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On the projection of future fire danger conditions with various instantaneous/mean-daily data sources

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  • S. Herrera
  • J. Bedia
  • J. Gutiérrez
  • J. Fernández
  • J. Moreno

Abstract

Fire danger indices are descriptors of fire potential in a large area, and combine a few variables that affect the initiation, spread and control of forest fires. The Canadian Fire Weather Index (FWI) is one of the most widely used fire danger indices in the world, and it is built upon instantaneous values of temperature, relative humidity and wind velocity at noon, together with 24 hourly accumulated precipitation. However, the scarcity of appropriate data has motivated the use of daily mean values as surrogates of the instantaneous ones in several studies that aimed to assess the impact of global warming on fire. In this paper we test the sensitivity of FWI values to both instantaneous and daily mean values, analyzing their effect on mean seasonal fire danger (seasonal severity rating, SSR) and extreme fire danger conditions (90th percentile, FWI90, and FWI>30, FOT30), with a special focus on its influence in climate change impact studies. To this aim, we analyzed reanalysis and regional climate model (RCM) simulations, and compared the resulting instantaneous and daily mean versions both in the present climate and in a future scenario. In particular, we were interested in determining the effect of these datasets on the projected changes obtained for the mean and extreme seasonal fire danger conditions in future climate scenarios, as given by a RCM. Overall, our results warn against the use of daily mean data for the computation of present and future fire danger conditions. Daily mean data lead to systematic negative biases of fire danger calculations. Although the mean seasonal fire danger indices might be corrected to compensate for this bias, fire danger extremes (FWI90 and specially FOT30) cannot be reliably transformed to accommodate the spatial pattern and magnitude of their respective instantaneous versions, leading to inconsistent results when projected into the future. As a result, we advocate caution when using daily mean data and strongly recommend the application of the standard definition for its calculation as closely as possible. Threshold-dependent indices derived from FWI are not reliably represented by the daily mean version and thus can neither be applied for the estimation of future fire danger season length and severity, nor for the estimation of future extreme events. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • S. Herrera & J. Bedia & J. Gutiérrez & J. Fernández & J. Moreno, 2013. "On the projection of future fire danger conditions with various instantaneous/mean-daily data sources," Climatic Change, Springer, vol. 118(3), pages 827-840, June.
    • Handle: RePEc:spr:climat:v:118:y:2013:i:3:p:827-840
    DOI: 10.1007/s10584-012-0667-2
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    References listed on IDEAS

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    1. Meg A Krawchuk & Max A Moritz & Marc-André Parisien & Jeff Van Dorn & Katharine Hayhoe, 2009. "Global Pyrogeography: the Current and Future Distribution of Wildfire," PLOS ONE, Public Library of Science, vol. 4(4), pages 1-12, April.
    2. Matthias Zahn & Hans von Storch, 2010. "Decreased frequency of North Atlantic polar lows associated with future climate warming," Nature, Nature, vol. 467(7313), pages 309-312, September.
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    1. Thibaut Fréjaville & Thomas Curt, 2015. "Spatiotemporal patterns of changes in fire regime and climate: defining the pyroclimates of south-eastern France (Mediterranean Basin)," Climatic Change, Springer, vol. 129(1), pages 239-251, March.
    2. Bruno A. Aparício & João A. Santos & Teresa R. Freitas & Ana C. L. Sá & José M. C. Pereira & Paulo M. Fernandes, 2022. "Unravelling the effect of climate change on fire danger and fire behaviour in the Transboundary Biosphere Reserve of Meseta Ibérica (Portugal-Spain)," Climatic Change, Springer, vol. 173(1), pages 1-20, July.
    3. J. Bedia & S. Herrera & D. Martín & N. Koutsias & J. Gutiérrez, 2013. "Robust projections of Fire Weather Index in the Mediterranean using statistical downscaling," Climatic Change, Springer, vol. 120(1), pages 229-247, September.
    4. A. Casanueva & J. Bedia & S. Herrera & J. Fernández & J. M. Gutiérrez, 2018. "Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool," Climatic Change, Springer, vol. 147(3), pages 411-425, April.

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