Trend Analyses Methodologies in Hydro-meteorological Records

Almazroui M., Şen Z.

Earth Systems and Environment, vol.4, no.4, pp.713-738, 2020 (ESCI) identifier identifier

  • Publication Type: Article / Review
  • Volume: 4 Issue: 4
  • Publication Date: 2020
  • Doi Number: 10.1007/s41748-020-00190-6
  • Journal Name: Earth Systems and Environment
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus
  • Page Numbers: pp.713-738
  • Keywords: Climate, Hydrology, Innovative, Trend, Variability, Whitening
  • Istanbul Medipol University Affiliated: Yes


In recent years, global warming and climate change impacts on hydro-meteorological variables and water resources triggered extensive focus on trend analyses. Especially, in historical records and climate change model scenario projections, trend feature searches help for better predictions prior to mitigation and adaptation activities. Each trend identification technique has a set of restrictive assumptions and limitations, but they are not cared for by many researchers. The major problem with trend research is that the researchers do not care for the basic assumptions of any methodology but use ready software to solve their problems. Among these assumptions, the most significant ones are the normal (Gaussian) probability distribution function (PDF) and serially independent structure of a given time series. It is the main objective of this review paper to present each trend identification methodology including classical ones with the new alternatives so that any researcher in need of trend analysis can have concise and clear interpretations for the choice of the most convenient trend method. In general, parametric, non-parametric, classical and innovative trend methods are explained comparatively including the linear regression, Mann–Kendall (MK) trend test with Sen slope estimation, Spearman’s rho, innovative trend analysis (ITA), partial trend analysis (PTA) and crossing trend analysis (CTA). Pros and cons are given for each methodology. In addition, for improvement of serial independence requirement of the classical trend analyses, methods are introduced briefly by pre- and over-whitening processes. Finally, a set of recommendations is suggested for future research possibilities.