Development of a Southern California Fire Weather Severity Index. by Timothy Brown. Final Report

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1 Development of a Southern California Fire Weather Severity Index by Timothy Brown Final Report Submitted to The Wildfire Hazard Reduction Training and Certification Program Federal Emergency Management Agency grant 5CA61600 University of California Berkeley Forest Products Laboratory May 1999

2 Forward This report describes the results of the study Development of a Southern California Fire Weather Severity Index under the Federal Emergency Management Agency (FEMA) grant 5CA61600, The Wildfire Hazard Reduction Training and Certification Program, at the University of California Berkeley Forest Products Laboratory. Dr. Timothy Brown, Desert Research Insitute, University of Nevada, performed the analysis under a consultant contract with the Forest Products Laboratory. For further information about this project, contact Dick Harrell, Wildland Fire Services, 2531 Morrene Drive, Placerville, CA, (Tel: ). 2

3 Introduction This report describes the results of the study Development of a Southern California Fire Weather Severity Index under the Federal Emergency Management Agency (FEMA) grant 5CA61600, The Wildfire Hazard Reduction Training and Certification Program, at the University of California Berkeley Forest Products Laboratory. The two primary objectives of the project were to 1) develop a fire weather severity index for southern California, and 2) verify this index with fire history. The results of this study can be included in the development of a procedure (model) for performing a Fire Hazard Assessment (FHA) in the six southern counties of California. FHA is quite distinct from Fire Hazard Classification and Fire Hazard Zoning. Classification is a broader, strategic analysis using many of the assessment parameters, and zoning is a planning and regulatory activity. FHA is a tactical, site specific measurement of the factors which affect fire behavior, fire suppression capability and effectiveness, structure survivability in a wildfire situation, firefighter and resident safety, etc. Fire hazard is a fuel complex which is defined by the volume, type, condition, arrangement and location which determines the ease of ignition and the difficulty of suppression. The primary components of hazard which this project addresses are condition, ease of ignition and suppression difficulty (all of which are primarily influenced by weather). Hence, the development of a weather severity index provides quantitative information for each of the hazard components. The condition is best described as a measure of fuel moisture. Live fuel moisture is the physiological response of a plant to growth processes, available soil moisture and weather factors (e.g., precipitation, atmospheric moisture, temperature, wind). Live fuel moistures are typically only important at certain times of the year. Direct sampling for over 10 years show critical living fuel moistures are reached in California chaparral fuels in late-august to mid-september (Countryman and Dean, 1979). While similar measurements are few, it is likely that the same conditions exist in non-deciduous tree species. Dead fuel moistures are a function of weather factors, in particular temperature and atmospheric moisture. The use of dead fuel moisture in this study is described in the next section. The ease of ignition can be described by components of the National Fire Danger Rating System (NFDRS) or the Fire Behavior Prediction System (FBPS). Fine fuel moisture, air temperature and fuel temperature all govern the ease of ignition. The suppression difficulty can be predicted using the spread algorithm in the BEHAVE and FIRECAST fire behavior prediction programs. Major factors related to suppression difficulty includes the relative ease of access, fire area/perimeter predictions based upon growth models and fireline construction and containment rates. Data and Methods The study region included the six southern California counties of San Diego, Riverside, Orange, San Bernardino, Los Angeles and Ventura. The California Division of Forestry (CDF) provided historical weather data for 16 Remote Automatic Weather Station (RAWS) sites 3

4 located in these counties (Figure 1). The Western Regional Climate Center provided RAWS data for an additional 11 federal agency sites. Hourly temperature, relative humidity and wind speed data were analyzed for the period 1991 through Some basic quality control was applied to these data, checking for unusual and erroneous values. In particular, checks were made for exceedence of predetermined values (wind speed 0-99 mph; temperature -20 F to 120 F; humidity 0 to 100%). Not all 27 RAWS sites were available for a given day. A minimum of five sites were used to compute an area average. If less than five sites were available, then the day was considered missing. Historical fire data for the counties of San Bernadino, San Diego and Riverside were obtained from the CDF Emergency Action Reporting System (EARS). Fire history data for Orange and Ventura counties were obtained from the California Fire Information Reporting System (CFIRS). Los Angeles County Fire Department provided historical data for their respective area. Only fires which exceeded a burn area of 100 acres or greater were included in the analysis. Figure 1 shows the location of 293 large fires during the period. Some fire locations were reported as township and range, which was then converted to latitude and longitude for plotting and analysis. Because of this conversion, there may be overlap in the location for some of the fires. During the seven year period ( ), 205 days were classified as having a large fire, and had RAWS weather data available. Fifty days had multiple large fire occurrence. Since RAWS sites are often not conveniently located near a fire, all RAWS variables were averaged for each hour of the day over the six counties. The mean distance to the nearest RAWS site for all fires was 38 miles. It was felt that computing average conditions over the entire region using a number of sites was actually more representative of the general conditions than using a single site many miles away. While this method would not be desirable in a study of fire behavior, it appears to be appropriate for establishing a fire weather hazard assessment. After some initial analysis of temperature and humidity, it was realized that humidity had far greater weight in large fire occurrence than did temperature. Also, given that relative humidity is a function of temperature, it was decided to combine the two variables into a single index - a 10-hour dead fuel moisture. Tables A, B, C, D, E and F provided in Rothermel (1983) were used to produce the index following procedures described on page 14 of that report. The tables allow for a construction of a 1-hour dead fuel moisture based on observed temperature and relative humidity readings depending on daytime or nighttime observations (Tables A and E, respectively). Corrections for season, aspect and elevation were applied using Tables B, C, D and F. Since aspect and elevation information was not readily available for all fires, values from the Table were picked based on a south aspect (and relative humidity 30%) and same elevation as the observing station (±1000 feet). The final step was an adjustment to approximate a 10-hour fuel by adding a value of 1% to the 1-hour computed values. This value and wind speed (analyzed separately) averaged over a day and the six counties comprise the severity index as discussed in the results below. 4

