Ground Heat Flux Reconstruction Using Bayesian Uncertainty Quantification Machinery and Surrogate Modeling

Zhou, Wenbo; Zhang, Liujing; Sheshukov, Aleksey; Wang, Jingfeng; Zhu, Modi; Sargsyan, Khachik; Xu, Donghui; Liu, Desheng; Zhang, Tianqi; Mazepa, Valeriy; Sokolov, Alexandr; Valdayskikh, Victor; Ivanov, Valeriy

doi:10.1029/2023ea003435

Title: Ground Heat Flux Reconstruction Using Bayesian Uncertainty Quantification Machinery and Surrogate Modeling

Journal Article · 01 March 2024 · Earth and Space Science

DOI: https://doi.org/10.1029/2023ea003435 · OSTI ID:2320357

^[1]; Zhang, Liujing ^[1];

^[2];

^[3];

^[4];

^[5]; Liu, Desheng ^[6];

^[6];

^[7]; Sokolov, Alexandr ^[8];

^[9];

^[1]

Univ. of Michigan, Ann Arbor, MI (United States)
Kansas State Univ., Manhattan, KS (United States)
Georgia Institute of Technology, Atlanta, GA (United States)
Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
The Ohio State Univ., Columbus, OH (United States)
Russian Academy of Sciences (RAS), Yekaterinburg (Russian Federation)
Russian Academy of Sciences (RAS), Labytnangi (Russian Federation)
Ural Federal Univ., Yekaterinburg (Russian Federation)

Ground heat flux (G₀) is a key component of the land-surface energy balance of high-latitude regions. Despite its crucial role in controlling permafrost degradation due to global warming, G₀ is sparsely measured and not well represented in the outputs of global scale model simulation. In this study, an analytical heat transfer model is tested to reconstruct G₀ across seasons using soil temperature series from field measurements, Global Climate Model, and climate reanalysis outputs. The probability density functions of ground heat flux and of model parameters are inferred using available G₀ data (measured or modeled) for snow-free period as a reference. When observed G₀ is not available, a numerical model is applied using estimates of surface heat flux (dependent on parameters) as the top boundary condition. These estimates (and thus the corresponding parameters) are verified by comparing the distributions of simulated and measured soil temperature at several depths. Aided by state-of-the-art uncertainty quantification methods, the developed G₀ reconstruction approach provides novel means for assessing the probabilistic structure of the ground heat flux for regional permafrost change studies.

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Research Organization:: Sandia National Lab. (SNL-CA), Livermore, CA (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF); Russian Foundation for Basic Research (RFBR)

Grant/Contract Number:: NA0003525; RFBR-19-05-00756; 2126792; 2126793; 2126797; 2126798; 1725654; 1724868; 1724633; 1724786; AC05-76RL01830

OSTI ID:: 2320357

Alternate ID(s):: OSTI ID: 2407286

Report Number(s):: SAND-2024-02762J; PNNL-SA-195022

Journal Information:: Earth and Space Science, Vol. 11, Issue 3; ISSN 2333-5084

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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