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- W4312964030 abstract "The recent upsurge in the incidence of extreme wildfire events, the expected impact of climate change on the frequency and severity of fires, and the progressive expansion of wildland-urban interface areas highlight the tangible need for improvement in our ability to predict, mitigate and manage the growing risk to which communities are exposed. The aim of this research is to contribute to deepen the knowledge on the spatial simulation of complex dynamics of wildland surface fire behaviour through the development and validation of a spatially distributed predictive model for the simulation of wildland surface fire spread intended for operational purposes. Given the position of one or more ignition points, the developed model allows to (i) obtain near real time dynamic estimates of the geo-environmental variables that control the fire spread, (ii) compute the direction and intensity of the maximum rate of fire spread in heterogeneous environments, and (iii) simulate the surface fire spread using agent-based models. The final aim is to provide competent authorities with timely information on the expected evolution of the flame front to optimise decision-making processes. The model, developed under synthetic conditions, is then applied to case studies recorded in the territory of the Autonomous Region of Sardinia, that offers institutional information on the ignition location, the evolution of the flame front, and the completed fire suppression activities, which are implemented in the model as well. Overall, the model showed a promising predictive capacity evaluated in quantitative terms of morphological matching between the observed and predicted fire spread patterns, returning more accurate results in areas with less complex morphologies and dominated by herbaceous rather than shrubby fuels. The model also made it possible to obtain accurate simulations in rapid processing times, compatible with its operational application as a tool for optimising and planning fire risk prevention and mitigation strategies and policies as well as fire management activities. Future research will be addressed at improving the predictive capacity of the model and estimating the propagation of the parametric uncertainty of the geo-environmental variables within the model." @default.
- W4312964030 created "2023-01-05" @default.
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- W4312964030 date "2022-01-01" @default.
- W4312964030 modified "2023-09-26" @default.
- W4312964030 title "Simulating wildland surface fire behaviour to support emergency management" @default.
- W4312964030 doi "https://doi.org/10.14195/978-989-26-2298-9_58" @default.
- W4312964030 hasPublicationYear "2022" @default.
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