Developing a particle tracking surrogate model to improve inversion of ground water – Surface water models

Yohann Cousquer, Alexandre Pryet, Olivier Atteia, Paul A Ferre, Célestine Delbart, Rémi Valois, Alain Dupuy

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The inverse problem of groundwater models is often ill-posed and model parameters are likely to be poorly constrained. Identifiability is improved if diverse data types are used for parameter estimation. However, some models, including detailed solute transport models, are further limited by prohibitive computation times. This often precludes the use of concentration data for parameter estimation, even if those data are available. In the case of surface water-groundwater (SW-GW) models, concentration data can provide SW-GW mixing ratios, which efficiently constrain the estimate of exchange flow, but are rarely used. We propose to reduce computational limits by simulating SW-GW exchange at a sink (well or drain) based on particle tracking under steady state flow conditions. Particle tracking is used to simulate advective transport. A comparison between the particle tracking surrogate model and an advective–dispersive model shows that dispersion can often be neglected when the mixing ratio is computed for a sink, allowing for use of the particle tracking surrogate model. The surrogate model was implemented to solve the inverse problem for a real SW-GW transport problem with heads and concentrations combined in a weighted hybrid objective function. The resulting inversion showed markedly reduced uncertainty in the transmissivity field compared to calibration on head data alone.

Original languageEnglish (US)
Pages (from-to)356-365
Number of pages10
JournalJournal of Hydrology
Volume558
DOIs
StatePublished - Mar 1 2018

Fingerprint

surface water
groundwater
inverse problem
mixing ratio
inversion
particle
transmissivity
solute transport
drain
advection
calibration

Keywords

  • Null-space Monte carlo
  • Particle-tracking
  • Stream-aquifer
  • Surrogate model

ASJC Scopus subject areas

  • Water Science and Technology

Cite this

Developing a particle tracking surrogate model to improve inversion of ground water – Surface water models. / Cousquer, Yohann; Pryet, Alexandre; Atteia, Olivier; Ferre, Paul A; Delbart, Célestine; Valois, Rémi; Dupuy, Alain.

In: Journal of Hydrology, Vol. 558, 01.03.2018, p. 356-365.

Research output: Contribution to journalArticle

Cousquer, Yohann ; Pryet, Alexandre ; Atteia, Olivier ; Ferre, Paul A ; Delbart, Célestine ; Valois, Rémi ; Dupuy, Alain. / Developing a particle tracking surrogate model to improve inversion of ground water – Surface water models. In: Journal of Hydrology. 2018 ; Vol. 558. pp. 356-365.
@article{aa5e96b161aa47c68d558ebefc4abf17,
title = "Developing a particle tracking surrogate model to improve inversion of ground water – Surface water models",
abstract = "The inverse problem of groundwater models is often ill-posed and model parameters are likely to be poorly constrained. Identifiability is improved if diverse data types are used for parameter estimation. However, some models, including detailed solute transport models, are further limited by prohibitive computation times. This often precludes the use of concentration data for parameter estimation, even if those data are available. In the case of surface water-groundwater (SW-GW) models, concentration data can provide SW-GW mixing ratios, which efficiently constrain the estimate of exchange flow, but are rarely used. We propose to reduce computational limits by simulating SW-GW exchange at a sink (well or drain) based on particle tracking under steady state flow conditions. Particle tracking is used to simulate advective transport. A comparison between the particle tracking surrogate model and an advective–dispersive model shows that dispersion can often be neglected when the mixing ratio is computed for a sink, allowing for use of the particle tracking surrogate model. The surrogate model was implemented to solve the inverse problem for a real SW-GW transport problem with heads and concentrations combined in a weighted hybrid objective function. The resulting inversion showed markedly reduced uncertainty in the transmissivity field compared to calibration on head data alone.",
keywords = "Null-space Monte carlo, Particle-tracking, Stream-aquifer, Surrogate model",
author = "Yohann Cousquer and Alexandre Pryet and Olivier Atteia and Ferre, {Paul A} and C{\'e}lestine Delbart and R{\'e}mi Valois and Alain Dupuy",
year = "2018",
month = "3",
day = "1",
doi = "10.1016/j.jhydrol.2018.01.043",
language = "English (US)",
volume = "558",
pages = "356--365",
journal = "Journal of Hydrology",
issn = "0022-1694",
publisher = "Elsevier",

}

TY - JOUR

T1 - Developing a particle tracking surrogate model to improve inversion of ground water – Surface water models

AU - Cousquer, Yohann

AU - Pryet, Alexandre

AU - Atteia, Olivier

AU - Ferre, Paul A

AU - Delbart, Célestine

AU - Valois, Rémi

AU - Dupuy, Alain

PY - 2018/3/1

Y1 - 2018/3/1

N2 - The inverse problem of groundwater models is often ill-posed and model parameters are likely to be poorly constrained. Identifiability is improved if diverse data types are used for parameter estimation. However, some models, including detailed solute transport models, are further limited by prohibitive computation times. This often precludes the use of concentration data for parameter estimation, even if those data are available. In the case of surface water-groundwater (SW-GW) models, concentration data can provide SW-GW mixing ratios, which efficiently constrain the estimate of exchange flow, but are rarely used. We propose to reduce computational limits by simulating SW-GW exchange at a sink (well or drain) based on particle tracking under steady state flow conditions. Particle tracking is used to simulate advective transport. A comparison between the particle tracking surrogate model and an advective–dispersive model shows that dispersion can often be neglected when the mixing ratio is computed for a sink, allowing for use of the particle tracking surrogate model. The surrogate model was implemented to solve the inverse problem for a real SW-GW transport problem with heads and concentrations combined in a weighted hybrid objective function. The resulting inversion showed markedly reduced uncertainty in the transmissivity field compared to calibration on head data alone.

AB - The inverse problem of groundwater models is often ill-posed and model parameters are likely to be poorly constrained. Identifiability is improved if diverse data types are used for parameter estimation. However, some models, including detailed solute transport models, are further limited by prohibitive computation times. This often precludes the use of concentration data for parameter estimation, even if those data are available. In the case of surface water-groundwater (SW-GW) models, concentration data can provide SW-GW mixing ratios, which efficiently constrain the estimate of exchange flow, but are rarely used. We propose to reduce computational limits by simulating SW-GW exchange at a sink (well or drain) based on particle tracking under steady state flow conditions. Particle tracking is used to simulate advective transport. A comparison between the particle tracking surrogate model and an advective–dispersive model shows that dispersion can often be neglected when the mixing ratio is computed for a sink, allowing for use of the particle tracking surrogate model. The surrogate model was implemented to solve the inverse problem for a real SW-GW transport problem with heads and concentrations combined in a weighted hybrid objective function. The resulting inversion showed markedly reduced uncertainty in the transmissivity field compared to calibration on head data alone.

KW - Null-space Monte carlo

KW - Particle-tracking

KW - Stream-aquifer

KW - Surrogate model

UR - http://www.scopus.com/inward/record.url?scp=85041404509&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85041404509&partnerID=8YFLogxK

U2 - 10.1016/j.jhydrol.2018.01.043

DO - 10.1016/j.jhydrol.2018.01.043

M3 - Article

AN - SCOPUS:85041404509

VL - 558

SP - 356

EP - 365

JO - Journal of Hydrology

JF - Journal of Hydrology

SN - 0022-1694

ER -