Figure 3.1: Location of Study Site

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III. METHODOLOGY AND MATERIALS 3.1 Study Site The research was conducted in Surabaya, East Java, Indonesia, which is geographically located at 112 36-112 21 East and 7 12-7 21 South.

1 III. METHODOLOGY AND MATERIALS 3.1 Study Site The research was conducted in Surabaya, East Java, Indonesia, which is geographically located at East and South. The extent of Surabaya city area is 34,465 ha, consisting of 31 sub-districts with 163 villages. Surabaya represents the city of Indonesia that has relatively high temperature, ranging from 20 to 34.7 C, with the humidity ranging from 35 to 98% and air pressure at about Mbs. The precipitation rate is about mm/year, with highest precipitation occurred in December to May. Wind speed average is 7.0 knot, with the maximum speed at about 26.2 knot. Historically, the lowland of Surabaya city came from the sediment formation of the sea area. Known by the soil type predominantly found, i.e.: clay-sand (alluvial) and limestone hill in part of western Surabaya. Figure 3.1: Location of Study Site 19

2 Surabaya municipal mostly belongs to lowland area with average altitude ranging from 3 to 9 meter above sea level. Meanwhile the hilly area is located in southwestern namely Bukit Lidah and Bukit Gayungan having altitude ranging from 25 to 50 meter. The coastal areas span from the west municipal border to Tanjung Perak harbor and Eastern area of Sidoarjo regency border. The coastal area includes 9 subdistricts and 17 districts. Generally the coastline is located in the Eastern and Northern as well as western part of the Surabaya city (Figure 3.2). In the Eastern part of Surabaya city, the predominant landuses are fish and salt ponds, wetland, fields, and swamp while in the Northern part is fish ponds, warehouse building, harbor with some settlement area. 3.2 Data The main and supporting data used in this study include: (a) IPCC data model for global sea level projection used from MRI CGCM model with SRES A1B scenario. Data global sea level was derived from WCRP (World Climate Reseach Program) that can be accessed from (b) Tidal gauge data from University of Hawaii Sea level center and Indonesian Navy annual report (2010). The tidal data were derived from UHSLC (University of Hawaii Sea Level Center) which can be accessed in Data were provided from 1991 to (c) SRTM (Shuttle Radar Topography Mission) acquired in 2006, with 30 m spatial resolution collected by National Mapping and Coordination Survey Agency (Bakosurtanal). SRTM/DEM data used for establish contour line and elevation model. (d) Sea wave level surface data from AVISO (Archiving, Validation and Interpretation of Satellite Oceanographic data) AVISO is Satellite altimeter data which is a combination (merge) from multiple satellite altimeters including TOPEX / Poseidon (T / P), GFO, Envisat, ERS-1 and 2, and Jason-1, available from October 1992 to October

3 This data was used by author as a parameter to define maximum value of sea level in transition month of El-nino La-nina. (e) RBI (Rupa Bumi Indonesia) Map 2001 at scale of 1: created by the National Mapping and Coordination Survey Agency (Bakosurtanal). The RBI map is topographic map in the form of digital vector data. The other data are landuse/ landcover map coupled with attribute data such as administration, river, road, etc. The RBI was used for mapping and tidal flood analysis. (f) Surabaya subsidence level data for 2003 ~ 2004 which were derived from Indonesian Geology Bureau. The subsidence data was used as a parameter to calculate relative sea level. (g) The Indonesian Navy tidal gauge annual report data were used as a reference and prediction of tidal floods occurrence can be done in Surabaya city. (h) Interview data by the local resident and observation at the potential inundation areas. 3.3 Required Tools To accomplish the study, the software and hardware used are as follows: (a) Software 1. Arc-Gis/view for GIS work and spatial analysis. 2. 3DEM for accessing and conversion of DEM. 3. WXTide 32 ver.4.7 for defining and measurement tides. 4. Panoply for open the sea level dataset from AVISO and converted to preferable file format. 5. Google Earth ver. 4.2 is used for landuse delineation. (b) Hardware 1. PC Intel Core2 CPU T GHz, 2 GB RAM, Mobile Intel 945GM Express Chipset Family. (c) Tools 1. Global Positioning System (GPS) Garmin 76Csx for measuring geoposition in field work. 2. Pocket Camera Fuji finepix A220 to capture the objects observed. 21

