Abstract
Understanding the fire prediction capabilities of fuel models is vital to forest fire management. Various fuel models have been developed in the Great Xing'an Mountains in Northeast China. However, the performances of these fuel models have not been tested for historical occurrences of wildfires. Consequently, the applicability of these models requires further investigation. Thus, this paper aims to develop standard fuel models. Seven vegetation types were combined into three fuel models according to potential fire behaviors which were clustered using Euclidean distance algorithms. Fuel model parameter sensitivity was analyzed by the Morris screening method. Results showed that the fuel model parameters 1-hour time-lag loading, dead heat content, live heat content, 1-hour time-lag SAV(Surface Area-to-Volume), live shrub SAV, and fuel bed depth have high sensitivity. Two main sensitive fuel parameters: 1-hour time-lag loading and fuel bed depth, were determined as adjustment parameters because of their high spatio-temporal variability. The FARSITE model was then used to test the fire prediction capabilities of the combined fuel models (uncalibrated fuel models). FARSITE was shown to yield an unrealistic prediction of the historical fire. However, the calibrated fuel models significantly improved the capabilities of the fuel models to predict the actual fire with an accuracy of 89%. Validation results also showed that the model can estimate the actual fires with an accuracy exceeding 56% by using the calibrated fuel models. Therefore, these fuel models can be efficiently used to calculate fire behaviors, which can be helpful in forest fire management.
Highlights
Weather, terrain, and fuel are major factors influencing wildfire occurrence and behaviors [1,2,3], among which fuel is arguably the only factor human can mediate
Forest fuel models description We derived seven vegetation types from the 1:1,000,000 vegetation map, which were combined into three fuel models according to their potential fire behaviors (Fig. 3) (Note: 1: Meadow; 2: Shrub; 3: Swamp; 4: Evergreen coniferous forest; 5: Deciduous broadleaf forest; 6: Deciduous coniferous forest; 7: Mixed coniferous and broad-leaf forest)
Grasses are well developed in these three vegetation types with the average high of 60,90 cm
Summary
Terrain, and fuel are major factors influencing wildfire occurrence and behaviors [1,2,3], among which fuel is arguably the only factor human can mediate. The shape, size, loading, moisture content, and spatial configuration of forest fuels affect the ignition, intensity, spread, and effects of wildfire [4,5]. Fuel is complex spatially and temporally, changing with vegetation type, succession stage, and environments [7,8,9]. Due to the infinite combinations of vegetation type, sucession stage, and environment present in a landscape, it is impossible to characterize all possible combinations that affect fuel. Generalizing fuels into finite number of fuel models has become a widely used approach to characterizing and mapping forest fuels across a landscape [10,11]. A fuel model is defined as ‘‘an identifiable association of forest fuel components of distinctive species, form, size, arrangement, and continuity that will exhibit characteristic fire behavior under defined burning conditions’’ [12]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
