Required Tools Loss Estimation

21 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 1:50.000 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-Gisview 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 2.9.4 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 T7200 2.00 GHz, 2 GB RAM, Mobile Intel 945GM Express Chipset Family. c Tools 1. Global Positioning System GPS Garmin 76Csx for measuring geo- position in field work. 2. Pocket Camera Fuji finepix A220 to capture the objects observed. 22 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. 23 Figure 3.2: Flowchart of General Method 24

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.

3.4.2 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 https:esg.llnl.gov:8443data 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. 25 Prediction of the sea level was done for 2010, 2030, and 2100. 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 1900. 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 t SLCC and extreme condition t SLEC of sea level projection can be seen as follows: LS TWL HW MSLM SLCC t t + + + = 2 t t t HEL LS TWL HWex MSLM SLEC + + + + = 3 Where: t MSLM : Global Mean Sea Level in t year observed data according IPCC model projection. HW : Average of high wave data. HWex : Maximum value of high wave data. TWL : Average range between maximum and minimum of tides data. LS : Land subsidence data. t HEL : 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.

3.4.3 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 26 condition related to the research, to conduct interviewed to the local residents, and to record the occurrence of tidal flood throughout 2010. 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. 27 Tidal Flood Normal Figure 3.4: Surveyed Area of Tidal Flood Condition 28

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: V H R Σ = 4 Where: R = Risk damage currency unit per unit area, H Σ = 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. 29 Table 3.1 presents the vulnerability values in relation to four different water depth inundation level intervals: 10 cm, 10–50 cm, 50–100 cm and 100–150 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 Landuse type Vulnerability values 10 cm 10-50 cm 50-100 cm 100-150 cm Extremely Low Very Low Low Moderate Residential 0.01 0.15 0.5 0.8 Warehouse Building 0.2 0.4 0.5 Embankment 0.25 0.5 1 Commercial 0.1 0.4

0.6 0.8

Agricultural field 0.05 0.1 0.2 Farm 0.01 0.1 0.2 0.25 Farm for crop 0.3 0.45 0.5 0.55 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 2100. The classified value expected could comprise the inundation level within 2010 to 2100. 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. 30 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 fishshrimp 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. 31

3.5 Sea Level Projection Component

3.5.1 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 2010 MSLM is about 0.292 m, in 2030 2030 MSLM is 0.287 m and in 2100 2100 MSLM is 0.406 m.

3.5.2 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 = 1.05430 = 0.316 m.

3.5.3 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 = 2.3830 = 0.715 m.

3.5.4 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 = 2.6 - - 0.2 = 1.4 m. 32

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 2003 – 2004 data observation. For this research these subsidence level data used as reference to predict land subsidence year by year. Subsidence value t SL is defined as the average of subsidence level data in a year with an assumption that the value of subsidence level is similar to 2003-2004. The average of subsidence level data SL can be calculated as follows, 1 t t = ∆ year projected t − year of available data, and the subsidence level formulated as follow: SL t SL t ∆ = 6 So that the value subsidence level in 2010, 2030 and 2100 is: 21 . 2004 2010 2010 − = SL = 0.12 m 21 . 2004 2030 2030 − = SL = 0.54 m. 21 . 2004 2100 2100 − = SL = 2.01 m.

3.5.6 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 t HEL is 0.4 m. 33 I V . R E S U L T A N D D I S C U S S I O N

4.1 Sea Level Prediction

4.1.1 Sea Level Prediction for 2010

The projection sea level of common climate condition SLCC in 2010 is: 2010 SLCC = 0.292 m + 0.316 m + 1.40 m + 0.125 m = 2.13 m. The projection sea level of extreme climate SLEC in 2010 is: 2010 SLEC = 0.292 m + 0.714 m + 1.40 m + 0.125 m + 0.40 m = 2.93 m. According prediction sea level in 2010, the prediction inundation level occurred was an impact of sea level in a range from 2.13 m until 2.93 m. The difference of the result between SLCC and SLEC, were influenced by climate condition the value of high wave and sea level in El Nino and La Nina transition period. The difference between SLCC and SLEC is about 0.8 m.

