Trend Analyses Methodologies in Hydro-meteorological Records
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REVIEW ARTICLE
Trend Analyses Methodologies in Hydro‑meteorological Records Mansour Almazroui1 · Zekâi Şen1,2 Received: 7 May 2020 / Accepted: 16 November 2020 © King Abdulaziz University and Springer Nature Switzerland AG 2020
Abstract 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. Keywords Climate · Hydrology · Innovative · Trend · Variability · Whitening
1 Introduction Since the final quarter of twentieth century, unprecedented human activities and fossil energy use led to environment and atmospheric pollution due to anthropogenic greenhouse gas (GHG) emissions. The consequent impacts are in the forms of global warming and climate change with increase in GHG emissions among which carbon dioxide plays the most important role (IPCC 2007, 2014). The most likely changes in physical climate variables or climate forcing agents are identified based on current knowledge following the IPCC AR5 uncertainty guidance ().
* Zekâi Şen [email protected]; [email protected] 1
Department of Meteorology, Center of Excellence for Climate Change Research, King Abdulaziz University, PO Box 80234, Jeddah 21589, Saudi Arabia
Engineering and Natural Sciences Faculty, Istanbul Medipol University, Beykoz, 34810 Istanbul, Turkey
2
Maass et al. (1962) assumed implicitly that hydro-meteorological time series (temperature, precipitation, and streamflow) are stationary, i.e., without trend compone
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