The modeled effects of fire on carbon balance and vegetation abundance in Alaskan tundra

• Main Author: Davidson, Carl
• Other Authors: Dietze, Michael C.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2012
• Subjects: envir; geo; Arctic; albedo; permafrost; Tundra; Alaska
• Online Access: http://hdl.handle.net/2142/34234

Arctic climate is warming at a rate disproportionately faster than the rest of the world. Changes have been observed within the tundra that are attributed to this trend, including active layer thickening, shrubland expansion, and increases in fire frequency. Whether tundra remains a global net sink of carbon could depend upon the effects of fire on vegetation, specifically concerning the speed at which vegetation reestablishes, the stimulation of growth after fire, and the changes that occur in species composition during succession. While rapid regeneration of graminoid vegetation favors the spread of this functional type in early succession, late succession appears to favor shrub vegetation at abundances greater than those observed before fire. Possible reasons for this latter observation include changes in albedo, soil insulation, and soil moisture regimes. Here we investigate the course of succession after fire disturbance within tundra ecosystems, and the mechanisms involved. A literature review was conducted over previous studies on burn sites, and a series of simulated burn experiments were attempted on the burn site left by the 2007 Anaktuvuk River fire to assess the behavior of version 2.1 of the Ecosystem Demography model (ED2) in the simulation of tundra fire. Though uniquely suited for the heterogeneous landscapes found within tundra, ED2 has not yet been applied to these ecosystems. Prior to validation, we parameterize and calibrate ED for the Alaskan tundra. The land surface sub-model within ED is modified to simulate permafrost through the effects of an increased soil-column depth, a specialized peat texture class, and the simulated effects of wind compaction and depth hoar on snow density. Parameterization was conducted through Bayesian techniques used to constrain parameter distributions based upon data from a literature survey, field measurements at Toolik Lake, Alaska, and an assimilation of three datasets. At each step, priority was assigned to measurements that could constrain parameters that ...