Identifying multiscale zonation and assessing the relative importance of polygon geomorphology and polygon types on carbon fluxes in an Arctic Tundra Ecosystem

Friday, 19 December 2014: 5:30 PM
Haruko Murakami Wainwright1, Baptiste Dafflon1, Lydia J Smith2, Melanie S Hahn2, Craig Ulrich1, Yuxin Wu3, John Peterson1, John Bryan Curtis1, Margaret S Torn4 and Susan S. Hubbard1, (1)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (2)University of California Berkeley, Berkeley, CA, United States, (3)Lawrence Berkeley National Lab, Berkeley, CA, United States, (4)Berkeley Lab/UC Berkeley, Berkeley, CA, United States
Quantifying the spatial distribution of surface and subsurface properties over a range of scales is critical for improved prediction of carbon cycling in the Arctic ecosystem. This is the first study to develop a multiscale zonation approach to characterize the spatial variability of geomorphic elements and to assess the relative controls of these elements on land surface and subsurface properties, and carbon fluxes. Working within an Arctic ice-wedge polygonal region near Barrow AK, we consider two-scales of zonation, including polygon features (troughs, centers, and rims of polygons) nested within polygon types (high, flat, and low-centered). The methodology includes (1) delineating polygons using the LiDAR digital elevation map (DEM), (2) identifying data-defined polygon types along the intensive observation transects by applying a clustering method to collocated ground-based geophysical imaging data and above-ground kite-based landscape imaging data, (3) distributing polygon types over the area using the polygon statistics extracted from the LiDAR DEM, and (4) characterizing the carbon fluxes and associated surface-subsurface properties including soil moisture, soil temperature, aqueous geochemistry, thaw depth and normalized difference vegetation index in each polygon type and polygon feature. Results show that nested zonation – polygon types and polygon features – can be used to represent distinct distributions of carbon fluxes and associated properties, as well as co-variability among those properties. Importantly, the results indicate that polygon types have more power to explain the variations in properties, including carbon fluxes, than polygon features. The developed approach is found to be valuable to tractably characterize multiscale zonation over large Arctic ecosystems using multiscale datasets as well as to derive information to assess the variability of the key components in the system, including carbon fluxes. This approach is expected to improve system understanding, site characterization, and parameterization of numerical models aimed at predicting ecosystem feedbacks to the climate.