Coupled land surfacea??subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra

摘要

pstrongAbstract./strong Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surfacea??subsurface hydrologicala??thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbona??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 hydrologicala??thermal processes associated with annual freezea??thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets a?? including soil liquid water content, temperature and electrical resistivity tomography (ERT) data a?? to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrologicala??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 surfacea??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 icea??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 hydrologicala??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 hydrologicala??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 0.3span class="thinspace"/spanm of soil) with high reliability. Due to the small variations of temperature and moisture within the shallow permafrost (here at about 0.6span class="thinspace"/spanm 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 surfacea??subsurface, deterministica??stochastic inversion approach, as well as the benefit of including multiple types of data to estimate OC and associated hydrologicala??thermal dynamics./p.