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Implications of Using Percentiles to Define Fire Danger Levels

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Implications of Using Percentiles to Define Fire Danger Levels P1.5 IMPLICATIONS OF USING PERCENTILES TO DEFINE FIRE DANGER LEVELS Faith Ann Heinsch *, Patricia L. Andrews, and Laurie L. Kurth U.S. Forest Service, Rocky Mountain Research Station, Missoula, MT 1. INTRODUCTION Weather Information Management System (WIMS; National Information Systems Group, 2009) and the The National Fire Danger Rating System (NDFRS) FireFamilyPlus program. An index is calculated using uses daily weather data to calculate indices that reflect historic weather data and the resulting distribution is seasonal changes. Indices, including Energy Release converted to percentiles. Cutoff values for the two upper Component (ERC) and Burning Index (BI), describe classes are based on percentile levels. Cutoff values for relative fire danger as opposed to observable fire the three lower classes are calculated from the value at behavior values (Deeming et al., 1977). Index values the 90th or 10th percentile, and are not affected by the are interpreted differently for individual regions and for data distribution (Table 2). different fuel models in the same region. Interpretation of indices is based on climatology, using selected years 3. RESULTS AND DISCUSSION and months. Climatology-based percentile levels are used to define the average and extreme conditions for 3.1 Defining Fire Danger Levels an area. Indices are often expressed in relative terms as In March 1974, standards were developed for percentiles (e.g., 90th and 97th percentiles). Percentiles specifying the level of fire danger for public information place indices in the same context for use in class (Helfman et al., 1987). The U.S. Forest Service (USFS) definitions, which are used for many applications specified that staffing levels (then called manning including public information (e.g., Smokey Bear signs), classes) be based on the 90th and 97th percentile firefighter pocket cards (Andrews et al., 1998: Burning Index (BI); the Bureau of Land Management Schlobohm, 2000), and Wildland Fire Assessment (BLM) specified using the 80th and 95th percentiles of System (WFAS) maps (Jolly et al., 2005). Fire behavior the BI (Helfman et al., 1975). While these climatological modelers may use fire danger percentiles to define fuel breakpoints are still predetermined by agency directive moisture values for programs such as FARSITE and (Standards for Fire and Fire Aviation Operations Task FlamMap (Finney, 1998; Finney, 2006). Group, 2009), the 90th and 97th percentiles are most Research use of NFDRS includes evaluating indices, commonly used today and are applied to all indices, not examining fuel models, and modeling the impacts of only BI. The weather station manager specifies the top climate change on fire danger. Research in these areas two breakpoints. While these breakpoints may be improves our understanding and informs future documented in the local fire management plan, there is development of fire danger rating. We summarize no formal documentation in the station catalog of the common methods for using percentiles to define fire data (years, months) used to set those breakpoints. danger levels, examine issues and deficiencies of Not using data distribution to set lower levels can be current approaches, and suggest the need for additional problematic in certain situations. For example, fire analysis and improved methods. danger levels are compared for Black Creek, Mississippi (MS) and Missoula, Montana (MT) because they have 2. METHODS similar index values at the 90th and 97th percentile levels but very different climates and fire seasons. Most fires in For the examples in this paper, several Remote southern MS occur during October-April, while fires in Automated Weather Stations (RAWS) were selected western MT occur primarily during June-September. that (1) represent a range of climatological conditions Figure 1a shows the distribution of ERC, fuel model D and (2) have relatively complete weather data, thereby [ERC(D)] for Black Creek, MS and ERC(G) for Missoula, minimizing problems related to missing data (Table 1). MT. The distributions are quite different. The most Station catalogs and weather data were downloaded common ERC for MS is 20, while the most frequent from Kansas City Fire Access Software (KCFAST). Fire value for MT is 39. The corresponding cumulative danger rating calculations were performed using curves with the similar index values at the 90th and 97th FireFamilyPlus version 4.0 (Bradshaw and Tirmenstein, percentile levels are shown in Figure 1b. For both sites, in prep.). by definition, 7% and 3% of the days are in the “Very The method of defining a five-class staffing level is High” and “Extreme” classes, respectively. Recall that described by Helfman et al. (1987), and is used by the the breakpoints for the lower classes are not based on data, but on the index value at the 90th percentile. The 90th percentile ERC value for both sites is 53. While the * Corresponding author address: Faith Ann Heinsch, breakpoints