Structural fire risk: The case of Portugal.

This study is focused in mapping the structural fire risk in the vegetated area of Portugal using a deterministic approach based on the concept of fire risk currently accepted by the scientific community which consists in the combination of the fire hazard and the potential economic damage. The fire susceptibility map of Verde and Zêzere (2010) was adopted and updated by the use of a higher resolution digital terrain model, longer burnt area perimeter dataset (1975-2013) and the entire set of Corine land cover inventories. The susceptibility was mapped with five classes to be in accordance with the Portuguese law and the results confirms the good performance of this model not only in the favourability scores but also in the predictive values. Three different scenarios of (maximum, mean, and minimum annual) burnt area were consider to estimate the fire hazard which allow to estimate the likelihood of a pixel to burn (ranging between 0% and 20%) depending on the class to which it belongs and on the future burnt area scenario. The potential economic damage was estimated with the vulnerability scores and monetary values of species defined in the literature and by law. The obtained fire risk map identifies the areas more prone to be affected by fires in the future and provides an estimate of the economic damage of the fire which will be a valuable tool for forest and fire managers and to minimize the economic and environmental consequences of vegetation fires in Portugal.

[1]  Paulo M. Fernandes,et al.  Fuel modelling and fire hazard assessment based on data from the Portuguese National Forest Inventory , 2006 .

[2]  L. S. Sanches Fernandes,et al.  A framework model for investigating the export of phosphorus to surface waters in forested watersheds: Implications to management. , 2015, The Science of the total environment.

[3]  A. Novara,et al.  Stakeholders' Perceptions about Fire Impacts on Lithuanian Protected Areas , 2016 .

[4]  C. Hardy Wildland fire hazard and risk: Problems, definitions, and context , 2005 .

[5]  L. S. Sanches Fernandes,et al.  Water resources planning for a river basin with recurrent wildfires. , 2015, The Science of the total environment.

[6]  P. Pereira,et al.  Modelling the Impacts of Wildfire on Ash Thickness in a Short‐Term Period , 2015 .

[7]  S. L. J. Oliveira,et al.  Modelling Fire Frequency in a Cerrado Savanna Protected Area , 2014, PloS one.

[8]  Charles W. McHugh,et al.  Numerical Terradynamic Simulation Group 10-2011 A simulation of probabilistic wildfire risk components for the continental United States , 2017 .

[9]  M. Lucas‐Borja,et al.  Soil microbiological properties and enzymatic activities of long-term post-fire recovery in dry and semiarid Aleppo pine (Pinus halepensis M.) forest stands , 2014 .

[10]  C. Chung,et al.  Systematic procedures of landslide hazard mapping for risk assessment using spatial prediction models , 2012 .

[11]  B. Law,et al.  Post-Wildfire Logging Hinders Regeneration and Increases Fire Risk , 2006, Science.

[12]  S. Keesstra,et al.  Short‐Term Vegetation Recovery after a Grassland Fire in Lithuania: The Effects of Fire Severity, Slope Position and Aspect , 2016 .

[13]  A. Shamseldin,et al.  River basin modelling for flood risk mitigation , 2005 .

[14]  Mário G. Pereira,et al.  The history and characteristics of the 1980-2005 Portuguese rural fire database , 2011 .

[15]  J. C. Verde Wildfire susceptibility modelling in mainland Portugal , 2015 .

[16]  D. Honoré,et al.  Slope effects on the fluid dynamics of a fire spreading across a fuel bed: PIV measurements and OH* chemiluminescence imaging , 2014 .

[17]  Geir-Harald Strand,et al.  CORINE Land Cover 2000. The Norwegian CLC2000 project , 2010 .

[18]  Trisalyn A. Nelson,et al.  Factors influencing national scale wildfire susceptibility in Canada , 2012 .

[19]  Mário G. Pereira,et al.  Modelling the impacts of wildfires on runoff at the river basin ecological scale in a changing Mediterranean environment , 2016, Environmental Earth Sciences.

[20]  Marj Tonini,et al.  Space-time clustering analysis of wildfires: The influence of dataset characteristics, fire prevention policy decisions, weather and climate. , 2016, The Science of the total environment.

[21]  Ricardo Costa,et al.  Space-time clustering analysis performance of an aggregated dataset: The case of wildfires in Portugal , 2015, Environ. Model. Softw..

[22]  Lia Duarte,et al.  Forest fire risk maps: a GIS open source application – a case study in Norwest of Portugal , 2013, Int. J. Geogr. Inf. Sci..

