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Hydrological Sciences Journal

ISSN: 0262-6667 (Print) 2150-3435 (Online) Journal homepage: http://www.tandfonline.com/loi/thsj20

An integrated tool for assessment of flood vulnerability of coastal cities to sea-level rise and potential socio-economic impacts: a case study in Bangkok, Thailand Dushmanta Dutta To cite this article: Dushmanta Dutta (2011) An integrated tool for assessment of flood vulnerability of coastal cities to sea-level rise and potential socio-economic impacts: a case study in Bangkok, Thailand, Hydrological Sciences Journal, 56:5, 805-823, DOI: 10.1080/02626667.2011.585611 To link to this article: http://dx.doi.org/10.1080/02626667.2011.585611

Published online: 12 Jul 2011.

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Date: 28 January 2017, At: 13:25

Hydrological Sciences Journal – Journal des Sciences Hydrologiques, 56(5) 2011

805

An integrated tool for assessment of flood vulnerability of coastal cities to sea-level rise and potential socio-economic impacts: a case study in Bangkok, Thailand Dushmanta Dutta1,2 1

SASE, Monash University, Churchill, Victoria 3842, Australia

2

CSIRO Land and Water, Black Mountain, ACT, Australia [email protected]

Received 28 September 2009; accepted 15 February 2011; open for discussion until 1 January 2012 Citation Dutta, D. (2011) An integrated tool for assessment of flood vulnerability of coastal cities to sea-level rise and potential socio-economic impacts: a case study in Bangkok, Thailand. Hydrol. Sci. J. 56(5), 805–823.

Abstract The paper introduces a comprehensive and integrated tool developed to analyse socio-economic impacts of floods due to sea-level rise (SLR) on coastal cities, and presents the outcomes of a case study application in Bangkok, Thailand. The study aimed to capture a macro picture of floods to present an overview of the severity of flooding under the projected SLR conditions. A physically-based distributed flood model, which combines surface and river flow, was adopted to simulate the flood scenarios due to different magnitudes of sea-level rise. The input rainfalls and upstream boundary conditions of a worst-case flood event of 1995 were considered as the baseline for the modelling, based on the available records of rainfall and water-level data sets of the last three decades. The outcomes of the case study present a detailed picture of floods and their socio-economic impacts in Bangkok City under the worst projected SLR scenarios in the 21st century. The simulated results show that for baseline conditions of 1995, the overall inundation area in Bangkok may increase up to 26% in 2050 due to a SLR of 32 cm, and to 81% in 2100 due to 88 cm SLR, compared to the extent of flood inundation in 1995. The number of flood affected buildings is likely to increase by a factor of 1.5 in the 75 years from 2025 to 2100. Key words floods; distributed flood model; AGENT-LUC model; socio-economic impacts; sea-level rise

Un outil intégré pour l’évaluation de la vulnérabilité des villes côtières aux inondations dues à l’élévation du niveau des mers et des impacts socio-économiques potentiels: une étude de cas à Bangkok, Thaïlande Résumé L’article introduit un outil exhaustif et intégré développé pour analyser les impacts socio-économiques des inondations dues à l’élévation du niveau des mers sur les villes côtières, et présente les résultats d’une étude de cas à Bangkok, Thaïlande. L’étude vise à capturer une image à grande échelle des inondations afin de présenter une vue d’ensemble de la sévérité des inondations sous les conditions d’élévation du niveau des mers projetées. Un modèle de crue distribué à base physique, qui combine écoulements de surface et fluvial, a été adopté pour simuler les scénarios de crues résultant de différentes valeurs de l’élévation du niveau des mers. Les pluies d’entrée et les conditions aux limites en amont de la pire inondation de 1995 ont été prises comme références pour la modélisation, sur la base des données enregistrées de pluie et de niveau d’eau disponibles pour les trois dernières décennies. Les résultats de l’étude de cas présentent un portrait détaillé des inondations et de leurs impacts socioéconomiques dans la ville de Bangkok, pour les pires scénarios d’élévation du niveau des mers prévus pour le 21e siècle. Les résultats simulés montrent que pour les conditions de base de 1995, la zone d’inondation globale de Bangkok pourrait augmenter jusqu’à 26% en 2050 avec une élévation du niveau des mers de 32 cm, et 81% en 2100 avec une élévation du niveau des mers de 88 cm, par rapport à l’étendue des inondations de 1995. Le nombre de bâtiments touchés par les inondations est susceptible d’augmenter d’un facteur de 1.5 dans les 75 années de 2025 à 2100. Mots clefs crues; modèle de crue distribué; modèle AGENT-LUC; impacts socio-économiques; élévation du niveau des mers

ISSN 0262-6667 print/ISSN 2150-3435 online © 2011 IAHS Press doi: 10.1080/02626667.2011.585611 http://www.informaworld.com

