Assessing accuracy of a probabilis@c model for very large fire in the Rocky Mountains: A case study of the High Park Fire E. Natasha Stavros1, John Abatzoglou2, Narasimhan K. Larkin3, Donald McKenzie3, E. Ashley Steel3 1University of Washington, School of Environmental and Forest Sciences, SeaAle, WA 2University of Idaho, Department of Geography, Moscow, ID 3Pacific Wildland Fire Sciences Lab, US Forest Service, SeaAle, WA Summary Across the western United States, the largest wildfires account for most of the area burned and substan9ally affect mountain forests and their associated ecosystem services, among which is pris9ne air quality. These fires commandeer na9onal aAen9on and significant fire suppression resources. Despite efforts to understand the influence of fuel loading, climate, and weather on annual area burned, few studies have focused on what abio9c factors enable and drive the very largest wildfires, here defined as megafires (>20,234 ha). We inves9gated the correla9on between both antecedent climate and in-­‐situ biophysical variables and megafires in the western United States from 1984 to 2009. We built logis9c regression models, at the spa9al scale of the na9onal Geographic Area Coordina9on Centers (GACCs), to es9mate the probability that a given day is conducive to a megafire. Models vary in accuracy and in which variables are the best predictors. In a case study of the condi9ons of the High Park Fire, neighboring Fort Collins, Colorado, occurring in early summer 2012, we evaluated the predic9ve accuracy of the Rocky Mountain model, and found that during the week of the High Park Fire igni9on, a megafire was approximately 1118 percent more likely to occur than during the historic record (1984-­‐2009). Figure 3 (below): Historical fire data for large fires over 404 hectares from the 1984-­‐2009 was used from the dataset: Monitoring Trends in Burn Severity (MTBS; www.mtbs.gov), a database of fire polygons, burn severity and 9ming. A) Is the number of fires greater than 20,234 hectares per PSA in each GACC. B) Is the percent of total hectares burned by fires greater than 20,234 ha in a given PSA in each GACC. B) A) Index Residence @mes (days) DC 46 FM1000 32 ERC 23 DMC 21 FM100 8 BI 8 FFMC 4 Table 1 (leS): A table of the dura9on in days that it takes for a calculated index to no longer have an effect on subsequent calculated indices, thus referring to the temporal autocorrela9on of each index Figure 4 (right): Historical weather and climate data at 4-­‐kilometer spa9al and daily temporal resolu9on from 1979-­‐2010 (Abatzoglou 2011) were used to calculate fuel and climate indices. In these composite graphs red denotes megafires, and blue denotes all other large fires (>404 ha). The dashed line is the megafire week as defined by day of year, e.g., week 1 = Jan 1-­‐7. The x-­‐axis shows weeks from discovery week. The lighter shades denote the 95% confidence interval of the mean. Model sensi9vity to megafire size classifica9on Comparing 20,234 ha threshold to 4,047 ha as this is a threshold Methods defined by Climate Central (2012) shows: •  Binomial logis9c regression: probability of a megafire week • The 4,047 ha threshold covers more range of probabili9es, as •  Determine predicted probabili9es given actual data there are more megafires by which to build the models •  Classify probability thresholds for determining megafire • ROC sta9s9cs are influenced by imbalanced data. The ROC •  Assess trade-­‐offs between recall and precision: sta9s9c is beAer for 20,234 ha threshold, but because of the •  Recall = p(predic9ng a megafire that is actually a megafire) heavily right-­‐skewed probability distribu9on, the model is •  Precision= p(correctly classifying a megafire) falsely assigning low probability more uniformly. Limita9ons/complica9ons Relate to High Park •  Models were built using imbalanced data (i.e. more large fire • Use energy release