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Selection: Ascoli:D [3 articles] 

Publications by author Ascoli:D.
 

Forest fires are changing: let’s change the fire management strategy

  
Forest@ - Rivista di Selvicoltura ed Ecologia Forestale, Vol. 14, No. 4. (31 August 2017), pp. 202-205, https://doi.org/10.3832/efor2537-014

Abstract

Forest fires in Italy are changing. More frequent heatwaves and drought increase the flammability of the vegetation; the abandonment of rural land produces 30.000 ha of newly afforested areas each year; and the wildland-urban interface is expanding with the sprawl of urbanized areas. However, forest fires are rarely understood and managed in their complexity. The public opinion is often misinformed on the causes and consequences of fires in the forest. Moreover, fire management relies almost exclusively on extinction and emergency response, ...

 

Forest disturbances under climate change

  
Nature Climate Change, Vol. 7, No. 6. (31 May 2017), pp. 395-402, https://doi.org/10.1038/nclimate3303

Abstract

Forest disturbances are sensitive to climate. However, our understanding of disturbance dynamics in response to climatic changes remains incomplete, particularly regarding large-scale patterns, interaction effects and dampening feedbacks. Here we provide a global synthesis of climate change effects on important abiotic (fire, drought, wind, snow and ice) and biotic (insects and pathogens) disturbance agents. Warmer and drier conditions particularly facilitate fire, drought and insect disturbances, while warmer and wetter conditions increase disturbances from wind and pathogens. Widespread interactions between agents are ...

 

Building Rothermel fire behaviour fuel models by genetic algorithm optimisation

  
International Journal of Wildland Fire, Vol. 24, No. 3. (2015), 317, https://doi.org/10.1071/wf14097

Abstract

A method to build and calibrate custom fuel models was developed by linking genetic algorithms (GA) to the Rothermel fire spread model. GA randomly generates solutions of fuel model parameters to form an initial population. Solutions are validated against observations of fire rate of spread via a goodness-of-fit metric. The population is selected for its best members, crossed over and mutated within a range of model parameter values, until a satisfactory fitness is reached. We showed that GA improved the performance ...

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