4 3. Printer HP 3420 for printing purpose. 3.4 General Method The research was beginning with examined the problem identification and proposed the objectives. Data used were divided into: a) Data to define the sea level projection and, b) Data to define the vulnerable area. Survey was conducted to collect the information about the existing condition of the city. According to the flowchart shown in Figure 3.3, this study was divided into four phases (1) preparation and data acquisition, (2) pre-processing, (3) fieldwork, (4) processing and reporting phase. 22

5 Figure 3.2: Flowchart of General Method 23

6 3.4.1 Preparation and data acquisition This phase consists of activities such as: literatures review, primary data collection, and problem identification. The literature studied deals with tidal flood, the climate hazard impact, sea level rise, el nino la nina phenomenon, and IPCC sea level projection by MRI model. This phase includes journals and previous research results collection which are related with this research. The primary data was collected in this phase from National Mapping and Coordination Survey Agency (Bakosurtanal), Indonesian Geology Bureau (Badan Geologi) Pre-processing Pre-processing phase includes the process for producing vulnerable map as a reference to determine feasible area at the fieldwork phase. The pre-processing data started by cropping DEM-SRTM, rectification (projected to UTM WGS 49S zone coordinate system), and convertion into grid format using 3DEM software. The geometric data were used to determine the inundation area using the sea level projection formula. Let say the sea level relative calculation resulted 4 meter, it means in area with 3 meter elevation above sea level, the inundation level will be 1 meter depth, and in elevation 4 meter or more there is no inundation or not affected. The next step is to define the value of sea level relative, by extracting raw data of high wave from AVISO and tide gauge raw data from UHSLC using Panoply software. The data were converted those data into Ms Excel format. Then putted the subsidence level data which gotten from Indonesian Geology Bureau in Ms Excel. Global mean sea level from IPCC model projection which has already downloaded from were extracted to text format file using Panoply software and converted into ms excel file format. All data has processed and calculated in Ms Excel software. The formula of sea level relative is modification of the Sofian (2008) sea level projection equation, as follow: T EE =M SL +H EL +H W +H PS +S L (1) Where T EE : sea level on extreme climate in year projected, M SL : average sea level from IPCC model projection, H EL : sea level in El Nino and La Nina transition period, H W : wave height, H PS : sea level caused by tidal wave, S L : Subsidence level. 24

7 Prediction of the sea level was done for 2010, 2030, and The 1900 used as a baseline year measurement; so that the value at 2.1 meter of 2010 for example, is the rising of the sea from The projection was divided into common and extreme conditions. Common condition is the sea level in general climate while extreme condition is the sea level in the extreme climate condition namely in the transition period of El Nino-La Nina. Thus prediction of sea level especially in year period of El Nino and La Nina, range in between common and extreme condition. The formula of common condition ( SLCC ( t ) ( SLEC ( t ) ) of sea level projection can be seen as follows: SLCC ( t ) = MSLM t) + HW + TWL + LS ( (2) SLEC = MSLM + HWex + TWL + LS + HEL (3) Where: ( t ) ( t) ( t) ) and extreme condition MSLM (t) : Global Mean Sea Level in (t) year observed data according IPCC model HW HWex TWL LS projection. : Average of high wave data. : Maximum value of high wave data. : Average range between maximum and minimum of tides data. : Land subsidence data. HEL (t) : Sea level in (t) El Nino and La Nina transition period. To define level inundation, the value of HW has to multiply by 30%, because wave energy to push sea water up to the mainland is decrease about 30% due to hit the material which stand on the coastline area. The value of sea level relatives has resulted and use as guidance to specify the level of inundation in year observation. Then in same time, the data resulted was overlaid with the topographic map (RBI map), to make a vulnerable map that possible to be displayed and geo-processed using Arc-view software Fieldwork Field survey was conducted in July 2010 and December 2010 according to the tidal data prediction by Indonesian Navy tidal report in Surabaya city. The fieldworks were intended to get information about tidal flood and to observe the 25

condition related to the research, to conduct interviewed to the local residents, and to record the occurrence of tidal flood

The condition of tidal flood in Surabaya can be seen in appendix 8. Flood impact might reduced by high sedimentation of Surabaya.

