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Flood risk assessment in the regionsurrounding the Bangkok SuvarnabhumiAirportYuddhana Keokhumcheng a , Tawatchai Tingsanchali b & Roberto S.Clemente aa Water Engineering and Management Program, Asian Institute ofTechnology , Pathumthani , Thailandb Program of Civil and Environmental Engineering, Nakhon PathomRajabhat University , Nakhon Pathom , ThailandPublished online: 13 Jun 2012.

To cite this article: Yuddhana Keokhumcheng , Tawatchai Tingsanchali & Roberto S. Clemente(2012) Flood risk assessment in the region surrounding the Bangkok Suvarnabhumi Airport, WaterInternational, 37:3, 201-217, DOI: 10.1080/02508060.2012.687868

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Water InternationalVol. 37, No. 3, May 2012, 201–217

Flood risk assessment in the region surrounding the BangkokSuvarnabhumi Airport

Yuddhana Keokhumchenga*, Tawatchai Tingsanchalib and Roberto S. Clementea

aWater Engineering and Management Program, Asian Institute of Technology, Pathumthani,Thailand; bProgram of Civil and Environmental Engineering, Nakhon Pathom Rajabhat University,Nakhon Pathom, Thailand

(Received 3 May 2010; final version received 29 March 2012)

Flood hazard, vulnerability, and risk assessments were conducted in the area sur-rounding the new Bangkok International Airport. A hydrodynamic model was usedto simulate the flood flow through the study area. Results show different levels of floodhazard for risk zones for 25-, 50- and 100-year return periods of rainfall.

Keywords: Flood hazard; flood damage vulnerability; flood risk; Bangkok floodplain;Suvarnabhumi Airport

Introduction

Bangkok, the capital of Thailand, is located in the lower basin of the Chao Phraya Riverand has faced severe flood disasters several times during the last three decades (especiallyin 1980, 1983, 1995, and 2011). In the eastern region of the flood-prone area where theSecond Bangkok International Airport (SBIA, formally Suvarnabhumi Airport) is located,floods occur frequently; the causes are mostly the heavy local rainfall and limited flooddrainage capacity. The presence of the SBIA and rapid urbanization reduce the flood stor-age and drainage capacity of the area. Government agencies are required to demonstratethe economic feasibility of a plan before government funds can be invested in flood mitiga-tion (RID 2004). The Royal Irrigation Department (RID), which is the government agencyin charge of flood mitigation, authorized three institutions (Asian Institute of Technology,Kasetsart University, and Thammasat University) to study and design a system to drainflood-waters from the areas surrounding the airport. They proposed the construction of adrainage canal, Klong Suvarnabhumi (klong means canal), and a pumping station with acapacity of 100 m3/s, to move the water from Klong Samrong (located to the south of theairport) to the Gulf of Thailand. Since the completion of this project in 2009, the floodingproblem has persisted, because these are depressed areas from which the water cannot bedrained solely by gravity. Extreme flood events are difficult to protect from because thecost of investment is so high. It is necessary to have a priority-based mitigation plan whenthe budget is limited.

This study presents a methodology for developing flood-risk maps for the floodplain inthe eastern region of Bangkok which can provide information to concerned authorities and

*Corresponding author. Email: keokhumcheng@gmail.com

ISSN 0250-8060 print/ISSN 1941-1707 online© 2012 International Water Resources Associationhttp://dx.doi.org/10.1080/02508060.2012.687868http://www.tandfonline.com

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local residents in flood-prone areas about flooding susceptibility. The focus here is on themethodology, and not the specific results, which, as will be noted, need to be modified toconsider the 2011 flood, which occurred after the research for this paper.

Description of study area

The overall study area covers an area of 624 km2 in the floodplain in the east of Bangkokand is located in the area surrounding SBIA as shown in Figure 1. The average ground levelis about 0.4 m to 1.0 m above mean sea level. The topography of the area is a flat plain fromwhich water drains slowly. The climate of the study area belongs to the tropical monsoon

Figure 1. Study area surrounding Suvarnabhumi International Airport.

