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How to apply the dependence structure analysis to extreme temperature and precipitation for disaster risk assessment

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Abstract

IPCC reports that a changing climate can affect the frequency and the intensity of extreme events. However, the extremes appear in the tail of the probability distribution. In order to know the relationship between extreme events in the tail of temperature and precipitation, an important but previously unobserved dependence structure is analyzed in this paper. Here, we examine the dependence structure by building a bivariate joint of Gumbel copula model for temperature and precipitation using monthly average temperature (T) and monthly precipitation (P) data from Beijing station in China covering a period of 1951–2015 and find the dependence structure can be divided into two sections, they are the middle part and the upper tail. We show that T and P have a strong positive correlation in the high tail section (T > 25.85 °C and P > 171.1 mm) (=0.66, p < 0.01) while they do not demonstrate the same relation in the other section, which suggests that the identification of a strong influence of T on extreme P needs help from the dependence structure analysis. We also find that in the high tail section, every 1 °C increase in T is associated with 73.45 mm increase in P. Our results suggested that extreme precipitation fluctuations by changes in temperature will allow the data dependence structure to be included in extreme affect for the disaster risk assessment under future climate change scenarios. Copula bivariate jointed probability distribution is useful to the dependence structure analysis.

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Acknowledgements

This work was supported by National Key Research and Development Program–Global Change and Mitigation Project: Global change risk of population and economic system: mechanism and assessment (2016YFA0602403); The Fundamental Research Funds for the Central Universities (310421101).

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Authors and Affiliations

  1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China

    Jieling Feng, Ning Li, Zhengtao Zhang & Xi Chen

  2. Key Laboratory of Environmental Change and Natural Disaster, MOE, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China

    Jieling Feng, Ning Li, Zhengtao Zhang & Xi Chen

  3. Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs & Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China

    Ning Li

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  1. Jieling Feng
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  2. Ning Li
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Correspondence to Ning Li.

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Feng, J., Li, N., Zhang, Z. et al. How to apply the dependence structure analysis to extreme temperature and precipitation for disaster risk assessment. Theor Appl Climatol 133, 297–305 (2018). https://doi.org/10.1007/s00704-017-2187-5

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  • DOI: https://doi.org/10.1007/s00704-017-2187-5

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