It was the fact that the predominant soil type of Surabaya is clay-sand (alluvial) from sedimentation formation.

Northern Surabaya (Dupak), build paving road and managed the water channel (along road side in Northern). Figure 3.

8 condition related to the research, to conduct interviewed to the local residents, and to record the occurrence of tidal flood throughout Tidal flood observation was based on the existing vulnerable hazard map which has been made. The condition of tidal flood in Surabaya can be seen in appendix 8. Flood impact might reduced by high sedimentation of Surabaya. According to the interview of local expert and survey, the sedimentation in Surabaya reaches 3 km per year. It was the fact that the predominant soil type of Surabaya is clay-sand (alluvial) from sedimentation formation. Several effort by local government to reduce the impact of the flood in Surabaya nowadays are: reforestation program, small dam in Northern Surabaya (Dupak), build paving road and managed the water channel (along road side in Northern). Figure 3.3: Efforts to Reduce Flood Impact Figure 3.4 shows tidal flood and normal condition where has surveyed in Pabean cantikan sub-district. In Pabean cantikan sub-district, inundation level ranging 0 to 30 cm. 26

9 Tidal Flood Normal Figure 3.4: Surveyed Area of Tidal Flood Condition 27

10 3.4.4 Processing and Reporting Phase The reporting phase is finalizing phase of the research including discussion, i.e., writing, do some revision, consultation to the supervisor, co-supervisor and the expert, also all activity for thesis report accomplishment. This phase also describe about the risk prediction of losses which caused by floods. According UN (2004), risk is defined as the probability of harmful consequences, or expected losses deaths, injuries, property, livelihoods, economic activity disrupted or environment damaged) resulting from interactions between natural or human-induced hazards and vulnerable conditions. Risk can be expressed as follows: Risk = Hazard x Vulnerability. (3) In this study the risk assessment is calculation of physical losses (tangible damage) from landuse types which was extracted from the Google Earth image. The risk assessment considered by vulnerable map that is resulted from previous phase (pre-processing). In this research, the risk estimation is only limited to the tangible damage or physical direct damage caused by tidal flood. The method of risk damage calculation is: R ΣH* V = (4) Where: R = Risk damage (currency unit per unit area), Σ H = total cost damage (per unit area), and V = Vulnerability value. (UN, 2004) Hypothetical prices of the landuse types were assigned based on previous of hazards flood report and on personal experience of the author in field survey. In this research, land use was divided into four classes because majority land use in Northern and Eastern Surabaya where located in range 0 ~ 6 km to the coastline are dominated by embankment zone (fish and salt ponds), residential, and warehouse building. So that only those four landuse type will be classified and calculated. The vulnerability value for estimation embankment zone was derived from interviews to the fish farmer (local resident). The cost damage can be calculated easily when the tide has reach more than 2 m high, because average height of main embankment is 2 m. In the embankment zone, it assumed that the 100% loss total loss occurs when the tide reach 3 m high. The potential of wave energy to break the embankment are not considered. 28

11 Table 3.1 presents the vulnerability values in relation to four different water depth inundation level intervals: <10 cm, cm, cm and cm from Coto (2002). Meanwhile the embankment vulnerable value was derived from author assumed according survey. Table 3.1: Vulnerability values for different landuse categories, in relation to four different water depth inundation level intervals Vulnerability values Landuse type <10 cm cm cm cm Extremely Low Very Low Low Moderate Residential Warehouse Building Embankment Commercial Agricultural field Farm Farm for crop Source from Coto (2002) The water depth inundation level in 2010, 2030 and 2100 were classified into: a. Extremely low; areas with inundation level from 0 to 10 cm. b. Very low; areas with inundation level from 10 to 50 cm. c. Low; areas with inundation level from 50 to 100 cm. d. Moderate; areas with inundation level from 100 to 150 cm. e. High; areas with inundation level from 150 to 250 cm. f. Very high; areas with inundation level from 250 to 400 cm and g. Extremely high areas with inundation level >400 cm. The classifications of inundation level were established from the combination class inundation level created by Coto (2000) and the predicted sea level in 2010 to The classified value expected could comprise the inundation level within 2010 to Compared with sea level in 2100 where predicted rise up to 400 cm, the sea level rise prediction in 2010 (0 to 50 cm) in categorized into low level. Hypothetical loss prices of the landuse types were assigned based on previous hazards flood report by Bappenas (2007) and based on personal experience of the author in field survey and some articles. The reference of cost damage parameter was obtained from the heavy damage value of each landuse type. 29

