urban flood case study

Decoding urban flood risk and democratizing risk assessment

Steven rubinyi, ross eisenberg.

Urban areas are increasingly prone to flooding due to climate change, rising urbanization, and inadequate stormwater management.  A recent paper estimated that 1.81 billion people, or 23% of the world population, are directly exposed to a 1-in-100-year flood event, posing significant risk to lives and livelihoods. As climate change further disrupts weather patterns, river flows, and sea levels, it is essential that cities develop strategies and plans to deal with the associated risks. Therefore, assessing urban flood risk is crucial to help ensure the safety of urban residents and prevent flood events from causing costly damage to infrastructure and local economies.

Urban flooding is a highly complicated problem to address, requiring deep technical expertise and complex engagement with many stakeholder groups. Cities often lack the know-how to fully gauge and act on the danger they face. By conducting urban flood risk assessments, local governments can better understand and manage their flood risk. This helps protect the lives, livelihoods, and assets of their communities and plan for long-term changes such as sea-level rise or increased storm frequency. By evaluating the probability of future flooding and the impact of different potential gray and green engineering solutions, such assessments provide valuable information for decision-makers allocating funds and other resources to flood preparedness and resilience programs.

The new Urban Flood Risk Handbook: Assessing Risk and Identifying Interventions , developed by the City Resilience Program at the Global Facility for Disaster Reduction and Recovery, offers practical guidance and best-practice methods for conducting an urban flood risk assessment and appraising options for mitigating flood risk. Designed for project managers, technical practitioners, local stakeholders, and anyone interested in strategic studies of urban flooding, the Handbook is enriched by case studies from six countries. It provides insights to help cities prepare for the increasingly frequent and severe floods around the world. By democratizing access to this highly specialized field for a broader audience, the Handbook also aids in scaling up the practice of high-quality flood risk assessment in cities globally. Key principles the Handbook highlights for developing urban flood risk assessments include:

Defining the aim and scope of the study

• Develop a clear understanding of the aim and scope of the assessment from the start, including defining the intended audience and geographical extent, and involve all relevant stakeholders early on.
• Consider the required effort (in time and cost) relative to the accuracy and resolution needed for the study, recognizing the important tradeoff between the two.
• Understand that the accuracy of the hazard and risk assessment heavily depends on the quality and availability of underlying data. Such data can be expensive and time-consuming to collect or purchase and should be examined as early as possible.
• Centralize stakeholder engagement in the process of assessing flood risk and appraising risk mitigation options to validate results and processes and ensure the uptake and support of the options.

Flood hazard and risk assessment

• Understand the local drivers of flooding – including high river discharges, local rainfall, extreme tides, and cyclone-induced storm surge – and the possibility of their joint occurrence in an urban area.
• Establish robust hazard and risk results sense checks at key stages, conducted by experienced and knowledgeable personnel, especially where adequate calibration or validation is challenging in data-poor environments; if necessary, global data may be used to validate outputs.
• Examine any remaining hazards and risk assessment uncertainties when evaluating intervention options and clearly disclose them when communicating findings.

Evaluation of potential interventions

• Adopt an open-minded and structured approach to consider all potential interventions across the gray-green-blue infrastructure spectrum, as well as non-structural options.
• Evaluate and account for both the direct benefits (e.g., reduced damage, fewer affected people) and the co-benefits (e.g., environmental, economic) of solutions.
• Analyze interventions against various future climate change and socioeconomic scenarios, prioritizing solutions effective across a broad range of potential situations.
• Consider the interaction of neighboring interventions with each other, accounting for the possibility of negative impacts as well as cumulative benefits.
• Assess the potential environmental and social impacts – including resettlement and land acquisition – of identified solutions early in the decision-making process to sidestep potential pitfalls later.

Grounded in detailed technical expertise, the Handbook assists governments and other stakeholders in enhancing the quality and actionability of urban flood risk assessments. Its comprehensive practical guidance and wide application range facilitate scaling in adapting to and mitigating the impact of urban floods, ensuring cities remain resilient in a changing climate.

• Climate Change
• Disaster Risk Management
• Urban Development
• Global Facility for Disaster Reduction and Recovery (GFDRR) 

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Senior Disaster Risk Management Specialist, World Bank

Disaster Risk Management Specialist

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Understanding urban flood vulnerability and resilience: a case study of Kuantan, Pahang, Malaysia

• Original Paper
• Open access
• Published: 14 March 2020
• Volume 101 , pages 551–571, ( 2020 )

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• M. Y. Safiah Yusmah   ORCID: orcid.org/0000-0002-4317-6124 1 ,
• L. J. Bracken   ORCID: orcid.org/0000-0002-1268-5516 2 ,
• Z. Sahdan 3 ,
• H. Norhaslina 1 ,
• M. D. Melasutra 4 ,
• A. Ghaffarianhoseini 1 ,
• S. Sumiliana 5 &
• A. S. Shereen Farisha 1  

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Malaysia is frequently affected by the annual flooding event caused by the seasonal monsoon which accounts for significant losses. Flood risk, exposure and damage potential are increasing, causing the level of poverty and vulnerability to rise. The annual occurrence of the flood hazard has forced residents to prepare beforehand to help them spring back to their daily life faster. This study aimed to investigate and understand the vulnerability and resilience of the victims towards floods in Kuantan, Pahang. A qualitative approach of focus group discussion (FGD) is used to obtain detailed and authentic information. A total of thirty-one (31) participants who were flood victims took part in the FGD. Six groups were formed for the FGD based on different criteria such as gender, age, education background, occupation, monthly income and social class. Each FGD group consisted of four to six participants. When the participants were asked to rank their top five daily challenges, many thought that flooding is not a threat compared to food, because flooding occurs annually and is predictable. The results showed that the participants are well aware of the causes of the vulnerability faced by them due to the flooding event. Reasons highlighted from the results for the flood occurrence are the demography of the area, the location of the houses, the improper and inaccurate information and evacuation plan, the management of the transit centre and the lack of preparation by the community. The participants also thought that poor dissemination of early warning information and flood control infrastructures from the government and other related agencies caused the victims to have insufficient time to prepare for emergencies, hence causing the recovery process to be slower. However, from their hands-on experiences, they were able to put forward suggestions on the resilience towards flood for future references.

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1 Introduction

The rapidly changing climate threatens to increase natural hazards and extreme weather such as floods, cyclones, hurricanes and drought, with floods being the most disastrous, frequent and widespread (Dhar and Nandargi 2003 ). Hydro-meteorological themed disaster has increased around Asia and South-East Asia countries over the last two decades. Countries such as China, India, Bangladesh and Pakistan are known as the supermarket for disaster especially in terms of disastrous floods (James 2008 ). Therefore, due to the global climate change, middle and rapidly developing countries are the victim of the economy as most of the damage caused by the disastrous events occur in poor countries with few assets. The occurrence of flooding are predicted to quadruple by 2080 as sea level is expected to continue rise due to global climate change (Small and Nicholls 2004 ). This is especially frightening for the population living in coastal areas as it is the most populated area in most countries with an estimate of 23% of world population living within 100 km and less than 100 m above sea level (Molua and Lambi 2007 ; Small and Nicholls 2004 ). Urban flooding is well known as it has caused damage and loss of life. Urban flooding inundates land or property in a built environment, particularly in densely populated areas. These floods are usually caused by flash floods, coastal floods or river floods, but urban flooding is often specifically due to poor drainage in urban areas. Urban flooding is often related to global climate change issues because most urban areas are the major contributors to greenhouse gas (GHG) emissions that eventually causes global warming. Over concentrated population, increasing infrastructure and economy cause the sustainability of an urban area to worsen. Over time it has become more challenging for the government and developers to create development plan that balances the demand of urbanization while minimizing the use of natural resources. Urban planners should concern and practice actions against climate-induced disasters (Godschalk 2003 ; Saavedra and Budd 2009 ; Kithia and Dowling 2010 ).

Malaysia, as a South-East Asia country, is located near the equator with climate categorized as equatorial. Equatorial climate is relatively hot and super-humid throughout the year with average rainfall of 250 cm annually (DID, 2007 ). In addition, it is essential to learn that the climates in Peninsular Malaysia differ to East Malaysia where the climate in West Malaysia is directly influenced by the monsoon wind from the northeast and southwest, while in East Malaysia the climate is mostly influenced by maritime weather. A yearly constant cycle of heavy rainfall at the east coast of Peninsular Malaysia and east of Malaysia (Sabah and Sarawak) between November and February are caused by the northeast monsoon wind while rain bearing winds from April to September caused by the southwest monsoon. The amount of rain from the southwest monsoon is lesser than the northeast monsoon that can reach up to 660 mm in 24 h. Annually, the average rainfall in Peninsular Malaysia can reach up to 2420 mm, while in Sabah and Sarawak, the amount of rainfall is more than the Peninsular with 2630 mm for Sabah and 3830 mm for Sarawak (DID 2007 ). According to Chia ( 1971 ), there are two types of rainfalls that cause the flood. They are (1) moderate intensity, long duration rainfall at a wide area and (2) high intensity, short duration localized rainfall. The occurrence of floods in Malaysia can be predicted. Usually, east coast and eastern Malaysia were affected by floods during December to January as the northeast monsoon sweeps, while the west coast of Peninsular is mainly affected in September to November with thunderstorms due to the inter-monsoon period. Generally, in Malaysia, most floods occur due to continuous heavy rainfalls that result in runoff due to the excess of water supplies that surpass the capacities of streams and rivers.

Several major flood events have occurred in Malaysia over the last few decades. For example, a gale force wind period in 1886 caused severe flooding in Kelantan. In 1926, the worst floods in Peninsular Malaysia caused scares among the people as it caused widespread damage to property, mental, physical, infrastructure and agriculture. After the initial days of the flood, projected losses to local business in and around the Klang River valley were estimated at around $12,000 Straits dollars. Even for those who did not suffer major flood damage, all businesses lost several day’s trade as the city stood at standstill. In Pahang, a private railway linking the plantation to mines at Sungei Lembing was partially washed away and trains had to be dug out of the mud afterwards (Williamson 2016 ). Flooding has become a significant yearly event occurrence in Malaysia especially at the end of the year. Most of the flood-prone areas can be found in several states in Peninsular Malaysia such as Kelantan, Johor, Pahang, Perak, Kuala Lumpur and Selangor while for east Malaysia it is Sabah and Sarawak (DID 2007 ). Most of the states in Malaysia are prone to flood risk due to (1) the natural physical topography and drainage, and (2) human geography of settlement and land use. Malaysia in the past is mostly riverine people that choose to inhabit banks and floodplain of the major river such as Pahang river, where most of the settlement is indeed high in flood risk (Chan 2012 ). Most of the floods occur due to the monsoon rainfall and intense rain storms. However, in recent decades, the cause of flood is not only due to natural events; the frequent occurrence of flash flood in cities such as Kuala Lumpur, Selangor and Kelantan was caused by poor drainage and area where rapid urbanization takes place. Chia ( 1971 ) stated several sources that cause flooding in Malaysia, loss of flood storage results from development that extend towards floodplain areas, the increase and rapid urbanisation that cause the rate of runoff to increases, faulty drainage system by the locals and continuous heavy rainfalls that cause the water storage to exceed the capacity of the river.

The annually frequent flood that occurs in Malaysia has taken a toll on the socio-economy in terms of flood damages. The flood damage is on the rise due to the increase in flood risks such as the urban development on flood plain area of major rivers. The damages and the losses caused by the flood can be direct or indirect, where direct flood damage is due to the contact of flood water with the building while indirect flood damage causes loss of work and production that eventually cause the victim to develop stress and suffering (Green et al. 1988 ). The estimated amount of damages due to the flooding is superior in compact and high densities urban areas compared to rural areas. According to Chan ( 1997 ), the chance of extreme flood damage occurrence is high in large urban centers such as Kuala Lumpur and Georgetown, Pulau Pinang. Moreover, aside from damages towards the economy, the flood may give permanent aftermath towards mental health and death. It is not unusual for the flood victims to suffer from trauma and mental health for the rest of their lives. Usually, those with a fragile mental state are more susceptible to mental collapse when they were engaged to the unwonted and wicked situation or events. Women and children are the group of people that easily suffer in a critical event. According to Jamaluddin ( 1985 ), to abate post-traumatic events, the victims themselves need to concur the situation in a more positive and appropriate ways for a chance of quick recovery.

Following the annual flooding event occurrence in Malaysia, the government (including the Drainage and Irrigation Department (DID)) has carried out several positive actions to mitigate the flood problem. These consist of structural measures and non-structural measures (Chan 2015 ). Structural measures focus more on how banks and embankments play their role in controlling flood flows. Example of structural measures taken by the government is the Storm Water Management and Road Tunnel (SMART) in Kuala Lumpur to alleviate flash flood problems that occur when heavy rainfalls hit Kuala Lumpur (Umar 2007 ). An example of non-structural measures relates land use planning and flood forecasting and warning systems to mitigate the impact of flooding, such as the flood forecasting and warning system (DID 1988 ). Most of the flood mitigation projects and actions undertaken by the government were structural measures such as canalization of rivers, raising river embankments and the building of a multi-purpose dam. Yet, the government has been upgrading the Flood Forecasting and Warning System for early warning. This infrastructure had been installed all over Malaysia with 233 telemetric rainfall stations, 190 telemetric water level stations, 256 manual stick gauges, 84 flood warning boards, 217 flood sirens and 9 real-time flood forecasting and warning system in 9 river basins (DID 2007 ). The DID has also taken an initiative to established an Internet-based National Flood Monitoring System ( http://infobanjir.moa.my ) where all the data on rainfall and water level can be accessed by the public. In brief, the flood management activities attempted by the government of Malaysia are (1) the National Resource Study, (2) Development of infrastructure for flood forecasting and warning system, (3) National Flood Monitoring System, (4) Flood Watch and (5) Urban Storm Water Management Manual for Malaysia (Hussaini 2007 ).