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Many of the large coastal cities in the Asian monsoon region are frequently affected by floods due to their geographical locations in alluvial floodplains in the lower basins of large perennial rivers. The statistics of the flood events from 1975 to 2002 clearly show an increasing trend of flood disasters in most Asian countries (Fig. 1). Annual flood events in Asia have tripled, with economic losses and human casualties increased more than five-fold during this period (Dutta 2003). Many of the most flood-affected regions are located in the coastal areas (Fig. 2). The coastal zone is expected to be home to nearly 75% of the Asian population by 2025 (Hinrichsen 1998, 1999). The combined effects of potential SLR and extreme rainfall events triggered by climate change may lead to catastrophic flood disasters in many of the coastal cities of Asia. The socio-economic impacts of such catastrophic floods will be enormous due to the rapid urbanization and high population growth. It is very important and urgent to assess the impacts of SLR on flooding in coastal cities and to prepare long-term plans for risk management (van Dam 2003, IPCC 2007b). This will assist policy makers to better understand the vulnerability of developing coastal cities under socio-economic and climatic changes. Studies conducted at global and regional levels provide a general overview of the situation in their respective contexts (Nicholls et al. 1999, Nicholls 2002); however, such results do not present comprehensive details to provide a sound basis for developing the best strategies for flood risk management. The flood mechanism is not adequately modelled in these

INTRODUCTION Many studies have predicted a strong impact of global warming on sea levels and its adverse effects on coastal zones around the world (Jones 2001, Jansson et al. 2003, McInnes et al. 2003, Caccamise et al. 2005, Carton et al. 2005). The Third Assessment Report (TAR) of the Intergovernmental Panel on Climate Change (IPCC) predicted that the global sea level may rise as much as 88 cm by the end of the 21st century (IPCC 2001). According to the Fourth Assessment Report (AR4) of the IPCC, global sealevel rise (SLR) in the second half of the 20th century was estimated as 1.8 ± 0.3 mm year-1 , which is consistent with the TAR estimate of 1.5 ± 0.5 mm year-1 for the 20th century (IPCC 2007a). Moreover, because of the thermal inertia of the oceans, the global mean temperature will probably increase beyond 2100 and the sea level will continue to rise in future centuries, even if greenhouse gas concentrations were stabilized by then (Nicholls 2002, Burroughs 2003, Antonov et al. 2005). Approximately 20% of the global population lives within 30 km of coastal areas and this number is expected to have doubled by 2025 (Cohen et al. 1997, Hinrichsen 1998). The SLR will have wide-ranging effects on coastal populations and ecosystems, such as saline water intrusion, erosion of shorelines, amplified intensity and frequency of coastal flood inundation (Srivastava 1998, Reeve 1998, Sherif and Singh 1999, van der Meji and Minnema 1999, Kim et al. 2005). For example, an SLR of one metre could displace up to 70 million people in Bangladesh and China (Burroughs 2003). Drought Floods Earthquake Extreme-temperature Famine Slide Volcano Wild-fire Windstorm

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Fig. 2 Spatial distribution of major flood events in Asia during 1975–2002.

studies, only uniform SLR is considered without essential details of topography, urban infrastructure, existing flood control measures, etc. and, thus, a comprehensive picture of the flooding situation and its impacts is not captured at the required level for appropriate management strategies to be devised by the responsible organizations. So far, only a few studies have been conducted in this direction, and a very few Asian countries have prepared long-term plans to deal with these problems. Significant progress has been made in twodimensional hydrological and hydrodynamic modelling in the last 10 years, and several models have been developed that are highly capable of simulating flood inundation in urban areas with high accuracy (Dutta et al. 1997, 2000, DHI 2002, Delft Hydraulics 2003), and of assessing the socio-economic impacts of floods (Parker 1992, Jonge et al. 1996, Dutta and Tingsanchali 2003, Dutta et al. 2003, Brouwer and van Ek 2004, Jonkman et al. 2008). The mathematical modelling approach has become well recognized as an efficient tool for mapping spatial dynamics, such as land-use/landcover change, and to understand and project the land-use dynamics and urban growth, and resulting socio-economic changes in urban areas. For example, agent-based models (ABM) are mathematical tools designed for urban growth analysis (Clarke et al. 1997, O’Sullivan and Torrens 2000, Parker et al. 2001, Silva and Clarke 2002). Agent-based models of land-use and land-cover change (ABM/LUCC) combine a cellular model representing the landscape of interest with an agent-based model that represents decision-making entities (Hoffmann et al. 2002). The ABM/LUCC models are well suited for analysis of spatial processes, spatial interactions and multi-scale phenomena (Parker et al. 2001).

However, there has not been much progress made so far on integration of the above two approaches to study the impacts of floods in urban areas under socio-economic and climatic changes. This study aimed to develop an integrated tool to comprehensively analyse the socio-economic impacts of floods in large coastal cities under projected climatic and socio-economic scenarios, and to conduct case study analyses in selected coastal cities in South and Southeast Asia. The integrated tool incorporated an agentbased urban growth model, a distributed flood model, and a socio-economic impact assessment model. The paper introduces the integrated tool and presents the outcomes of a case study application of the tool to analyse the flood characteristics in Bangkok under different SLR scenarios. PROJECT FRAMEWORK AND INTEGRATED TOOL A comprehensive framework (Fig. 3) was devised to implement the project including the development of the integrated tool and its applications in several selected coastal cities. The project took a systematic approach for developing the integrated assessment tool and its implementation to achieve the following two major objectives: –



to accurately capture the changes of flooding characteristics in coastal cities in the context of climate change and anthropogenic forcing; and to identify sound metrics to assess the impacts of these changes on socio-economic conditions.

