flood vulnerability of the town of tanay, rizal,...

*Corresponding author: [email protected]

Philippine Journal of Science146 (2):117-127, June 2017 ISSN 0031 - 7683Date Received: ?? Feb 20??

Key words: flood simulation, flood vulnerability, GIS mapping, socio-economic index, social vulnerability, storm hydrograph

Flood Vulnerability of the Town of Tanay, Rizal, Philippines

University of Rizal System, Sampaloc, Tanay, Rizal

Romeo C. Pati* and Amabel P. Cruz

Flood and social vulnerability analyses were used to assess the dynamics and social impact of flood in the flood plains of Tanay. Flood simulation was carried out using the derived hydrograph as input in the simulation model. The social vulnerability of each of the flood-prone barangays in the town was also determined using proxy indices such as strength of public infrastructure, demographic and socio-economic factors. The model successfully predicted the flood depths and delineated the spatial extent of flooding in the different barangays of the town. This was shown by the simulated flood depths that were comparable with the observed flood depths of the communities in seven out of nine flood-prone communities in Tanay. Barangay Tabing Ilog had the highest overall vulnerability index, indicating that this barangay is the most vulnerable to flood and needs a comprehensive flood risk preparedness and social development plan to increase the coping capacity of the residents to flooding.

INTRODUCTIONMany populations around the world are vulnerable to various disasters. During the 20th century, natural disasters particularly floods are occurring around the world with increased frequency and strength as a consequence of land-use changes and increased climate variability (Balica et al. 2009). In the Philippines, the intense and beyond-normal downpour of rainfall during Typhoon Ondoy last 2009 created devastating effects to life and properties and to the development of the affected areas in the low-lying areas of Metro Manila and Rizal Province. Losses suffered by these places were overwhelming and set back economic and social development (Pati et al. 2014). Rehabilitation costs were almost unaffordable and the affected individuals and families suffered psychologically and financially (Borga et al. 2011; Ouma and Tateishi 2014).

The tragic consequences of this natural calamity clearly indicate the need to design effective physical and social programs that must be implemented in order to mitigate

the impacts of flood. Understanding how a range of factors such as demographic and socio-economic as well as geo-physical factors are interlinked with flood and vulnerability can be an essential input in generating a sound flood mitigation program.

Vulnerability is a measure of harm due to exposure, susceptibility and/or lack of resilience to floods (Balica et al. 2009). Exposure describes who is affected and the extent of flood in the area. Susceptibility on the other hand is a manifestation of exposure in conjunction with the capacity/incapacity to be resilient, to cope with, and to recover from floods (Balica et al. 2012).

Factors such as low awareness on floods, lack of mobility and capacity to move quickly, weak social networks may combine together within deprived communities making them more vulnerable to natural hazards. This can be further exacerbated by lack of investment or maintenance of the infrastructure and built environment. It is important to recognize that these are factors of vulnerability to natural hazards (Balica et al. 2012).

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The capacity of households to respond to hazards is equally important for the assessment of hazard impacts and the successful implementation of policy measures aimed at reducing risk, such as stimuli for individual risk mitigation, evacuation plans, as well as insurance coverage for natural disaster risk. This capacity to respond is largely a function of a household’s socio-demographic status that is related to its social vulnerability (Cutter et al. 2003). These factors have been lately considered in the developed countries in the assessment of vulnerability to a stressor but have not been given due attention in many flooded areas of the Philippines, particularly in the town of Tanay, Rizal.

Coupling the geophysical elements of flooding with the socio-economic conditions of people living in a given area enriches understanding on the impacts of flooding on social systems (Balica et al. 2012). Flood mapping delineates the flood-prone areas using geophysical conditions and hydrologic parameters. Social vulnerability analysis, on the other hand, determines the capacity of a community or group of individuals to cope with and recover from the impact of a flood (Balica et al. 2012).

Flood and social vulnerability analysis using a geographic information system (GIS) can be used as means of studying threats to life and properties. It is an approach

Figure 1. Map of the study area.

used to identify locations with properties and specific populations who are vulnerable. It is also used as a tool in designing a mitigation and emergency plan that can be used to reduce the vulnerability of a given population (Ousmane et al. 2015).

