case study of floods

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The 2019 floods in the central u.s..

Lessons for Improving Health, Health Equity, and Resiliency

In spring 2019, the Midwest region endured historic flooding that caused widespread damage to millions of acres of farmland, killing livestock, inundating cities, and destroying infrastructure. CS_52

The Missouri River and North Central Flood resulted in over $10.9 billion of economic loss in the region, making it the costliest inland flood event in U.S. history. CS_52 Yet, this is just the beginning, as climate change continues to accelerate extreme precipitation, increasing the likelihood of severe events previously thought of as “once in 100 year floods.” CS_53 , CS_54

This 2019 disaster exhibited the same health harms and healthcare system disruptions seen in previous flooding events, and vulnerable populations – notably tribal and Indigenous communities – were once again disproportionately impacted. Thus, there is an enormous need for policy interventions to minimize health harms, improve health equity, and ensure community resilience as the frequency of these weather events increases.

Before-and-after images of catastrophic flooding in Nebraska. Left image taken March 20, 2018. Right image taken March 16, 2019.

Source: NASA Goddard Space Flight Center, with permission

The role of climate change, widespread devastation, and compounding inequities

The Missouri River and North Central Flood were the result of a powerful storm that occurred near the end of the wettest 12-month period on record in the U.S. (May 2018 – May 2019). CS_55 , CS_56 The storm struck numerous states, specifically Nebraska (see Figure 1), Iowa, Missouri, South Dakota, North Dakota, Minnesota, Wisconsin, and Michigan. Two additional severe flooding events occurred in 2019 in states further south, involving the Mississippi and Arkansas Rivers.

This flood event exhibits two key phenomena that have been observed over the last 50 years as a result of climate change: annual rainfall rates and extreme precipitation have increased across the country. CS_57 The greatest increases have been seen in the Midwest and Northeast, and these trends are expected to continue over the next century. Future climate projections also indicate that winter precipitation will increase over this region, CS_57 further increasing the likelihood of more frequent and more severe floods. For example, by mid-century the intensity of extreme precipitation events could increase by 40% across southern Wisconsin. CS_58 While it is too early to have detection and attribution studies for these floods, climate change has been linked to previous extreme precipitation and flood events. CS_59 , CS_60

Hundreds of people were displaced from their homes and millions of acres of agricultural land were inundated with floodwaters, killing thousands of livestock and preventing crop planting. CS_52 , CS_61 , CS_62 Federal Emergency Management Agency (FEMA) disaster declarations were made throughout the region, allowing individuals to apply for financial and housing assistance, though remaining at the same housing site continues to place them at risk of future flood events.

In Nebraska alone, 104 cities, 81 counties and 5 tribal nations received state or federal disaster declarations. FEMA approved over 3,000 individual assistance applications in Nebraska, with more than $27 million approved in FEMA Individual and Household Program dollars. In addition to personal property, infrastructure was heavily affected, with multiple bridges, dams, levees, and roads sustaining major damage (see Figure 2). CS_52

Destruction of Spencer Dam during Missouri River and North Central Floods. CS_63

• Oglala Sioux Tribe, Cheyenne River Sioux Tribe of the Cheyenne River Reservation, Standing Rock Sioux Tribe (North Dakota and South Dakota), Yankton Sioux Tribe of South Dakota, Lower Brule Sioux Tribe of the Lower Brule Reservation, Crow Creek Sioux Tribe of Crow Creek Reservation, Sisseton-Wahpeton Oyate of the Lake Traverse Reservation, Rosebud Sioux Tribe of the Rosebud Sioux Indian Reservation, Santee Sioux Nation, Omaha Tribe of Nebraska, Winnebago Tribe of Nebraska, Ponca Tribe of Nebraska, Sac & Fox Nation of Missouri (Kansas and Nebraska), Iowa Tribe of Kansas and Nebraska, and Sac & Fox Tribe of the Mississippi in Iowa.

Source: Nebraska Department of Natural Resources, with permission.

