Open AccessSpatial analysis of return period based copula on extreme rainfall data in South Sulawesi
Author(s) -
Reski Wahyu Yanti,
Anwar Fitrianto,
Muhammad Nur Aidi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1842/1/012050
Subject(s) - return period , extreme value theory , copula (linguistics) , natural disaster , spatial distribution , generalized extreme value distribution , spatial analysis , autocorrelation , geography , spatial dependence , index (typography) , environmental science , physical geography , climatology , statistics , mathematics , meteorology , flood myth , econometrics , geology , remote sensing , archaeology , world wide web , computer science
The extreme rainfall in an area makes the area vulnerable to various disasters. To reduce the risk of damage caused by floods, it is important to know the characteristics of extreme rainfall. Generally, the characteristics of extreme rainfall are described by one variable. However, most of the extreme rainfall events also need to be explained based on the return period they occur using several variables with copula approach. This study model to the characteristics of extreme rainfall with two variables, they are namely extreme rainfall intensity and extreme rainfall volume. The purpose of this study was to analyze the spatial distribution pattern of the return period from extreme rainfall in South Sulawesi. To determine the characteristics of the return period distribution in South Sulawesi, spatial analysis is carried out using the Moran’s index and the LISA index. The results of the spatial autocorrelation analysis with the Moran’s index show that there is a relationship between several return period values in South Sulawesi, with the Moran’s index value of 0.209. This means that the value of the return period in South Sulawesi has a clustered relationship pattern. Furthermore, the results of the spatial autocorrelation analysis with LISA show that there are seven sub-districts identified as having local spatial autocorrelation. The conclusion obtained from Moran’s scatterplot is that 15 sub-districts are the main concern in preventing natural disasters because extreme rainfall in these 15 sub-districts tends to occur more frequently, so that it can lead to various natural disasters.