Articles | Volume 23, issue 9
https://doi.org/10.5194/hess-23-3787-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-3787-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Sep 2019
Research article |
| 18 Sep 2019
| 18 Sep 2019
Global sensitivity analysis and adaptive stochastic sampling of a subsurface-flow model using active subspaces
Daniel Erdal
CORRESPONDING AUTHOR
Center for Applied Geosciences, University of Tübingen, Hölderlinstr. 12, 72074 Tübingen, Germany
Olaf A. Cirpka
Center for Applied Geosciences, University of Tübingen, Hölderlinstr. 12, 72074 Tübingen, Germany
Viewed
Total article views: 3,260 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 24 Apr 2019)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,956 | 1,241 | 63 | 3,260 | 181 | 63 | 61 |
- HTML: 1,956
- PDF: 1,241
- XML: 63
- Total: 3,260
- Supplement: 181
- BibTeX: 63
- EndNote: 61
Total article views: 2,288 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 Sep 2019)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,590 | 640 | 58 | 2,288 | 99 | 55 | 55 |
- HTML: 1,590
- PDF: 640
- XML: 58
- Total: 2,288
- Supplement: 99
- BibTeX: 55
- EndNote: 55
Total article views: 972 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 24 Apr 2019)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 366 | 601 | 5 | 972 | 82 | 8 | 6 |
- HTML: 366
- PDF: 601
- XML: 5
- Total: 972
- Supplement: 82
- BibTeX: 8
- EndNote: 6
Viewed (geographical distribution)
Total article views: 3,260 (including HTML, PDF, and XML)
Thereof 2,857 with geography defined
and 403 with unknown origin.
Total article views: 2,288 (including HTML, PDF, and XML)
Thereof 2,110 with geography defined
and 178 with unknown origin.
Total article views: 972 (including HTML, PDF, and XML)
Thereof 747 with geography defined
and 225 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
18 citations as recorded by crossref.
- Accelerating Groundwater Data Assimilation With a Gradient‐Free Active Subspace Method H. Yan et al. 10.1029/2021WR029610
- Surrogate‐Model Assisted Plausibility‐Check, Calibration, and Posterior‐Distribution Evaluation of Subsurface‐Flow Models J. Allgeier & O. Cirpka 10.1029/2023WR034453
- Sampling behavioral model parameters for ensemble-based sensitivity analysis using Gaussian process emulation and active subspaces D. Erdal et al. 10.1007/s00477-020-01867-0
- Stochastic inverse modeling of transient laboratory-scale three-dimensional two-phase core flooding scenarios A. Dell'Oca et al. 10.1016/j.ijheatmasstransfer.2022.123716
- Parameter estimation and uncertainty analysis in hydrological modeling P. Herrera et al. 10.1002/wat2.1569
- Technical Note: Improved sampling of behavioral subsurface flow model parameters using active subspaces D. Erdal & O. Cirpka 10.5194/hess-24-4567-2020
- A Bayesian framework to assess and create risk maps of groundwater flooding P. Merchán-Rivera et al. 10.1016/j.jhydrol.2022.127797
- Joint Optimization of Measurement and Modeling Strategies With Application to Radial Flow in Stratified Aquifers R. Maier et al. 10.1029/2019WR026872
- Identifying relevant hydrological and catchment properties in active subspaces: An inference study of a lumped karst aquifer model D. Bittner et al. 10.1016/j.advwatres.2019.103472
- A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System L. Puppo et al. 10.1016/j.ress.2021.107963
- Legacy pollutants in fractured aquifers: Analytical approximations for back diffusion to predict atrazine concentrations under uncertainty E. Petrova et al. 10.1016/j.jconhyd.2023.104161
- Factors influencing calibration of a semi-distributed mixed runoff hydrological model: A study on nine small mountain catchments in China L. Wen et al. 10.1016/j.ejrh.2023.101418
- Temporal Scale‐Dependent Sensitivity Analysis for Hydrological Model Parameters Using the Discrete Wavelet Transform and Active Subspaces D. Bittner et al. 10.1029/2020WR028511
- A Stochastic Framework to Optimize Monitoring Strategies for Delineating Groundwater Divides J. Allgeier et al. 10.3389/feart.2020.554845
- Coupling saturated and unsaturated flow: comparing the iterative and the non-iterative approach N. Brandhorst et al. 10.5194/hess-25-4041-2021
- A two-step Bayesian network-based process sensitivity analysis for complex nitrogen reactive transport modeling H. Dai et al. 10.1016/j.jhydrol.2024.130903
- Hydrologic multi-model ensemble predictions using variational Bayesian deep learning D. Li et al. 10.1016/j.jhydrol.2021.127221
- Bayesian Calibration and Sensitivity Analysis for a Karst Aquifer Model Using Active Subspaces M. Teixeira Parente et al. 10.1029/2019WR024739
17 citations as recorded by crossref.
