ABSTRACT
The Bangkok Metropolitan Administration's expansion, driven by economic growth, includes extending Metro systems and altering land use. This transformation, especially noticeable near these routes, has led to shifts in land cover – reduced green spaces, increased impervious surfaces, and decreased water storage. These changes, coupled with denser population and higher waste-generation, have compromised drainage efficiency, amplifying flood vulnerability. This study focuses on Pink Line's 34.5-kilometre stretch from Khae Rai intersection along Tiwanon Road to Min Buri, aiming to evaluate Flood Vulnerability Index (FVI) near the Metro line, identify critical influencing factors and propose mitigation strategies. The analysis divided the study area into five zones based on characteristics, evaluating FVI using population density (PO), drainage efficiency (DE), impervious ratio (IR), garbage management (GB), and pond area ratio (PA). FVI values ranged from 0.41 to 0.55. Sensitivity analysis showed minor FVI impacts at Lat Pla Khao and Ram Intra km.4 stations due to DE and IR, with reduced FVI at Khu Bon and Eastern Ring Road stations from PA and GB improvements. PA and IR enhancements consistently lowered FVI, while fluctuations were observed with GB and DE changes, with notable impacts at Min Buri Market Station. Understanding factor sensitivity aids in planning local mitigation-strategies.
HIGHLIGHTS
Flooding is the number one risk in Thailand.
Water disaster risk statistics show that the proportion of the population in Thailand that is at a point of concern that they will be at risk from disasters is as high as 34%.
INTRODUCTION
Floods present significant global challenges, damaging lives, properties, and national economies (UNESCO & UN-Water 2020). They are widely regarded as among the world's most critical natural disasters, impacting millions annually (World Bank Report 2012). Over the past few decades, regions globally, including Italy, Germany, China, Bangladesh, and Thailand, have witnessed the effects, with over 10 million people affected thus far. These regions have faced increased severity of flooding due to factors such as climate change, rapid urbanization, and changes in land use.
The focus on flood risk highlights the uneven distribution of climate change impacts, necessitating adaptation to human-induced alterations, particularly for urban poor communities (Gran Castro & Ramos De Robles 2019). This exposure is especially acute regarding land use practices, which significantly contribute to flood-related damage. Urban areas, as economic centers with dense populations and diverse activities, face heightened vulnerability in this regard. This emphasis underscores the importance of addressing land use practices, especially in metropolitan areas serving as economic hubs with dense populations and diverse activities, thus amplifying their vulnerability.
Identifying flood risks in vulnerable areas is imperative for effective disaster management, especially in urban contexts (Gran Castro & Ramos De Robles 2019), a topic extensively explored in disaster risk science (Gain et al. 2015). The United Nations outlined flood vulnerability by encompassing various factors, including human settlement conditions, infrastructure, policy, social imbalances, and economic patterns (Kumar & Bhattacharjya 2020). To assess flood vulnerability, the Flood Vulnerability Index (FVI) has been developed, incorporating physical, climate, social, ecological, economic, and urban factors. Researchers such as Balica et al. (2012) have classified these factors into indicators: exposure (E), susceptibility (S), and resilience (R). Researchers have proposed innovative approaches to assess flood vulnerability, such as the Climate Change Flood Vulnerability Index (CCFVI) introduced by Balica et al. (2012). Studies, including Waghwala & Agnihotri (2019) in Surat City, India, a social–economic and environmental assessment of urban sub-catchment flood risk using a multi-criteria approach: in Mumbai City, India (Pathak et al. 2020), and Udnoon et al. (2021) in Bangkok, Thailand, have quantified land use and land cover (LULC) changes, evaluating their impact on flood risks.
