The Effectiveness and Limitations of Fuel Modeling Using the Fire and Fuels Extension to the Forest Vegetation Simulator

Fuel treatment effectiveness is often evaluated with fire behavior modeling systems that use fuel models to generate fire behavior outputs. How surface fuels are assigned, either using one of the 53 stylized fuel models or developing custom fuel models, can affect predicted fire behavior. We collect...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Forest science 2014-04, Vol.60 (2), p.231-240
Hauptverfasser: Noonan-Wright, Erin K, Vaillant, Nicole M, Reiner, Alicia L
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Fuel treatment effectiveness is often evaluated with fire behavior modeling systems that use fuel models to generate fire behavior outputs. How surface fuels are assigned, either using one of the 53 stylized fuel models or developing custom fuel models, can affect predicted fire behavior. We collected surface and canopy fuels data before and 1, 2, 5, and 8 years after prescribed fire treatments across 10 national forests in California. Two new methods of assigning fuel models within the Fire and Fuels Extension to the Forest Vegetation Simulator were evaluated. Field-based values for dead and downed fuel loading were used to create custom fuel models or to assign stylized fuel models. Fire was simulated with two wind scenarios (maximum 1-minute speed and maximum momentary gust speed) to assess the effect of the fuel model method on potential fire behavior. Surface flame lengths and fire type produced from custom fuel models followed the fluctuations and variability of fine fuel loading more closely than stylized fuel models. However, results of 7 out of 10 statistical tests comparing surface flame length between custom and stylized fuel models were not significant (P < 0.05), suggesting that both methods used to assign surface fuel loads will predict fairly similar trends in fire behavior.
ISSN:0015-749X
1938-3738
DOI:10.5849/forsci.12-062