title: Probabilistic fire spread forecast as a management tool in an operational setting.
abstract: BACKGROUND
  An 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.
  
  
  RESULTS
  Uncertainty 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.
  
  
  CONCLUSIONS
  This 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.
authors: Centro de Estudos Florestais, Tapada da Ajuda, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisbon, Portugal.
authors: Renata M S
authors: RM
authors: Pinto
authors: Centro de Estudos Florestais, Tapada da Ajuda, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisbon, Portugal.
authors: Akli
authors: A
authors: Benali
authors: Centro de Estudos Florestais, Tapada da Ajuda, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisbon, Portugal.
authors: Ana C L
authors: AC
authors: Sá
authors: Centro de Investigação e de Tecnologias Agro-Ambientais e Biológicas, Universidade de Trás-os-Montes e Alto Douro (UTAD), Apartado 1013, 5001-801 Vila Real, Portugal.
authors: Paulo M
authors: PM
authors: Fernandes
authors: Instituto Dom Luiz, Faculdade de Ciências da Universidade de Lisboa, 1749-016 Lisbon, Portugal.
authors: Pedro M M
authors: PM
authors: Soares
authors: Instituto Dom Luiz, Faculdade de Ciências da Universidade de Lisboa, 1749-016 Lisbon, Portugal.
authors: Rita M
authors: RM
authors: Cardoso
authors: Instituto Dom Luiz, Faculdade de Ciências da Universidade de Lisboa, 1749-016 Lisbon, Portugal.
authors: Ricardo M
authors: RM
authors: Trigo
authors: Centro de Estudos Florestais, Tapada da Ajuda, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisbon, Portugal.
authors: José M C
authors: JM
authors: Pereira
keywords: FARSITE; Fire growth modelling; MODIS; Satellite active fires; Uncertainty assessment and propagation; VIIRS