Advertisement

Analysis of Trend and Variability in Time Series of Extreme Daily Temperature of Abu Dhabi City (UAE)

  • Nishi BhuvandasEmail author
Chapter
Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

Trend identification plays a significant role in climate change studies to make future predictions about possible consequences on the urban environment, agriculture, water availability, etc. The Variation of Extreme Temperature intensity is observed in many regions all over the globe as an outcome of abrupt changes in climate. Keeping in mind the non-stationary climate conditions, it is essential to incorporate potential future changes in climatological studies. In the present study, the statistical parameters and the variability in time series of extreme daily temperature were analyzed using Generalized Extreme Value (GEV) distribution and its suitability has been examined by superimposing onto Gringorten plotting positions. The trend analysis of extreme daily temperatures was carried out using an innovative trend template proposed by Şen [7] and non-parametric Mann-Kendall (MK) trend test. The trend detection and variability of extreme daily temperature of Abu Dhabi City station, UAE was also carried out in this study. 34 years of temperature data were used for the analysis. Also, the extreme daily temperatures time series were checked for their serial correlation.

Keywords

Abu Dhabi city Climate variability Extreme daily temperature Generalized extreme value Trend analysis 

References

  1. 1.
    Chow, V.T., Maidment, D.R., Mays, L.W.: Applied Hydrology. Tata McGraw-Hill, New York (1988)Google Scholar
  2. 2.
    Cunnane, C.: Unbiased plotting positions—a review. J. Hydrol. 37, 205–222 (1978)CrossRefGoogle Scholar
  3. 3.
    Douglas, E.M., Vogel, R.M., Kroll, C.N.: Trends in floods and low flows in the United States: impact of spatial correlation. J. Hydrol. 240, 90–105 (2000)CrossRefGoogle Scholar
  4. 4.
    Huang, Y.: Rapid flood risk assessment using GIS technology. Int. J. River Basin Manag. 7(1), 3–14 (2010)CrossRefGoogle Scholar
  5. 5.
    IPCC: Climate change 2007: impacts, adaptation and vulnerability. In: Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden P.J., Hanson, C.E. (eds.) Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, 976pp (2007)Google Scholar
  6. 6.
    Şen, Z.: An innovative trend analysis methodology. J. Hydrol. Eng. 17(9), 1042–1046 (2012)CrossRefGoogle Scholar
  7. 7.
    Yue, S., Pilon, P., Cavandis, G.: Power of Mann-Kendall and Spearman’s rho test for detecting monotonic trends in hydrological series. J. Hydrol. 259, 254–271 (2002)CrossRefGoogle Scholar
  8. 8.
    Yue, S., Pilon, P., Phinney, B., Cavandis, G.: The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol. Process. 16, 1807–1829 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Abu DhabiUAE

Personalised recommendations