Natural Disaster Risk Assessment and Area Business Continuity Plan Formulation for Industrial Agglomerated Areas in the ASEAN Region
Risk Profile Report - Bekasi and Karawang of Indonesia -
February 2015
AHA CENTRE Japan International Cooperation Agency
OYO International Corporation Mitsubishi Research Institute, Inc. CTI Engineering International Co., Ltd. Outline of the Pilot Area
Country name Indonesia
Pilot areas Industrial clusters and surrounding areas in Karawang Regency, Bekasi Regency and Bekasi City
Location of pilot Pilot areas are located in the east of Jakarta. Industrial clusters are scattered across areas Bekasi Regency, Karawang Regency and Bekasi City. There are 12 industrial parks. (The approximate area where industrial parks are scattered is indicated by a red broken line.)
Jakarta
Industrial clusters
Local Local government of Indonesia consists of two levels: the higher level is Provinces administrative (Provinsi), under which Regencies (Kabupaten) and Cities (Kota) are placed as the agencies in pilot second level. Regencies and Cities, in particular, are independent administrative areas organizations, unlike in Japan where Prefectures (Regencies) encompass Cities.
Area and Local administrative Area (km2) Population* population of pilot agencies areas Karawang Regency
Bekasi Regency
Bekasi City
West Java Province * As of 2000, partially as of 2005
Natural conditions Tropical rainforest climate with rainy and dry seasons. The risk of flooding increases of pilot areas in some parts of the areas during the rainy season.
Hazards Four rivers (Citarum, Cibeet, Cikarang and Bekasi) flow across the pilot areas. The (disasters) in pilot Cibeet River converges with the Citarum River in Karawang. The Cikarang River areas: converges with the Bekasi River. Therefore, there are two water systems: the Citarum Flood River system and the Cikaran-Bekasi River system. There are vulnerable areas where flooding occurs frequently. The areas with high risk of floods are: ・ regions lower than the point where the Citarum River meets the Cibeet River; ・ flood-prone areas of the Bekasi River; and ・ flood-prone areas of the Cikaran River.
i The industrial parks in Bekasi and Karawang are located relatively high above the sea level with less direct risk of flooding. However, there are flood-prone areas in the surrounding areas, where social infrastructures and local communities are vulnerable to floods. As a result, distribution systems, employees, etc. would be directly affected, and industrial parks would not be able to avoid indirect damage.
Hazards The Sunda Trench extends east and west in the area south of the island of Java. Along (disasters) in pilot the trench, the Indo-Australian plate dives under the Eurasian plate, which causes areas: earthquake activity similar to that in Japan. However, pilot areas would not be heavily Earthquake damaged by a huge earthquake occurring in the Sunda Trench since the areas are located in the area north of the island of Java, far away from the seismic center. That is why no seismic disasters with many casualties in Bekasi and Karawang have been recorded in the disaster database since the 20th century. However in the central mountain range of West Java Province, two inland earthquakes occurred in 1975 and 2005, which killed two persons, one in each case. Moreover, another earthquake occurred in the 19th century, which killed 7 people in the north of West Java Province.
Hazards No tsunamis with casualties in the surrounding regions of pilot areas have been (disasters) in pilot recorded in the disaster database. Earthquakes occurring at trenches generate tsunamis areas: and cause damage in the area south of the island of Java. Tsunamis would thus pass the Tsunami Sunda Channel before reaching Jakarta with a decreased wave height.
Hazards There are approximately 150 volcanoes in Indonesia, 80 of which are active. Mount (disasters) in pilot Kiaraberes-Gagak, Mount Salak, Mount Gede, Mount Tangkuban Perahu, and others are areas: within a 100 km range of the pilot areas. Volcanoes Mount Krakatau, which erupted in 1883 as the second largest eruption in the history of Indonesia, is located about 170 km west of the pilot areas. Mount Merapi, which has erupted repeatedly throughout history and also has been active in recent years, is located about 350 km east, and Mount Kelut is located about 570 km east of the pilot areas. Mount Galunggung, which erupted in 1982 and caused economic damage of 160 million dollars (0.12 % of GDP), is located about 120 km southeast of the pilot areas.
Industrial clusters There are five industrial parks developed by Japanese-affiliated companies around in pilot areas Karawang and Bekasi. KIIC (Karawang International Industrial City), chosen as the representative industrial park of the pilot areas, is one of them. The outline of KIIC is as follows. Constructed in 1992. (Planned for five stages of construction, with the third completed at present.) Developed by ITOCHU Corporation, and managed by the ITOCHU Group. 114 tenants are currently operating, 95 of which are Japanese-affiliated. Aims to provide a steady supply of electricity through preferential contract with the PLN. ・ PLN substations were built on the site and have no rental costs. Staffs are stationed at the PLN office within the industrial park. (comprehensive transformation contract) ・ The park has a guaranteed electricity supply and is excluded from the list of planned outages. Electricity supply had been insufficient until 2008, causing frequent planned outages. Subsequently, infrastructure was developed to balance supply and demand, stabilizing the electricity supply to industrial parks Industrial water: Taken from the West Tarum Canal and treated in-house at a water treatment facility (30,000 tons/day). Telecommunication: 1,000 lines from PT. Telekom. Natural gas: Direct contracts between the PGN (public gas corporation) and tenants.
ii General economic The roads along expressways in Bekasi City, Bekasi Regency and Karawang Regency conditions of pilot are the major base for Japanese-affiliated companies. The speed of economic areas development in Karawang Regency is said to be the fastest in Indonesia. Most of the laborers in this area commute from Jakarta, and these areas form the Jakarta Metropolitan area (JABODETABEK, an acronym for Jakarta, Bogor, Depok, Tangerang and Bekasi). With the benefits of economic development, the minimum wage in Jakarta has increased by 44% from the previous year, reaching the level of a little over 20,000 yen per month. Increased fuel and labor costs have, in part, imposed a heavy burden on labor-intensive industries. Consequently, some companies may start considering relocating to rural areas. However, immediate relocation to other areas is expected to be difficult, because infrastructure development around Jakarta is currently substantial compared to other areas and because Japanese-affiliated companies in particular are concentrated there (eastern Jakarta). According to the latest statistical data from the Statistical Yearbook Indonesia 2012, the regional GDP of West Java Province is 861 trillion Indonesian rupiah (as of 2011), accounting for 11.6 % of the total GDP of Indonesia. This is the third largest following Jakarta Special Province (the first at 13.2 %) and East Java Province (the second at 11.9 %). The growth rate of the regional GDP of West Java Province is 6.48 %, which is higher than the 5.16 % of East Java Province, but short of the 6.71 % of Jakarta Special Province, which also exceeds 6.32 %, the average rate of Indonesia.
BCP Various disasters such as earthquakes, volcanoes and floods are expected to occur in dissemination in Indonesia. Companies seem to consider policies on disaster prevention, however, few Indonesia cases are known in which specific plans have been discussed and formulated. Furthermore, general business operators do not understand the concept of BCP. Public lifeline providers have not developed disaster prevention plans or a BCP either. Conversely, large foreign manufacturers and trading companies have established BCPs. The national government has developed disaster prevention strategies/plans at the national level. Disaster prevention systems of companies are expected to comply with the national plans and the urban master plan under development. However, aspects other than natural disasters, such as protests and demonstrations, are taken more seriously for the risk management plans of companies.
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Contents
Outline of the Pilot Area Page Chapter 1 Disaster Risks of the Pilot Area ...... 1-1 1.1 Overview ...... 1-1 1.2 Identification of Predominant Hazards ...... 1-1 1.3 Disaster Risk for Floods ...... 1-7 1.4 Hazard and Risk Information Sources ...... 1-7 Chapter 2 Natural Hazards in the Pilot Area ...... 2-1 2.1 Floods ...... 2-1 2.2 Typhoons/Hydrometeorology ...... 2-2 2.3 Storm Surges ...... 2-2 2.4 Earthquakes ...... 2-2 2.5 Tsunamis ...... 2-3 2.6 Volcanoes ...... 2-3 Chapter 3 Outline of Natural Hazard Assessments ...... 3-1 3.1 Seismic Hazard Assessment ...... 3-1 3.2 Tsunami Hazard Assessment ...... 3-5 3.3 Flood Hazard Assessment ...... 3-9 Chapter 4 Profile of Pilot Area ...... 4-1 4.1 Outline of the Pilot Area ...... 4-1 4.2 Outline of Local Authorities ...... 4-2 4.2.1 Local Administration System of Indonesia ...... 4-2 4.2.2 West Java Province ...... 4-3 4.2.3 Karawang Regency ...... 4-4 4.2.4 Bekasi Regency ...... 4-4 4.2.5 Bekasi City ...... 4-4 4.3 Present State of Industrial Agglomerated Areas ...... 4-4 4.3.1 Industrial Parks in the Industrial Agglomerated Areas ...... 4-5 4.3.2 Japanese Chemical Products Manufacturer in KIIC ...... 4-8 4.4 Transport Infrastructure Conditions ...... 4-9 4.4.1 Roads ...... 4-9 4.4.2 Ports ...... 4-12 4.4.3 Railways ...... 4-14 4.4.4 Airports ...... 4-15 4.5 Lifeline Facilities and Public Services ...... 4-17 4.5.1 Electricity ...... 4-17
1 4.5.2 Water ...... 4-18 4.5.3 Communications ...... 4-21 4.5.4 Gas ...... 4-22 4.5.5 Waste ...... 4-23 4.5.6 Schools ...... 4-25 4.5.7 Hospital ...... 4-25 4.6 Economic Relations with Neighboring Regions and Japan ...... 4-26 4.6.1 Overview of the Economy of Pilot Area ...... 4-26 4.6.2 Major Economic Policy ...... 4-27 4.6.3 Economic Ties with Japan ...... 4-28 4.7 BCP Implementation Conditions ...... 4-31 4.7.1 Major Natural Disasters and Disaster Management Awareness ...... 4-31 4.7.2 Implementation of BCP ...... 4-32 4.7.3 Efforts for Implementing BCP ...... 4-33 4.7.4 BCP Implementation Problems ...... 4-33 4.8 Current State of Disaster Risk Management ...... 4-34 4.8.1 Questionnaire Surveys ...... 4-34 4.8.2 Review of the Questionnaire Surveys for Industrial Parks ...... 4-34 4.8.3 Review of the Questionnaire Surveys for Business Enterprises ...... 4-36 4.8.4 Review of the Questionnaire Surveys for Lifeline Utility Companies ...... 4-38 4.8.5 Review of the Questionnaire Surveys for Traffic Infrastructure Companies .. 4-40 4.8.6 Review of the Questionnaire Surveys for Local Governments ...... 4-42
Appendix Details of Natural Hazard Assessments ...... A-1 A.1 Seismic Hazard Assessment ...... A-1 A.1.1 Methodology of Probabilistic Seismic Hazard Analysis ...... A-1 A.1.2 Amplification Analysis of the Surface Ground ...... A-4 A.1.3 Expression of the Results ...... A-5 A.1.4 Simulation and Results ...... A-6 A.1.5 Evaluation of the Results ...... A-12 A.2 Tsunami Hazard Assessment ...... A-14 A.2.1 Theory of Tsunami Propagation and Selection of Simulation Model ...... A-14 A.2.2 Input Data ...... A-15 A.2.3 Output Data ...... A-19 A.2.4 Return Period of Scenario Earthquake ...... A-20 A.2.5 Results of Simulation ...... A-22 A.2.6 Evaluation of the Results ...... A-31 A.3 Flood Hazard Assessment ...... A-33 A.3.1 Overview ...... A-33
2 A.3.2 Data Collection ...... A-34 A.3.3 Target Return Period ...... A-34 A.3.4 Rainfall Analysis ...... A-34 A.3.5 Runoff Analysis ...... A-40 A.3.6 Inundation Analysis ...... A-49 A.3.7 Evaluation of the Results ...... A-59
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Disaster Risks of the Pilot Area
Chapter 1 Disaster Risks of the Pilot Area
1.1 Overview The disaster risks of the pilot area were assessed by the distribution of facilities that are mentioned in Chapter 4 overlaid on the hazard maps for seismic intensity or inundation depth, among others. (See Appendix) The main subjects of the assessment are traffic infrastructures and lifeline facilities, which are important for the continuation of businesses.
For tsunamis, floods and storm surges, the facilities in areas that will be inundated and/or are projected to be submerged are generally expected to receive damage. Past disaster records for the pilot area are the most valuable data for the assessment, because the extent of damage varies depending on the type and structure of the facilities, as well as their location. However, as past disaster records for the pilot area are not available for this study, disaster risk was assessed by referencing the disaster records from other areas.
For earthquakes, the extent of damage is determined by the intensity of seismic vibration at the location of the facility and their seismic performance. Several relationships between seismic intensity and the extent of damage of typical facilities have been proposed based on past earthquake disasters as the damage functions. The notable examples of functions are ATC-131, ATC-252 and Hazus3, which were based on damage that occurred in the U.S.A. For this study, the extent of damage and the time needed to recover are assessed based on these damage functions. Therefore, it should be noted that the assessed results in this study can be improved by referring to the local situation of the facilities.
1.2 Identification of Predominant Hazards Impacts to business continuity were assessed and are shown in Figure 1.2.1. The hazard that will most likely interrupt business in Bekasi and Karawang is flooding, followed by earthquake. This assessment is based on the overlaid maps of hazards and important facilities shown in Figure 1.2.2 – Figure 1.2.5.
The hazards in the pilot area with a probability of occurring once in 200 years are as follows.
Earthquake: Seismic intensity of 7 to 8 on the MMI Scale.
Tsunami: Maximum wave height in Jakarta of around 0.3 m for the model with a return period of over 1,000 years.
Flood: Maximum inundation depth is 4 m and the duration is more than 2 weeks.
1 ATC, 1985, ATC-13: Earthquake Damage Evaluation Data for California, Federal Emergency Management Agency, Applied Technology Council, California, U.S.A. 2 ATC, 1991, ATC-25: Seismic Vulnerability and Impact of Disruption on Lifelines in the Conterminous United States, Federal Emergency Management Agency, Applied Technology Council, California, U.S.A. 3 FEMA, 2011, Hazus -MH 2.1, Multi-hazard Loss Estimation Methodology, Earthquake Model.
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Large Risk byNatural Hazards Risk Disaster
Small Low (1/200 - 1/100) High
Probability Figure 1.2.1 Identification of Predominant Hazards
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Disaster Risks of the Pilot Area
yearfacilitiesthe important andof distribution probability a 200- with xpected xpected :
ntensity e Earthquake source model
: EZ - FRISK, Software
: NEHRP, Ground Data: NEHRP, : 1/100,000 geological map by PSG, amplification 1.2.2 Seismic 1.2.2 Figure i Probabilistic Seismic Hazard Analysis, Ground classification and map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard.
This [Analytical condition] FRISK,EZ- Conversion from PGA to MMI:Trifunac and Brady (1975), Return period : 200 years.
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: GEBCO_08, Bathymetry chart by Hydro : TUNAMI data by Tohoku Univ., Bathymetry : 1350m, 450m, 150m, 50m, Simulation 50m, duration150m, 450m, : hours,24 1350m, : Returnperiod: morethan be used for disaster scenario creation. This map is not the forecast of the future hazard. Grid size Grid
This map is intendedto [Analytical condition] Software OceanographicOffice, 1,000 years. Maximum tsunami wave height for most severe case (return period is over 1,000 years) 1,000 is over period (return case severe most for height wave tsunami Maximum 1.2.3 Figure Water Level (m) Level Water
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Disaster Risks of the Pilot Area
important facilities and the important of distribution probability year - 200 with a
Rainfall data :3B42RT based rainfall data., Elevation data GTOPO: 30, ASTER
: IFAS for Runoff analysis and iRIC for Inundation analysis, analysis, Inundation for iRIC and analysis Runoff for IFAS : Boundary condition : Five hydrographscalculated runoff with model aregiven upper as Software : 200 years. Return period Grid size : 200m, Maximum inundationexpected Maximum depth 1.2.4 Figure This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard.[Analyticalcondition] 3hours interval data are enlarged to the scale of ground - GDEM, boundary conditions.Assume thatJatiluhur damis filled andrunoff inflow from catchment isreleased control.,withno
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year probability and the distribution of important facilities distributionthe important andof probability year - 200 with a
Rainfall data :3B42RT based rainfall data., Elevation data GTOPO: 30, ASTER
: IFAS for Runoff analysis and iRIC for Inundation analysis, analysis, Inundation for iRIC and analysis Runoff for IFAS : Boundary condition : Five hydrographscalculated runoff with model aregiven upper as Software : 200 years. Inundation duration expected duration Inundation 1.2.5 Figure Return period Grid size : 200m, This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard.[Analyticalcond ition] 3hours interval data are enlarged to the scale of ground - GDEM, boundary conditions.Assume that Jatiluhurdam is filled and runoff inflow fromcatchment is releasedcontrol., withno
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Disaster Risks of the Pilot Area
1.3 Disaster Risk for Floods The disaster risk for floods, which has been identified as the predominant hazard for business interruption with a 200-year probability, was assessed and is shown in Table 1.3.1.
Table 1.3.1 Disaster Risk for Floods Buildings in Industrial parks are not inundated. Industrial Parks Substations and water treatment plants in or near industrial parks will not damaged. Lifeline Facilities Substation in Karawang City will be inundated to depth of over 2m. Some telephone/mobile phone base stations will cease operations because of the shortage of electric power. Traffic Jakarta-Cikampek Toll Road will be closed for more than 2 weeks. Infrastructures Highway No. 1 will be closed in Karawang City for more than 2 weeks. Karawang City and the surrounding area will be inundated for more than 2 weeks. Industrial Park Many employees will be absent from work because of the inundation of their Workers homes. Traffic conditions will become worse and many workers will be late for their shifts.
1.4 Hazard and Risk Information Sources
Earthquakes, Tsunamis, Volcanoes [BNPB] Badan Nasional Penanggulangan Bencana (National Agency for Disaster Management) http://www.bnpb.go.id/ [GEOSPASIAL] Badan Nasional Penanggulangan Bencana http://geospasial.bnpb.go.id/ [DIBI] Data dan Informasi Bencana Indonesia (Indonesian Disaster Information and Data) http://dibi.bnpb.go.id/DesInventar/dashboard.jsp?lang=ID [BMKG] Badan Meteorologi, Klimatologi, dan Geofisika (Meteorological, Climatological and Geophysical Agency) http://www.bmkg.go.id/BMKG_Pusat/Depan.bmkg [InaTEWS] Indonesia Tsunami Early Warning System http://inatews.bmkg.go.id/new/ [BIG] Badan Informasi Geospasial (Geospatial Information Agency) http://www.bakosurtanal.go.id/
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[BPBA] Badan Penanggulangan Bencana Aceh (Aceh Disaster Management Agency) http://bpba.acehprov.go.id/ [TDMRC] Tsunami and Disaster Mitigation Research Center, Syiah Kuala University http://www.tdmrc.org/en/ Badan Geologi (Geological Agency) http://www.bgl.esdm.go.id/ [PVMBG] Pusat Vulkanologi dan Mitigasi Bencana Geologi (Center for Volcanology and Geological Hazard Mitigation) http://www.vsi.esdm.go.id/static_content.php?id_kategori=1
Meteorological Hazards, Floods [BNPB] Badan Nasional Penanggulangan Bencana (National Agency for Disaster Management) http://www.bnpb.go.id/ [BPBD] Badan Penanggulangan Bencana Daerah (Regional Disaster Management Agency), DKI Jakarta http://bpbd.jakarta.go.id/ [BPBD] Badan Penanggulangan Bencana Daerah (Regional Disaster Management Agency), Jawa Tengah http://bpbdjateng.info/ [BPBD] Badan Penanggulangan Bencana Daerah (Regional Disaster Management Agency), Jawa Barat http://bpbd.jabarprov.go.id/ [BMKG] Badan Meteorologi, Klimatologi, dan Geofisika (Meteorological, Climatological and Geophysical Agency) http://www.bmkg.go.id/BMKG_Pusat/Depan.bmkg [LIPI] Lembaga Ilmu Pengetahuan Indonesia (Indonesian Institute of Sciences) http://www.lipi.go.id/www.cgi?depan&&&&&eng [ITB] Institut Teknologi Bandung http://www.itb.ac.id/en/ [AHA Centre] ASEAN Coordinating Centre for Humanitarian Assistance on Disaster Management http://www.ahacentre.org/
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Natural Hazards in the Pilot Area
Chapter 2 Natural Hazards in the Pilot Area
2.1 Floods
(1) The pilot area is the part of Kab. Bekasi and Kab. Karawang where the industrial estates are located. Floods in the pilot area are caused by storm rainfalls from monsoons; large floods were recorded in February 2002, February 2005, February 2007, March 2010 and January 2013. Overflowing of the Citarum (4,500 km²), Cibeet (534 m²), Cikarang (218 m²) and Bekasi Rivers (333 m²) has caused floods. Habitual inundation areas have been identified downstream of the confluence of the Cibeet and the Citarum at Karawang City, and also in the lower basins of the Cikarang and the Bekasi Rivers.
(2) The industrial estates are generally located at a higher ground level and are thus not in danger of direct flooding. However, social infrastructure such as roads and other lifelines in the surrounding areas are vulnerable to flood disaster risks, possibly affecting the logistics system and workforce. As a result, the industrial estates cannot escape indirect disaster risks.
(3) The pilot area is located in the middle basins of the rivers, and is affected by the development of their upper basins, where there are decreasing forest areas, changing land uses and increasing runoff. The upper basin area of the Citarum is the large city of Bandung and the upper basin of the Cibeet, Cikarang and Bekasi is the development area of Bogor. It is clear that both areas will be developing and expanding, and flood runoff will increase. The construction of flood control facilities is suggested in order to cope with the increased runoff. Though Kab. Bekashi and Kab. Karawang have prepared their future development plans, it is necessary for the area to develop sustainable development plans from the aspect of disaster risk reduction management.