As the capital city of Thailand, Bangkok is a central hub for governmental, educational, and transportation institutions. To cope with the challenges arising from its rapid expansion, Bangkok has focused on developing its rail infrastructure (Siridhara 2021). The Bangkok rail system has expanded considerably over the last two decades, increasing its coverage from 13% in 2017 to 33% in 2022, with further expansions anticipated until 2042. The operational Metro rail system in the Bangkok Metropolitan Area spans 210.94 km, encompassing 11 routes. The initial development began with the inauguration of the first BTS Skytrain lines, ‘Sukhumvit–Mochit–On Nut’ and ‘Silom Line, National Stadium–Saphan Taksin,’ in December 1999, covering an initial distance of 23.5 km. Subsequent expansions have extended the Metro rail network to neighboring provinces such as Samut Prakan, Nonthaburi, and Pathum Thani. A significant ongoing project is the MRT Pink Line, which aligns with the city's master plan for rail transport. It entails the construction of a secondary straddle monorail line, elevated throughout its route. The line spans 34.5 km from Khae Rai intersection along Tiwanon Road, Chaengwattana Road, Lak Si Intersection Ram Intra to Min Buri intersection. It is evident that the project has led to changes in the LULC of the area, raising concerns about its vulnerability to flooding. Therefore, this study aims to evaluate the FVI according to the existing conditions and conducts sensitivity analyses to assess the degree of FVI changes due to exposure, susceptibility, and resilience concerns. Further, recommendations will be proposed.
Stations along the Pink Line Metro Rail and the selected stations for the study (shown in pink letters).
Stations along the Pink Line Metro Rail and the selected stations for the study (shown in pink letters).
METHODS OF THE STUDY
To evaluate the impact of the Metro rail extension on flood risk in the vicinity of its route, it is essential to analyze the FVI both pre- and post-construction. Data pertaining to critical local characteristics, such as LULC permeability, drainage capacity, runoff storage, as well as spatial and temporal variability, were gathered from authoritative sources, including the Department of Public Works, Town & Country Planning (Department of Public Works and Town & Country Planning (DPT) (2023)), and BMA. Following this, a sensitivity analysis was conducted to ascertain the magnitude of FVI alteration and to recommend regionally suitable flood mitigation strategies.
Data collection
The data collection process entailed gathering pertinent information encompassing rainfall patterns, population density (PO), drainage efficiency (DE), impervious area ratio (IR), pond storage capacity, and garbage collection. These criteria were determined based on area-specific characteristics outlined by Balica et al. (2012). The primary influencing factors contributing to spatial vulnerability were categorized as follows:
– Rainfall: Annual average rainfall data were sourced from the Thai Meteorological Department (TMD) (Meteorological Department of Thailand 2023), serving as a climatic indicator highlighting potential flooding events.
– PO: Calculated as individuals per square kilometre, PO provided insights into risk exposure. Data were obtained from the Department of Public Works and Town & Country Planning (DPT) (2023).
– DE: Expressed as a percentage, DE gauged the drainage system's effectiveness in managing rainwater absorption. Factors considered included service life, land subsidence conditions, pipe sag, and dredging frequency. Data sources encompassed the Department of Drainage and Sewerage (DDS) (2023).
– IR: Defined by the proportion of impermeable surfaces such as residential, commercial, and road areas to the total land area, the IR was assessed using aerial photographs and Geographic Information System (GIS) techniques.
– Pond Storage Area Ratio: This factor quantified the relationship between pond storage capacity and area, indicating the proportion of storage available. Information was referenced from the Department of Drainage and Sewerage (DDS) (2023).
– Garbage Collection: Reflecting social behaviors, garbage collection data provided insights into urban residents' habits. Accumulated roadside garbage could potentially block drainage sewer manhole inlets. The data source is the Bangkok Metropolitan Administration (BMA) (Bangkok Metropolitan Administration 2023).
LULC changes analysis
This study focuses on the Pink Line Project (mrta-pinkline.com 2022), which took place from April 2020 to July 2023, covering a construction period of 39 months and involving 30 stations, spanning a total distance of 34.5 km. The project timeline encompassed several crucial stages:
– 2012–2013: Feasibility and environmental impact assessment (EIA) studies were conducted.
– 2013–2015: Initiatives were undertaken to explore land ownership.
– 2016–2019: Area management and expropriation activities were carried out in overlapping project zones.
– April 2020–Present: Construction commenced, albeit delayed from the initially planned start in 2019.
The study hinged on LULC data spanning 2013, 2019, and 2022, offering insights into historical urban expansion patterns and facilitating predictions about future urban growth. These datasets, derived from aerial photographs, surveys, and assessments conducted by the Department of Public Works and Town & Country Planning (Department of Public Works and Town & Country Planning (DPT) (2023)), were pivotal in discerning past trends and forecasting potential urban developments. Aerial photographs and GIS techniques were employed in the study, focusing on five specific stations, particularly the Lak Si Station, an intersection of the Green Line at Wat Phra Si Mahathat Station. The analysis divides the study area into five distinct zones based on area characteristics.