The holistic tool includes three major components: (a) an urban growth model; (b) a distributed flood model; and (c) an impact analysis model. The urban

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Fig. 3 Comprehensive project framework.

growth model was designed to predict the land-use and land-cover changes as result of human activities and the resulting urban growth. The distributed flood model was used to simulate flood inundation characteristics under projected climate change conditions. The impact analysis model, which can include a series of impact response functions or other indicators, was used to analyse the economic and social impacts of floods on different sectors. Various social and economic sectors affected by floods were identified and a set of qualitative indicators relating to the impacts were established. Using these indicators and predicted floods due to SLR, a detailed assessment of social and economic impacts under anthropogenic developments was carried out. Urban growth model The urban growth model was designed to forecast the spatial extent of urban areas, incorporating economic growth, migration and sprawl effect. The model was based on the Anthropogenically Engineered Transformations of Land Use and Land Cover (AGENT-LUC) model, which is a nationalscale integrated and dynamic time-series simulation

model for assessing land-use and land-cover changes as a result of human activities (Rajan and Shibasaki 2000). This consists of four models: a biophysical crop yield model, a rural income model, an urban land-use model, and an agent-decision model; a submodel for migration was also incorporated. All these model components interact and have feedback loops to determine the new course of action by the agent at the next step. The model structure is sequential: the biophysical crop yield model calculates the potential productivity of the land unit for the given conditions of soil, topography, water availability and climatic parameters (Fig. 4). The distribution of water availability takes into account the soil conditions, amount of rain received, and the existence of irrigation facilities. The main assumption of this model is that there is a strong linkage between the climate and crop distributions (Leemans and Solomon 1993). The crop yield estimates are derived by modifying the approach as described in the EPIC model (Sharpley and Williams 1990). The central concept of this approach is the growing period and the photosynthetic efficiency of the crops. The rural income model calculates the economic potential of the land unit, based on both agricultural and non-agricultural

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revenues and expenditures. It also takes into account the accessibility, terrain conditions and current land use in calculating the costs. Urban land use is the other major land use that is primarily influenced by the activities of humans. The urban land requirement is estimated as it competes with agricultural areas due to increasing population pressures and the rise in the economic levels of a region. The model takes into account the “locational” value—neighbourhood and accessibility of the land unit—in assessing the new areas that will be urbanized. The final step in the simulation is the agent-decision model, which uses the estimated income, urban land needs and the existing land use in the land unit under consideration as its input to predict the land use. The agent is the decision maker in this model, who arrives at a decision by taking into account the prevailing conditions in the respective grids. In addition to economic factors, the demographic condition (age distribution and educational levels) and land-use history are considered to help in arriving at a reasonable estimate for the change in land-use patterns. As an addition to the land-use change decision, the model has a migration sub-model that simulates the changes in population of each grid as a consequence of the changes in economic welfare and demographic distribution that will exist in the grid after the changes in the landuse/land-cover patterns (Rajan and Shibasaki 2000).

Distributed flood model The surface and river components of the Institute of Industrial Science Distributed Hydrological Model (IISDHM), a physically-based distributed hydrological model, were used to formulate the distributed flood model for inundation simulation (Jha et al. 1997, Dutta et al. 1997, 2000). This model

uses one-dimensional (1D) Saint Venant equations of continuity and momentum (equations (1) and (2)) for river network flow simulation, and the twodimensional (2D) form of these equations for surface flow routing. The mass conservation (continuity) equation is: ∂Q ∂A + =q ∂x ∂t

(1)

and the momentum equation is: ∂Q ∂ + ∂t ∂x



Q2 A

 + g(A

∂z + Sf ) = 0 ∂x

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where t is time; x is the distance along the longitudinal axis of the water course; A is the cross-sectional area; Q is the discharge through A; q is the lateral inflow or outflow distributed along the x-axis of the watercourse; g is a gravity acceleration constant; z is the water surface level with reference to datum; and Sf is the friction slope. The governing equations for 2D gradually varied unsteady flow can be derived from the conservation of mass and momentum equations. The overland flow equations are the 2D expansion of 1D open-channel flow Saint Venant equations. The mass conservation (continuity) equation is written as: ∂uh ∂vh ∂h + + =q ∂x ∂y ∂t

(3)

and the momentum equations are, in the x-direction: ∂u ∂u ∂u ∂Z + u + v + g( + Sfx ) = 0 ∂t ∂x ∂y ∂x and in the y-direction:

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∂v ∂v ∂Z ∂v + u + v + g( + Sfy ) = 0 ∂t ∂x ∂y ∂y

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where u and v are the velocities of flow in the xand y-directions, and Sfx and Sfy are friction slopes in x- and y-directions. Diffusive approximations of the 1D and 2D Saint Venant equations are used in river and surface flow routing by solving them implicitly using finite difference schemes with a uniform network of square grids. The exchange of flow between the channel network and floodplains is simulated using a floodplain compartment concept. The floodplain compartments are surface grids along the river channels, which are considered as boundary conditions in overland flow routing. The flow transfer between floodplain compartment and river is assumed to occur along x reaches, which adjoin the river and floodplain compartments; this flow is assumed to be a broad-crested weir with submergence correction. The flow can be either away from the river or to the river, depending on the relative water-surface elevations of the river and the floodplain compartment. The river elevations are computed using the 1D diffusive wave model solution for channel network, and the floodplain elevations are computed by 2D diffusive wave model for overland flow. The exchange of flow between flood compartment and river reach at time step, t, is computed by a simple storage routing relation, i.e.: Vlt+1 = Vlt + (I t+1 − Ot+1 )t

(6)

in which Vl is the volume of water in the floodplain compartment at time t+1 or t, I is inflow from the river grids to adjacent floodplain compartments, and O is outflow from the floodplain compartments to adjacent river grids (Dutta et al. 2000).

