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Jump point identification in hydro-meteorological time series by crossing methodology

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Abstract

The climate change impact appears as a decreasing or increasing monotonic trend in hydro-meteorology time series records due to greenhouse gas (GHG) emissions causing to global warming and climate change impacts. On the other hand, there may be abrupt changes in the form of jumps in these series due to natural and engineering activities. Although trend identification methods are rather common in the literature, jump determination conventional methodologies are rather rare and their applications present some restrictive asumptions like the serial independence and normal (Gaussian) probability distribution function (PDF). The methodology presented in this paper is away from each of such assumptions and it depicts the minimum number of upcrossing along horizontal truncation levels within the time series variation domain. The applications of the methodology are given for annual Danube River discharge records, Romania; New Jersey rainfall and temperature records, USA; monthly rainfall records and Van Lake level fluctuations, Turkey.

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Code in Matlab is available upon request.

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Authors and Affiliations

  1. Engineering and Natural Sciences Faculty, Istanbul Medipol University, Kavacık, Istanbul, Turkey

    Zekâi Şen

  2. Center of Excellence for Climate Change Research (CECCR), Faculty of Arid Lands, Meteorology and Agriculture Department, King Abdulaziz University, Jeddah, Saudi Arabia

    Zekâi Şen

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Şen, Z. Jump point identification in hydro-meteorological time series by crossing methodology. Theor Appl Climatol 144, 769–777 (2021). https://doi.org/10.1007/s00704-021-03576-2

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