HR: 1340h
AN: H43F-1698    [Abstracts]
TI: Predicting Fire Severity and Hydrogeomorphic Effects for Wildland Fire Decision Support
AU: * Hyde, K
EM: kdhyde@fs.fed.us
AF: USFS RMRS Missoula Forestry Sciences Lab, 800 East Beckwith Ave, Missoula, MT 59801, United States
AU: Woods, S W
EM: scott.woods@cfc.umt.edu
AF: The University of Montana, 32 Campus Drive, Missoula, MT 59812, United States
AU: Calkin, D
EM: decalkin@fs.fed.us
AF: USFS Missoula Fire Sciences Lab, 5775 US Highway 10, Missoula, MT 59808, United States
AU: Ryan, K
EM: kryan@fs.fed.us
AF: USFS Missoula Fire Sciences Lab, 5775 US Highway 10, Missoula, MT 59808, United States
AU: Keane, R
EM: rkeane@fs.fed.us
AF: USFS Missoula Fire Sciences Lab, 5775 US Highway 10, Missoula, MT 59808, United States
AB: The Wildland Fire Decision Support System (WFDSS) uses the Fire Spread Probability (FSPro) model to predict the spatial extent of fire, and to assess values-at-risk within probable spread zones. This information is used to support Appropriate Management Response (AMR), which involves decision making regarding fire-fighter deployment, fire suppression requirements, and identification of areas where fire may be safely permitted to take its course. Current WFDSS assessments are generally limited to a binary prediction of whether or not a fire will reach a given location and an assessment of the infrastructure which may be damaged or destroyed by fire. However, an emerging challenge is to expand the capabilities of WFDSS so that it also estimates the probable fire severity, and hence the effect on soil, vegetation and on hydrologic and geomorphic processes such as runoff and soil erosion. We present a conceptual framework within which derivatives of predictive fire modelling are used to predict impacts upon vegetation and soil, from which fire severity and probable post-fire watershed response can be inferred, before a fire actually occurs. Fire severity predictions are validated using Burned Area Reflectance Classification imagery. Recent tests indicate that satellite derived BARC images are a simple and effective means to predict post-fire erosion response based on relative vegetation disturbance. A fire severity prediction which reasonably approximates a BARC image may therefore be used to assess post-fire erosion and flood potential before fire reaches an area. This information may provide a new avenue of reliable support for fire management decisions.
DE: 0468 Natural hazards
DE: 1817 Extreme events
DE: 1819 Geographic Information Systems (GIS)
DE: 1847 Modeling
SC: Hydrology [H]
MN: 2007 Fall Meeting