Predicting large wildfires across western North America by modeling seasonal variation in soil water balance
Document Type: Journal Article
Author(s): R. H. Waring; N. C. Coops
Publication Year: 2016

Cataloging Information

  • climate change
  • Daily Minimum Temperature
  • fire danger rating
  • fire frequency
  • fire hazard reduction
  • fire management
  • fire size
  • forest management
  • general model
  • LAI - leaf area index
  • leaves
  • moisture content
  • Oregon
  • season of fire
  • soil management
  • soil moisture
  • Washington
  • wildfires
  • wildland fire
Record Maintained By:
Record Last Modified: June 13, 2018
FRAMES Record Number: 55093
Tall Timbers Record Number: 33004
TTRS Location Status: Not in file
TTRS Call Number: Available
TTRS Abstract Status: Fair use, Okay

This bibliographic record was either created or modified by the Tall Timbers Research Station and Land Conservancy and is provided without charge to promote research and education in Fire Ecology. The E.V. Komarek Fire Ecology Database is the intellectual property of the Tall Timbers Research Station and Land Conservancy.


A lengthening of the fire season, coupled with higher temperatures, increases the probability of fires throughout much of western North America. Although regional variation in the frequency of fires is well established, attempts to predict the occurrence of fire at a spatial resolution < 10 km2 have generally been unsuccessful. We hypothesized that predictions of fires might be improved if depletion of soil water reserves were coupled more directly to maximum leaf area index (LAImax) and stomatal behavior. In an earlier publication, we used LAImax and a process-based forest growth model to derive and map the maximum available soil water storage capacity (ASWmax) of forested lands in western North America at l km resolution. To map large fires, we used data products acquired from NASA's Moderate Resolution Imaging Spectroradiometers (MODIS) over the period 2000-2009. To establish general relationships that incorporate the major biophysical processes that control evaporation and transpiration as well as the flammability of live and dead trees, we constructed a decision tree model (DT). We analyzed seasonal variation in the relative availability of soil water (fASW) for the years 2001, 2004, and 2007, representing respectively, low, moderate, and high rankings of areas burned. For these selected years, the DT predicted where forest fires > 1 km occurred and did not occur at ~ 100,000 randomly located pixels with an average accuracy of 69 %. Extended over the decade, the area predicted burnt varied by as much as 50 %. The DT identified four seasonal combinations, most of which included exhaustion of ASW during the summer as critical; two combinations involving antecedent conditions the previous spring or fall accounted for 86 % of the predicted fires. The approach introduced in this paper can help identify forested areas where management efforts to reduce fire hazards might prove most beneficial. © The Author(s) 2015. This article is published with open access at

Online Link(s):
Waring, R. H., and N. C. Coops. 2016. Predicting large wildfires across western North America by modeling seasonal variation in soil water balance. Climatic Change, v. 135, no. 2, p. 325-339. 10.1007/s10584-015-1569-x.