for the lower classes are the same (13 and U.S. Forest Service, Rocky Mountain Research 27), the percent of days in the classes are quite different Station, Fire, Fuel, and Smoke Science Program, (Fig. 1c; Table 3). The “High” class includes 40% of the 5775 W. U.S. Highway 10, Missoula, MT 59808; days for MS and 64% for MT, a difference of 24%. e-mail: [email protected]. Table 1. Weather Stations used in the analysis. Station Climate Division Elevation Years of ID Site Name State (Bailey 1995) Latitude Longitude (ft) Data Used 045709 Mt. Laguna CA Mediterranean 32.880°N 116.420°W 5760 1970-2008 086704 Chekika FL Humid tropical 25.625°N 80.580°W 5 1999-2008 203802 Baldwin MI Hot Continental 43.5°N 85.5°W 832 1999-2008 227802 Black Creek MS Humid subtropical 30.849°N 89.034°W 275 1978-2008 241513 Missoula MT Temperate steppe 46.82°N 114.1°W 3200 1970-2008 261705 Red Rock NV Tropical/Subtropical Desert 36.135°N 115.427°W 3760 1999-2008 290801 Tower NM Temperate steppe 35.779°N 106.626°W 6500 2003-2008 415109 Conroe TX Humid subtropical 30.236°N 95.4823°W 120 1999-2008 Table 2. Calculation of fire danger class thresholds for a five-class staffing level, after Helfman (1987). Lower limit of the class for indices for Upper limit of the class for indices for which higher values indicate higher fire which lower values indicate higher fire danger (e.g., ERC, BI, Maximum danger (e.g., 1000-h Fuel Moisture, Fire Danger Class Temperature) Relative Humidity) E–Extreme 97th percentile value 3rd percentile value VH–Very High 90th percentile value 10th percentile value H–High ½ of the value at the 90th percentile ½ of the difference between the maximum value and the 10th percentile value M–Moderate ¼ of the value at the 90th percentile ¾ of the difference between the maximum value and the 10th percentile value L–Low Zero Maximum value When fire danger rating was used primarily for 3.2 Using Percentiles to Interpret Indices determining conditions under which fire potential was “Extreme”, climatology breakpoints at the lower levels NFDRS is a complex system that includes the fire were less important. As the use of fire danger rating has danger indices of Spread Component (SC), Energy broadened to include a range of applications (e.g., Release Component (ERC), Burning Index (BI), Ignition prescribed fire), more attention should be paid to proper Component (IC), and Keetch-Byram Drought Index definition of the lower classes. (KBDI) (Burgan, 1988: Deeming et al., 1977), as well as There can be distinct differences in the number of days intermediate values (e.g., 1000-h fuel moisture) that can in a fire danger rating class between the 90th/97th and serve as indices. A number of input options can be the 80th/95th percentile standards. The percentile levels customized in the Station catalog, including Slope used to define the upper two classes are specified by Class, Climate Class, and Green Up Date. Additionally, the station manager. The difference between the the user selects from 20 fuel models from the 1978 90th/97th standard and the 80th/95th standard was NFDRS as well as 20 fuel models from the 1988 update. examined for a single weather station (Missoula, MT). Of primary consideration is the fact that NFDRS Figure 2a shows those percentile levels on seasonal produces relative indices, not absolute fire behavior plots of the mean and maximum ERC(G) for 1999-2008 values. The Burning Index (BI) is an index whose and the ERC(G) values calculated for the year 2000. calculation is based on a model for flame length. The “Very High” class, by definition, includes 7% or 15% However, BI is not a prediction of flame length – proper of the historic data used in the analysis. Depending on interpretation of index values requires analysis of the standard used to set the classes, there would have historical climate data to ascertain the relative been either 27 or 56 days in the “Very High” class in significance of a given value. Fire managers can use a 2000, a difference of 29 days (Fig. 2b). Analysts percentile analysis to compare indices based on various comparing the number of days with fire danger in the options and fuel models to assess differences and guide highest classes should be aware of the method used to selection. set the levels. Fuel model selection is also an important consideration. As an example, fuel models G and H are compared for weather stations in both western Montana and eastern Texas. Fuel model G is labeled “Closed, a 185 Black Creek, MS Missoula, MT days 1978-1987 1978-1987 ↑ 100 50 80 40 60 30 40 20 Number of Days of Number 20 10 0 0 0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80 ERC(D) ERC(G) b th 100 97th Percentile 100 97 Percentile th 90th Percentile 90 90 Percentile 90 80 80 70 70 60 60 50 50 Percentile 40 40 30 30 20 20 10 10 13 27 53 60 13 27 53 61 0 0 0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80 ERC(D) Model: 8D1PE3 ERC(G) Model: 7G3PE2 2687 Wx observations 1034 Wx observations FF+4.0.2 FF+4.0.2 c 70 70 60 60 50 50 40 40 30 30 20 20 % Days inClass Days % 10 10 0 0 L M H VH E L M H VH E Fire Danger Class Fire Danger Class Figure 1.
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