Socio-economic impact assessment model In this study, socio-economic impact is defined as the potential loss from a flood event, derived by assessing the vulnerability of population, buildings and infrastructure to this hazard. It identifies the characteristics and potential consequences of floods, how much of the community could be affected by it, and the impact on community assets. The lack of a globally or regionally accepted method for carrying out assessments that could determine the socio-economic impact of floods has greatly limited the capacity to

present a comprehensive picture in this study. The current assessment method relies on the total number of people and buildings, and the lengths of roads and railways affected by floods. Detailed questionnaire surveys were conducted in several flood-affected areas of four selected cities of South and Southeast Asia to identify the most important socio-economic issues for flood impact analysis, and to establish a set of impact indices for the selected issues (Dutta and Tingsanchali 2003, Bhuiyan et al. 2005, Dutta et al. 2005). From the questionnaire survey, it was found that the residential and non-residential buildings and the transportation infrastructure (roads and railways) were the most important economic issues, and population was the most important social issue, in terms of their vulnerability to floods. Other social and economic issues affected by floods were not included in the analysis due to the lack of sufficient data to establish impact indices. The population was divided into three subcategories: persons less than 6 years of age, persons of 6–65 years, and persons above 65 years. Based on the responses to the questionnaire, two major flood parameters—depth and duration— were used to classify the flood hazard, and each of the parameters was divided into five ranges to assign the flood-hazard intensity. The flood depth categories used were: 3.5 m, and the flood duration ranges were: 7 days. The impact indices were prepared based on the percentage of damage, as shown in Table 1. For the population, damage of less than 25% indicates a minor health problem, damage within the range 25–50% implies a major problem, and damage between 50–75% causes irrecoverable illness. There will be loss of life when the health damage exceeds 75%. For buildings, damage means total damage to contents, structure and outside properties. Less than 25% damage to buildings indicates a minimum level of damage to the structure and outside properties and little damage to the contents, damage of 25–50% Table 1 Impact indices for different damage category of floods. Percentage of damage

Impact index

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Socio-economic impacts of floods in Bangkok

indicates a medium level of damage to structure and contents, and damage of 50–75% indicates very high damage to building contents and high damage to building structure. Damage over 75% means that building contents are completely damaged and the building structure and outside properties have become unusable. For roads and railways, damage of less than 25% indicates transportation interruption for a few hours, damage of 25–50% indicates minor damage, and damage of 50–75% will cause minor to major road damage and transportation interruption for a few days. Damage of over 75% would imply severe to complete collapse of the transportation system and the need to reconstruct roads and railway lines. STUDY AREA Bangkok, the capital city of Thailand (latitude 13◦ 45 N and longitude 100◦ 31 E), is one of the largest cities in Asia and a regional hub. It is located on the lower flat basin of the Chao Phraya River, the largest and most important river in Thailand with a drainage area of 160 103 km2 and an annual suspended sediment discharge of 11 × 106 t (Milliman et al. 1995). The Chao Phraya originates in the northern-most part of Thailand and discharges into the Gulf of Thailand after flowing approximate 1200 km (Fig. 5). The average annual discharge is about 770 m3 /s, with a peak of 4560 m3 /s recorded in 1995 (Thammasittirong 1999). The coastal environment of the Chao Phraya Delta is classified as lowenergy micro-tidal. Somboon (1992) showed that the shoreline has migrated about 90–100 km southward from the centre of the central plain in Thailand over

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the last 6000 years, which corresponds to a migration rate of about 15 m year-1 . Saito et al. (2000a) also studied delta-front migration of the Chao Phraya: radiocarbon dates from deltaic sediments indicate a migration rate of about 20 m year-1 for the last 4000 years. Moreover, based on a reconstructed map of the palaeo-delta front in the central plain in Thailand, they calculated the approximate palaeo-sediment discharge of the river system to the northern part of the Gulf of Thailand. It is estimated that the average sediment volume for the last 3000 years (on a 1000-year time scale) was about 2 × 107 m3 year-1 (land increment of 2 × 103 km2 , thickness of 10 m, duration of 1000 years). This value is almost equivalent to the present sediment-discharge rate of the Chao Phraya and Mae Klong rivers (Somboon 1992). Recent topographic maps and satellite images show a rapid shoreline retreat because of coastal erosion at the mouth of the Chao Phraya. The western shore of the river mouth lost 1.8 × 106 m2 between 1969 and 1973, 1.5 × 106 m2 between 1973 and 1979, and 3.9 × 106 m2 between 1979 and 1987, with a maximum shoreline retreat of 500 m between 1969 and 1987 (Vongvisessomjai et al. 1996). Moreover, Saito et al. (2000b) showed that the shoreline retreated 200–300 m between 1987 and 1992. The major reason for this rapid shoreline retreat is thought to be land subsidence due to groundwater pumping. In the city of Bangkok and its vicinity, land subsidence has been a critical issue since 1978; it continued throughout the 1980s. The total subsidence of Bangkok up to 1988 ranged from less than 20 cm to more than 160 cm, with a maximum rate of about 12 cm year-1 . This means that relative sea level rose

