Testing for increasing weather risk

Original Paper

Abstract

It is an undisputed fact that weather risk increases over time due to climate change. However, qualification of this statement with regard to the type of weather risk and geographical location is needed. In this paper we compare alternative tests for trend detection and discuss their sensitivity. We use local t tests, change point tests and Mann–Kendall tests to analyze the trends of weather risk indices that are relevant from an agricultural viewpoint. Local test procedures offer more information about the timing and the kind of change in weather risk than global tests do. We also use quantile regression to analyze changes in the tails of weather index distributions. These methods are applied to temperature and rainfall based weather indices in three different climatic zones. Our results show that weather risk follows different patterns depending on the type of risk and the location. We also find differences in the sensitivity of the statistical test procedures.

Keywords

Weather extremes Agricultural risk Change point test Quantile regression 

References

  1. Aguilar E, Auer I, Brunet M, Peterson TC, Wieringa J (2003) Guidelines on climate metadata and homogenization, WCDMP 53, WMO-TD 1186, World Meteorol Org Geneva, pp 55Google Scholar
  2. Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Klein Tank A, Haylock M, Collins D, Trewin B, Rahimzadeh F, Tagipour A, Ambenje P, Kumar KR, Revadekar J, Griffiths G, Vincent L, Stephenson DB, Burn J, Aguliar E, Brunet M, Taylor M, New M, Zhai P, Rusticucci M, Vazquez-Aguirre JL (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res 111:1–22CrossRefGoogle Scholar
  3. Anderson RL (1941) Distribution of the serial correlation coefficients. Ann Math Stat 8(1):1–13Google Scholar
  4. Andriyashin A, Benko M, Härdle W, Timofeev R, Ziegenhagen U (2006) Color harmonization in car manufacturing processes. Appl Stoch Models Bus Ind 22:519–532CrossRefGoogle Scholar
  5. Bayazit M, Önöz B (2007) To prewhiten or not to prewhiten in trend analysis? Hydrol Sci J 52(4):611–624CrossRefGoogle Scholar
  6. Beniston M, Stephenson DB (2004) Extreme climatic events and their evolution under changing climatic conditions. Glob Planet Change 44:1–9CrossRefGoogle Scholar
  7. Bertranda P (2000) A local method for estimating change points: the “Hat-function”. Statistics 34(3):215–235CrossRefGoogle Scholar
  8. Dawdy DR, Matalas NC (1964) Statistical and probability analysis of hydrologic data, part III: analysis of variance, covariance and time series. In: Chow VT (ed) Handbook of applied hydrology, a compendium of water-resources technology. McGraw-Hill Book Company, New York, pp 868–890Google Scholar
  9. Environmental Protection Administration (EPA) (2009) Extreme events and disasters are biggest threat to Taiwan. Typhoon Morakot. Executive Yuan, ROC Taiwan. http://www.epa.gov.tw
  10. Fischer T, Gemmer M, Liu L, Su B (2012) Change-points in climate extremes in the Zhujiang River Basin, South China, 1961–2007. Clim Change 110:783–799CrossRefGoogle Scholar
  11. Frich P, Alexander L, Della-Marta P, Gleason B, Haylock M, Klein Tank A, Peterson T (2002) Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim Res 19:193–212CrossRefGoogle Scholar
  12. Härdle WK (1990) Applied nonparametric regression. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  13. Härdle WK, Song S (2010) Confidence bands in quantile regression. Econom Theory 26:1180–1200CrossRefGoogle Scholar
  14. Härdle WK, Cabrera BL, Okhrin O, Wang W (2011) Localising temperature Risk. SFB 649 Discussion Paper 2011-001. Humboldt Universität zu, BerlinGoogle Scholar
  15. Härdle WK, Spokoiny V, Wang W (2011) Locale quantile regression. SFB 649 Discussion Paper 2011-005. Humboldt Universität zu, BerlinGoogle Scholar
  16. Hegerl GC, Zwiers F, Kharin S, Stott P (2007) Detectability of anthropogenic changes in temperature and precipitation extremes. J Clim 17:3683–3700CrossRefGoogle Scholar
