Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework Article Swipe
Georgios Karagiannis
,
Zhangshuan Hou
,
Maoyi Huang
,
Guang Lin
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/computation10050072
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/computation10050072
In this work, generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We perform inverse modeling and compute the posterior distribution of the critical hydrological parameters that are subject to great uncertainty in the Community Land Model (CLM) for a given value of the output LH. The unknown parameters include those that have been identified as the most influential factors on the simulations of surface and subsurface runoff, latent and sensible heat fluxes, and soil moisture in CLM4.0. We set up the inversion problem in the Bayesian framework in two steps: (i) building a surrogate model expressing the input–output mapping, and (ii) performing inverse modeling and computing the posterior distributions of the input parameters using observation data for a given value of the output LH. The development of the surrogate model is carried out with a Bayesian procedure based on the variable selection methods that use gPC expansions. Our approach accounts for bases selection uncertainty and quantifies the importance of the gPC terms, and, hence, all of the input parameters, via the associated posterior probabilities.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/computation10050072
- https://www.mdpi.com/2079-3197/10/5/72/pdf?version=1652179131
- OA Status
- gold
- Cited By
- 1
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2980360413
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https://openalex.org/W2980360413Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/computation10050072Digital Object Identifier
- Title
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Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian FrameworkWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
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2022-05-05Full publication date if available
- Authors
-
Georgios Karagiannis, Zhangshuan Hou, Maoyi Huang, Guang LinList of authors in order
- Landing page
-
https://doi.org/10.3390/computation10050072Publisher landing page
- PDF URL
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https://www.mdpi.com/2079-3197/10/5/72/pdf?version=1652179131Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2079-3197/10/5/72/pdf?version=1652179131Direct OA link when available
- Concepts
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Polynomial chaos, Applied mathematics, Posterior probability, Bayesian probability, Inverse, Mathematics, Inversion (geology), Mathematical optimization, Uncertainty quantification, Computer science, Algorithm, Statistics, Monte Carlo method, Geology, Paleontology, Structural basin, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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