(4) In the Citarum River Basin, there are three (3) dams (Saguling, Cirata and Jatilhur) and two (2) weirs (Curug and Walahar), which control 59% of the water resources of the Citarum River Basin. However, the other three (3) river basins of the Cibeet, Cikarang and Bekasi Rivers have no control facilities except irrigation weirs. With the increase of flood runoff, the formulation of necessary countermeasures is suggested.
Water resources in the area are managed as the Citarum Integrated Basin (12,000 km²) by the Jasa Tilta Public Corporation together with the Directorate General of Water Resources, Ministry of Public Work and the West Java Governor.
(5) Regional Disaster Risk Reduction and Management Plans (RDRRMP) for Kab. Bekasi and Kab. Karawang are still in the preparation stage. The industrial estates are not involved in the RDRRMP. However, it would be better in formulating the Area BCP for them to consider participating in the Regional Disaster Risk Reduction and Management Plan as part of the regional community.
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2.2 Typhoons/ Hydrometeorology
The pilot area is located in the Southern Hemisphere and off the typhoon route. The floods in the pilot area are caused by storm rainfalls from monsoons; large floods were recorded in February 2002, February 2005, February 2007, March 2010 and January 2013.
2.3 Storm Surges
Storm surges generated by tropical cyclones have caused severe damage on the southern coast of Java, which is the Indian Ocean side. There have been no storm surges on the northern coast of Java, the Java Sea side.
2.4 Earthquakes
The Indo-Australia Plate is subducting under the Eurasia Plate and forms the Sunda Trench to the south of Java island from east to west. Similar to Japan, this area has a great deal of seismic activity. However, the northern part of Java island where the pilot area is located is far from the seismic source area and for that reason, is less affected by the large earthquakes occuring along the Sunda Trench. Therefore, there is no record of earthquake disaster with many casualties after the 20th century in and around Bekasi and Karawang region. There were two inland earthquakes which caused one fatality each in 1975 and 2005 that occurred in the mountainous region of middle Java Barat. Also, an earthquake which caused 7 deaths occurred in 19th century.
The Global Seismic Hazard Assessment Program (GSHAP) modeled seismic activity globally and conducted probabilistic seismic hazard analysis. The published peak ground acceleration (PGA) for the pilot area with a 10% probability of exceedence in 50 years (475 years in return period) is about 300 gal on the rock site. However, according to the geological map by PSG (Geological Survey Institute), some of the pilot area is covered by Quaternary soft sediment, which is expected to amplify seismic motion by 50% on the ground surface. Conversely, when a 100-year return period is adopted for a BCP, a smaller PGA would be expected. According to an analysis by the Study Team, 120 - 150 gal (MMI 7 - 8) is expected. It is assumed that this ground motion level will cause some damage to the facilities.
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Natural Hazards in the Pilot Area
Figure 2.4.1 Earthquakes with Casualties in the Pilot Area
2.5 Tsunamis
There is no record in the disaster database of tsunami causing casualties in or around the pilot area. Earthquakes that may cause disastrous tsunami occur off the south coast of Java island along the Sunda Trench. For that reason, tsunami height becomes much lower as it passes through the Sunda Strait to the area around Jakarta. An analysis by the Study Team shows that tsunami height near Jakarta caused by a magnitude 9 earthquake along the Sunda Trench is expected to be less than 0.5 m.
2.6 Volcanoes
There are about 150 volcanoes in Indonesia, 80 of which are active volcanoes. Mt. Kiarabberes-Gegak, Mt. Salak, Mt. Gede and Mt. Tangkubanparahu are located within 100km of the pilot area.
Mt. Krakatau, which erupted in 1883 as the second largest eruption in Indonesian history, is located about 170 km west of the pilot area. Mt. Merapi is located about 350 km east and Mt. Kelut is 570 km east of the site. They have erupted repeatedly throughout history and are still active in recent years. Mt. Galunggung, whose eruption caused US$ 160 million in economic losses (0.12% of GDP) in 1982, is located about 120 km southwest of the site.
The Center for Volcanology and Geological Hazard Mitigation (CVGHM) has developed 80 hazard maps of volcanoes. On the maps, the volcanic hazardous areas are classified into the following 3 categories: “Region I: Affected by secondary risk from eruption (lahars and ash
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clouds)”, “Region II: Affected by material eruption by climatic condition” and “Region III: Directory affected by material eruption (pyroclastic flow, debris and gasses)”
An early warning system for volcanic eruption is operated by the CVGHM. The warning levels for volcanic eruption are classified into the following 4 categories: “Level I: Normal - Volcanic activity stays in normal without any difference from its background levels”, “Level II: Alert - Volcanic activity begins to increase and has passed over its background levels”, “Level III: Standby - Volcanic activity has shown its precursor before eruption” and “Level IV: Danger - Started with volcanic ash eruption, and then approaching the main eruption”.
Figure 2.6.1 Volcanos around the Pilot Area
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Outline of Natural Hazard Assessments
Chapter 3 Outline of Natural Hazard Assessments
3.1 Seismic Hazard Assessment The basic procedure of earthquake hazard assessment is shown in Figure 3.1.1. The details of each item are stated below.
Step 1: Collection and Analysis of Existing Information
Earthquake-related Information Social & Natural Information
1) Past Earthquake Disasters 5) Infrastructure Facilities
2) Existing Research 6) Natural Conditions
3) Earthquake Catalogue
4) Active Faults
Step 2: Setting of Hazard Probability
7) Probabilistic Seismic Hazard Analysis Method
Step 3: Analysis and Evaluation
8) Selection of Software for Analysis
9) Input Data Preparation
9)-1 Source Model
9)-2 Attenuation Model
10) Calculation of Baserock Motion
11) Amplification Analysis of Surface Ground
12) Expression of the Results
Figure 3.1.1 Basic Procedure of Earthquake Hazard Assessment
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Step 1: Collection and Analysis of Existing Information Earthquake-related Information
1) Past earthquake disaster records Seismic intensity distribution data and disaster records of past earthquakes in the study area are collected. The frequency and the extent of earthquake disasters can be understood by analyzing the year of occurrence, earthquake magnitude, seismic intensity distribution and damage distribution.
2) Existing research and study Existing research and study results of earthquake hazards in the area are collected. The information collected focuses on historical earthquake research, seismic hazard maps and studies on ground amplification of earthquake motion.
3) Earthquake catalogue The earthquake catalogue is a list of past earthquakes including origin, depth, year/month/day/time of occurrence, seismic magnitude and so on. As the earthquake catalogue is a base for earthquake hazard analysis, a catalogue that covers a longer period is preferable. The catalogue should include the earthquakes that occurred within a 100 km range of the study area.
4) Active faults An active fault is a fault which may generate earthquakes in the future. Data on active faults such as location, length and activity are necessary for earthquake hazard assessment.
Social and Natural Information
5) Infrastructure facilities The distribution of infrastructure facilities on which industrial agglomerated areas are dependent is studied. Transportation facilities and lifeline facilities are the main target for study. The actual region where earthquake hazard is to be assessed is decided based on the distribution of infrastructure facilities. As infrastructure facilities are widely spread outside of industrial agglomerated areas, the region of hazard analysis is not limited to industrial agglomerated areas.
6) Natural conditions Topography maps or DEM are collected as the basic information of the study area. Geological, geomorphological and land use maps are also collected to assess the amplification of earthquake motion caused by subsurface ground.
Step 2: Setting of Hazard Probability
7) Methodology of Probabilistic Seismic Hazard Analysis
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Outline of Natural Hazard Assessments
Probabilistic methodology is used in the seismic hazard assessment because the probability of hazards is important for the Area BCP. Using the probabilistic method, the expected earthquake ground motion within a certain period at the study point is calculated, considering all the earthquake sources around the study point to reflect the possibilities of each source. Any hazard that has a high possibility of occurring during the lifetime of the industrial facilities is taken into account for the Area BCP. Therefore, estimating the probability of hazards is an essential component of the analysis.
Step 3: Analysis and Evaluation
8) Selection of software for analysis As the probabilistic seismic hazard analysis involves complicated numerical calculations, many computing programs have been developed for this purpose and some of them are freely available. However, they are intended to be used by researchers or engineers with expert knowledge. EZ-FRISK is commercial software offered by Risk Engineering Inc. Analysis using EZ-FRISK is comparatively easy because the earthquake source model and attenuation formula are provided with the program.
9) Input data preparation 9)-1 Source model The earthquake source model should include all the active faults within several 100 km of the study point. The earthquakes for which sources are unknown and for which it is difficult to make a definite estimation of the magnitude/location of future events are modeled as background seismic activities. 9)-2 Attenuation formula The empirical attenuation formula is used to calculate earthquake ground motion based on the magnitude of the earthquake and the distance between epicenter and the study point. It is desirable to use an attenuation formula that was created for use on the study site. It is generally more desirable to use a new proposed attenuation formula because this newer formula is derived from precise, recent earthquake observation records.
10) Calculation of baserock motion The earthquake ground motion calculated with a probabilistic method is expressed as follows. a) The probability that the study site experiences a certain earthquake ground motion. e.g. The probability of experiencing 100 gal or more is 10% in 50 years. b) The earthquake ground motion value for a certain probability. e.g. 100 gal or more will be experienced with a probability of 10% in 50 years.
11) Amplification analysis of surface ground Seismic waves are amplified by the surface ground. There are several methodologies to evaluate the amplification characteristics of surface ground; for example, evaluation can be based on the surface soil, the average S wave velocity of surface soil layers, and numerical response analysis using the
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ground structure model. The most suitable method is selected by considering the available data, necessary work and budget.
12) Expression of the results Calculated values for ground movement are physical quantities, such as peak ground acceleration or velocity. Seismic intensity is another way to express the strength of the ground vibration caused by earthquakes and is a more popularly understandable way of expressing values. Seismic intensity is also used to estimate damage based on past earthquake disasters. Though the relationship between PGA or PGV and seismic intensity is not a one-to-one ratio, empirical formulas are used to convert the former to the latter.
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Outline of Natural Hazard Assessments
3.2 Tsunami Hazard Assessment The basic procedure of hazard assessment for tsunamis generated by earthquakes is shown in Figure 3.2.1. The details of each item are stated below.
Step 1: Collection of Existing Information
Tsunami-related Information Social & Natural Information
1) Historical tsunami disasters 4) Social infrastructures
2) Existing research and study 5) Natural conditions
3) Earthquake data
Step 2: Setting of Scenario Earthquake
6) Statistical Analysis (G-R Law) 7) Setting of Scenario
Step 3: Analysis and Evaluation
8) Setting of tsunami (earthquake) model
9) Selection of tsunami simulation model
10) Preparation of input data
10)-1 Topographical data
10)-2 Roughness data
10)-3 Sea defense data
10)-4 Initial water height
11) Tsunami simulation
Figure 3.2.1 Basic Procedure of Tsunami Hazard Assessment
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Step 1: Collection of Existing Information Tsunami-related Information
1) Records of historical tsunami disasters Inundation data and disaster records of past tsunamis in the study area are collected. The frequency and the extent of tsunami disaster can be understood by analyzing the year of occurrence, tsunami wave height, inundation and damage distribution.
2) Existing research and study Existing research and study results of tsunami hazards in the area are collected. The information collected focuses on historical tsunami research and tsunami hazard maps.
3) Earthquake data (earthquake catalogue) Data on earthquakes that have caused tsunamis is collected. The earthquake catalogue is the list of past earthquakes including the origin, depth, year/month/day/time of occurrence, seismic magnitude and so on. Earthquakes which generated tsunami are selected.
Social and Natural Information
4) Social infrastructures Information on industrial agglomerated areas and social infrastructure that would be potentially affected by tsunami will be collected. Social infrastructures are categorized into transportation infrastructures and lifelines. The actual region where tsunami hazard is to be assessed is decided based on the distribution of infrastructure facilities. As infrastructure facilities are widely spread outside of industrial agglomerated areas, the region of hazard analysis is not limited to industrial agglomerated areas.
5) Natural conditions Topography maps, bathymetry maps or DEM are collected as the basic information of the study area. Land use maps and geological maps are also useful for more precise analysis.
Step 2: Setting of Scenario Earthquake A scale of tsunami disaster for risk assessment is decided based on the collected data. The scale of tsunamis is defined as the scale of the earthquake which generates the tsunami.
6) Statistical analysis of earthquake (Gutenberg-Richter Law) The recurrence interval of scenario earthquakes that generate tsunami is decided based on the earthquake catalogue around the source area of scenario earthquakes. The relationship of earthquake magnitude and the frequency of earthquake occurrence, which is known as the Gutenberg-Richter Law is used. The annual probability or recurrence interval of the scenario earthquake of arbitrary magnitude can be estimated.
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Outline of Natural Hazard Assessments
7) Setting of scenario earthquake A scenario earthquake and its magnitude should be set for the Area BCP. If a larger magnitude is assumed, the components of the Area BCP increase and the process to formulate it becomes more complicated. However, business continuity after a disaster will be more stable. Conversely, if a smaller magnitude is assumed, the Area BCP can be more easily formulated. However if the disaster is larger than the estimated scenario, business continuity may become difficult. Therefore, it is desirable to decide the magnitude of the scenario earthquake by holding discussions with stakeholders, including citizens, on local disaster management planning, governmental policy and feasibility of the plan.
Step 3: Analysis and Evaluation
8) Setting of tsunami (earthquake) model The fault parameters of the scenario earthquake such as location, size, slip, etc. are decided for numerical simulation.
9) Selection of tsunami simulation model Tsunami propagation simulations require complicated numerical calculations. There are several theories to describe tsunami propagation, depending on the relationship between wavelength and depth of water or distance to the tsunami source area. Several computing programs have been developed in for these theories; however, most of them are intended to be used by researchers or engineers with expert knowledge.
10) Preparation of input data The general input data for tsunami simulation is as follows. This data is given to each grid, which is explained below. 10)-1 Topographical data For the simulation, it is necessary to create a topographical model for the area that includes the source area of the scenario earthquake, the objective area, and the route of tsunami propagation. The topographical model includes the topography of the sea floor, the topography of the land surface where tsunami might run up, and the sea defense structures. The simulation area is divided and covered by a square grid, and altitude and roughness data are allotted to each grid. The size of grid is appropriate defined, taking into account the complexity of the topography and the wave length of the tsunami. The grid size is usually defined from larger to smaller according to the distance from coast, considering that the topography becomes more complex and shorter wave components become dominant near the coast. This methodology is called "nesting." The grid size is defined as, for instance, 1350 m → 450 m → 150 m → 50 m from the tsunami source region to the coast.
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10)-2 Roughness data The effect of friction for tsunami wave propagation is expressed by Manning's roughness coefficient (n). The roughness coefficient usually adopted for marine areas is n=0.025. 10)-3 Sea defense data Embankments and other sea defense structures are modeled as height data in the grid. 10)-4 Initial water height data (= deformation of sea floor) Changes of water heights caused by fault movement are given as initial conditions for tsunami simulations. Changes of water height are assumed to be same as the vertical components of sea floor deformation.
11) Tsunami simulation The general output of the tsunami simulation is as follows. Output items are obtained for each grid. 1. Maximum water height or maximum inundation height 2. Maximum velocity 3. Elapsed time of maximum water height 4. Elapsed time of given water height (x cm, for instance)
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Outline of Natural Hazard Assessments
3.3 Flood Hazard Assessment The basic procedure of Flood Hazard Assessment is shown in Figure 3.3.1. The detailed procedure is described below.
Flood-related Information Social & Natural Information
1) Collection and organization 3) Information on social Step 1 of existing records infrastructure Collection 2) Collection and organization 4) Information on natural of existing of hydrological data conditions information
5) Frequency analysis of hydrological data Step 2
Setting of target flood 6) Setting of design flood scale scale
7) Selection of runoff analysis model
Step 3
8) Construction of runoff analysis model and conducting the analysis Flood analysis
8)-1 Analysis of past floods
8)-2 Analysis of designed flood
Figure 3.3.1 Basic Procedure of Flood Hazard Assessment
Step 1: Collection of existing information Flood-related Information
1) Collection and organization of existing flood record Flood-related information in the target area for flood risk evaluation is collected. Collecting data such as rainfall during floods, water level, and river discharge makes it possible to grasp the characteristics of floods. Inundation areas, duration time, water depth, and the cause of the flood as indicated in damage reports or photographs are also helpful in understanding the phenomena of inundation. If a flood hazard map is available in the target area, the information (inundation area, duration time and depth) shown in the map will be utilized.
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2) Collection and organization of hydrological data Existing hydrological data in target area is collected. Data on rainfall (hourly data or daily data), river water level, river discharge and tidal level should be collected. If there are river facilities such as dams or gates, it is advisable to collect the operational reports of these facilities during floods. Before organizing this data, confirm if there is incorrect or missing data. Based on the collected hydrological data, flood duration, probability of flood occurrence, and situation when the largest recorded flood occurred will be analyzed. Cross-cutting profile data is also helpful in this step of model construction.
Information on Social Infrastructure and Natural Conditions
3) Collection and organization of information on social infrastructure Information on industrial estates and social infrastructure that would be affected by flooding is collected. Social infrastructure can be divided into two categories: the transportation infrastructure related to transport to and from industrial estates and the lifeline infrastructures necessary to maintain business operations. The actual region where flood hazard is to be assessed is decided based on the distribution of infrastructure facilities. As infrastructure facilities are widely spread outside of industrial agglomerated areas, the region of hazard analysis is not limited to industrial agglomerated areas.
4) Collection and organization of information on natural conditions Topographic maps are collected, and data relating to natural conditions such as altitude, land use pattern and geology is organized. From the aspect of data accuracy, it is advisable to use a detailed map with a scale of 1/5,000 or more.
Step 2: Setting of target flood scale Based on the data collected in Step 1, the target flood scale for formulating the Area BCP is set. The basic flood scale set as the largest recorded flood, 50-year return period, 100-year return period and 200-year return period.
5) Frequency analysis on hydrological data Probable hydrological value is calculated using collected hydrological data. The procedure for processing statistics will involve applying probability density functions such as exponential distribution, evaluating the probability density function, and then determining the appropriate probability density function. For reliable probability density function results, samples for at least 50 years are needed.
6) Setting of design flood scale The design flood scale for formulating the Area BCP is set. If the designed flood is large in scale, there are more components for formulating the Area BCP. In this case, a considerable amount of work is necessary to formulate the Area BCP, but level of safety in the plan is high. Conversely, if the designed
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Outline of Natural Hazard Assessments flood scale is small, the target scope of the Area BCP becomes limited, which makes it easier to formulate the Area BCP. In this case, there is a possibility the Area BCP will not be effective. Hence, design flood scale should be set in accordance with regional city plans, administrative strategy, and feasibility of plan, and upon discussions with stakeholders, including local residents.
Step 3: Runoff analysis/evaluation
7) Selection of runoff analysis model The appropriate model for analyzing the characteristics of floods in target area is selected. The appropriate analysis model should be selected from the viewpoint of runoff characteristics, required resolution, and financial capacity for purchasing software. At first, it is preferable to use free software like IFAS for formulating the Area BCP.
8) Construction of flood analysis model and conducting the analysis 8)-1 Analysis of past floods Flood analysis with the selected model and past rainfall data is conducted. After that, simulation accuracy is confirmed by comparing the results of the simulation with actual discharge records. If the precision of the simulation is not satisfactory, attempts to improve the accuracy will be made by modifying parameters. 8)-2 Analysis of designed scale flood Runoff analysis on the designed flood is conducted.
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Chapter 4 Profile of the Pilot Area
4.1 Outline of the Pilot Area
In Indonesia, the following three local governments in West Java Province were selected as the pilot area. Karawang Regency (in Indonesian: Kabupaten Karawang) Bekasi Regency (in Indonesian: Kabupaten Bekasi) Bekasi City (in Indonesian: Kota Bekasi)
In Karawang Regency and Bekasi Regency, there are many industrial parks where many Japanese companies are engaged in operations. Almost 60-70 percent of the total industry in West Java Province is concentrated in these areas.
The local governance system of Indonesia is divided into provinces (Provinsi), regencies (Kabupaten), and cities (Kota). Each are said to be highly independent and have a relatively large amount of power and authority. Regencies and cities are on the same level, and are mutually independent, unlike Japan.
Figure 4.1.1 Distribution of Pilot Areas1
Area West Java Province: 34,816.96 km2 Karawang Regency : 1,737.3 km2 Bekasi Regency : 1,484.4 km2 Bekasi City : 210.49 km2
1 POSKOTA Website: http://www.poskota.co.id/berita-terkini/2011/04/08/pemekaran-jawa-barat-terhambat-dana ASEAN-Japan Centre Website: http://www.asean.or.jp/ja/asean/know/country/indonesia.html (in Japanese)
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Population (as of 2010) West Java province: 43,053,732 Karawang Regency : 2,135,200 Bekasi Regency : 2,642,578 Bekasi City : 2,378,211 Natural conditions The pilot areas have a tropical rain forest climate, with rainy and dry seasons. In the rainy season, the flood risk becomes high in some parts of the areas. Other features Most industry in West Java province is concentrated in these pilot areas.
4.2 Outline of Local Authorities
4.2.1 Local Administration System of Indonesia
As mentioned previously, the local governance system of Indonesia is divided into provincial governments and regency or city governments.
(Apart from the above, there are also the units of subdistrict (Kecamatan) and administrative village (Kelurahan), which are internal agencies of the regency and city governments. There are also natural villages, which perform traditional self-governance.)
Each local government has lower administrative organizations such as a secretariat office, administration offices, technical organizations and agencies, etc. under the local chief executive and the vice local chief executive, and each body performs various assigned tasks. For example, BAPPEDA (Badan Perencanaan Pembangunan Daerah = Regional Development Planning Agency) is in charge of city planning and infrastructure development, and BPBD (Badan Penanggulangan Bencana Daerah = Regional Disaster Management Agency) is in charge of natural disaster management.
BPBD is regionally affiliated agency of BNPB (Badan Nasional Penanggulangan Bencana = National Disaster Mitigation Agency), which is a central administrative body. BNPB, which was established in 2008 as a non-departmental agency similar to the ministries, is a comprehensive disaster management implementation and coordination body. A BPBD is to be established in every province, regency, and city. As of 2012, all 33 provinces have established BPBD, while only 395 of all regencies and cities have BPBD.
