GSA Annual Meeting in Denver, Colorado, USA - 2016

Paper No. 286-2
Presentation Time: 8:30 AM

INVESTIGATING THE LINK BETWEEN HYDRAULIC CONDUCTIVITY AND SOIL CHARACTERISTICS OF PERMAFROST CORES FOR THE NEXT GENERATION ECOSYSTEM EXPERIMENT (NGEE)-ARCTIC


LÓPEZ, Robin D., WU, Yuxin, ULRICH, Craig, CHOU, Chunwei Nick, KNEAFSEY, Timothy J. and MCKNIGHT, Catherine, Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, rdlopez@lbl.gov

The Arctic environment is rich with permafrost. However, as Earth experiences changes in its global temperature, there is a critical concern of permafrost thawing in the Arctic tundra. This could result in the release of vast stores of organic material locked frozen within the permafrost. The research efforts of this group is just one of many components that aims to address the overall goal of developing a predictive model of the Arctic ecosystem in response to climate change, as part of the Next Generation Ecosystem Experiment (NGEE) – Arctic. Specifically, permafrost samples derived from the Arctic at the Barrow Environmental Observatory in northern Alaska are evaluated for various properties, including hydraulic conductivity. Hydraulic conductivity is a fundamental parameter in understanding and predicting movement of water through soils. Additionally, lab analysis of soil yields key information that could possibly be correlated with hydraulic conductivity. One of which is soil particle analysis. Particle analysis is a qualitative and quantitative measurement performed in the field and laboratory, respectively. In the case of this particular research project, a laser diffraction method is utilized to conduct the soil particle analysis. Differences observed in soil analysis amongst samples, can offer indications on a soils ability to retain moisture, organic material concentration, and infiltration rate. The research group aims to develop a reasonable connection interlinking properties of soil particle analysis, hydraulic conductivity, and geomorphic features. Ideally, this ultimately will provide better insight of surface and subsurface water movement that is vital in predicting permafrost thaw. As well, these contributions will assist in developing a predictive model for climate feedback in the Arctic environment.