On the projection of future fire danger conditions with various instantaneous/mean-daily data sources
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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.
KeywordsClimate change Fire Weather Index Fire regime Regional Climate Models Reanalysis data Iberian Peninsula
The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007–2013) under grant agreement 243888 (FUME Project). J.F. acknowledges financial support from the Spanish R&D&I programme through grant CGL2010-22158-C02 (CORWES project).
The ESCENA project (200800050084265) of the Spanish “Strategic action on energy and climate change” provided the WRF RCM simulation used in this study. We acknowledge three anonymous referees for their useful comments that helped to improve the original manuscript.
- Camia A, Amatulli G, San Miguel-Ayanz J (2008) Past and future trends of forest fire danger in Europe. Tech. Rep. EUR 23427 EN—2008, Institute for Environment and Sustainability, Joint Research Centre, European Comission, Ispra, ItalyGoogle Scholar
- Chandler C, Cheney P, Thomas P, Trabaud L, Williams D (1983) Fire in forestry. Forest fire behavior and effects, vol 1. Wiley, New York, USAGoogle Scholar
- Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette J, Park B, Peubey C, de Rosnay P, Tavolato C, Thépaut JN, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quart J R Meteorol Soc 137:553–597CrossRefGoogle Scholar
- Dowdy A, Mills G, Finkele K, deGroot W (2010) Index sensitivity analysis applied to the Canadian Forest Fire Weather Index and the McArthur Forest Fire Danger Index. Meteorol Appl 17:298–312Google Scholar
- Fugioka FM, Gill A, Viegas DX, Wotton B (2009) Fire danger and fire behavior modeling systems in Australia, Europe, and North America. In: Bytnerowicz A, Arbaugh M, Riebau A, Andersen C (eds) Developments in environmental science. Elsevier B.V., The NetherlandsGoogle Scholar
- Hennessy K, Lucas C, Nicholls N, Bathols J, Suppiah R, Ricketts J (2005) Climate change impacts on fire-weather in South-East Australia. Tech. rep., CSIRO Marine and Atmospheric Research and Bushfire CRC and Australian Bureau of Meteorology, AustraliaGoogle Scholar
- Moreno J, Zavala G, Martin M, Millán A (2010) Forest fire risk in spain under future climate change. In: Settele J, Penev L, Georgiev T, Grabaum R, Grobelnik V, Hammen V, Klotx S, Kotarac M, Kuehn I (eds) Atlas of biodiversity risks, Pensoft, Sofia & Moscow, pp 72–73Google Scholar
- Roeckner E (2007) ENSEMBLES ECHAM5-MPI-OM 20C3M run2, monthly mean values. World Data Center for Climate. CERA-DB “ENSEMBLES_MPEH5_20C3M_2_MM”. Available at http://cera-www.dkrz.de/WDCC/ui/Compact.jsp?acronym=ENSEMBLES_MPEH5_20C3M_2_MM. Accessed Jan 2012
- Rothermel R (1972) A mathematical model for predicting fire spread in wildland fuels. Research Paper INT-115, USDA Forest Service, Intermountain Forest and Range Experiment Station, p 40Google Scholar
- Seneviratne S, Nicholls N, Easterling D, Goodess C, Kanae S, Kossin J, Luo Y, Marengo J, McInnes K, Rahimi M, Reichstein M, Sorteberg A, Vera C, Zhang X (2012) Changes in climate extremes and their impacts on the natural physical environment. In: Field C, Barros V, Stocker T, Qin D, Dokken D, Ebi K, Mastrandrea M, Mach K, Plattner GK, Allen S, Tignor M, Midgley P (eds) Managing the risks of extreme events and disasters to advance climate change adaptation. Cambridge University Press, Cambridge, UK, and New York, USA, pp 109–230Google Scholar
- Skamarock W, Klemp J, Dudhia J, Gill D, Barker D, Duda M, Huang XY, Wang W, Powers J (2008) A description of the advanced research WRF Version 3. NCAR Technical Note 475, NCAR, Boulder, CO, USAGoogle Scholar
- Strauss D, Bednar L, Mees R (1989) Do one percent of the forest fires cause ninety-nine percent of the damage? For Sci 35:319–328Google Scholar
- van der Linden P, Mitchell J (2009) ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES project. Tech. rep., Met Office Hadley Centre, Exeter, UKGoogle Scholar
- van Wagner CE (1987) Development and structure of the Canadian forest Fire Weather Index. Forestry Tech. Rep. 35, Canadian Forestry Service, Ottawa, CanadaGoogle Scholar
- van Wagner CE, Pickett TL (1985) Equations and FORTRAN program for the Canadian forest fire weather index system. Forestry Tech. Rep. 33, Canadian Forestry Service, Ottawa, CanadaGoogle Scholar
- Vázquez A, Moreno J (1995) Patterns of fire occurrence across a climatic gradient and its relationship to meteorological variables in Spain. In: Moreno J, Oechel W (eds) Global change and Mediterranean–type ecosystems, ecological studies, vol 117. Springer, New York, USAGoogle Scholar
- Wotton B, Alexander M, Taylor S (2009) Updates and revisions to the 1992 Canadian forest fire behavior prediction system. Information Report GLC-X-10, Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, Sault Ste. Marie, Ontario, CanadaGoogle Scholar