[23]  Raphaele Blanchi,et al.  Forest fire risk assessment and cartography - a methodological approach. , 2002 .

[24]  F. Moreira,et al.  Modeling and mapping wildfire ignition risk in Portugal , 2009 .

[25]  P. Santoni,et al.  Combustion of forest litters under slope conditions: burning rate, heat release rate, convective and radiant fractions for different loads , 2014 .

[26]  Mark A. Finney,et al.  The challenge of quantitative risk analysis for wildland fire , 2005 .

[27]  Artemi Cerdà,et al.  Influence of vegetation recovery on soil hydrology and erodibility following fire: an 11-year investigation , 2005 .

[28]  Emilio Chuvieco,et al.  Earth observation of wildland fires in Mediterranean ecosystems , 2009 .

[29]  Mário G. Pereira,et al.  Land cover fire proneness in Europe , 2014 .

[30]  A. Cerda,et al.  Wildland fire ash: Production, composition and eco-hydro-geomorphic effects , 2014 .

[31]  T. Lasanta,et al.  Long-term erosional responses after fire in the Central Spanish Pyrenees: 2. Solute release , 2005 .

[32]  A. C. Ricardo MODELAÇÃO DA PROBABILIDADE DE OCORRÊNCIA DE INCÊNDIO EM POVOAMENTOS FLORESTAIS DE PORTUGAL CONTINENTAL , 2010 .

[33]  S. Mukherjee,et al.  Forest fire risk zone mapping from satellite imagery and GIS , 2002 .

[34]  J. Zêzere,et al.  Assessment and validation of wildfire susceptibility and hazard in Portugal , 2009 .

[35]  S. Keesstra,et al.  Physically‐Based Modelling of the Post‐Fire Runoff Response of a Forest Catchment in Central Portugal: Using Field versus Remote Sensing Based Estimates of Vegetation Recovery , 2016 .

[36]  Fantina Tedim,et al.  Vulnerabilidade aos incêndios florestais: reflexões em torno de aspetos conceptuais e metodológicos , 2013 .

[37]  E. Chuvieco,et al.  Application of remote sensing and geographic information systems to forest fire hazard mapping. , 1989 .

[38]  Marco Bindi,et al.  Potential impact of climate change on fire risk in the Mediterranean area , 2006 .

[39]  L. S. Sanches Fernandes,et al.  Controls and forecasts of nitrate yields in forested watersheds: A view over mainland Portugal. , 2015, The Science of the total environment.

[40]  Carlos C. DaCamara,et al.  Effects of regional climate change on rural fires in Portugal , 2013 .

[41]  José Manuel Mendes,et al.  Avaliação do Risco de Incêndio Florestal no Concelho de Arganil , 2011 .

[42]  Brigitte Leblon,et al.  Forest wildfire hazard monitoring using remote sensing: A review , 2001 .

[43]  J. Pereira,et al.  Generating Annual Fire Risk Maps Using Bayesian Hierarchical Models , 2014 .

[44]  Marco Marengo,et al.  Oblique impacts of water drops onto hydrophobic and superhydrophobic surfaces: outcomes, timing, and rebound maps , 2014 .

[45]  K. Denef,et al.  Fire affects root decomposition, soil food web structure, and carbon flow in tallgrass prairie , 2016 .

[46]  S. Keesstra,et al.  The influence of fire history, plant species and post-fire management on soil water repellency in a Mediterranean catchment : The Mount Carmel range, Israel , 2016 .

[47]  B. R. Ramesh,et al.  Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India , 2012 .

[48]  R. Figueira,et al.  Temporal and spatial accumulation of heavy metals in the sediments at Paiva Castro Reservoir (São Paulo, Brazil) , 2015, Environmental Earth Sciences.

[49]  Fernando Bação,et al.  Characterizing and modelling the spatial patterns of wildfire ignitions in Portugal: fire initiation and resulting burned area. , 2008 .

[50]  C. Vinchon,et al.  Comprehensive Vulnerability Assessment of Forest Fires and Coastal Erosion: Evidences from Case-Study Analysis in Portugal , 2014 .

[51]  Douglas G. Altman,et al.  Adequate sample size for developing prediction models is not simply related to events per variable , 2016, Journal of clinical epidemiology.

[52]  E. Chuvieco,et al.  Development of a framework for fire risk assessment using remote sensing and geographic information system technologies , 2010 .