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at a rate of 2–3 m/100 year, which is higher than the predicted rate of SLR in the 21st century due to global warming. Land subsidence occurred not only onshore but also offshore. The slope of the near-shore zone of the Chao Phraya Delta is very gentle with a gradient of 1 m/km. Therefore, subsidence of 10–20 cm induced an increase in water depth of 10–20% at a point 1 km offshore and an increase of 5–10% 2 km offshore. This increase in water depth is the main cause of the increase in wave energy that resulted in coastal erosion (Saito et al. 2000b). Bangkok has a hot and humid tropical climate; the hottest month is April with average maximum temperatures of 35◦ C and average minimum of 26◦ C; December is the coolest month with average maximum temperatures of 31◦ C and average minimum of 21◦ C. The rainy season is from May to October, and the average annual rainfall in Bangkok is 1500 mm. Floods, caused mainly by upstream inflow and highintensity rainfall, are the most frequent natural disasters in Bangkok, affect a large number of people and cause huge economic damage almost every year. Due to its low elevation, ranging from 0 to 4 m above sea level, the tidal effect is prominent in the Chao Phraya River up to several kilometres inside Bangkok, and that contributes significantly to floods (Engkagul 1993). There are usually two high tides and two low tides per day in the Gulf of Thailand, but these are often asymmetrical with amplitude of 1–2 m. The daily variation of tides is normally from –0.5 to 1.5 m with a peak of 2.5 m recorded in 1995. In the Bangkok area of the Chao Phraya Delta, groundwater extraction during 1960–1994 increased the average relative SLR by 17 mm year-1 due to land subsidence. For Bangkok, the steady rise in sea level poses a threat for the investment, operation and safety levels of the flood-control system, which could have an estimated annual pumping cost of up to US$20 million (Sabhasri and Suwarnarat 1996). The IPCC AR4 has highlighted the grave consequences of SLR, including catastrophic floods to several lowlying coastal cities around the world such as Bangkok (IPCC 2007b).

CASE STUDY ANALYSIS Identification of climate change scenarios: sea-level rise The projected SLR by different general circulation models (GCM) in different parts of the world for the IPCC SRES scenarios (IPCC 2000) varies

significantly. The highest projected rise of global average sea level in the IPCC TAR is 88 cm by the end of the 21st century (Church et al. 2001, IPCC 2001). For analysis of the impacts of SLR on floods in Bangkok, the worst-case scenario was considered, and the analyses were carried out for four time horizons: 2025, 2050, 2075 and 2100 to gain a comprehensive understanding of the impacts in every quarter of the 21st century. Urban growth modelling Bangkok and six of its surrounding provinces, namely: Chachoengsao, Nakhon Pathom, Nonthaburi, Pathum Thani, Samut Prakan and Samut Sakhon, were selected for simulation of the urbanization and population growth, using the AGENT-LUC model for the selected time periods. Based on the availability of past data, 1980 was selected as the base year, and the data of 1990 were used for verification of the model. The spatial simulation of the future population and urbanization was carried out for the IPCC SRES B1 Scenario. The maximum population density was designed as 20 000 people km-2 . The projected urban expansion in 2025, 2050, 2075 and 2100 is shown in Fig. 6. The results show that urbanization in Bangkok would continue to increase rapidly until 2025 (51% growth between 2000 and 2025), and that this would be followed by a significant growth (17%) between 2025 and 2050. The growth would stabilize thereafter (3% for 2050–2075 and less than 1% for 2075–2100). Figure 7 shows the growth of population in Bangkok in the selected years, which features a trend similar to urban growth; however, the urban population growth in Bangkok is expected to exceed the total population growth rates in the region until 2050, and then it will stabilize, as shown Fig. 8. The projection reveals that the population of Bangkok will approximately double in 100 years from the current level of 9.2 million. Flood inundation modelling Data collection, collation and model set-up The data and information required for flood simulations by the modelling tools were collected from various existing sources, including the relevant government departments and regional and global databases produced by various international organizations. The major temporal data sets collected include hourly rainfall data, evaporation data within the study area, and water-level data at the upstream and downstream boundaries of the river. The spatial

