A Comparative analysis of the application of trend analysis methods to hydro-meteorological data

OpenAlex İngilizce

Climate change has significantly increased the need to identify long-term trends in hydro-meteorological time series. In this study, trend analysis methods applied to hydro-meteorological variables such as precipitation, temperature, discharge, evaporation and drought indices were examined within the framework of a comprehensive literature review. The methods considered have been classified into four main groups: parametric methods (such as linear regression), non-parametric methods (such as the Mann-Kendall test, Sen’s slope estimator, and Spearman’s rho), innovative trend analysis approaches (Innovative Trend Analysis (ITA), Innovative Polygon Trend Analysis (IPTA), and wavelet-based methods) and breakpoint analysis methods (such as the Pettitt test, Standard Normal Homogeneity Test, and Buishand test). A systematic review of over 130 studies published between 2015 and 2026 across different geographical regions revealed that precipitation and temperature data were the most commonly analysed variables, whilst the Mann-Kendall test was found to be the dominant method. The increasing adoption of innovative methods such as ITA and IPTA in recent years indicates a methodological shift towards approaches capable of capturing non-linear and complex trends. The findings indicate that forward-looking modelling and multivariate integrated analyses are insufficient; they suggest that machine learning and artificial intelligence-based hybrid approaches offer promising avenues for future research.