Vulnerability and resilience act as a leading tool to quantify and map human aftermath from hazards. In the context of social–ecological systems, resilience refers to the magnitude of disturbance that can be absorbed before a system changes to a radically different state as well as the capacity to self-organize and the capacity for adaptation to emerging circumstances (e.g., Carpenter et al. 2001 ; Berkes et al. 2003 ; Folke 2006 ). Vulnerability, by contrast, is usually portrayed in negative terms as the susceptibility to be harmed. The central idea of the often-cited IPCC definition (McCarthy et al. 2001 ) is that vulnerability is the degree to which a system is susceptible to and is unable to cope with adverse effects (of climate change). According to Proag ( 2014 ) vulnerability is defined as a measure of hazard that compliance with physical, economy and social and the implication that results in the ability to cope with the event occurrence. While the concept of resilience itself has taken two broad forms of (1) hard resilience and (2) soft resilience (Moench 2009 ) where hard resilience is a direct strength when placed under pressure and soft strength is the ability to absorb and recover from the impact of destructive event (Rufat et al. 2015 ). According to Balica and Wright ( 2010 ), resilience is the ability of a system to handle commotions while maintaining the efficiency in social, economic, physical and environment. It is in the nature of human being to become vulnerable when their normal daily activities, facilities and consumption were affected in critical factor from the disaster. Moreover, the demographic characteristic, socioeconomic status and health is the leading driver of vulnerability due to flooding events. Resilience is a system that functions to work in a certain way under normal circumstances. Therefore, resilience is important in several sectors such as technical, political, environment, ecology, economy, legal and in an organization. Variables that usually concerned in measuring the degree and magnitude of vulnerability and resilience from disastrous events is employment, income, health and educational status. Jackson ( 2006 ) stated that resilience, vulnerability and adaptive capacity are inter-related with each other when it comes to natural disaster events. Not only that, it also stated that a community with low vulnerability has the potential to have high resilience. Several other definitions of resilience can be found in Table  1 . Vulnerability deals more with the environmental risk and hazards while for resilience, it deals more with the change and persistence of an ecosystem (Carpenter et al. 2001 ; Gunderson 2000 ). Furthermore, in flood-prone rural areas, the norms of poverty have heightened vulnerability among the poor while in the urban area, the vulnerability is much lower compared to the rural area as more strategies, planning, investment and development were undertaken to curb the problem (resilience). In short, the poor suffer more from hazards compared to the wealthy, although poverty and vulnerability are not always related or in line with each other (Chan and Parker 1996 ). The vulnerability experienced by the poor was due to the lack of opportunity and access to structures of power where knowledge and resources of the hazard or disaster were limited. In addition, aside from the poor, vulnerability is familiar among the lower income groups, Malaysia is dominated by the Bumiputera communities where fatalities were common while low-level vulnerability can be expected in the urban settlement where it is mostly populated by the Chinese and Indians. This is because, back in the past before the Chinese and Indians ethnics populated Malaysia, the Bumiputera that originated from the Peninsular choose to settle in the area near the coastal and major rivers where they can have easy access to food and transportation. Resilience actions need to cultivate and be implemented in order to prevent damages and loss of life. This needs to be one of the main purposes of development rather than a characteristic of a good development (Bene et al. 2012 ). Resilience needs to be applied in urbanization process as urban areas were known to be complicated social ecological systems (Simon 2007 ; Swyngedouw and Heynen 2003 ).

The aim of this paper is to investigate the perceptive of the urban community on the vulnerability of flood. Next, this paper studies on the various suggestion from the local community on future resilience towards flood event. A method of focus group discussion (FGD) was applied in order to obtained detail and compact information from the stakeholder in a different category of people or group of people. From the results, it showed that the urban community was aware of the cause of the vulnerability in their neighbourhood towards the flood event. The data obtained from the FGD were solely from the real-life experience of the stakeholder that were involved in the group discussion. From the discussion, various suggestions have been put forward by the stakeholder on their future resilience actions towards flooding.

2 Materials and method

Information regarding the urban flooding situation and experiences was collected through the qualitative method known as focus group discussions (FGD) that consists of structured discussion that are usually used to obtain in-depth information from a group of people about a specific topic. The group discussion was engaged by the flooding victims themselves; therefore information regarding sensitive topics can be obtained. The district of Kuantan which is located in the state of Pahang was selected as the study area due to the lack of research done in the state compared to other flood-prone states such as Selangor and Kelantan. In this study, the researchers were interested in the vulnerability faced by the flooding victims, annually, in the district of Kuantan and the local resilience measures taken to minimize the natural disasters.

2.1 Study area

Figure  1 shows a map of Kuantan, Pahang, Malaysia. The district of Kuantan is situated in the state of Pahang. Pahang is the third largest state in Malaysia after Sarawak and Sabah. Geographically, Pahang is the biggest state in Peninsular Malaysia and has the longest river in Peninsular Malaysia at 459 km. Since Peninsular Malaysia is affected by two monsoons (northeast and southwest monsoon) and two inter monsoons (Suhaila et al. 2010 ), the Pahang Basin receives a high total of rainfall during the northeast monsoon period that contributes and causes flooding events along the river in the basin (DID 2005 ). Other main sources of flooding in the basin are the extreme increase in river discharge due to the monsoon and the sea waves from the South China Sea. The overflow water results in floods within the basin area which occur yearly, particularly from November to December (Lun et al. 2010 ). Kuantan has a total area of 2453 km 2 and is situated 250 km east from Kuala Lumpur. The monsoon that brought the heavy rainfall in November to December every year is 2.3 times higher than the normal average rainfall. The Kuantan River Basin (KRB) is an important watershed of Kuantan city. The basin starts from Sungai Lembing, passing through Kuantan and finally drains to the South China Sea. Heavy rainfall causes spill over of rivers and flooding in low areas that encompass human activities, both social and economic. In December 2001 to January 2002, Kuantan experienced a massive flood caused by continuous heavy rainfall during the northeast monsoon. Most of the city area was submerged under water when nearby rivers overflowed. This incident affected 18,000 people and 22.94 km 2 of land (EKA 2002 ). Another dreadful flood condition and incident occurred 10 years after the massive flood happened in 2001/2002; an unexpected flood due to continuous rainfall that caused 6000 victims to lose property and assets. Due to the poor drainage system in Kuantan, the intercity roads that connect people from city to city were badly flooded causing hundreds of vehicles to be trapped. The recent flood occurrence in 2013 was caused by the prolonged heavy rainfall, high tides and rapid urbanization process. More development was undertaken in the low-lying area that are easily flooded. The 2013 flood in Kuantan caused 14,044 people to be evacuated from their houses. Furthermore, the flood resulted in major damages towards basic facilities such as electricity, road structures, buildings and personal belongings. These have cost a fortune for the government to repair the damages done by the flood hazard (Jamaludin et al. 2013 ). Aside from heavy rainfall caused by the northeast monsoon, the occurrence of flooding in Kuantan can be due to the rise in temperature that causes heavy rainfall and rise in sea level.

The study area

2.2 Participants

The victims of the flooding hazard were identified in the study area. A total of 31 participants joined the focus group discussion. This method was applied to conceptualize the relationship between vulnerability and resilience of the urban community from people with different backgrounds. Six focus group discussions (FGDs) were formed during this study. The groups were formed to discuss the urban flood vulnerability and resilience based on different criteria of gender, age, education background, occupation, monthly income and social class. Each of the FGD group consisted of four to six participants from various backgrounds. The list of FGD participant for each group is shown in Table  2 which highlights differences in the number of participants in each group due to the difficulty in getting participants despite earlier preparations. Most of the participants involved were selected from the haphazard settlement around Kuantan River, Isap River, Belat River, Pandan River and Galing River. The discussion was participated by the flooding victims with age range from 16 to 69 years old. A wide range of ages helped in widening the answer and opinion on the highlighted issues in the group discussion due to differences in generations and ways of thinking. The group discussion was participated in 16 females and 15 males.

2.3 Data collection

Data collection was carried out in March 2016. Qualitative data collection method was applied to extract ground information on the relationship between vulnerability and resilience of the urban community from different background. The two-qualitative method applied was the focus group discussion (FGD) and field observation. In this study, the semi-conducted interview was the main approach for data collection. FGD is a qualitative method that has been defined as a discussion that has been carefully designed to gain or gather impressions or viewpoints on a defined circle of interest in a non-threatening environment (Kruger 1994 ). Moreover, the group discussion, focuses on perceptions, opinions and the motives underlying their acts and behaviour (Greenbaum 2000 ; Hyden and Bulow 2003 ; Maykut and Morehouse 1994 ). This method was chosen for the study because it is particularly useful for exploring people’s or the victim’s experience and knowledge (Kitzinger 1995 ).

An informed consent agreement was obtained from those who agreed to take part in the discussion, and a suitable time for the discussion was agreed. All the participants in the group discussion were asked to describe their various experiences. At the beginning of each focus group session a moderator introduced themselves and gave a brief explanation about the procedures of the discussion to the participants. Besides conducting the discussion in the group, the moderator helped the participants to focus on the topic discussed. Each focus group session was conducted for no longer than 45 min. The key questions prepared earlier for the moderator to discuss with their respective group are shown in Fig.  2 . All of the conversations that occurred in the discussion group were recorded on video for further analysis.

FGD key questions

2.4 Data analysis

The FGD was video recorded and later analysed using qualitative inductive content analysis, also known as qualitative data analysis. The analysis was carried out in several steps which involved coding, whereby raw data were raised to conceptual level. It is pertinent to analyse data for context as it involves identifying conditions, nature of the situation, circumstances or problems from the participants response. This study analysed the qualitative data by utilizing computer-aided software or computer-aided qualitative data software (CAQDAS), Atlas.ti. Atlas.ti is known for its capability in workbench for qualitative data analysis particularly for audio data. This software analysed and interpreted text and audio using coding and annotating activities. For analysing, the video and audio data were transcribed into word processing documents. Every word, sentence and paragraph needed to be analysed attentively for further interpretation of the data. Therefore, it is important to organize, reduce and describe the data delicately in order to avoid unnecessary mistakes that will affect the results produced. According to Schwandt ( 1997 ), the analysis must be done in a rigorous, systematic, disciplined and imitative manner with the documented methodology. Thereupon, to analyse simply means to break down the data into coding (Miles and Huberman 1994 ) and categories (Dey 1993 ). Initial coding using CAQDAS is time-consuming to ensure that the building of the codes is systematic. As the data are broken up for classification, it is then developed into a concept where connections are made between them to enable new descriptions to be made. Next, once the data were classified, they were checked for regularities, variation and peculiarities in patterns. This helps in identifying potential connections by data linking and associations among the categories. All in all, the most important steps in qualitative analysis are to select a sufficient amount of data in one time and to process the raw data (video interview) into coding (Dey 1993 ) before running it into Atlas.ti software for further analysis and results.

Using constant comparative analysis, data from interview and observation, resulted in several primary categories such as daily life challenges, vulnerability due to the urban flooding and finally the resilience towards flooding. These are presented in order below.

3.1 Top daily life challenges

One of the questions asked during the FGD was how the participants ranked their top daily life challenges including flooding. The results showed that most of the participants rank flooding as the least important challenge because for them, the floods only occur once a year when the northeast monsoon season passes by the east coast of Peninsular Malaysia. On the contrary, food supplies, health, education, economy and social issues are the top daily life challenges, respectively, as all these challenges are applicable on a daily basis. Hence, only when their environment is affected by flood, will it disturb their daily activities. Figure  3 shows the results for top daily life challenges faced by the participants and the comments on the challenges they faced.

Top daily life challenges ranked by the participants

The top daily challenges consist of seven elements: food, flood, health, education, economy, safety and social. The participants were expected to rank their top five daily challenges according to their priorities. Two sets of results were obtained and new challenges were added and discussed. Figure  4 shows the results of the two sets of daily life challenges ranked by the participants. Social and safety were added in the discussion as additional challenges where the participant ascertains that it is important to be included.

Result of top daily life challenges ranked by the participants

Figure shows that from the two sets of results on the daily challenges, the first set shows that food is the priority while the flood was ranked last. However, in the second set of results some participants believed flooding should be ranked first. Those participants who ranked flood first live in areas that are seriously affected by the annual flood. Not only that, they also stated that the flooding event causes them hardships and disturbed their normal daily life. Conversely, those who ranked flood as their last priority in the daily challenges are from areas where the occurrence of flood is predictable and happens once a year; thus they have the opportunity to prepare beforehand. The constant occurrence and the signal given by the nature such as changes of the wind and cloud, and tidal water level at coastal and river basin help the victims to prepare themselves. Garai ( 2017 ) stated that understanding the sign and signal from the changes in nature has helped the people in the past to predict the upcoming flooding event. Moreover, technologies and experts from the meteorology department were able to predict and warn people about the upcoming event. Most of the participants ranked food first because it is important to have continuous supply for survival. Flooding will cause damages to most of the goods including foods. Food stalls, supermarket, mini markets and sundry shops will be closed down due to the flood. This will cause disruption in food supplies; hence, it is important to prepare and stock up the food supplies before the flood occurs. Equally important to food is health. For most of the participants, health is crucial. The impact of health from flooding comes in many forms. According to Rufat et al. ( 2015 ), one-third of the deaths during flood events occur away from the floodwater. Examples of deaths that can occur away from floodwater include deaths from dehydration, stroke, lack of medicine supplies and negligence of health issues prior to flood events (Jonkman et al. 2009 ).

Besides death, flooding can affect the mental health and psychology of a victim. The psychological effects are different according to anxiety and stress, age, gender, previous health condition and recovery duration, effects can usually acute after the event (Stanke et al. 2012 ). Other health issues such as water borne diseases due to contaminated water, malnutrition, fever and other infectious diseases are easily spread during and after the flood when the victims interact with each other at the transit centre. While discussing the top daily challenges, participants highlighted the importance of social and security issues. Social problems involving the teenagers and young adults usually increased. Most of the social problems that involve teenagers are vandalism, burglary and theft. Adger ( 1999 ) in his study stated that the actions displayed by the teenagers may be due to coping behaviour, stress or access to certain resources or needs. However, social problems are not limited to teenagers only. Adults who are also desperate took advantage from the flood event usually caused social problems. They usually break into the flood victim houses and steal any valuable goods such as electric appliance, jewelleries, car, motorcycles and even the house parts such as steels, wires and cable. This is when the social security elements from the discussion among the participants surfaced. Security in this context is not limited to assets and goods, but also the safety of individuals during the flood to avoid any casualties and death. Making sure all the important and valuable goods and assets are in a safe place before the flood occurred ensures the safety of the people’s belongings. On the other hand, the security and safety of individuals during and after the flood is the top priority while it is encouraged to help those affected by flood, but not to the extent of risking their own life. For example, it is reported that there was a case of death at Kampung Isap where a man died due to drowning while trying to rescue another flood victim. Therefore, it is important to prioritize and take care of one’s own life and safety during and after the flood events.

