We developed and tested a novel 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 organic carbon content and its associated uncertainty in the Arctic tundra. The results show that jointly using multiple datasets helps to better estimate the organic carbon content, especially at the active layer.
Quantitative characterization of soil organic carbon content is essential due to its significant impacts on surface-subsurface hydrological-thermal processes and microbial decomposition of organic carbon, which both in turn are important for predicting carbon-climate feedbacks. We present a novel approach to estimate this soil property and its impacts on the hydrological-thermal regime including the freeze-thaw transition in the Arctic tundra based on observations of soil moisture, soil temperature and electrical resistivity data.
This study developed and tested a novel approach to estimating soil organic carbon content using inverse modeling that can incorporate diverse hydrological, thermal and ERT datasets. In addition, the study permitted exploration of surface-subsurface hydrological-thermal dynamics and spatiotemporal variations associated with freeze-thaw transitions. Given the importance of characterizing organic carbon content as part of ecosystem and climate studies, the typical challenges associated with collecting and analyzing “sufficient” core data to characterize the vertical and horizontal variability of organic carbon associated with a field study site, and the increasing use of electrical resistivity data to characterize vertical, horizontal, and temporal variability in shallow systems, the new inversion approach offers significant potential for improved characterization of organic carbon content over field-relevant conditions and scales. It also offers significant potential for improving our understanding of hydrological-thermal behavior of naturally heterogeneous permafrost systems.
Earth & Environmental Sciences
The Next-Generation Ecosystem Experiments (NGEE Arctic) project is supported by the Office of Biological and Environmental Research in the DOE Office of Science. This NGEE Arctic research is supported through contract number DE-AC02-05CH11231 to Lawrence Berkeley National Laboratory.
Tran, A. P., B. Dafflon, and S. S. Hubbard. 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”, The Cryosphere, 11(5), 2089-2109. DOI: 10.5194/tc-11-2089-2017.
SC-23.1 Climate and Environmental Sciences Division, BER
BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER
May 10, 2019
Quantifying Decision Uncertainty in Water Management via a Coupled Agent-Based Model
Considering risk perception can improve the representation of human decision-making processes in age [more...]
May 09, 2019
Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Scenarios
Study provides country-specific urban area growth models and the first dataset on country-level urba [more...]
May 05, 2019
Calibrating Building Energy Demand Models to Refine Long-Term Energy Planning
A new, flexible calibration approach improved model accuracy in capturing year-to-year changes in bu [more...]
May 03, 2019
Calibration and Uncertainty Analysis of Demeter for Better Downscaling of Global Land Use and Land Cover Projections
Researchers improved the Demeter model’s performance by calibrating key parameters and establi [more...]
Apr 22, 2019
Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles
Representation of warm temperature events varies considerably among global climate models, which has [more...]
List all highlights (possible long download time)