  17. Hu Y, Maskey S, Uhlenbrook S (2012) Trends in temperature and rainfall extremes in the Yellow River source region, China. Clim Change 110:403–429CrossRefGoogle Scholar
  18. Iowa Climate Change Impacts Committee (ICCIC) (2011) Climate change impacts on Iowa 2010. Report to the Governor and the Iowa General Assembly. www.energy.iowa.gov
  19. Klein Tank AMG, Zwiers FW, Zhang X (2009) Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation. Climate Data and Monitoring WCDMP 72. World Meteorological OrganizationGoogle Scholar
  20. Koenker R, Bassett G (1978) Regression quantiles. Econometrica 46(1):33–50CrossRefGoogle Scholar
  21. Kürbis K, Mudelsee M, Tetzlaff G, Brázdil R (2009) Trends in extremes of temperature, dew point, and precipitation from long instrumental series from central Europe. Theory Appl Climatol 98:187–195CrossRefGoogle Scholar
  22. Liu X, Xu Z, Yu R (2011) Trend of climate variability in China during the past decades. Clim Change 109:503–516CrossRefGoogle Scholar
  23. Mercurio D, Spokoiny V (2004) Statistical inference for time-inhomogeneous volatility models. Ann Stat 32:577–602CrossRefGoogle Scholar
  24. Nadolnyak D, Vedenov D, Novak J (2008) Information value of climate-based yield forecasts in selecting optimal crop insurance coverage. Am J Agric Econ 90(5):1248–1255CrossRefGoogle Scholar
  25. Noguchi K, Gel YR, Duguay CR (2011) Bootstrap-based tests for trends in hydrological time series, with application to ice phenology data. J Hydrol 410(3–4):150–161CrossRefGoogle Scholar
  26. Siliverstovs B, Rainald Ö, Kemfert C, Jaeger C, Armin H, Kremers H (2010) Climate change and modelling of extreme temperatures in Switzerland. Stoch Environ Res Risk Assess 24:311–326CrossRefGoogle Scholar
  27. Spokoiny V (2009) Multiscale local change point detection with applications to value at risk. Ann Stat 37(3):1405–1436CrossRefGoogle Scholar
  28. Tebaldi C, Hayhoe K, Arblaster JM, Meehl GA (2006) Going to the extremes: an intercomparison of model-simulated historical and future changes in extreme events. Clim Change 79(3–4):185–211CrossRefGoogle Scholar
  29. Trenberth KE (2011) Changes in precipitation with climate change. Clim Res 47:123–138CrossRefGoogle Scholar
  30. Vasiliades L, Loukas A, Patsonas G (2009) Evaluation of a statistical downscaling procedure for the estimation of climate change impacts on droughts. Nat Hazards Earth Syst Sci 9:879–894CrossRefGoogle Scholar
  31. Wilson LT, Yang Y, Lu P, Wang J, Nielsen-Gammon JW, Smith N, Fernandez CJ (2007) Integrated Agricultural Information and Management System (iAIMS): World Climatic Data. http://beaumont.tamu.edu/ClimaticData/
  32. World Bank (2005) Managing agricultural production risk: innovations in developing countries, www.worldbank.org/rural
  33. Xu W, Filler G, Odening M, Okhrin O (2010) On the systemic nature of weather risk. Agric Finance Rev 70(2):267–284CrossRefGoogle Scholar
  34. Yue S, Pilon P, Caradias G (2002) Power of the Mann–Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series. J Hydrol 259:254–271CrossRefGoogle Scholar
  35. Zhang Q, Xu CY, Zhang Z, Chen Y (2009) Changes of temperature extremes for 1960–2004 in Far-West China. Stoch Environ Res Risk Assess 23(6):721–735CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • W. Wang
    • 1
  • I. Bobojonov
    • 2
  • W. K. Härdle
    • 1
  • M. Odening
    • 3
  1. 1.Ladislaus von Bortkiewicz Chair of StatisticsSchool of Business and Economics, Humboldt-Universität zu BerlinBerlinGermany
  2. 2.Department of Agricultural Markets, Marketing and World Agricultural TradeLeibniz Institute of Agricultural Development in Central and Eastern Europe (IAMO)Halle (Saale)Germany
  3. 3.Department of Agricultural Economics, Farm Management GroupHumboldt-Universität zu BerlinBerlinGermany

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