Abstract

In recent years water-related issues are increasing globally, some researchers even argue that the global hydrological cycle is accelerating, while the number of meteorological extremities is growing. With the help of large number of available measured data, these changes can be examined with advanced mathematical methods. In the outlined research we were able to collect long precipitation datasets from two different climatical regions, one sample area being Ecuador, the other one being Kenya. Using the methodology of spectral analysis based on the discrete Fourier-transformation, several deterministic components were calculated locally in the otherwise stochastic time series, while by the comparison of the results, also with previous calculations from Hungary, several global precipitation cycles were defined in the time interval between 1980 and 2019. The results of these calculations, the described local, regional, and global precipitation cycles can be a helpful tool for groundwater management, as precipitation is the major resource of groundwater recharge, as well as with the help of these deterministic cycles, precipitation forecasts can be delivered for the areas.

Highlights

  • In recent years the impact of the ever-changing climate across the globe has caused several water-related problems (Szűcs and Ilyés 2019), more researchers argue that the hydrological cycle is in some way accelerated (Szöllősi-Nagy 2018), causing the growing number of meteorological extremes

  • The long-term precipitation data of two mainly different climatic regions were examined with the method of spectral analysis based on the discrete Fourier-transformation

  • The used method is capable of the definition of deterministic components in another way stochastic time series, such as precipitation

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Summary

Introduction

In recent years the impact of the ever-changing climate across the globe has caused several water-related problems (Szűcs and Ilyés 2019), more researchers argue that the hydrological cycle is in some way accelerated (Szöllősi-Nagy 2018), causing the growing number of meteorological extremes. This means that finding deterministic patterns getting more. Defining periodic components into precipitation datasets is important because with the help of these cycles and the use of advanced mathematical methods forecasts can be calculated for a given area for different time intervals, and different scales (Ilyés et al 2019)

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