Critical live fuel moisture in chaparral ecosystems: a threshold for fire activity and its relationship to antecedent precipitation

Large wildfires in southern California typically occur during periods of reduced live fuel moisture (LFM) and high winds. Previous work has found evidence that a LFM threshold may determine when large fires can occur. Using a LFM time series and a fire history for Los Angeles County, California, we found strong evidence for a LFM threshold near 79%. Monthly and 3-month total precipitation data were used to show that the timing of this threshold during the fire season is strongly correlated with antecedent rainfall. Spring precipitation, particularly in the month of March, was found to be the primary driver of the timing of LFM decline, although regression tree analysis revealed that high winter precipitation may delay the timing of the threshold in some years. This work further establishes relationships between precipitation and fire potential that may prove important for anticipating shifts in fire regimes under climate-change scenarios.

[1]  David R. Weise,et al.  Assessing Live Fuel Moisture For Fire Management Applications , 1998 .

[2]  D. Roberts,et al.  Evaluation of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Moderate Resolution Imaging Spectrometer (MODIS) measures of live fuel moisture and fuel condition in a shrubland ecosystem in southern California , 2006 .

[3]  D. Roberts,et al.  Use of Normalized Difference Water Index for monitoring live fuel moisture , 2005 .

[4]  T. Swetnam,et al.  Warming and Earlier Spring Increase Western U.S. Forest Wildfire Activity , 2006, Science.

[5]  J. Michaelsen,et al.  Sensitivity of Fire Regime in Chaparral Ecosystems to Climate Change , 1995 .

[6]  R. Burgan,et al.  1988 Revisions to the 1978 National Fire-Danger Rating System , 1988 .

[7]  Max A. Moritz,et al.  SPATIOTEMPORAL ANALYSIS OF CONTROLS ON SHRUBLAND FIRE REGIMES: AGE DEPENDENCY AND FIRE HAZARD , 2003 .

[8]  R. Minnich Fire Mosaics in Southern California and Northern Baja California , 1983, Science.

[9]  Marilyn N. Raphael,et al.  The Santa Ana Winds of California , 2003 .

[10]  M. J. Schroeder,et al.  SYNOPTIC WEATHER TYPES ASSOCIATED WITH CRITICAL FIRE WEATHER , 1964 .

[11]  G. De’ath,et al.  CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS , 2000 .

[12]  David W. Bacon,et al.  Estimating the transition between two intersecting straight lines , 1971 .

[13]  D. Roberts,et al.  Spectral shape-based temporal compositing algorithms for MODIS surface reflectance data , 2007 .

[14]  P. Andrews BEHAVE : Fire Behavior Prediction and Fuel Modeling System - BURN Subsystem, Part 1 , 1986 .

[15]  Philip E. Dennison,et al.  Evaluating predictive models of critical live fuel moisture in the Santa Monica Mountains, California , 2008 .

[16]  S. Shapiro,et al.  An analysis of variance test for normality ( complete samp 1 es ) t , 2007 .

[17]  Fang Yang,et al.  A statistical topographic model for exciton luminescence spectra , 1992 .

[18]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[19]  Keeley,et al.  Reexamining fire suppression impacts on brushland fire regimes , 1999, Science.

[20]  Michael D. Dettinger,et al.  CLIMATE AND WILDFIRE IN THE WESTERN UNITED STATES , 2003 .

[21]  Roger D. Peng,et al.  Detection of non-linearities in the dependence of burn area on fuel age and climatic variables , 2003 .

[22]  T. Kitzberger,et al.  Climatic and human influences on fire regimes in ponderosa pine forests in the Colorado Front Range. , 2000 .

[23]  M. Finney FARSITE : Fire Area Simulator : model development and evaluation , 1998 .

[24]  M. Lesperance,et al.  PIECEWISE REGRESSION: A TOOL FOR IDENTIFYING ECOLOGICAL THRESHOLDS , 2003 .

[25]  Ross Ihaka,et al.  Gentleman R: R: A language for data analysis and graphics , 1996 .

[26]  D. Roberts,et al.  The effects of vegetation phenology on endmember selection and species mapping in southern California chaparral , 2003 .

[27]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[28]  Jon E. Keeley,et al.  Testing a basic assumption of shrubland fire management: how important is fuel age? , 2004 .

[29]  T. Swetnam,et al.  Century scale climate forcing of fire regimes in the American Southwest , 2000 .

[30]  C. Daly,et al.  A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain , 1994 .

[31]  Dar A. Roberts,et al.  Mapping live fuel moisture with MODIS data: A multiple regression approach , 2008 .