The following is the governance system of local authorities in Indonesia and Indonesia’s disaster management structure.
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Province 33 in total
Regency (Kabupaten) 349 in total, City (Kota) 91 in total Regency / City Government Internal organization Subdistrict Government 5,656 in total Internal organization Administrative Village Natural Village
71,563 in total Figure 4.2.1 Governance System of Local Authorities in Indonesia2
National Level------President
Ministries/Departments Non Departments/Institutions
BNPB
Governor Provincial Level------
Services Agencies BPBD
Regency/Municipal Level------Regent/ Mayor
Services Agencies BPBD
District Level------CAMAT (Head of District)
Head of Village Village level------
Community
Legend: order/ guidance coordination request/ report/ coordination/ (downward) guidance
Figure 4.2.2 Indonesia’s Disaster Management Structure3
The outline of each local government in the pilot areas is described in section 4.2.2.
4.2.2 West Java Province
West Java is one of the provincial governments located in western part of Java Island. There are 17 regencies and 9 cities within the province including Karawang Regency, Bekasi Regency and
2 Council of Local Authorities for International Relations. (2009). Local Authorities in Indonesia. (in Japanese) 3 Japan International Cooperation Agency (JICA). (2012). Data collection survey on ASEAN regional collaboration in disaster management, Draft final report, Country report Indonesia.
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Bekasi City, which are the pilot areas of the study. It has the largest population and the highest population density in Indonesia. It is also one of the main industrial areas in Indonesia, and many industrial parks, including those with investments from foreign companies in Japan and South Korea, have gathered in this province. The provincial capital is Bandung.
4.2.3 Karawang Regency
In Karawang Regency, there are many industrial parks such as KIIC (Karawang International Industrial City), which is the pilot industrial park for this study. KIIC was developed and is managed by the Itochu Corporation together with the Sinar Mas Group (local Indonesian capital). Many Japanese companies are tenants of KIIC. Karawang Regency is less than 60 km (over an hour by car) from the central part of Jakarta.
4.2.4 Bekasi Regency
There are also many industrial parks here, similar to Karawang Regency. Since it is also close to Jakarta, it is one of the leading industrial areas in the West Java Province. The Jababeka Industrial Park, which is the largest industrial park in Southeast Asia, is located in Cikarang, a central city of Bekasi Regency. Cikarang is located at the midpoint between Bekasi City and Karawang Regency, and it is about 50 minutes by car from the central part of Jakarta.
4.2.5 Bekasi City
Bekasi City is a part of Jabotabek, which refers to the city zone around Jakarta. It is about 30-40 minutes by car from the central part of Jakarta. It has large population as a commuter town. There are also many industries, and many local and foreign companies including Japanese companies are developing operations in this area. However, within the city, there are very few industrial areas with concentrations of manufacturing companies.
4.3 Present State of Industrial Agglomerated Areas
The team visited Karawang International Industry City (KIIC), which is one of the industrial parks in West Java Province, since it was selected as the industrial park to be used for simulations, representing other parks in the industrial agglomerated areas. The team selected the following industrial parks in West Java Province for the questionnaire survey. 1) Bekasi International Industrial Estate (BIIE) 2) East Jakarta Industrial Park (EJIP) 3) Greenland International Industrial Centre (GIIC) 4) Jababeka Industrial Park (JIP) 5) Lippo Cikarang Industrial Park (LPIP) 6) MM2100 Industry Town (MMIT)
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7) Marunda Industry Park (MIP) 8) Karawang International Industry City (KIIC) 9) Kujang Cikampek Industrial Estate (KCIE) 10) Mitra Karawang Industrial Park (MKIP) 11) Bukit Indah City Industrial Estate (BCIE) 12) Surya Cipta Industrial Estate (SCIE)
Twelve industrial parks are shown as the shaded areas on the map issued by BIG4 in Figure 4.3.1.
Source: CRM, Local Consultant Figure 4.3.1 Industrial Parks in the Industrial Agglomerated Areas in West Java Province
4.3.1 Industrial Parks in the Industrial Agglomerated Areas
For a field survey conducted in August 2013, a JICA Team visited KIIC as the model park and MM2100 for interviews. Both parks were developed by major Japanese trading houses. The following is a summary of these industrial parks.
Karawang International Industrial City (KIIC)
KIIC was constructed in 1992 as a modern industrial park located in the tree-line region of West Karawang Regency. It is a joint venture between Sinar Mas Land of Indonesia and the Itochu Corporation of Japan. It encompasses an area of 1,400 hectares. There are 125 business enterprises as tenants, including 102 Japanese affiliated companies (82%).5
4 Bakosurtanal, a special government agency dealing with geospatial and maps 5 Source: www.kiic.webs.com
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KIIC is the first industrial park in Indonesia to acquire ISO 9001/2000 certification It also acquired certifications for ISO 14001/2004 for Quality and Environmental Management Systems in 2002 and OHSAS 18002/2007 for Health and Safety Management Systems.
Source: http://www.itochu.co.jp/en/news/2012/121022.html Figure 4.3.2 KIIC and Infrastructure
2013 marks KIIC’s 21st anniversary from its inception in 1992. More than 80% of the customers are Japanese affiliated companies due to the high quality of park management and administration for the past 20 years. Large national and multi-national corporations who are tenants at KIIC include Toyota Motor Manufacturing Indonesia, HM Sampoerna, Yamaha Motor Indonesia, Astra Daihatsu Motor, Panasonic Semiconductor Indonesia and Sharp Semiconductor Indonesia.
The master plan of KIIC development consists of the five phases in total. So far, the third has been completed. According to KIIC management, decisions on further development will be made based on careful business evaluations.
KIIC entered into a premium electrical power purchase agreement with PLN, which ensures a stable supply of power to tenants at KIIC. This agreement is the first contract with premium status in Indonesia. KIIC provides land for transformers free of charge, which is where PLN has installed transformers6 that are used exclusively by KIIC.
Scheduled black-outs were frequently seen up to 2008 due to power shortages. With PLN’s investments in infrastructure improvement, the power supply and demand balance for industrial parks has been stable since 2009. 1) Even if a black-out is planned, the power supply to KIIC is ensured at all times because of the premium power purchase agreement with PLN. 2) In the area developed for the third phase, there are additional transformers installed off the KIIC site. This allows for a dual power supply when combined with the original transformers on the
6 180MW (60MW x 3)
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KIIC site. 3) KIIC assists tenant companies until their power is connected. After this, tenant companies will contract directly with PLN. 4) Voltage: 20kV (To reduce voltage to 220V or 380V, each tenant company must install their own step-down transformers.) 5) Frequency: 50Hz 6) PLN Tariff system ( over 201kVA) - Connection Fee: Rp. 430,000/kVA - Down Payment: Rp. 550,518/kVA (deposit, advance payment) - Basic Charge: Rp. 42,644/kVA (monthly charge) - Usage Charge: Rp. 1,121/kWh (flat rate)
7) Industrial Water: Intake from West Tarum Canal / In-house treatment by KIIC (30,000 tons/day) 8) Telecommunication: PT. Telekom, 1,000 lines available 9) Natural Gas: Direct contract with PGN
MM2100 Industrial Town (MMIT)
Constructed in 1990, MM2100 Industrial Town is a fully integrated industrial park developed by PT Megalopolis Manunggal Industrial Development (MMID), which was established by the Marubeni Corporation of Japan and the Manunggal Group of Indonesia (owned by the Nin King). The 3-phase master plan was completed in 1998.
Figure 4.3.3 MM2100
Located in Cibitung, Bekasi Regency, MM2100 is situated on 1,450 hectares of land. Part of the land (200 hectares) is developed and managed by PT Bekasi Fajar Industrial Estate, with the remaining 1,250 hectares controlled by PT Megalopolis Manunggal Industrial Estate. MM2100
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offers one-stop service for securing investment licenses. It also has also adequate infrastructure, similar to the other industrial parks. As of September 2013, with 176 tenants (167 in operation) and 406 supporting companies, MM2100 Industrial Town is one of the best value industrial parks in Indonesia, housing 120 Japanese affiliated companies (68%). The total number of employees is around 13,350 workers.7 Thirty-five percent of the tenants are companies related to automobile and motorcycle manufacturing.
A stable source of electric power is supplied by IPP’s PT. Cikarang Listrindo. None of the tenants have back-up generators, because a stable power supply is ensured by IPP. Since PLN increased their rates, IPP has become quite competitive. MM2100 does not use a back-up system, because the sale of electric power by MM2100 is not allowed by regulations. 1) Charges and tariffs for electric power Deposit: charges for a 2-month billing period Connection charge: Fixed amount paid to IPP (PT. Cikarang Listrindo) Medium Voltage 1,600kVA~: US$49,000 / 202kVA~1,600kVA: US$35,000 Low Voltage ~201kVA: Rp. 450,000 × kVA Variable Amount paid to MMID US$66 × kVA Charges: Capacity Charge: Rp. 9,000/kVA × Exchange rate factor (Rp. To US$) Usage Charge: Rp. 140 × Exchange rate factor (Rp. To US$) Electricity tax8 2.4% × (Capacity Charge + Usage Charge) 2) Industrial water is taken from the West Tarum Canal. MM2100 treats water using their own system with a capacity of 42,000m3/day. Connection charge (depending on size of water pipe) Size 1~1.5: US$ 20,000 Size 1.5 : US$ 40,000 Size 2~ : US$ 60,000 3) Telecommunications: 3,000 lines available through PT. Telekom. 4) Natural Gas: Supplied through direct contracts with PGN.
4.3.2 Japanese Chemical Products Manufacturer in KIIC
KIIC provided an introduction to a Japanese chemical manufacturer for an interview. This interview focused on their previous experiences with natural hazards, lifeline utilities, and traffic
7 Source: www.mm2100.co.id 8 Local Government (Bekasi Regency) Regulation No.1, Year 2011
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infrastructure during disasters. This factory commenced operations in September 1998. Their main product is silicon wafers.
There were two earthquakes in the 16 years since the commissioning of the factory. The president of the company worries more about the smoke from burning fields and volcanic ash than normal natural disasters. This is because this smoke and/or ash may compromise production quality. There is annual flooding, which prevents the employees from commuting to work. However, only 10% of employees fail to come to the factory, which does not harm daily manufacturing. The company is not concerned with the disaster risk of floods because of the higher9 level of the land. 1) Business Continuity Plan Every three months, the company reports the business risk analysis to their headquarters in Japan as one of the measures in their Business Continuity Plan. 2) Inventory control Issues on inventory control are handled by having three months of products in stock. 3) Lifeline utilities Prior to 2008, there were a many power failures because of the gap between electricity supply and demand. However, since 2009, the electricity supply to industrial parks has been stable because of improvement to the infrastructure. 4) Traffic infrastructure The company complains about insufficient infrastructure. For the moment, there is only one highway. (i.e. the Jakarta-Cikampek toll road.) Continuous traffic jams are a major issue for daily business.
4.4 Transport Infrastructure Conditions
4.4.1 Roads
(1) Outline
Target roads are mainly the roads in the Special Capital District of Jakarta (DKI Jakarta), Bekasi City, Bekasi Regency, and Karawang Regency ― the last three areas are in the West Java Province. These areas are those around the capital and the primary industrial agglomerated areas. The important road that connects them is the Jakarta-Cikampek Highway (toll road) from Jakarta to Cikampek.
This road connects to the highway from Cikampek to Bandung, and has become a trunk road connecting Jakarta, industrial agglomerated areas, and Bandung. However, chronic traffic congestion is caused by a heavy concentration of traffic inbound for Jakarta due to the increase in
9 40 m above sea level
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the traffic demand, freight transportation and commuters in recent years. The congestion between the Jakarta and Bekasi is especially remarkable.
Figure 4.4.1 Traffic Environment around Industrial Agglomerated Areas A rapid increase in the population of DKI Jakarta and Bekasi areas has been observed, and it is one cause of highway traffic congestion. Traffic congestion has also been caused by an increase in traffic due to the industrial estates.
Table 4.4.1 Population and its Density around the Highway Area Population Density Population (x 1,000) 2 Area (km ) (2010) 2 1980 1990 2000 2010 2010 (People/km ) DKI Jakarta 6,503 8,210 8,364 9,588 663 14,470 Bekasi Area 1,143 2,073 3,200 5,021 1,480 3,393 Source：Population Census of Indonesia
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Traffic Capacity
(pcu / day) (pcu 8 Lanes 6 Lanes Traffic Volume Traffic
Source: Ministry of Economy, Trade and Industry’s 2nd Jakarta-Cikampek Highway Feasibility Study Report of Indonesia 2013.2 Figure 4.4.2 Congestion on Jakarta-Cikampek Highway
National Route 1 (Trans Jakarta), which is a part of the Asian Highway, serves as a trunk road from Jakarta, passing through northern Java on the north side of the Cikampek Highway. It connects main cities and has played an auxiliary role to the Cikampek Highway. In industrial agglomerated areas, regency roads in the direction of north to south can be seen. These roads, along with Kabupaten roads make an important community road network for local resident traffic.
(2) Disaster Example
Direct damage caused by flooding has not occurred in industrial agglomerated areas or to the highway itself. However, on the 72 km highway, inundation due to the insufficient drainage capacity of the rivers on both sides of the highway has occurred often in the residential areas at the 14-km, 19-km and the 41-km points. The highway itself serves as the evacuation area. Additionally, the road between Bekasi and Jakarta experiences particularly excessive traffic congestion during flooding, causing complete gridlock.
Almost all areas on the north side of the highway are low-lying, making them prone to flooding and consequently, frequent gridlock.
(3) Commuting Means
Although shared busses or vans are used to transport workers in industrial estates, general commuters use mass transit (bus, station wagon bus, etc.).
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Table 4.4.2 Commuters’ Means of Transportation Commuters’ Means of Transportation Urban Area Public transportation Individual transport (Mass Transit) (passenger car, motorbike, Walking etc.) Jakarta City East 55.4 39.3 5.4 Bekasi City 57.9 38.5 3.6 Depok City 58.4 34.6 7.0 Bandung Regency 48.6 31.4 20.0 Bogor Regency 82.5 14.1 3.4
Source: Profil Komuter, 2005 Badan Pusat Statistik
(4) Roads for Physical Distribution
Physical distribution has generally arisen between industrial agglomerated areas and the Tanjung Priok Port in Jakarta, which generates a large amount of truck traffic on the Cikampek Highway.
However, due to traffic congestion, round-trip physical distribution from industrial agglomerated areas to the port takes an entire day, making it far from efficient. JASA MARGA, which manages the highway, is making efforts to strengthen the surveillance system, aiming to find and resolve traffic problems within 30 minutes of their discovery.
4.4.2 Ports
(1) Outline
The Tanjung Priok Port in the northeast of Jakarta is located at the center of not only the Jakarta metropolitan area, but also the largest industrial, commercial, and consumer belt in Indonesia. It is also an international industrial port used as Indonesia’s base for marine shipping.
The port covers a mooring area of 422 ha and land area of 630 ha, spanning 6 km in an east/west direction. It consists of five ports, three container terminals, and five berths. The length of the berths is 18,185 m. In 2011, there were 18,914 ship calls and 5,649,119 TEU were shipped. The port is also an important international port among ASEAN countries, where average annual growth has been recorded at 26%.
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⑥
⑦ ⑧ ③ ④
① ② ⑤
Legend: Terminal I, Terminal II, Terminal III, JICT (Jakarta International
①Container Terminal)②③ , Koja Terminal④ , TPT (T⑤⑥anjung Priok Car Terminal) Figure⑦ 4.4.3 Tanjung⑧ Priok Port Table 4.4.3 Container Cargo Throughput Terminal 2007 2008 2009 2010 2011 Jakarta International 1,821,292 1,995,781 1,675,395 2,095,008 2,265,202 Container Terminal Koja Container 702,861 704,618 620,172 754,592 839,245 Terminal Conventional 1,165,630 1,283,879 1,509,338 1,762,912 2,544,672 Total (TEUs) 3,689,783 3,984,278 3,804,905 4,612,512 5,649,119 Source: Statistical Yearbook of Indonesia
There are two bay entrances, with one on the east side and one on the west. Ordinarily, only the west side, with a depth of 14 m, is used. Off the harbor, a 72 ha area of reclaimed land has been extended. Additionally, a port extension development project (North Calibaru Development Project) which will build a quay of 1,200 m is underway, which will resolve the shortage of harbor facilities and meet the rapidly increasing demand.
Although trucks are generally used to move container in and out of the port, rail transport is also used to the extent possible, with 26-car train making 12 round trips per day.
(2) Issues
The major issues for the port are as follows.
1) It is becoming difficult for port facilities to accommodate the increase in container transaction volume.
2) A new elevated road (JOOR: Jakarta Outer Ring Road) from Tanjung Priok Port to the industrial agglomerated areas is under construction. However, even upon its completion, it will not resolve
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present traffic congestion. Thus, a new bridge/road over the sea it planned to connecting to Marunda.
3) The IPC (Indonesia Port Corporation), which manages the Tanjung Priok Port, does not have a disaster prevention plan because the port is generally not impacted by floods, with no damage from earthquakes or tsunamis on record.
4.4.3 Railway
The Java Trunk Line runs along the north side of the industrial agglomerated areas from the east to the west. It is a single line, and not yet electrified. However, since the amount of transportation by rail and the number of trains connecting between cities is increasing, the facilities have reached maximum capacity in recent years.
In particular, the Bekasi line (between Manggarai station and Bekasi station) uses tracks shared between long-distance lines and the commuter lines, and with many grade crossings, problems have arisen in terms of operation and management.
Moreover, at the Cikarang station on the north side of the center of industrial agglomerated areas, only about five trains depart and arrive per day. Therefore, the transport capacity of the Java arterial railway is low. The increase of the railroad users in the JABODETABEK area has also stopped. The construction of a 4-track line on the Java arterial railway and a new transportation system for feeder traffic to the MM2100 industrial complex is thus being considered, as improvements to the level of service for commuter transportation are necessary.
(1,000 persons) Sumatera Jabodetabek (Java) Sumateranes Jabodetabbek
Java Lines
JABODETABEK area: Jakarta and its surrounding 4 cities and 3 districts. Source: JBIC, Indonesia Investment Environment 2012 Figure 4.4.4 Transition of the Number of Railroad Users
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4.4.4 Airport
In the Jakarta Metropolitan area, there are currently four airports: Soekarno Hatta International Airport, Halim Perdanakusuma Airport, Pondoc Cabe Airport, and Chrug Airport, each with their own role.
Soekarno Hatta International Airport is located at about 20 km west of Jakarta. It started operations at 1985, mainly targeting international flights. Passenger traffic in 2012 was 58 million people, which is an increase of 12.1% over 2011. The airport does not have the capacity to respond to the rapid increase of users. It has become the ninth busiest airport in the world. Currently, the airport uses the following three terminals, with plans to add one more. 1) Domestic terminal 2) Terminal for international flights and for P.T. Garuda Indonesia 3) Dedicated terminal for LCC
There are also terminals for cargo and for Hajj. There are two runways, which are 3,660 m x 60 m, respectively. The area of the apron is 830,142 m². The area of the passenger terminal is 338,728 m² and the cargo facility is 70,794 m². The volume of cargo handled in 2012 was 342,473 metric tons.
Source: Ministry of Economy, Trade and Industry’s, Indonesia Soekarno Hatta International Airport Extension Project Study Report, 2012.2 Figure 4.4.5 Soekarno Hatta International Airport
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Table 4.4.4 Soekarno Hatta International Airport Usage Air cargo Number of arrivals and Year Passengers (tons) departures 2008 32,172,114 465,799 245,482 2009 37,143,719 538,314 287,868 2010 44,355,998 633,391 338,711 2011 52,446,618 617,716 345,495 2012 57,772,762 342,473 369,740 Source: Wikipedia, Soekarno-Hatta International Airport
Since traffic infrastructure going to this airport from within Jakarta does not include a railway, the airport is only accessible by car or bus. Traffic congestion is a daily occurrence, diminishing the convenience of the airport. Moreover, road embankments were built on areas with soft ground, causing flooding during the rainy season. This also obstructs traffic. Additionally, the BCP does not include measures for electric power equipment is not made, and air traffic control system is not operated in a stable manner.
Halim Perdanakusuma Airport is located 11 km east of central Jakarta, and access from the eastern industrial agglomerated areas and central Jakartat is convenient. Although this airport mainly handles the arrivals and departures of VIP chartered flights and warplanes, it is also in charge of transporting precision components and products from the industrial agglomerated areas. However, regular air service has been restricted for non-commercial flights, and the number of users is decreasing. Table 4.4.5 Halim Perdanakusuma Airport Usage Number of arrivals and Year Passengers Air cargo (tons) departures 2011 69,499 531,139 5,281 2012 42,094 259,512 5,484 Source: PT Angkasa Pura II
Since the airport was formerly an international airport the runaway is 3,000 m x 45 m, which is sufficient for domestic flights. Five airlines currently use this airport.
The other two airports have problems regarding access, and are thus not included in this report.
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4.5 Lifeline Facilities and Public Services
4.5.1 Electricity
(1) Outline
Power generation and power transmission is conducted by National Perusahaan Listrik Negara (PLN). The Bekasi and Karawang branch offices are in charge of supplying electric power to the region. Electric power borne by the Province of Java Barat is as follows. Although small-scale thermal power plants are located in the Province of Java Barat, most electric power is generated and provided by the other provinces. Table 4.5.1 Electricity in West Java Province Installed Capacity Produced Electricity Sold Electricity Year (MW) (x 1,000 MWh) (x 1,000 MWh) 2007 1 14 32,337 2008 1 49 34,051 2009 1 40 35,701 2010 1 96 38,671 2011 1 34 41,328 Source: Statistics-Indonesia, Statistical Yearbook of Indonesia 2012
There are various means of generating power including many coal-fired thermal power stations and combined cycles, followed by hydro-power generation. Table 4.5.2 Production of Electricity of PLN Item Production Means 2010 (GWh) PNL Generation Hydro 15,827.35 Facility Coal-fired 54,407.02 Gas Turbine 7,861.70 Combined Cycle 26,811.70 Geo thermal 3,398.02 Diesel 5,096.98 Diesel Gas 73.56 Solar 0.50 Wind 0.02 Rental 8,233.21 Subtotal 131,710.07 Purchase 38,076.16 Total 169,786.23 Comparison with 7.39 % the previous year Source: PLN, PLN Statistics 2010
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Figure 4.5.1 Power Grid
(2) Electric Power in Industrial Parks
Serious electric power shortages have not arisen in the industrial parks since priority contracts for supply have been signed with PLN. Therefore, the frequency of power failure is limited to a few times per year, with each blackout being short.