The parameters for analysis were selected based on the Bangkok City Plan for 2013, 2019, and 2022 from the Department of Public Works and Town & Country Planning. The selected variables for each sub-area were:
– Flooding behaviors: Examination of prominent and side streets prone to flooding,
– Increase in impervious surface ratio: Assessment of the expansion rate of impervious surfaces,
– Characteristics of LULC changes: Analysis of alterations from 2013, 2019, and 2022 using aerial photographs,
– PO: Evaluation of the population ratio per unit area in each sub-area,
– Pond area ratio (PA): Examination of the ratio of pond areas to the total area under focus.
FVI of pre- and post-Metro line construction and sensitivity analysis
The evaluation of flood vulnerability has prompted the development of the FVI, which integrates various factors such as physical, climatic, social, ecological, economic, and urban elements. Balica et al. (2012) have categorized these factors into indicators known as exposure (E), susceptibility (S), and resilience (R). However, contextual intricacies can make defining specific components within these categories complex. For instance, public awareness may blur the lines between susceptibility and resilience, creating nuanced relationships among these indicators. Advanced methodologies like fuzzy inference, as demonstrated by Udnoon et al. (2021), have been employed to classify factors such as rainfall, existing drainage performance, PO, and traffic to assess the FVI.
Description and range of the FVI
Designation . | Index value . | Description . |
---|---|---|
Very small vulnerability | ≤0.10 | Very small vulnerability to floods. The area recovers fast, flood insurance exists, and investment in the area is high. |
Small to moderate vulnerability | 0.10–0.25 | Slight to moderate vulnerability to floods. The area is vulnerable to floods, and the recovery process is fast. |
Moderate vulnerability | 0.25–0.50 | Moderate vulnerability to floods. Measures should be taken to reduce vulnerability; the area can recover in months. |
High vulnerability | 0.50–0.75 | High vulnerability to floods. Measures should be taken with action plans to manage floods to reduce risks; the recovery process is prolonged, low resilience. |
Very high vulnerability | 0.75–1.00 | Very high vulnerability to floods. The area is very vulnerable to floods, and the recovery process is prolonged. The area would recover in years. Budget is scarce. |
Designation . | Index value . | Description . |
---|---|---|
Very small vulnerability | ≤0.10 | Very small vulnerability to floods. The area recovers fast, flood insurance exists, and investment in the area is high. |
Small to moderate vulnerability | 0.10–0.25 | Slight to moderate vulnerability to floods. The area is vulnerable to floods, and the recovery process is fast. |
Moderate vulnerability | 0.25–0.50 | Moderate vulnerability to floods. Measures should be taken to reduce vulnerability; the area can recover in months. |
High vulnerability | 0.50–0.75 | High vulnerability to floods. Measures should be taken with action plans to manage floods to reduce risks; the recovery process is prolonged, low resilience. |
Very high vulnerability | 0.75–1.00 | Very high vulnerability to floods. The area is very vulnerable to floods, and the recovery process is prolonged. The area would recover in years. Budget is scarce. |
Source: developed from Balica et al. (2012).
Fuzzy inference system workflow. Source: developed from Fuzzy Logic Toolbox in MATLAB (Jang & Gulley 1995).
Fuzzy inference system workflow. Source: developed from Fuzzy Logic Toolbox in MATLAB (Jang & Gulley 1995).
To assess the impact of the Pink Line Metro Rail on LULC changes and their consequent effects on flood vulnerability in the surrounding areas, FVI evaluations were conducted based on conditions observed in 2018, 2019, and 2022 at five Pink Line Metro Rail stations: Lat Pla Khao, Ram Intra km.4, Khu Bon, Eastern Ring, and Min Buri Market stations. Fuzzy inference techniques were employed for these assessments. The parameters considered in the evaluation included PO, DE, IR, PA, and garbage management (GB). Furthermore, a sensitivity analysis was performed to determine the influence of various factors. During this analysis, a constant rainfall intensity of 76 mm/h, corresponding to the designed rainfall for the Bangkok Metropolitan Administration (BMA) drainage system, was used across all scenarios, with variations only in the parameters under consideration.
Scenario analysis based on spatial constraints
Upon determining the sensitivity of flood vulnerability to parameter variations and their respective ranges, the subsequent phase of this study entails conducting experiments involving parameter adjustments while considering spatial constraints and the practicality of proposed measures.
This phase is divided into five cases, each serving distinct investigative purposes. Cases 1 through 4 aim to identify optimal measures for each area, while the fifth case delves into assessing the areas' vulnerability to flooding under altered rainfall conditions due to climate change. It is assumed throughout the analysis that Bangkok's garbage collection efficiency remains at 100%, and the PO values from actual 2022 data, as shown in Table 2, are utilized, recognizing the inherent challenge and lack of control in modifying population (PO).
– Case 1: Existing FVI – The assumed rainfall aligns with the 76 mm/h design value for Bangkok's drainage system, with PA, IR, and DE conditions set based on actual 2022 data from the area.
– Case 2: Varying PA – The assumed rainfall aligns with the 76 mm/h design value for Bangkok's drainage system, adjusting PA conditions while keeping IR and DE constant as the actual values in 2022.
– Case 3: Varying IR – Similar to Case 2, the assumed rainfall corresponds to Bangkok's drainage system design parameters, while IR conditions are modified, with PA and DE maintained constant as the actual values in 2022.
– Case 4: Varying DE – Similar to cases 2 and 3, the assumed rainfall corresponds to Bangkok's drainage system design parameters, while DE conditions are modified, with PA and IR maintained constant as actual values in 2022.
– Case 5: FVI under altered rainfall intensity – Parameters including PO, DE, IR, and PA are set according to actual 2022 data for the area, with rainfall intensity manipulated to evaluate the area's vulnerability under various rain scenarios.
Spatial and temporal changes of parameters of concern around five stations during each stage of the Pink Line project construction
Stations . | Area . | 2013–2018 Feasibility study phase . | 2018–2019 Site preparation phase . | Apr. 2020–Jul. 2022 Construction phase . | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PO . | IR . | DE . | PA . | PO . | IR . | DE . | PA . | PO . | IR . | DE . | PA . | ||
sq. km . | ![]() | (%) . | (%) . | (%) . | ![]() | (%) . | (%) . | (%) . | ![]() | (%) . | (%) . | (%) . | |
Lat Pla Khao | 18.41 | 5,314.63 | 75 | 77 | 1.73 | 4,874.44 | 88 | 77 | 1.73 | 4,808.92 | 97 | 77 | 1.56 |
Ram Intra km.4 | 23.72 | 3,909.56 | 79 | 77 | 1.40 | 4,117.64 | 88 | 77 | 1.40 | 4,118.86 | 92 | 77 | 1.35 |
Khu Bon | 13.06 | 3,781.37 | 69 | 64 | 5.57 | 3,752.35 | 77 | 64 | 5.57 | 3,715.92 | 81 | 64 | 5.57 |
Eastern Ring | 14.45 | 2,840.40 | 65 | 64 | 5.08 | 3,274.48 | 77 | 64 | 5.08 | 3,259.40 | 89 | 64 | 5.08 |
Min Buri Market | 28.46 | 3,357.71 | 48 | 58 | 1.09 | 3,408.83 | 51 | 58 | 1.09 | 3,383.18 | 61 | 58 | 1.09 |
Stations . | Area . | 2013–2018 Feasibility study phase . | 2018–2019 Site preparation phase . | Apr. 2020–Jul. 2022 Construction phase . | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PO . | IR . | DE . | PA . | PO . | IR . | DE . | PA . | PO . | IR . | DE . | PA . | ||
sq. km . | ![]() | (%) . | (%) . | (%) . | ![]() | (%) . | (%) . | (%) . | ![]() | (%) . | (%) . | (%) . | |
Lat Pla Khao | 18.41 | 5,314.63 | 75 | 77 | 1.73 | 4,874.44 | 88 | 77 | 1.73 | 4,808.92 | 97 | 77 | 1.56 |
Ram Intra km.4 | 23.72 | 3,909.56 | 79 | 77 | 1.40 | 4,117.64 | 88 | 77 | 1.40 | 4,118.86 | 92 | 77 | 1.35 |
Khu Bon | 13.06 | 3,781.37 | 69 | 64 | 5.57 | 3,752.35 | 77 | 64 | 5.57 | 3,715.92 | 81 | 64 | 5.57 |
Eastern Ring | 14.45 | 2,840.40 | 65 | 64 | 5.08 | 3,274.48 | 77 | 64 | 5.08 | 3,259.40 | 89 | 64 | 5.08 |
Min Buri Market | 28.46 | 3,357.71 | 48 | 58 | 1.09 | 3,408.83 | 51 | 58 | 1.09 | 3,383.18 | 61 | 58 | 1.09 |
RESULTS AND DISCUSSION
Spatial and temporal LULC changes
