Abstract
Flooding is a common problem that occurs in some regions of Indonesia, including Makassar city. In the planning of flood control, rainfall variables are very necessary as the frequency, intensity and duration of rainfall. The relationship of these variables can be expressed in a curve Intensity-Duration-Frequency (IDF). The objectives of this study are to identify the best fitting distribution of rainfall data of Makassar city and also to model the relationship between rainfall intensity, rainfall duration and rainfall frequency that is described through IDF curves. The annual maximum daily rainfall data from Ujung Pandang rainfall station of Makassar is used in this study for the period 1986-2015. Data collection was performed at the Department of Water Resources Management in South Sulawesi province. Five distributions which are considered are Gumbel, Generalized Extreme Value (GEV), Generalized Pareto (GPA), Generalized Logistic (GLO) and Pearson type III (P3) distributions. The study result found that the probability distribution of rainfall data in Makassar city has a generalized extreme value distribution. Meantime, based on IDF curves shown that the longer the rainfall duration, the rainfall intensity decreases for various return periods. The results of this study are expected to be valuable information for designers of water management.
Highlights
A flood event is a natural disaster that can cause losses for human life’s and its environment
The other impacts of flood event are the emergence of tropical diseases as dengue fever disease (Syafruddin and Noorani, 2013)
Five types of distributions are selected in fitting for the daily rainfall amount, namely Gumbel, Generalized Extreme Value distribution (GEV), Generalized Pareto (GPA), Generalized Logistic (GLO) and P3 distributions
Summary
A flood event is a natural disaster that can cause losses for human life’s and its environment. The other impacts of flood event are the emergence of tropical diseases as dengue fever disease (Syafruddin and Noorani, 2013). An effort to anticipate the impact of flood events is through wetness estimating techniques of an area. Du et al (2013) investigated the spatiotemporal variation of dry/wet conditions with the Standardized Precipitation Index (SPI) in Hunan, China. Sanusi and Ibrahim (2012) predicted the wet class transition using a log linear model. Cai (2010) estimated the wet and low water of precipitation with the weighted Markov chain methods
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