As with other climate-related disasters, the 2019 floods had devastating effects on already vulnerable communities as numerous tribes and Indigenous peoples were impacted,° adding to centuries of historical trauma. CS_64 , CS_65 Accounts of flooding on the Pine Ridge Reservation in South Dakota demonstrate the challenges that resource-limited communities face in coping with extreme weather events. CS_64 Delayed response by outside emergency services left tribal volunteers struggling to help residents stranded across large distances without access to supplies, drinking water, or medical care.66 Lack of equipment and limited transportation hampered evacuations. CS_67

Health harms and healthcare disruptions

There were three recorded deaths from drowning, but hidden health impacts were widespread and extended well beyond the immediate risks and injuries from floodwaters. In the aftermath, individuals in flooded areas were exposed to hazards like chemicals, electrical shocks, and debris. CS_68 Water, an essential foundation for health, was contaminated as towns’ wells and other drinking water sources were compromised. This put people, especially children, at increased risk for health harms like gastrointestinal illnesses. CS_69 Stranded residents relied on shipments of water from emergency services and volunteer organizations and the kindness of strangers ( see Box 1 ).

BOX 1: “We just remember the trust and commitment to each other”

Linda Emanuel, a registered nurse and farmer living in the hard-hit rural area of North Bend in Nebraska, helped organize flood recovery efforts. She recalled wondering, “How are we going to handle this? How do we inform the people of all the hazards without scaring them?” In addition to her educational role, she administered a limited supply of tetanus shots, obtained and distributed hard-to-find water testing kits, and coordinated PPE usage. In the first days of the flooding, she hosted some 25 stranded individuals in her home. Reminiscing about how community members came together amidst the devastation, Emanuel remarked, “We just remember the trust and the commitment to each other and to our town. We are definitely a resilient city.” CS_70

Standing water remained in many small town for months, and a four-year old child at the Yankton Sioux reservation in South Dakota likely contracted Methicillin-resistant Staphylococcus aureus (MRSA) after playing in a pond. CS_71 The mold and allergens that developed in the aftermath of the floods exacerbated respiratory illness. CS_72 Flooding also backed up sewer systems into basements; clean up required personal protective equipment (PPE) to prevent the potential spread of infectious diseases. The significant financial burdens, notably the loss of property in the absence of adequate insurance, can contribute to serious mental and emotional distress in flood victims. CS_73 , CS_74

Infrastructure disruptions, like flooded roads, meant that many individuals in rural areas were unable to access essential services including healthcare. In an interview with the New York Times, Ella Red Cloud-Yellow Horse, 59, from Pine Ridge Indian Reservation, recounts her own struggle to get to the hospital for a chemotherapy appointment. CS_64 After being stranded by flooding for days, she had contracted pneumonia, but she couldn’t be reached by an ambulance or tractor because her driveway was blocked by huge amounts of mud. She was forced to trudge through muddy flood waters for over an hour to get to the highway.

She told the Times, “I couldn’t breathe, but I knew I needed to get to the hospital.” Her story is an increasingly common occurrence as critical infrastructure is damaged by climate change-intensified extreme events. These infrastructure challenges are also often superimposed on top of the challenges of poverty and disproportionate rates of chronic diseases ( see the Case Study ). Multiple hospitals sustained damage and several long-term care facilities were forced to evacuate, with some closing permanently, as a result of the rising floodwaters, CS_75 likely exacerbating existing diseases.

A path towards a healthier, equitable, and more resilient future

As human-caused climate change increases the likelihood of precipitation events that can cause severe flooding disasters, public health systems must serve as a first line of defense against the resulting health harms. As such, the broader public health system needs to develop the capacity and capability to understand and address the health hazards associated with climate-related disasters. Often funds and resources for these efforts are focused on coastal communities; however, inland states face many climate-related hazards that are regularly overlooked. Building on or expanding programs similar to CDC’s Climate-Ready States and Cities Initiative will help communities in inland states prepare for future climate threats. CS_76

Additionally, public health officials, health systems, and climate scientists should collaborate to create robust early warning systems to help individuals and communities prepare for flood events. Education regarding the health impacts of flooding should not be limited to the communities affected, but it should also include policymakers and other stakeholders who can implement systemic changes to decrease and mitigate the effects of floods. Local knowledge offered by community members regarding water systems, weather patterns, and infrastructure will be essential for effective and context-specific adaptation. By implementing these changes and executing more inclusive flood emergency plans, communities will be better situated to face the flood events that are projected to increase in the years to come.