(3) Power Outages in Residential Areas
Although power outages in residential areas have occurred with remarkable frequency during every flood, the War Room set up at each branch office responds immediately to restore power within one day.
4.5.2 Water
(1) Water Supply
The tap water in Bekasi City, Bekasi Regency, and Karawang Regency comes from the Citarum River, which flows from the Jatiluhur Dam. Water is taken from the WTC (West Tarum Canal), where water flows from east to west.
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Source: JICA MPA Study Team
Figure 4.5.2 Present Flow in Water Supply
The water demand in Bekasi City/Regency and Karawang Regency is as follows. Table 4.5.3 Water Demand 2010 Bekasi Karawang Item City/Regency Regency Population 4,966,040 2,125,234 Water Supply Population 1,087,772 369,216 Water Supply Diffusion Rate 21.9 17.4 Water Supply per Capita 125 78 Basic Demand 136,486 28,673 Rate of Non-revenue Water (%) 18.5 39 Water Demand (m3/day) 167,447 47,018 Water Demand (l/s) 1,938 544 Special Demand (l/s) 0 0 Total Demand (l/s) 1,938 544 Source: MPA Study 2012.11
(2) Industrial Water
The present condition of industrial water and the demand in 2020 are shown below. Table 4.5.4 Industrial Water Demand 2010 Increase (2010 – 2020) 2020 Area Present Use Industrial Park Area Water Demand Water Demand (l/s) (ha) (l/s) (l/s) Bekasi 2,242 3,370 1,348 3,590 Karawang 3,630 620 248 3,878 Total 5,872 3,990 1,596 7,468 Note: Water demand increase unit: 35m3/day/ha = 0.4 l/s Source: MPA Study Team
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A steep increase in demand for water for both domestic and industrial use is expected in the future. Table 4.5.5 Future Water Demand 2010 2020 Area Item Present Use Water Demand (m3/s) (m3/s) Bekasi PDAM 1.9 6.0 Industrial Use 2.2 3.6 Subtotal 4.2 9.6 Karawang PDAM 0.5 2.4 Industrial use 3.6 3.9 Subtotal 4.2 6.2 Total 8.4 15.8 Source: MPA Study Team
Source：JICA MPA Study Team MPA: Master Plan for establishing Metropolitan Priority Area for Investment and Industry in Jabodetabek Area-Final Report Figure 4.5.3 Future Water Demand
(3) Sewerage
There is no sewer system in Bekasi City, Bekasi Regency, or Karawang. Sewage treatment is performed using septic tanks.
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4.5.3 Communications
(1) Internet Broadband Market
There are 204 companies that have acquired licenses related to internet access services as of 2011. Moreover, there are 2,740,000 broadband subscribers people and with a yearly upward trend shown. Table 4.5.6 Subscribers and Diffusion Rates for Broadband Services Nationwide Year 2007 2008 2009 2010 2011 Number of Broadband Subscribers (x 1,000) 779 982 1,864 2,280 2,736 Diffusion Rate of Broadband (%) 0.3 0.4 .08 1.0 1.1 Source: ITU-World Telecommunication/ICT Indicators Database 2012
(2) Mobile Phone Market
The main cellular phone business operators are Telkomsel, Indosat, and XL Axiata. This market shows rapid growth and has a diffusion rate of one phone per person.
Table 4.5.7 Subscribers and Diffusion Rates for Mobile Phone Services Nationwide Year 2007 2008 2009 2010 2011 Number of Mobile Phone Subscribers (x 1,000) 93,387 140,578 163,677 211,290 236,799 Diffusion Rate of Mobile Phone (%) 40.2 59.8 68.9 88.1 97.7 Source: ITU-World Telecommunication/ICT Indicators Database 2012
(3) Fixed-line Phone Market
Six companies provide local telephone services. One satellite is held to provide satellite communications services. Table 4.5.8 Subscribers and Diffusion Rates for Fixed-line Telephone Services Nationwide Year 2007 2008 2009 2010 2011 Number of Fixed-line Telephone Subscribers 19,530 30,378 34,807 40,929 38,617 (x 1,000) Diffusion Rate of Fixed-line Telephone % 8.4 12.9 14.7 17.1 15.9 Source: ITU-World Telecommunication/ICT Indicators Database 2012
(4) Radio Market
There are 800 radio broadcasting stations nationwide, including RRI public broadcasting, commercial broadcasting, university broadcasts, army broadcasts, and community broadcasting.
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(5) Broadcast Market
After TVRI began broadcasting in 1962, commercial broadcasting began from 1989 with 11 companies airing wide-area broadcast. The satellite broadcasting Indovision started broadcasting in 1994, with about 650,000 current subscribers.
Although there are two main cable TV companies, subscribers remain low at about 160,000. From within the project target region, cable TV can only be viewed in the Jakarta metropolitan area. The government aims at shifting to digital broadcasting completely by 2018
4.5.4 Gas
There are no city gas systems in Bekasi City, Bekasi Regency, or Karawang Regency. The heat source used for cooking in the area is propane, which consists mostly of butane propane. The equivalent of 0.5 kg of propane is one liter of kerosene. Since the cost of gas is half that of kerosene, it is more widely used. However, there are many accidents caused by gas leaks.
The distribution pipe network compared to gas sellers is shown below. There are a total of 26 stores.
Figure 4.5.4 Gas Distribution to Depot (Customer)
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4.5.5 Waste
(1) Outline
In the West Java Province, waste disposal is carried out at about 40 landfill disposal sites, at which about 4,500 tons of city garbage is dumped per day. Is not sorted, but instead disposed of as mixed garbage. However, a 3R program is being promoted to reduce the quantity of generated garbage, and a plan to compost 20% of generated garbage is being formulated.
(2) Final Disposal Site
The amount of generated garbage and the landfill disposal sites in the pilot area are shown below. Table 4.5.9 Final Disposal Site Karawang City / Regency Bekasi Regency Bekasi City Total Regency Generated Volume 1,686 4,307 1,299 7,292 (m3/day) Final Disposal Site Burangkeng Bantar Gebang Jalupang Setu Sumur Batu Amount Landfill 900 5,000 tons 230 Garbage (m3/day) 1,392 TPA Area (ha) 7.60 108.00 3.50 10.00 Start of Operations 1994 1989 2002 2003 Operational Controlled C.L. Open dumping Conditions landfill C.L. Source: Ministry of Environment, Indonesia Waste Treatment Program CDM Business Study in West Java Province 2008
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Figure 4.5.5 Map of Final Disposal Sites
(3) Composition of Garbage
Garbage samples were extracted from two dumping sites in Bandung in November 2008, and the following results were found. Table 4.5.10 Composition of Garbage in Bandung City No. Garbage Composition (%) 1 Organic 56 2 Wood/Branch 1 3 Egg bale 1 4 Paper 15 5 Cloths 2 6 Rubber 2 7 Bone 1 8 Glass 2 9 Plastic (bottle) 2 10 Plastic (sheet) 15 11 Metals 0 12 Residues 2 Source：Ministry of Environment, Indonesia Waste Treatment Program CDM Business Study in West Java Province 2008
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4.5.6 Schools
The numbers of schools in the target region are as follows. Table 4.5.11 Number of School Kab. Bekasi Kota. Bekasi Kab. Karawang City / Regency Total Public Private Public Private Public Private Kindergarten Schools 2 664 716 - 1 105 1,488
Primary Schools 702 391 753 174 848 158 3,026
Junior High Schools 109 247 39 170 82 93 740
High Schools 37 92 18 75 24 70 316
University/College 6 8 10 24 Source: Interviews by local consultant
4.5.7 Hospitals
The numbers of hospitals in the target region are as follows. Table 4.5.12 Number of Hospitals City / Regency Kab. Bekasi Kota. Bekasi Kab. Karawang Total Number of Beds More than 200 0 2 1 3 More than100 4 10 5 19 100 or less 7 9 8 24 Unknown 0 6 0 6 Total 11 27 14 52 Source: Interviews by local consultant
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Figure 4.5.6 Map of Hospitals and Commercial Centers
4.6 Economic Relations with Neighboring Regions and Japan
4.6.1 Overview of the Economy of the Pilot Area
Areas along the highway running through Bekasi City, Bekasi Regency and Karawang Regency are regarded as a major base for Japanese companies to expand their business. The speed of economic development of Karawang Regency is regarded the fastest in Indonesia. Many workers in the area commute from Jakarta, which is contributing to the emergence of the Jakarta metropolitan area (JABODETABEK: An acronym for Jakarta, Bogor, Depok, Tangerang and Bekasi).
Machinery, agriculture, textiles, and electronics have been identified as major industries in the region. For example, according to statistical data from Karawang Regency, the total number of employees in 2010 was 133,558. Breaking down by sector, machinery (22,146) account for the highest number, followed by agriculture (21,223), textiles (16,077), and electronics (12,687).
Owing to the benefits of this economic development, the minimum wage in Jakarta has increased 44% from the previous year, and has reached the level of over 20,000 yen per month. Adding the cost of fuel has also contributed to the continuing rise of labor costs, placing a great strain on the labor intensive industries. Therefore, it is expected that some companies may consider moving to rural areas in the future. Nevertheless, since infrastructure development in Jakarta area is much
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more developed compared to other areas and because Japanese companies are concentrated in this area (East Jakarta), immediately moving to other areas is likely difficult under current circumstances.
According to the latest data in the Statistical Yearbook Indonesia 2012, the regional GDP of West Java province is IDR 861 trillion (as of 2011), representing 11.6% of Indonesia’s national GDP and the third largest regional GDP, behind the Special Capital Territory of Jakarta (ranking first with 13.2%), and East Java province (ranking second with 11.9%). Also, the GDP growth rate of West Java province was 6.48%. While it is still behind the Special Capital Territory of Jakarta, which marked a growth rate of 6.71%, it is higher than the national average of 6.32% and it surpassed the growth rate of East Java province at 5.16%.
4.6.2 Major Economic Policy
In order to promote foreign investment, the government of Indonesia offers three major economic incentives: (i) granting of tax exemptions (given only to a limited number of industrial sectors including pioneer industries), (ii) granting of tax allowances (given to a wider range of industrial sectors), and (iii) import duty exemptions in bonded areas (corresponds to export processing zones). Currently, Japan, Singapore and South Korea are said to be enthusiastic in investing in Indonesia. Japan and South Korea have strong manufacturers in sectors such as the automobile industry, and Singapore is particularly interested in investing in service and commercial sectors.
In May 2011, the government of Indonesia formulated the Master plan for Acceleration and Expansion of Indonesia’s Economic Development (MP3EI) as a specific measure to solve the problem of infrastructure development. Under the plan, economic development is promoted by dividing Indonesia into six economic corridors and placing emphasis on the key industries identified for each region. The country aims to increase its nominal GDP six times over the level of 2010, and to become one of the world’s top ten economies by year 2025.
The Master Plan for Establishing Jakarta Metropolitan Priority Area for Investment and Industry (MPA) announced in October 2012 was developed jointly by the governments of Japan and Indonesia. The plan proposes a set of measures including the development of the new Cilamaya International Airport and improvement of road access (to the Bekasi-Karawang area). If these improvements to the infrastructure are made, it will contribute to the alleviation of traffic congestion in the Bekasi and Karawang areas and to over-concentration at Tanjung Priok Port.
Also, Jakarta is located at the junction of the Java Economic Corridor and the Sumatra Economic Corridor. Therefore, if the MPA is successfully established, it is expected to contribute to the economic development of Indonesia through the MP3EI as well. In particular, companies in the Bekasi and Karawang areas have high expectations. Results of interviews held in the field survey
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show that both government officers and private sector companies in Karawang place high expectations on the realization of the MPA.
However, the plan is currently stalled due to issues such as land expropriations. Similarly, no progress is being made toward actual construction of new airport in the Karawang area (in addition to Scarno Hatta Airport) or the second Jakarta-Cikampek highway, for which plans have already been approved.
Network hub of economic development
corridors between Java and Sumatra
Figure 4.6.1 Network Hub of Economic Development Corridors between Java and Sumatra
4.6.3 Economic Ties with Japan
Looking at the trade value between Japan and Indonesia, the export value to Japan reached USD 33,715 million (based on FOB price: 2011), which made Japan the largest export destination. This is followed by China with USD 22,941 million, and the USA with USD 16,459 million. As for commodities exported from Indonesia to Japan, mineral fuels comprise most of the export items, and items such as raw materials not suitable for food (mineral resources), products classified by materials (e.g. wood products, metal products), and machinery and transport equipment also account for a substantial portion.
The import value from Japan was USD 19,437 million (based on CIF price: 2011), which made Japan the third largest exporter to Indonesia after China with USD 26,212 million, and Singapore with USD 25,965 million. Examining the commodities imported by Indonesia from Japan, fossil fuel and mineral resources, wood, and seafood were previously the primary products until the 1990s. However, the proportion of industrial products such as machinery/electronic products and their parts have been increasing since the 2000s.
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Japanese foreign direct investment (FDI) in Indonesia amounts to USD 1,516 million (2011) and is the second largest after Singapore with UDS 5,123 million. While Singapore’s FDI in Indonesia is mostly in the sectors of manufacturing, wholesale/retail, and finance, Japanese FDI in Indonesia is characterized by its concentration in the manufacturing sector.
2009 2010 2011
Japan 679 713 1,516 United States 172 931 1,488 Europe 2,109 2,918 2,697 Singapore 4,341 5,006 5,123 China (w/Taiwan) 32 48 243 Rest of the world 3,483 6,600 8,407 Total 10,815 16,215 19,475 Source: Statistical Yearbook of Indonesia 2012 Figure 4.6.2 Foreign Direct Investment by Country (million US$), 2009–2011
Foreign direct investment by location in Indonesia is shown below. FDI in the Java region accounts for 56% of the total. Of this, FDI in West Java Province and DKI-Jakarta accounts for more than one-third of the total.
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Table 4.6.1 Foreign Direct Investment by Location
2012 Lokasi Project Value US$1000 % Sumatera Aceh 26 172,272.8 0.7% Sumatera Utara 133 645,321.8 2.6% Sumatera Barat 45 75,020.2 0.3% Riau 81 1,152,854.9 4.7% Jambi 30 156,321.8 0.6% Sumatera Selatan 107 786,448.5 3.2% Bengkulu 21 30,431 0.1% Lampung 57 114,320.3 0.5% Kepulauan Bangka Belitung 30 59,183.4 0.2% Kepulauan Riau 165 537,110.7 2.2% Total(Provinsi) 695 3,729,285.4 15.2% Java Daerah Khusus Ibukota Jakarta 1,148 4,107,720.8 16.7% Jawa Barat 682 4,210,703.8 17.1% Jawa Tengah 141 241,512.6 1.0% Daerah Istimewa Yogyakarta 28 84,939.2 0.3% Jawa Timur 403 2,298,776.2 9.4% Banten 405 2,716,263.7 11.1% Total(Provinsi) 2,807 13,659,916.3 55.6% Bali dan Nusa Bali 324 482,037.8 2.0% Tenggara Nusa Tenggara Barat 133 635,790 2.6% Nusa Tenggara Timur 20 8,723.7 0.0% Total(Provinsi) 477 1,126,551.5 4.6% Kalimantan Kalimantan Barat 45 397,534.8 1.6% Kalimantan Tengah 89 524,738 2.1% Kalimantan Selatan 54 272,291.3 1.1% Kalimantan Timur 167 2,014,085 8.2% Total(Provinsi) 355 3,208,649.1 13.1% Sulawesi Sulawesi Utara 70 46,651.9 0.2% Sulawesi Tengah 27 806,531 3.3% Sulawesi Selatan 29 582,579.2 2.4% Sulawesi Tenggara 41 35,723.2 0.1% Gorontalo 17 35,314.6 0.1% Sulawesi Barat 3 228.5 0.0% Total(Provinsi) 187 1,507,028.4 6.1% Maluku Maluku 10 8,518.1 0.0% Maluku Utara 9 90,253.7 0.4% Total(Provinsi) 19 98,771.8 0.4% Papua Papua Barat 18 32,035.1 0.1% Papua 21 1,202,432.6 4.9% Total(Provinsi) 39 1,234,467.7 5.0% Total (Wilayah) 4,579 24,564,670.2 100% Source: BKPM (Investment Coordinating Board)
Next, foreign direct investment by sector in Indonesia is shown below. Primary industry accounts for 22.1%, secondary industry accounts for 54.1%, and tertiary industry accounts for 23.8%. FDI in secondary industry is large, with food industry (12.1%), Nonmetallic mineral industry (11.6%) and Metal, machinery & electronic industry (7.8%) sharing a large portion of the total.
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Table 4.6.2 Foreign Direct Investment by Sector
2012 Sektor Project Value Rp. Million % Primary Food Crops & Plantation 180 9,631,484.3 10.4% Livestock 31 97,444.7 0.1% Forestry 9 144,542.2 0.2% Fishery 7 14,729.3 0.0% Mining 39 10,480,900.3 11.4% Total (Sektor) 266 20,369,100.8 22.1% Secondary Food industry 222 11,166,685.3 12.1% Textile industry 51 4,450,911.0 4.8% Leather goods & footwear industry 9 76,678.5 0.1% Wood industry 15 56,968.1 0.1% Paper and printing industry 64 7,561,039.0 8.2% Chemical and pharmaceutical industry 94 5,069,454.8 5.5% Rubber and plastic industry 110 2,855,009.6 3.1% Nonmetallic mineral industry 37 10,730,662.3 11.6% Metal, machinery & electronic industry 81 7,225,667.2 7.8% Medical preci. & optical instru, watches & clock 0.0% industry Motor vehicles & other transport equip. industry 21 664,417.7 0.7% Other industry 10 31,450.8 0.0% Total (Sektor) 714 49,888,944.3 54.1% Tertiary Electricity, gas & water supply 42 3,796,780.1 4.1% Construction 17 4,586,618.3 5.0% Trade & repair 35 1,030,439.9 1.1% Hotel & restaurant 34 1,015,033.6 1.1% Transport, storage & communication 33 8,612,042.0 9.3% Real estate, industrial estate & business activities 6 58,004.8 0.1% Other services 63 2,825,050.6 3.1% Total (Sektor) 230 21,923,969.3 23.8% Total (Sektor Utama) 1,210 92,182,014.4 100% Source: BKPM (Investment Coordinating Board)
4.7 BCP Implementation Conditions
4.7.1 Major Natural Disasters and Disaster Management Awareness
Earthquakes, volcanic disasters, floods, and storms are natural disasters expected in Java, for which preparations should be made. Although risk management for these natural disasters has already been considered in private enterprises and civil organizations, preparations for disaster management have not yet been made. Moreover, most business people are not familiar with the concept of the BCP, nor is the necessity of a BCP for corporate disaster risk management commonly recognized.
Even at institutions such as the Chamber of Commerce and Industry (KADIN), more weight is placed on efforts for developing business activities than on the management of disaster risk reduction. Thus, most SMEs in Indonesia have little concern regarding corporate disaster risk.
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4.7.2 Implementation of BCP
(1) Implementation of BCP by Enterprises
Enterprises are required to prepare risk management structures for natural disaster in accordance with the plans and strategies for national disaster management established by the central government. A central committee has been established for occupational health and safety to study the improvement of safety measures at enterprises. Enterprises conduct disaster risk management according to these schemes; however, few enterprises and organizations have developed disaster management plans or emergency response plans.
Actions for corporate disaster risk management have not been implemented well, due to the low awareness of disaster risk. This low awareness is caused, in party, by the low incidence of heavy corporate damage caused by natural disasters in Jakarta and surrounding areas.
Some enterprises have prepared a disaster preparedness plan or contingency plans. These enterprises include those that rely on road networks such as logistic companies and those who are involved with other companies with BCP.
(2) Implementation of BCP by Utility Suppliers and Distributors
Private or public companies dealing with any hazardous materials such as oil or gas are obligated by laws of public safety or environmental conservation to establish risk management systems pertaining to the environment or crisis.
Electric utility companies are also required to prepare crisis management plans through the development of Standard Operating Procedures. Although emergency responses are also defined in this procedure, disaster management plans and contingency plans have not been established. Furthermore, at water utility agencies, structured disaster risk management is not currently implemented.
At other utility companies or agencies for water resource management, telecommunications, and road networks, no instances of developed BCP were found. Most companies have not established any plans or manuals including contingency plans or disaster recovery plans, nor have they considered the risks of a large-scale disaster.
(3) Implementation of BCP by Foreign Capital Companies and Japanese Companies
It is assumed that foreign capital companies such as major manufacturers and trading companies have prepared a BCP. In the industrial parks where the main factories of foreign capital companies are located, utility redundancy is ensured since water distribution systems and electric transmission systems are basically independent from the surrounding area. In addition, some individual
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companies or industrial parks have electric power facilities for emergency. The preparedness in terms of utilities is thus established.
Enterprises are concerned more with labor demonstration, accidents, and traffic congestion than natural disaster risks. Because foreign capital companies also commonly devote their resources to these problems, it seems that the implementation of disaster risk management has a relatively low priority.
4.7.3 Efforts for Implementing BCP
(1) Regulations or Guidelines for the Implementation of BCP
National strategies for disaster risk management and action plans have been developed in Indonesia and enterprises are required to implement risk management along with these strategies and the master plans developed regionally.
