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.
Highlights
The geographical location of Tunisia plays an important role in the availability of water resources
This study has shown the constraints that limit the application of conventional methods of generating rain in a semi-arid environment such as Tunisia
The model developed on Tunisian rainfall stations generates the occurrence and amount separately
Summary
The geographical location of Tunisia plays an important role in the availability of water resources. This country is characterized by a limited resource that is irregularly distributed in space and time. The lack of long series of rainfall observations considerably limits the estimation of water supplies. It has been shown in the literature that the generation of sets of data using short time series can lead to errors. The most appropriate mode concerns the generation of rainfall series in conjunction with a rainfall-runoff model (Chandler, 2003 [5], Chandler et al, 2005 [6], Selvalingam and Miura, 1978 [7], Rodriguez-Iturbe et al, 1987 [8], Kim and Olivera, 2010 [9])
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
