TITLE:
Using the Markov Chain for the Generation of Monthly Rainfall Series in a Semi-Arid Zone
AUTHORS:
Mouelhi Safouane, Nemri Saida, Jebari Sihem, Slimani Mohamed
KEYWORDS:
Stochastic Generation, Rainfall Series, Markov Chain, Semiarid, Central Tunisia
JOURNAL NAME:
Open Journal of Modern Hydrology,
Vol.6 No.2,
April
6,
2016
ABSTRACT: Numerous methodologies have been developed in the literature for the generation of rain. However, in semi-arid areas where the irregularity of rain is contrasted, the question of the applicability of these models is still relevant. The objective of this article is to propose a development method of stochastic generator of monthly rainfall series. The present work is based on the modeling of the occurrence and the quantity of rain in a separate way. The occurrence is treated in two stages. The first step considers the Markov chain according to the occurrence of annual statements (dry, average and wet). The second step uses the monthly rankings. The amount of rain is calculated based on historical series according to the monthly rank and the annual statement noted. This method is applied to rainfall data recorded at five rainfall stations in semi-arid region of Central Tunisia. The usual and conventional statistical tests of the generated series have shown the validity of this method.