Introduction – Figure 1: Nebraska Flooding The Role of Climate Change – Figure 2: Destruction of Spencer Dam Health Harms and Healthcare Disruptions – Box 1: Remember the Trust A Path Towards Equality

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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 her e .

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Climate change has caused billions of dollars in flood damages, according to Stanford researchers

Flooding has caused hundreds of billions of dollars in damage in the U.S. over the past three decades. Researchers found that 36 percent of the costs of flooding in the U.S. from 1988 to 2017 were a result of intensifying precipitation, consistent with predictions of global warming.

In a new study, Stanford researchers report that intensifying precipitation contributed one-third of the financial costs of flooding in the United States over the past three decades, totaling almost $75 billion of the estimated $199 billion in flood damages from 1988 to 2017.

Water rescue crew searches by boat for survivors after a dangerous flooding event. In a new analysis, researchers attribute about one-third of the cost of flooding damages in the past 30 years to climate change. (Image credit: Roschetzky / iStockPhoto)

The research , published Jan. 11 in the journal Proceedings of the National Academy of Sciences , helps to resolve a long-standing debate about the role of climate change in the rising costs of flooding and provides new insight into the financial costs of global warming overall.

“The fact that extreme precipitation has been increasing and will likely increase in the future is well known, but what effect that has had on financial damages has been uncertain,” said lead author Frances Davenport, a PhD student in Earth system science at Stanford’s School of Earth, Energy & Environmental Sciences (Stanford Earth). “Our analysis allows us to isolate how much of those changes in precipitation translate to changes in the cost of flooding, both now and in the future.”

The global insurance company Munich Re calls flooding “the number-one natural peril in the U.S.” However, although flooding is one of the most common, widespread and costly natural hazards, whether climate change has contributed to the rising financial costs of flooding – and if so, how much – has been a topic of debate, including in the most recent climate change assessments from the U.S. government and the Intergovernmental Panel on Climate Change.

At the crux of that debate is the question of whether or not the increasing trend in the cost of flooding in the U.S. has been driven primarily by socioeconomic factors like population growth, housing development and increasing property values. Most previous research has focused either on very detailed case studies (for example, of individual disasters or long-term changes in individual states) or on correlations between precipitation and flood damages for the U.S. overall.

In an effort to close this gap, the researchers started with higher resolution climate and socioeconomic data. They then applied advanced methods from economics to quantify the relationship between historical precipitation variations and historical flooding costs, along with methods from statistics and climate science to evaluate the impact of changes in precipitation on total flooding costs. Together, these analyses revealed that climate change has contributed substantially to the growing cost of flooding in the U.S., and that exceeding the levels of global warming agreed upon in the United Nations Paris Agreement is very likely to lead to greater intensification of the kinds of extreme precipitation events that have been most costly and devastating in recent decades.

“Previous studies have analyzed pieces of this puzzle, but this is the first study to combine rigorous economic analysis of the historical relationships between climate and flooding costs with really careful extreme event analyses in both historical observations and global climate models, across the whole United States,” said senior author and climate scientist Noah Diffenbaugh , the Kara J Foundation Professor at Stanford Earth.

“By bringing all those pieces together, this framework provides a novel quantification not only of how much historical changes in precipitation have contributed to the costs of flooding, but also how greenhouse gases influence the kinds of precipitation events that cause the most damaging flooding events,” Diffenbaugh added.

The researchers liken isolating the role of changing precipitation to other questions of cause and effect, such as determining how much an increase in minimum wage will affect local employment, or how many wins an individual player contributes to the overall success of a basketball team. In this case, the research team started by developing an economic model based on observed precipitation and monthly reports of flood damage, controlling for other factors that might affect flooding costs like increases in home values. They then calculated the change in extreme precipitation in each state over the study period. Finally, they used the model to calculate what the economic damages would have been if those changes in extreme precipitation had not occurred.

“This counterfactual analysis is similar to computing how many games the Los Angeles Lakers would have won, with and without the addition of LeBron James, holding all other players constant,” said study co-author and economist Marshall Burke , an associate professor of Earth system science.