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type. The mean annual rainfall is approximately 1,234 mm. The rainy season is betweenMay and October, during which about 89% of the total annual rainfall occurs (RID 2004).

Research methodology

This study identifies flood-risk areas for priority-based flood mitigation planning byusing a hydrodynamic model in conjunction with a geographic information system (GIS).A hydrodynamic model was used to simulate the flood flow through the canal networkand floodplain. In the past few years, various researchers have used the hydrodynamicmodelling approach to simulate flood inundation in many floodplains. Dutta et al. (2007)adopted a distributed hydrodynamic modelling approach for simulating a flood inunda-tion phenomenon in the lower Mekong basin. Patro et al. (2009) used a coupled 1D–2Dhydrodynamic model, MIKE FLOOD, to simulate the inundation extent and flood depthin the delta region of the Mahanadi River basin in India. Tingsanchali and Karim (2010)used the MIKE 11 hydrodynamic model to simulate flood-wave propagation through themain channel and over the floodplain of the Yom River basin to develop flood-risk mapsfor northern Thailand. The MIKE FLOOD hydrodynamic model (DHI 2003a) was used tosimulate flood inundation in the present study. Initially, the model was calibrated and veri-fied using observed flood-water levels. After model calibration and verification, the modelwas used to simulate the flood flow in terms of flood depth for 25-, 50- and 100-year returnperiods of rainfall. Flood-risk assessments were performed by incorporating flood-hazardand flood-damage vulnerability. Flood-hazard maps were developed from hazard assess-ments done by considering flood depth and duration. The flood-damage vulnerability ofassets and population at risk were considered in terms of asset value per unit area and pop-ulation density, respectively. Based on relative magnitudes of flood risk in the study area,flood-risk maps for various return periods of rainfall were developed.

Methodology of flood inundation simulation

Design time series rainfall

For assessment of hazard caused by flood, the probable maximum precipitation is one ofthe key variables (Faulkner, 1999). There are seven rainfall stations available inside thestudy area; the data can be obtained from RID and the Thai Meteorological Department(RID 2004). The Thiessen polygon method was applied to the study area to determinethe effective area of each rainfall station. For rainfall frequency analysis the data obtainedwere records of annual maximum rainfall from 1955–2004. The length of the data recordis quite long and covers the most severe flood years of 1980, 1983, 1990, and 1995. From2004 until 2010, floods still persisted in the study area but their severity was less than theone in 1995, which was the biggest flood in last 30 years (Siripong et al. 2009). Figure A-1 in the appendix shows the average monthly rainfall at seven rainfall stations in the studyarea from 1955–2004. (The appendix may be obtained from the corresponding author orviewed online at http://dx.doi.org/10.1080/02508060.2012.687868.)

A Gumbel probability distribution was used to carry out the rainfall frequency analysis.The average daily rainfall distribution pattern over one decade (1994–2004, which coversthe big flood in 1995, as shown in Figure A-2) was used to develop a dimensionless timeseries for rainfall. The average daily rainfall of 1994–2004 from May to November (therainy season), which has a significant effect on the flood situation in the study area, was

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used to develop hyetographs for 25-, 50- and 100-year return periods by rainfall frequencyanalysis. Examples of hyetographs for the 100-year return period are shown in Figure A-3.

Hydrodynamic model

Schematization of canal network and floodplains

There are 121 canals in the study area, forming a criss-cross network. Based on thefloodplain topography and flood flow directions, the floodplain storage areas are connectedto the canal system. The existing dikes prevent inflow from outside into the study area.A schematic diagram of the existing canal network is given in Figure A-4.

Flood routing by the MIKE 11 hydrodynamic model

Propagation of flood waves along the canals was computed in space and time using theMIKE 11 hydrodynamic model (DHI 2003b). The hydrodynamic module contains animplicit, finite difference computation of unsteady flows in rivers or water courses. Thecanal roughness coefficient or Manning’s n value (Chow 1973) was initially set to 0.023 forthis study.

