Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework
Abstract
:1. Introduction
2. Dataset and Parameterization
3. Bayesian Methodology
3.1. Bayesian Inverse Problem Setup
Algorithm 1 Updates of a single MCMC swap. 

3.2. Surrogate Model Specification
3.2.1. Generalized Polynomial Chaos Expansion
3.2.2. Bayesian Training Procedure
Algorithm 2 Updates of the Gibbs swap. 

Bayesian Model Averaging
Median Probability Model Based Evaluation
4. Analysis of the USARM DataSet
Surrogate Model Building Step
4.1. Inversion Step
4.2. Model Validation
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Months  Measurement  

JanuaryApril  $15.542$  $22.017$  $41.365$  $59.095$ 
MayJuly  $58.377$  $58.813$  $45.107$  $41.362$ 
AugustDecember  $31.250$  $28.645$  $17.635$  $12.778$ 
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Karagiannis, G.; Hou, Z.; Huang, M.; Lin, G. Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework. Computation 2022, 10, 72. https://doi.org/10.3390/computation10050072
Karagiannis G, Hou Z, Huang M, Lin G. Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework. Computation. 2022; 10(5):72. https://doi.org/10.3390/computation10050072
Chicago/Turabian StyleKaragiannis, Georgios, Zhangshuan Hou, Maoyi Huang, and Guang Lin. 2022. "Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework" Computation 10, no. 5: 72. https://doi.org/10.3390/computation10050072