12 In residential area the cost value of heavy damage is 20 million rupiahs per unit, while light damage in residential area is about 5 million rupiahs per unit, and warehouse building is about 200 million rupiahs per unit (Bappenas, 2007). According to survey, usually the resident builds a dike after occurrence of the floods in the inundation of 10 cm depth, and it is reference to the low vulnerable value for residential. Total costs to build dikes per house approximately achieve 200 thousand rupiahs. The embankment is dividing into fish and salt, where for in fish/shrimp embankment the harvest price, is about 4.5 million per hectare per farming season (6 months) (Bappenas, 2000). Meanwhile in salt embankment, the harvest price is about 300 thousand rupiahs per hectare per farming at every 3 days (Purbani, 2003). The detail of conversion amount of unit of each landuse type in one hectare can be illustrated as follows: a. Residential area; the assumption per hectare of residential area are filled by 100 houses. Every house has size about 60 square meters so that for housing complex fill in area about 6000 square meter, and the rest area about 4000 square meter is using for terrace and collector road. b. Warehouse; the warehouse criteria in this study is defined as more than one-story building which has an area exceeding 500 square meters and is used as commercial properties. The assumption of general building size is 500 square meter for each building and the rest about 5000 square meter for collector road and parking area, so that in one hectare will be consist of 10 buildings. c. Embankment; the embankment usually has already been divided per plot in hectare area. 30

13 3.5 Sea Level Projection Component MRI data Model In order to obtain the sea level rise, the available model data in certain year was reduced by data in 1900, which were used as the baseline year when the first measurement of tidal gauge. Appendix shows that the value of sea level rise in 2010 ( MSLM 2010 ) is about m, in 2030 ( MSLM 2030 ) is m and in 2100 ( MSLM 2100 ) is m Average High Wave Data High wave data were obtained from University of Hawaii Sea Level Center (UHSLC), the complete data can be seen in Appendix 7. Calculation for defining inundation, the average value of high wave data has been multiplied by 30% due to hit to the material which stand on the coastline area. HW = 1.054*30% = m Maximum High Wave Data The maximum of high wave data was obtained from the highest or maximum value of high wave data and also multiplied by 30% due to hit to the material which stands on the coastline area. HWex = 2.38*30% = m Average between Maximum and Minimum Tides The tide pattern was stated (see chapter 2.7.1) where there has a maximum and a minimum value from average data in any day. To predict the sea level, the distance value between maximum and minimum value has been measured. The tide data in this research were derived from WX Tide Software. TWL = (- 0.2) = 1.4 m. 31

14 3.5.5 Subsidence level estimation Subsidence s in Surabaya is predicted and caused by the pressure of the heavy material such as building and heavy vehicles especially in Northern Surabaya which filled up by warehouse and freight transportation. Subsidence level data have been obtained from Badan Geology with data observation. For this research these subsidence level data used as reference to predict land subsidence year by year. Subsidence value ( SL (t) ) is defined as the average of subsidence level data in a year with an assumption that the value of subsidence level is similar to The average of subsidence level data ( SL ) can be calculated as follows, t = t 1 (year projected) t0 (year of available data), and the subsidence level formulated as follow: SL t = t * SL (6) ( ) So that the value subsidence level in 2010, 2030 and 2100 is: SL 2010 = ( ) *0.21 SL = 0.12 m = ( = 0.54 m. 2004) *0.21 SL 2100 = ( ) *0.21 = 2.01 m High wave in El Nino and La Nina The value of high wave in El Nino and La Nina period (see Appendix 1), has been obtained from the high peak which can be seen from altimeter data (see Appendix 4). This case the data value ( HEL (t) ) is 0.4 m. 32

FLOOD HAZARD AND RISK ASSESSMENT IN MID- EASTERN PART OF DHAKA, BANGLADESH

FLOOD HAZARD AND RISK ASSESSMENT IN MID- EASTERN PART OF DHAKA, BANGLADESH Muhammad MASOOD MEE07180 Supervisor: Prof. Kuniyoshi TAKEUCHI ABSTRACT An inundation simulation has been done for the mid-eastern