Flood routing by the MIKE 21 hydrodynamic model

The hydrodynamic model in MIKE 21 (DHI 2003c) is a numerical modelling system forthe simulation of water levels and flows in lakes, estuaries, bays, and coastal areas. In thisstudy, the ground elevation data for MIKE 21 simulation were obtained from the topo-graphic data of the study by RID (2004). A digital elevation model with 200 m × 200 mgrid was developed for the entire floodplain. Since the floodplain is rice fields, Manning’sn for the floodplain was set to 0.05, following Chow (1973).

MIKE FLOOD model

MIKE FLOOD (DHI 2003a) is a tool that integrates the 1D MIKE 11 model and the 2DMIKE 21 model into a single, dynamically coupled modelling system. The connectionbetween MIKE 11 and MIKE 21 – flood waves flowing over the canal levees – is calculatedusing a broad-crested weir coefficient. In this study, an initial value of 1.705 m1/2/s wasassumed for the weir coefficient (Henderson 1966); the accumulation of flood water is dueto rainfall over the study area and the pumped discharge of 12 m3/s from the SBIA intothe floodplain.

Simulation of flood inundation

Computation of flood flow through the study area was performed by MIKE FLOOD model.The simulations were performed for the two months of September and October, which havethe highest monthly rainfall. The results are expressed in terms of hourly flood depth foreach square of the 200 m × 200 m grid that was assigned to the floodplain.

Classification of maximum flood depth. The simulated maximum flood depth for a partic-ular return period of rainfall was classified into four depth categories (low, medium, high,and very high) based on three marginal depths (0.45, 0.90, and 1.20 m). The basis forselecting these marginal depths was the data from questionnaire surveys which are briefly

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described herein. In general, the average plinth level above the ground of most buildingsand houses in the study area is about 0.45 m. When the flood depth is less than or equal to0.45 m, damage is minor. When the flood depth is greater than 0.45 m but not exceeding0.90 m, damage is moderate. When the flood depth is greater than 0.90 m but not exceed-ing 1.20 m, there is enormous damage and the possibility of death. In flood depths greaterthan 1.20 m, there is a high probability of death.

Classification of flood duration. Determination of flood durations is laborious and time-consuming. To overcome this obstacle in this study, flood durations were determined byusing the three inundation maps of flood depths for rising flood, peak flood, and recedingflood from the model simulation. The flood depths were classified into insignificant-floodareas and flood-affected areas on the basis of impending depth that causes difficulties to thelivelihood of people. The impending depth used in this study was the average flood depthobtained from questionnaire surveys of people who experienced flooding in the study area.It was found that 0.22 m is the onset of flood depth that causes difficulties for their liveli-hood. The areas belonging to the flood-affected area category were used to determine floodduration. Using the same criterion as Islam and Sado (2000), the duration of flooding wasdivided into four categories: short, medium, long, and very long. The flood duration of dif-ferent categories in each grid unit was determined by superimposing the three inundationmaps. The areas shown as flooded in all inundation maps were considered to have a verylong duration of flooding. Those shown as flooded in any two flooded maps were consid-ered to have a long duration of flooding. Areas shown as flooded in only one inundationmap were considered to have a medium duration of flooding. Areas that showed as free offlooding in all inundation maps were considered to have short duration of flooding.

Methodology of flood risk assessment

Flood hazard assessment

Generally, flood hazard assessment is an estimation of the overall adverse effect of flooding,which depends on many parameters. One or more parameters can be considered, dependingon the characteristics of the study area and floods (United Nations 1991, pp. 31–91). Sincethe study area has a large floodplain with flat topography, two major parameters, flooddepth and flood duration, were considered for flood hazard assessment.

Hazard indicators for flood depth and flood duration

The severity of flood hazard is usually given with respect to a relative scale representingthe degree of flood hazard. In this study it is called the flood hazard indicator (FHI). Therelative scale of each flood hazard category is determined by trial and error. Karim andChowdhury (1995) made a sensitivity analysis for alternative types of hazard index. Theyfound that a linear scale was the best alternative. Their study area was flat, as in this study;a linear scale is therefore used in this analysis as well.

