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Climatic Change

, Volume 127, Issue 3–4, pp 547–560 | Cite as

Statistical downscaling of climate impact indices: testing the direct approach

  • A. CasanuevaEmail author
  • M. D. Frías
  • S. Herrera
  • D. San-Martín
  • K. Zaninovic
  • J. M. Gutiérrez
Article

Abstract

Climate Impact Indices (CIIs) are being increasingly used in different socioeconomic sectors to transfer information about climate change impacts to stakeholders. Typically, CIIs comprise into a single index several weather variables —such as temperature, wind speed, precipitation and humidity— which are relevant for a particular problem of interest. Moreover, most of the CIIs require daily (or monthly) physical coherence among these variables for their proper calculation. This constraints the number of statistical downscaling techniques suitable for a component-wise approach to this problem. We test the suitability of the alternative “direct” downscaling approach in which the downscaling method is applied directly to the CII, thus circumventing the multi-variable problem and allowing the use of a wider range of downscaling methods. For illustrative purposes, we consider two popular CIIs —the Fire Weather Index (FWI) and the Physiological Equivalent Temperature (PET), used in the wildfire and tourism sectors, respectively— and compare the performance of the two approaches using the analog method, a simple and popular method providing inter-variable dependence. The results obtained with ‘perfect’ reanalysis predictors are comparable for both approaches, although smaller accuracy is obtained in general with the direct approach. Moreover, similar climate change ‘deltas’ are obtained with both approaches when applied to an illustrative future global projection using the ECHAM5 model. Overall, there is a trade-off between performance and simplicity which needs to be balanced for each particular application.

Keywords

Statistical Downscaling Physiological Equivalent Temperature Analog Method Fire Danger Local Standard Time 
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.

Notes

Acknowledgments

Authors are grateful to the data providers and also to Dr. Matzarakis for providing the RayMan software and to J. Bedia por their helpful comments. We also acknowledge the financial support from the European Commision’s Seventh Framework Programme under CLIM-RUN Project (contract FP7-ENV-2010-265192). A.C. thanks to the Spanish Ministry of Science and Innovation for the funding provided within the FPI programme (CORWES project, CGL2010-22158-C02: BES-2011-047612) and J.M.G. for the grant EXTREMBLES (CGL2010-21869). We thank three anonymous referees for their useful comments that helped to improve the original manuscript.

