气候变化领域集成服务门户(CCPortal): Global sensitivity analysis and adaptive stochastic sampling of a subsurface-flow model using active subspaces

CCPortal

DOI	10.5194/hess-23-3787-2019
	Global sensitivity analysis and adaptive stochastic sampling of a subsurface-flow model using active subspaces
	Erdal D.; Cirpka O.A.
发表日期	2019
ISSN	1027-5606
起始页码	3787
结束页码	3805
卷号	23 期号:9
英文摘要	Integrated hydrological modeling of domains with complex subsurface features requires many highly uncertain parameters. Performing a global uncertainty analysis using an ensemble of model runs can help bring clarity as to which of these parameters really influence system behavior and for which high parameter uncertainty does not result in similarly high uncertainty of model predictions. However, already creating a sufficiently large ensemble of model simulation for the global sensitivity analysis can be challenging, as many combinations of model parameters can lead to unrealistic model behavior. In this work we use the method of active subspaces to perform a global sensitivity analysis. While building up the ensemble, we use the already-existing ensemble members to construct low-order meta-models based on the first two active-subspace dimensions. The meta-models are used to pre-determine whether a random parameter combination in the stochastic sampling is likely to result in unrealistic behavior so that such a parameter combination is excluded without running the computationally expensive full model. An important reason for choosing the active-subspace method is that both the activity score of the global sensitivity analysis and the meta-models can easily be understood and visualized. We test the approach on a subsurface-flow model including uncertain hydraulic parameters, uncertain boundary conditions and uncertain geological structure. We show that sufficiently detailed active subspaces exist for most observations of interest. The pre-selection by the meta-model significantly reduces the number of full-model runs that must be rejected due to unrealistic behavior. An essential but difficult part in active-subspace sampling using complex models is approximating the gradient of the simulated observation with respect to all parameters. We show that this can effectively and meaningfully be done with second-order polynomials. © Author(s) 2019.
语种	英语
scopus关键词	Sensitivity analysis; Stochastic models; Stochastic systems; Geological structures; Global sensitivity analysis; Hydraulic parameters; Integrated hydrological modeling; Parameter combination; Parameter uncertainty; Second-order polynomial; Uncertain parameters; Uncertainty analysis; boundary condition; flow modeling; geological structure; hydrological modeling; parameter estimation; sampling; sensitivity analysis; simulation; subsurface flow; uncertainty analysis
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159607
作者单位	Erdal, D., Center for Applied Geosciences, University of Tübingen, Hölderlinstr. 12, Tübingen, 72074, Germany; Cirpka, O.A., Center for Applied Geosciences, University of Tübingen, Hölderlinstr. 12, Tübingen, 72074, Germany
推荐引用方式 GB/T 7714	Erdal D.,Cirpka O.A.. Global sensitivity analysis and adaptive stochastic sampling of a subsurface-flow model using active subspaces[J],2019,23(9).
APA	Erdal D.,&Cirpka O.A..(2019).Global sensitivity analysis and adaptive stochastic sampling of a subsurface-flow model using active subspaces.Hydrology and Earth System Sciences,23(9).
MLA	Erdal D.,et al."Global sensitivity analysis and adaptive stochastic sampling of a subsurface-flow model using active subspaces".Hydrology and Earth System Sciences 23.9(2019).