The idea of the FHI is to find a small set of numbers suited for representing the variationin flood hazard due to local topography. That is, the flood-hazard levels are denoted bysmall integers. In the present study, FHIy is the flood-depth hazard indicator while FHIt isthe flood-duration hazard indicator.

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Flood hazard factor

The flood hazard factor FHF represents a single indication of adverse effect based on acombination of the FHIs for flood depth and flood duration. That is, the FHF for each gridunit is a linear combination of the FHIy and FHIt for that grid unit:

FHF = μFHIy + βFHIt (1)

where μ and β are weighting factors, and μ + β = 1.The proper values of FHIy and FHIt can be simultaneously determined with the deter-

mination of μ and β by performing a sensitivity analysis of Equation (1). Thus, smallinteger numbers were assigned to FHIy and FHIt.

A smaller FHIy was assigned for a lower flood depth, while a larger FHIy was used toindicate a higher flood depth. The values of FHIy for different categories of flood depthhazard are shown in Table 1. The lowest FHIy was 0, 1, and 2 for the first, second, and thirdoption, respectively. The increment for higher flood hazard was 1 for all options.

Generally, flood duration is linearly proportional to flood depth. Because the studyarea is mainly utilized for agricultural purposes, the damages due to flood duration followthe same trend as that of flood depth (Tingsanchali and Karim 2005; Tingsanchali andKeokhumcheng, 2006). Hence, the previous relationships between flood duration and flooddepth (Tingsanchali and Keokhumcheng, 2006) are applied to this study. So far, there isno standard time limit to define the hazard category for flood duration. In this study, fourcategories of flood duration were used and a linear scale was adopted for the flood-durationhazard indicator FHIt as it was for FHIy. The FHIt values for different categories of floodduration hazard are shown in Table 2.

Ideally, the number of options for both FHIy and FHIt, is the minimum necessary toindicate a slight change in FHF, as found by sensitivity analysis. It was found that when

Table 1. Flood hazard indicator for flood depth.

Flood hazard indicator FHIy

Flood depth y (m) Hazard category Option 1a Option 2a Option 3a

0.00 < y ≤ 0.45 Low 0 1 20.45 < y ≤ 0.90 Medium 1 2 30.90 < y ≤ 1.20 High 2 3 4y > 1.20 Very high 3 4 5

Table 2. Flood hazard indicator for flood duration.

Flood hazard indicator FHIt

Flood duration Hazard category Option 1b Option 2b Option 3b

Short Low 0 1 2Medium Medium 1 2 3Long High 2 3 4Very long Very high 3 4 5

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Table 3. Patterns for flood hazard factor calculation.

Flood hazard indicator optionsCase studies for floodhazard assessment Flood depth Flood duration

A 1a 1bB 1a 2bC 1a 3bD 2a 1bE 2a 2bF 2a 3bG 3a 1bH 3a 2bI 3a 3b

the options for both FHIy and FHIt were more than 3 there were slight changes in the FHF;hence, 3 options each for FHIy and FHIt were sufficient for determining the value of FHF.

The sensitivity analysis of weighting factors FHIy and FHIt was performed for threesets of factors {μ, β}: {0.25, 0.75}, {0.50, 0.50}, and {0.75, 0.25}. From the analysis,the maximum variation of FHF was found when both μ and β were 0.50. Therefore, forsimplicity, the same weighting factor of 0.50 was used for both FHIy and FHIt. Nine casestudies were performed to determine the FHF in each grid unit for different sets of FHIy

and FHIt, as shown in Table 3. From the obtained FHF in each case study, the maximumFHF was converted to 100 for convenience. The other values of FHF were converted pro-portionally. To provide more alternatives in classifying flood hazard zones, the new FHFvalues were subdivided into four equal intervals, designated low, medium, high, and veryhigh flood hazard.