References

  1. Abatzoglou J, Brown T (2012) A comparison of statistical downscaling methods suited for wildfire applications. Int J Climatol 32:772–780. doi: 10.1002/joc.2312 CrossRefGoogle Scholar
  2. Anandhi A, Srinivas V, Kumar D, Nanjundiah R, Gowda P (2014) Climate change scenarios of surface solar radiation in data sparse regions: a case study in Malaprabha river basin, India. Clim Res 59(3):259–270CrossRefGoogle Scholar
  3. 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
  4. Bedia J, Herrera S, San-Martín D, Koutsias N, Gutiérrez J M (2013) Robust projections of fire weather index in the Mediterranean using statistical downscaling. Clim Chang 120(1–2):229–247. doi: 10.1007/s10584-013-0787-3 CrossRefGoogle Scholar
  5. Bedia J, Herrera S, Camia A, Moreno J M, Gutiérrez J M (2014) Forest fire danger projections in the mediterranean using ENSEMBLES regional climate change scenarios. Clim Chang 122(1–2):185–199. doi: 10.1007/s10584-013-1005-z CrossRefGoogle Scholar
  6. Bourqui M, Mathevet T, Gailhard J, Hendrickx F (2011) Hydrological validation of statistical downscaling methods applied to climate model projections. In: IAHS-AISH publication, international association of hydrological sciences, pp 32–38Google Scholar
  7. Brands S, Herrera S, San-Martín D, Gutiérrez J (2011) Validation of the ENSEMBLES global climate models over southwestern Europe using probability density functions: a downscaler’s perspective. Clim Res 48:145–161. doi: 10.3354/cr00995 CrossRefGoogle Scholar
  8. 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
  9. Casanueva A, Herrera S, Fernández J, Frías M, Gutiérrez J (2013) Evaluation and projection of daily temperature percentiles from statistical and dynamical downscaling methods. Nat Hazards Earth Syst Sci 13:2089–2099. doi: 10.5194/nhess-13-2089-2013
  10. Charles S, Bari M, Kitsios A, Bates B (2007) Effect of gcm bias on downscaled precipitation and runoff projections for the serpentine catchment, western Australia. Int J Climatol 27(12):1673–1690CrossRefGoogle Scholar
  11. Cheung C, Hart M (2014) Climate change and thermal comfort in Hong Kong. Int J Biometeorol 58(2):137–148CrossRefGoogle Scholar
  12. Chu J, Xia J, Xu CY, Singh V (2010) Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe river, China. Theor Appl Climatol 99(1–2):149–161CrossRefGoogle Scholar
  13. Curry C, van der Kamp D, Monahan A (2012) Statistical downscaling of historical monthly mean winds over a coastal region of complex terrain. I. Predicting wind speed. Clim Dyn 38:1281–1299CrossRefGoogle Scholar
  14. 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
  15. Dehn M (1999) Application of an analog downscaling technique to the assessment of future landslide activity - a case study in the Italian alps. Clim Res 13(2):103–113CrossRefGoogle Scholar
  16. Dehn M, Brger G, Buma J, Gasparetto P (2000) Impact of climate change on slope stability using expanded downscaling. Eng Geol 55(3):193–204CrossRefGoogle Scholar
  17. Dimitrakopoulos A, Bemmerzouk A, Mitsopoulos I (2011) Evaluation of the Canadian fire weather index system in an eastern Mediterranean environment. Meteorol Appl 18:83–93CrossRefGoogle Scholar
  18. Fealy R, Sweeney J (2008) Statistical downscaling of temperature, radiation and potential evapotranspiration to produce a multiple gcm ensemble mean for a selection of sites in Ireland. Irish Geogr 41(1):1–27CrossRefGoogle Scholar
  19. Freitas CRD, Scott D, McBoyle G (2008) A second generation climate index for tourism (CIT): specification and verification. Int J of Biometeorol 52:399–407CrossRefGoogle Scholar
  20. Frías M, Herrera S, Cofiño A, Gutiérrez J (2010) Assessing the skill of precipitation and temperature seasonal forecasts in Spain. Windows of opportunity related to ENSO events. J Clim 23:209–220CrossRefGoogle Scholar
  21. Fu G, Charles S, Chiew F, Teng J, Zheng H, Frost A, Liu W, Kirshner S (2013) Modelling runoff with statistically downscaled daily site, gridded and catchment rainfall series. J Hydrol 492:254–265CrossRefGoogle Scholar
  22. García-Bustamante E, Conzález-Rouco J, Navarro J, Xoplaki E, Luterbacher J, Jiménez PA, Montávez J, Hidalgo A, Lucio-Eceiza E (2013) Relationship between wind power production and North Atlantic atmospheric circulation over the northeastern Iberian Peninsula. Climate Dyn 40:935–949CrossRefGoogle Scholar
