Climatic Change

, Volume 120, Issue 1–2, pp 229–247 | Cite as

Robust projections of Fire Weather Index in the Mediterranean using statistical downscaling

  • J. Bedia
  • S. Herrera
  • D. San Martín
  • N. Koutsias
  • J. M. Gutiérrez


The effect of climate change on wildfires constitutes a serious concern in fire-prone regions with complex fire behavior such as the Mediterranean. The coarse resolution of future climate projections produced by General Circulation Models (GCMs) prevents their direct use in local climate change studies. Statistical downscaling techniques bridge this gap using empirical models that link the synoptic-scale variables from GCMs to the local variables of interest (using e.g. data from meteorological stations). In this paper, we investigate the application of statistical downscaling methods in the context of wildfire research, focusing in the Canadian Fire Weather Index (FWI), one of the most popular fire danger indices. We target on the Iberian Peninsula and Greece and use historical observations of the FWI meteorological drivers (temperature, humidity, wind and precipitation) in several local stations. In particular, we analyze the performance of the analog method, which is a convenient first choice for this problem since it guarantees physical and spatial consistency of the downscaled variables, regardless of their different statistical properties. First we validate the method in perfect model conditions using ERA-Interim reanalysis data. Overall, not all variables are downscaled with the same accuracy, with the poorest results (with spatially averaged daily correlations below 0.5) obtained for wind, followed by precipitation. Consequently, those FWI components mostly relying on those parameters exhibit the poorest results. However, those deficiencies are compensated in the resulting FWI values due to the overall high performance of temperature and relative humidity. Then, we check the suitability of the method to downscale control projections (20C3M scenario) from a single GCM (the ECHAM5 model) and compute the downscaled future fire danger projections for the transient A1B scenario. In order to detect problems due to non-stationarities related to climate change, we compare the results with those obtained with a Regional Climate Model (RCM) driven by the same GCM. Although both statistical and dynamical projections exhibit a similar pattern of risk increment in the first half of the 21st century, they diverge during the second half of the century. As a conclusion, we advocate caution in the use of projections for this last period, regardless of the regionalization technique applied.


Regional Climate Model Statistical Downscaling Analog Method Fire Danger Regional Climate Model Simulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We are grateful to the Spanish Meteorological Agency (AEMET) and to the Hellenic National Meteorological Service (HNMS) for providing the observational data used in this study. We would also like to thank Erik van Meijgaard from the Royal Netherlands Meteorological Institute for making available ENSEMBLES RACMO2 climate model output verifying at 12:00 UTC and to the Max Planck Institute for providing the appropriate data for the ECHAM5 model used in this work. This work was partly funded by European Union’s Seventh Framework Programme (FP7/2007–2013) under grant agreements 243888 (FUME Project) and from Spanish Ministry MICINN under grant EXTREMBLES (CGL2010-21869). We thank two anonymous referees for their useful comments that helped to improve the original manuscript.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • J. Bedia
    • 1
  • S. Herrera
    • 2
    • 3
  • D. San Martín
    • 2
  • N. Koutsias
    • 4
  • J. M. Gutiérrez
    • 1
  1. 1.Instituto de Física de CantabriaCSIC-Universidad de CantabriaSantanderSpain
  2. 2.Predictia Intelligent Data SolutionsSantanderSpain
  3. 3.Dept. Applied Mathematics and Computer SciencesUniversity of CantabriaSantanderSpain
  4. 4.Department of Environmental and Natural Resources ManagementUniversity of IoanninaAgrinioGreece

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