Development of flood-hazard zones

The computed floodplain areas in each flood-hazard zone of all nine case studies wereexamined to determine the best combination of FHIy and FHIt. The best combination ofboth FHIy and FHIt provided computed values for the floodplain area in each flood-hazardzone close to the actual floodplain areas based on flood depth and duration. The obtainedclassification of flood hazard zones in the case study that had the best combination of FHIy

and FHIt was used to develop the flood-hazard map.

Flood damage vulnerability assessment

Flood damage vulnerability is a measure of the intrinsic susceptibility of elements at riskfrom exposure to flood. Elements at risk in this study are quantified by using the populationand the value of assets in the study area. The number of population and a GIS database ofland-use classification are required. A questionnaire survey was also done to collect thevalue of assets in different categories of land use.

Flood damage vulnerability indicators of elements at risk

Flood damage vulnerability is expressed on a linear scale by using an indicator from 0 (noflood damage vulnerability) to 1 (very high flood damage vulnerability). The flood damage

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vulnerability indicator of the population at risk FVIp of each sub-district (the smallestadministrative area) was calculated by dividing the population density of each sub-districtby the maximum population density of all sub-districts. The flood damage vulnerabilityindicator of assets at risk FVIa of each land-use category was calculated by dividing theaverage asset value per unit area of each land-use category by the maximum asset valueper unit area amongst all land-use categories.

Flood-damage vulnerability factor

The flood-damage vulnerability factor FVF represents the resultant susceptibility of popu-lation and assets at risk exposed to flood. Similar to the concept for determination of floodhazard factor, the magnitude of FVF was determined by:

FVF = λFVIp + γ FVIa (2)

where λ and γ are weighting factors and λ + γ = 1.Both population and assets can be directly damaged by flood hazard and cause eco-

nomic losses. Therefore, for simplicity, a weighting factor of 0.50 is used for both FVIp

and FVIa.

Development of flood damage vulnerability zones

From the calculated FVF, the maximum value was converted to 100 to make a conve-nient scale and the other values were converted proportionally. The new FVF values weresubdivided into four equal intervals: low, medium, high, and very high flood damage vul-nerability. The FVF rank represents the degree of susceptibility of elements at risk todamage caused by floods.

Flood risk zoning and assessment

Risk is the probability of harmful consequences or expected loss resulting from interac-tions between hazards and vulnerable conditions. Risk is conventionally expressed by theequation

risk = hazard × vulnerability (3)

The magnitude of flood risk in this study is expressed by the product of FHF and FVFwhich determines the flood risk factor FRF of the corresponding area. The FRF representsthe comparative flood risk intensity amongst the grid units. For a systematic representa-tion, the initial maximum FRF was converted to 100. The other FRFs were proportionallyconverted to the same scale accordingly. Similarly to the studies of Chowdhury and Karim(1997), BUET (1997), and Tingsanchali and Karim (2005), the study area was divided intofour categories of flood-risk zones based on the adjusted FRF.

Results and discussion

Model calibration and verification

The hydrodynamic model was calibrated by using the observed data on daily water level ofthe biggest flood in 1995 at Bang Phli Watergate station on Klong Samrong (RID 2004).During the calibration, Manning’s n was adjusted by trial and error to produce agreement

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Observed data in 1995

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Observed data in 2001

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Figure 2. Results of model calibration and verification at Bang Phli Watergate station.

between the observed and simulated water levels. In some cases small changes were madein floodplain bathymetry. The calibrated Manning’s n was found to be within 0.026 to0.035 for canals and 0.062 for the floodplain. The broad-crested weir coefficient was alsoadjusted and was found to be 1.74 m1/2/s. A comparison of the observed and computedwater levels is shown in Figure 2. The model was verified by using the data of a smallerflood in 2001; this comparison is also shown in Figure 2. The results show good agreementbetween the observed and computed data for both model calibration and verification. Themodel performance was determined based on three parameters: the coefficient of efficiencye, the relative error RE, and the correlation coefficient r. The values of e and RE were foundto vary from 0.82 to 0.93 and –0.05 to +0.03, respectively (they would be 1 and 0 in thecase of perfect agreement between computed and observed data). The r values between thecomputed and observed data were found to vary from 0.90 to 0.95, which is satisfactory.