The study was conducted in 2013 in nine lowland barangays of Tanay, Rizal, Philippines. A barangay is the smallest administrative area of a town, also called village. The interaction of a flood hazard and the socio-economic conditions of the people were assessed using GIS-hazard mapping and vulnerability-resilience indicators.

METHODOLOGY

The Study AreaThe flood plain of Tanay, Rizal (Figure 1), the seat of the nine lowland barangays of the town, is at the foot of the Sierra Madre Mountain Range and adjacent to Laguna Lake. It is within the grid coordinates of 121°16’ 30” to 121° 19’ 30” N and 14° 32’ to 14° 29’ E. It is the economic hub of most towns located in the eastern part of Rizal Province. The climate is characterized by

Pati and Cruz: Flood Vulnerability of the Town of Tanay

Philippine Journal of ScienceVol. 146 No. 2, June 2017

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a dry summer that usually starts in January and ends in May. Rainy season usually starts in June with occasional intense precipitation events. The water that comes from the mountains and drains into Tanay River is the main source of irrigation water for the rice fields and arable lands during the dry season, but causes heavy flooding during an intense rainfall event.

Hydrograph Derivation The peak discharges of a river flow due to an extreme rainfall event can be obtained from the storm hydrographs derived from unit hydrographs using established methods. In this study, the unit hydrographs of the river flow were derived using a deconvolution technique from the measured river flows and total rainfall data of 213.4 mm during Typhoon Pedring. They were used to calculate the storm hydrographs of the river during an extreme rainfall event (Typhoon Ondoy) in given locations using the cross-section and the total rainfall data of 418.0 mm. This was accomplished using a convolution technique.

River Cross-Section Measurement and GIS ProcessingThe cross-sections of the river network and riverbanks were surveyed using an engineer’s transit and a hand-held Geographic Positioning System (GPS). The locations of the river cross-section measurements were based on the HEC-RAS (Hydraulic Engineering Center-River Analysis System) following the User’s Manual (USACE 2010).

The surveyed data was overlaid in the Triangulated Irregular Network (TIN) of the study area. It was used in processing the geometric datasets such as the river centerline, main channel banks (left and right), flowpaths, and cross-section cutlines of the river using the HEC-GeoRAS pre-processing of ARCGIS. The procedures used in processing the datasets were based on the procedure outlined in the HEC-GeoRAS Manual.

The processed data was used to create the RAS GIS Import file and imported in the HEC-RAS software. The file contains the TIN of the study area and the required layers tab should have the river XSCutlines and XSCutlines3D for Stream Centerline, XS Cutlines and XSCutlines Profiles.

HEC-RAS Flood SimulationHEC-RAS is one of the most-often applied hydraulic models in flood studies (Carling et al. 2010). Its performance is comparable with and at times superior to the other softwares that are commonly used in flood modelling. It is capable of performing one-dimensional water surface profile calculations for unsteady and steady gradually varied flows in natural or constructed channels (Alaghmand et al. 2012). Beside these advantages, the

software is a free ware that can be easily downloaded from the Internet.

The simulation was carried out by first examining the geometric datasets that were imported to ensure that they were consistent with the data that was derived in ARCGIS. The river cross-section data was manually drawn and locations were specified using the surveyed latitude and longitude coordinates. Manning’s roughness coefficients derived through the initiative of the United States Geological Survey (Jarret 1985; Philips and Tadayon 2007) were likewise manually entered into the Geometric Data Editor of the software.

The peak flow in the different locations of the river network was defined in the flow data editor of the software. The external boundary condition was defined in the last river cross-section of the lower river reach by calculating the slope of the river network.

Before running the model, the software was set to first check for the correctness of the data before executing the flood flow simulation. This was accomplished by clicking Option in the main program window and click-checking the box before the entry Check Data before Execution.

After the simulation, a RAS GIS export file was created and re-imported in ARCGIS. Post-processing of the HEC-RAS output was done using the HEC-GeoRAS utility. It converts the HEC-RAS output into a continuous water surface that interacts with the digital terrain model to produce flood plain extents, water depth grids and other geospatial analyses.