  23. Giorgi F (1990) Simulation of regional climate using limited area model nested in a general circulation model. J Clim 3:941–963CrossRefGoogle Scholar
  24. Guo B, Zhang J, Gong H, Cheng X (2014) Future climate change impacts on the ecohydrology of Guishui river basin, China. Ecohydrol Hydrobiol 14(1):55–67CrossRefGoogle Scholar
  25. Gutiérrez J, San-Martín D, Brands S, Manzanas R, Herrera S (2013) Reassessing statistical downscaling techniques for their robust application under climate change conditions. J Clim 26:171–188CrossRefGoogle Scholar
  26. Hamlet AF, Elsner MM, Mauger GS, Lee SY, Tohver I, Norheim RA (2013) An overview of the columbia basin climate change scenarios project: approach, methods, and summary of key results. Atmos-Ocean 51(4):392–415. doi: 10.1080/07055900.2013.819555 CrossRefGoogle Scholar
  27. Hempel S, Frieler K, Warszawski L, Schewe J, Piontek F (2013) A trend-preserving bias correction the ISI-MIP approach. Earth Syst Dyn Discuss 4(1):49–92. doi: 10.5194/esdd-4-49-2013 CrossRefGoogle Scholar
  28. Hewitson BC, Crane RG (1996) Climate downscaling: techniques and application. Clim Res 7:85–95CrossRefGoogle Scholar
  29. Hoffmann P, Krueger O, Schlnzen K (2012) A statistical model for the urban heat island and its application to a climate change scenario. Int J Climatol 32(8):1238–1248CrossRefGoogle Scholar
  30. Höppe P (1999) The physiological equivalent temperature - a universal index for the biometeorological assessment of the thermal environment. Int J of Biometeorol 43:71–75CrossRefGoogle Scholar
  31. Huang S, Krysanova V, sterle H, Hattermann F (2010) Simulation of spatiotemporal dynamics of water fluxes in Germany under climate change. Hydrol Process 24(23):3289–3306CrossRefGoogle Scholar
  32. Hur J, Ahn JB (2014) The change of first-flowering date over south Korea projected from downscaled Ipcc Ar5 simulation: peach and pear. Int J Climatol. doi: 10.1002/joc.4098
  33. Hur J, Ahn JB, Shim KM (2014) The change of cherry first-flowering date over south korea projected from downscaled ipcc ar5 simulation. Int J Climatol 34(7):2308–2319CrossRefGoogle Scholar
  34. Kilsby CG, Wilby RL (2007) A daily weather generator for use in climate change studies. Environ Model Softw 22(12):1705–1719CrossRefGoogle Scholar
  35. Krause P, Hanisch S (2009) Simulation and analysis of the impact of projected climate change on the spatially distributed waterbalance in Thuringia, Germany. Adv Geosci 21:33–48CrossRefGoogle Scholar
  36. Li Z, Zheng F L, Liu W Z (2012) Spatiotemporal characteristics of reference evapotranspiration during 1961-2009 and its projected changes during 2011-2099 on the Loess plateau of China. Agric Forest Meteorol 154–155:X147–155CrossRefGoogle Scholar
  37. Maak K, Von Storch H (1997) Statistical downscaling of monthly mean air temperature to the beginning of flowering of galanthus nivalis l. in northern Germany. Int J Biometeorol 41(1):5–12CrossRefGoogle Scholar
  38. Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ,Widmann M, Brienen S, Rust HW, Sauter T, Themel M, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M, Thiele-Eich I (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48:RG3003. doi: 10.1029/2009RG000314.CrossRefGoogle Scholar
  39. Matzarakis A, Mayer H, Iziomon M (1999) Applications of a universal thermal index: physiological equivalent temperature. Int J of Biometeorol 43:76–84CrossRefGoogle Scholar
  40. Matzarakis A, Rutz F, Mayer H (2007) Modelling radiation fluxes in simple and complex environments-application of the RayMan model. Int J of Biometeorol 51:323–334CrossRefGoogle Scholar
  41. Matzarakis A, Rutz F, Mayer H (2010) Modelling radiation fluxes in simple and complex environments: basics of the RayMan model. Int J of Biometeorol 54:131–139CrossRefGoogle Scholar
  42. Mieczkowski Z (1985) The tourism climatic index: a method of evaluating world climates for tourism. Can Geogr 29:220–233CrossRefGoogle Scholar
  43. Morgan R, Gatell E, Junyent R, Micallef A, Özhan E, Williams A (2000) An improved user-based beach climate index. J Coast Conserv 6:41–51CrossRefGoogle Scholar