Flood hazard map development

Extreme hazard events generally ranging from 20- to 400-year return periods are consid-ered in planning and designing of water resource systems based on the purpose and type ofstructure (Biringer and Danneels 2000, Stedinger and Crainiceanu 2000). However, giventhe lifetime of any infrastructural development, flood-disaster mitigation projects are oftendesigned for a 100-year return period (United Nations 1991, pp. 31–91, Dutta et al. 2003).The emphasis in these analyses is therefore on a 100-year return period; 25- and 50-yearreturn periods were investigated in relation to the 100-year return period of rainfall.

From the simulation of flood inundation for 100-year return period of rainfall, it wasfound that for the classification of maximum flood depth, the floodplain areas are 49.49,34.93, 10.46, and 5.12% for low, medium, high, and very high flood depth zones, respec-tively. For the classification of flood duration, the floodplain areas are 40.93, 19.99, 21.46,and 17.63% for short, medium, long, and very long duration zones, respectively.

The FHF of each grid unit in the floodplain was computed by Equation (1) for the ninecase studies in Table 3. The computed FHF values were then normalized within the range of

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Table 4. Floodplain areas of each flood hazard zone for 100-year return period of rainfall.

Floodplain area (% of total study area)

Case studies for floodhazard assessment

Lowhazard

Mediumhazard

Highhazard

Very highhazard

A 55.74 24.23 9.45 10.58B 57.85 22.12 9.45 10.58C 53.42 20.47 12.58 13.53Da 41.27 20.22 22.54 15.97E 26.64 24.40 29.49 18.47F 24.23 22.45 33.75 19.57G 22.14 21.21 34.51 22.14H 21.45 16.78 34.45 27.32I 21.45 14.45 34.12 29.98

aThis case yielded the most consistent results.

0 to 100, in which the minimum and maximum computed FHF values were normalized as0 and 100, respectively. Table 4 shows the percentages of floodplain areas by subdividingthe normalized FHF values into four equal intervals, designated low, medium, high andvery high flood hazard.

The results in Table 4 were examined to determine the best combination of FHIy andFHIt. The results of all case studies were checked for consistency in different flood-hazardzones and with other case studies. Amongst all case studies, more consistent results werefound for Cases A, B, C, and D. It can be seen that in Cases E, F, G, H, and I, the floodplainunder the low hazard zone is lower than the other cases. On the other hand, in Cases A,B, C, and D, the floodplain under low hazard zone is consistently the highest amongst allcategories. This is the same as the actual situation in 1995, in which the magnitude ofrainfall corresponded to the 100-year return period (Siripong et al. 2009) and the majorpart of the floodplain was under the low hazard zone. Moreover, the floodplain in the lowhazard zone in Case D is less than 50% of the total area, again, close to the actual situation.Therefore, the results from Case D were used to develop flood-hazard maps for the 100-year return period of rainfall as shown in Figure 3. It should be noted that the airport hasits own flood-protection system, which includes an earthen dyke 3 m high and 23.5 kmlong and two pumping stations each with a capacity of 6 m3 per second to pump out theflood-water, which flows south to the Gulf of Thailand (RID 2004). These make the airportimmune to flooding, so the area was not considered in the analysis.

Flood damage vulnerability map development

Population numbers for each sub-district are only available in 2004. The population densityin each sub-district can be calculated. In the same year, RID (2004) also studied land-useclassification of the study area.