component (ERC) percen9les as a proxy to (>404 ha) events than megafire (>20,234 ha)) weekly Burning Index (BI) percen9les because BI is calculated •  Daily temporal autocorrela9on in predictor variables (CFFDRS, using ERC and a spread component, which is not available to us NFDRS, and PDSI variables) (gacc.nifc.gov/rmcc/predic9ve/erc_10_100_1000_13.htm) •  Indices were developed using averages across broad spa9al • High Park discovery date: June 9, 2012 or Day-­‐of-­‐Year= 161, scales and therefore do not account for local spa9al week = 23 = 2nd week of June had a BI.7 ~ 62.5 compared to heterogeneity of fuel condi9ons and climate. the “normal” over the climatological record where BI.7 ~ 50 • 1118% more likely to have a megafire (> 20,234 ha) week than the normal condi@ons for that week over the climatological 4047 ha record Discussion • Even the largest fires are subject to other controls besides climate. Fuel structure or composi9on, and topographic complexity, exert fine-­‐scale controls on fire spread (Kennedy and McKenzie 2010), although extreme weather can diminish the effect of both (Bessie and Johnson 1995). • Our models are not spa9ally explicit, and therefore do not account for proximity to the wildland urban interface or the 9me between the fire start and ini9al aAack, which can vary widely depending on the proximity of resources needing Figure 6: Normalized precision and recall against probability thresholds for protec9on. classifying megafires, e.g. given a probability of >= 0.2, then the predicted • Our models predict that a megafire was more than 6 9mes as probability was classified as a megafire. likely that week (of the High Park Fire) than in a typical week in Table 2: the sta9s9cal models to determine a megafire given fuel condi9ons and climate. the historical record, but there is an element of uncertainty associated with igni9ons that we do not consider. Megafire P(megafire) ROC • What defines a "megafire" goes beyond area burned (Kasischke Size (ha) Sta@s@c et al. 2005). Lives lost, structures destroyed, economic cost in 20,234 1/(1+e12.694-­‐0.140*BI.7) 0.861 general, and degrada9on of air quality can be very different for 4,047 1/(1+ e13.500 -­‐ 0.003*DMC.7 -­‐ 0.135*ERC.7 -­‐ 0.194*TEMP.n1) 0.859 two fires that burned the same total area. Sta@s@cal Models RM where megafire > 4046 ha
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Figure 1: Use 20,234 ha as the megafire threshold as this is the threshold for fires in the top 2 percent of fire sizes, thus classifying them as “extreme” events 0.2
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Acknowledgements Joint Fire Science Program (JFSP) funded this research. Also, a special thanks to Rob Norheim with the University of Washington for providing the References maps. Abatzoglou, J. T. 2011. Development of gridded surface meteorological data for ecological applica9ons and modeling. Interna9onal Journal of Climatology. <hAp://onlinelibrary.wiley.com/doi/10.1002/joc.3413/abstract;jsessionid=8B96FF43570FADBC4CC8CB20C2BECF64.d04t03?
Contact deniedAccessCustomisedMessage=&userIsAuthen9cated=false> E. Natasha Stavros ENStavros@gmail.com Bessie, W. C. and E. A. Johnson. 1995. The rela9ve importance of fuels and weather on fire behaior in sub-­‐alpine forests. Ecology 76:747-­‐762. Climate Central. 2012. The Age of Western Wildfires. <hAp://www.climatecentral.org/news/report-­‐the-­‐age-­‐of-­‐western-­‐wildfires-­‐14873> Kasischke, E. S., E. J. Hyer, P. C. Novelli, L. P. Bruhwiler, N. H. F. French, A. I. Sukhinin, J. H. Hewson, and B. J. Stocks. 2005. Influences of boreal fire emissions on Northern Hemisphere atmospheric carbon and carbon monoxide. Global Biogeochemical Cycles 19:GB1012. Kennedy, M. C. and D. McKenzie. 2010. Using a stochas9c model and cross-­‐scale analysis to evaluate controls on historical low-­‐severity fire regimes. Landscape Ecology 25:1561-­‐1573.