At the central and local levels, government authorities related to disaster risk management such as the National Disaster Management Agency (BNPB) and the Regional Disaster Management Agency (BPBD) are in charge of the establishment and coordination of a disaster management scheme. However, no laws that compel enterprises to develop disaster management plans have been established. The implementation of contingency plans or BCP has not progressed at private or government-owned enterprises.
(2) Private Sector Efforts in BCP Education and Dissemination
There have been no efforts detected among enterprises in the private sector in promoting BCP creation. In terms of improving corporate management of disaster risk, no cases of providing support for enterprises in the development of disaster prevention plans or for conducting risk assessments have been observed.
However, KADIN functions to support business growth among SMEs, holding business seminars and symposiums on a regular basis. However, issues regarding business continuity or disaster management have never been included as the main topic. KADIN also believes that companies will show more interest in the implementation of BCP in the near future.
4.7.4 BCP Implementation Problems
To implement BCP, it is necessary to improve utility infrastructures. The inadequacy of the infrastructure in Indonesia is one problems hindering BCP dissemination. Road networks are a major issue, and the continuous traffic congestion around Jakarta greatly impacts physical distribution. In addition, the improvement of road and drain facility infrastructures is an urgent task
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because of flood vulnerability. These weaknesses in social infrastructure are a concern of private enterprises including foreign capital companies.
The lack of knowledge and know-how in the private sector on BCM/BCP is also considered a reason for the lack of progress its development.
4.8 Current State of Disaster Risk Management
In order to review the current state of disaster risk management, questionnaire surveys were conducted by the local consultant.
4.8.1 Questionnaire Surveys
Questionnaires were prepared for five organizations deeply or markedly involved with Area Business Continuity Plans (A-BCP). The purpose of the questionnaires was to analyze the current practices and readiness of both A-BCP and individual BCP. A review of these questionnaires review will be used as a basis for formulating the framework of the JICA’s Area Business Continuity Plan.
The questionnaires are conducted for the assessment of: 1) twelve selected industrial parks in Bekasi City, Bekasi Regency and Karawang Regency; 2) disaster risk management applied by business tenants at KIIC; 3) lifeline utility services; 4) traffic infrastructure operators; and 5) disaster risk management applied by local governments.
The questionnaires were distributed to tenants by KIIC in order to keep their contact information confidential. Replies were collected from 16 business enterprises within KIIC. Due to national security reasons, very few details could be obtained on infrastructure related to national strategic lifeline utility operators such as Pertamina, PLN and Telkom.
4.8.2 Review of the Questionnaire Surveys for Industrial Parks
The following industrial parks in the industrial agglomerated Kota Bekasi, Kabupaten Bekasi and Kabupaten Karawang of West Java Province were selected. 1) Bekasi International Industrial Estate (BIIE) 2) East Jakarta Industrial Park (EJIP) 3) Greenland International Industrial Centre (GIIC) 4) Jababeka Industrial Park (JIP) 5) Lippo Cikarang Industrial Park (LPIP) 6) MM 2100 Industrial Town (MMIT) 7) Marunda Industrial Park (MIP)
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8) Karawang International Industry Centre (KIIC) 9) Kujang Cikampek Industrial Estate (KCIE) 10) Mitra Karawang Industrial Park (MKIP) 11) Bukit Indah City Industrial Estate (BCIE) 12) Surya Cipta Industrial Estate (SCIE)
The results of the questionnaire survey given to industrial parks are as follows. 1) Interruption of lifeline utility services and causes of the interruption Interruption time is within 0 (no stop) – 3 hours. Two companies had an interruption time of 12 hours, and one company had an interruption time of up to 48 hours (two days). Most of the interruptions were caused by routine maintenance and network trouble. 2) Alternatives for lifeline utility services 3) Damages to roads and other traffic infrastructure caused by natural disasters
Most of the roads are not affected by natural disasters. Only one industrial park suffered damage from flooding. Have Experienced All Roads Flooded Simultaneously
Serious Damages to the Industrial Park due to Natural Disasters
4) Business continuity in disasters
Most of the industrial parks did not have trouble continuing business while suffering from natural disasters. Only one industrial park suffered flood damage.
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Employees’ Availability to Work in Cases of Disasters
5) The following are the major requests with respect to disaster control measures: To local governments To maintain a cooperative relationship the industrial estate (1 IP) To put more care into regulation problems (1 IP) To accelerate the construction/widening of roads (1 IP) To coordinate a disaster prevention plan (1 IP) To lifeline utility service providers To complete lifeline utility services (1 IP) None (1 IP) To traffic infrastructure service providers Roads to alleviate traffic congestion (1 IP) Alternative public transportation and road access (1 IP) To improve road quality, lighting, and to reduce traffic congestion (1 IP) To widen road access choices (3 IP) Repairing roads damaged by floods (1 IP) Most industrial parks are concerned by narrow roads, ask for them to be widened.
4.8.3 Review of the Questionnaire Surveys for Business Enterprises
The results of the questionnaire surveys for business enterprises are as follows: 1) Interruption of lifeline utility services Interruption time is 3 hours or less. (One tenant experienced a 12-hour outage.) Backup system One tenant out of 16 uses satellite phones. 2) Normally used traffic infrastructure and roads Expressway: Jakarta-Cikampek Toll road (100% of respondents. i.e. 16 tenants) Port: Tanjung Priok Port Airport: Soekarno Hatta International Airport Railway: Jakarta-Cikampek (1 tenant out of 16 tenants)
3) Alternatives for traffic infrastructure
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Roads and expressways are the major means of transportation. No alternative expressway. Alternative port: Surabaya, Semarang, Tanjung Intan-Cirebon Alternative airport: Bandung, Halim Perdanakusuma 4) Disaster prevention plans, Business Continuity Plans and concerns
BCP is not well recognized among the tenants.
Major concerns with respect to business continuity are as follows: 4 tenants out of 16 are concerned about the current insufficient infrastructure, i.e. lifeline utilities and traffic infrastructure in general. Tenants replied on insufficient road and expressway for the following issues: Transportation/ Traffic (General) (Yes: 3 tenants) Availability, commuting of employees during disasters (Yes: 3 tenants) Transport of products and raw materials forming the core of their business (Yes: 3 tenants) The following are concerns or measures for business continuity: Problems with production facility Storage in remote location Time required for recovery from disasters Financial support for recovery from disaster damages Public sector involvement and efforts
5) Requests with respect to disaster control measures
The following are the main requests: To local governments Accurate and quick information about disasters (4 tenants) Establishment of temporary crisis centers for support in recovering lifeline utilities and traffic infrastructure (3 tenants) Restoration of flood damaged residential areas (2 tenants) Improvement of current insufficient infrastructure (2 tenants) To lifeline utility service providers Alternative facilities (7 tenants) Quick recovery from damage in disasters (5 tenants)
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The traffic infrastructure service providers To build alternative expressways (4 tenants) To match traffic infrastructure with traffic volume (4 tenants) To build alternative traffic infrastructure (3 tenants) To build alternative roads ( 3 tenants) Quick recovery from damage in disasters ( 3 tenants) To build alternative ports (2 tenants) To build alternative airports (2 tenants) To build alternative railways (2 tenants)
Tenants are concerned with the risk of disaster damage and the inadequacy of road and expressway transportation.
4.8.4 Review of the Questionnaire Surveys for Lifeline Utility Companies
The results of the questionnaire survey for lifeline utility companies are as follows: 1) Disasters covered by disaster mitigation plans The disaster most commonly included in disaster mitigation plans is flooding. 2) Emergency supply of electricity and water Emergency water supplies are available for up to 18 hours, but electricity is only available for 3 hours.
Emergency Communications (Satellite-based mobile phones)
Disaster Management Plan
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Business Continuity Plan
3) Interruption of supply and the causes of the interruption The only interruption occurred for electricity suppliers due to flooding. The supply was stopped for four days. Interruption due to Incidents / Natural Disasters
4) Requests regarding disaster control measures: To local governments More water delivery units Formation of an executive coordination unit Flood-proofing of the substation To lifeline utility services providers Better on-site communication Provision of an early warning system To traffic infrastructure service providers More help to distribute clean water Increased security at damaged locations
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Lifeline utility companies are concerned mostly with the lack of clean water delivery units to help distribute clean water in the field.
4.8.5 Review of the Questionnaire Surveys for Traffic Infrastructure Companies
The results of the questionnaire survey for traffic infrastructure companies are as follows. 1) Disasters covered by disaster mitigation plans The disaster most commonly included in disaster mitigation plans is flooding. 2) Emergency supply of electricity Roads and highway have an available supply of electricity up to 15 hours, whereas the port has an emergency supply of more than 24 hours. Emergency Electricity Supply
Only Pelindo II (port) has an emergency satellite-based mobile phone. Emergency Communications (Satellite-based mobile phones)
Most of the traffic infrastructure companies have a disaster management plan, but most do not have a business continuity plan. Disaster Management Plan
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Business Continuity Plan
3) Interruption of supply and the causes of the interruption 50% of interruptions are caused by floods, while the other 50% have not experienced an interruption in supply.
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4) Requests regarding disaster control measures: To local governments A solid prevention team and facility Better coordination Routine flood check every year or every 5 years To lifeline utility service providers More health services More electricity networks To traffic infrastructure service providers Additional disaster relief funds More consideration of traffic infrastructure More disaster warning facilities
Traffic infrastructure companies are mainly concerned about the lack of a solid prevention team and facilities such as an early disaster warning system.
4.8.6 Review of the Questionnaire Surveys for Local Governments
The results of the questionnaire survey for local governments are as follows: 1) Disasters covered by disaster mitigation plan The main disaster included in disaster mitigation plans by local governments is flooding. Other types of disasters are also included, but with minimal possibilities. 2) Typical disasters targeted for damage prediction The main disaster targeted for damage prediction is also flooding. Storms and storm surges are the second most important disaster type for damage prediction. 3) Disaster response for residents
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All disaster response measures for residents such as securing evacuation houses, supplying emergency toilets, and supplying water and food are equally important for the local government. 4) Disaster response for companies regarding the recovery of lifeline utilities or traffic infrastructure Five out of eight local government agencies have a policy to help companies recover lifeline utilities.
Six out of eight local government agencies have a policy on the recovery of traffic infrastructure.
5) Disaster response regarding financial support
Seven out of eight local government agencies will give financial support to whoever needs it in case of disasters.
6) Disaster damaged that has impacted local governments Recent natural disasters have been floods, which cause extensive damage to public infrastructures, residential areas, roads, and farms. The local government operations suffered minimal damage. Most were undamaged. 7) Public disclosure of information on hazards/risk All local government agencies disclose all information on disaster hazards and risks to the public.
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8) Requests regarding disaster control measures: To the central government Comprehensive disaster management and planning (1) Immediate physical and financial support (4) River normalization (1) Flood control pool (1) Water pumps (1) Transportation equipment (1) Disaster equipment/evacuation, ambulances, instant food (2) Better appreciation from the government (2) Logistical support (1) To lifeline utility service providers Swift repair of damage to public facilities (1) Developing lifeline utility emergency procedures (1) Providing clothes, food, shelter, etc. (1) Providing emergency rafts (1) Prioritizing direct action on-site (1) To traffic infrastructure service providers Improvement of roads (1) Determining disaster response routes (1) Quick repair of facilities and infrastructure (1) Building multi-functional bridges (1) Determining evacuation routes (1)
The local government is concerned mostly with the need for immediate physical and financial support from the central government.
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Appendix Details of Natural Hazard Assessments
A.1 Seismic Hazard Assessment The methodology of seismic hazard simulation is roughly divided into deterministic methodology and probabilistic methodology. By the deterministic methodology, earthquake ground motion is calculated if the specific earthquake source fault has been activated. The earthquake ground motion distribution by the future possible earthquake can be calculated precisely, but to estimate when the calculated earthquake ground motion will be realized is difficult because it is impossible to predict the future earthquake by current technology. By the probabilistic method, the expected earthquake ground motion within a certain period at the study point is calculated considering all the earthquake sources around the study point reflecting the possibility of each sources. Therefore, the earthquake motion distribution by probabilistic method is not the estimation of the earthquake motion distribution by future probable earthquake but the ensemble of independent expected earthquake motion at each point. The deterministic method is commonly used for disaster management purpose and the probabilistic methodology is usually used for zoning in the building seismic code or earthquake insurance system etc.
The probabilistic method is adopted in the seismic hazard analysis for area BCP because the probability of the hazard is important. The hazard that has high possibility to occur in the lifetime of the industrial facilities is considered in area BCP, therefore to estimate the probability of the hazard is essential component in the analysis.
A.1.1 Methodology of Probabilistic Seismic Hazard Analysis
(1) Summary The combination of earthquake ground motion at a certain point and the probability to experience at least the ground motion in a certain period is calculated by the probabilistic seismic hazard analysis method. The flow of the analysis is shown in Figure A.1.1.
The general steps of the analysis are as follows.
1) Set up the model of seismic activity around the study point. Not only the earthquakes that the source faults are clearly known but the earthquakes that the earthquake sources are not known and definite estimation of the magnitude and the location of future event is difficult should be included.
2) Estimate the probability of the magnitude, probability of the distance from the study point and the probability of the occurrence of the modeled earthquakes.
3) Set up the probability model to estimate the earthquake ground motion if the magnitude of the earthquake and the distance from the study point are given. The empirical attenuation equation and the dispersion of the equation are usually used.
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4) Calculate the probability that the earthquake ground motion at the study point by modeled earthquake become larger than a certain value in a certain period.
5) Steps 1) to 4) are carried out for each modeled earthquake and all the probabilities are aggregated. The probability to experience a certain earthquake ground motion at least once in a certain period at the study point is calculated as a result.
McGuire, R. K. (2004)1) is recommended as a textbook of probabilistic seismic hazard analysis.
Figure A.1.1 Flowchart of probabilistic seismic hazard analysis (NIED(2005)2), original in Japanese)
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(2) Software for analysis As the probabilistic seismic hazard analysis involves complicated numerical calculation, many computing programs are developed and some of them are freely available. Among them, SEISRISK, FRISK, CRISIS, NSHMP and OpenSHA are famous but they are intended to be used by the researchers or the engineer with expert knowledge. Danciu et al. (2010)3) compiled the precise information about the probabilistic seismic hazard analysis programs. Table A.1.1 is the general information of the major software for probabilistic seismic hazard analysis by Danciu et al. (2010).
Table A.1.1 Major software for probabilistic seismic hazard analysis
EZ-FRISK is the expanded commercial version of FRISK and offered by Risk Engineering Inc. The analysis by EZ-FRISK is comparatively easy because the earthquake source model and attenuation formula are provided with computing program. EZ-FRISK Ver. 7.62 was used in this study.
(3) Source model The earthquake source model should include all the seismic activities within several 100km from the study point. The active faults in the area are modeled at first because earthquake basically occurs by the activity of the fault. However, not all the active faults are known or studied of the properties, the earthquakes that the earthquake sources are not known and definite estimation of the magnitude and the location of future event is difficult are modeled as the background seismic activities also. As the creation of the earthquake source model needs high grade capacity and expert knowledge, earthquake modeling is usually conducted in university or public research institute. Therefore, to conduct originally new probabilistic seismic hazard analysis, the earthquake source model should be given from academic organization.
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As mentioned above, the source models provided with EZ-FRISK are used in this study.
(4) Attenuation formula The so-called empirical attenuation formula is used to calculate the earthquake ground motion from the magnitude of the earthquake and the distance between epicenter and the study point. Many researchers proposed various attenuation formulas for several decades. They have different features based on the database and the algorithm that were used to create the formula, and they also have the limitation of applicability. It is desirable to use the attenuation formula that was intended to use the study site. The newer proposed attenuation formula is generally desirable to use because newer formula is derived based on the more precise recent earthquake observation records.
The empirical attenuation formula for ASEAN region is not known. In this study, following formula based on the world wide earthquake observation records are adopted.
[for shallow crustal earthquake]
Abrahamson and Silva (2008)4) Boore and Atkinson (2008)5) Campbell and Bozorgnia (2008)6) Chiou and Youngs (2008)7) [for deep plate boundary earthquake]
Atkinson and Boore (2003)8) Youngs et al. (1997)9)
A.1.2 Amplification Analysis of the Surface Ground
(1) Summary The earthquake ground motion is affected by not only the magnitude and the distance but by the ground condition around the study area. The seismic wave is amplified by the surface grounds and the extent of amplification is different depending on the structure of the surface ground. Some of the empirical attenuation formula includes the effect of surface ground amplification but another method is usually used to evaluate the wide area. At first, the earthquake motion at the bedrock is calculated by the empirical attenuation formula and the amplification of the surface ground is multiplied to get the surface ground motion.
There are several methodology to evaluate the amplification characteristics of surface grounds; for example, based on the surface soil, based on the average S wave velocity of surface soil layers and numerical response analysis using the ground structure model. The suitable method is selected considering the available data, necessary work and budget.
In this study, the method based on the surface soil is used.
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(2) Ground classification and amplification factor The classification of the ground and the amplification factor by FEMA (1995)10) is adopted in this study (see Table A.1.2 and Table A.1.3). They are developed in U.S.A. and used in many countries recently. The soil profile, average S wave velocity of upper 30 meters and N value are used to define the site class.
Followings are the adopted ground classification for Indonesia based on the geologic time read from collected geologic maps.
Class B: Tertiary and before Class C: Pleistocene Class D: Holocene Class E: River deposit and marine deposit in Holocene
Table A.1.2 NEHRP Classification Site Average S-wave velocity of Profile N value Class the upper 30 meters A Hard rock > 1500 m/sec B Rock 1500 ≥ Vs > 760 m/sec C Very dense soil and soft rock 760 ≥ Vs > 360 m/sec N > 50 D Stiff soil 360 ≥ Vs > 180 m/sec 50 ≥ N ≥ 15 E Soil 180 m/sec ≥ Vs 15 > N Table A.1.3 NEHRP Amplification
A.1.3 Expression of the Results
(1) Methodology of expression The calculated earthquake ground motion by the probabilistic method is expressed as follows. a) The probability that the study site experiences a certain earthquake ground motion. ex. The probability is 10% in 50 years to experience 100 gals or more. b) The earthquake ground motion value for a certain probability. ex. 100 gals or more will be experienced on the probability of 10% in 50 years.
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The probability is expressed as the combination of the period and the probability in the period. If the seismic activity is uniform not depending on the year, probability can be expressed by annual probability. In this study, the calculated results are expressed by the method b) above. It should be noticed that the earthquake motion distribution by probabilistic method is the ensemble of independent expected earthquake motion at each point and it is not related to the activity of a certain fault.
(2) Seismic Intensity The calculated value is a physical quantity, such as peak ground acceleration or velocity. The seismic intensity is another expression of the strength of the ground vibration by the earthquake and more popularly understandable. The seismic intensity is also used to estimate the damage based on the past earthquake disaster experiences. In this study, peak ground acceleration is converted to seismic intensity in MMI scale following empirical formula by Trifunac and Brady (1975)11).
log PGA = 0.014 + 0.30*I PGA: peak ground acceleration (gal), I: seismic intensity (MMI)
A.1.4 Simulation and Results
(1) Source model The source model for Indonesia is shown in Figure A.1.2. The surface projection of source models along the plate boundary is shown in rectangular shape and the inland faults are shown as folded lines. The seismic activities that the location and the magnitudes are not identified in advance are modeled as the comparatively broad area sources.
Figure A.1.2 Source model for Indonesia
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(2) Baserock motion The acceleration distribution on the ground with Vs=760 m/sec is shown in Figure A.1.3 to Figure A.1.6. The expected probability of occurrence is at least once in 50, 100, 200 or 500 years (50, 100, 200 or 500 years probability). The calculated acceleration is larger in south because the active seismic sources exist along the south coast of Java island.
This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. [Analytical condition] Probabilistic Seismic Hazard Analysis, Software: EZ-FRISK, Earthquake source model: EZ-FRISK, Return period: 50 years. Figure A.1.3 Baserock acceleration distribution (expected for 50 years, unit: g)
This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. [Analytical condition] Probabilistic Seismic Hazard Analysis, Software: EZ-FRISK, Earthquake source model: EZ-FRISK, Return period: 100 years. Figure A.1.4 Baserock acceleration distribution (expected for 100 years, unit: g)
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This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. [Analytical condition] Probabilistic Seismic Hazard Analysis, Software: EZ-FRISK, Earthquake source model: EZ-FRISK, Return period: 200 years.
Figure A.1.5 Baserock acceleration distribution (expected for 200 years, unit: g)
This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. [Analytical condition] Probabilistic Seismic Hazard Analysis, Software: EZ-FRISK, Earthquake source model: EZ-FRISK, Return period: 500 years. Figure A.1.6 Baserock acceleration distribution (expected for 500 years, unit: g)
(3) Ground classification The 1/100,000 scale geological maps published by Geological Institute of Indonesia (PSG) are purchased and digitized. The ground of study area is classified based on the geological time. The ground classification map is shown in Figure A.1.7
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Figure A.1.7 Ground classification
(4) Surface motion The acceleration at the ground surface is calculated multiplying the amplification factor to the acceleration value at the baserock. The results are shown in Figure A.1.8 to Figure A.1.11. The acceleration value is converted to seismic intensity in MMI scale by empirical equation and shown in Figure A.1.12 to Figure A.1.15.
