Articles | Volume 23, issue 9
https://doi.org/10.5194/hess-23-3787-2019
https://doi.org/10.5194/hess-23-3787-2019
Research article
 | 
18 Sep 2019
Research article |  | 18 Sep 2019

Global sensitivity analysis and adaptive stochastic sampling of a subsurface-flow model using active subspaces

Daniel Erdal and Olaf A. Cirpka

Related authors

Presentation and discussion of the high-resolution atmosphere–land-surface–subsurface simulation dataset of the simulated Neckar catchment for the period 2007–2015
Bernd Schalge, Gabriele Baroni, Barbara Haese, Daniel Erdal, Gernot Geppert, Pablo Saavedra, Vincent Haefliger, Harry Vereecken, Sabine Attinger, Harald Kunstmann, Olaf A. Cirpka, Felix Ament, Stefan Kollet, Insa Neuweiler, Harrie-Jan Hendricks Franssen, and Clemens Simmer
Earth Syst. Sci. Data, 13, 4437–4464, https://doi.org/10.5194/essd-13-4437-2021,https://doi.org/10.5194/essd-13-4437-2021, 2021
Short summary
Coupling saturated and unsaturated flow: comparing the iterative and the non-iterative approach
Natascha Brandhorst, Daniel Erdal, and Insa Neuweiler
Hydrol. Earth Syst. Sci., 25, 4041–4059, https://doi.org/10.5194/hess-25-4041-2021,https://doi.org/10.5194/hess-25-4041-2021, 2021
Short summary
Technical Note: Improved sampling of behavioral subsurface flow model parameters using active subspaces
Daniel Erdal and Olaf A. Cirpka
Hydrol. Earth Syst. Sci., 24, 4567–4574, https://doi.org/10.5194/hess-24-4567-2020,https://doi.org/10.5194/hess-24-4567-2020, 2020
Short summary
High-Resolution Virtual Catchment Simulations of the Subsurface-Land Surface-Atmosphere System
Bernd Schalge, Jehan Rihani, Gabriele Baroni, Daniel Erdal, Gernot Geppert, Vincent Haefliger, Barbara Haese, Pablo Saavedra, Insa Neuweiler, Harrie-Jan Hendricks Franssen, Felix Ament, Sabine Attinger, Olaf A. Cirpka, Stefan Kollet, Harald Kunstmann, Harry Vereecken, and Clemens Simmer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-557,https://doi.org/10.5194/hess-2016-557, 2016
Manuscript not accepted for further review
Short summary
Joint inference of groundwater–recharge and hydraulic–conductivity fields from head data using the ensemble Kalman filter
D. Erdal and O. A. Cirpka
Hydrol. Earth Syst. Sci., 20, 555–569, https://doi.org/10.5194/hess-20-555-2016,https://doi.org/10.5194/hess-20-555-2016, 2016
Short summary

Related subject area

Subject: Groundwater hydrology | Techniques and Approaches: Modelling approaches
Data-driven modelling of hydraulic-head time series: results and lessons learned from the 2022 Groundwater Time Series Modelling Challenge
Raoul A. Collenteur, Ezra Haaf, Mark Bakker, Tanja Liesch, Andreas Wunsch, Jenny Soonthornrangsan, Jeremy White, Nick Martin, Rui Hugman, Ed de Sousa, Didier Vanden Berghe, Xinyang Fan, Tim J. Peterson, Jānis Bikše, Antoine Di Ciacca, Xinyue Wang, Yang Zheng, Maximilian Nölscher, Julian Koch, Raphael Schneider, Nikolas Benavides Höglund, Sivarama Krishna Reddy Chidepudi, Abel Henriot, Nicolas Massei, Abderrahim Jardani, Max Gustav Rudolph, Amir Rouhani, J. Jaime Gómez-Hernández, Seifeddine Jomaa, Anna Pölz, Tim Franken, Morteza Behbooei, Jimmy Lin, and Rojin Meysami
Hydrol. Earth Syst. Sci., 28, 5193–5208, https://doi.org/10.5194/hess-28-5193-2024,https://doi.org/10.5194/hess-28-5193-2024, 2024
Short summary
The impact of future changes in climate variables and groundwater abstraction on basin-scale groundwater availability
Steven Reinaldo Rusli, Victor F. Bense, Syed M. T. Mustafa, and Albrecht H. Weerts
Hydrol. Earth Syst. Sci., 28, 5107–5131, https://doi.org/10.5194/hess-28-5107-2024,https://doi.org/10.5194/hess-28-5107-2024, 2024
Short summary
Assessing groundwater level modelling using a 1-D convolutional neural network (CNN): linking model performances to geospatial and time series features
Mariana Gomez, Maximilian Nölscher, Andreas Hartmann, and Stefan Broda
Hydrol. Earth Syst. Sci., 28, 4407–4425, https://doi.org/10.5194/hess-28-4407-2024,https://doi.org/10.5194/hess-28-4407-2024, 2024
Short summary
Short high-accuracy tritium data time series for assessing groundwater mean transit times in the vadose and saturated zones of the Luxembourg Sandstone aquifer
Laurent Gourdol, Michael K. Stewart, Uwe Morgenstern, and Laurent Pfister
Hydrol. Earth Syst. Sci., 28, 3519–3547, https://doi.org/10.5194/hess-28-3519-2024,https://doi.org/10.5194/hess-28-3519-2024, 2024
Short summary
Laboratory heat transport experiments reveal grain size and flow velocity dependent local thermal non-equilibrium effects
Haegyeong Lee, Manuel Gossler, Kai Zosseder, Philipp Blum, Peter Bayer, and Gabriel C. Rau
EGUsphere, https://doi.org/10.5194/egusphere-2024-1949,https://doi.org/10.5194/egusphere-2024-1949, 2024
Short summary

Cited articles

Aquanty Inc.: HydroGeoSphere User Manual, Tech. rep., Aquanty Inc., Waterloo, ON, Canada, 2015. a
Beven, K. and Binley, A.: The future of distributed models: Model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, https://doi.org/10.1002/hyp.3360060305, 1992. a
Cirpka, O. A. and Kitanidis, P. K.: Sensitivities of temporal moments calculated by the adjoint-state method and joint inversing of head and tracer data, Adv. Water Resour., 24, 89–103, 2000. a
Constantine, P. G. and Diaz, P.: Global sensitivity metrics from active subspaces, Reliab. Eng. Syst. Saf., 162, 1–13, https://doi.org/10.1016/j.ress.2017.01.013, 2017. a, b, c
Constantine, P. G. and Doostan, A.: Time-dependent global sensitivity analysis with active subspaces for a lithium ion battery model, Stat. Anal. Data Min., 10, 243–262, https://doi.org/10.1002/sam.11347, 2017. a
Download
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.