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  4. Mathematical modeling and statistical analysis of Moroccan mean annual rainfall using EXPAR processes

Mathematical modeling and statistical analysis of Moroccan mean annual rainfall using EXPAR processes

MMC.
2023;
Volume 10, Number 3
: pp. 607–616
https://doi.org/10.23939/mmc2023.03.607
Received: February 15, 2023
Revised: July 06, 2023
Accepted: July 08, 2023

Mathematical Modeling and Computing, Vol. 10, No. 3, pp. 607–616 (2023)

Authors:
  1. N. Azouagh
  2. S. El Melhaoui
1
Université Hassan II, Faculté des Sciences et Techniques
2
Laboratoire de Modélisation Stochastique et Déterministe, Université Mohammed Premier, Faculté des Sciences; Université Mohammed Premier, Faculté de Droit, Département d'Économie

In this work, we propose a study of the mean annual rainfall time series in order to evaluate the climate changes pattern over time.  If the analysis of this time series is carried out correctly, it can contribute to improve planning and policy development.  That is why we consider the problem of mathematical modeling and analysis of the mean annual rainfall of Morocco between 1901 and 2020 using descriptive statistics, structure changes analysis, spectral analysis and a nonlinear Exponential Autoregressive (EXPAR) processes to reproduce the behavior of this time series.  The results indicate three main breakpoints and show that the time series has a remarkable cycles about 60, 18 and 6 years with a global decrease tendency about 0.56 mm per year.  Furthermore, we have justified the choice of using a non-linear EXPAR processes rather than a linear traditional one and provided a good fitted EXPAR model.

time series
Moroccan rainfall
nonlinearity test
exponential autoregressive models
spectral analysis
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