The general method adopted for the flood hazard simulation and overall vulnerability mapping using HEC-RAS and HEC-GeoRAS in ARCGIS is shown in Figure 2.

Validation of Modelled Flood DepthThe simulated flood depths in different locations were compared with the surveyed depths of floodwaters during Typhoon Ondoy. Surveying was done by asking residents what they remembered as the heights of the water relative to the walls, trees, fences and others, and then measuring them.

Flood Mapping and ClassificationThe hazard map that was developed was further classified according to its potential to flooding. This was accomplished in the ARCGIS using the Spatial Analyst tool of the software. The criteria were based on the depths of flooding within the area. The levels of flooding along with their corresponding depths are as follows:

Low 0.20 to 0.59 m Extreme 1.50 to 3.50 m

Moderate 0.60 to 1.49 m Very Extreme 3.55 and above



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Figure 2. Flow chart of flood vulnerability analysis used in the study (following Maidment 1993).

Socio-Economic DataThe socio-economic and demographic data sets were obtained from various government offices of Tanay. Other unavailable demographic and socio-economic data needed in the social vulnerability analysis were obtained via interviews using a semi-structured survey questionnaire. The questionnaire was edited by selected faculty in the university and validated in one of the barangays of the town. The survey was conducted in the identified flood-prone barangays of the town. A team composed of 12 members was organized to do the survey.

The desired sample size of respondents was computed following the Cochran formula (1977) and the proportional sampling method. The desired sample size of the respondents was statistically inferred at a confidence level of 96%. The computed statistical number of respondents was 603.

Social Vulnerability AnalysisThe assessment of social vulnerability was carried out using proxies or vulnerability indicators. The major variables as well as sub-variables that were used to measure the social vulnerability are depicted in Table 1. These variables had been considered in numerous studies conducted in other countries. The weight distribution of each variable was determined through factorial analysis of the computed data of the four variables.

Calculation of Social Vulnerability IndexThe social vulnerability index was determined following the formula of Cutter et al. (1997):

SoVI = 0.3061*DI + 0.2781*SEI +0.1788*NRDI +0.2371*SPII (1)



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Where:SoVI = Social Vulnerability Index of a barangay in

the study areaDI = Demographic Index of a barangay in the study

areaSEI = Socio-economic Index of a barangay in the

study areaNRDI = Natural Resource Dependence Index of a

barangay in the study areaSPII = Strength of Public Infrastructure of a

barangay in the study areaThe variable index is the sub-index of the major variables used in social vulnerability analysis in the flood-prone barangays of the town (Table 1). It was determined using the following formula:

variable index = Maximum value of XX (2)

The X (variable) is the fraction of the variable used in calculating the variable index. It was computed using the formula:

Table 1. Major and sub-variables used in social vulnerability analysis.

Major Variable Weight (%)

Sub-Variable

Demographic Index

30.61 Number of renters and separated couples

Number of individuals per household

Number of disabilities

Age and GenderEducational Attainment

Socio-Economic Index

27.80 Number of families with income below poverty level in a community

Number of family members with permanent jobs

Number of families with cars

Natural Resources Dependence Index

Average monthly income of the family

17.88 Number of environmental resources which provide income to people within the area

Number of families whose jobs are located within the community/barangay

Strength of Public Infrastructure Index

23.71 Travel time and road condition from the house to the evacuation area

Number of health centers within the community

Number of communication systems available in the area

X(variable) = Raw value of corresponding

variable/parametervariable/parameter total of

the town(3)

The measure of social vulnerability for the monthly family income and barangay monthly revenue was adjusted from a range of zero to one to permit the addition or multiplicative averaging. This was accomplished by a normalization procedure the average of which is called standardized vulnerability index. The formula is:

Vij = (Xij – Min Xi) / (MaxXi – MinXi) (4)

where

Vij = the standardized vulnerability index for a vulnerability parameter i for a vulnerable place j

Xij = the observed value of a particular place j for a particular vulnerability parameter i

Min Xi and Max Xi = the minimum and maximum values of the observed range of values of a vulnerability parameter

The overall social vulnerability index for each barangay was determined by getting the average of the computed indices of the different social indicators. The computed index was rescaled to five using the weights shown in Table 1.

