Articles | Volume 13, issue 2
https://doi.org/10.5194/nhess-13-311-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-13-311-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression
J. Martínez-Fernández
INIA-CIFOR, Forest Research Centre, Department of Forest Ecology and Genetics, Carretera de La Coruña, Km. 7,5 28040, Madrid, Spain
E. Chuvieco
Department of Geography and Geology, University of Alcalá, Calle Colegios 2, 28801 Alcalá de Henares, Spain
N. Koutsias
Department of Environmental and Natural Resources Management, University of Western Greece, G. Seferi 2, 30100 Agrinio, Greece
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Elena Aragoneses, Mariano García, Michele Salis, Luís M. Ribeiro, and Emilio Chuvieco
Earth Syst. Sci. Data, 15, 1287–1315, https://doi.org/10.5194/essd-15-1287-2023, https://doi.org/10.5194/essd-15-1287-2023, 2023
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We present a new hierarchical fuel classification system with a total of 85 fuels that is useful for preventing fire risk at different spatial scales. Based on this, we developed a European fuel map (1 km resolution) using land cover datasets, biogeographic datasets, and bioclimatic modelling. We validated the map by comparing it to high-resolution data, obtaining high overall accuracy. Finally, we developed a crosswalk for standard fuel models as a first assignment of fuel parameters.
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Magí Franquesa, Melanie K. Vanderhoof, Dimitris Stavrakoudis, Ioannis Z. Gitas, Ekhi Roteta, Marc Padilla, and Emilio Chuvieco
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Matthias Forkel, Niels Andela, Sandy P. Harrison, Gitta Lasslop, Margreet van Marle, Emilio Chuvieco, Wouter Dorigo, Matthew Forrest, Stijn Hantson, Angelika Heil, Fang Li, Joe Melton, Stephen Sitch, Chao Yue, and Almut Arneth
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Weather, humans, and vegetation control the occurrence of fires. In this study we find that global fire–vegetation models underestimate the strong increase of burned area with higher previous-season plant productivity in comparison to satellite-derived relationships.
Emilio Chuvieco, Joshua Lizundia-Loiola, Maria Lucrecia Pettinari, Ruben Ramo, Marc Padilla, Kevin Tansey, Florent Mouillot, Pierre Laurent, Thomas Storm, Angelika Heil, and Stephen Plummer
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We present a new global burned area product, generated from MODIS information and thermal anomalies data, providing the highest spatial resolution (approx. 250 m) global product to date. The dataset comprises the 2001–2016 time series of the MODIS archive, and includes two types of BA products: monthly full-resolution continental tiles and biweekly global grid files at a degraded resolution of 0.25 °, supplemented with several auxiliary variables useful for different applications.
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Wildfires affect infrastructures, vegetation, and the atmosphere. However, it is unclear how fires should be accurately represented in global vegetation models. We introduce here a new flexible data-driven fire modelling approach that allows us to explore sensitivities of burned areas to satellite and climate datasets. Our results suggest combining observations with data-driven and process-oriented fire models to better understand the role of fires in the Earth system.
M. Lucrecia Pettinari and Emilio Chuvieco
Biogeosciences, 13, 2061–2076, https://doi.org/10.5194/bg-13-2061-2016, https://doi.org/10.5194/bg-13-2061-2016, 2016
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This paper presents the first global fuel data set, containing all the parameters required to be input in the Fuel Characteristic Classification System (FCCS). It was developed from different spatial variables, both based on satellite Earth Observation products and fuel databases.
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A. Venäläinen, N. Korhonen, O. Hyvärinen, N. Koutsias, F. Xystrakis, I. R. Urbieta, and J. M. Moreno
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