Urban resilience to floods in parts of Makassar, Indonesia

Thoban, Meydelin Isani; Hizbaron, Dyah Rahmawati

doi:10.1051/e3sconf/202020001007

Cited by 11 publications

(4 citation statements)

References 12 publications

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“…For instance, in mid-December 2017, suburbs of Makassar City experienced flooding with a height of one meter (Halim et al, 2019). In January 2019, a flood disaster hit the city and inundated 1658 houses, affecting 9328 residents (Thoban & Hizbaron, 2020). Even recently, on February 13, 2023, heavy rains with an intensity of 166.8 mm/day resulted in flooding in most parts of Makassar (VOI, 2023).…”

Section: Resultsmentioning

confidence: 99%

Modeling Extreme Rainfall Events in the Tropical Monsoon Region: A Generalized Pareto Distribution Approach

Muzakir¹,

Sunusi²,

Thamrin³

2023

IJRP

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Extreme weather events have become more frequent due to climate change, and extreme rainfall is a common occurrence in the tropical monsoon areas. Makassar City was chosen as a representative area of the tropical monsoon climate. The objective of this research is to utilize the generalized Pareto distribution (GPD), along with its nested model (exponential), to predict the monthly return levels of extreme rainfall in the designated geographical region. The study utilized daily precipitation data from January 1980 to December 2022. To determine the monthly daily rainfall data that exceeds a certain threshold, the peaks over threshold (POT) method was applied. The result discovered that the exponential distribution is the most appropriate for extreme rainfall series in most months, except for February and July, where the GPD is more appropriate. No trends or seasonal patterns were identified in any of the months. The calculated return levels of extreme rainfall for each month at the 2, 3, 5, and 10-year return periods indicate that February has the highest rainfall return level for all selected return periods compared to other months with December and January following closely behind. These findings are expected to assist the government in developing flood prevention strategies and mitigating their effects, particularly during the rainy season's peak months in the city, which are December to February.

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Section: Resultsmentioning

confidence: 99%

Modeling Extreme Rainfall Events in the Tropical Monsoon Region: A Generalized Pareto Distribution Approach

Muzakir¹,

Sunusi²,

Thamrin³

2023

IJRP

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“…As a result, floods are frequent during this period. It was recorded that in January 2019, a flood disaster hit the city of Makassar and inundated 1658 houses, affecting 9328 residents [34]. Heavy rains with an intensity of 166.8 mm/day that occurred on February 13, 2023, caused flooding in most parts of Makassar [35].…”

Section: Return Levels and Probability Of Exceedancesmentioning

confidence: 99%

Stochastic Model of the Annual Maximum Rainfall Series Using Probability Distributions

Musakkir,

Sunusi,

Thamrin

2023

Mal. J. Fund. Appl. Sci.

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Rainfall is a natural process that is often characterized by significant variability and uncertainty. Stochastic models of rainfall typically involve the use of probability distributions to describe the likelihood of different outcomes occurring. This study aimed to model the annual maximum of daily rainfall in Makassar City, Indonesia for the period 1980–2022, specifically focusing on the rainy season (November to April) using probability distributions to estimate return periods. The study used the Generalized Extreme Value (GEVD) and Gumbel distributions. The Kolmogorov-Smirnov test was used to determine the suitability of each distribution, and the likelihood ratio test was employed to determine the best-fit model. The Mann-Kendall test was used to detect any trends in the data. The results indicated that the Gumbel distribution was the best-fit model for data in November, December, January, March, and April, while GEV was appropriate for February. No trends were observed in any of the months. The study then estimated the maximum rainfall for various return periods. January produced the highest maximum rainfall estimates for the 2, 3, and 5-year return periods, while February produced the highest maximum rainfall estimates for the 10 and 20-year return periods. Information about maximum rainfall can be valuable for the government and other stakeholders in developing flood prevention strategies and mitigating the effects of heavy rainfall, particularly during the peak months of the rainy season in Makassar City, which are December, January, and February.

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“…Musliadi et al [6] modeled the potential flood depth of the Tallo watershed in Makassar using the Log-Pearson type III method on rainfall data. Thoban and Hizbaron [7] conducted research with spatial multi-criteria evaluation (SMCE) to find the resilience level of the Makassar community to flooding. Djamaluddin et al [8] and Indrayani et al [9] predicted the flood vulnerability in Makassar City by considering social, physical, economic, and environmental aspects.…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based Machine Learning for Flood Policy Recommendations in Makassar City, Indonesia

Rimba,

Arumansawang,

Utama

et al. 2023

Water

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Makassar City frequently experiences monsoonal floods, typical of a tropical city in Indonesia. However, there is no high-accuracy flood map for flood inundation. Examining the flood inundation area would help to provide a suitable flood policy. Hence, the study utilizes multiple satellite data sources on a cloud-based platform, integrating the physical factors of a flood (i.e., land use data and digital elevation model—DEM—data) with the local government’s urban land use plan and existing drainage networks. The research aims to map the inundation area, identify the most vulnerable land cover, slope, and elevation, and assess the efficiency of Makassar’s drainage system and urban land use plan. The study reveals that an uncoordinated drainage system in the Tamalanrea, Biringkanaya, and Manggala sub-districts results in severe flooding, encompassing a total area of 35.28 km2. The most affected land use type is cultivation land, constituting approximately 43.5% of the flooded area. Furthermore, 82.26% of the urban land use plan, covering 29.02 km2, is submerged. It is imperative for the local government and stakeholders to prioritize the enhancement of drainage systems and urban land use plans, particularly in low-lying and densely populated regions.

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Urban resilience to floods in parts of Makassar, Indonesia

Cited by 11 publications

References 12 publications

Modeling Extreme Rainfall Events in the Tropical Monsoon Region: A Generalized Pareto Distribution Approach

Modeling Extreme Rainfall Events in the Tropical Monsoon Region: A Generalized Pareto Distribution Approach

Stochastic Model of the Annual Maximum Rainfall Series Using Probability Distributions

Cloud-Based Machine Learning for Flood Policy Recommendations in Makassar City, Indonesia

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