This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. [Analytical condition] Probabilistic Seismic Hazard Analysis, Software: EZ-FRISK, Earthquake source model: EZ-FRISK, Ground classification and amplification: NEHRP, Ground Data: 1/10,000 geological map by PSG, Return period: 50 years. Figure A.1.8 Surface acceleration distribution (expected for 50 years, unit: g)
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This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. [Analytical condition] Probabilistic Seismic Hazard Analysis, Software: EZ-FRISK, Earthquake source model: EZ-FRISK, Ground classification and amplification: NEHRP, Ground Data: 1/10,000 geological map by PSG, Return period: 100 years. Figure A.1.9 Surface acceleration distribution (expected for 100 years, unit: g)
This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. [Analytical condition] Probabilistic Seismic Hazard Analysis, Software: EZ-FRISK, Earthquake source model: EZ-FRISK, Ground classification and amplification: NEHRP, Ground Data: 1/10,000 geological map by PSG, Return period: 200 years. Figure A.1.10 Surface acceleration distribution (expected for 200 years, unit: g)
This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. [Analytical condition] Probabilistic Seismic Hazard Analysis, Software: EZ-FRISK, Earthquake source model: EZ-FRISK, Ground classification and amplification: NEHRP, Ground Data: 1/10,000 geological map by PSG, Return period: 500 years. Figure A.1.11 Surface acceleration distribution (expected for 500 years, unit: g)
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This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. [Analytical condition] Probabilistic Seismic Hazard Analysis, Software: EZ-FRISK, Earthquake source model: EZ-FRISK, Ground classification and amplification: NEHRP, Ground Data: 1/10,000 geological map by PSG, Conversion from PGA to MMI: Trifunac and Brady (1975), Return period: 50 years.
Figure A.1.12 Seismic intensity distribution (expected for 50 years in MMI scale)
This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. [Analytical condition] Probabilistic Seismic Hazard Analysis, Software: EZ-FRISK, Earthquake source model: EZ-FRISK, Ground classification and amplification: NEHRP, Ground Data: 1/10,000 geological map by PSG, Conversion from PGA to MMI: Trifunac and Brady (1975), Return period: 100 years. Figure A.1.13 Seismic intensity distribution (expected for 100 years in MMI scale)
This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. [Analytical condition] Probabilistic Seismic Hazard Analysis, Software: EZ-FRISK, Earthquake source model: EZ-FRISK, Ground classification and amplification: NEHRP, Ground Data: 1/10,000 geological map by PSG, Conversion from PGA to MMI: Trifunac and Brady (1975), Return period: 200 years. Figure A.1.14 Seismic intensity distribution (expected for 200 years in MMI scale)
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This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. [Analytical condition] Probabilistic Seismic Hazard Analysis, Software: EZ-FRISK, Earthquake source model: EZ-FRISK, Ground classification and amplification: NEHRP, Ground Data: 1/10,000 geological map by PSG, Conversion from PGA to MMI: Trifunac and Brady (1975), Return period: 500 years.
Figure A.1.15 Seismic intensity distribution (expected for 500 years in MMI scale)
A.1.5 Evaluation of the Results
(1) Probabilistic seismic hazard analysis As the most software for probabilistic seismic hazard analysis is made based on the same theorem, there is no big difference between them except user interface. The largest component that may affect to the result is the earthquake source model. Not only the earthquake catalogue of recorded events and historical earthquakes, the active faults with no record of earthquakes in the history or the area source model should be considered. In this study, the earthquake source model included in the commercial analysis package is used. This source model was made based on the existing documents in the world and the accuracy is enough for the hazard assessment of the pilot study site in this project. However, the opinions about the active faults by the researchers are not usually same and the consensus between them is difficult in many cases. To consult the local researcher and reflect the opinion to the analysis may be necessary to assess the seismic hazard in smaller area.
(2) Amplification analysis of the surface ground There are plenty of analysis methods for amplification by the surface ground. The methodology that is adopted in this study is the comparatively easy to use. Basically, the structure of the soil layers of underground is necessary to know the amplification characteristics. In this study, the amplification factor is estimated based on the information that is available from geological maps. If the results of ground profiling or the results of scientific research of the ground by local researchers are available, the amplification in part of study area can be evaluated more precisely. The scientific research may has been conducted or will be conducted in other region in ASEAN. The research such information is effective for the assessment.
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References
1) McGuire, R. K. (2004). Seismic Hazard and Risk Analysis, Earthquake Engineering Research Institute, Berkeley. 2) National Research Institute for Earth Science and Disaster Prevention (NIED), 2005, A Study on Probabilistic Seismic Hazard Maps of Japan, Technical Note of the National Research Institute for Earth Science and Disaster Prevention, No. 275. 3) Danciu, L., M. Pagani, D. Monelli and S. Wiemer (2010), GEM1 Hazard: Overview of PSHA Software, GEM Technical Report 2010-2. 4) Abrahamson N. and W. Silva, 2008, Summary of the Abrahamson & Silva NGA Ground-Motion Relations, Earthquake Spectra, Vol. 24, Issue 1, pp. 67-97. 5) Boore D. M. and G. M. Atkinson, 2008, Ground-Motion Prediction Equations for the Average Horizontal Component of PGA, PGV, and 5%-Damped PSA at Spectral Periods between 0.01 s and 10.0 s, Earthquake Spectra, Vol. 24, Issue 1, pp. 99-138. 6) Campbell K. W. and Y. Bozorgnia, 2008, NGA Ground Motion Model for the Geometric Mean Horizontal Component of PGA, PGV, PGD and 5% Damped Linear Elastic Response Spectra for Periods Ranging from 0.01 to 10 s, Earthquake Spectra, Vol. 24, Issue 1, pp. 139-171. 7) Chiou B. S.-J. and R. R. Youngs, 2008, An NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra, Earthquake Spectra, Vol. 24, Issue 1, pp. 173-215. 8) Atkinson G. M. and D. M. Boore, 2003, Empirical Ground-Motion Relations for Subduction-Zone Earthquakes and Their Application to Cascadia and Other Regions, Bull. Seism. Soc. Amer., Vol. 93, No. 4, 1703-1729. 9) Youngs, R. R., S. -J. Chiou, W. J. Silva, and J. R. Humphrey, 1997, Strong Ground Motion Attenuation Relationships for Subduction Zone Earthquakes, Seism. Res. Let., Vol. 68, No. 1, 58-73. 10) Federal Emergency Management Agency, 1995. FEMA 222A and 223A - NEHRP Recommended Provisions for Seismic Regulations for New Buildings, 1994 Edition, Washington, D. C., Developed by the Building Seismic Safety Council (BSSC) for the Federal Emergency Management Agency (FEMA) 11) Trifunac M. D. and A. G. Brady, 1975, On the Correlation of Seismic Intensity Scales with the Peaks of Recorded Strong Ground Motion, Bull. Seism. Soc. Amer., Vol. 65.
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A.2 Tsunami Hazard Assessment The flowchart of tsunami simulation is shown in Figure A.2.1. The detailed procedure is described hereinafter and the results of simulation for three pilot areas are shown next.
Fault Model Input Grid data contains; Sea area: Bathymetric chart - Topography (= altitude) Land area: Topographic map - Coefficient of roughness - Height of sea defense - Initial sea-level Deformation of seabed
Tsunami Simulation Model
Output - Max tsunami height (all grid) - Max tsunami velocity (all grid) - Arrival time of max height (all grid) - Arrival time of x cm height (all grid) - Tsunami height wave form (selected grid)
Figure A.2.1 Flowchart of tsunami simulation
A.2.1 Theory of Tsunami Propagation and Selection of Simulation Model There are several theories to describe the behavior of tsunami such as linear long-wave theory, non-linear long-wave theory, linear disperse wave theory and non-linear disperse wave theory. Each of them is a theory based on the long-wave approximation which can be applied to the wave which wave length is long enough to the depth of water. The wave length of tsunami is generally from several 10 km to 100 km relative to the average depth of the ocean is 4 km. That is the reason why the long-wave approximation can be proper for tsunami.
Linear long-wave theory can be applied to the deep water area, over around 50 m in depth, where the amplitude of the wave is small enough to the depth of water and the friction on the sea floor can be
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The velocity of shorter wave is slower than longer wave. Therefore, when tsunami wave consists of several waves of different length, dispersion of short wave and long wave is observed. This difference in velocity by the wavelength is called dispersibility and the theory considering this dispersibility is the disperse wave theory. The simulation on the disperse wave theory is not so common at present, however in case of distant tsunami which occurs over 1,000 km far from the object site, linear disperse wave theory is to be applied. And when the effect of dispersibility is concerned at shallow water area, non-linear disperse wave theory is to be applied. Please see reference 1) and 2) when more detailed explanation of those theories are required.
Table A.2.1 shows the sample tsunami simulation models based on the long-wave theory and the disperse wave theory introduced above. Tsunami simulation program "TUNAMI" which is developed by Tohoku University based on the non-linear long wave theory is used in this study.
Table A.2.1 Tsunami simulation model Program Name Organization Source URL Long Wave Theory TUNAMI Tohoku University, Japan Open https://code.google.com/p/tunami/ The Port and Airport http://www.pari.go.jp/cgi-bin/search-en/de STOC Open Research Institute, Japan tail.cgi?id=2005060440205 http://ceeserver.cee.cornell.edu/pll-group/ COMCOT Cornell University, USA Open comcot.htm Washington University, http://depts.washington.edu/clawpack/geo GeoClaw Open USA claw/ MOST NOAA, USA Closed http://nctr.pmel.noaa.gov/model.html ComMIT NOAA, USA Closed http://nctr.pmel.noaa.gov/ComMIT/ Disperse Wave Theory Disperse National Defense Academy, http://www.nda.ac.jp/cc/kensetsu/index-e. Open Potential Model Japan html University of Delaware, http://chinacat.coastal.udel.edu/programs/f FUNWAVE Open USA unwave/funwave.html NEOWAVE University of Hawaii, USA Open http://www.ore.hawaii.edu/OE/index.htm http://ceeserver.cee.cornell.edu/pll-group/ COULWAVE Cornell University, USA Closed doc/COULWAVE_manual.pdf
A.2.2 Input Data General input data for tsunami simulation is as follows. These data is given to each grid which is explained below.
1) Topographical data 2) Roughness data
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3) Sea defense data 4) Initial water height data (= deformation of sea-floor)
(1) Topographical Data Topographical model for the area which includes the source region of the scenario earthquake, the objective area and the route of tsunami between them is required for tsunami simulation. Topographical model includes the topography of sea-floor, the topography of the land surface where tsunami might run up and sea defense structures. Orthogonal coordinate system is usually used for the modeled area of less than 1,000 km x 1,000 km and polar coordinate system is used for the wider area. The orthogonal UTM coordinate system is used for this analysis.
The simulation area is divided and covered by square grid and altitude and roughness data are given to each grid. Altitude describes topographic features and roughness is used for considering friction between water and sea-floor or land surface. The size of grid is properly defined considering the complexity of topography and the wavelength of tsunami. The grid size is usually defined from bigger to smaller according to the distance from coastlines, considering the condition that is the topography becomes more complex and shorter wave component becomes dominant at the nearer area to the coastlines. This methodology is called "nesting". The grid size is defined as, for instance, 1350m -> 450m -> 150m -> 50m from tsunami source region to the coast. Grid is connected with 1/3 size grid step by step in this case and this 1/3 connection is the most general way, however 1/2 connection and 1/5 connection is also used for nesting.
Topographical data is obtained from bathymetry chart and topographical map. Nowadays, digital data of those maps are often available. However, if there is no digital data, it is required to generate digital data by digitizing those maps. Table A.2.2 shows digital topographical data provided by international organization for free. Those data can be also used for the modeling. Following shows a case study preparing topographical model from source region to coastal area by GEBCO_08 data (Figure A.2.2) and from coastal area to the coastline and inland area by more detailed bathymetry chart (Figure A.2.3).
Table A.2.2 Open-source Data of Bathymetric Chart Name Explanation GEBCO_08 Grid - Organization: British Oceanographic Data Centre (BODC) - Topographic data of 30 sec grid covering sea-floor and land surface - URL: http://www.gebco.net/data_and_products/gridded_bathymetry_data/ SRTM30_PLUS - Organization: Scripps Institution of Oceanography, University of California San Diego - Topographic data of 30 sec grid covering sea-floor and land surface - URL: http://topex.ucsd.edu/
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1350m grid area
Depth (m)
0 1000 2000 3000 4000 5000 6000
Figure A.2.2 Wide Area Topography Model by GEBCO Data (South China Sea Area)
Figure A.2.3 Topography Model of Coast Area by Bathymetry Data (Manila Bay area)
(2) Roughness Data The effect of friction for tsunami wave propagation is considered by Manning's roughness coefficient (n). "0.025" is often used as roughness coefficient for marine area. Table A.2.33) and Table A.2.44) show Manning's roughness coefficients for different types of ground surfaces.
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Table A.2.3 Comparison of Manning's roughness coefficients 3) Fukuoka et al. (1994) Aida (1977) Goto and Shuto (1983) Kotani et al. (1998) estimated equivalent setup estimated category roughness category roughness category category roughness coefficient coefficient coefficient coefficient high 80% 0.1 0.01 density 50 ～ 8 0.096 dense zone 0.07 high density residential zone 0.080 rather high density mid 20 ～ 5 0.084 0.05 0.05 mid density residential zone 0.060 zone density low 0 ～2 0.056 0.03 low density residential zone 0.040 density forest zone road 0.043 other land zone 0.02 0.030 (inc. garden, tide protection forest) field zone 0.020 (inc. waste land) Shoreline (inc. tide sea and river zone (w/o tide 0.04 0.025 protection forest) protection forest)
Table A.2.4 Manning values for land cover classes 4)
(3) Sea defense data Embankment and other sea defense structures are modeled as a grid height data.
(4) Initial Water Height Data (=deformation of sea-floor)
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Change of water height caused by fault movement should be prepared as an initial condition for tsunami simulation. Change of water height is assumed to be same as vertical component of sea-floor deformation. The sea-floor deformation is calculated as a displacement caused by a slip on the fault in the semi-finite elastic body using fault parameters shown in Table A.2.5.
The theory of above calculation is described in the following references like Mansinha and Smilie (1971) 5) or Okada (1992) 6) and others. The website of Cornell University7) and National Research Institute for Earth Science and Disaster Prevention (NIED) 8) are also informative.
Figure A.2.4 shows a calculated deformation of sea-floor by the program named DC3D0 / DC3D 8) developed by Okada.
Table A.2.5 Example of fault parameters Sample Value Fault Parameter (corresponding to Figure A.2.4) Depth (km) 18 Strike angle (degree) 177 Dip angle (degree) 24 Rake angle (degree) 90 Length (km) 313 Width (km) 70 Slip (m) 9.6
Water Level (m)
Figure A.2.4 Example of calculated vertical deformation
A.2.3 Output Data The general output of the tsunami simulation is as follows. Output items are obtained for each grid.
1) Maximum water height or maximum inundation height (for all grid)
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2) Maximum velocity (for all grid) 3) Elapsed time of maximum water height (for all grid) 4) Elapsed time of given water height ( x cm, for instance) (for all grid) 5) Time history of water height (selected grid) 6) Time history of water velocity (selected grid)
Figure A.2.5 shows a sample result of tsunami simulation by the program "TUNAMI" developed by Tohoku University in Japan. The left side of Figure A.2.5 shows maximum water height distribution map and the right side of Figure A.2.5 shows time history of water height at the selected grid.
Water Level (m)
Figure A.2.5 example of tsunami simulation results
A.2.4 Return Period of Scenario Earthquake The relationship of earthquake magnitude and return period is estimated by Gutenberg-Richter Low using earthquake data, often called earthquake catalog, around the targeted earthquake zone. It is well known that larger earthquake occurs less frequently. It was Gutenberg and Richter who formulate this relation as the following equation in 1941. It is the reason why this relation is called as Gutenberg-Richter Low or G-R Low.
log n(M) = a - b M or log N(M) = A - b M
The relation between occurrence frequency, n(M) dM, and cumulative frequency which is magnitude M and over, N(M), and 0.1 interval magnitude, dM = 0.1, is arranged in Table A.2.6 for the earthquake data which occurred during 1965 to 1999 in and around Japan area. Figure A.2.6 is the plot of the data with magnitude on x-axis and frequency on y- axis. The formula on the figure is a regression curve of M and N relation derived by the least square method.
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M = 7.5 corresponds to N = 6.8 on the formula, for instance. It means that the occurrence frequency of the earthquake with magnitude 7.5 and over is 6.8 times on average in 35 years, during 1965 to 1999. And its annual probability is calculated as 6.8 / 35 = 0.19 / year. It is recognized that the earthquake with magnitude 7.5 and over is expected to occur 0.19 time on average in and around Japan area. On the other hand, inverse number of the annual probability, 1 /0.19 = 5.1 years in this case, is called recurrence time. Then it is expressed that the recurrence time of the earthquake with magnitude 7.5 and over is 5.1 years.
As described above, an annual probability or a recurrence time of the scenario earthquake can be estimated using G-R Low in and around the scenario earthquake area.
Table A.2.6 Number of Shallow Earthquakes Occurred in and around Japan (1965 - 1999) 9)
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● N(M)
○ n(M) dM (dM=0.1)
N=6.8
M=7.5
Figure A.2.6 Data Plot of Table A.2.6 9)
A.2.5 Results of Simulation
(1) Collection of Existing Information The following information and data are collected for setting scenario earthquake and conducting tsunami simulation.
Literatures related to scenario earthquakes - Irsyam, M et al. (2010) 10): Development of Seismic Hazard Maps of Indonesia for Revision of Seismic Hazard Map - EMILE A. OKAL et al. (2011) 11): Tsunami Simulations for Regional Sources in the South China and Adjoining Seas Literatures related to earthquake environment - Earthquake catalog from January 1983 to May 2013 prepared by BMKG Data related to bathymetric feature - Wide area: GEBCO_08 Grid
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- Vicinity of objective area: Bathymetry chart by Hydro Oceanographic Office (Dinas Hidro-Oseanografi)
(2) Setting of Scenario Earthquake
1) Statistical analysis of earthquake (Gutenberg-Richter Law) Figure A.2.7 shows the expected maximum magnitude, Mw, and "a-value" and "b-value" of the Gutenberg-Richter Law of the large inter-plate earthquakes which occur along the Sunda Trench like the 2004 Sumatra earthquake10).
The figure shows that the maximum magnitude, Mw, of the expected largest earthquake along from southern Sumatra to Java is 8.1 to 8.2 and the G-R Law's "a-value" and "b-value" for Southern Sumatra area are 5.76 and 1.05, 6.14 and 1.10 for Java area.
On the other hand, Figure A.2.8 shows the earthquake data around Jakarta from January 1983 to May 2013 prepared by BMKG and Figure A.2.9 shows the picked up data along the Sunda Trench. Figure A.2.10 shows the overlay of the plot of the data and the formulas defined in Figure A.2.7. The plot of the data is little higher than the formulas but both seems roughly consistent.
According to this figure, the return period of the earthquake with Mw = 8.1 to 8.2, which is expected in the existing study1), may be around 600 to 700 years. The return period of the scenario earthquake of this study with Mw = 9.0 can be estimated more than 1,000 years.
2) Setting of scenario earthquake There is no record of major tsunami disaster around the Java Sea side of the Java Island where Bekasi and Karawang are located. In this study, the possible tsunami that may cause impact to the study area, though the possibility is very low, is simulated considering that the result will be used in the future.
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Figure A.2.7 Segmentation model and parameter used in subduction “Megathrust” zone of Indonesia (Source: Irsyam, M et al. (2010))
Figure A.2.8 Epicenter Data around Jakarta (1000km * 1000km) by BMKG
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Figure A.2.9 Epicenter Data along Sunda Trench Zone
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Figure A.2.10 Return Period of the Earthquakes along Sunda Trench (Gutenberg–Richter law)
(3) Analysis and Results
1) Setting of tsunami (earthquake) model There is no record of major tsunami disaster around the Java Sea side of the Java Island where Bekasi and Karawang are located. Therefore, the scenario earthquake which has the possibility to affect the objective area is selected among the earthquakes2) which occur along the Sunda Trench .
Table A.2.7 shows fault parameters of scenario earthquake and Figure A.2.11 shows the location of the scenario earthquake.
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Table A.2.7 Source Model Scenario Depth Strike Dip Rake Length Width Slop Mw No. (km) (degree) (degree) (degree) (km) (km) (m) 111) 9.0 10 300 10 102 500 150 10.0
Scenario 1 (M9.0)
Figure A.2.11 Tsunami Source Model (Scenario 1)
2) Selection of tsunami simulation model Tsunami simulation program "TUNAMI" which is developed by Tohoku University based on the non-linear long wave theory is used for the analysis. Analysis period is set to be 24 hours after earthquake occurrence.
3) Preparation of Input data UTM coordinate system is used. Square grids are adopted for topographical model. The size of grids are defined as 1350m, 450m, 150m and 50m, from tsunami source region to the coast based on the nesting method.
Topographical data The depth of the grids in the ocean area with the size of 1350m, 450m and 150m and the inland 50m grids are generated based on "GEBCO_08 Grid" data prepared by BODC (British Oceanographic Data Centre).
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The point water depths of the bathymetry charts prepared by Hydro Oceanographic Office (Dinas Hidro-Oseanografi) are digitized and used for creating the depth data of the 50m grids around Jakarta Bay and its vicinity.
Figure A.2.12 shows the wide area topographical model prepared from "GEBCO_08 Grid". Figure A.2.13 shows the topographical model around the Jakarta Bay area prepared from bathymetry charts.
Roughness data Roughness coefficient "0.025" is used both for ocean and land area.
Sea defense data Embankment and other sea defense structures are not considered in this study.
Initial water height data (= deformation of sea-floor) Deformation of sea-floor is calculated by Okada's program "DC3D0 / DC3D" with using fault parameters of scenario earthquakes and the vertical component of the deformation of the sea-floor is given to corresponding grid as initial water height.
Calculated deformation of sea-floor is shown in Figure A.2.14.
1350m grid area
Depth (m)
0 1000 2000 3000 4000 5000 6000
Figure A.2.12 Wide Area Topography Model by GEBCO Data (Around West Jawa)
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Figure A.2.13 Topography Model around Jakarta Bay
4) Tsunami simulation Figure A.2.15 to Figure A.2.16 show the results of the simulation for Scenario 1. Table A.2.8 shows the maximum tsunami height at Jakarta Bay and the expected return period calculated from magnitude of scenario earthquake.
The tsunami height by the huge earthquakes with magnitude 9.0, which occurs along the Sunda Trench, is estimated less than 1m by the simulation. The earthquake with magnitude 9.0 has not expected in the past study11) and if it may occur, the return period may be larger than thousand years.
