Available at: http://digitalcommons.calpoly.edu/theses/646
Date of Award
MS in Forestry Sciences
Natural Resources Management
Christopher A. Dicus
I utilized forest growth model (FVS-FFE) and fire simulation software (FlamMap, Randig), integrated through GIS software (ArcMap9.3), to quantify the impacts varied landscape-scale fuel treatments have on short-term onsite carbon loss, long-term onsite carbon storage, burn probability, conditional flame length, and mean fire size. Thirteen fuel treatment scenarios were simulated on a 42,000 hectare landscape in northern California: one untreated, three proposed by the US Forest Service, and nine that were spatially-optimized and developed with the Treatment Optimization Model in FlamMap. The nine scenarios developed in FlamMap varied by treatment intensity (10%, 20%, and 30% of the landscape treated) and treatment type (prescribed fire, mastication and thin + burn). Each scenario was subjected to 10,000 simulated wildfires with random ignition locations in order to develop burn probability and average flame length values for each scenario. I also recorded mean fire size for each scenario. I used the burn probability values to represent the likelihood of future wildfire occurrence, which I incorporated into our long-term onsite carbon storage projections.
Our results suggest that the influence landscape-scale fuel treatments have on carbon dynamics and fire behavior metrics (mean burn probability, flame length and mean fire size) are highly dependent upon the treatment arrangement, type, and intensity. The results suggest that treating 20% of the landscape maximizes long-term carbon storage and that prescribed fire minimizes short-term carbon loss and maximizes onsite long-term carbon storage. Treating 20% of the landscape also appears to be the optimal treatment intensity for reducing fire behavior metrics, and treating beyond this level produces diminishing returns in reduction of fire behavior. When treating 20% of the landscape, site-specific treatments appear to perform well in comparison to spatially-optimized treatments.