Applying this framework, the research team found that – when totaled across all the individual states – changes in precipitation accounted for 36 percent of the actual flooding costs that occurred in the U.S. from 1988 to 2017. The effect of changing precipitation was primarily driven by increases in extreme precipitation, which have been responsible for the largest share of flooding costs historically.

“What we find is that, even in states where the long-term mean precipitation hasn’t changed, in most cases the wettest events have intensified, increasing the financial damages relative to what would have occurred without the changes in precipitation,” said Davenport, who received a Stanford Interdisciplinary Graduate Fellowship in 2020.

The researchers emphasize that, by providing a new quantification of the scale of the financial costs of climate change, their findings have implications beyond flooding in the U.S.

“Accurately and comprehensively tallying the past and future costs of climate change is key to making good policy decisions,” said Burke. “This work shows that past climate change has already cost the U.S. economy billions of dollars, just due to flood damages alone.”

The authors envision their approach being applied to different natural hazards, to climate impacts in different sectors of the economy and to other regions of the globe to help understand the costs and benefits of climate adaptation and mitigation actions.

“That these results are as robust and definitive as they are really advances our understanding of the role of historical precipitation changes in the financial costs of flooding,” Diffenbaugh said. “But, more broadly, the framework that we developed provides an objective basis for estimating what it will cost to adapt to continued climate change and the economic value of avoiding higher levels of global warming in the future.”

Diffenbaugh is also the Kimmelman Family Senior Fellow at the Stanford Woods Institute for the Environment and an affiliate of the Precourt Institute for Energy . Burke is also deputy director of the Center on Food Security and the Environment and a fellow at the Stanford Woods Institute, the Freeman Spogli Institute for International Studies and the Stanford Institute for Economic Policy Research .

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Disaster Risk Reduction for Resilience pp 161–190 Cite as

Flood Resilient Plan for Urban Area: A Case Study

• Anant Patel 3 , 4 ,
• Neha Keriwala 5 ,
• Darshan Mehta 6 , 7 ,
• Mohamedmaroof Shaikh 8 &
• Saeid Eslamian 9  
• First Online: 30 March 2023

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Extreme rainfall and sea-level rise due to climate change may have disastrous consequences. In order to take action on the city’s issues with climate change, a new approach called flood resilient urban design was created. Floods caused by global warming and urban growth prompt cities to have various disaster response and preparation strategies included in their master planning efforts. In order to decrease risk, susceptibility and general disaster preparation, it is essential to include the improvement of flood resilience in development planning. Natural disasters cause damage in many sectors such as water management, energy, ecosystems and health. An efficient water management system keeps cities safe from floods and droughts. For flood damage reduction, several regulation methods and public-private collaboration are being used for people’s safety. This chapter proposes a novel flood management plan and land use planning techniques in response to urban flooding. The most significant hydraulic construction constructed on rivers is a dam. It is also a well-known truth that dam collapse causes catastrophe in the downstream river reach, resulting in the loss of human life, property and economic resources. As a result, it is critical to conduct research and develop a flood mitigation strategy for the metropolitan city downstream of each dam and determine the region that would be flooded in the worst-case scenario of a dam collapse. This chapter focuses on research being conducted for Ahmedabad, situated in the lower basin of the Sabarmati River, India. This research will aid in the development of an emergency action plan for the evacuation of the general population and minimising property damage.

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Patel, A., Keriwala, N., Mehta, D., Shaikh, M., Eslamian, S. (2023). Flood Resilient Plan for Urban Area: A Case Study. In: Eslamian, S., Eslamian, F. (eds) Disaster Risk Reduction for Resilience. Springer, Cham. https://doi.org/10.1007/978-3-031-22112-5_8

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Urban Flooding in the United States

An ad hoc committee will organize a series of regional workshops or case studies to explore the issue of urban flooding in several metropolitan areas. The committee’s report will identify any commonalities and variances among these metropolitan areas in terms of causes, adverse impacts, unexpected problems in recovery, effective mitigation strategies, and key themes of urban flooding; provide an estimate of the size or importance of flooding in these urban areas; and relate, as appropriate, causes and actions of urban flooding to existing federal resources or policies.