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data sets consisted of land-use, topography, soil and river cross-section data. Elevation data were obtained from the HYDRO1K digital elevation model (DEM) of the US Geological Survey (USGS) and land-use data from the USGS global land-use database. Surface roughness coefficients for different grids were assigned based on the land-use types. The flood model was set up for 1 km × 1 km spatial and hourly temporal resolutions. For the river network, only the main stream of the Chao Phraya River was considered, with the hydrological gauging station C22 as the upstream boundary and Pom Phrachul as the downstream boundary nodes (Fig. 5). The river crosssection data, hourly rainfall data for the four gauging stations located within the study area, and hourly water-level data for upstream and downstream boundary stations were obtained from various local sources, including the Bangkok Metropolitan Administration (BMA), the Royal Irrigation Department (RID) and the Port of Thailand Authority. Figure 9 shows the observed hourly water-level data for the upstream and downstream boundary stations in the study area

for the periods of calibration and verification of the model. Calibration and verification of model The model was calibrated and verified for two selected periods of 1995. The calibrated parameters were the Manning’s roughness coefficient in the river and the runoff coefficients for different land-use types. The calibration and verification were performed using the observed water-level data for the C12 waterlevel gauging station in the Chao Phraya River (refer to Fig. 3 for location). The comparison of hourly simulated and observed water levels at C12 for the calibration period is shown in Fig. 10. The simulated water levels matched well with the observed water-level data for the 3-day period of calibration, with a very close match between the simulated and observed peak water levels. In the calibration, emphasis was on obtaining the best match between the simulated and observed peak water levels. Figure 11 shows a comparison of the simulated and observed water levels at the C12 station during the verification

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Gulf of Thailand

S

0

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10

20

30 Kilometers

20

90

18

80

16

70

14

60

12 50 10 40 8 30

6

20

4

10

2 0

Percentage

Population (in Million)

Fig. 7 Projected population within the study area in: (a) 2025, (b) 2050, (c) 2075 and (d) 2100.

0 1980

1990

2000

Sim Total Popln (BKK+5 provinces)

2025 Year

2050

Only Urban Popln

2075

2100

% Urban Popln

Fig. 8 Urban population in Bangkok vs total population in the region (urban population changes 1980, 1990–2100).

Socio-economic impacts of floods in Bangkok

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3.2 2.8 Water Level (m)

2.4 2.0 1.6 1.2 0.8 0.4 0.0 –0.4 –0.8

0

50

100

Upstream Water Lever

150

200 Hour

Downstream Water Lever

Fig. 9 Hourly water levels observed at upstream and downstream boundary nodes (23–31 October 1995). 1.4 Observed WL

1.2

Simulated WL

1

Water Level (m)

0.8 0.6 0.4 0.2 0 –0.2 –0.4 –0.6

1

6

11

16

21

26

31 36 Time (hours)

41

46

51

56

61

66

Fig. 10 Comparison of simulated and observed water levels at station C12 for the calibration period (23–25 October 1995).

period. The simulated water level agrees well with the observed water levels at C12 for the peaks and pattern of flow. Figure 12 shows the scatter plots of the observed and simulated data for this together with the trend line with zero intercept. The overall R2 value is 0.64; the agreements between observed and simulated data are very high above the water level of 0.6 m, as can be clearly seen from Fig. 12. The simulated maximum surface inundation area during the period of verification for the flood event of 1995 is shown in Fig. 13. This shows that the surface inundation depth was the highest at the lowest part of the study area, confirming the observations made by the local government officials. The inundation depths in most of the areas were within 1 m, as mentioned in several news reports on the 1995 floods. However, in the

absence of any recorded ground-observation map or data points of the actual flood inundation depths, the simulated results could not be verified at any point level or for the actual extent. Impacts of SLR on floods For analysis of the impacts of SLR on floods in Bangkok, the river boundary condition data were selected from the baseline flood event of 1995, one of the most catastrophic floods in Bangkok in the last five decades, with the highest recorded flood water levels at different gauging stations along the Chao Phraya River. The projected SLR values for the four selected periods were added to the observed sea-level data during the flood event of 1995 to generate the new downstream boundary conditions for these periods. The peak

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Dushmanta Dutta 1.40

Observed WL Simulated WL

1.20 1.00 Water Level (m)

0.80 0.60 0.40 0.20 0.00 –0.20 –0.40 –0.60

1

6

11

16

21

26

31

36

41

46

51 56 61 Time (hours)

66

71

76

81

86

91

96 101 106

Fig. 11 Comparison of simulated and observed water levels at station C12 for the verification period (26–30 October 1995). 1.30 R2 = 0.635 1.10

0.90

Simulated WL (m)

0.70

0.50

0.30

0.10

–0.10

–0.30

–0.50 –0.50

–0.30

–0.10

0.10

0.30 0.50 Observed WL (m)

0.70

0.90

1.10

1.30

Fig. 12 Scatter plot of observed and simulated water levels at station C12 together with trend line.

upstream discharge recorded at the upstream gauging station (C22) during this period was used as the upstream boundary condition. The highest recorded hourly rainfall data of 1995 (for 4–13 August) in Bangkok were taken as the input rainfall data for the simulation (Fig. 14).

