Statistical and trend analyses of long-term rainfall data: a case study for Mauritius Online publication date: Wed, 30-Sep-2020
by Reena Hansa Seebocus; Michel Roddy Lollchund; Miloud Bessafi
International Journal of Hydrology Science and Technology (IJHST), Vol. 10, No. 5, 2020
Abstract: This paper focuses on the analysis of monthly rainfall data for the period 1950-2016 for Mauritius using statistical techniques and decomposition method. For the statistical analysis, the data are fitted to commonly used probability distribution function for which parameters are estimated using the method of maximum likelihood estimation (MLE). The Anderson-Darling (A-D), Kolmogorov-Smirnov (K-S) and chi-square (C-S) tests are then employed to determine which PDF best fits the data. The results obtained indicate that the log-normal, GEV and inverse Gaussian PDFs best fit the rainfall data at less than 5% significance level. The ensemble empirical mode decomposition (EEMD) method is employed to study the trends in the data. Results obtained are in terms of intrinsic mode functions (IMFs) and the trendline. The analysis reveals that there is a general linear decrease of 1 mm/year in the amount of rainfall.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Hydrology Science and Technology (IJHST):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook