气候变化领域集成服务门户(CCPortal): Coupled land surface-subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra

CCPortal

DOI	10.5194/tc-11-2089-2017
	Coupled land surface-subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra
	Tran A.P.; Dafflon B.; Hubbard S.S.
发表日期	2017
ISSN	19940416
卷号	11 期号:5
英文摘要	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 liquid water content, temperature and electrical resistivity tomography (ERT) data-to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrological-Thermal parameters and, therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. 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 (e.g., solar radiation balance, evapotranspiration, snow accumulation and melting) and ice-liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate a posteriori distributions of desired model parameters. For hydrological-Thermal-To-geophysical variable transformation, the simulated subsurface temperature, liquid water content and ice content are explicitly linked to soil electrical 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 quantify the propagation of uncertainty from the estimated parameters to prediction of hydrological-Thermal responses. We find that, compared to inversion of single dataset (temperature, liquid water content 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 (top 0.3ĝ€m of soil) with high reliability. Due to the small variations of temperature and moisture within the shallow permafrost (here at about 0.6ĝ€m depth), the approach is unable to estimate OC with confidence. However, if the soil porosity is functionally related to the OC and mineral content, which is often observed in organic-rich Arctic soil, 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. © Author(s) 2017.
学科领域	active layer; decomposition; electrical resistivity; estimation method; geophysical method; hydrodynamics; land surface; microbial activity; organic carbon; parameterization; quantitative analysis; soil carbon; stochasticity; thermodynamics; vertical distribution; Arctic
语种	英语
scopus关键词	active layer; decomposition; electrical resistivity; estimation method; geophysical method; hydrodynamics; land surface; microbial activity; organic carbon; parameterization; quantitative analysis; soil carbon; stochasticity; thermodynamics; vertical distribution; Arctic
来源期刊	Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/119338
作者单位	Climate and Ecosystems Division, Earth and Environmental Sciences Area, Lawrence National Berkeley Lab, Berkeley, CA 94720, United States
推荐引用方式 GB/T 7714	Tran A.P.,Dafflon B.,Hubbard S.S.. Coupled land surface-subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra[J],2017,11(5).
APA	Tran A.P.,Dafflon B.,&Hubbard S.S..(2017).Coupled land surface-subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra.Cryosphere,11(5).
MLA	Tran A.P.,et al."Coupled land surface-subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra".Cryosphere 11.5(2017).