Based on the land use statistics for Bangkok Metropolitan Administration (BMA), in 2019, the distribution of land was delineated as follows: community and buildings occupied 659,114 rai (1,055 km2), comprising 67.36%; agricultural land covered 231,441 rai (370 km2), representing 23.67%; forested areas spanned 1,582 rai (3 km2) (0.16%); water bodies encompassed 28,821 rai (46 km2) (2.96%); and miscellaneous regions accounted for 57,305 rai (92 km2) (5.85%) (source: Department of Land Development). The new Bangkok city plan has introduced strategic modifications to transform Bangkok into a compact city. These alterations include relaxing land-zoning regulations in previously restricted areas and fostering diverse and extensive property developments in midtown and suburban regions. Consequently, these changes are anticipated to enhance the variety and density of developments across various locations. According to the Department of Lands, designated rural and agricultural zones will be reduced by 23.73%, and rural and agricultural conservation zones will see a reduction of 64.21% (source: Department of Land Development).
Considering the Pink Line project construction stage, as previously mentioned in Section 2.2, this research centered on LULC data of 2013, 2019 as the pre-construction stage, and 2022 as the post-construction stage. The FVIs of the five selected stations, Lat Pla Khao, Ram Intra km.4, Khu Bon, Eastern Ring, and Min Buri Market stations, were evaluated. The parameters under consideration encompass the PO, DE, IR, PA ratio, and GB across different years, reflecting notable changes. This summary is presented in Table 2 as follows.
Physical characteristics of land use changes in the five stations are:
1. Lat Pla Khao Station:
– Population Decrease: 9,308 people (9.15%)
– Household Increase: 9,553 households (18.71%)
2. Ram Intra km.4 Station:
– Population Increase: 4,964 people (5.35%)
– Household Increase: 10,228 households (22.88%)
– Land-use change characteristics: Expansion of sub-streets into residential areas such as dormitories and apartments. Surface runoff increased, and overwhelmed drainage systems caused flooding during the rainy season.
3. Khu Bon Station:
– Population Decrease: 833 people (1.73%)
– Household Increase: 3,304 households (23.53%)
4. Eastern Ring Station:
– Population Increase: 6,054 people (14.75%)
– Household Increase: 3,714 households (25.17%)
– Land-use change characteristics: High-rise buildings, commercial expansions like department stores and malls, transforming vacant land into residential and commercial spaces.
5. Min Buri Market Station:
– Population Increase: 725 people (0.76%)
– Household Increase: 8,052 households (19.80%)
– Land-use change characteristics: Transformation from a community area into high-rise condos and commercial buildings along the electric train lines. Expectations for future developments include tall buildings, offices, and shopping centers.
It is observable that the efficiency of the drainage system and the proportion of area suitable for water retention remain consistent across the three time-periods. Conversely, areas impervious to water, such as concrete-covered surfaces, show a gradual increase corresponding to the project's construction phases. On the other hand, the PO value fluctuates without a discernible pattern, which could have implications for the FVI of each area in every period, as described in Section 3.2.
Pre and post-Pink Line construction FVI
Spatial and temporal changes of the FVI at five stations along the Pink Line Metro.
Spatial and temporal changes of the FVI at five stations along the Pink Line Metro.
Sensitivity analysis
The parameters used to evaluate FVI consist of four manageable variables: PO, DE, IR, and PA. Therefore, to assess the sensitivity of FVI values, four cases were studied, with details as follows.
Sensitivity analysis of PO
Results from the sensitivity analysis of the FVI on changes in population density (PO).
Results from the sensitivity analysis of the FVI on changes in population density (PO).
Sensitivity analysis of DE
Results from the sensitivity analysis of the FVI on changes in drainage efficiency (DE).
Results from the sensitivity analysis of the FVI on changes in drainage efficiency (DE).
