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

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. Ground heat flux is the energy that goes into or comes out from belowground that controls the soil freeze-thaw process in high-latitude regions. Its changes under climate warming will influence variations in the soil's seasonal thawing depth and permafrost thickness and spatial extent. Available data on ground heat flux are very sparse from both direct field measurements and large-scale model outputs in the Arctic. This study combines detailed modeling and uncertainty quantification methods to accurately reconstruct the ground heat flux from shallow soil temperature observations and estimates from predictive models, which are more readily available for the Arctic. Since the approach relies on several assumptions, we also quantify the uncertainty of the estimated ground heat flux. The reconstructed ground heat fluxes using the method developed in this study match well with the fluxes observed or derived from the predictive model. The soil properties inferred from the developed process are also consistent with the values observed for typical soils. Ground heat flux is reconstructed from various types of shallow soil temperature and auxiliary data using an analytical heat transfer model Uncertainty quantification methods are applied to infer model parameters and increase simulation efficiency drastically The efficacy of the proposed ground heat flux reconstruction framework is shown by agreement between simulation and observation