impact of disinformation in the observational data reiterates the necessity of thorough data validation and the need for accounting for both model structural and observational errors in model conditioning. It further suggests caution when translating results of synthetic modeling examples to real-world applications. Still, applying diverse conditioning data types was found to be essential to constrain uncertainty, especially in the transport of solutes in the model.},
  author       = {Delsman, Joost R and Winters, Pieter and Vandenbohede, Alexander and Oude Essink, Gualbert HP and Lebbe, Luc},
  issn         = {0043-1397},
  journal      = {WATER RESOURCES RESEARCH},
  keyword      = {WATER MODELS,NETHERLANDS,NEW-ZEALAND,INVERSE METHODS,POROUS-MEDIA,numerical modeling,parameter estimation,GLUE,groundwater salinity,real world,PARAMETER-ESTIMATION,SENSITIVITY-ANALYSIS,SALINE GROUNDWATER,UNCERTAINTY ASSESSMENT,VIRUS TRANSPORT},
  language     = {eng},
  number       = {3},
  pages        = {1652--1672},
  title        = {Global sampling to assess the value of diverse observations in conditioning a real-world groundwater flow and transport model},
  url          = {http://dx.doi.org/10.1002/2014WR016476},
  volume       = {52},
  year         = {2016},
}

Chicago
Delsman, Joost R, Pieter Winters, Alexander Vandenbohede, Gualbert HP Oude Essink, and Luc Lebbe. 2016. “Global Sampling to Assess the Value of Diverse Observations in Conditioning a Real-world Groundwater Flow and Transport Model.” Water Resources Research 52 (3): 1652–1672.
APA
Delsman, J. R., Winters, P., Vandenbohede, A., Oude Essink, G. H., & Lebbe, L. (2016). Global sampling to assess the value of diverse observations in conditioning a real-world groundwater flow and transport model. WATER RESOURCES RESEARCH, 52(3), 1652–1672.
Vancouver
1.
Delsman JR, Winters P, Vandenbohede A, Oude Essink GH, Lebbe L. Global sampling to assess the value of diverse observations in conditioning a real-world groundwater flow and transport model. WATER RESOURCES RESEARCH. 2016;52(3):1652–72.
MLA
Delsman, Joost R, Pieter Winters, Alexander Vandenbohede, et al. “Global Sampling to Assess the Value of Diverse Observations in Conditioning a Real-world Groundwater Flow and Transport Model.” WATER RESOURCES RESEARCH 52.3 (2016): 1652–1672. Print.