Building a Better Mousetrap to Estimate Dead Grass Fuel Moisture

Who says you can’t build a better mousetrap?  Local BLM Alaska Fire Service ecologist Eric Miller recently published a study using his extensive data on dead grass fuel moisture in Alaska to compare the performance of several models to predict moisture content from environmental variables.  Currently, Van Wagner’s (1969) model, with tweaks and add-on’s, is used in the Canadian Forest Fire Danger Rating System fire weather indices.  Eric notes that statistically Van Wagner’s model is overly complex, and at least 3 models used in industry or other processes that account for ambient temperature and relative humidity (or alternatively, dewpoint depression) can model standing dead grass fuel moisture in Alaska pretty well.  Fuel moisture content is integral to fire behavior prediction and fire danger ratings, and the reason standing dead grass moisture content can be predicted so reliably is that its thin structure and aeration keeps it very close to equilibrium with atmospheric moisture.  The drying lag time may be even less than 1 hour, although it is often referred to as a “1-hour lag time” fire fuel.  Eric’s working on some applications of his findings for the spring prescribed fire season in Alaska’s military land holdings.  Check out the paper:  Miller, Eric A. 2019. Moisture Sorption Models for Fuel Beds of Standing Dead Grass in Alaska. MDPI Fire 2(2):1-18. https://www.mdpi.com/2571-6255/2/1/2

Eric has several tools available for practitioners in Alaska on his website: http://www.taigafire.org/,  including simple rules-of-thumb and CFFDRS calculators for fuel moisture content.  These are partially described in a 2015 AFSC Research Brief What is the moisture content of standing dead grass?

 

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