Journal cover Journal topic
The Cryosphere An interactive open-access journal of the European Geosciences Union
doi:10.5194/tc-2017-1
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
07 Feb 2017
Review status
This discussion paper is under review for the journal The Cryosphere (TC).
Coupled Land Surface-Subsurface Hydrogeophysical Inverse Modeling to Estimate Soil Organic Content and explore associated Hydrological and Thermal Dynamics in an Arctic Tundra
Anh Phuong Tran, Baptiste Dafflon, and Susan S. Hubbard Climate & Ecosystems Division, Earth and Environmental Sciences Area, Lawrence National Berkeley Lab, Berkeley, California, CA 94720, USA
Abstract. Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface–subsurface hydrological-thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon-climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological-thermal processes associated with annual freeze-thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets, including soil water liquid, temperature and electrical resistivity data (ERT), to estimate the vertical distribution of OC content. We subsequently explore the control of OC on hydrological-thermal behavior. We employ the Community Land Model to simulate nonisothermal surface-subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes and ice/liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate posterior distributions of desired model parameters. For hydrological-thermal to geophysical variable transformation, the simulated subsurface temperature, liquid and ice water content are explicitly linked to the soil apparent resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit of joint inversion on the estimation of OC and other parameters. We also quantified the propagation of uncertainty from the estimated parameters to prediction of hydrological-thermal responses. We find that compared to inversion of single dataset (either temperature or liquid or apparent resistivity), joint inversion of these datasets significantly reduces parameter uncertainty. We find that the joint inversion approach is able to estimate OC and sand content within the shallow active layer (0.3 m) with high reliability. Due to the small variations of temperature and moisture within the shallow permafrost (0.6 m), the approach is unable to estimate OC with confidence. However, if the soil porosity is functionally related to the OC and mineral content, the uncertainty of OC estimate at this depth remarkably decreases. Our study documents the value of the new surface-subsurface, deterministic-stochastic inversion approach, as well as the benefit of including multiple types of data to estimate OC and associated hydrological-thermal dynamics.

Citation: Tran, A. P., Dafflon, B., and Hubbard, S. S.: Coupled Land Surface-Subsurface Hydrogeophysical Inverse Modeling to Estimate Soil Organic Content and explore associated Hydrological and Thermal Dynamics in an Arctic Tundra, The Cryosphere Discuss., doi:10.5194/tc-2017-1, in review, 2017.
Anh Phuong Tran et al.
Anh Phuong Tran et al.
Anh Phuong Tran et al.

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Short summary
Soil organics carbon (SOC) and its influence on terrestrial ecosystem feedbacks to global warming in permafrost regions is particularly important for the prediction of future climate variation. Our study proposes a new surface-subsurface, joint deterministic-stochastic hydrological-thermal-geophysical inversion approach and documents the benefit of including multiple types of data to estimate the vertical profile of SOC content and its influence on hydrological-thermal dynamics.
Soil organics carbon (SOC) and its influence on terrestrial ecosystem feedbacks to global...
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