Table A.2.8 Maximum Tsunami Height at Jakarta Return Period Max Tsunami Height Scenario No. Mw (year) (m) 1 9.0 over 1,000 0.34
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Simulation result of Scenario 1
This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. [Analytical condition] Software: TUNAMI by Tohoku Univ., Bathymetry data: GEBCO_08, Bathymetry chart by Hydro Oceanographic Office, Grid size: 1350m, 450m, 150m, 50m, Simulation duration: 24 hours, Return period: more than 1,000 years.
Water Level (m)
Figure A.2.14 Vertical deformation (Scenario 1)
This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. [Analytical condition] Software: TUNAMI by Tohoku Univ., Bathymetry data: GEBCO_08, Bathymetry chart by Hydro Oceanographic Office, Grid size: 1350m, 450m, 150m, 50m, Simulation duration: 24 hours, Return period: more than 1,000 years.
Jakarta
Water Level (m)
Figure A.2.15 Maximum Water Height (Scenario 1)
Figure A.2.16 Wave form of Tsunami Height at Jakarta (Scenario 1)
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A.2.6 Evaluation of the Results
1) Return period of the tsunami The probabilistic analysis methodology for tsunami is not established as for the earthquake. The main reason is that there is no simple simulation method for tsunami compared to the earthquake. The earthquake motion can be evaluated by attenuation formula using only the magnitude of the earthquake and the distance from the epicenter to the study point; however simple formula to estimate tsunami is not exist because the influence of the bathymetry and the shape of the shore line have a great influence to the tsunami. Therefore, the tsunami simulation for the previously decided probability of occurrence like the other hazards is not possible. In this study, the tsunami was simulated based on the existing fault models in the existing scientific papers, and the probability of the occurrence of the earthquake at the fault was estimated based on the earthquake catalogue around the fault separately. The tsunami simulation in this study is not the probabilistic analysis but the simulation of tsunami by the existing fault model and the evaluation of the probability of fault activity.
2) Bathymetry data The open source bathymetry data of around 1km grid is used to make the bathymetry model. The quality of the data is enough to be used to make offshore bathymetry model. The bathymetry data near the coast should be more precise, for example 50m grid data is necessary. The chart or bathymetry map are digitized and used in this study. The precise bathymetry data is sometimes unavailable or not exist at all. This limitation may become an obstacle to the tsunami simulation.
3) Run-up The run-up is not simulated in this study because the precise and accurate elevation model, height of dyke, land cover and distribution of the buildings are necessary. The high level of technique is also required for precise analysis. The hurdle to realize the run-up simulation for Area BCP is high.
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References
1) IUGG/IOC Time Project: Numerical Method of Tsunami Simulation with the Leap-frog Scheme, IOC Manuals and Guides No.35, UNESCO 1997 http://www.jodc.go.jp/info/ioc_doc/Manual/122367eb.pdf 2) Imamura, Yalciner and Ozyurt (2006): TSUNAMI MODELLING MANUAL (TUNAMI model) http://www.tsunami.civil.tohoku.ac.jp/hokusai3/E/projects/manual-ver-3.1.pdf 3) Tsunami Dictionary (Japanese) (2007) : Edited by Shuto, Imamura, Koshimura, Satake, Matsutomi, Asakura Publishing Co., Ltd 4) Kaiser, Scheele1, Kortenhaus, Løvholt, Romer, Leschka (2011): The influence of land cover roughness on the results of high resolution tsunami inundation modeling, Nat. Hazards Earth Syst. Sci., 11, 2521–2540 http://www.nat-hazards-earth-syst-sci.net/11/2521/2011/nhess-11-2521-2011.pdf 5) Mansinha, Smilie (1971): The Displacement Fields of Inclined Faults, Bulletin of the Seismological Society of America, Vol. 61, No. 5, pp. 1433-1440 http://ceeserver.cee.cornell.edu/pll-group/doc/Mansinha_Smylie_1971.pdf 6) Okada (1992): Internal deformation due to shear and tensile faults in a half-space, Bull. Seism. Soc. Am., 82, 1018-1040. 7) COMCOT, Cornell University http://ceeserver.cee.cornell.edu/pll-group/comcot_fault.htm 8) Program to calculate deformation due to a fault model DC3D0 / DC3D http://www.bosai.go.jp/study/application/dc3d/DC3Dhtml_E.html 9) Utsu (2001): Seismology, 3rd edition (Japanese), Kyoritsu Shuppan Co., Ltd. 10) Irsyam, M., Sengara, I.W., Asrurifak, M., Ridwan, M., Aldiamar, F., Widiyantoro, S., Triyoso, W., Natawijaya, D.H., Kertapati, E., Meilano, I., and Suhardjono (2010a). Summary: Development of Seismic Hazard Maps of Indonesia for Revision of Seismic Hazard Map in SNI 03-1726-2002, research report submitted to the Ministry of Public Works by Team for Revision of Seismic Hazard Maps of Indonesia, July.
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A.3 Flood Hazard Assessment
A.3.1 Overview According to the topographical characteristics, past disaster situations, and hearing survey conducted so far, it is proved that the inundation has been caused by river water. Industrial estates are located along the Cikampek Highway. Main local road and urban areas are located to the downstream side of Cikampek Highway. In this study, inundation situation of these areas are analyzed and evaluated. Basic procedures of flood analyses are as follows.
・Collect hydrological data and conduct rainfall analyses to calculate the probable rainfall and determine the target rainfall. ・Runoff analyses are conducted using target rainfall calculated by rainfall analyses as input conditions. In this study, satellite rainfall data (3B42RT) are utilized to complement the ground rainfall data because the number of gauging stations of ground rainfall is insufficient considering the scale of target area. IFAS is used for runoff simulation. ・With the runoff discharges calculated by runoff simulation, inundation analyses are conducted. In this study, Nays2dFlood, one of the iRIC software, is employed for inundation simulation. ASTER GDEM is set as basic topographic condition, and it is corrected partially by spot elevation data observed on the Cikampek Highway.
1. Rainfall Analysis ・ Collection of hydrological data ・ Calculation of probable rainfall ・ Setting up the target rainfall
2. Runoff Analysis ・ Setting catchment area ・ Setting topographic and geological conditions ・ Complement the ground rainfall data with satellite rainfall data
・ Setting parameters ・ Calculation of runoff discharge
3. Inundation Analysis ・ Setting topographical condition with ASTER GDEM ・ Correction of topographical condition using spot elevation data
・ Setting parameter (Roughness coefficient) ・ Execute inundation analyses ・ Evaluation results Figure A.3.1 Procedures for Flood Analysis
IFAS (Integrated Flood Analysis System) is Flood Forecasting System developed by International Centre for Hazard and Risk Management (ICHARM). Main features of IFAS are as follows.
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・Not only ground-based but also satellite-based rainfall data are applicable. ・GIS data analyses are available for setting of river basin and river networks. ・Two types of runoff model (PWRI distributed model and BTOP model) are available, which enables users to conduct analyses in various situations. ・Free download for the execution program from ICHARM website iRIC (International River Interface Cooperative) is a river flow and riverbed variation analysis software package developed by the USGS (U.S. Geological Survey) and the Foundation of Hokkaido River Disaster Prevention Research Center. In this study, Nay2dFlood, which is one of the iRIC software, is employed. Main features of iRIC are as follows.
・Elevation data, inflow hydrograph and roughness coefficient reflecting land use conditions are the minimum requirements as input data to conduct calculation. Therefore, it is said Nays2dFlood demands relatively little information to conduct analysis compared to other software. ・Functions for creating calculation lattices and setting calculation conditions, and for visualization and analysis of calculation results are well prepared. ・Free download for the execution program from iRIC website.
A.3.2 Data Collection The status of data collection is as follows.
Table A.3.1 Status of Data Collection Collected Data Area Water Level Topographic Data Rainfall Inundation Map Others (Tidal Level) Spot elevation data along Daily rainfall data Inundation Maps Indonesia the Cikampek Highway from 19 stations - of past major - Bekasi/Karawang and major local road (1999-2013) flood events
A.3.3 Target Return Period Target return period should be determined taking into consideration the policy of the central or local government, willingness of residents, and feasibility. In this study, largest recorded flood, 50-year return period flood, 100-year return period flood, and 200-year return period flood are set up as target floods.
A.3.4 Rainfall Analysis
(1) Data Collection The rainfall data from 19 stations located in the catchment area of Cirarum river and Bekasi river are collected. The status of data collection is shown in Table A.3.2, and the location of gauging stations is shown in Figure A.3.2.
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The collected data are daily precipitation. The observation period of them is from 1999 through 2013. Data obtained from fourteen stations denoted as No.6 to 19 in Table A.3.2 are observed only in the years when flood occurs, and only around the industrial areas. Data seems to be insufficient. Data obtained from five stations denoted as No.1 to 5 in Table A.3.2 are relatively in good condition with no missing values, but the observation period is from 2003 through 2012, which is not enough for the hydrological analysis. Considering the above situation, Cisomang, which is located at approximately center of the Citarum river basin, is selected as representative station for rainfall analysis. Probable rainfalls based on the annual maximum rainfall and maximum 3-days rainfall are calculated for reference.
Table A.3.2 Collected Rainfall Data
Location Year No Station Name Latitude Longitude 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 1 Cisomang 6.683333 107.407500 × × ●●●●●●●●●●●● × 2 Tunggilis 6.419694 107.031139 × × × × ● ● ● ● ● ● ● ● ● ● × 3 Ciherang 7.036944 107.580278 × × ●●●●●●●●●●●● × 4 Cileunca 7.193056 107.544722 ●●●●●●●●●●●●●● × 5 Kertamanah 6.190278 107.610556 × × × × ● ● ● ● ● ● ● ● ● ● × 6 Rawamerta-96.A 6.263389 107.367056 ―――△――△―△―――――△ 7 Teluk Jambe-89.a 6.396806 107.266611 ―――△――●―△―――――△ 8 Pangkalan 86 6.450622 107.217297 ―――●――●―△―――――△ 9 Tegalwaru 80 6.527744 107.240625 ― ― ― ● ― ― △ ― × ――――― × 10 Bendung.Cibeet_No.8a-CH 6.391028 107.221306 ―――●――●―●―――――△ 11 Bd.Cikarang-9a-CH 6.292167 107.118250 ―――●――△―●―――――△ 12 Bd.Bekasi-10a-CH 6.250028 106.997389 ―――●――△―●―――――△ 13 Bd.Cipamingkis-89.aCH 6.510992 107.068169 ―――●――●―●―――――△ 14 Cibarusah-85 6.429158 107.068442 ―――△――△―●―――――△ 15 Setu-Bks.13 6.331825 107.044625 ―――●――△―●―――――△ 16 Lemahabang_100 6.290692 107.131131 ―――●――△―●―――――△ 17 Babakan-Bma.27 6.243636 107.001472 ― ― ― ● ― ― × ― ● ― ― ― ― ― × 18 Cikeas-Dkt.26 6.294208 106.969061 ― ― ― ● ― ― × ― ● ― ― ― ― ― △ 19 Cibitung 6.270511 107.001472 ――― × ――△―●―――――△ ●：データありCollected △：一部欠測Collected but partially missing ×：なしNo data
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Legnd ：Gauging Station ：Indusrial Estate ：Basin Boundary
Figure A.3.2 Location of Rainfall Stations
(2) Frequency Analysis
1) Conditions of Analysis Conditions of frequency analysis are shown in the following table.
Table A.3.3 Conditions of Frequency Analysis No Items Description 1 Software Hydrological statistical tool developed by JICE1 Annual maximum daily rainfall and annual maximum 3-days rainfall in 2 Sample Cisomang Station from 2001 to 2012 (AMS) Selected from following 13 functions Exponential Distribution, Gumbel Distribution, Square-root Exponential Type Maximum Distribution, Extreme Value Distribution, Peason Type III Distribution (Real Space), Peason Type III Distribution Probability distribution 3 (Logarithmic Space), Iwai Method, Ishihara ・ Takase Method, Log-normal model Distribution (Quantile Method), Log-normal Distribution 3 (Slade II), Log-normal Distribution 2 (Slade I, L-moment method), Log-normal Distribution 2 (Slade I, Product moment method), Log-normal Distribution 4 (Slade IV, Product moment method)
1 Japan Institute of Country-ology and Engineering http://www.jice.or.jp/sim/t1/200608150.html
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4 Plotting position Cunnane plot (α=0.4) ・SLSC is less than 0.04. 5 Evaluation of validity ・Jack-knife error estimate is lower. ・Correlation coefficient is higher.
2) Results Daily maximum rainfall is more than 100 mm in 2007, 2008, 2009, and 2012. Three-days rainfall is more than 250 mm in 2007 and 2012 (Table A.3.4). Therefore, rainfall event on November 2007 is selected as a representative rainfall in Cisomange gauging station in Citarum River. As the results of statistical analyses, the values of SLSC obtained from all 13 functions are greater than 0.04. These results are due to the shortage of sample data. In this study, “Exponential Distribution” is selected as the probability distribution model since it gives the smallest SLCS of 0.062 of all (Figure A.3.3). When “Exponential Distribution” is applied, the return period of the rainfall event on November 2007 is evaluated as 5 years (Figure A.3.3). Annual maximum rainfall of 241 .0 mm is corresponding to 50-year return period, 271.4 mm to 100-year return period, and 301.9 mm to 200-year return period (Figure A.3.3).
Table A.3.4 Annual Maximum Rainfall and Maximum 3-days Rainfall by Years Daily Rainfall 3-days Rainfall Year Annual Maximum Annual Maximum Period Period Value (mm) Value (mm) 2001 94.00 11/15 199.20 11/13 - 11/15 2002 87.60 12/8 157.60 1/29 - 1/31 2003 79.00 10/12 135.00 5/13 - 5/15 2004 80.40 5/5 160.80 5/5 - 5/7 2005 79.50 10/23 138.40 2/22 - 2/24 2006 79.50 10/23 138.40 2/22 - 2/24 2007 140.00 11/11 272.50 11/9 - 11/11 2008 173.00 8/31 240.00 8/30 - 9/1 2009 163.50 12/28 225.50 12/26 - 12/28 2010 99.00 10/26 202.00 10/22 - 10/24 2011 99.00 10/26 202.00 10/22 - 10/24 2012 182.00 4/21 283.50 4/4 - 4/6
A-37 Risk Profile Report - Bekasi and Karawang of Indonesia - －－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－ －－－－－－－－－－－－－－－ －－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－ －－－－－－ 16.1 22.1 31.6 48.7 65.4 95.7 10.9 93.4 -55.4 134.7 157.6 207.2 249.4 378.9 385.4 435.5 473.1 569.6 0.065 116.9 0.805 0.899 0.126 108.0 131.1 171.5 222.9 258.1 308.1 359.3 0.893 0.974 LN3Q LN3PM LN2LM LN2PM IshiTaka －－－－－－－－－－－－－－－－－－－－－－ －－－－－－－－ －－－－－－－－－－－－－－－－－－－－－－－－－－－－－－ －－－－ Iwai
Annual Volume Series (Sumple size N=12) Gev LP3Rs LogP3 SqrtEt 0.069 0.089 0.069 0.087 0.068 Gumbel 8.8 10.2 9.2 14.6 137.3 14.1 17.123.6 17.630.2 22.7 17.034.1 27.7 23.0 26.5 181.338.9 30.5 29.3 29.9 205.9 23.3 43.4 34.0 33.2 27.8 226.8 25.7 37.3 38.4 23.9 237.9 25.1 43.4 19.0 251.0 24.0 24.5 262.4 24.0 28.8 12.4 13.7 12.7 21.0 159.2 19.2 99.6 106.4 100.6 99.7 292.6 103.0 -57.4 -59.4 -58.5 -58.1 -59.7 -57.8 ○ 118.8 122.80.872 0.888 1210.913 0.874 122.3 0.9140.100 0.847 125.5 0.896 0.118 0.906 121.6 0.900 0.167 0.935 117.4 0.921 0.124 123.4 0.958 139.8 0.177 112.6 142.3 0.120 115.4 126.6 340.7 136.3 120.4 394.0 141.3 0.961 0.9370.062 0.948 0.957 0.929 0.958 0.947 0.937 0.940 0.932 0.931 0.935 Exp Item
pAIC
1/5 1/10 1/20 1/30 1/50 1/80 1/1001/1501/200 45.61/400 49.4 38.8 52.2 41.6 45.8 58.8 43.6 50.4 32.8 267.6 48.4 53.7 55.7 276.8 33.5 62.1 77.6 151.1 283.2 46.6 298.1 59.4 102.4 1/1501/2001/400 289.2 253.41/2 301.9 223.7 262.61/3 332.3 322.7 232.8 284.6 689.0 339.1 255.3 276.7 711.5 372.7 285.7 764.8 302.2 1/2 1/3 1/5 1/101/201/30 170.31/50 166.1 200.7 145.31/80 188.9 218.5 167.8 164.31/100 202.0 241.0 460.1 203.6 175.7 218.4 169.6 261.6 522.1 226.3 190.4 271.4 233.4 198.2 557.1 256.4 240.5 204.4 214.9 600.0 211.2 285.0 235.8 298.5 638.6 656.6 254.2 262.6
jackknife error estimates error jackknife SLSC(99%) SLSC(50%) Value probable
X-COR(99%) X-COR(50%) P-COR(99%) log likelihood P-COR(50%) Selected method Selected Probability Distribution (Daily (Daily Distribution Maximum Rainfall) .3 Probability Figure A.3 Figure
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Appendix Details of Natural Hazard Assessments LN3Q LN3PM LN2LM LN2PM IshiTaka 19.320.1 18.919.1 19.5 14.917.1 19.0 15.8 19.118.0 17.7 17.0 19.8 13.921.0 17.7 18.9 19.2 15.8 13.9 27.1 18.6 21.2 17.8 18.5 15.6 34.6 21.1 22.7 17.6 22.5 18.1 38.6 24.3 24.8 18.7 26.8 21.7 46.5 26.2 26.8 21.4 29.5 25.6 52.7 29.9 27.8 25.0 32.9 28.0 68.8 32.8 29.7 27.1 36.1 31.1 40.6 31.1 31.2 37.7 34.0 34.5 34.4 40.6 35.4 42.9 42.7 38.0 47.9 39.9 44.5 -63.0 -63.1 -63.1 -63.1 -63.1 -63.0 0.970 0.9730.976 0.971 0.9790.052 0.973 0.978 0.051 0.972 0.979 0.054 0.972 0.978 0.051 0.977 0.049 0.051 131.9 132.20.953 132.2 0.9630.971 132.2 0.966 0.9710.073 130.1 0.963 0.970 0.065 0.956 130 0.971 0.075 0.957 0.972 0.065 0.971 0.067 0.069 186.6 192.0 192.1 192.1 188.4 188.4 209.8 214.5 213.9 214.6 211.4 210.4 235.0 236.6 235.5 236.8 235.8 233.7 265.2 260.9 259.6 261.1 265.0 261.5 292.7 281.3 280.2 281.4 291.7 286.9 307.9 292.0 291.1 291.9 306.7 301.0 326.2 304.3 304.1 304.1 325.1 318.3 342.4 314.9 315.3 314.5 341.6 333.9 349.9 319.6 320.5 319.1 349.3 341.1 363.2 327.9 329.5 327.2 363.3 354.2 372.5 333.5 335.8 332.6 373.1 363.4 393.9 346.1 350.2 344.8 396.5 385.4 Iwai － － － － － － － － － － － － － － － － － － － － － － － － － － － － － － － －
0.060 Annual Volume Series (Sumple size N=12) ○ 0.042 Gev LP3Rs LogP3 days Rainfall) SqrtEt - Gumbel 13.414.5 13.716.7 15.0 14.320.9 17.0 17.7 20.725.7 20.3 22.1 22.1 20.0 28.7 23.8 28.3 21.3 19.8 32.6 25.9 34.9 18.9 17.7 36.3 28.7 39.0 19.9 14.5 31.3 44.5 23.7 14.4 49.7 31.5 16.5 40.8 20.8 25.6 -60.7 -63.1 -63.1 -63.1 -62.9 0.938 0.967 0.958 0.973 0.969 0.963 0.9750.064 0.972 0.047 0.979 0.053 0.979 125.4 130.10.940 130.2 0.9500.972 132.2 0.942 0.9700.092 131.9 0.961 0.969 0.073 0.974 0.973 0.080 0.974 0.061 0.079 176.8 185.8 183.1 189.2 190.5 200.3 208.1 205.7 213.1 214.3 229.8 233.1 232.3 237.8 238.3 Exp Item
pAIC
1/2 1/3 1/5 1/10 269.9 264.3 267.7 265.8 264.7 1/20 309.9 294.4 303.9 288.9 286.2 1/30 333.4 311.6 325.6 300.5 297.0 1/50 362.9 333.2 353.8 313.0 309.1 1/80 390.1 353.0 380.5 322.7 318.7 1/100 403.0 362.3 393.4 326.6 322.9 1/150 426.4 379.3 417.4 332.8 329.8 1/200 443.1 391.3 434.9 336.4 334.3 1/400 483.1 420.3 478.2 342.3 343.5 1/2 1/3 1/5 1/10 1/20 1/30 1/50 1/80 1/1001/1501/200 38.11/400 41.3 32.6 43.7 34.9 52.3 49.3 36.5 57.1 45.7 40.5 60.6 55.4 28.0 69.5 62.6 32.6 81.3 35.9 44.0
jackknife error estimates error jackknife SLSC(99%) SLSC(50%) Value probable
X-COR(99%) X-COR(50%) P-COR(99%) log likelihood P-COR(50%) Selected method Selected Probability Distribution .4 Probability (Maximum 3 Figure A.3 Figure
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A.3.5 Runoff Analysis Runoff analyses are conducted using target rainfall calculated by rainfall analyses as input conditions.
(1) Conditions of Analysis Conditions of frequency analysis are shown in the following table.