3.2 Vulnerability

This study investigated the vulnerability to flooding of the community of the study area. The results from the analysis are shown in Fig.  5 . From the FGD, there are several reasons that cause the vulnerability to flooding in the community. Grounds and claims such as houses built near to the river banks and lowland areas, improper and ineffective flood evacuation plans, mismanagement of flood transit centres, lack of instant and accurate flood information and the lack of preparation for the flood by the community members have caused increases in the vulnerability of people to the flood event. Figure  5 shows that middle-aged participants ranging from 26 to 45 years old were concerned about the location of the housing area that can be easily affected by the flood, such as lowland area, near the river banks and swamp area. Meanwhile, the vulnerability of the older generation, from 56 to 65 years old, is mostly due to their refusal to stay at the transit centre, the type of their houses that are usually made from wood and the location of their houses which are mostly located near the beach that make it difficult for them to evacuate. In terms of income, for the participants with monthly income of less than RM1000 and less than RM3000, flooding has made them vulnerable and insecure. However, they refused to move to the transit centre as it may put their house at risk of burglary. Also, some of the transit centres are located far from their houses and some transit centres have imposed payment on the victims for shelter. For gender category, both males and females agree that uncertain flood warnings and warning issued only on high tides have caused them to be more vulnerable. The victims that are involved in the JKKK (village committee) have different vulnerability compared to the residents. The JKKK often faced problems during evacuation of other flood victims because most of them refused to be evacuated as they prefer to wait and see what unfolds. Different from the vulnerability faced by the JKKK committee, the residents are vulnerable towards the flood in terms of government aid that won’t be enough to recover their damaged goods.

The participants’ flood vulnerability

Victims with different education background seem to have made different choices about their house location, hence faced different vulnerability. Victims with high school education background mostly live near the riverbank which is more vulnerable, while those with college education background choose to live near the highways which can be easily accessed for an escape. Lastly, government, self-employment and private occupational victims have different aspect of vulnerability. Most of the victims that work with the government are more concerned on the accuracy of information circulated by the social media, the challenges in evacuation of sick victims and those whose houses are at low ground. As for self-employed victims, they are vulnerable because they can only afford houses situated on lower ground. They are more worried about losing their house and not having the ability to repair or build a new house. Evacuation is difficult because unlike the government and private sectors, they do not have holidays and have to work almost everyday. Those working in the private sector are more concerned about the condition of the transit centre during the flood. They are worried if the transit centre is not safe. All in all, in terms of health, most victims lack preparation for medicine and other basic medical facilities even before the flood occurs.

4 Discussion

This study has examined aspects related to urban flooding in the study area. The main aspects are daily life challenges, vulnerability from the flooding, and the local future resilience towards floods. Everyone who was involved in the disaster will be impacted to some extent. Therefore, this study explores the response, reactions and the resilience of the victims before, during and after the event and why such behaviour and action were taken and displayed.

Vulnerability has been known as a leading tool to quantify and map human dimensions of hazards. The vulnerability of people to flooding is usually affected by variables such as income, ethnicity, education, age and gender. According to Rufat et al. ( 2015 ), income and poverty are the key drivers in vulnerability. Ajibade et al. ( 2013 ) think that women and children are more vulnerable compared to men because they are physically weaker than men and that the roles and responsibilities of women during flood event are more dangerous. The harder it is for someone to reconstruct their lives after the disastrous event, the more vulnerable they are. Contextual aspects of vulnerable populations obtained from the discussion are shown in Fig.  5 . From the discussion, the vulnerability of the participants towards the flood can be grouped into geographical setting (location), socioeconomic, related agencies (societal network and insurance company) and the disaster’s phase (during the flood). All the variables listed are the important keys to deconstruct vulnerability. Most of the vulnerability stated by the participants in the discussion is related to the location of the housing area. Houses located close to the riverbank, swamp and lowland area are vulnerable to the flood. This is because these areas can be easily flooded when the rivers overflow due to heavy rainfall and runoff from the higher area. Rapid urban development without consideration of the local housing area has also increased flooding. Intensity of the improper drainage systems and an imbalance in the embankment of lowland area for development has resulted in negative impacts on the locals, where local residences have to face unpredictable flood events caused by the embankment and incomplete drainage system designed by the developer. Moreover, the location of houses that are difficult to access, such as the beach area, hinder the process of evacuation and hence caused the victims to be vulnerable during the monsoon season. Meanwhile, the vulnerability in socioeconomic factors is measured through household income, poverty, unemployment, educational status and wealth (Rufat et al. 2015 ). According to Chan and Parker ( 1996 ), age and gender are related to income, those over 50 years of age have a comparatively low source of income. It is believed that income and poverty are the key drivers for vulnerability. According to Friend and Moench ( 2013 ), poverty and vulnerability are related but not the same; individuals with greater wealth experience are less vulnerable to flooding event. Therefore, since most of the victims in the study area have low income, the environment and housing conditions of the neighbourhood are poor. The material of the house is old and easily destroyed by flood and hence increased the vulnerability of the victims in the total loss of their house.

Nevertheless, the vulnerability in agencies such as insurance company and social network company is varied. For example, some of the victims may falsify claims towards the insurance agency for flood support. This is due to the shortage of money and funding necessary to get back to their daily life. Then, the vulnerability in social media towards the victims is because of the amount of outdated information that may lead to misunderstanding within groups of flood victims. The social media network needs to be sharp and alert in updating information regarding on the weather forecast from the meteorology stations. This is because most of the victims rely on the news they heard on TV, radio and even online social media for further actions. Finally, the victims are most vulnerable during the disaster phase. Some of the victims refused to stay at the flood transit centre because they are worried that their house may be at the risk of being robbed or the transit centre is situated a long way from their housing area. The risk perception that influences the vulnerability of the victims resulting in refusal to be transported to the transit centre is fear, uncertainty and worry of the safety of their family members, assets and properties (Willis et al. 2011 ). Nevertheless, the victims that faced a total loss of their house were evacuated to the transit centre for temporary shelter. Sometimes unexpected incidents such as black outs happened at the transit centre due to the electricity failure, which may cause trauma and panic attack to the flood victims at the transit centre where they do not feel safe and secure. For sick and elderly victims who are in wheel chairs, or others who are on machines and medical support, evacuation due to the flooding could be challenging, both for the victims and the evacuators. Furthermore, the uncertainties of flood warning and forecast caused them to be mentally tired all the time. In addition, some of the victims used the approach of ‘wait and see’; hence they refused to be evacuated in the early phase of the flood. They will only evacuate when the situation worsens. This is a challenge to the volunteers. On the other hand, when the victims are evacuated, they are forced to leave their food stuff behind; therefore, they are depending solely on the food aid by the government at the transit centre. The continuous rain fall over a long period will caused the victims to be more vulnerable as more materials and properties will be damaged and destroyed by the floods and these will not be recovered by the government. Hence, more expenditure is needed to compensate their losses.

Towards the end of the discussion, the participants were asked about their resilience towards flooding in the future. The results from the discussion can be seen in Fig.  6 . Resilience is defined as the ability of a system to bear any commotion while sustaining certain levels of efficiency in its social, economic, physical and environment component (Balica and Wright 2010 ). There are several dimensions of resilience that have been highlighted by the participants such as the construction of flood barriers, information and updates on the flood, distribution of material support, transit centre and development on lowland. The participants acknowledged the present effort by relevant government agencies in helping and handling the flood hazard, but in their opinion, more can be done. The participant’s perceptions regarding their resilience are that improvements need to be planned and supported by the government and other related agencies. By providing awareness programs for the public and information and updates on the flood situation, it helps the victims to understand more about flooding and hence help them in preparing and building resilience for future hazards. The participants also hoped that the authorities will be more sensitive to risk reduction including housing, infrastructure, utilities systems and regulation of land development according to the level of risk. In their opinion, there should not be any more development of lowland areas and the reclamation built by the developer need to be looked through of the side effects towards the present housing area. This is because some of the reclamation of land for new urban development areas has caused flooding to the present housing areas. The reclamation wall may accidentally block the water ways or the bypass for the overflow water to be discharged to the sea, thus causing unwanted and extreme flooding to the lowland area. The building of the water barrier has also caused the housing settlement between the barriers to be turned into a pond when overflow waters become stagnant in the middle of the lowland area. On the other hand, according to the participants, the distribution of the food aid given by the government to the victims needs to be supervised because there were cases where some of the victims were overlooked and they missed the aid that was distributed. Finally, the provision of community facilities such as evacuation centre, transit centres and temporary shelters is important in order to minimize residents’ exposure to flood hazard. The participants stated that the victims preferred transit centres close to their houses. Public schools are typically used as evacuation grounds and temporary shelters during the disaster which are short in facilities, such as shower rooms. Victims are also worried about the cleanliness and hygiene of the public school. Developer and the state government are urged to build a proper transit centre for the victims to shelter where inadequacy of water and sanitary facilities should be the primary consideration. Therefore, the commitment and the involvement of government, NGO and urban dwellers in long-term flood management and risk will help in assisting the community in minimizing the damages resulted from the hazard; hence, they will manage to get back to their normal daily life in a short time.

The suggested future flood resilience

5 Conclusion

All of the detail and genuine information on the vulnerability and the resilience of the floods from this study were obtained from the qualitative method called focus group discussion (FGD). The study was designed to capture the full range of perceptions on flooding from the urban community. The participants of the group discussion were volunteers who were interviewed for research purpose. Participants were divided into six groups with different demographic background for holistic results. The participants were willing to describe in detail the event, including their feelings and emotions towards the disaster. The information and data obtained are valuable and crucial to understanding the urban flood resilience and vulnerability theory and perceptions. Hence, it is equally important to take into consideration the response, actions and the reactions behind the participants’ behaviour that were displayed. These will help in formulating future planning for effective flood hazard management.

The outcomes when the participants were asked to rank their top five daily challenges were obtained, and they showed that most felt that flooding is not the uppermost daily threat to them as the flood comes annually and is mostly predictable. The victims are most concerned with continuous food supply. Those who felt flood is a threat to them is due to the fact that they live in flood-prone areas. On the other hand, several participants believed that health is the most important variable in their daily life because without good health, they will not be able to work or execute their daily activities. After food and health, other challenges according to participants’ priorities are education, economy, security and social. A large proportion of the population of the study area remains poor and vulnerable to floods, especially in the rural settlement. In other aspects, poor dissemination of early warning information and flood control infrastructures from the government and other related agencies have caused the victims to have little time to prepare for emergencies and hence cause the recovery process to be slower. Moreover, the housing location for most of the participants is within lowland areas which made them more vulnerable. Lowland areas are easily flooded due to heavy rainfall and tidal water from the South China Sea. Thus, it causes an abrupt increase in water volume that exceeds the river basin capacity. New development such as housing ranging from the hill to the valley is one of the major causes of increased flooding in the study area. What determines one’s vulnerability is the gender roles, place, employment, health care, income and social status. The outcome shows that gender has no significance in determining vulnerability. Both male and females voiced concerns about the inefficiency in flood warning and forecasts issued by the government and media during the flood. Results also showed that participants with high or low income faced the same level of vulnerability. However, they refused to move to the designated flood transit centre due to the risk of their house being robbed and the location of the transit centre is far from their house. Notably, the low income victims were hit harder compared to the high income victims because they need more money and other sources of income in order to get back to their daily life. Lots of everyday appliances and goods need to be repaired or replaced which are costly to the poor. Therefore, in order to decrease the vulnerability due to the flooding event, resilience need be cultivated. A lesson should be learnt from the past event and actions should be taken to avoid losses. Structural and non-structural flood mitigation solutions should be taken and adapted to the flood-prone areas. A better flood prevention, mitigation response and rehabilitation should be implied in the disaster risk management.

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Acknowledgements

The authors would like to acknowledge the financial support provided by University of Malaya Research Grant under the Frontier Science Grant RG358-15AFR and Equitable Society Grant RP026B-15SBS, the engineers, assistance engineers and staff of Water Resources and Hydrology Unit, Drainage and Irrigation Department (DID), Kuantan, Pahang, Malaysia, the Kuantan District Officer, Kuantan Assistant District Officer, Penghulu Mukim (Headman) of Mukim Kuala Kuantan 1, staff of Kuantan District Office, Kuantan, Pahang and the Kuantan residents who have contributed in this article.

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M. Y. Safiah Yusmah, H. Norhaslina, A. Ghaffarianhoseini & A. S. Shereen Farisha

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Safiah Yusmah, M.Y., Bracken, L.J., Sahdan, Z. et al. Understanding urban flood vulnerability and resilience: a case study of Kuantan, Pahang, Malaysia. Nat Hazards 101 , 551–571 (2020). https://doi.org/10.1007/s11069-020-03885-1

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The 2015 Chennai Flood: A Case for Developing City Resilience Strategies

Soumita Chakraborty , Umamaheshwaran Rajasekar

Over the last 25 years, the world has seen a rise in the frequency of natural disasters in rich and poor countries alike. Today, there are more people at risk from natural hazards than ever before, with those in developing countries particularly at risk. This essay series is intended to explore measures that have been taken, and could be taken, in order to improve responses to the threat or occurrence of natural disasters in the MENA and Indo-Pacific regions. Read More . ..  

The Chennai metropolitan region (CMA), with an area of 1,189 sq kms and a population of 8,653,521, is the fourth-largest populated city in India. [1] This city, located in north eastern part of Tamil Nadu is a flat plain bounded on the east by Bay of Bengal and on the remaining three sides by Chengalpattu and Thiruvallur districts. Expansion in terms of area as well as population has led to a shift in land use and land cover patterns across the region.

Situated along the eastern coast of India, Chennai is exposed to violent storm surges and flooding during northeast monsoons (September to November). Although local flooding is an annual phenomenon in selected parts of the city, extreme events, such as the 1918 cyclone and 1985 floods, had faded from people’s memory. [2]  However, history repeated itself in the city and neighboring coastal districts in November-December 2015, when a devastating flood affected more than 4 million people, claimed more than 470 lives and resulted in enormous economic loss. [3]

The sudden and unprecedented nature of the flood led to ad hoc and uncoordinated relief and response activities by different governmental and non-governmental agencies. Industrial and commercial centers were forced to temporarily shut down their production due to loss of power, shelter and limited logistics. Amid the chaos and widespread impact, the event brought people and institutions in and outside Chennai together, to provide support to the victims affected by the flood. Help reached the affected areas and their residents from different sections of society and in variety of forms. The lessons from this case study and others like it can help urban centers elsewhere in Asia to plan for similar eventualities.

