Where fire management and carbon studies connect . . .

Screen capture of the WFEIS calculator (http://wfeis.mtri.org)

On the surface Alaska fire management and boreal ecosystem carbon studies have little in common.  But a deeper look reveals the connections between them.  Carbon scientists in the last decade have become increasingly interested in fire effects on the legacy carbon locked up in permafrost and the deep, slow-to-decompose organic layer of boreal forest floor (Kasischke et al. 2013, Genet et al. 2013).  Projections indicating more extensive, frequent and/or severe fires in northern latitudes with a rapidly warming climate, longer fire seasons, and more lightning (Romps, et al. 2014) lend a certain urgency to attempts to quantify the potential impacts of fire-released carbon on greenhouse warming.  Fire management agencies are less interested in long-term impacts of fire-released gasses but they are more and more driven to assess impacts of smoke on communities.  Work at the boundary between the two sets of interests has started to yield some interesting results.  For example, Michigan Tech Research Institute has joined their consumption field data from NASA studies to the USFS Consume Model and FCCS fuels maps and LANDFIRE fire perimeters in a web-based tool that provides users a simple interface for computing wildland fire emissions (1-km spatial resolution). The Wildland Fire Emissions Information System (WFEIS) can calculate tons of CO2 or other gases from large fires across the US and Canada from 1984-2010.  Although this tool is  for post-facto emissions analysis it is a good example of how large spatial data sets and complex equations can be united in a simple graphical interface allowing one to–say–query the forest fire emissions from the 231,000 acres burned in Alaska in 2010 (10.9 million tons CO2, 95,000 tons PM 2.5).  The hope is that weather modeling and research linkages with the common fire danger and risk rating system used in northern latitudes (CFFDRS) will soon bring this kind of application into the real-time and forecast prediction realm.


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