- Accelerating Groundwater Data Assimilation With a Gradient‐Free Active Subspace Method H. Yan et al. 10.1029/2021WR029610
- Surrogate‐Model Assisted Plausibility‐Check, Calibration, and Posterior‐Distribution Evaluation of Subsurface‐Flow Models J. Allgeier & O. Cirpka 10.1029/2023WR034453
- Sampling behavioral model parameters for ensemble-based sensitivity analysis using Gaussian process emulation and active subspaces D. Erdal et al. 10.1007/s00477-020-01867-0
- Stochastic inverse modeling of transient laboratory-scale three-dimensional two-phase core flooding scenarios A. Dell'Oca et al. 10.1016/j.ijheatmasstransfer.2022.123716
- Parameter estimation and uncertainty analysis in hydrological modeling P. Herrera et al. 10.1002/wat2.1569
- Technical Note: Improved sampling of behavioral subsurface flow model parameters using active subspaces D. Erdal & O. Cirpka 10.5194/hess-24-4567-2020
- A Bayesian framework to assess and create risk maps of groundwater flooding P. Merchán-Rivera et al. 10.1016/j.jhydrol.2022.127797
- Joint Optimization of Measurement and Modeling Strategies With Application to Radial Flow in Stratified Aquifers R. Maier et al. 10.1029/2019WR026872
- Identifying relevant hydrological and catchment properties in active subspaces: An inference study of a lumped karst aquifer model D. Bittner et al. 10.1016/j.advwatres.2019.103472
- A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System L. Puppo et al. 10.1016/j.ress.2021.107963
- Legacy pollutants in fractured aquifers: Analytical approximations for back diffusion to predict atrazine concentrations under uncertainty E. Petrova et al. 10.1016/j.jconhyd.2023.104161
- Factors influencing calibration of a semi-distributed mixed runoff hydrological model: A study on nine small mountain catchments in China L. Wen et al. 10.1016/j.ejrh.2023.101418
- Temporal Scale‐Dependent Sensitivity Analysis for Hydrological Model Parameters Using the Discrete Wavelet Transform and Active Subspaces D. Bittner et al. 10.1029/2020WR028511
- A Stochastic Framework to Optimize Monitoring Strategies for Delineating Groundwater Divides J. Allgeier et al. 10.3389/feart.2020.554845
- Coupling saturated and unsaturated flow: comparing the iterative and the non-iterative approach N. Brandhorst et al. 10.5194/hess-25-4041-2021
- A two-step Bayesian network-based process sensitivity analysis for complex nitrogen reactive transport modeling H. Dai et al. 10.1016/j.jhydrol.2024.130903
- Hydrologic multi-model ensemble predictions using variational Bayesian deep learning D. Li et al. 10.1016/j.jhydrol.2021.127221
1 citations as recorded by crossref.
Latest update: 20 Nov 2024
Short summary
Assessing how sensitive uncertain model parameters are to observed data can be done by analyzing an ensemble of model simulations in which the parameters are varied. In subsurface modeling, this involves running heavy models. To reduce time wasted simulating models which show poor behavior, we use a fast polynomial model based on a simple parameter decomposition to approximate the behavior prior to
full-model simulation. This largely reduces the cost for the global sensitivity analysis.
Assessing how sensitive uncertain model parameters are to observed data can be done by analyzing...
