Climate change is no longer debated in the context of whether or not it is occurring, but rather in the context of how rapid and extensive that change will be. This is the global situation to which the biomes of national parks in Canada and the United States must adapt. Through the use of the MC1 Dynamic Global Vegetation Model (DGVM) this thesis constructs projections of possible vegetation response of ten biome classifications to the impacts of continental-scale climate change in seven regions: Atlantic, Great Lakes, Mountain, Northern, Pacific, Prairie, and Southern. It then analyzes the potential ways in which DGVMs can be utilized by park management schemes in accommodating for future climate change in the selection, creation, and maintenance of national parks.
As the latest generation of vegetation modelling systems, the advantages of Dynamic Global Vegetation Models over pre-existing equilibrium biogeography models are examined in this thesis. DGVMs highlight the degree to which ecosystems are interconnected, and are able to provide continental-scale data necessary in coordinating an integrated planning approach for national parks in North America. They are utilized in this study for generating projections of future biome distribution, based on climate information from three General Circulation Models: CGCM2, CSIRO Mk2, and HadCM3. Following the generation of possible climate scenarios, the impact of changes to biome distribution within national parks is discussed. The thesis findings provide valuable modelling analysis and scenarios for use in future planning by the US National Park System and Parks Canada. Utilization of DGVMs will help in creating flexible, coordinated management strategies that take into account projected vegetation responses to climate shifts that lie ahead.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/3568 |
| Date | January 2007 |
| Creators | Wood, Lyle Daniel |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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