Sensitivity analysis of IR
Results from the sensitivity analysis of the FVI on changes in impervious ratio (IR).
Results from the sensitivity analysis of the FVI on changes in impervious ratio (IR).
It is noteworthy that all stations exhibit nearly identical FVI values. This consistency underscores the importance of including this phase in the fuzzy inference process for further analysis and decision-making regarding flood vulnerability.
Sensitivity analysis of PA
Results from the sensitivity analysis of the FVI on changes in pond storage ratio (PA).
Results from the sensitivity analysis of the FVI on changes in pond storage ratio (PA).
Scenario analysis based on spatial constraints
The sensitivity analysis of FVI, conducted by adjusting the manageable parameters PO, DE, IR, and PA, revealed unique sensitivities for each parameter across different areas. Moreover, specific parameters indicated thresholds that signify limitations in reducing vulnerability to flooding. This emphasizes the importance of spatially considering preventive flood-relief measures and considering the constraints imposed by each parameter. During this phase, the environmental context of each area is carefully considered.
The scenario analysis of each station was conducted for the five cases, as mentioned in Section 2.4. The results are described below.
Scenario 1: Lat Pla Khao Station
The vicinity surrounding Lat Pla Khao Station, characterized by a PO of 4,808.92 cap/sq.km, exhibits an IR of 97%, DE of 77%, and PA of 1.56%. At a rainfall intensity of 76 mm/h, the FVI was estimated at 0.55. The sensitivity analysis of these parameters revealed that FVI begins to decline when DE surpasses 70% and IR falls below 50%, and exhibits sensitivity to PA variations.
Scenario results for the Lat Pla Khao Station: (a) FVI changes due to %PA variation, (b) FVI changes due to %DE variation, (c) FVI changes due to %IR variation, (d) FVI changes due to rainfall variation.
Scenario results for the Lat Pla Khao Station: (a) FVI changes due to %PA variation, (b) FVI changes due to %DE variation, (c) FVI changes due to %IR variation, (d) FVI changes due to rainfall variation.
Nevertheless, under extreme rainfall due to climate change surpassing design thresholds, the area faces heightened vulnerability to flooding. This is evidenced by the increase in FVI from 0.55 to 0.64, as demonstrated in Figure 9(d).
Scenario 2: Ram Intra km.4 Station
The vicinity surrounding Ram Intra km.4 Station, characterized by a PO of 4,118.86 cap/sq.km, exhibits an IR of 92%, DE of 77%, and PA of 1.35%. At a rainfall intensity of 76 mm/h, the FVI was estimated at 0.55. The sensitivity analysis of these parameters revealed that FVI begins to decline when DE surpasses 70% and IR falls below 50%, and exhibits sensitivity to PA variations.
Scenario results for the Ram Intra km.4 Station: (a) FVI changes due to %PA variation, (b) FVI changes due to %DE variation, (c) FVI changes due to %IR variation, (d) FVI changes due to rainfall variation.
Scenario results for the Ram Intra km.4 Station: (a) FVI changes due to %PA variation, (b) FVI changes due to %DE variation, (c) FVI changes due to %IR variation, (d) FVI changes due to rainfall variation.
However, during intense rainfall events caused by climate change exceeding planned thresholds, the region experiences increased susceptibility to flooding. This is substantiated by the rise in FVI from 0.55 to 0.71, as illustrated in Figure 10(d).
Scenario 3: Khu Bon Station
In the vicinity surrounding Khu Bon Station, characterized by a PO of 3,715.92 cap/sq.km, notable features include an IR of 81%, a DE of 64%, and a PA of 5.57%. The FVI calculated under a rainfall intensity of 76 mm/h stood at 0.42. The sensitivity analysis conducted on these parameters revealed that FVI exhibits a decreasing trend when DE exceeds 70% and IR falls below 50%, and it demonstrates sensitivity to variations in PA.
Scenario results for the Khu Bon Station; (a) FVI changes due to %PA variation, (b) FVI changes due to %DE variation, (c) FVI changes due to %IR variation, (d) FVI changes due to rainfall variation.
Scenario results for the Khu Bon Station; (a) FVI changes due to %PA variation, (b) FVI changes due to %DE variation, (c) FVI changes due to %IR variation, (d) FVI changes due to rainfall variation.