The simulated maximum flood inundation maps for the four selected scenarios are shown in Fig. 15. The simulated results show that almost 48% of the study area would be affected by floods due to 32 cm SLR in 2050 and 69% due to 88 cm SLR in 2100. Table 2 shows the projected areas of inundation under

Socio-economic impacts of floods in Bangkok

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River Study Area Bangkok

Inundation (cm) 10–20 21–50 50–100 101–150 151–200 201–250 251–300 301–350 No Data N E

W S

Gulf of Thailand 0

10

20

30 Kilometers

Fig. 13 Simulated maximum flood inundation extent and depths in 1995. 55.0 50.0 45.0

Rainfall (mm)

40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0

1

16

31

46

61

76

91

106

121

136

151

166

181

196 Hour

Fig. 14 Hourly average rainfall data (4–13 August 1995).

different flood depths as compared to the baseline flood event of 1995. This shows that overall inundation in the study area would increase by 26% due to SLR in 2050 and 81% in 2100. Similarly, the inundation depths would increase greatly in many locations. These results show that SLR would significantly worsen the flooding conditions in Bangkok city by the end of the 21st century. Assessment of socio economic impacts For the assessment of the socio-economic impacts of floods, four main socio-economic aspects were considered, namely: population, buildings, roads and railway infrastructure. The baseline data and information for these four categories were collected at the district level. Secondary data relevant for socio-economic

analysis were collected from the respective government departments and then collated with the model output for scenario analyses. Due to limited availability of data, analysis in the “buildings” category was concentrated on residential buildings only, and the “transportation” category incorporated only main roads and railway network laid within the study area boundary. A set of qualitative indices was developed for the estimation of impacts of floods on four selected economic and social issues, based on the outcomes of a series of questionnaire surveys conducted in two frequently flood-affected districts in Bangkok, namely: Bangkapi and Bengkum (Dutta and Tingsanchali 2003, Dutta and Wongwiwat 2006). Table 3 presents the flood impact indices for buildings, population, roads and railways. Using these indices, and the

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Dushmanta Dutta 2050

2025 River Bangkok Study Area

River Bangkok Study Area Inundation (cm)

Inundation (cm) 10–20 21–50 51–100 101–150 151– 200 201–250 251–300 301–350 No Data N E

W

Gulf of Thailand 0

10

20

10–20 21–50 51–100 101–150 151– 200 201–250 251–300 301–350 No Data N E

W

Gulf of Thailand

S 0

30 Kilometers

2050

10

20

S 30 Kilometers

2100 River

River Bangkok

Bangkok Study Area

Study Area

Inundation (cm) 10–20 21–50 51–100 101–150 151– 200 201–250 251–300 301–350 No Data N E

W

Gulf of Thailand 0

10

20

Inundation (cm) 10–20 21–50 51–100 101–150 151– 200 201–250 251–300 301–350 No Data N E

W

Gulf of Thailand

S 0

30 Kilometers

10

20

S 30 Kilometers

Fig. 15 Simulated flood inundation maps due to SLR by 14 cm (2025), 32 cm (2050), 58 cm (2075), and 88 cm (2100). Table 2 Flood inundation area, in km2 (%), for different scenarios. Inundation depth (cm) 10–20 21–50 51–100 101–150 151–200 201–250 251–300 301–350 Total

Simulated inundation area for different years: 1995

2025

2050

2075

2100

987 (29.6) 188 (5.6) 37 (1.1) 37 (1.1) 24 (0.7) 6 (0.2) 0 (0.0) 0 (0.0) 1279 (38)

1002 (30.1) 304 (9.1) 56 (1.7) 30 (0.9) 24 (0.7) 13 (0.4) 0 (0.0) 0 (0.0) 1429 (43)

970 (29.1) 457 (13.7) 5 (0.15)

1031 (31.0) 663 (19.9) 152 (4.5) 1 (0.03) 39 (1.2) 24 (0.7) 7 (0.2) 0 (0.0) 1917 (57)

1003 (30.1) 1034 (31.1) 163 (5.0) 41 (1.2) 12 (0.36) 48 (1.4) 9 (0.3) 1 (0.03) 2311 (69)

outcomes of flood inundations for the projected SLR and anthropogenic growth, the socio-economic impacts of floods were estimated for the selected periods. The estimated flood impacts on buildings for the selected years are presented in Fig. 16 and Table 4(a). The impacts of floods on the buildings sector are given by the categories “low” (up to 25% damage) and “moderate” (25–50% damage). The results show

44 (1.3) 21 (0.63) 1 (0.03) 0 (0.0) 1611 (48)

that the number of affected buildings in the “low” impact category would increase by 1.5 times in the 75 years from 2025, and the number of buildings in the “moderate” impact category would increase by 30%. The increase is not only the result of the expansion of flood inundation area, but also of the growth of urbanization, as can be seen in Fig. 16: due to the rapid urbanization up to 2050, the rate of increase of impact will be also high until 2050.

Socio-economic impacts of floods in Bangkok

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Table 3 Flood impact indices of buildings, population, roads and railways (A: no impact, B: low impact, C: moderate impact, D: high impact, E: very high impact). Depth of flood (m)

