Monitoring, Scaling and Predicting Interactions Across Critical Zone Compartments using Geophysical Data

Thursday, 27 July 2017: 10:30 AM
Paul Brest West (Munger Conference Center)
Susan S. Hubbard, Haruko M Wainwright, Anh Phuong Tran, Emmanuel Leger, Yuxin Wu and Baptiste Dafflon, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
Quantifying how terrestrial systems respond to climate change is challenging due to the complexity of bedrock-to-canopy interactions that over a wide range of scales. This presentation will describe the development of several new geophysical approaches to help bridge across critical zone compartments and scales. We demonstrate the approaches in an Arctic tundra ecosystem, where increasing temperatures are thawing the permafrost, potentially leading to significantly increased production of greenhouse gasses.

We first describe a stochastic approach to estimate the distribution of terrestrial system functional zones, which are regions in the landscape that have unique distributions of properties that influence system behavior. We considered the control of geomorphology on above and below ground properties important for carbon cycling using UAS imaging, surface geophysics, and a range of point measurements in an ice wedge polygon region. Results show that geophysics-based zonation results were useful for explaining the distribution of soil microbiome assemblanges as well as resulting landscape-scale carbon fluxes, thus offering powerful approach for translating between these two common end-member scales.

A networked approach was developed to coincidentally monitor above and below ground critical zone processes. The approach takes advantage of autonomous acquisition UAV and ground based acquisition platforms. The dense datasets enabled the first ‘visualization’ of interactions that occur across key critical zone compartments, leading to improved understading of critical zone freeze-thaw behavior and suggesting new ways to use remote sensing data to estimate soil properties.

Finally, we estimated the distribution of soil organic carbon, required for accurate prediction of carbon cycling. We performed analysis and X-ray CT scanning of Arctic core, and used neural network approaches and DEM based metrics to quantify and extrapolate measurements to larger scales, respectively. We developed a new CLM-based capability to jointly invert and assimilate streaming data (incl. ERT and met data) to estimate soil organic carbon and its influence over water and heat fluxes. We found that joint inversion of electrical resistivity and other datasets significantly improved estimation of organic content.