Challenges Faced During and Following the Event

Flooding often handicaps the affected community by adversely affecting its educational system, food availability, mobility and access to energy on a daily basis. Chennai was no exception: daily functions became a challenge for the entire city.

School authorities faced numerous challenges, ranging from the sudden need to shift and secure school records / admit cards and postpone exams, to maintaining physical infrastructure and equipping schools to serve as shelters. Following the event, school authorities faced yet another set of daunting tasks related to the resumption of the academic session (e.g. repairing and replacing furniture, etc.) in schools that had been shuttered (for 10 to 33 days) in various parts of the city.

Flooding often handicaps the affected community by adversely affecting its educational system, food availability, mobility and access to energy on a daily basis.

Food logistics arrangements across the affected communities included the unavailability of manufacturing capacity and delivery mechanisms. The lack of accessibility to several parts of Chennai due to severe flooding made identification of delivery points and transport routes more difficult, which deprived some local communities of basic food supplies required for survival. During the first 24 hours of flooding, the main concern of the local supermarkets providing food supplies to surrounding areas, was to safeguard perishable items not only from getting wet but also to keep them from spoiling (since there was no electricity). However, it was critical for them to meet customer demand, keeping in mind the limited food availability and lack of communication within their management team.

First responders and information providers faced difficulties in providing accurate real time information to local communities on flooded areas, accessibility of roads, road condition, traffic flow and current weather scenario.

Flooding of roads, tracks and supporting infrastructure, delayed and suspended provision of necessary services. Moreover, several hospital staff were unable to get to work or extend their support due to being affected by the flood themselves. It was a greater challenge for hospital authorities, to safeguard patients admitted to Intensive and Critical Care units (ICU) or those under ventilation through maintenance of power supply.

The Chennai flood had a devastating impact on businesses, especially on small and medium-sized enterprises (SMEs), who were unprepared and vulnerable to both direct and indirect impacts. Flood water entered the first level of most of the offices and shops, reaching a height of approximately two meters in some areas. This damaged products, stocks, storage units, electrical equipment. In post disaster scenario, several businessmen in Chennai were unable to operate for three months due to lack of process-service delivery, finance, logistics, management implications and loss of customer base. Service station owners too had a hard time in recovering broken cars, fixing damaged engines, car interiors, upholsteries and external impact damages. In post flood scenario fungal attack and rusting were additional issues faced by them to continue their business.

Community-Based Organizations (CBOs) faced a plethora of challenges and obstacles, as did official first responders ...

Community-Based Organizations (CBOs) faced tough challenges, such as contingency planning at zone/ district level, stock piling of relief materials/supplies, arranging for inter-agency coordination, preparing evacuation plans, providing public information and conducting field exercises. Service providers in the transport sector had to undertake route planning and ensure priority management. Situation worsened due to lack of mechanisms to mitigate impacts of flood, such as road closure notification, absence of traffic control warning signs, emergency detour routes, etc. which are essential during such extreme events. Thus, they procured boats and hired fishermen to commute to inundated parts of the city.

Likewise, government officials — first responders, such as the fire department, the National Disaster Response Force (NDRF) and the police, in particular — faced a plethora of challenges and obstacles. They not only had the responsibility of conducting rescue operations, but also of road clearance and provision of other facilities to ensure supply of basic necessities throughout the affected communities. The fire department managed calls, coordinated between departments and controlled water distribution system, in the absence of power for prolonged periods. They had to function with disrupted utility services, clear streets of debris, waste and fallen trees in low lying areas and also ensure steady and quick pumping out of water from flooded pockets. NDRF on the other hand, was required to conduct timely rescue operations with small teams, coordinate with local officials, mobilize limited human resources to priority areas and commute using limited transport vehicles and boats. They also had electricity constraints in setting up onsite operational coordination control room (OSOCC) and shelters for both their team as well as the local community. In some instances, the Chennai police were unable to ensure effective and timely response, due to lack of common command system, clear assignment of duties and demarcation of roles to respective officials, for times of emergency.

Resilience Efforts

Various segments of society assisted local communities and relief providers in affected parts of Chennai to cope with the flood. The Chennai government, private schools and the Parent Association were three strong pillars which supported victims in the aftermath of the flood. School children from Hosur made artefacts for sale at an art show to raise funds for a severely affected government school in Poonamallee. Another group of 15 teachers and 40 alumni of the TVS Academy School of Hosur, travelled to Chennai to help improve the infrastructure of Aringar Anna Government Girls Higher Secondary School, Poonamallee. These groups extended help in painting damaged walls, blackboards and building new toilets. During and post flood, government schools were used as relief camps where food and health issues were partially covered by government and parent association.

Various segments of society assisted local communities and relief providers in affected parts of Chennai to cope with the flood.

Private enterprises, such as restaurants, taxi service providers and automobile service centers, also joined hands with the government to provide relief to the flood affected population. Kolapasi, a Chennai-based restaurant, was turned into a temporary food relief agency. Social media was used for awareness generation on the initiative and also to raise funds. Individuals of all age groups and across all professions, supported this initiative by volunteering to cook, wash utensils, pack and deliver food. About 1.7 lakhs food boxes were distributed across the city.

The ride-hailing company Ola started operating boats, which also provided an important learning for future preparedness measures. They strategically identified water routes for providing service to even the most inaccessible areas. They also helped the Fire Department in conducting their rescue operations. Similarly, a vegetable and milk supply chain, Heritage Fresh, sold their commodities at a subsidized rate when prices in parts of Chennai were on the rise. Mobile vegetable shops also put in efforts to reach out to as many flood affected people as possible. Online food service providers, such as Zomato, added one extra meal on behalf of the company for every order that was placed for the stranded people.

The impact of flood on health sector was a complex issue, as the threats to health were both direct (for example, flash flood) and indirect (for example, a hospital needing to be closed due to flooding). To protect and promote health of patients and minimize health risks, sustained treatment for chronic infectious disease were provided through voluntary camps. 51 patients were evacuated and ICU wards were shifted to first floor; special care was taken while shifting new born babies, mental patients, elderly or patients with disabilities; cleanliness was ensured by internal experts using prescribed norms and dosage of chemicals and sump pumps were installed in hospitals to drain out water. Adequate stock of medicine, injections and IV fluids (intravenous) were available for continued medical care of the patients. Immediate actions in response to the flash flood situation from the ESIC was to direct all capacities of the existing health care system towards flood relief, prevention of disease outbreak, water disinfection and vigilance for future outbreaks.

Funds for energy and fuel supply were of least priority, but their demand was high in slums and remote areas where it was required for the survival of sick family members, the elderly and children. Organizations like Oxfam, provided support through the provision of energy and fuel supply to households. Private companies like Servals Pvt Ltd. initiated a similar program of providing specially designed rehabilitation kit, which included a kerosene stove, water filter, utensils, disinfectant, etc. to the slum dwellers, manual laborers and villagers in the worst hit areas, who were not covered under government programs. Along with the kit, training was also provided to ensure optimum utilization of the given products. 

Small- and medium-sized enterprises (SMEs) suffered both direct (physical) and indirect (man-days/ sales) loss. They demanded government to provide interest free loans and delay their tax payment along with other repayments. SMEs took adequate measures to build resilience against future floods through installation of electrical points at a raised height and flood defense barriers within their premises, securing databases by using online recovery systems, etc.

Vehicle service stations, such as Harsha Toyota collected and repaired cars that broke down due to water logging. Company ordered its dealerships to take extra space for flood affected cars while insurance companies were asked to clear their claims on time. They also provided discounted service packages, such as completely waiving labor charges, and offering ten percent discounts on spare parts, roadside assistance, loyalty points of up to Rs. 20,000, 50 percent discounts on car renewal and an exchange bonus up to Rs. 30,000 to flood-affected areas. The 2015 Chennai flash flood made all the car companies (e.g., Toyota, BMW, Renault, Maruti, Hyundai, Nissan, etc.) rethink and develop more sustainable business continuity plan for production, maintenance and parking. Several online and local sellers including a number of automobile portals, such as Copart, has a separate page exclusively for cars damaged in Chennai floods for holding auctions.

Hotel authority liaised with local authorities (i.e., police and fire service and incorporated emergency plans and services wherever possible. Guests were relocated and although flood kits (water proof clothing, blanket, candle/torches, etc.) was provided to all, there is a need to strengthen response and relief capacity of hotels.

Community-Based Organizations (CBOs), such as Tamil Nadu Thowheed Jamath (TNTJ) mobilized over 700 volunteers for carrying out rescue, relief, rehabilitation and reconstruction work, which included arranging food, shelter, cleaning up after flood water resided, waste management, spraying of insecticides and distribution of relief kit. They used half-cut plastic tank boats to rescue stranded people, conducted community based training programmes in health risks and fostered behavioral changes to support all social groups. TNTJ also became one of the coordinating facilitator through establishment of community, zone and district level mechanism with local partners, frontline workers and line departments.

Social media, such as Facebook, Twitter, and Google Maps, played an important role in bringing all the service providers and individuals to work together for reducing the impact and helping the flood affected population recover better. These platforms helped disseminate information, broadcast further warnings, inform people of the undertaken initiatives, call for volunteers in respective sectors, crowdsource and map the waterlogged or inundated areas. Professor Amit Sheth and his team at Wright State University in the United States carried out a new National Science Fund (NSF)-funded project, the Social and Physical Sensing Enabled Decision Support for Disaster Management and Response. This technology was mobilized  to monitor and analyze social media and crowdsourcing for better situational awareness of Chennai flood. Companies, such as BSNL, Paytm, Airtel and Zomato, also pitched in to help Chennai flood victims.

Towards Building Urban Disaster Risk Resilience

The 2015 Chennai flood caused by the torrential downpour brought city life to a standstill. It affected socio-economic condition of the district, maimed critical infrastructure, stranded animals and humans, disrupted services and flooded major parts of the city. The incorporation of flood preparedness measures will help reduce the extent of their impact on people, their life and property in future, along with giving them better coping abilities.

Best practices from Chennai flood case study should be used to strengthen existing risk handling capacities as well as learn lessons, to help replicate similar initiatives for preparedness of other Indian cities. This will also enable the government to coordinate and collaborate with similar service providers across the city for conducting efficient rescue and response operations in future. Best practices extrapolated from this case study could also prove useful to local and national officials from countries throughout Asia and the Middle East, all of whom continue to wrestle with the complex challenges associated with responding to responding to natural disasters in urban settings.    

Prioritized interventions and emergency responses which can be used to reduce urban risk, redevelop city plans and ensure effective disaster relief operations in future are listed below.

➢ As was reflected in the initiatives undertaken by several CBOS, particularly TNTJ, disaster response should address the humanitarian imperative; adhere to the principles of neutrality and impartiality; and ensure local participation and accountability, along with respecting local culture and custom. Thus, awareness generation and capacity building programs should promote inclusive flood disaster management approaches. Operational and sustainable livelihood models should be developed in the aftermath of such emergencies for weaker sections of the society. Disaster resistant shelters, public buildings and critical infrastructure, such as water and sewerage networks, need to be improved in order to avoid water logging and enhance community resilience.

➢ Cities need to develop broadcasting systems to inform the affected community about real time extreme events in different locales and provide updates on current road, flood, weather, food and energy supply scenario. Social media helps develop a two-way communication which helps acquire real time information from the community itself.

➢ Development of city disaster risk resilience strategy will better enable government and non-government organizations in phasing out adaptation and mitigation measures during normalcy.

➢ To ensure community level disaster preparedness, designed trainings should include actions or steps to be taken by citizen prior to, during and after disaster scenarios. Emergency respondents need to have basic first aid skills, such as airway management, bleeding control and simple triage.

➢ Emotional impact of the event on both workers as well as victims need to be addressed and documented for informing city disaster management plan.

➢ GIS-based evacuation plans, including current flood water flow, emergency routes, water depth, obstacles and possible search and rescue (SAR) interventions, need to be prepared. Existing capacity needs to be strengthened and assistance programmes should be provided to existing or new SAR teams at district and state level, for future preparedness. In addition, there is also a need to prepare Flood Risk Maps highlighting availability of grocery stores, restaurants, public utilities, food storage units, hospitals, residential homes for elderly people, high flood prone areas, etc.

➢ Communication systems, including early warning and public awareness mechanisms, need to be established in order to disseminate information during adverse conditions. (There is also an urgent need to prioritize child protection for the prevention of child trafficking during disasters.)

➢ Adaptation strategies need to ensure raised utility and reduced food cost through development and strengthening of local food suppliers. Food supply chain should be maintained by improved coordination and efficiency between producers, suppliers and retailers.

➢ Local flood plain maps, should inform construction practices (e.g., selection of appropriate materials for walls and floors).

➢ In flood-prone areas, water proofing should be mandated for emergency facilities like- power control room, water treatment plants, sewerage plants, etc. Emergency food and assets (generator sets, fuel) area should be at an elevated level to prevent inundation due to flooding.

Note: The detailed assessment of interventions undertaken during and post Chennai floods was funded by Rockefeller Foundation under the Asian Cities Climate Change Resilience Network program. The study was conducted by Taru Leading Edge and IFMR Chennai.

[1] “Chennai Metropolitan Urban Region Population 2011 Census,” accessed May 29, 2017, http://www.census2011.co.in/census/metropolitan/435-chennai.html .

[2] Deepa H. Ramakrishnan, “Memories of Rain Ravaged Madras,” The Hindu, December 9, 2015, accessed May 29, 2017, http://www.thehindu.com/news/cities/chennai/floods-in-madras-over-years… .

[3] “Letter from Chennai- Saving a home from floods,” The National, January 17, 2015, accessed May 29, 2017, http://www.thenational.ae/world/south-asia/20151213/letter-fromchennai-saving-a-home-from-the-floods ; “When Chennai was logged out and how,” Deccan Chronicle, accessed March 29, 2017; and http://www.deccanchronicle.com/151203/nation-currentaffairs/article/when-chennai- was-logged-out-and-how.B. Narasimhan, “Storm water drainage of Chennai: Lacuna, Assets, and Way Forward.” Presentation made at “Resilient Chennai: Summit on Urban Flooding,” hosted by 100 Resilient Cities in partnership with the Corporation of Chennai (2016). 

