4.1 Article

Development of a Fuel Model for Cistus spp. and Testing Its Fire Behavior Prediction Performance

Journal

FIRE-SWITZERLAND
Volume 6, Issue 7, Pages -

Publisher

MDPI
DOI: 10.3390/fire6070247

Keywords

fuel model; destructive fuel sampling; fire behavior prediction; Cistus spp

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This paper describes the development of a fuel model for Cistus spp. in Greece and its application in predicting surface fire rate of spread. The fuel model, adjusted with published parameter values, is suitable for Cistus spp.-dominated areas with low vegetation height. However, the model underpredicted the actual rate of spread, and a linear regression equation was developed to improve the predictions. This equation can be used to adjust BehavePlus predictions for a more accurate reflection of fire behavior.
This paper presents the development of a fuel model that can describe fuel situations in areas dominated by Cistus spp. (rockrose) in Greece. In order to obtain the necessary fuel data, thirty (30) 1 m(2) plots were destructively sampled in phryganic areas dominated by Cistus creticus, Cistus parviflorus, and Cistus salvifolius in western Greece. To develop the fuel model for Cistus spp., field measurements were supplemented with published parameter values for Cistus salvifolius. The resulting fuel model (with a height of 9.44 cm) is suitable for describing Cistus spp.-dominated phryganic areas of relatively low vegetation height in southeastern Europe. Once developed, the fuel model was inputted into the BehavePlus system to produce surface fire rate of spread predictions (ROSpred, m & BULL;min(-1)), which were then compared to 21 surface fire ROS field observations (ROSobs) obtained from wildfires or prescribed burns in areas covered by Cistus spp. It was found that the ROS for the Cistus spp. fuel model significantly underpredicted the actual ROS. A statistically significant linear regression equation was developed to mathematically describe the relationship between the predicted and observed ROS. This equation can be used to adjust BehavePlus predictions to more accurately reflect the real-world fire behavior for this fuel type.

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