TY  - JOUR
PY  - 2023//
TI  - Uncertainty simulation of large-scale discrete grassland fire spread based on Monte Carlo
JO  - Fire safety journal
A1  - Liu, Xing-peng
A1  - Tong, Zhi-jun
A1  - Zhang, Ji-quan
A1  - Song, Chuan-tao
SP  - e103713
EP  - e103713
VL  - 135
IS  - 
N2  - An improved fire spread model was proposed for large-scale grassland areas. In this study, based on a semi-physical model, the fuel moisture was calculated using grassland area weather data. The fuel continuity was introduced into grassland spread model to judge whether fire can burn continuously across the grassland. To overcome the uncertainty in grassland fire spread simulation, Monte Carlo (MC) method was used to deal with the uncertainty of the key factors of the model, e.g. fuel characteristics (continuity, load, and moisture) and wind speed. The fire spread rate was established based on the grassland fire behaviour field experiment and the Rothermel model. Finally, according to the spread patterns of large-scale grassland fires, meteorological and remote sensing (RS) data were used to simulate the propagation process of a grassland fire. Based on Monte Carlo method, a method to effectively improve the maximum and minimum possible range of grassland fire spread simulation is proposed. The fire spread field experiment error analysis and simulation results revealed that the results are highly consistent. This study can provide decision support for fire management departments during grassland firefighting.<p />  <p>Language: en</p>
LA  - en
SN  - 0379-7112
UR  - http://dx.doi.org/10.1016/j.firesaf.2022.103713
ID  - ref1
ER  -