Forecasting Fine Fuels in the Intermountain West Rangelands

Mira Ensley-Field
The objective of this thesis project was to develop a fine fuels forecast to help fire managers anticipate spatial variation in fuel loads before the start of the fire season. In Chapter 1 we compile and analyze the methodologies of the historical record of fine fuel loads reported to the Great Basin Coordination Center. Based on our data analysis, we developed a series of recommendations for improving the methods used to sample fine fuels in the future as well as more broad ideas for how land
more » ... anagers can use emerging technologies to more effectively monitor fine fuels. In Chapter 2, we combine this historic record of fine fuel measurements with a newly available remotely-sensed dataset of herbaceous productivity from the Rangeland Analysis Product and model the ecological process of fine fuels accruing in and leaving a system. We build a model where the amount of fuel at a location depends on the fuel load of the previous year and the current year's productivity. We then forecasted the remotely-sensed herbaceous productivity. Finally, we combine these models to forecast fuel loads based on forecasted productivity. These results left us with three main takeaways; 1) improvements and a greater quantity of fine fuel data measured are needed to produce a fine fuels forecast with usefully narrow confidence intervals, 2) remotely-sensed productivity datasets are meaningfully related to on-the-ground fine fuels and would be a useful tool for land managers in early spring, and 3) ecological forecasts of remotely-sensed productivity is a promising future direction of research. Our efforts have useful implications for how land managers can use already existing remotely-sensed data and remotely-sensed data forecasts for early spring fire planning decisions as well as needed recommendations for how to better focus the large amount of effort that goes into fine fuels monitoring in early spring.
doi:10.26076/7f30-dbaf fatcat:wsbhqt62r5c6vm5kqlxuffvzcy