Evaluation of future flood risk in Asian megacities: a case study of Jakarta

Januriyadi, Nurul Fajar; Kazama, So; Moe, Idham Riyando; Kure, Shuichi

doi:10.3178/hrl.12.14

Cited by 28 publications

(20 citation statements)

References 34 publications

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“…According to Bricker et al (2014), the reduced capacity of the drainage system generated by trash-clogging flood gates is a factor that causes flooding. Budiyono et al (2016) and Januriyadi et al (2018) reported that climate change in the future would increase flood risk in Jakarta.…”

Section: Introductionmentioning

confidence: 99%

Flood inundation simulations based on GSMaP satellite rainfall data in Jakarta, Indonesia

Priyambodoho

Kure

Yagi

et al. 2021

Prog Earth Planet Sci

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Jakarta is the capital of Indonesia and is considered one of the most vulnerable cities to climate-related disasters, including flooding, sea-level rise, and storm surges. Therefore, the development of a flood-forecasting system for Jakarta is crucial. However, the accurate prediction of flooding in Jakarta is challenging because of the short flood concentration time in highly urbanized basins and the shortage of rainfall data in poorly gauged areas. The aim of this study is to simulate recent flood inundation using global satellite mapping of precipitation (GSMaP) products. The GSMaP products (NRT and Gauge V7) were evaluated and compared with hourly observation data from five ground stations in the Ciliwung River Basin. In addition, a rainfall-runoff and flood inundation model was applied to the target basin. The results of the analysis showed that the GSMaP Gauge data were more accurate than the GSMaP NRT data. However, the GSMaP Gauge cannot be used to provide real-time rainfall data and is, therefore, inadequate for real-time flood forecasting. We conclude that the GSMaP Gauge is suitable for replicating past flood events, but it is challenging to use the GSMaP NRT for real-time flood forecasting in Jakarta.

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Section: Introductionmentioning

confidence: 99%

Flood inundation simulations based on GSMaP satellite rainfall data in Jakarta, Indonesia

Priyambodoho

Kure

Yagi

et al. 2021

Prog Earth Planet Sci

Self Cite

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“…In Can Tho, Vietnam climate change-related changes such as sea-level rise and increased river flow have projected to increase flood risk within the city (Huong and Pathirana 2013). In Jakarta, mean future flood risk is projected to increase by 300-400% (Januriyadi et al 2018). However, climate change and its impacts have received little attention within research focused on FSA identification.…”

Section: Climate Change and Future Adaptationmentioning

confidence: 99%

A review of modelling methodologies for flood source area (FSA) identification

et al. 2021

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Flooding is an important global hazard that causes an average annual loss of over 40 billion USD and affects a population of over 250 million globally. The complex process of flooding depends on spatial and temporal factors such as weather patterns, topography, and geomorphology. In urban environments where the landscape is ever-changing, spatial factors such as ground cover, green spaces, and drainage systems have a significant impact. Understanding source areas that have a major impact on flooding is, therefore, crucial for strategic flood risk management (FRM). Although flood source area (FSA) identification is not a new concept, its application is only recently being applied in flood modelling research. Continuous improvements in the technology and methodology related to flood models have enabled this research to move beyond traditional methods, such that, in recent years, modelling projects have looked beyond affected areas and recognised the need to address flooding at its source, to study its influence on overall flood risk. These modelling approaches are emerging in the field of FRM and propose innovative methodologies for flood risk mitigation and design implementation; however, they are relatively under-examined. In this paper, we present a review of the modelling approaches currently used to identify FSAs, i.e. unit flood response (UFR) and adaptation-driven approaches (ADA). We highlight their potential for use in adaptive decision making and outline the key challenges for the adoption of such approaches in FRM practises.

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“…Betweenness centrality (BC) is based on the shortest path between two nodes in the network. It measures how frequently a node lies on the shortest paths among all possible node pairs (Freeman, 1977;Wang et al, 2018). The betweenness centrality of a nodes denotes the fraction of shortest paths which pass through the focal node between all node pairs (Equation Eq.…”

Section: Network Analysismentioning

confidence: 99%

Flood Impact Assessment on Road Network and Healthcare Access at the example of Jakarta, Indonesia

Klipper

Zipf

Lautenbach

2021

AGILE GIScience Ser.

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Abstract. Climate change leads to an increasing number of flood events that poses threats to a large share of the global population. In addition to direct effects, flooding leads to indirect effect due to damages of the road infrastructure that might limit accessibility of health sites. For disaster preparedness it is important to know how flood events impact accessibility in that respect. We analyzed this at the example of the capital of Indonesia, Jakarta based on the flood event of 2013. The analysis was based on information about the road network and health sites from OpenStreetMap. We assessed impacts of the flood event by comparing centrality indicators of the road network as well as by an accessibility analysis of health sites before and during the event. The flooded areas were home to 2.75 million inhabitants and hosted 79 clinics and hospitals. The flood split the road network into several subgraphs. The city center maintained its importance for time-efficient routing as well as for easily accessible healthcare but might be prone to traffic congestion after such an event. Indirect effects via interrupted road traffic through flooded areas affected around 1.5 million inhabitants and led to an increase of travel time to the nearest hospital by five minutes based on normal traffic conditions.

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Evaluation of future flood risk in Asian megacities: a case study of Jakarta

Cited by 28 publications

References 34 publications

Flood inundation simulations based on GSMaP satellite rainfall data in Jakarta, Indonesia

Flood inundation simulations based on GSMaP satellite rainfall data in Jakarta, Indonesia

A review of modelling methodologies for flood source area (FSA) identification

Flood Impact Assessment on Road Network and Healthcare Access at the example of Jakarta, Indonesia

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