Modeling of Rainfall Characteristics for Monitoring of the Extreme Rainfall Event in Makassar City

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.

Estimated Parameters of Rain Flow Distribution Using L-Moment Method in South Sulawesi, Indonesia

“…According to Hosking (1997) the cumulative function of the LN3 distribution does not have an explicit quantile function solution [13]. So that the reparameterization of the lognormal distribution is carried out in this case the parameters ξ, α, and κ are small modifications of the reparameterization [14], so that the parameters for the lognormal distribution III (LN3) is…”

“…The parameterization was extended to include the usual type III pearson distribution, with positive slope and lower bound ξ, inverse type III pearson distribution, with negative slope and upper limit ξ, and the normal distribution which is included as a special case of the type III pearson distribution. It is possible that the type III Pearson distribution is used when the slope of the observed data may have a negative value [13].…”

“…The application of the L-Moment method approach in determining the type of frequency distribution of extreme rainfall data has been carried out by several researchers [12][13][14][15][16] which have been carried out in various regions around the world including Indonesia including Selangor, Malaysia; Sicily, Italy; Makassar City, South Sulawesi Province; West Sumatra and Medina, Saudi Arabia. The L-Moment method is used because it is easier to use, has the ability to characterize a wider range of probability distributions and is more prone to biased estimation.…”

Frequentist and Bayesian Approaches in Modeling and Prediction of Extreme Rainfall Series: A Case Study from Southern Highlands Region of Tanzania

“…Extreme rainfall is characterized as the highest amount of rainfall experienced within a 24-hour period annually [20]. One way to analyze changes in extreme rainfall is to use statistical distribution models.…”

“…However, it is important to note that the data frequently exhibits nonnormal distribution characteristics across multiple regions. While the Anderson-Darling and Kolmogorov-Smirnov tests have often been used for ftting probability distributions, they may not be sensitive enough to identify substantial deviations from an assumed distribution at a particular location [20]. To address this issue, researchers suggest utilizing L-moments as a reliable method to estimate higher statistical moments.…”