5 Figure 1. Map showing six southern California counties along with locations of RAWS sites (flags) and fire occurrence locations (solid circles). Size of fire is indicated by relative size of symbol. For some fire locations township and range values were converted to latitude and longitude, and thus there may be overlap in the symbols. Results Figure 2 shows monthly values of the 10-hour dead fuel moisture and wind speed over the six southern California counties. Hourly values were used to compute monthly means. A well-defined annual cycle can be seen in the two curves. The lowest dead fuel moisture values occur during the warm season months of June through October. The strongest hourly average wind speeds occur during the winter and spring season months of November through May. Though not readily apparent in this plot, Santa Ana wind events are most common in October through December. This type of wind condition was not explicitly examined in the present 5

6 study. However, as will be seen below, high wind days during these months are associated with large fire occurrence Dead Fuel Moisture (%) DFM SPD Wind Speed (mph) Jan Feb Mar Apr May Jun Figure 2. Monthly mean 10-hour dead fuel moisture and wind speed values based on 27 hourly RAWS sites averaged over the six counties for the period. Jul Aug Sep Oct Nov Dec 5 Figure 3 shows the number of large fire days by month during the period. These values are also given in Table 1. Most large fires occurred during the summer months June through August (57% of the annual total), though May through November can be considered the primary fire season with 94% of the annual fires occurring during these months. Only February and March were absent of large fires. Table 1 provides monthly mean values of 10-hour dead fuel moisture and wind speed averaged over the six southern California counties. Hourly values were used to compute daily values, and these values are divided into three categories days with large fire, three days prior to a day with large fire, and days with no large fire. Figures 4 and 5 show these results graphically. The choice of three days prior to large fire occurrence is based upon experience and research that has shown it typically takes three days of drying before fuels drop to readily 6

7 ignitable levels (i.e., only 2/3 of the fuel-to-atmosphere equilibrium moisture content is lost in the first 10-hour drying period, another 2/3 in the second period and another 2/3 in the third period. The three day period of drying and wetting is fairly constant given an absence of subsidence winds or high humidity events Number of days Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 3. Total number of days with large fire occurrence within the six counties during the period Figure 4 shows the monthly mean 10-hour dead fuel moisture for each category in Table 1. There is a clear distinction between all three categories. Days with large fires have the lowest average 10-hour dead fuel moisture and days with no large fire have the highest dead fuel moisture values. The mean difference between these two categories over all months is approximately 3%. Larger differences occur during the winter months (approximately 4%) and smaller differences occur during summer (approximately 2%). However, this 2% difference can still be considered large since the value is based upon a relatively large number of days (i.e., 39, 46 and 32 days for June, July and August, respectively). Monthly mean 10-hour dead fuel moisture values averaged over the three day period prior to a large fire falls within the other two 7

8 categories. This suggests that fuels tend to dry substantially at a minimum of a few days prior to the large fire event. Table 1. Monthly mean values of 10-hour dead fuel moisture and wind speed for the six southern California counties for the period Fire days (205 days total) refer to days having events exceeding 100 acres. Non-fire days (2,350 days total) refer to days having no fire occurrence exceeding 100 acres. 10-hr dead fuel moisture (%) Wind speed (mph) Number of fire days Non-fire days 3 days prior to fire Fire days Non-fire days 3 days prior to fire Fire days January February March April May June July August September October November December Figure 5 shows the monthly mean wind speed for each category in Table 1. During May through September, there appears to be little difference between average wind speed in the three categories. The largest wind speeds associated with large fire events occur during the cool season. In January and March there is little difference between values on large fire days and the three days preceding the fire event. However, wind values on large fire days during October through December stand out as substantially larger values than during any other month. Though each daily wind pattern was not analyzed separately, these values likely reflect Santa Ana conditions which occur frequently during these months. 8