RESULTS AND DISCUSSION

Hydrograph AnalysisThe computed unit hydrograph in the upper portion of the river had a peak discharge of 46.45 m3s-1. The equivalent peak storm discharge in this cross-section for extreme flood event is 146.56 m3s-1. At the middle of the river network, the calculated peak storm discharge is 153.91 m3s-1. The peak discharge values of the storm hydrograph slightly increases as it progresses downstream (Figure 3).

HEC-RAS Model CalibrationPrior to flood simulation, the model was calibrated using the water elevation data that was gathered during Typhoon Pedring. The measured water depths of 3.4 and 5.11 m in the upper and middle portions of the river network during Typhoon Pedring were slightly lower than the depths of simulated water elevation in HEC-RAS. The simulated depths were 3.65 and 5.45 m, respectively. The flood depths taken at the lower portion of the river as well as near the national highway were almost the same as the simulated flood depths (Figure 4).



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Figure 3. Unit and storm hydrograph in the different locations of Tanay River.

Figure 4. Simulated and measured flood depths during Typhoon Pedring.

Flood Extent and Flood Depths SimulationThe spatial distribution of the flooded area was wide in the barangays of the town. The flood depths ranged from 0.2 to 15.4 m. The deepest simulated flood depths were found mostly along the shore of Laguna Lake and in the middle of the river channel. Small patches of deep floodwaters were observed in Katipunan Bayani, Kay Buto and Plaza Aldea. These areas are fallow lands and others are creeks that discharge to a swamp. Most parts in the heart of the town may experience low to extreme flooding with depths ranging from 0.2 to 3.51 m. Except for Barangays Tandang

Kutyo and Plaza Aldea, the whole area of the seven barangays is expected to be totally subjected to flooding during an extreme event such as the flooding brought by Typhoon Ondoy (Figure 5).

Although portions of Barangays Tandang Kutyo and Plaza Aldea may not be affected by flooding, majority of these areas are uninhabited. Most of the commercial establishments and residential areas in these barangays are found in the flood-prone areas just like in the other barangays (Figure 5).



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A small island is observed along the highway (Figure 5) going to the town of Baras several meters after the intersection of Rodriguez Avenue and Barangay Aldea National Highway. Based on the interview, the area has been landfilled with boulders and soil to give way to the construction of the Tanay cockpit and some residential houses.

Landfilling has been the common mitigation measure done by many residents in the flood-prone areas of Tanay. In spite of this, most of the areas have been flooded during Typhoon

Figure 5. Overlaid simulated depths and extent of flooding during the Typhoon Ondoy on a digital model of the study area.

Table 2. Estimated flooded area (ha) and classification of the different barangays in Tanay, Rizal

Barangay Low Moderate Extreme Very Extreme Total

Plaza Aldea 18.7 49.5 137.2 24.2 229.6

Tabing Ilog 0.0 0.4 2.1 1.5 4.0

Mag-ampon 0.0 2.4 7.2 3.6 13.2

Katipunan Bayani 2.8 10.9 57.9 2.6 74.2

Tandang Kutyo 3.4 6.2 8.7 10.8 29.1

Kay Buto 1.0 7.1 41.1 35.6 84.8

San Isidro 0.0 9.3 11.9 2.9 24.1

Pinagkamaligan 0.0 1.5 2.6 1.0 5.1

Wawa 1.8 36.2 11.3 3.5 52.8

TOTAL 27.7 123.5 280.1 85.7 517.0

Ondoy due to the increased rainfall intensity in a shorter period of time resulting in higher waves of flood waters.

A total area of 517 ha is affected by low to extreme flooding under an extreme flooding scenario (Table 2). Almost half of the flooded area is in Barangay Plaza Aldea, the largest barangay in the lowland part of Tanay. An area which is classified as extremely flooded is 137.2 ha while an area of 18.7 ha is affected by low flooding.



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The total areas exposed to extreme flooding and very extreme flooding are 280.1 ha and 85.7 ha, respectively. Most of the areas that are exposed to low flooding were found in Barangays Plaza Aldea, Katipunan Bayani, Tandang Kutyo and Wawa.

