Open AccessInnovative trend analysis of annual maximum precipitation in Gowa regency
Author(s) -
Wahidah Sanusi,
Muhammad Abdy,
Sulaiman Sulaiman
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 - 10.1088/1742-6596/1899/1/012092
Subject(s) - precipitation , trend analysis , human settlement , environmental science , agriculture , tourism , water resources , lag , climatology , geography , physical geography , water resource management , meteorology , statistics , mathematics , computer science , ecology , computer network , archaeology , biology , geology
Floods and drought are two events that can have a negative impact on human survival. In addition, the impact of these two events can also affect agriculture, fisheries, tourism, housing, transportation and others. Trend analysis is an analysis that can be used to identify extreme rainfall events such as floods and drought. The results of the trend analysis can be useful for water resource planning and management. Therefore, the aim of this study is to obtain an overview of the annual maximum precipitation trend in Gowa Regency. The study uses the daily precipitation data from the Sungguminasa and Bonto Sallang Stations of Gowa Regency for 31 years (1988 – 2018). The data was obtained from the Water Resources, Human Settlements, Spatial Planning and Development Office of South Sulawesi Province. The method used is the Mann-Kendall test, Theil-Sen approach, and innovative trend analysis. The results of a lag-one serial correlation test found that all stations are serially independent. Based on the MK method that both stations show negative trend, but significant only at Sungguminasa station at the 95% confidence level. The results of the ITA method show that all stations are significant negative trends at the 95% level. One of advantages of the ITA method is the ability to detect the significant hidden trends in time series.