Climatic Change

, Volume 118, Issue 3–4, pp 827–840

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

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

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.

Keywords

Climate change Fire Weather Index Fire regime Regional Climate Models Reanalysis data Iberian Peninsula 

References

  1. Andrews P, Loftsgaarden D, Bradshaw L (2003) Evaluation of fire danger rating indexes using logistic regression and percentile analysis. Int J Wildland Fire 12:213–226CrossRefGoogle Scholar
  2. Bedia J, Herrera S, Gutiérrez J, Zavala G, Urbieta I, Moreno J (2012) Sensitivity of Fire Weather Index to different reanalysis products in the Iberian Peninsula. Nat Hazards Earth Syst Sci 12:699–708. doi:10.5194/nhess-12-699-2012 CrossRefGoogle Scholar
  3. Brands S, Gutiérrez J, Herrera S, Cofiño A (2012) On the use of reanalysis data for downscaling. J Clim 25:2517–2526. doi:10.1175/JCLI-D-11-00251.1 CrossRefGoogle Scholar
  4. Brown T, Hall B, Westerling A (2004) The impact of twenty-first century climate change on wildland fire danger in the Westerns United States: an applications perspective. Clim Change 62:365–388CrossRefGoogle Scholar
  5. 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
  6. Carvalho A, Flannigan MD, Logan K, Miranda AI, Borrego C (2008) Fire activity in Portugal and its relationship to weather and the Canadian Fire Weather Index System. Int J Wildland Fire 17:328–338CrossRefGoogle Scholar
  7. Carvalho A, Flannigan MD, Logan KA, Gowman L, Miranda AI, Borrego C (2010) The impact of spatial resolution on area burned and fire occurrence projections in Portugal under climate change. Clim Change 98:177–197CrossRefGoogle Scholar
  8. 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
  9. Christensen J, Carter T, Rummukainen M, Amanatidis G (2007) Evaluating the performance and utility of Regional Climate Models: the PRUDENCE project. Clim Change 81:1–6. doi:10.1007/s10584-006-9211-6 CrossRefGoogle Scholar
  10. Costa L, Thonicke K, Poulter B, Badeck F (2011) Sensitivity of Portuguese forest fires to climatic, human, and landscape variables: subnational differences between fire drivers in extreme fire years and decadal averages. Reg Environ Change 11:543–551CrossRefGoogle Scholar
  11. de Groot W, Goldammer J, Keenan T, Brady M, Lynham T, Justice C, Csiszar I, O’Loughlin K (2006) Developing a global early warning system for wildland fire. For Ecol Manag 234:S10. doi:10.1016/j.foreco.2006.08.025 CrossRefGoogle Scholar
  12. 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
  13. 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
  14. Flannigan M, Harrington J (1988) A study of the relation of meteorological variables to monthly provincial area burned by wildfire in Canada 1953–80. J Appl Meteorol 27:441–452CrossRefGoogle Scholar
  15. Flannigan M, Logan K, Amiro B, Skinner W, Stocks B (2005) Future area burned in canada. Clim Change 72(1–2):1–16. doi:10.1007/s10584-005-5935-y CrossRefGoogle Scholar
  16. 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
  17. Giannakopoulos C, Le Sager P, Bindi M, Moriondo M, Kostopoulou E, Goodess C (2009) Climatic changes and associated impacts in the Mediterranean resulting from a 2 °C global warming. Glob Planet Change. doi:10.1016/j.gloplacha.2009.06.001 Google Scholar
  18. Good P, Moriondo M, Giannakopoulos C, Bindi M (2008) The meteorological conditions associated with extreme fire risk in Italy and greece: relevance to climate model studies. Int J Wildland Fire 17:155–165CrossRefGoogle Scholar
  19. 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
  20. Krawchuk M, Moritz MA, Parisien MA, van Dorn J, Hayhoe K (2009) Global Pyrogeography: the current and future distribution of wildfire. PLoS ONE 4:e5102. doi:10.1371/journal.pone.0005102 CrossRefGoogle Scholar
  21. Littell J, McKenzie D, Peterson D, Westerling A (2009) Climate and wildfire area burned in Western U.S. ecoprovinces, 1916–2003. Ecol Appl 19:1003–1021CrossRefGoogle Scholar
  22. 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
  23. Moriondo M, Good P, Durao R, Bindi M, Giannakopoulos C, Corte-Real J (2006) Potential impact of climate change on fire risk in the mediterranean area. Clim Res 31:85–95CrossRefGoogle Scholar
  24. Nakićenović N (2000) Greenhouse gas emissions scenarios. Technol Forecast Soc Change 65:149–166. doi:10.1016/S0040-1625(00)00094-9 CrossRefGoogle Scholar
  25. Palheiro P, Fernandes P, Cruz M (2006) A fire behaviour-based fire danger classification for maritime pine stands: comparison of two approaches. For Ecol Manag 234:S54CrossRefGoogle Scholar
  26. Pechony O, Shindell DT (2010) Driving forces of global wildfires over the past millennium and the forthcoming century. P Natl Acad Sci USA 107(45):19,167–19,170. doi:10.1073/pnas.1003669107 CrossRefGoogle Scholar
  27. Räisänen J (2007) How reliable are climate models? Tellus A 59:2–29CrossRefGoogle Scholar
  28. 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
  29. 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
  30. 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
  31. 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
  32. Sterl A (2004) On the (in)homogeneity of reanalysis products. J Clim 17(19):3866–3873CrossRefGoogle Scholar
  33. Stocks B, Fosberg M, Lynham T, Mearns L, Wotton B, Yang Q, Jin JZ, Lawrence K, Hartley G, Mason J, McKenney D (1998) Climate change and forest fire potential in Russian and Canadian boreal forests. Clim Change 38:1–13CrossRefGoogle Scholar
  34. 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
  35. 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
  36. van Wagner CE (1987) Development and structure of the Canadian forest Fire Weather Index. Forestry Tech. Rep. 35, Canadian Forestry Service, Ottawa, CanadaGoogle Scholar
  37. 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
  38. Vázquez A, Moreno J (1993) Sensitivity of fire occurrence to meteorological variables in Mediterranean and Atlantic areas of Spain. Landsc Urban Plan 24:129–142CrossRefGoogle Scholar
  39. 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
  40. 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
  41. Wotton BM (2009) Interpreting and using outputs from the Canadian Forest Fire Danger Rating System in research applications. Environ Ecol Stat 16:107–131. doi:10.1007/s10651-007-0084-2 CrossRefGoogle Scholar
  42. Zahn M, von Storch H (2010) Decreased frequency of North Atlantic polar lows associated with future climate warming. Nature 467:309–312CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • S. Herrera
    • 1
  • J. Bedia
    • 2
  • J. M. Gutiérrez
    • 2
  • J. Fernández
    • 3
  • J. M. Moreno
    • 4
  1. 1.Predictia Intelligent Data Solutions S.L. CDTUC Fase ASantanderSpain
  2. 2.Instituto de Física de Cantabria (IFCA-CSIC)Universidad de CantabriaSantanderSpain
  3. 3.Dpto. de Matemática Aplicada y CC de la computaciónUniversidad de CantabriaSantanderSpain
  4. 4.Dpto. Ciencias AmbientalesUniversidad de Castilla La ManchaToledoSpain

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