(a) Buildings: 3.50 (b) Population: 3.50 (c) Roads: 3.50 (d) Railways: 3.50

Duration of flood (d) 7

A B B C D

A B C C D

A B C C D

A B C D D

A B C D D

A B C E E

A B C E E

A C C E E

A C C E E

A C D E E

A B C C C

A B C C C

A B C C D

A B C D D

A B C D D

A B C D D

A B C D D

A B C D D

A C D D D

A C D D D

The numbers of flood-affected people in different categories for the selected years are presented in Table 4(b) and Fig. 17. Table 4(b) shows that the number of affected people in all the three categories will increase significantly during the 75-year period of 2025–2100. The affected road lengths in the study area during the selected years are presented in Table 4(c). The total affected road lengths for 2100 are much higher than those for 2025, which may be attributed to larger flood inundation area in 2100 compared to 2025. Finally, the affected lengths of railway in the study area are presented in Table 4(d). The total affected lengths of railway in future scenarios increase significantly, while the affected railway lengths under the “high impact” category remain constant. CONCLUSIONS The paper has introduced a comprehensive and integrated tool to analyse the socio-economic impacts of floods due to SLR on coastal cities along with a case study application in Bangkok. The simulated results show that, for rainfall and upstream and downstream river flow conditions the same as the baseline flood

event of 1995, up to 48% of the study area is likely to be affected by extreme floods in 2050 and 69% of the area in 2100 under the worst-case projected scenario of SLR. The overall inundation area in Bangkok is likely to increase by 26% in 2050 due to SLR of 32 cm and by 81% in 2100 due to SLR of 88 cm compared to the area inundated by the flood event of 1995. Similarly, the inundation depths would increase significantly in many locations. These results show that SLR would have serious impacts on flooding conditions in Bangkok City, and that, in turn, will have high socio-economic consequences. The outcomes of the socio-economic impacts analysis show that the number of people and buildings and the lengths of roads likely to be affected by floods would increase significantly due to SLR. The number of flood-affected buildings (with possible damage less than 25%) is likely to increase by 1.5 times in the 75-year period of 2025–2100. Similarly, the number of affected buildings with damage of 25–50% can increase by 30% in the same period. Although the flood model has been calibrated and verified for the baseline flood event of 1995, it may be possible that the severity of the simulated flood magnitudes due to SLR are overestimated to a certain extent due to the exclusion of some of the existing and planned

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Dushmanta Dutta

River

River

Study Area

Study Area

Bangkok

Bangkok

Urban Area

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Impact Index

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Impact on buildings in 2100

Fig. 16 Flood impacts on buildings in due to SLR in 2025, 2050, 2075 and 2100. Table 4 Summary of the affected buildings, population, roads and railways. Impact index

(a) Buildings: Low Moderate (b) Population: Low Moderate Very high (c) Roads: Low Moderate (d) Railways: Low Moderate High

No. of affected buildings in study area: 2025

2050

2075

2100

1 145 980 82 008

1 451 322 97 071

1 629 826 105 328

1 791 502 110 577

5 720 020 134 405 293 047

6 804 316 227 777 311 898

7 632 711 340 657 312 903

8 969 668 446 810 329 816

569 742 65 522

624 118 80 041

695 961 110 741

837 332 133 836

89 050 – 1 430

95 627 – 1 430

105 913 – 1 430

122 016 966 1 430

structural flood control measures, as well as the micro-details of the topography in the model set-up. The outcomes of the case study present a comprehensive picture of floods due to extreme

hydro-climatic conditions and their socio-economic impacts in Bangkok City under the projected worstcase SLR scenario in every quarter of the 21st century. The integrated tool enabled both projected SLR and

Socio-economic impacts of floods in Bangkok

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River

River

Study Area

Study Area

Bangkok

Bangkok Urban Area

Urban Area

Impact Index

Impact Index

Low Impact

Low Impact Moderate Impact

Moderate Impact

Very high Impact

Very high Impact N

N W

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Impact on population in 2025

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Impact on population in 2075

0

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Impact on population in 2100

Fig. 17 Flood impacts on population due to SLR in 2025, 2050, 2075 and 2100.

anthropogenic development to be taken into account in the flood inundation analysis. The outcomes of the case study demonstrate that the integrated tool can be utilized effectively to develop a comprehensive understanding of the socio-economic impacts of floods under the projected SLR scenario in coastal cities for long-term flood disaster mitigation planning. Acknowledgements The author gratefully acknowledges the financial support received from the APN Project Grant 2004-CB01-NSY for conducting this study, and the Asian Institute of Technology for hosting the project. REFERENCES Antonov J.I., Levitus, S. and Boyer, T.P., 2005. Thermosteric sea level rise, 1955–2003. Geophysical Research Letters, 32, L12602. Bhuiyan, J.A.N, Dutta, D., Das Gupta, A. and Babel, M.S., 2005. Flood simulation and its socio-economic impact analysis in Meghna Delta, Bangladesh under climate change conditions. In: Proceedings of the International symposium on Floods in

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Thammasittirong ,1999. Operational flood forecasting for Chao Phraya River Basin, Thesis WM-99-23, AIT, Thailand. van Dam, J.C., ed., 2003. Impacts of climate change and climate variability on hydrological regimes, 1st edn, Cambridge: Cambridge University Press. van der Meij, J.L. and Minnema, B., 1999. Modelling of the effect of a sea-level rise and land subsidence on the evolution of the groundwater density in the subsoil of the northern part of The Netherlands. Journal of Hydrology, 226, 152–166. Vongvisessomjai, S. et al., 1996. Coastal erosion in the Gulf of Thailand. In: J.D. Milliman and B.U. Haq, eds. Sea-level rise and coastal, subsidence, Dordrecht: Kluwer Academic, 131–150. Watson, R.T., 2001. Climate change 2001. Presentation to the sixth conference of Parties to the United Nations Framework Convention on Climate Change, 19 July 2001.