Identification of sub-trends from hydro-meteorological series

By C. Onyutha. Stochastic environmental research and risk assessment (Print), 2015

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On Rigorous Drought Assessment Using Daily Time Scale: Non-Stationary Frequency Analyses, Revisited Concepts, and a New Method to Yield Non-Parametric Indices (2017, methodology): “The rescaled time series c in terms of the exceedance and non-exceedance counts of data points can be obtained by Onyutha [30]:”
Trends and variability in African long-term precipitation (2018, methodology): “For instance, a period of 30 years or more can be used to characterize climate fluctuations, 15-year data period is relevant for planning crop water requirements (Onyutha 2016a), water supply projects, or riskbased water resources applications, etc.”
Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches (2020, methodology): “Sen 55 developed the innovative trend analysis that has been utilized for detecting trend in meteorological, hydrological and environmental variables 56–60 .”