Akli Benali
Instituto Superior de Agronomia
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Featured researches published by Akli Benali.
SpringerPlus | 2016
Renata Machado dos Santos Pinto; Akli Benali; Ana C. L. Sá; Paulo M. Fernandes; Pedro M. M. Soares; Rita M. Cardoso; Ricardo M. Trigo; José M. C. Pereira
BackgroundAn approach to predict fire growth in an operational setting, with the potential to be used as a decision-support tool for fire management, is described and evaluated. The operational use of fire behaviour models has mostly followed a deterministic approach, however, the uncertainty associated with model predictions needs to be quantified and included in wildfire planning and decision-making process during fire suppression activities. We use FARSITE to simulate the growth of a large wildfire. Probabilistic simulations of fire spread are performed, accounting for the uncertainty of some model inputs and parameters. Deterministic simulations were performed for comparison. We also assess the degree to which fire spread modelling and satellite active fire data can be combined, to forecast fire spread during large wildfires events.ResultsUncertainty was propagated through the FARSITE fire spread modelling system by randomly defining 100 different combinations of the independent input variables and parameters, and running the correspondent fire spread simulations in order to produce fire spread probability maps. Simulations were initialized with the reported ignition location and with satellite active fires. The probabilistic fire spread predictions show great potential to be used as a fire management tool in an operational setting, providing valuable information regarding the spatial–temporal distribution of burn probabilities. The advantage of probabilistic over deterministic simulations is clear when both are compared. Re-initializing simulations with satellite active fires did not improve simulations as expected.ConclusionThis information can be useful to anticipate the growth of wildfires through the landscape with an associated probability of occurrence. The additional information regarding when, where and with what probability the fire might be in the next few hours can ultimately help minimize the negative environmental, social and economic impacts of these fires.
Agricultural and Forest Meteorology | 2015
Joaquín Bedia; S. Herrera; José Manuel Gutiérrez; Akli Benali; Swen Brands; Bernardo Mota; José M. Moreno
Remote Sensing | 2016
Akli Benali; Ana Russo; Ana C. L. Sá; Renata Machado dos Santos Pinto; Owen F. Price; Nikos Koutsias; José M. C. Pereira
Global Ecology and Biogeography | 2017
Akli Benali; Bernardo Mota; Nuno Carvalhais; Duarte Oom; Lee Miller; Manuel L. Campagnolo; José M. C. Pereira
Science of The Total Environment | 2016
Akli Benali; Ana R. Ervilha; Ana C. L. Sá; Paulo M. Fernandes; Renata Machado dos Santos Pinto; Ricardo M. Trigo; José M. C. Pereira
Remote Sensing of Environment | 2017
Ana C. L. Sá; Akli Benali; Paulo M. Fernandes; Renata Machado dos Santos Pinto; Ricardo M. Trigo; Michele Salis; Ana Russo; Sonia Jerez; Pedro M. M. Soares; Wilfrid Schroeder; José M. C. Pereira
Science of The Total Environment | 2017
Akli Benali; Ana C. L. Sá; Ana R. Ervilha; Ricardo M. Trigo; Paulo M. Fernandes; José M. C. Pereira
arXiv: Applications | 2018
Jose Ameijeiras-Alonso; Akli Benali; Rosa M. Crujeiras; Alberto Rodríguez-Casal; José M. C. Pereira
Archive | 2014
Akli Benali; Gerardo López-Saldaña; Ana Russo; Renata Machado dos Santos Pinto; Koutsias Nikos; Owen F. Price; José M. C. Pereira
Archive | 2014
Renata Machado dos Santos Pinto; Ana Sá; Paulo M. Fernandes; José M. C. Pereira; Akli Benali