Most of the extremely flooded areas found in Barangay Kay Buto were in the low-lying places, along the shore of Laguna Lake (Figure 5). On the other hand, the extremely flooded areas of Barangay Katipunan Bayani were mostly rice fields in between two national highways with higher elevations (Pati 2014).

Validation of the Simulated Flood Depths and Other ObservationsThe results of the flood simulation using HEC-RAS indicated that the model can be used to reliably predict the spatial extent and depth of flood events. Based on the validation conducted, the model was able to spatially delineate the flood event during Typhoon Ondoy. The flood depths during the flood event caused by Typhoon Ondoy, particularly in the residential areas of Barangays Tabing Ilog, Plaza Aldea, Pinagkamaligan and San Isidro, coincided with the simulated flood depths (Pati 2014).

The simulated flood depths in Barangays Tabing Ilog and Plaza Aldea were 1.5 and 2.9 m, respectively. The actual flood depths in these areas, based on the measured flood depths as described by the residents, were 1.4 m and 2.8 m, respectively (Figure 6). However, there were instances where the model over-estimated the flood depths, particularly in areas where there had been extensive land modification. These areas were found in Barangays Katipunan Bayani, Mag-ampon and Tandang Kutyo. The simulated flood depths in Barangays

Katipunan Bayani and Mag-ampon were 2.1 and 4.6 m, respectively. On the other hand, the actual flood depths in these areas were 1.1 and 2.7 m only (Pati 2014).

Social Vulnerability Analysis and Mapping

Demographic Indicators of the Flood-Prone Barangays in Tanay, RizalThe demographic indices of the flood-prone barangays in Tanay ranged from 1.74 to 3.11. The barangay with the lowest index was Kay Buto while the highest was Tabing Ilog (Table 3). Barangay Tabing Ilog had the highest number of occupants with disabilities, number of residents with ages under 10 and over 65. The number of females and female-headed households was also high in this barangay. As a result, this barangay was the most vulnerable to flood when considering demography as indicator.

Women have a more difficult time than men during recovery due to added family care responsibilities (Morrow 1999). Social or cultural vulnerability factors are also driven by family structure or social networks. These social variables include the presence of a large number of dependents, either the young or elderly, female-headed households, and educational attainment (Morrow 1999).

Each of the other barangays had a demographic index lower than 2.5, which means they were less vulnerable to flood compared to Barangay Tabing Ilog.

Socio-Economic Indicators of the Flood-Prone Barangays in Tanay, RizalBarangay Kay Buto had the highest socio-economic index of 3.48 and therefore was the most vulnerable barangay. It had the highest index among four of five factors used in

Figure 6. Simulated and measured flood depths during Typhoon Ondoy.



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determining the socio-economic vulnerability to floods. Unstable jobs, low family income and number of families below poverty level were the major factors contributing to its total vulnerability. Barangay Pinagkamaligan had the lowest index of 2.52. It had higher family income and lesser number of families below poverty level (Table 4).

The economic status of an individual or community is a manifestation of their ability to absorb losses and cope from the negative effects of flood or any natural hazards. Higher family incomes and stable jobs enable communities to absorb and recover from the impacts of floods quickly due to safety nets and entitlement programs (Cutter et al. 2000).

Overall Social Vulnerability Index of Different barangays in Tanay, RizalThe computed overall social vulnerability indices of the different barangays in Tanay ranged from 1.97 to 2.84 from a scale of 5. Three barangays in Tanay were slightly socially vulnerable to flooding (Table 5).

All of the socio-economic indices of the flood-prone barangays were above the median scale, indicating that

improvements need to be done to raise the resilience of the residents to flooding. On the other hand, five barangays had good demographic indices, with only one slightly vulnerable to flooding.

The infrastructure index was higher than the other indicators due to better roads, available public service system and communication system. Health services were also accessible due to the proximity of the flood-prone barangays to the hospitals and health centers of the town.