In the event of climate change leading to an escalation in rainfall intensity from 76 to 200 mm/h, it would result in an elevation of the FVI value for the area from 0.42 to 0.52, as demonstrated in Figure 11(d).
Scenario 4: Eastern Ring Station
In the vicinity surrounding Eastern Ring Station, which boasts a PO of 3,259.40 cap/sq.km, notable features include an IR of 89%, a DE of 64%, and a PA of 5.08%. The FVI, calculated under a rainfall intensity of 76 mm/h, was recorded at 0.41. An analysis of these parameters revealed that FVI exhibits a decreasing trend when DE exceeds 70% and IR falls below 50%, and it shows sensitivity to variations in PA.
Scenario results for the Eastern Ring Station: (a) FVI changes due to %PA variation, (b) FVI changes due to %DE variation, (c) FVI changes due to %IR variation, (d) FVI changes due to rainfall variation.
Scenario results for the Eastern Ring Station: (a) FVI changes due to %PA variation, (b) FVI changes due to %DE variation, (c) FVI changes due to %IR variation, (d) FVI changes due to rainfall variation.
In the climate change scenario causing a rise in rainfall intensity from 76 to 200 mm/h, the FVI value for the area would increase from 0.42 to 0.52, as demonstrated in Figure 12(d).
Scenario 5: Min Buri Market Station
In the vicinity surrounding Min Buri Market Station, where the PO stands at 3,383.18 cap/sq.km, notable characteristics include an IR of 61%, a DE of 58%, and a PA of 1.09%. The FVI, assessed under a rainfall intensity of 76 mm/h, was documented at 0.48. An examination of these factors revealed a declining trend in FVI when DE surpassed 70% and IR dropped below 50%, and the index exhibited sensitivity to PA variations.
Scenario results for the Eastern Ring Station and Min Buri Market Station. (a) FVI changes due to %PA variation. (b) FVI changes due to %DE variation. (c) FVI changes due to %IR variation. (d) FVI changes due to rainfall variation.
Scenario results for the Eastern Ring Station and Min Buri Market Station. (a) FVI changes due to %PA variation. (b) FVI changes due to %DE variation. (c) FVI changes due to %IR variation. (d) FVI changes due to rainfall variation.
However, in the climate change scenario leading to increased rainfall intensity from 76 to 200 mm/h, the area's FVI value would rise from 0.48 to 0.60, as depicted in Figure 13(d).
CONCLUSION
This study critically examines the flood vulnerability associated with the extension of the Bangkok Metropolitan Region's Metro rail system, particularly focusing on the Pink Line project's impact. The research is motivated by the escalating global flood risks due to climate change, rapid urbanization, and changes in land use, especially pronounced in metropolitan areas like Bangkok, where growing population densities and economic activities intersect with environmental vulnerabilities. Analyzing the FVI covering exposure, susceptibility, and resilience indicators reveals the dynamic nature of flood vulnerability in five key stations along the Pink Line extension. Post-construction evaluation shows an increasing trend in FVIs, mainly due to expanded impervious surfaces and population dynamics. Sensitivity analyses uncover nuanced interactions among PO, DE, imperviousness, and water retention capacities, emphasizing the delicate balance required between urban development and sustainable flood risk management.
LULC changes significantly influence flood vulnerability, altering surface runoff patterns, drainage capacities, and flood mitigation potentials. Increased impervious surfaces and reduced water-retention areas accentuate flood vulnerabilities, necessitating adaptive strategies integrating green infrastructure, enhanced drainage systems, and community awareness initiatives to mitigate flood risks effectively. The nexus between infrastructure expansion, exemplified by the Metro rail system, and flood vulnerability reveals complex dynamics, with urban transportation development altering land use and influencing hydrological processes. Despite benefits for connectivity and urban development, the Pink Line extension inadvertently contributes to heightened flood risks due to changes in surface permeability, runoff patterns, and localized drainage capacities. These findings highlight the need for integrated planning approaches that reconcile infrastructure expansion with sustainable land use practices and disaster-resilience measures.
The spatial sensitivity analysis and several scenario results detail the flood vulnerability across each sub-study area. These findings recommend devising strategies to mitigate and prevent flooding in the respective areas.