Table A.3.5 Conditions of Runoff Analysis No Items Description 1 Software IFAS Ver.1.32 8 – 14 November, 2007 (7 days) 2 Calculation Period *Period the largest rainfall recorded at Cisomang gauging station. ・Downstream side of Jatiluhur dam in Citarum river (Figure A.3.7) *Assume that Jatiluhur dam is filled and runoff inflow from catchment is released 3 Calculation Point with no control. (Inflow = Outflow) ・Other 4 representative rivers DEM data: GTOPO30 4 Topographical Data Land use: GLCC Geology: CGWM Rainfall data: 3B42RT(V5) 3 hours interval 5 Rainfall Data *Satellite rainfall data are enlarged to the scale of ground-based rainfall data. Default value of IFAS Ver.1.3 6 Parameter Setting *Parameter identification is impossible due to the lack of discharge data.
1) Runoff Model IFAS (Integrated Flood Analysis System) is a Flood Forecasting System developed by ICHARM. IFAS provides interfaces to input not only satellite-based but ground-based rainfall data, as well as GIS functions to create river channel network and to estimate parameters of a runoff analysis engine and interfaces to display output results.
In this study, The PWRI Distributed Model Ver.2.0 is employed. The PWRI Distributed Model comprises a two-layer non-linear tank model in the slope, and Kinematic Wave Model for tracking river channels. The PWRI Distributed Model has been applied to a lot of studies in Japan.
2 International Centre for Hazard and Risk Management (ICHARM) http://www.icharm.pwri.go.jp/index.html
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Satellite-based rainfall data: internet 3B42RT(NASA) GSMaP(JAXA) Rainfall data QMORPH, CMORPH(NOAA) Ground-based rainfall data: GPV(JMA)
Building channel network: DEM data GTOPO30(USGS) Hydro1k(USGS) GlobalMap(ISCGM) Modeling Estimation of parameter (estimated from the geophysical data) Land-use: GLCC(USGS) Soil: soil texture (UNEP), soil depth (NASA), soil moisture (UNEP) Geology: CGWM
1) PERI distributed model Ver2.0 Runoff analysis 2) BTOP Model
time-series, horizontal distribution, outline of tank Display of results tables & graphs, animation output to General-purpose geographic information system (Google Earth) Figure A.3.5 Main functions of IFAS
Figure A.3.6 Image of PWRI Distributed Model
2) Calculation Period The period from November 8 to 14, 2007 is set up as calculation period considering that maximum 3-days rainfall of representative rainfall event occurs from November 9 to 11, 2007.
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3) Catchment Area In this study, representative five rivers including Citarum River are taken into consideration as the inflow conditions for flood analyses. The calculation points are shown in Figure A.3.7.
Point5 Point4 Point3 Point2 Point1
Legend ：discharge calculation point ：Industrial estate ：Basin boundary ：Inundation simulation area
Figure A.3.7 Catchment Area
4) Topographic Data Elevation GTOPO30, which is managed and provided by USGS, is utilized as elevation data for runoff analysis.
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Figure A.3.8 Elevation Data for Runoff Analysis (Indonesia)
Land use GLCC (GLOBAL LAND COVER CHARACTERIZATION), which is managed and provided by USGS, is utilized as land use data for runoff analysis.
Figure A.3.9 Land Use Data for Runoff Analysis
5) Rainfall Data The number of ground-based gauging stations is insufficient considering the scale of river catchment, so it is difficult to comprehend the distribution of rainfall in the target area. In addition, despite it is
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Therefore, satellite rainfall data (3B42RT) is utilized to complement the ground-based rainfall data.
a) Calculation of daily maximum rainfall using the satellite rainfall data (3B42RT) during the period of rainfall event on November 2007 at Cisomang gauging station.
b) Calculation of enlargement rate by comparing the daily maximum rainfall calculated in a) with 5-year return period rainfall (Considered to be corresponding to 2007-year event: 140.0mm), and other target return period rainfalls (50-year: 241.0mm,
100-year: 271.4mm, 200-year: 301.9mm)
c) Enlargement of satellite rainfall data according to the rate calculated in b)
Figure A.3.10 Procedures for Enlargement of Satellite Rainfall Data
Table A.3.6 Results of Enlargement of Satellite Rainfall Data (Daily Data)
Unit:(mm) Observation of 3B42RT Item St.Cisomang Original_3B42RT Ajusted ― ― 2007 y50 y100 y200 Probable daily rainfall 140.0 86.4 140.0 241.0 271.4 301.9 Enlarging ratio ― ― 1.620 2.789 3.141 3.494 2007/11/8 12.0 13.8 22.3 38.4 43.3 48.1 2007/11/9 71.0 3.2 5.2 9.0 10.1 11.2 2007/11/10 61.5 16.8 27.2 46.8 52.7 58.6 2007/11/11 140.0 86.4 140.0 241.0 271.4 301.9 2007/11/12 50.0 48.4 78.4 135.0 152.0 169.1 2007/11/13 11.0 5.2 8.5 14.6 16.4 18.2 2007/11/14 30.3 24.7 40.1 69.0 77.7 86.4 Total 375.8 198.5 321.6 553.6 623.5 693.5
500 Comparison of the daily precipitation Observation_St.Cisomang 400 Original_3B42RT 2007 300 y50 y100 200 y200 Pricipitation(mm) 100
0 2007/11/8 2007/11/9 2007/11/10 2007/11/11 2007/11/12 2007/11/13 2007/11/14
Figure A.3.11 Results of Enlargement of Satellite Rainfall Data (Daily Data)
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250 Comparison of the hourly precipitation Original_3B42RT Ajusted_3B42RT_2007 200 Ajusted_3B42RT_y50 Ajusted_3B42RT_y100 150 Ajusted_3B42RT_y200
100 precipitation(mm)
50
0 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 2007/11/8 2007/11/9 2007/11/10 2007/11/11 2007/11/12 2007/11/13 2007/11/14
Figure A.3.12 Results of Enlargement of Satellite Rainfall Data (Hourly Data)
3B42RT(V5) Before Enlargement（2007/11/11 13:00）
Cisomang Station
Enlargement Rate: 140.0mm／86.4mm
3B42RT(V5) After Enlargement（2007/11/11 13:00）
Cisomang Station
Figure A.3.13 Results of Enlargement of Satellite Rainfall Data Distribution (Daily Data)
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6) Parameter Setting Table A.3.7 and Table A.3.8 show the parameters set up in this study for IFAS. Default values set up in IFAS Ver1.3 are employed because parameter identification is impossible due to the lack of discharge data.
Table A.3.7 Model Parameters for Runoff Analysis ・Surface Runoff Parameters (IFAS Default Value)
Land use type SKF HFMXD HFMND HFOD SNF FALFX HIFD
Forest 0.0005 0.1 0.01 0.005 0.7 0.8 0 Water Bodies, Snow 0.00001 0.05 0.01 0.005 2 0.5 0 or Ice Cropland and 0.00001 0.05 0.01 0.005 2 0.5 0 Pasture, Wetland Urban and Built-Up 0.000001 0.001 0.0005 0.0001 0.1 0.9 0 Land
Others 0.00002 0.05 0.01 0.005 2 0.6 0
・Ground water Runoff Parameters (IFAS Default Value)
Land use type AUD AGD HCGD HIGD
ALL 0.1 0.003 2 2
・River Channel Parameters (IFAS Default Value)
RBW RBS RNS RRID RGWD RHW RHS RBH RBET RLCOF
7 0.5 0.035 0.2 0 9999 1 0.5 0.05 1.4
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Table A.3.8 Description of Model Parameters for IFAS
Source: Integrated Flood Analysis System (IFAS Version 1.2) User’s Manual June 2009 P.122 / ICHARM
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(2) Results The results of runoff analyses are shown from Figure A.3.14 to Figure A.3.18. These results are used for inundation analyses as upper boundary conditions.
Chitarum River 10,000 2007 year Flood Return Period 50y 8,000 Return Period 100y Return Period 200y 6,000
4,000 Discharge(m3/s)
2,000
0 11/8 11/9 11/10 11/12 11/13 11/14 11/11
Figure A.3.14 Result of Runoff Analysis (Point 1)
Tributary of Chitarum River (1) 2,000 2007 year Flood Return Period 50y 1,600 Return Period 100y Return Period 200y 1,200
800 Discharge(m3/s)
400
0 11/8 11/9 11/12 11/10 11/11 11/13 11/14 Figure A.3.15 Result of Runoff Analysis (Point 2)
Tributary of Chitarum River (2) 2,000 2007 year Flood Return Period 50y 1,600 Return Period 100y Return Period 200y 1,200
800 Discharge(m3/s)
400
0 11/8 11/9 11/10 11/11 11/12 11/13 11/14
Figure A.3.16 Result of Runoff Analysis (Point 3)
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Tributary of Bekasi River (1) 250 2007 year Flood Return Period 50y 200 Return Period 100y Return Period 200y 150
100 Discharge(m3/s)
50
0 11/8 11/9 11/10 11/11 11/12 11/13 11/14
Figure A.3.17 Result of Runoff Analysis (Point 4)
Tributary of Bekasi River (2) 250 2007 year Flood Return Period 50y 200 Return Period 100y Return Period 200y 150
100 Discharge(m3/s)
50
0 11/8 11/9 11/11 11/12 11/14 11/10 11/13 Figure A.3.18 Result of Runoff Analysis (Point 5)
A.3.6 Inundation Analysis Inundation analyses are conducted using the results of the run-off simulation described above. Simulation cases are as shown below;
・Case 1： 2007 year flood as target flood ・Case 2： 50 -year return period flood as target flood ・Case 3： 100 -year return period flood as target flood ・Case 4： 200 -year return period flood as target flood
(1) Conditions of Analysis Simulation conditions are tabulated in the following table.
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Table A.3.9 Conditions of Inundation Analysis No Items Description 1 Software Nays2dFlood (iRIC Software)3 Two-dimensional plane flow analysis model using runoff discharge 2 Model calculated with runoff model as boundary conditions 3 Target Area See Figure A.3.19 4 Grid Size 200m Set up with ASTER GDEM (See Figure A.3.21) 5 Elevation Data (Correct ASTER GDEM with spot data measured on Cikampek Highway.) Five hydrographs calculated with runoff model are given as upper boundary conditions. (See Figure A.3.19) Boundary 6 Assume that Jatiluhur dam is filled and runoff inflow from catchment is Condition released with no control. (Inflow = Outflow) Downstream conditions are set up to be free flow. Roughness Given at each grid according to land use conditions (0.047 - 0.060) 7 Coefficient Assume the drainage capacity of 2m3/s/ km2 and deduct the amount of 8 Drainage water from each grid.
1) Inundation Model Nays 2D Flood is a flood flow analysis solver that relies on unsteady 2-dimensional plane flow simulation using boundary-fitted coordinates as the general curvilinear coordinates. This solver adopts the 2-dimensional plane flow simulation of the Nays2D Solver developed by Professor Yasuyuki Shimizu of Hokkaido University for flood flow analysis.
The solver easily enables the user to set the inflow conditions of an arbitrary number of inflow rivers that enter from the upstream end or sides of a river. It has been applied to the flood flow analysis of small/mid-scale rivers. Because the solver does not require river channel data, it is also used for the flood process analysis of primitive rivers and rivers in developing countries.
The basic procedures for inundation analysis using Nays2d Flood are as follows.
Create Calculation Lattices Set Calculation Conditions Execute Calculation - Input topographic data (DEM) - Upper inflow - Roughness Coefficient
2) Target Area The simulation area is shown in Figure A.3.19, which covers the industrial estates located along the Cikampek Highway, and city area located in the downstream side.
As to the inflow condition, representative five rivers including Citarum river are taken into consideration as the inflow conditions.
3 iRIC project http://i-ric.org/en/
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Cikampek Highway
Inflow Inflow Inflow ：Industrial Estate Inflow ：Simulation Area Inflow Figure A.3.19 Target Area
3) Elevation Data ASTER GDEM4 (Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model) is employed as topographical data.
ASTER is an earth observing sensor developed in Japan to be onboard the satellite "Terra". It is the high efficiency optical sensor which covers a wide spectral region from the visible to the thermal infrared by 14 spectral bands. ASTER acquires data which can be used in various fields in earth science.
DEM is generated from a stereo-pair of images acquired with nadir and backward angles over the same area. ASTER GDEM will be developed based on this data.
The data obtained from Website of ASTER GDEM is raster format (*tif), so it needs to be converted to text format using software such as ArcGIS to input Nays2dFlood.
Grid size is set as 200 m considering computing power.
4 ASTER GDEM http://gdem.ersdac.jspacesystems.or.jp/
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Table A.3.10 Outline of Elevation Data (ASTER GDEM) Items Description Remarks 30m Grid size is 200m Resolution (Grid Number: 268 x 271 = 72,628) X: 715,934 - 782,934 m Y: 9,280,209 – 9,346,691 m Area *Coordination System WGS1984 UTM Zone 48S
In this study, whether the Cikampek Highway, which is significant network for traffic and transport would be inundated or not is one of the focus points. However, due to the limitation of resolution of ASTER GDEM (30m), it is possible that the elevation of highway would be incorrectly described.
Spot elevation data are measured with handy GPS. Using these data with aerial photographs and other topographical information obtained from Google Earth, the elevation described by ASTER GDEM is corrected.
1 m 2 m 3 m 4 m 5 m 6 m
Figure A.3.20 Correction of ASTER GDEM
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Elevation(m)
Figure A.3.21 Elevation of Target Area
4) Boundary Condition Five hydrographs calculated with runoff model are given as upper boundary conditions. (See from Figure A.3.14 to Figure A.3.18 and Figure A.3.19)
Assume that Jatiluhur dam is filled and runoff inflow from catchment is released with no control. (Inflow = Outflow) Downstream conditions are set up to be free flow.
5) Roughness Coefficient Roughness coefficient is given at each grid according to land use conditions. In this study, global map5 managed by ISCGM (International Steering Committee for Global Mapping) is employed for determination of land use conditions to which the values of roughness coefficient are corresponding. The data obtained from Website of ISCGM is raster format (*tif), so it needs to be converted to polygon format using software such as ArcGIS to input Nays2dFlood.
The relationship between land use conditions and roughness coefficient is as shown in Table A.3.11, These values are generally used in Japan.
5 ISCGM http://gdem.ersdac.jspacesystems.or.jp/
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Table A.3.11 Relationship between Land Use Conditions and Roughness Coefficient Agriculture Road Others (Paddy Field, Cropland, Orchard) 0.060 0.047 0.050
Figure A.3.22 Land Use Conditions of Target Area
6) Drainage Effect Assume the drainage capacity of 2m3/s/ km2 and deduct the amount of water from each grid.
(2) Results Results of inundation analyses (maximum depth and inundation duration) are shown in the following figures (from Figure A.3.23 to Figure A.3.30).
Industrial estates are not inundated even in the case of 200-year return period flood. Some parts of Cikampek Highway are inundated. The local road located in the north side of Cikampek Highway is largely inundated, which would cause the suspension of traffic. Inundation duration is more than two weeks in almost all area.
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This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. ： [Analytical condition] Software: IFAS for Runoff analysis and iRIC for Inundation analysis, Rainfall data: Industrial Estate 3B42RT 3hours interval data are enlarged to the scale of ground-based rainfall data., Elevation data: GTOPO 30, ASTER GDEM, Grid size: 200m, Boundary condition: Five hydrographs calculated with runoff model are given as upper boundary conditions. Assume that Jatiluhur dam is filled and runoff inflow from catchment is released with no control., 2007-year flood.
Depth(m) ～ 1.0 1.0 ～ 2.0 2.0 ～ 3.0 3.0 ～ 4.0 4.0 ～ 5.0 5.0 ～
Figure A.3.23 Result of Inundation Analysis (Maximum Depth Case 1: 2007-year Flood)
This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. ： [Analytical condition] Software: IFAS for Runoff analysis and iRIC for Inundation analysis, Rainfall data: Industrial Estate 3B42RT 3hours interval data are enlarged to the scale of ground-based rainfall data., Elevation data: GTOPO 30, ASTER GDEM, Grid size: 200m, Boundary condition: Five hydrographs calculated with runoff model are given as upper boundary conditions. Assume that Jatiluhur dam is filled and runoff inflow from catchment is released with no control., 2007-year flood.
Duration (day) ～ 5 5 ～ 10 10 ～ 15 15 ～ 20 20 ～
Figure A.3.24 Result of Inundation Analysis (Inundation Duration Case 1: 2007-year Flood)
A-55 Risk Profile Report - Bekasi and Karawang of Indonesia -
This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. ： [Analytical condition] Software: IFAS for Runoff analysis and iRIC for Inundation analysis, Rainfall data: Industrial Estate 3B42RT 3hours interval data are enlarged to the scale of ground-based rainfall data., Elevation data: GTOPO 30, ASTER GDEM, Grid size: 200m, Boundary condition: Five hydrographs calculated with runoff model are given as upper boundary conditions. Assume that Jatiluhur dam is filled and runoff inflow from catchment is released with no control., Return period: 50 years.
Depth(m) ～ 1.0 1.0 ～ 2.0 2.0 ～ 3.0 3.0 ～ 4.0 4.0 ～ 5.0 5.0 ～
Figure A.3.25 Result of Inundation Analysis (Maximum Depth Case 2: 50-year Return Period Flood)
This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. ： [Analytical condition] Software: IFAS for Runoff analysis and iRIC for Inundation analysis, Rainfall data: Industrial Estate 3B42RT 3hours interval data are enlarged to the scale of ground-based rainfall data., Elevation data: GTOPO 30, ASTER GDEM, Grid size: 200m, Boundary condition: Five hydrographs calculated with runoff model are given as upper boundary conditions. Assume that Jatiluhur dam is filled and runoff inflow from catchment is released with no control., Return period: 50 years.
Duration (day) ～ 5 5 ～ 10 10 ～ 15 15 ～ 20 20 ～
Figure A.3.26 Result of Inundation Analysis (Inundation Duration Case 2: 50-year Return Period Flood)
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Appendix Details of Natural Hazard Assessments
This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. ： [Analytical condition] Software: IFAS for Runoff analysis and iRIC for Inundation analysis, Rainfall data: Industrial Estate 3B42RT 3hours interval data are enlarged to the scale of ground-based rainfall data., Elevation data: GTOPO 30, ASTER GDEM, Grid size: 200m, Boundary condition: Five hydrographs calculated with runoff model are given as upper boundary conditions. Assume that Jatiluhur dam is filled and runoff inflow from catchment is released with no control., Return period: 100 years.
Depth(m) ～ 1.0 1.0 ～ 2.0 2.0 ～ 3.0 3.0 ～ 4.0 4.0 ～ 5.0 5.0 ～
Figure A.3.27 Result of Inundation Analysis (Maximum Depth Case 3: 100-year Return Period Flood)
This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. ： [Analytical condition] Software: IFAS for Runoff analysis and iRIC for Inundation analysis, Rainfall data: Industrial Estate 3B42RT 3hours interval data are enlarged to the scale of ground-based rainfall data., Elevation data: GTOPO 30, ASTER GDEM, Grid size: 200m, Boundary condition: Five hydrographs calculated with runoff model are given as upper boundary conditions. Assume that Jatiluhur dam is filled and runoff inflow from catchment is released with no control., Return period: 100 years.
Duration (day) ～ 5 5 ～ 10 10 ～ 15 15 ～ 20 20 ～
Figure A.3.28 Result of Inundation Analysis (Inundation Duration Case 3: 100-year Return Period Flood)
A-57 Risk Profile Report - Bekasi and Karawang of Indonesia -
This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. ： [Analytical condition] Software: IFAS for Runoff analysis and iRIC for Inundation analysis, Rainfall data: Industrial Estate 3B42RT 3hours interval data are enlarged to the scale of ground-based rainfall data., Elevation data: GTOPO 30, ASTER GDEM, Grid size: 200m, Boundary condition: Five hydrographs calculated with runoff model are given as upper boundary conditions. Assume that Jatiluhur dam is filled and runoff inflow from catchment is released with no control., Return period: 200 years.
Depth(m) ～ 1.0 1.0 ～ 2.0 2.0 ～ 3.0 3.0 ～ 4.0 4.0 ～ 5.0 5.0 ～
Figure A.3.29 Result of Inundation Analysis (Maximum Depth Case 4: 200-year Return Period Flood)
This map is intended to be used for disaster scenario creation. This map is not the forecast of the future hazard. ： [Analytical condition] Software: IFAS for Runoff analysis and iRIC for Inundation analysis, Rainfall data: Industrial Estate 3B42RT 3hours interval data are enlarged to the scale of ground-based rainfall data., Elevation data: GTOPO 30, ASTER GDEM, Grid size: 200m, Boundary condition: Five hydrographs calculated with runoff model are given as upper boundary conditions. Assume that Jatiluhur dam is filled and runoff inflow from catchment is released with no control., Return period: 100 years.
Duration (day) ～ 5 5 ～ 10 10 ～ 15 15 ～ 20 20 ～
Figure A.3.30 Result of Inundation Analysis (Inundation Duration Case 4: 200-year Return Period Flood)
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Appendix Details of Natural Hazard Assessments
A.3.7 Evaluation of the Results
(1) Assumptions for the simulation It is assumed that Jatiluhur dam is filled and runoff inflow from catchment is released with no control; namely all flooded water in the upper river goes down to the Karawang City along the lower stream. On the other hand, the rain fall in Karawang City is not considered and the function of the aqueducts and drainage pumps are supposed to be maintained. These conditions are difficult to be supposed in advance and the above mentioned assumption is reasonable for the simulation considering certain probability of occurrence. It should be noted that the actual flooding situation may be affected by these conditions.
(2) Used data and the accuracy of the analysis The rainfall data at ground surface is available for only one station. The rain fall data is complemented by satellite rainfall data and used in the analysis; but it is not enough and the runoff analysis is limited to the outline study level. The inundation analysis is also outline level because the cross section data of the river is not available. However, the characteristics of the inundation in the study area can be evaluated properly and the analyzed results are adequate for the disaster scenario formulation. The consistency of the simulated results and the actual flooding is confirmed by the hearing survey of the actual duration period of the inundation in this area.
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