4.1.2 Flood Prediction for 2010

Figure 4.1 Inundation level of Tidal Flood in 2010 per Landuse Class 34 100 200 300 400 500 600 0-10 10-50 50-100 Extremely Low Very Low Low Inundation level cm H e c ta r e Figure 4.1 shows prediction inundation of common condition in 2010, based on calculation of SLCC Sea Level in Common Condition, which has been classified into 3 inundation depth levels namely extremely low 0~10 cm, very low 10~50 cm, and low 50~100 cm. The inundated area in 2010; range approximately between 0 to 4 km from the coastline, covered some area in Northern and Eastern Surabaya. The inundation divided into three inundation classes level as shown in Table 4.1 and Figure 4.2. It shows several inundation levels of landuse type and their corresponding area. Total inundated area in 2010 is about 1048.4 hectares. The residential is the most area which affected by flooding, approximately 963.3 hectares are flooded. Table 4.1: Prediction total flood area 2010 in three inundation level 0-10cm 10-50cm 50-100cm Extremely low Very low Low Building 36.581 28.580 19.920 Residential 256.012 528.128 179.194 Embankment unaffected unaffected unaffected Inundation Level Area Ha Landuse Type Figure 4.2 Graph of Tidal Flood in 2010 Area per Inundation Level 35 Based on the survey, it has been recorded that the tidal flood occured on the 11, 12, and 13 of July 2010 at 9.45am until 11.30am, in Jln. Kebalen wetan sub- district Pabean Cantikan in Northern of Surabaya with elevation about 1.7 m asl above sea level, reached about 0.3 – 0.35 m. In Krembangan sub-district with elevation about 1.4 m asl, the inundation reached about 0.6 m. While the tidal flood occurred on 22 December 2010 at 10.30pm until 11.45pm, the inundation of tidal flood reached 0.6 m. Field survey reported that inundation was occurred mostly in Northern and Eastern part of Surabaya, where the predominant includes are residential and building warehouse area. The embankment zone is unaffected by the tidal flood, because according interview with the local resident, the embankment zone will be flooding when the tides reach about 3 meter height or more. Adaptability of local resident to the tidal flood in 2010, are performed constructing the small dam in front of the door, raised house building, and made paving road Figure 4.3. Figure 4.3 Field Survey in Pabean Cantikan sub-District 4.1.3 Sea Level Prediction for 2030 The projection sea level of common climate condition SLCC in 2030 is: 2030 SLCC = 0.286 m + 0.316 m + 1.40 m + 0.544 m = 2.54 m. The projection sea level of extreme climate SLEC in 2010 is: 2030 SLEC = 0.286 m + 0.714 m + 1.40 m + 0.544 m + 0.40 m = 3.34 m. Sea level projection in common climate condition for 2030 is about 2.5 m and 3.3 m in extreme climate. 36

4.1.4 Flood Prediction for 2030

Based on calculation of SLCC Sea Level in Common Condition, inundation area in 2030 are predicted to spread within range of 6 km from the coastline. Sea level projected is ranging from 2.5 to 3.3 m. Figure 4.4 Inundation level of Tidal Flood in 2030 per Landuse Class Figure 4.3 shows that the prediction of inundation level in 2030, are in range 10 cm to 150 cm. The inundation level was then classified into 3 inundation depth levels namely: very low 10~50 cm, low 50~100 cm, and moderate 100~150 cm as tabulated in Table 4.2. Figure 4.5 shows several inundation levels of landuse type and their corresponding area in 2030. Table 4.2: Prediction total flood area 2030 in three inundation level 10-50cm 50-100cm 100-150cm Very low Low Moderate Building 176.619 87.814 60.745 Residential 855.077 596.383 449.426 Embankment 2155.650 − − Inundation Level Area Ha Landuse Type 37 500 1000 1500 2000 2500 3000 3500 10-50 50-100 150-250 Very Low Low Moderate Inundation level cm H e c ta r e Figure 4.5 Graph of Tidal Flood in 2030 Area per Inundation Level In 2030, it is predicted that 4381.7 hectares of embankment area in 2030 predicted will be inundated. This is because the inundation level predicted by SLCC Sea Level in Common Condition will reach more than 2 m.

