M.P. Calef | Search USGS

USGS2015

Recent changes in annual area burned in interior Alaska: The impact of fire management

The Alaskan boreal forest is characterized by frequent extensive wildfires whose spatial extent has been mapped for the past 70 years. Simple predictions based on this record indicate that area burned will increase as a response to climate warming in Alaska. However, two additional factors have affected the area burned in this time record: the Pacific decadal oscillation (PDO) switched from cool and moist to warm and dry in the late 1970s and the Alaska Fire Service instituted a fire suppression policy in the late 1980s. In this paper a geographic information system (GIS) is used in combination with statistical analyses to reevaluate the changes in area burned through time in Alaska considering both the influence of the PDO and fire management. The authors found that the area burned has increased since the PDO switch and that fire management drastically decreased the area burned in highly suppressed zones. However, the temporal analysis of this study shows that the area burned is increasing more rapidly in suppressed zones than in the unsuppressed zone since the late 1980s. These results indicate that fire policies as well as regional climate patterns are important as large-scale controls on fires over time and across the Alaskan boreal forest.

Alaska↗

USGS2005

Analysis of vegetation distribution in Interior Alaska and sensitivity to climate change using a logistic regression approach

Aim: To understand drivers of vegetation type distribution and sensitivity to climate change. Location: Interior Alaska. Methods: A logistic regression model was developed that predicts the potential equilibrium distribution of four major vegetation types: tundra, deciduous forest, black spruce forest and white spruce forest based on elevation, aspect, slope, drainage type, fire interval, average growing season temperature and total growing season precipitation. The model was run in three consecutive steps. The hierarchical logistic regression model was used to evaluate how scenarios of changes in temperature, precipitation and fire interval may influence the distribution of the four major vegetation types found in this region. Results: At the first step, tundra was distinguished from forest, which was mostly driven by elevation, precipitation and south to north aspect. At the second step, forest was separated into deciduous and spruce forest, a distinction that was primarily driven by fire interval and elevation. At the third step, the identification of black vs. white spruce was driven mainly by fire interval and elevation. The model was verified for Interior Alaska, the region used to develop the model, where it predicted vegetation distribution among the steps with an accuracy of 60-83%. When the model was independently validated for north-west Canada, it predicted vegetation distribution among the steps with an accuracy of 53-85%. Black spruce remains the dominant vegetation type under all scenarios, potentially expanding most under warming coupled with increasing fire interval. White spruce is clearly limited by moisture once average growing season temperatures exceeded a critical limit (+2 ??C). Deciduous forests expand their range the most when any two of the following scenarios are combined: decreasing fire interval, warming and increasing precipitation. Tundra can be replaced by forest under warming but expands under precipitation increase. Main conclusion: The model analyses agree with current knowledge of the responses of vegetation types to climate change and provide further insight into drivers of vegetation change. ?? 2005 Blackwell Publishing Ltd.

Journal of Biogeography↗