The Middle East Institute (MEI) is an independent, non-partisan, non-for-profit, educational organization. It does not engage in advocacy and its scholars’ opinions are their own. MEI welcomes financial donations, but retains sole editorial control over its work and its publications reflect only the authors’ views. For a listing of MEI donors, please click here .

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An urban flood model development coupling the 1d and 2d model with fixed-time synchronization.

1. Introduction

2. numerical models, 2.1. two-dimensional surface flow model, 2.2. one-dimensional drainage network model, 2.3. coupling method, 3. test and validation, 3.1. laboratory experiment setup, 3.2. numerical model setup, 3.3. test results, 3.3.1. mass balance check, 3.3.2. comparison of inflow capacities by element, 4. urban inundation modeling, 4.1. study area, 4.2. test setup, 4.3. fixed-time synchronization, 5. analysis results, 5.1. analysis results of flow exchange by synchronization time, 5.2. analysis of calculation time by synchronization interval, 6. discussion, 7. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.

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Sim, S.-B.; Kim, H.-J. An Urban Flood Model Development Coupling the 1D and 2D Model with Fixed-Time Synchronization. Water 2024 , 16 , 2726. https://doi.org/10.3390/w16192726

Sim S-B, Kim H-J. An Urban Flood Model Development Coupling the 1D and 2D Model with Fixed-Time Synchronization. Water . 2024; 16(19):2726. https://doi.org/10.3390/w16192726

Sim, Sang-Bo, and Hyung-Jun Kim. 2024. "An Urban Flood Model Development Coupling the 1D and 2D Model with Fixed-Time Synchronization" Water 16, no. 19: 2726. https://doi.org/10.3390/w16192726

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• Published: 27 September 2024

Exposing disparities in flood adaptation for equitable future interventions in the USA

• Lidia Cano Pecharroman   ORCID: orcid.org/0000-0001-9018-0241 1 &
• ChangHoon Hahn 2  

Nature Communications volume  15 , Article number:  8333 ( 2024 ) Cite this article

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As governments race to implement new climate adaptation solutions that prepare for more frequent flooding, they must seek policies that are effective for all communities and uphold climate justice. This requires evaluating policies not only on their overall effectiveness but also on whether they benefit all communities. Using the USA as an example, we illustrate the importance of considering such disparities for flood adaptation through a FEMA dataset of  ~ 2.5 million flood insurance claims. We use C ausal F low , a causal inference method based on deep generative models, to estimate the treatment effect of flood adaptation interventions based on a community’s income, racial demographics, population, flood risk, educational attainment, and precipitation. We find that the program saves communities $5,000–15,000 per household. However, these savings are not evenly spread across communities. For example, for low-income communities savings sharply decline as flood-risk increases in contrast to their high-income counterparts. Even among low-income communities, savings are >$6,000 per household higher in predominantly white communities. Future flood adaptation efforts should go beyond reducing losses overall and aim to equitably support communities in the race for climate adaptation.

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Introduction.

Flooding constitutes nearly a third of all losses from natural disasters worldwide 1 . In the US alone, flooding causes more damage than any other severe weather-related event, with annual losses averaging over $5 billion 2 . These losses are only expected to multiply as climate change raises the sea level and increases the frequency of extreme weather events 3 . By the end of the century, rising sea levels and coastal flooding are estimated to cost the global economy $14.2 trillion, a fifth of the global GDP, in damaged assets 4 . In response, communities are rapidly enacting flood adaptation measures 5 , 6 , 7 . As these measures have emerged so has evidence of their success 8 , 9 , 10 , 11 , 12 , 13 . However, there is still a gap in understanding whether and how the effectiveness varies across different communities.

A better understanding of the effectiveness of flood adaptation policies and their connections to the communities implementing them can ensure that they have the intended effect. It can also ensure that flood adaptation investments deliver on the desired goals and effectively allocate limited resources for climate adaptation. Furthermore, it can prevent climate interventions from unknowingly replicating historical patterns of discrimination 14 , 15 , 16 . This is especially critical in light of recent evidence highlighting patterns of inequality in flood preparedness and recovery processes in the US 17 , 18 , 19 , 20 , 21 .

In 1990, FEMA initiated the National Flood Insurance Program (NFIP) Community Rating System (CRS) in order to improve community flood adaptation and resilience. The program is based on a set of prescriptive activities recognized as best practices for flood risk reduction. These constitute flood adaptation recommendations that are prevalent in flood planning across the world. To join the CRS, communities must implement a series of flood adaptation activities: e.g., floodplain mapping, open space preservation, stormwater management activities, or public information and participation programs. In exchange, residents of the community receive a discount on their flood insurance premium rates. More than 1500 out of roughly 20,000 communities in the NFIP are currently part of the CRS program 22 .

In 2019, FEMA released the NFIP Redacted Claims data set that contains roughly 2.5 million flood insurance claims. It contains claims from communities participating in the CRS as well as claims from communities who did not participate in the CRS, but were eligible for insurance coverage because they complied with minimum floodplain regulation requirements. Thus, the Redacted Claims data set provides an ideal quasi-experimental setup (Fig.  1 ). Insurance claims from flood losses, which can be used as a proxy for flood loss, can be compared between CRS participants and non-participants to quantitatively assess the effectiveness of the CRS flood adaptation activities.

Communities that participated in the CRS are part of the treated group (orange), while the communities that did not are part of control group (blue).

Capitalizing on this quasi-experimental setup, past literature has examined whether the CRS led to a reduction in flood claims 9 , 12 , 13 , 23 , 24 . Despite some studies finding the contrary 10 , the overall consensus is that flood losses are reduced by the CRS. There has been, however, little investigation on whether the program’s effectiveness varies across different types of communities. There is also a lack of systematic analyses on the disparities in flood adaptation initiatives across communities in the broader international literature.

Beyond the CRS, past work has shown that low-income communities respond to risks differently to safeguard their livelihoods 25 and deploy flood-coping mechanisms in the absence of flood protection 26 . Studies have also highlighted the need to promote pro-poor climate adaptation initiatives and to strengthen low-income household’s asset base to improve their adaptation 27 . Furthermore, some studies have predicted flood losses and vulnerabilities for different types of communities 28 , 29 , 30 . Nevertheless, no work so far quantifies how the benefits/savings of flood adaptation activities are distributed across different communities. That is the main goal of this paper.

In this work, we reexamine the effectiveness of flood adaptation interventions through the CRS. We evaluate whether they are effective and also identify the types of communities that benefit the most from their implementation. We use C ausal F low to measure the effectiveness of the CRS as a function of key community characteristics such as population, income, race, and educational attainment at high-resolution on a zip code level over the entire continental US. C ausal F low is a novel data-driven method that leverages deep generative models and accurately measures the causal effect of flood adaptation policies while accounting for its non-linear, complex, and correlated dependence on community characteristics. Our results shed light on the community characteristics most associated with the effectiveness of flood adaptation interventions and provide key insight for how communities should tailor such interventions. They also provide a path forward to re-envisioning flood adaptation in ways that can benefit a broader spectrum of communities in the face of climate change.

Results and discussion

The effectiveness of flood adaptation measures.

With C ausal F low , we can estimate the impact of the CRS flood adaptation activities on the total insurance claims paid per policy for any set of community (zip code) characteristics. In other words, we can measure the “treatment effect” of implementing CRS activities and quantify how much a policyholder saves on average thanks to the program. In order to systematize our results, we define 27 distinct community typologies by population, income, and racial composition. For each community property, we identify three bins (low, medium, and high) corresponding to the 16, 50, and 84th percentiles of the full data set (Table  1 ). Then the typologies are defined by distinct combinations of the three bins of the three different properties. For further detail see Section  D in Supplementary Information. Then, we compute their CATE’ for different values of average precipitation, flood risk, renter fraction, and educational attainment.

In Fig.  2 , we present the CATE’ of all 27 community typologies as a function of (a) average precipitation, (b) educational attainment, and (c) FSF flood risk score. In each panel, we vary a single characteristic while keeping all others fixed. We do this for each typology, represented by a single line. This enables us to isolate and examine the effect of a specific characteristic on the CATE’. We represent the uncertainties on CATE’, derived using standard error of the mean, in one of the high income communities in panel (c). The uncertainties on CATE’ for the communities do not vary significantly and range between $180 to $300. Overall, we find that the CRS saving for policyholders can range from $5000 to $15,000 per policy. For certain communities, the savings can exceed $20,000. Our findings are consistent with previous evidence, which found that the CRS led to a 40 percent reduction in losses at the county level 9 and a $2.8–$5.5 million reduction in damages for a particular flood event 23 .

In each panel, we vary a single community characteristic to highlight its impact on CRS savings: ( a ) average monthly precipitation, ( b ) educational attainment, and ( c ) flood risk score. All other characteristics are fixed to fiducial values. For reference, we include the uncertainties on CATE' for one of the high income communities in ( c ). In ( a ), we highlight predominantly racial and ethnic minority communities with high population and mid to high income (black dotted), which are mostly in urban areas. In ( c ), we mark the communities with high and low income in teal and cyan, respectively. The CRS saves policyholders an average of $ 5000– $ 15,000 per policy. However, the efficacy of flood adaption depends significantly on income, population, race, precipitation, educational attainment, and flood risk.

Beyond estimating the overall savings on flood losses, we can also assess the CRS by examining the effect of average precipitation on savings. Flood losses are typically worsened by compounded water runoff from higher precipitation [e.g., refs. 31 , 32 , 33 ]. Yet in Fig.  2 a, we find that for nearly all of the community typologies (>95%), the CRS savings increase with higher average precipitation. Our results firmly illustrate the CRS program’s effectiveness in mitigating flood losses.

The program’s overall success, however, does not paint the full story. Our results show that while the CRS is effective, the benefits are not felt evenly across different communities. This serves as critical evidence for rethinking and reimagining future flood adaptation policies that aim to equitably reduce flood losses for all communities. In the following, we present two lines of action in designing and evaluating future strategies.

Tailored Requirements and Resources

Community characteristics play a major role in the effectiveness of current flood adaptation measures. To increase flood savings for all communities will require tailoring such measures. For example, while the CRS is effective at reducing flood losses at higher precipitation, certain communities go against this trend. In particular, communities with high percentages of racial and ethnic minorities, with high population and mid to high income (black dotted in Fig.  2 a), see their savings steeply decrease with higher precipitation—i.e., flood adaptation measures are less effective. A geospatial analysis of these communities indicates that nearly all of them are in urban areas (see Section  E in Supplementary Information). Urbanized areas have higher percentages of impervious surfaces that increase water runoff and can cause or worsen flooding 34 . For these urban communities, flood adaptation programs should require activities geared towards e.g., decreasing surface imperviousness.

We also find a significant population dependence. In Fig.  3 , we present the CRS savings for the 27 community typologies as a function of precipitation, flood risk score, and educational attainment, same as in Fig.  2 . We highlight the communities with a high population in orange and a low population in blue. Overall, flood adaptation measures are more effective for high population communities. They save  ~$4000 per policy more than less populated communities. Combined with previous work, which found that highly populated communities are also more likely to adopt CRS activities 10 , our results suggest that flood adaptation programs favor populous communities.

We present the CRS savings for the 27 community typologies, same as in Fig.  2 , highlighting the high population (orange) and low population (blue) communities. The rest of the communities, with mid population, are marked in gray. Flood adaptation measures are more effective for high population communities, who save  ~$4 000 per policy more than low population communities.

Less populated communities may not have the personpower required to implement the prescribed activities, e.g., lacking access to public servants and workers with a wide range of technical expertise. The smaller tax base may also limit the resources available to implement flood adaptation. In field interviews, we found that the acquisition or relocation of flood-prone buildings was limited by the lack of resources available at the local level (Section III C). The implementation of some CRS activities may also require the types of public services or resources that are not economically feasible for small communities. For instance, the implementation of a flood mapping exercise may prove resource-intensive. Hence, future programs should provide the necessary technical support and incentivize collaboration among adjacent communities. Although not explored in this study, population density, beyond population alone, could also play a role in the effectiveness of programs like the CRS.

Finally, we find that the effectiveness of the CRS depends on the communities’ educational attainment. In Fig.  2 b, we present the CRS savings for the 27 community typologies as a function of educational attainment, which we define as the fraction of inhabitants with a Bachelor’s or higher degree. Past evidence already suggests that there is an initial barrier to joining the CRS related to education, where communities with higher educational attainment are more likely to participate 35 , 36 , 37 . Our results show that even after communities join, the effectiveness hinges on their education. The dependence on education translates into a gap of up to  ~$2 000 per policy in savings.

Out of the 19 creditable activities that communities can implement, many require capacity building and technical expertise: e.g., the establishment of flood warning systems and the building and inspection of levees. Communities with lower educational attainment may find barriers to access the required resources or, as suggested in past literature community buy-in for flood management may be more difficult. In response, future programs should include interventions that reduce the educational/technical barriers and provide the necessary technical assistance along with tailored community outreach resources.

The trends presented in this section make it clear that future flood adaptation programs should tailor their required adaptation measures. This is particularly important when there are rewards or incentives for implementing the measures, such as the lowering of insurance premiums in the CRS. If programs reward a community’s adaptation, then not all “low-hanging fruit” interventions should qualify for the reward. Instead, communities should be required to implement the types of flood adaptation interventions that are most effective for their needs. At the same time, future programs must furnish communities with the necessary resources to overcome any financial/technical/educational barriers in complying with such requirements. Only then we can expect a just and equitable distribution of the benefits of climate adaptation. Next, we illustrate the importance of considering the inequities and disparities of flood adaptation programs.

Climate justice

An effective and far-reaching flood adaptation strategy requires embedding climate justice at its core. Our results show that the effectiveness of a program is tied to economic disparities and disparities between predominantly white communities, and predominantly racial and ethnic minority communities. For example, although flood adaptation is effective overall for low-income communities, it is less effective when they are located in high flood-risk zones. The trend is reversed for affluent communities. We highlight this in Fig.  2 c, where we show CRS savings as a function of flood risk for low (light blue) and high-income (dark blue) communities. Our results suggest that the most economical CRS activities may only be effective at lower risk. Meanwhile, interventions that fare well at higher risk can only be afforded by high-income communities. Evidently, flood adaptation cannot be left to a community’s resources alone, especially since average annual flood losses are disproportionately borne by poorer communities 20 .

