Using the Markov Chain for the Generation of Monthly Rainfall Series in a Semi-Arid Zone

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.

Characterization of Soil Moisture Regime in the Kairouan Region, Tunisia

“…The annual potential evapotranspiration rate is estimated to be around 1460 mm. [12] Monthly rainfall -Sidi Saad Station [13] Monthly minimum, average and maximum temperature curve in Kairouan [14] Figure 3 Precipitation and temperature in the study area [13 14]…”

Stochastic Modeling Technology for Grain Crops Storage Application : Review

“…Some of studies presented the weather forecasting as shown in Table 1. Among them include: estimation of the rainfall sequences during the rainy season in Kurdufan [30], rainfall prediction at the Daspalla Region in Odisha, Eastern India [31] for crop planning, daily rainfall occurrence forecasting in Peninsular Malaysia [32], the rainfall estimation during monsoon season over major station in Gangetic West Bengal [33], and a stochastic generator of monthly rainfall series in Tunisia [34]. In the study [35], Markov chain model with weights was applied to predict Standardized Precipitation Index (SPI) drought intensity by using standardized self coefficients as weights.…”