Flood vulnerability Analysis in Tanay, RizalThe overall vulnerability map (Figure 7) encompassing both flood and social factors shows that most of the flood-prone barangays of the town had moderate to extreme overall vulnerability. These were also the areas where most of residential houses and commercial establishments were located.

The barangays that were classified as extremely vulnerable to flooding were Tabing Ilog and Kay Buto. On the other hand, the areas that were classified as low to moderately vulnerable were scattered in many parts of the flood-prone areas. Some were also situated in the outskirts of the flood-prone barangays.



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Table 4. Socio-economic index of the flood-prone barangays in Tanay, Rizal.

BARANGAY INCOME POVERTY JOB CAR SOCIO-ECONOMIC INDEX

Plaza Aldea 2.9823 3.1820 2.8016 2.4830 2.8666

Tabing Ilog 2.8736 2.9344 2.6354 3.1366 2.8915

Mag-ampon 2.5440 2.5677 2.7679 2.9484 2.6858


Tandang Kutyo 2.7331 2.6664 2.4356 2.8740 2.6699

Kay Buto 3.5446 3.7052 3.2490 3.4672 3.4763

San Isidro 2.9489 2.4857 3.0456 3.1809 2.8887

Pinagkamaligan 2.3905 1.8340 2.8816 3.1122 2.5196

Wawa 2.6900 2.5610 2.8832 3.5237 2.8917

Table 3. Demographic index of the flood-prone barangays in Tanay, Rizal.

Barangay Family Size Age and Gender Disabilities Separated

and Renters Educational Attainment

Demographic Index

Plaza Aldea 1.9941 3.3334 1.9055 1.6055 3.0156 2.3601

Tabing Ilog 2.6558 3.8196 4.9350 1.2150 2.0154 3.1098

Mag-ampon 3.2410 1.6698 3.1575 2.3176 1.4971 2.4314

Katipunan Bayani 4.7656 0.4133 0.7492 1.1842 1.5691 1.7654

Tandang Kutyo 3.3667 0.8929 1.1820 1.9610 2.4810 1.9168

Kay Buto 2.1317 1.1731 1.5209 1.8842 2.2738 1.7372

San Isidro 1.1719 1.6289 1.7067 3.7039 1.9880 1.9091

Pinagkamaligan 2.3462 0.3500 2.7089 1.0745 2.3742 1.7585

Wawa 2.2811 1.4056 1.4895 4.8973 2.4866 2.3192

Table 5. Overall social vulnerability index of the flood-prone barangays in Tanay, Rizal.

Barangay Demographic Index

Socio-Economic

Index

Resource Dependence Index

Infrastructure Index

Overall Vulnerability Index

Plaza Aldea 2.3601 2.8666 2.5378 2.1424 2.5041

Tabing Ilog 3.1098 2.8915 2.7741 2.3807 2.8391

Mag-ampon 2.4314 2.6858 3.0719 2.2595 2.6232


Tandang Kutyo 1.9168 2.6699 2.2550 2.5669 2.3226

Kay Buto 1.7372 3.4763 2.5725 2.5223 2.5591

San Isidro 1.9091 2.8887 2.3466 2.2079 2.3386

Pinagkamaligan 1.7585 2.5196 1.7868 1.7226 1.9704

Wawa 2.3192 2.8917 2.1944 2.0064 2.3928

Figure 7. Overall vulnerability map of the flood-prone barangays in Tanay, Rizal.



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CONCLUSIONFlood simulation and social vulnerability analysis are practical ways of predicting scenarios and can be used to mitigate the risk of flood. All the barangays in the lowland portion of Tanay are vulnerable to flood. A total area of 517 ha is expected to be flooded under an extreme rainfall event. The flood depths in low-lying areas may reach 3.5 m.

In terms of social vulnerability, the most socio-economically vulnerable with an index of 3.48 is Barangay Kay Buto. Barangay Tabing Ilog is the most demographically vulnerable with an index of 3.10 and the most socially vulnerable with an overall index of 2.84.

Based on the interplay of the social and physical aspects of flooding, Barangays Tabing Ilog and Kay Buto are the most vulnerable to flooding, hence the urgent need for comprehensive flood risk preparedness and social development plan to increase the coping capacity of the residents to flooding.

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