Furthermore, we find that even within low-income communities, there is a systematic gap in the CRS savings between predominantly white and racial and ethnic minority communities. In Fig.  4 , we present the CRS savings for predominantly white (dotted) and racial and ethnic minority (solid) communities with low income. Since the highlighted communities have the same low income, the comparison between the solid and dotted lines illustrates the sole effect of race. Overall, the program is significantly less effective for racial and ethnic minority communities. In some cases, the gap exceeds $6000 per policy. This points to the program’s inability to breach existing patterns of discrimination and racial inequality 14 , 15 , 16 .

We include the other community typologies for reference (gray). Although both communities have the same income level, highly diverse communities have significantly lower flood loss savings. The gap between white and racial and ethnic minority communities exists for low (blue; top), mid (green; middle), and high (orange; bottom) populations (pop.). This racial gap exceeds $6000 per policy in certain cases.

This gap is consistent with patterns of inequality found in flood preparedness and recovery more broadly 17 , 18 , 19 , 20 . Reference 21 exposed inequalities in the delineation of flood zones in FEMA maps, where “Black and Asian neighborhoods experience disproportionate risk in federally overlooked pluvial and fluvial flood zones” (p.1). Other works have shown that the type of flood adaptation measures employed correlate with racial diversity 38 . Measures like “retreat” correlate with high racial diversity while measures like “shoreline armoring” correlate with low racial diversity. Such differences reflect the real choices that historically disenfranchised communities make based on the accessibility of certain types of solutions.

We show that flood adaptation programs can perpetuate institutional racism, through tangible effects on savings, or lack thereof. Future programs should re-examine the processes, structures, and existing assumptions of flood adaptation prescriptions and incentives under the lens of equity, race, and inclusion. This will be particularly salient as the evidence overwhelmingly shows that the most disadvantaged communities are projected to suffer the most from the consequences of climate change. Embedding these priorities at the core of future programs will be crucial to close existing gaps and support all communities in mitigating flood losses.

Considerations moving forward

In this work, we find clear trends between flood loss savings and community characteristics. This provides strong evidence that the success of future flood adaptation interventions should not only be measured based on whether they reduce losses, but also on whether the benefits are equitably distributed. Below, we discuss some of the caveats and limitations of our work and outline future research.

First, we note that our results are measured using data from households with access to flood insurance. Hence, our results do not reflect the communities without flood insurance. Even though the threshold to access it is relatively low, it is not negligible. While some of the communities without insurance face no significant flood risks, those that do disproportionately include households without access.

39 found that income of policyholders was higher than of non-policyholders: a quarter of policyholders are classified as lower income, while the fraction is over a half for non-policyholders 40 . The income disparity between policyholders and non-policyholders is also found in special flood hazard areas 41 , thus suggesting that affordability is a significant barrier to access insurance. While we cannot quantify flood losses for uninsured communities using our dataset, given the gaps in savings that we find between high income and low income communities, we expect including the losses of uninsured communities would only further widen the gaps.

We also note that the savings estimates in this work are conservative. In calculating the CRS savings, we correct for the outreach component, where communities receive information on how to successfully file their claims ( Δ Y in Eq. ( 4 )). This correction is estimated by comparing non-CRS communities to CRS communities that participated in activities for public information and scored below 10% on all other activities. The ideal comparison would be to compare non-participants to CRS communities that only participated in public information activities. However, this includes very few communities, so we relax this selection. This likely leads to an underestimate of Δ Y and, thus, the CRS savings. Furthermore, our control group consists of communities that did not participate in the CRS but have access to the NFIP, which requires them to regulate floodplain development. Although the requirements are minimal, they may already have a slight effect in reducing losses, which would also make our savings estimate conservative. Subsequent work explores the impact of the outreach component in further detail by examining the proportion of successful payments before and after the implementation of the CRS. Along these lines, we also find signs that the outreach component could be driving elite capture. Savings on flood losses decline for communities with the highest levels of educational attainment (>50%; Fig.  2 c). Field interviews support the possibility that some households are learning to “game the system” in order to refurbish their homes after a flooding event. Further research, however, is necessary for a more systematic understanding.

Our estimate of flood loss savings is also conservative because preventing damage to homes mitigates further ripple effects in the livelihoods and well-being of communities. Exposure to flood-damaged homes is linked to an increase in mental and health disorders 42 , death and injury risk, disease outbreaks [e.g., gastroenteritis 43 ], trauma, anxiety 44 , and work disruption 45 . For every dollar saved in flood losses in our results, we can expect a far larger reduction in the true loss.

This study underscores the importance of accounting for complex dependencies when evaluating the effectiveness of adaptation efforts and designing future ones. The CRS, as a program that prescribes a wide range of flood adaptation interventions, serves as an ideal example to showcase the importance of evaluating these efforts from a climate justice lens.

In subsequent work, we will extend this analysis to go beyond assessing the impact of simply participating in the CRS. We will examine the impact of CRS activities and provide specific insights on how communities can capitalize on different solutions depending on their characteristics.

The complex dynamics of climate change require a granular understanding of its effect as well as the impact of interventions aimed to combat it. In this regard, the rapid development of deep generative models presents a unique opportunity to move beyond current causal inference approaches. Together with increased investment in data generation, C ausal F low and similar approaches will be capable of addressing previously intractable causal inference queries that are crucial in designing future policy interventions.

In summary, this study shows that even though current flood adaptation practices reduce flood losses, the savings vary greatly across communities. Future adaptation pathways need to consider key community characteristics in providing the necessary solutions and resources. They must also embed equity priorities at their core to break existing patterns of inequality and discrimination so that all communities can benefit from climate adaptation investment into the future.

For this work, we compile a dataset based on the FEMA Flood Insurance Mitigation Administration NFIP Redacted Claims data with additional information on community characteristics. We use the publicly available NFIP dataset at https://www.fema.gov/openfema-data-page/fima-nfip-redacted-claims-v1 . From this dataset, we use data on CRS participation, the date of flood loss, the total claims paid on building damages and content from the loss, and the number of policies per claim. Each zip code is labeled as either a CRS participant or nonparticipant. CRS participation takes place at the jurisdictional level (municipality, city, borough, and sometimes county). If a zip code is embedded within a participating jurisdiction, it is considered a participant. We combine all the entries for a zip code and calculate the average amount paid per policy for each claim.

For each zip code, which we refer to as a community, we quantify its flood risk using scores compiled in the First Street Foundation (FSF) dataset, available at https://firststreet.org/data-access/getting-started-with-first-street-data/ . The risk scores are computed based on factors including risk of flooding from high-intensity rainfall, overflowing rivers and streams, high tides, and coastal storm surges. They are also based on hydrological models at the watershed scale, which considers geographic characteristics. While the consideration of risk at the zip code level is not reflective of the exact flood risk of a particular property, it provides the overall flood risk surrounding the property. We further supplement the dataset with census data from the US Census Bureau American Community Survey: https://www.census.gov/programs-surveys/acs/news/data-releases.html . The census is compiled in 4 year intervals: 2008–2012, 2012–2016, and 2016–2020. We assign median income and number of residents (population) of the communities based on their zip code and date of loss. We also calculate the fraction of residents that rent, have a Bachelor’s degree or more advanced degrees, and do not identify as only white. We refer to each of the characteristics as the renter fraction, educational attainment, and fraction of racial and ethnic minorities, respectively.

Lastly, we include average precipitation in millimeters during the month of the flood loss, as in ref. 46 . This considers the average precipitation over the entire month of the flooding event and, thus, accounts for the potential impact of compound rainfall. This data was extracted by splitting the PRISM climate group data ( https://prism.oregonstate.edu/ ) compiled by the Northwest Alliance for Computational Science and Engineering using US zip code boundaries from 2020. The use of the average precipitation in mm during the month of loss aims at capturing the potential effects of compounded precipitation as a factor that may influence the effects of flooding and the effectiveness of flood prevention measures.

In total, our dataset includes 14,729 unique communities. In Fig.  1 , we plot the communities that are included in our dataset (blue and orange) on a map of the US. Our final dataset is publicly available at https://doi.org/10.5281/zenodo.13135690 .

C ausal F low

One of the main goals of causal inference is to measure the treatment effect of a policy, like the CRS. For heterogeneous treatments, the effect is quantified using the conditional average treatment effect (CATE), the ATE as a function of covariates. By revealing the dependence of the treatment effect on covariates, CATE provides a more detailed understanding of the causal path. Given outcome Y , covariates X , and variable T that indicates the control ( T  = 0) or treated ( T  = 1) groups, CATE is estimated as:

E [ Y   ∣   X ,  T  = 0, 1] represents the expected value of Y given X for the control and treated groups, respectively.

Typically, CATE is estimated using either matching or linear regression. In matching, samples in the treated group are matched to ones in the control based on their X values. CATE is then estimated by comparing the outcomes of the matched samples. Even prevalent methods, such as synthetic control 47 , 48 or propensity score matching 49 , match samples based on some finite volume in covariate space, which can lead to incorrect estimates of the CATE.

The other approach is regression, most commonly with linear models 50 , 51 . A model of Y as a linear function of X is fit to the data and then used to estimate CATE. In many scenarios, assuming a linear model is incorrect. For instance, there is no reason to expect flood losses to depend linearly on its population, or median household income. Furthermore, there is often no a priori knowledge of the functional form that should be adopted for a model of Y .

We can instead estimate the CATE without any of these assumptions. We rewrite Eq. ( 1 ) as

where p ( Y   ∣ X ,  T  = 1) and p ( Y   ∣ X ,  T  = 0) are the conditional probability distribution of Y given X for the treated and control groups. If we can estimate p ( Y   ∣ X ,  T  = 1) ≈  q T ( Y   ∣ X ) and p ( Y   ∣ X ,  T  = 0) ≈  q C ( Y ∣ X ) and sample from them, \({Y}_{T,i}^{{\prime} } \sim {q}_{T}(Y| X)\,{{{{\rm{and}}}}}\,{Y}_{C,\, j}^{{\prime} } \sim {q}_{C}(Y| X)\) , we can estimate CATE using Monte Carlo integration:

Deep generative models from machine learning (e.g., ChatGPT, Dall-E) enable us to accurately estimate and sample from p ( Y   ∣ X ,  T  = 0, 1). In this work, we use normalizing flow models 52 , 53 , 54 , 55 , which use a bijective transformation, f  :  z ↦ x , that maps a complex target distribution, p ( x ), to a simple base distribution, π ( z ), in our case a Gaussian. f is defined to be invertible and to have a tractable Jacobian so that target distribution can be evaluated from the base distribution: \(p(x)=\pi (z)| \det {\frac{\partial f}{\partial x}}^{-1}|\) . A neural network is used for f to provide an extremely flexible mapping that can estimate complex distributions. This neural density estimation approach has been used extensively in a variety of fields spanning neuroscience [e.g., ref. 56 ] to astrophysics [e.g., refs. 57 , 58 ]

Using normalizing flows, in particular the Masked Autoregressive Flow [MAF 59 ] models implemented by 60 , 61 , we estimate p ( Y   ∣ X ,  T  = 1) ≈  q T ( Y   ∣   X ) and p ( Y ∣ X ,  T  = 0) ≈  q C ( Y   ∣ X ) for the treated and control groups separately. We describe the training and validation of our normalizing flows q T and q C in Section  A of Supplementary Information. Our outcome, Y , is the total insurance claims payments per policy in dollars. We use seven covariates, X : precipitation, flood risk, income, population, renter fraction, educational attainment, and fraction of racial and ethnic minorities (Section III A). The treated group consists of communities participating in the CRS, while the control group consists of non-participants. In Fig.  1 , we mark the communities in the treated (orange) and control (blue) groups of our dataset.

Once trained, we can evaluate CATE at any given value of the covariates using q T , q C , and Eq. ( 3 ), as long as it is within the support of the covariates in our data. We detail how we ensure this in Section  B of Supplementary Information. Our approach, which we call C ausal F low , relaxes the strong assumptions made in standard causal inference methods. It learns the detailed relationship between X and Y from the data to provide an accurate and robust estimate of the treatment effect. C ausal F low was specifically developed for this work. However, it is designed more broadly to answer causal inference questions that involve high-dimensional and nuanced relations between outcome and covariates. We emphasize that C ausal F low can be applied to other quasi- experimental setups across the social sciences. The software is publicly available at https://github.com/changhoonhahn/causalCRS .

Lastly, we introduce a correction, Δ Y , to the CATE to account for the outreach component of the CRS program:

Communities in the treated group are informed of how to successfully file their claims. The outreach component alone increases the total insurance claims paid per policy by Δ Y  ~ $9, 780 (see Section  C of Supplementary Information for details). Since this increase is not a reflection of any change in flood losses, the CATE in Eq. ( 3 ) underestimates the impact of the CRS. Thus, we include Δ Y and correct for the effect of outreach to more accurately quantify the treatment effect on flood loss. Throughout this work, we refer to \({{{{{\rm{CATE}}}}}}^{{\prime} }\) as the CRS savings.

To provide additional insight and intrepretation of the analysis in the paper, we conducted 15 elite interviews with academics and government employees from FEMA, Massachusetts Emergency Management Agency, National Aeronautics and Space Administration, and the National Academy of Sciences. We also conducted 21 semi-structured interviews across two CRS communities to government officials, civil society, business owners, and community members. Stratified sampling was used to select interviewees according to the following categories: government officials, civil society, first responders, and business owners. The questionnaires used in the interviews were submitted to the MIT Committee on the Use of Humans as Experimental Subjects (COUHES). Due to the subject and content of the study, Institutional Review Board (IRB) approval was waived via Protocol ID: E-2829 - PhD.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

All of the data generated in this study have been deposited in the Zenodo database under the accession code: https://doi.org/10.5281/zenodo.13135690 . Restrictions apply to the availability of First Street Foundation data, which were used under license for the current study and are not publicly available. Interview data are not publicly available as per compliance of IRB protocol to remain anonymous.

Code availability

All of the code used in this paper is publicly available at https://github.com/changhoonhahn/causalCRS .

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Acknowledgements

It’s a pleasure to thank Mariana Arcaya, Peter Melchior, A.R Siders, and Lawrence Susskind for valuable discussions at different stages of this research. L.C.P. was supported by the La Caixa Foundation and by the Martin Family Society of Fellows. C.H. was supported by the AI Accelerator program of the Schmidt Futures Foundation.

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Cano Pecharroman, L., Hahn, C. Exposing disparities in flood adaptation for equitable future interventions in the USA. Nat Commun 15 , 8333 (2024). https://doi.org/10.1038/s41467-024-52111-0

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Future of the subsurface: urban water management in the UK (annex)

Published 3 October 2024

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Future of the subsurface: urban water management in the UK  

This document is an annex to the Future of the subsurface report completed by the Foresight team in the Government Office for Science. It has been informed by a review of literature and stakeholder engagement and draws on a series of UK and international case studies to highlight current issues and potential solutions for urban water management.   