  44. 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
  45. Ouyang FLH, Zhu Y, Zhang J, Yu Z, Chen X, Li M (2014) Uncertainty analysis of downscaling methods in assessing the influence of climate change on hydrology. Stoch Enviro Res Risk A 28(4):991–1010CrossRefGoogle Scholar
  46. Pons M, San-Martín D, Herrera S, Gutiérrez JM (2010) Snow trends in northern Spain. Analysis and simulation with statistical downscaling methods. Int J Climatol 30:1795–1806Google Scholar
  47. Räisänen J (2007) How reliable are climate models? Tellus A 59(1):2–29. doi: 10.1111/j.1600-0870.2006.00211.x CrossRefGoogle Scholar
  48. Rehana S, Mujumdar P (2013) Regional impacts of climate change on irrigation water demands. Hydrol Process 27(20):2918–2933Google Scholar
  49. Samadi S, Carbone G, Mahdavi M, Sharifi F, Bihamta M (2013) Statistical downscaling of river runoff in a semi arid catchment. Water Resour Manag 27(1):117–136CrossRefGoogle Scholar
  50. Stocks B, Lawson B, Alexander M, Wagner CV, McAlpine R, Lynham T, Dube D (1989) The canadian forest fire danger rating system: an overview. For Chron 65:450–457CrossRefGoogle Scholar
  51. Sultana Z, Coulibaly P (2011) Distributed modelling of future changes in hydrological processes of spencer creek watershed. Hydrol Process 25(8):1254–1270CrossRefGoogle Scholar
  52. Tian D, Martinez C, Graham W (2014) Seasonal prediction of regional reference evapotranspiration based on climate forecast system version 2. J Hydrometeorol 15(3):1166–1188CrossRefGoogle Scholar
  53. Timbal B, McAvaney B (2001) An analogue-based method to downscale surface air temperature: application for Australia trends. Clim Dyn 17:947–963CrossRefGoogle Scholar
  54. Tisseuil C, Vrac M, Lek S, Wade A (2010) Statistical downscaling of river flows. J Hydrol 385:279–291CrossRefGoogle Scholar
  55. Tukimat NNA, Harun S, Shahid S (2012) Comparison of different methods in estimating potential evapotranspiration at muda irrigation scheme of Malaysia. J Agric Rural Dev Trop Subtrop (JARTS) 113(1):77–85Google Scholar
  56. Viegas D, Bovio G, Ferreira A, Nosenzo A, Sol B (1999) Comparative study of various methods of fire danger evaluation in southern Europe. Int J Wildland Fire 9:235–246CrossRefGoogle Scholar
  57. van Wagner C, Pickett T (1985) Equations and FORTRAN program for the canadian forest fire weather index system. Forestry Tech. Rep. 33. Canadian Forestry Service. Ottawa, CanadaGoogle Scholar
  58. van Wagner C E (1987) Development and structure of the Canadian forest fire weather index. Forestry Tech. Rep. 35. Canadian Forestry Service. Ottawa, CanadaGoogle Scholar
  59. Wilby R (2008) Constructing climate change scenarios of urban heat island intensity and air quality. Environ Plan B: Plan Des 35(5):902–919CrossRefGoogle Scholar
  60. Wilcke RAI, Mendlik T, Gobiet A (2013) Multi-variable error correction of regional climate models. Clim Chang 120(4):871–887. doi: 10.1007/s10584-013-0845-x CrossRefGoogle Scholar
  61. Willis C, van Wilgen B, Tolhurst K, Everson C, DAbreton P, Pero L, Fleming G (2001) The development of a national fire danger rating system for South Africa. Tech. rep., Department of Water Affairs and Forestry, PretoriaGoogle Scholar
  62. 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
  63. Zorita E, von Storch H (1999) The analog method as a simple statistical downscaling technique: comparison with more complicated methods. J Clim 12:2474–2489CrossRefGoogle Scholar
  64. Zuo DP, Xu ZX, Li JY, Liu ZF (2011) Spatiotemporal characteristics of potential evapotranspiration in the weihe river basin under future climate change. Shuikexue Jinzhan/Adv Water Sci 22(4):455–461Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • A. Casanueva
    • 1
    Email author
  • M. D. Frías
    • 1
  • S. Herrera
    • 1
    • 2
  • D. San-Martín
    • 2
  • K. Zaninovic
    • 3
  • J. M. Gutiérrez
    • 4
  1. 1.Grupo de Meteorología, Dpto. Matemática Aplicada y Ciencias de la ComputaciónUniv. de CantabriaSantanderSpain
  2. 2.Predictia Intelligent Data Solutions S.L.SantanderSpain
  3. 3.Meteorological and Hydrological Service of CroatiaZagrebCroatia
  4. 4.Grupo de Meteorología, Instituto de Física de CantabriaCSIC-Univ. de CantabriaSantanderSpain

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