A questionnaire survey is also required for flood damage vulnerability development.The object is to collect data from which to quantify the asset values which will beused to develop elements at risk from exposure to flood hazard. The element at risk isa component that is used to define flood vulnerability. The questionnaire surveys wereperformed by interviewing stakeholders within the study area. Interviews were accom-plished in person or by telephone. The questions in the surveys were about the flooding

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Figure 3. Flood hazard map for 100-year return period of rainfall.

experienced by the stakeholders and their assets that were exposed to flood. The samplesize used was determined based on the expense of data collection, time, spatial cover-age, and the need to have sufficient statistical power. The study area was classified intoseven major types of land use: (1) agricultural (paddy fields, fish farms, shrimp farms,orchards, plantation), (2) residential, (3) commercial, (4) government compound, (5) indus-trial estate, (6) road/highway, and (7) wasteland (mangrove swamp forest, water resources,grassland). The largest area was found for agricultural area (56.58% of the total area).Hence, the representative samples comprised agriculturists, fishers, residents, merchants,and government officers. The 3,303 stakeholders were proportioned to the types of landuse. From the questionnaire surveys the values of assets of each land-use type could beobtained, and hence the average asset value per unit area could be calculated.

Amongst all of the sub-districts, the maximum population density was984 persons/km2. The FVIp of all sub-districts was computed as previously described andfound to vary from 0.07 to 1.

The maximum average asset value per unit area was THB18,321/m2 for commercialor business area (THB30 ∼= US$1). The FVIa of all land-use categories was computed aspreviously described and found to vary from 0.001 to 1.

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Figure 4. Flood damage vulnerability map.

From the obtained FVIp and FVIa, the FVF of each sub-district was determined byEquation (2). The obtained maximum value was converted to 100 and the other values wereconverted proportionally. The converted FVF were subdivided into four equal intervalsto classify the susceptibilities of elements at risk exposed to flood hazard. The result isrepresented schematically in Figure 4.

Risk area zoning and assessment

As previously described for flood-risk assessment, the results show that the FRF among thegrid units varies from 0 to 8000 for the 100-year return period of rainfall. For a systematicrepresentation, the initial maximum FRF of 8000 was converted to 100. The other FRFwere proportionally converted to the same scale accordingly. The adjusted FRF in eachgrid unit were categorized into four zones based on scores of equal intervals: low flood risk(LFR), moderate flood risk (MFR), high flood risk (HFR), and very high flood risk (VFR).The percentage areas of the floodplain were found to be 38.42, 35.28, 10.8,5 and 15.45%for LFR, MFR, HFR and VFR, respectively.

Figure 5 shows the flood-risk map for a 100-year return period of rainfall. By compar-ing Figure 5 with the flood hazard map in Figure 3 and the flood damage vulnerability mapin Figure 4, it can be seen that some areas in the high flood risk zone (HFR) and very highflood risk zone (VFR) in Figure 5 have only medium hazard level but high flood damagevulnerability due to high population density or high asset values.

Flood risk assessments were also performed for 25- and 50-year return periods ofrainfall and a comparative summary of flood-risk areas is given in Table 5.

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In this study, flood damage vulnerability was considered only for population and assetsat risk for the year 2004. From the obtained flood-risk maps under the same flood dam-age vulnerability, it is apparent that the floodplains in HFR and VFR zones continuouslyincrease with increasing return period of rainfall. This shows that the degree of returnperiod of rainfall has potential effects on the degree of flood risk. It also appears that mostof the HFR and VFR zones are east and south of the airport, which is consistent with thestudy by RID (2004).

Discussion on impact of flood of the study area in 2011

In October and November 2011, a catastrophic flood occurred in Thailand. This floodoccurred after the completion of this research work, so data from it was not consid-ered in the analysis. It was caused by heavy rainfall in the northern and central partsof Thailand due to tropical storms with an approximate return period of 100 years. Theflood affected the northern and central parts of Thailand, including some parts of Bangkok.Several districts in the northern and western parts of Bangkok which lie outside Bangkok’sflood wall were flooded heavily. Since the study area is located in the eastern floodplain ofBangkok, diversion of flood-water through the study area was one of the main approachesto protect the center of Bangkok. In November 2011, the northern parts of the study areawere affected by floods, as shown in Figure 6. Ironically, these areas belong to the low-flood-hazard zone and low-flood-risk zone in Figures 3 and 5 respectively. The reason is

Figure 5. Flood risk map for 100-year return period of rainfall.