Background : the review begins by introducing water management in urban areas. Over 80% of the UK ’s population lives in urban areas, and relies on water management systems for essential services such as provision of drinking water, wastewater removal and treatment, and flood protection [footnote 1] . These water systems, which exist both within urban areas and across the wider water catchment, comprise both built and natural components, including pipes and other distribution infrastructure, wastewater treatment works, groundwater, and rivers. 

Current UK status : the review then describes different aspects of urban water management in more detail. These include challenges associated with managing urban water systems and the crucial services they provide. Water supply, for example, is challenged by drought, demand, and water leakages. The management of flood risk, groundwater, wastewater and pollution are also considered. In the UK , the responsibility for managing urban water systems is shared by multiple bodies, and the policy landscape varies by location. 

International examples of innovative urban water management : the review then considers some international examples. These include a rainwater harvesting system in Australia, a smart city with smart urban water management in South Korea, and the Cloudburst Management Plan in Denmark. 

Future challenges and opportunities in urban water management : the review then highlights some challenges and opportunities in urban water management in the UK . Many problems faced by urban water management are likely to become exacerbated in the future, as urbanisation increases, climate change brings more extreme weather, and water infrastructure increasingly competes for subsurface space with other uses. There are also opportunities to innovatively manage urban water, that utilise emerging technologies or take a more holistic approach. These can be efficient in targeting more than one urban water management issue at once, whilst providing benefits for the citizens of urban areas.  

Interactions with the wider subsurface system : the review ends by considering how urban water systems interacts with the wider subsurface system. Examples of these interactions include groundwater flooding affecting other subsurface infrastructure, tree roots interfering with buried infrastructure, and competition for space between subsurface water infrastructure and other assets. The review also considers a specific subsurface interaction relating to urban water management – the potential implications of an increase in the installation of sustainable drainage systems ( SuDS ).

Background 

Water systems, both within the urban footprint and across the wider water catchment, have a crucial role providing essential services for cities, and comprise both natural and built systems. Over 80% of the UK ’s population live in urban areas, and rely on these essential services including the delivery of drinking water, removal of wastewater, management of surface water and flood protection. This case study focuses on parts of water systems that fall within the urban catchment specifically (‘urban water systems’), and primarily their subsurface aspects. However, urban water systems form part of the wider water catchment which includes rural areas, and activity at the surface strongly influences the subsurface.  

The natural water system comprises a variety of components, including rainfall, moisture in the soil, groundwater, blue infrastructure (blue infrastructure is urban water infrastructure, which includes rivers, canals and ponds) such as rivers and ponds, and supporting green infrastructure (green infrastructure refers to natural spaces in urban areas, such as hedges, fields, parks and gardens) such as trees. Blue-green infrastructure provides a multitude of benefits, including improving an area’s natural flood resilience, and increasing biodiversity. Components of the natural water system have often been altered in urban areas, for example, many rivers in urban areas have been culverted or heavily modified over a period of many years, for purposes such as supporting industrial processes or to free up surface space.  

Groundwater is both a source of water and can be used to provide heating and cooling. It stores geothermal energy which can be extracted from the ground by either open or closed loop geothermal heating systems. Open loop systems pump groundwater from aquifers directly, utilising a heat pump to extract low-grade heat and upgrade it to higher temperatures (>40˚C) required for the heating of buildings. They then return the colder groundwater to the subsurface. Closed loop systems use a carrier fluid, rather than the groundwater itself, to exchange heat. For a more detailed description of geothermal energy and heating, please see the evidence review Geothermal energy in the UK . 

Engineered water systems consist of infrastructure such as pipes and other distribution elements, and water treatment and storage facilities, which provide services such as the provision of water supply, wastewater treatment and flood protection. Water is supplied by pipes which pump water to urban areas from reservoirs, and wastewater is carried away from buildings through a different set of pipes to sewers. In urban areas, rainwater often cannot drain naturally due to high levels of impermeable surfaces, so positive drainage is required to manage surface water runoff.  

Traditional drainage systems form part of the built water system and are designed to remove water as quickly as possible where it falls, by directing it into public sewers. Around a third of sewers in the UK are ‘combined sewer systems’, which carry both wastewater and surface water. The volume of surface water entering the wastewater system puts it under significant pressure, which is often alleviated through ‘combined sewer overflows’ [footnote 2] . These discharge excess water (diluted sewage) directly into watercourses at times of high flow and are regulated, for example by the Environment Agency ( EA ) in England under the Permitting Regime [footnote 3] . Otherwise, wastewater from sewers is cleaned in water treatment facilities before being returned to rivers.

Current status in UK  

This section covers different aspects of urban water management in the UK , including the management of water supply, flood risk, urban groundwater, wastewater and pollution. It also covers information on the policy landscape of urban water systems and urban water management in Manchester.  

Water supply 

The delivery of safe drinking water is a crucial function of the urban water system, and is challenged by drought, increasing demand and ageing infrastructure. The daily household usage of water increased from 85 litres per person in the 1960s to 143 litres per person in 2020 [footnote 4] . 

Leaks exacerbate water demand issues, causing around one fifth of public water supply to be lost each year [footnote 5] . They can be caused by ageing pipes or ground movement, which results from ground water abstraction, loading from construction, or temperature changes. As shown in Figure 1, the volume of water lost from leaks decreased during the 1990s, then remained relatively constant in the 2000s and 2010s. Although the amount of water lost from leaks has recently started to decline again, leaks remain frequent and difficult to detect. 

Challenges from these issues can exist in parallel. For example, London experiences significant water loss through leaks (around 500 megalitres of water per day in 2019), whilst being in the South-East which is currently classified as ‘seriously water stressed’ [footnote 6] . Significant portions of its pipes are over 60 years old, with some being older than 150 years. 

Water companies are responsible for the delivery of water supply, as well as manging the supporting infrastructure. Ofwat, the Water Services Regulation Authority, are the economic regulator of water companies, and are responsible for ensuring that water companies properly carry out their functions and can finance their functions [footnote 7] .  

Figure 1: Graph showing water leakage from pipes in ML /day in England and Wales. Source: © Crown Copyright ( Ofwat ) 

A graph showing water leakage from pipes in ML per day in England and Wales. The graph shows a decrease throughout the 1990s before remaining relatively constant throughout the 2000s and 2010s, before starting to decline again in recent years up to 2022.

Flood risk 

Flood risk is one of the main hazards faced by urban areas, and can be dangerous to life, as well as causing infrastructural and economic damage. For example, annual losses from flood damage are around £700 million [footnote 8] , and over 60% of properties in England use services supplied by infrastructure sites and networks located in (or dependent on others located in) areas at risk of flooding [footnote 9] . 

Permeable surfaces such as grass and soft landscaping help absorb rain and allow it to infiltrate into underlying soils, but in urbanised areas these surfaces are often replaced with less permeable or impermeable materials, such as concrete. This results in rain collecting on the surface, which requires formalised drainage systems to remove. ‘Surface water flooding’ is a type of flooding which occurs when urban drainage systems become overwhelmed by severe or extended rainfall or as a result of localised failures or blockages. Approximately 3.4 million properties in England were at risk of surface water flooding in the year 2022-2023, with over 900,000 of these having an annual probability of flooding greater than 1% [footnote 10] [footnote 11] . 

‘Groundwater flooding’ is another type of flooding, in which groundwater emerges from the ground at the surface or causes the flooding of buildings or infrastructure below ground including basements. This also affects urban areas, with an estimated 122,000-290,000 properties estimated to be at risk in England in 2021-2022 [footnote 10] . Fluvial (river) flooding (when rivers and streams break their banks causing water to flow out onto adjacent low-lying areas) and sea flooding can also affect urban areas. 

There have been recent changes to legal drainage requirements for new developments to help mitigate the flood risk in urban areas. Schedule 3 of the 2010 Flood Water Management Act has a provision for the approval and adoption of drainage systems, in which drainage approval is required from a SuDS approval body ( SAB ) before commencing any construction work which has drainage implications. This was adopted by Wales and came into force in 2019 [footnote 12] . A recent review by Defra recommended making SuDS mandatory for new developments, and there will likely be new standards on the design, construction, operation and maintenance of SuDS [footnote 13] . Schedule 3 is currently in the process of being enacted in England, however the way it will be implemented is still being determined by Defra. 

Urban groundwater 

Groundwater refers to water found beneath the Earth’s surface in spaces between rocks and soil and is a key element of the urban water system. Whilst accounting for a third of public water supply in the UK , and up to two thirds in some regions, groundwater is sensitive to human interference with a multitude of factors determining pollution and water table levels. Regulation and coordination of these various factors is sparse, and without adequate risk management, can lead to issues including subsidence and groundwater flooding. 

Groundwater has been referred to as “both an asset and a problem” [footnote 14] , because it’s a very valuable resource, but can also pose health risks. As well as being used for human consumption, it has other applications such as being used in industrial processes and for the cooling of air [footnote 15] . Compared to other water sources, groundwater is typically more resilient to the effects of climate change although groundwater drought does occur [footnote 16] . 

The interaction between groundwater and buried urban infrastructure can be a significant problem. Groundwater can infiltrate and flood buried infrastructure such as basements and tunnels, and the prolific construction of underground infrastructure can impede groundwater flow and lead to higher water table levels. This can be considered during the planning stages of basement construction, for example through Basement Impact Assessments [footnote 17] . 

The balance between natural groundwater recharge and the abstraction of groundwater also affects water table levels and leads to issues if not monitored. Over-abstraction, where the amount of groundwater being abstracted is greater than the amount being replenished by the rain, can cause negative effects including the deterioration of groundwater quality, increased salinity concentrations and declining water table levels, which can lead to subsidence issues [footnote 18] . Groundwater rebound, where abstraction decreases and groundwater levels recover, can result in ground uplift, and flooding of buried infrastructure [footnote 19] [footnote 20] . This has been a particular issue in cities such as Birmingham, Nottingham, Liverpool and London [footnote 21] . 

In the late 1980s in London, groundwater rebound was identified as a potential problem as the water table was rising by up to 3m per year following decreases in groundwater abstraction. However, this was addressed via the GARDIT strategy which was devised between Thames Water, the EA and London Underground. Now groundwater levels in London are continuously monitored and are broadly stable [footnote 22] . 

Managing and treating wastewater is a crucial function of urban water systems. A huge volume of wastewater requires treatment in urban areas each year, for example Thames water, which supplies areas in the South of England, treated 4.6 billion tonnes of wastewater between March 2021 and March 2022 [footnote 23] . Around a third of sewers in the UK are combined, in which wastewater and surface water are discharged to the same piped network.  

Water companies often discharge dilute raw sewage into rivers via storm overflows to alleviate pressure on combined sewer systems, for example in 2021, discharged raw sewage was discharged into rivers 372,533 times over a period of 2.75 million hours (over 300 years) [footnote 24] . The EA regulates the use of sewage overflows by water companies through Environmental Permitting Regulations [footnote 25] , however overflows occur regularly and outside of these permitted events, increasing pollution. Recent analysis has attributed sewage overflow events to insufficient infrastructure capacity and highlighted that investment into water infrastructure has not kept up with demand over a prolonged period of time, with most wastewater treatment works receiving a volume of flow much greater than they are designed to manage. This demand increase is partly attributable to urban population growth, whilst urbanisation has also increased surface run-off volumes into combined sewers [footnote 26] . 

Recent upgrades to urban sewer systems include the construction of the Tideway Tunnel in London. This is a 25km long tunnel underneath the Thames to reduce sewage overflows and is due for completion in 2025 [footnote 27] . Water companies have recently pledged to invest £10bn this decade to modernise sewers and reduce sewage overflows into England’s waterways, tripling their current investment plans. This will fund a significant upgrade to the sewer system, which Water UK has stated will reduce sewage overflows by up to 140,000 per year by 2030, compared with 2020 [footnote 28] . 

Aside from storm overflows discharging diluted sewage into watercourses, other areas of the natural water environment in urban areas which are susceptible to pollution include groundwater and other bodies of water such as ponds. Pollution can enter the natural water environment directly from the source, such as phosphorous from water mains leaks or wastewater treatment works [footnote 29] . Similarly, thermal pollution from infrastructure can pollute natural water environments by raising their temperature [footnote 30] . For example, ground source heating and cooling systems can thermally pollute groundwater, altering microbe growth rates and chemical concentrations [footnote 31] . 

The natural environment can also become polluted by diffuse sources, including pollutants relating to both industrial and municipal activities, such as run-off from impervious surfaces and built areas, and pollutants from leaking sewers [footnote 32] . The adverse impacts of these effects on water can include harmful impacts to aquatic life [footnote 33] , and increases in the cost of purification of groundwater to produce drinking water [footnote 34] . Government have committed to producing plans to mitigate against diffuse pollution [footnote 35] . 

Policy landscape 

In the UK , the responsibility for managing urban water systems is shared by multiple bodies, and the policy landscape varies across different locations.  

For example, water supply management involves Defra which oversees the policy landscape, Ofwat which economically regulates water companies and water companies who are responsible for water delivery infrastructure. Groundwater abstraction is primarily controlled through a licensing system. This is overseen by the Environment Agency in England [footnote 36] , the Environment Agency Wales in Wales [footnote 37] , the Scottish Environment Protection Agency in Scotland [footnote 38] and the Northern Ireland Environment Agency in Northern Ireland [footnote 39] . 

Other aspects of urban water management are also regulated by multiple authorities and bodies. For example, a number of bodies have joint responsibility to manage flood risk, and this varies by devolved administration. In England, Defra are the policy lead on flood risk, and national policies are delivered by Risk Management Authorities ( RMAs ). These include the Environment Agency, Lead Local Flood Authorities, District and Borough Councils and Water, Sewerage Companies and Internal Drainage Boards [footnote 40] . In Wales, Flood and Coastal Erosion Risk Management involves organisations including National Resources Wales and 28 RMAs . In Scotland, bodies involved in flood risk management include local authorities and SEPA which is responsible for national flood risk forecasting. In Northern Ireland ( NI ), multiple organisations are involved in planning for risk of flooding, including NI water, NI Environmental Agency and NI Rivers. See Table for a list of authorities involved in urban water management in Manchester.  