Spatial recommendations for flood vulnerability decreasing
Based on the comprehensive analysis of FVIs, sensitivity assessments, and several scenarios across the five Pink Line Metro Rail stations – Lat Pla Khao, Ram Intra km.4, Khu Bon, Eastern Ring, and Min Buri Market stations – a series of local specific recommendations emerge. These recommendations aim to enhance the flood resilience of these areas while also considering the broader context of climate change and urban development in Bangkok.
Lat Pla Khao Station
Lat Pla Khao Station faces significant challenges due to its high PO and extensive impervious surface ratios. The simulated results for Lat Pha Khao Station indicate that modifying DE and IR had minimal impact on the FVI. Conversely, increasing PA by 15%–100% reduced FVI from 0.55 to 0.42.
To mitigate flood risks in Lat Pla Khao effectively, designated water retention areas or rain gardens should be created to capture and store rainwater during heavy rainfall events and reduce peak flows.
Ram Intra km.4 Station
The simulated results for Ram Intra km.4 Station indicate that modifying DE and IR had minimal impact on the FVI. Conversely, increasing PA by 15%–100% reduced FVI from 0.55 to 0.45, decreasing from 1.83% to 18.48%.
Optimal measures should involve reducing impervious surfaces to approximately 50%, enhancing water retention areas by 50%, and maintaining the current high DE of 77%. These strategies are considered feasible and effective in reducing FVI.
Khu Bon Station
Khu Bon Station requires a harmonious approach integrating urban development and flood resilience. Simulation outcomes for this station showed that changes in PA and IR had a negligible effect on the FVI. In contrast, raising %DE from 64% to 80% resulted in a slight decrease in FVI from 0.42 to 0.38.
Therefore, flood vulnerability mitigation strategies in the Khu Bon Station area should incorporate sustainable urban design principles in future developments, such as using sustainable drainage systems, preserving natural waterways, enhancing water infiltration capacities, and investing in green infrastructure projects that improve water absorption, reduce surface runoff, and promote ecosystem services, such as wetlands restoration and urban green spaces.
Eastern Ring Station
While Eastern Ring Station demonstrates a moderate vulnerability to flooding, flood prevention and relief planning should be integrated to support future growth. The station encompasses commercial and residential areas, necessitating tailored flood mitigation strategies. Analyzing the simulated outcomes for Eastern Ring Station reveals that variations in PA and IR had minimal impact on FVI. However, adjustments to %DE modestly influenced FVI. Specifically, increasing %DE from 64% to 80% decreased FVI from 0.42 to 0.38.
As a result, flood vulnerability mitigation strategies in the Eastern Ring Station area should prioritize enhancing the drainage system's efficiency to 70% and integrating climate-resilient design standards in new construction projects, including sustainable drainage solutions, flood-resistant building materials, and elevated infrastructure designs.
Min Buri Market Station
Min Buri Market Station, characterized by moderate PO and DE challenges, necessitates targeted interventions for improved flood resilience. Analysis of the simulated outcomes for this station reveals notable effects of different parameters on FVI. Augmenting PA from PA + 15% to PA + 100% reduced FVI from 0.45 to 0.30, while increasing %DE from 58% to 75% decreased FVI from 0.47 to 0.41. Conversely, elevating %IR from 60% to 80% raised FVI to 0.48 and 0.61, representing increases of 5.59% and 35.28%, respectively.
The findings highlight the Min Buri area's vulnerability to physical alterations, for example installing green infrastructure features such as swales, detention ponds, and vegetated buffers, conducting regular monitoring and maintenance of drainage systems to address blockages, sedimentation, and infrastructure degradation, ensuring optimal performance and longevity.
In addition to the detailed station-specific recommendations, it is imperative to incorporate climate change projections into flood resilience strategies. The results from scenarios concerning the increasing rainfall intensities indicate the necessity of enhanced infrastructure design standards, integration of climate resilience criteria into urban development plans, and investments in green infrastructure projects to mitigate flood impacts effectively and enhance overall resilience. These detailed recommendations are tailored to each station's specific challenges and vulnerabilities, aiming to enhance flood resilience, promote sustainable development practices, and foster community engagement and preparedness in mitigating flood risks effectively.
ACKNOWLEDGEMENTS
Many thanks to my advisor, Prof. Emer. Dr Suwatana Chittaladakorn and Asst. Prof. Dr Sitang Pilailar, for their advice, encouragement, and support. Without the immensely valuable and motivational feedback at weekly meetings and on multiple drafts, this paper would never have been completed.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.