| Summary: | The goal of this study is to (1) determine how land cover in the Beringian Arctic changed in the last half century; (2) assess what biophysical properties control peak growing season land-atmosphere CO2 and CH4 exchange in multiple landscapes and land cover classes in Beringia; and (3) model how decadal land cover change in Beringia has altered peak growing season CO2 and CH4 exchange and global warming potential. Using a campaign-style, snapshot sampling approach sixteen sites were visited in ten different landscapes throughout the Beringian Arctic between 2005 and 2008. Sites represented a broad range of arctic terrestrial ecosystems, and data collection included CO2 exchange, CH4 exchange, and a number of biophysical and spectral properties for the purpose of spatial scaling and model development. For seven landscapes, ground-truthed land cover maps were created from recent high-resolution Quickbird imagery. Using conservative assumptions regarding land cover change, modern land cover maps were used as baselines for the development of historic high-spatial-resolution land cover maps derived from aerial photography and declassified military imagery dating back to 1948. Using these multi-temporal coverages, trends in decade time scale land cover change were determined for each study landscape. Within Alaska, drier landscapes and open water cover classes expanded whereas wet vegetated land cover classes decreased in area. For Russian landscapes, shrub dominated land cover expanded wherever these were present and land cover generally shifted towards an expanse of wetter landscape vegetation types. Multiple regression models were developed using field data. These were able to effectively predict CO2 and CH4 flux (R 2 = 0.70 and 0.66 respectively) for a range of vegetation types and landscapes at multiple locations in the Beringian Arctic. Originating from measurements taken during the snapshot sampling campaign, the models were relatively simple, spatially scalable models whose input parameters could be derived ...
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