UK example: Greater Manchester 

Greater Manchester is the third most populated county in the UK and has a variety of different authorities and organisation managing its water, as described in Table 1. It faces many of the water management risks and challenges described above. Some of these relate to: 

Floods and flood risk : Several parts of Greater Manchester are Zone 2 (0.1% - 1% annual probability of flooding) or Zone 3 (>1% annual probability of flooding) flood zones [footnote 41] , with 15,000 properties in Greater Manchester having a medium or high flood risk. The 2015 Boxing Day floods caused £11.5 million in infrastructure damage and left 31,200 properties without power [footnote 41] .  

Upgrading ageing infrastructure : The aqueduct which supplies most of Greater Manchester with its drinking water from the Haweswater reservoir is almost 100 years old and 100km in length [footnote 41] . This is currently undergoing significant upgrades with a £1.75 billion investment to replace six sections of pipeline [footnote 42] . Many of Manchester’s buried, culverted rivers are uncharted and at risk of collapse and blockage.  

Combined drainage systems : Like many urban areas, Greater Manchester has a higher proportion of combined sewers compared with the UK average, with 54% of public sewers combining surface and wastewater. 

Leakage and water loss : Leaks caused United Utilities (which is the water supplier for the North-West including Greater Manchester) to lose 133 litres per day per person in 2017-2018 [footnote 41] .  

Water usage : Domestic usage has increased from 106 litres per day per person in 2013-2014 to 123 litres per day per person in 2017-2018 [footnote 41] .

Table 1: Some authorities and organisations roles involved in urban water management in Greater Manchester. [footnote 41]  

International examples of urban water management 

This section covers international examples of urban water management. Examples from Australia, South Korea and Denmark are highlighted as innovative solutions or approaches to urban water management challenges.  

Australia – Rainwater harvesting in tanks for irrigation in parks  

Australia is the 17th most water-stressed nation globally. Up to 90% of the rainwater which falls in its cities runs off hard surfaces and enters waterways, carrying pollutants with it. To help re-use this water efficiently and prevent pollutants entering waterways, some parks are installing huge water tanks beneath them to store and process rainwater [footnote 45] . 

One example of this is the Fitzroy Gardens Harvesting System in the city of Melbourne. It uses wetland principles and underground water storage tanks to collect rainwater. Stored rainwater goes through a filtration process to remove waste, sediment and other pollutants, before being disinfected with UV to remove bacteria and viruses. Processed water is sprayed at night to limit any potential health risks to park users. The system has been operating since 2013 and captures around 30 million litres of storm water per year for irrigation [footnote 46] . 

South Korea — Smart cities with smart urban water management  

Busan is the 2nd most populated city in South Korea, where the metropolitan government are currently developing an adjoining waterfront city, located between two rivers, called the Busan Eco Delta City. Within this area, a region called the Busan Eco Delta Smart City ( BEDSC ) is being piloted as a smart city. This is currently in the testing phase in which citizens are experiencing living with smart technologies to provide feedback. Technologies include radar, sensors and automated drains to restore the natural water cycle and reduce flood risk. BEDSC utilises water from its adjacent river, both as a drinking water supply and to generate hydropower [footnote 47] .i 

Denmark – Cloudburst Management Plan  

Following extensive flooding in Copenhagen in 2010 and 2011, Copenhagen City Council adopted a Cloudburst Management Plan [footnote 48] . This plan aims to protecting against cloudbursts (very heavy rainfall), whilst creating more recreational urban green spaces. Once fully implemented, it will combine sewer-based drainage solutions such as tunnels with around 300 surface projects. These surface projects will facilitate drainage and storage of rainwater near to the surface and utilise it to develop green and blue infrastructure. 

The plan will be implemented over twenty years and each year the city decides which projects to undertake, prioritising projects which are in high-risk areas, areas where they will be easy to implement, or areas where new investments can be connected to ongoing urban development projects. Tunnel solutions should only be used where there are no opportunities for drainage solely at ground level [footnote 48] . Infrastructural changes have been presented as ‘improved city green spaces’, which has helped generate acceptance and enthusiasm for the upgrades [footnote 49] . Currently, the project is ongoing, with 11 major surface projects and 2 major cloudburst tunnels already having been constructed, and 60 more surface projects under development [footnote 50] .

Future challenges and opportunities for urban water management 

This section considers future challenges for urban water management in the UK , and potential opportunities, reflecting on the examples shown above. Whilst urban water systems face significant challenges currently, many will be exacerbated in the future by changes such as increased urbanisation and climate change. However, there are also opportunities to innovatively manage urban water, in ways that utilise emerging technologies or take a more holistic approach. These can be efficient in targeting more than one urban water management issue at once, whilst providing benefits for the citizens of urban areas.  

Future challenges 

Urban populations : Urban populations are increasing, and at a faster rate than rural populations. Population projections show that city region populations will increase by 7.6% between 2015 and 2025, compared with the UK average of 6.7% [footnote 1] . This will put more pressure on infrastructure to deliver water supply and remove and treat wastewater.  

Climate – flood risk : Flood risk is likely to increase in the future, with the number of properties in England at high risk of surface water flooding expected to increase by 230,000 by 2055 [footnote 51] . This is partly due to climate change causing increases in extreme daily rainfall events [footnote 52] . Analysis has shown that in a ‘worst-case’ scenario where carbon reduction targets are missed and climate sensitivity is high, 1% annual probability flood losses could increase by up to 37% from 1990 levels [footnote 53] . Urbanisation also contributes to increased flood risk. New developments are also expected to increase the number of properties in areas at high risk of surface flooding, whilst increases in areas covered by impermeable surfaces, such as front gardens being paved over, may also cause increases. Groundwater flood risks are also likely to change in the future too, as changes to rainfall patterns affect groundwater recharge and rising sea levels cause the water table level to increase. 

Climate – water supply and demand : Water supply is set to face increased pressures in the future, with reports suggesting that parts of the South of England will run out of water within the next 20 years unless greater action is taken [footnote 54] . Total water supply is forecasted to decrease by 7% between 2020 and 2045 due to drier weather, and the need to abstract water more sustainably. However, an additional supply of 4 billion litres of water per day is expected to be needed by the 2050s [footnote 54] . In cities in particular, water can have a role in climate change adaptation, as it can be used in cooling to help cope with heat waves, through water fountains or spraying water onto streets [footnote 55] . Options for increasing water supply are limited, time-consuming and expensive, so reductions in water demand will be needed, which will likely require a combination of methods [footnote 54] .  

Changes in groundwater recharge will also place increasing stress on water supply. Groundwater accounts for a third of the UK ’s water supply and this is far higher in some regions, such as over 75% in South East England [footnote 56] . Groundwater recharge is the process by which surface water moves downwards and replenishes groundwater. Modelling has suggested that compared to today by the 2080s the groundwater recharge season will be shorter, and that recharge potential over summer will be lower, but that the annual recharge potential will be broadly unchanged. This is due to likely changes in rainfall and evaporation caused by climate change, and could lead to more variable groundwater levels and heighten risk of drought [footnote 57] . Understanding how these changes could affect groundwater recharge can help ensure future water supplies are resilient.  

Ageing infrastructure : Leaks and challenges with infrastructure associated with urban water systems may also increase with time, as infrastructure becomes increasingly old. Climate change may also exacerbate leakage, as it is expected to cause greater variation in the levels of moisture in the soil, leading to increased ground movement which damages pipes [footnote 58] . 

Future opportunities 

Integrated water management : Integrated water management can help address the combination of issues surrounding water management including high and low flows, and water pollution. This process involves treating different uses of water as interdependent, and uses a coordinated water management approach, rather than the traditional fragmented sectoral approach. This can involve using modelling to create a water management strategy for a particular area and to create mechanisms to deliver this [footnote 59] . 

Smart urban water systems and emerging technologies : Smart urban water systems could be used in the future to integrate digitalisation into the water systems in a way which would reduce water demand and flood risk. Smart urban water systems combine novel techniques of data collection and transmission, modelling, analysis and automation to increase flexibility within the water system. For example, data from smart water meters and noise loggers could be used to detect leaks and identify their location. Then automated infrastructure could control the water pressure to reduce water loss. However, smart water systems can carry potential risks, especially around privacy and security [footnote 60] .  

Another emerging technique being developed uses unused optical fibre strands to detect leaks from water and wastewater networks. In this technique, lasers would detect noise at regular intervals along the strand, providing data on the location of leaks, ground stability and activity on network assets [footnote 61] . Pipebots are another new technology being developed, in which high-accuracy sensing technology is installed in micro-robots able to enter pipes and ducts. These may help utility companies to monitor their infrastructure, reducing the need for road excavations [footnote 62] . 

Rainwater harvesting : Rainwater harvesting is a technique which treats water as a natural resource by collecting and using it for purposes such as irrigating parks and gardens, toilet flushing and firefighting, but not typically as a supply of drinking water. This can provide direct benefits such as improving water security and mitigating against pollution from surface runoff, and can also lead to further benefits such as improving natural spaces [footnote 63] [footnote 64] . Additionally, another benefit of rainwater harvesting is that generally water can be collected near to the location where it is required [footnote 63] .  

Sustainable drainage systems : There are also future opportunities in urban water management surrounding sustainable drainage systems ( SuDS ) and working with natural processes ( WWNP ). SuDS aim to imitate the natural drainage system of an undeveloped site to allow the infiltration of surface water into the ground where possible and manage surface water in an alternative way to traditional drainage systems. They comprise a variety of features and interventions, depending on the site-specific constraints. These can include a mixture of green and engineered components used for both the storage and conveyance of surface water, such as retention ponds, permeable paving and geocellular drainage systems, which are storage tanks which contain honeycomb-shaped structures [footnote 65] . SuDS can also include measures above ground, such as green roofs which can reduce surface run-off by up to 70% [footnote 66] . 

Analysis of an urban area in London has shown that retrofitting SuDS would be a cost-effective method of managing flood risk, if all wider benefits are considered [footnote 67] . The most significant wider benefits were shown to be from reduced flood risk, rainwater harvesting effects and decreasing surface water charges, which are fees paid by property owners to account for the water that drains from their property into public sewers [footnote 68] . Studies have also been carried out in Greater Manchester which show that retrofitting SuDS and incorporating urban nature-based solutions can provide extensive benefits in addition to reducing flood risk, including cost savings and creating new green and blue spaces [footnote 69] [footnote 70] . SuDS can also improve groundwater recharge, which as described above is likely to reduce as a result of climate change [footnote 71] . 

There are challenges associated with the implementation of infiltration SuDS , including specific issues relating to adoption. They are not suitable for all locations, and if installed inappropriately can lead to issues such as sinkhole formation and subsidence issues. For example, the installation of infiltration-based SuDS in gypsum karst (a type of soluble rock) areas may wash fine minerals from the ground, increase geohazard frequency and lead to sinkhole formation [footnote 72] . This demonstrates the importance of understanding the physical system when incorporating SuDS into urban areas.

Interactions with the wider subsurface system 

The urban water system is affected by, and linked to, many other parts of the subsurface system. Additionally, external factors can affect or change the requirements of the urban water system. This section outlines some of these subsurface elements and external factors.  

Flooding affecting other subsurface infrastructure : Between October 2019 and August 2021, there were 55 incidents of Transport for London stations being closed due to flooding, including flooding from surface water, and from water mains bursts and leaks [footnote 73] . Damage to water and energy infrastructure typically accounts for 3-10% of the total cost of floods [footnote 74] . 

SuDS interactions : Infiltration SuDS could change the subsurface properties due to facilitating increased infiltration. 

Net zero : Interactions between urban water systems and net zero include the groundwater system, which could provide renewable heat. This includes water in disused coal mines, which 25% of UK homes and businesses are located over [footnote 75] . Heating currently accounts for one third of the UK ’s annual carbon footprint [footnote 76] , with geothermal heating offering potential for heating more sustainably. For example, a recent analysis estimated that all sources of geothermal heat combined could fulfil UK residential heating demand for 100 years [footnote 77] .  

Competition for space with other uses : Increasingly deep infrastructure is being developed for urban water management, such as the Tideway Tunnel in London for sewage at up to 70m deep [footnote 27] . Construction and tunnelling can require dewatering of subsurface space, and conversely water can be used in construction. 

Interaction with tree roots : Proximity of buried infrastructure and tree roots in urban areas means interactions are frequent. Tree roots can break into water or sewage pipes with minor cracks or loose joints, causing extensive damage, blockages and subsequent flooding [footnote 78] . However, there are mitigation measures which allow tree roots and utilities to co-exist nearby to each other, such as providing tree roots with good quality uncompacted soil, meaning they don’t need to seek out water and nutrients from other areas [footnote 79] . 

Ground movement damaging infrastructure : Heavy rain and leaking pipes can cause ground movement which damages infrastructure. For example, in 2015 in Manchester a sinkhole opened up following heavy rain, believed to be caused by a collapsed water pipe [footnote 80] . This caused a carriageway to collapse and damaged a major sewer beneath [footnote 81] . Ground movement typically costs the utility sector £300-500 million/year [footnote 82] . Climate change is expected to exacerbate subsidence issues caused by varying moisture levels in soil, with 10% of UK properties expected to be at risk by 2070 [footnote 83] .  

Example systems interaction 

To illustrate some specific subsurface interactions, we have isolated and refined three example interactions, chosen in consultation with stakeholders as particularly interesting and illustrative of subsurface challenges, one of which is shown below. These include both ‘feedback loops’ and linear chains of interactions, described below. Further information about the example interactions, including their development, is shown in the main Future of the subsurface report.   

Figure 2 shows one set of interactions developed, which includes the installation of SuDS to mitigate flood risks, the occurrence of surface floods, and how these interact over time. 

As shown, installing SuDS will reduce surface runoff, thus decreasing surface flood risk. When flood risk is reduced, less new flood mitigation measures need to be installed. Depending on the specific site and scale of installation, reduced surface runoff could also entail a reduction in sewage overflow events, reducing pollution of open waters. This would also reduce the need for investment in new wastewater infrastructure. 

SuDS installations have to be planned carefully not only to maximise their benefits but also to prevent negative effects such as the potential to trigger geohazards such as sinkhole formation. For simplicity, these side specific effects have not been included in the included in Figure 2. 

Figure 2: This figure shows an example interaction of the installation of SuDS and other aspects of the subsurface system. After the installation of SuDS is increased, this decreases surface runoff, which decreases flood risk as well as the volume of water in sewerage.

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