9 14 13 Non-fire days 3 days prior to fire Fire day Dead Fuel Moisture (%) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 4. Monthly mean 10-hour dead fuel moisture averaged over the six counties for the period. Solid circles represents days with large fire occurrence, solid triangle represents three day period preceding day with large fire, and crosses represents days without large fire occurrence. 9

10 10 9 Non-fire days 3 days prior to fire Fire days Wind Speed (mph) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 5. Monthly mean wind speed averaged over the six counties for the period. Solid circles represents days with large fire occurrence, solid triangle represents three day period preceding day with large fire, and crosses represents days without large fire occurrence. A statistical method known as resampling was used to test the validity of the observed 10-hour dead fuel moisture and wind speed values associated with large fire occurrence. The test consisted of randomly selecting a predetermined number of days for each month, computing the average 10-hour dead fuel moisture for those days, and comparing this average to the originally observed values shown in Figures 4 and 5. For example, Table 1 shows that in July there were 46 days with large fire occurrence for the period. Conceptually in the resampling process, all July days (7years times 31days=217days) were thrown into a hat, and 46 days randomly selected with replacement. That is, when one day was pulled from the hat, it was included in the calculation, then thrown back in so it could be selected again. The selection may or may not include a large fire day. Once the 46 days were selected, the daily average 10-hour dead fuel moisture was computed, then an average of these 46 values determined. This process was performed 1000 times for each month. The number of days used for each month are given in column 2 of Table 1. 10

11 The results for the 10-hour dead fuel moisture resampling analysis are shown in Figure 6. For each month, the resampled 10-hour dead fuel moisture is greater than the observed value for days with large fire occurrence, indicating that the original values based on large fire days are statistically different from those on non-large fire days. This result is further assessed by computing the proportion of times that the resampled monthly 10-hour dead fuel moisture value was equal to or less than the original observed value. These proportions are shown in Table 2, and represent the probability that the observed value occurred by random chance. The proportions, and hence the probabilities, are small for all months with the possible exception of January. No values are given for February and March since there were no large fire occurrences during these months Dead Fuel Moisture (%) Resampled DFM 8 Observed DFM 7 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 6. Monthly values of resampled (curve with solid triangle symbols) and observed 10-hour dead fuel moisture for days with large fire occurrence (curve with solid circles). A similar analysis was performed for wind speed. In this case, the proportion is for those monthly wind speed values that were equal to or greater than the observed values for days with large fire occurrence. Figure 7 provides a plot of monthly resampled and observed values, and 11

12 the percent probabilities are given in Table 2. These results suggest that the strong wind speeds during the autumn months are indeed significant in relation to large fire occurrence and have a low probability of occurring by random chance. Table 2. Probability (in percent) of having 10-hour dead fuel moisture (DFM) values less than or equal to the observed large fire occurrence DFM value, and wind speed values greater than or equal to the observed large fire occurrence value based upon the statistical resampling method described in the text. No values are given for February and March since there were no large fire occurrences during these months. Month JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC DFM% Wind Speed% Observed wind speed Wind Speed (mph) Resampled wind speed Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 7. Monthly values of resampled (curve with solid triangle symbols) and observed wind speed for days with large fire occurrence (curve with solid circles). 12

13 Summary This report has summarized the results of a study to produce a fire hazard index based on weather conditions for six southern California counties. Hourly values of temperature, relative humidity and wind speed were analyzed from 27 RAWS sites for the period Temperature and humidity was used to compute a 10-hour dead fuel moisture value. Monthly mean values of 10-hour dead fuel moisture and wind speed average over the six counties were then related to three categories of large fire ( 100 acres) occurrence days with no large fire, three days preceding large fire occurrence, and days with large fire. The key results can be summarized as follows: There is a substantial decrease in 10-hour dead fuel moisture on days with large fire occurrence compared to days with no large fire or the three days preceding a large fire event. The annual average difference is approximately 3% (approximately 2% during the summer months). There is a substantial decrease in 10-hour dead fuel moisture during the three days preceding a day with large fire, confirming a build-up of drying conditions for a least a few days before the fire occurrence. The annual average is approximately 1% more than on days with large fire and 2% less than days with no large fire occurrence. Wind speed appears to be associated with large fire occurrence primarily during the months of October through January and in April. During the summer months, there is little difference between monthly mean wind speed values on days with large fire and days without large fire occurrence. Acknowledgements The author wishes to thank Dick Harrell for providing the opportunity to work on this project, for tracking down fire history data, and for providing general guidance during the project. The author also wishes to thank Pete Guilbert from CDF for providing RAWS data, and Beth Hall from the Desert Research Institute CEFA program for providing analysis assistance. References Countryman, C., and W. Dean, 1979: Measuring moisture content in living chaparral. General Technical Report GTR-PSW-36, U.S.D.A. Forest Service Pacific Southwest Research Station, September Rothermel, R., 1983: How to predict the spread and intensity of forest and range fires. General Technical Report GTR-INT-143, U.S.D.A. Forest Service Intermountain Research Station, February

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