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Table 5. Flood-risk areas under 25-, 50- and 100-year return periods of rainfall.

Percentage of flood-risk areasa

Flood-riskzone

25-year returnperiod

50-year returnperiod

100-year returnperiod

Low 45.39 32.45 38.42Medium 44.71 47.38 35.28High 8.32 9.34 10.45Very high 1.58 5.83 15.45

aTotal floodplain area = 592 km2.

that flood-hazard areas in this study were determined based on direct rainfall occurringwithin the study area. This is the situation most likely to confront local decision makers indeveloping priority-based flood-mitigation plans; the runoff situation of the 2011 flood islikely to be quite rare.

Conclusions

This study aimed to prepare flood risk–based zoning maps for 25-, 50- and 100-year returnperiods of local rainfall in the area surrounding the SBIA in the eastern part of the Bangkokfloodplain. The presence of the airport and rapid urbanization adversely affect flood riskin the area. Because of the high cost of investment, Thailand cannot afford to prevent alllevels of flooding or protect all risk areas simultaneously. Yet until this study, flood-riskassessment had never been done in this area.

From the study it was found that the main factors that affect the degree of flood risk arethe severity of flood hazard, the population density, and the types of land use. The findingshows that flood-risk maps can provide information and guidelines for concerned agenciesin developing priority-based flood-mitigation planning to alleviate the flood-related prob-lems of stakeholders in the study area. Among the different zones of the study area,the VFR zone should take priority for flood mitigation because the social and economicimpacts there are likely to be much higher than the others. Moreover, the prioritization ofimplementation of flood-hazard evacuation training courses for residents or stakeholdersis also required. It is necessary to note that water needs space. If an area is protected fromflooding, this will automatically exacerbate the flood problem in another area. To overcomethis hindrance, systematic basin-wide planning based on a flood-risk map is necessary indeveloping an effective flood-risk management strategy. Also, people who are planningto develop the land can be forewarned about flood-risk areas through publication of theflood-risk maps.

The results from the study also show that for the 100-year return period of rainfall,the main areas in the VFR zone are located to the east and south of the airport. This isconsistent with the fact that after the completion of the flood drainage canal to the Gulfof Thailand (Klong Suvarnabhumi) the flooding problem still persists, especially to theeast and south of the airport. This is because these areas have low-lying terrain, causingflood-waters to drain away slowly. Furthermore, most of the canals surrounding the airportare shallow, clogged with weeds, rubbish, and silt, and encroached by residents’ activities.To reduce possible flood damage and improve the efficiency of flood-water drainage, multi-lateral works are required such as the dredging or widening of the canals that convey water

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Figure 6. Actual flood inundation of the study area in November 2011.

to Klong Suvarnabhumi, and the installation of pumping stations to accelerate drainage ofwater to Klong Suvarnabhumi and the sea.

The theoretical contribution from this study is the determination of flood duration.It was determined by superimposing the three inundation maps for rising flood, peak flood,and receding flood from the hydrodynamic model simulation. This is a good alternative forflood duration assessment in case of the unavailability of the support data.

It is also observed that for any return period of rainfall, some medium flood-hazardzones have a high risk of flood damage. This can happen if these areas have high populationdensity or high asset value. It can be concluded that flood risk depends on the degree of

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flood-hazard and flood-damage vulnerability. Both potential flood hazard and elements atrisk from exposure to flood are necessary for flood risk assessment.

The assessments of flood hazard and flood risk in this study were only based on directrainfall occurring within the study area. Therefore, they could not determine the floodhazard and flood risk caused by the recent flood in 2011, in which the flood was caused byintentional diversion of flood runoff from the area outside Bangkok into the study area.

For more accurate flood-risk assessment, other parameters of flood hazard such as floodflow velocity and rate of water-level rise should be considered in conjunction with otherparameters of elements at risk such as public services or infrastructure exposed to potentialflood hazard.

AcknowledgementsGratitude is expressed to the Royal Thai Government for financial support for this research, which isa part of the dissertation work of the first author.

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