4.1.5 Sea Level Prediction for 2100

The projection sea level of common climate condition SLCC in 2100 is: 2100 SLCC = 0.405 m + 0.316 m + 1.40 m + 2.0112 m = 4.133 m. The projection sea level of extreme climate SLEC in 2100 is: 2100 SLEC = 0.286 m + 0.714 m + 1.40 m + 4.931 m + 0.40 m = 4.931 m. According SLCC and SLEC the sea level in 2100 will rise about 2 times higher than in 2010. The values of SLCC and SLEC in 2100 respectively are 4.13 m and 4.93 m. 38

4.1.6 Flood Prediction for 2100

Based on the sea level prediction in 2100, the inundation level occurred as will have a range between 4.1 m and 4.9 m. Figure 4.6 Inundation level of Tidal Flood in 2100 per Landuse Class For the prediction in 2100, the inundation was classified into 3 inundation depth levels namely: moderate 100~150 cm, high 150~250 cm and very high 250~400 cm. As shown in Figure 4.6 we may conclude, that the inundation in 2100, will reach 25 total area of Surabaya city. The inundation in 2100 will in range at about 8 km from the coastline. Almost all area in Northern and Eastern Surabaya will be inundated, ranging from 1 to 1.5 m depth. Table 4.3 and Figure 4.7 show several inundation levels of the landuse type and their corresponding area in 2100. Table 4.3: Prediction total flood area 2100 in three inundation level 100-150cm 150-250cm 250-400cm Moderate High Very High Building 306.666 278.685 65.411 Residential 1467.466 1747.302 488.444 Embankment 1500.208 3888.836 − Inundation Level Area Ha Landuse Type 39 1000 2000 3000 4000 5000 6000 2010 2030 2100 Ye ar Proje cte d H e c ta r e Building Residential Embankment 1000 2000 3000 4000 5000 6000 7000 100-150 150-250 250-400 Moderate High Very High Inundation level cm H e c ta r e Sea level predicted will rise about 2 meters high within 90 years, and if there is no effort to avoid that condition, approximately total 9743 hectares area in 2100 predicted will be inundated. Figure 4.8 is shows trend of the inundation area beyond 2100. Figure 4.7: Graph of Tidal Flood in 2100 Area per Inundation Level Figure 4.8: Graph of Trend Inundation Area 40

4.2 Loss Estimation

From the prediction in 2010, 2030 and 2100, the following are their loss estimation for each landuse type and flood water depth see Table 4.4 ~ 4.7. Basically the calculation of damage due to floodwater is derived from the costs per ha area. Table 4.4: Loss estimation of landuse type in relation to the inundation intervals Prices are given in Indonesian Rupiah 1 USD = 8500 IDR, approximately. 10 cm 10-50 cm 50-100 cm 100-150 cm 150-400 cm Residential 20.00 300.00 1,000.00 1,600.00 2,000.00 Warehouse Building 0.00 400.00 800.00 1,000.00 2,000.00 Fishpond 0.00 1.13 2.25 4.50 4.50 Saltpond 0.00 0.08 0.15 0.30 0.30 Landuse type Cost damage per Ha in million idr Table 4.5: Estimation loss of landuse type related with inundation level in 2010 10 cm 10-50 cm 50-100 cm Residential 5,120.24 158,438.40 179,194.00 Warehouse Building 0.00 11,432.00 15,936.00 Fishpond − − − Saltpond − − − Landuse type Cost damage in million idr As shown in Table 4.5, estimation of total loss in 2010 is about 370 billion rupiahs. While estimation of total loss in 2030, is about 2 trillion rupiahs Table 4.6. Then estimation of total loss in 2100 is about 3.6 trillion rupiahs Table 4.7. Table 4.6: Estimation loss of landuse type related with inundation level in 2030 0-50 cm 50-150 cm 150-250 cm Residential 256,523.10 596,383.00 898,852.00 Warehouse Building 70,647.60 70,251.20 121,490.00 Fishpond 1,745.51 − − Saltpond 45.31 − − Landuse type Cost damage in million idr Table 4.7: Estimation loss of landuse type related with inundation level in 2100 0-150 cm 150-250 cm 250-400 cm Residential 2,347,945.60 3,494,604.00 976,888.00 Warehouse Building 306,666.00 557,370.00 130,822.00 Fishpond 939.92 12,913.46 − Saltpond 199.42 305.75 − Landuse type Cost damage in million idr 41 Based on the result of loss estimation, residential region has biggest loss than other landuse type region. This is due to the high density population are usually live in the coastal area. Residential area which located in Northern and Eastern part of Surabaya, predicted will suffer severe losses. Total loss in residential area is about 3 trillion rupiahs. 42 V . C O N C L U S I O N S A N D R E C O M M E N D A T I O N

5.1 Conclusions