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Framing the Challenge of Urban Flooding in the United States

Flooding is the natural hazard with the greatest economic and social impact in the United States, and these impacts are becoming more severe over time. Catastrophic flooding from recent hurricanes, including Superstorm Sandy in New York (2012) and Hurricane Harvey in Houston (2017), caused billions of dollars in property damage, adversely affected millions of people, and damaged the economic well-being of major metropolitan areas. Flooding takes a heavy toll even in years without a named storm or event. Major freshwater flood events from 2004 to 2014 cost an average of $9 billion in direct damage and 71 lives annually. These figures do not include the cumulative costs of frequent, small floods, which can be similar to those of infrequent extreme floods.

Framing the Challenge of Urban Flooding in the United States contributes to existing knowledge by examining real-world examples in specific metropolitan areas. This report identifies commonalities and variances among the case study metropolitan areas in terms of causes, adverse impacts, unexpected problems in recovery, or effective mitigation strategies, as well as key themes of urban flooding. It also relates, as appropriate, causes and actions of urban flooding to existing federal resources or policies.

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An ad hoc committee will organize a series of regional workshops or case studies to explore the issue of urban flooding in 3 to 8 metropolitan areas in order to gain an initial understanding of its extent and causes in the chosen locations. These case study/information gathering sessions will provide information from federal, state, and local government agencies, and other relevant stakeholders responsible for flood control, flood response, recovery, or mitigation on questions related to urban flooding both outside and inside the floodplain, such as : -- How big is the problem of flooding in each metropolitan area; i.e., how bad can floods be or have floods been and how much do floods cost? -- What causes the worst impacts of flooding, including structural and human impacts (human life and property)? -- How could the worst impacts be avoided or mitigated? -- Who is affected most by floods in the metropolitan area? -- Which regions of the metropolitan areas see the longest lasting or most costly effects of flooding? Based on information gathered from the case study cities, the committee will produce a consensus report that: 1.  Identifies any commonalities and variances among the case study metropolitan areas in terms of causes, adverse impacts, unexpected problems in recovery, or effective mitigation strategies, as well as key themes of urban flooding. 2.  Provides an estimate of the size or importance of flooding in those urban areas; and 3.  Relates, as appropriate, causes and actions of urban flooding to existing federal resources or policies, including but not limited to the National Flood Insurance Program, non-disaster grants, Stafford Act authorities, or others.

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• Published: 18 January 2021

Role of dams in reducing global flood exposure under climate change

• Julien Boulange   ORCID: orcid.org/0000-0003-2167-8761 1 ,
• Naota Hanasaki   ORCID: orcid.org/0000-0002-5092-7563 1 ,
• Dai Yamazaki   ORCID: orcid.org/0000-0002-6478-1841 2 &
• Yadu Pokhrel   ORCID: orcid.org/0000-0002-1367-216X 3  

Nature Communications volume  12 , Article number:  417 ( 2021 ) Cite this article

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• Climate-change impacts

Globally, flood risk is projected to increase in the future due to climate change and population growth. Here, we quantify the role of dams in flood mitigation, previously unaccounted for in global flood studies, by simulating the floodplain dynamics and flow regulation by dams. We show that, ignoring flow regulation by dams, the average number of people exposed to flooding below dams amount to 9.1 and 15.3 million per year, by the end of the 21 st century (holding population constant), for the representative concentration pathway (RCP) 2.6 and 6.0, respectively. Accounting for dams reduces the number of people exposed to floods by 20.6 and 12.9% (for RCP2.6 and RCP6.0, respectively). While environmental problems caused by dams warrant further investigations, our results indicate that consideration of dams significantly affect the estimation of future population exposure to flood, emphasizing the need to integrate them in model-based impact analysis of climate change.

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Introduction

Global warming is expected to increase flood risk by altering the distribution, variability, and intensity of precipitation events 1 , 2 . While global estimates of populations exposed to river flooding vary widely across studies, a 4–20 fold increase by the end of the 21 st century is commonly predicted 3 , 4 , 5 . To mitigate the destructive potential of floods and maximize water availability for human consumption, an estimated 2.8 million dams 6 have been constructed globally with a total water impoundment capacity ranging from 7,000 to 10,000 km 3 , which represents over one-sixth of the annual continental discharge to global oceans 7 , 8 , 9 . Currently, about half of the planet’s major river systems are regulated by dams 10 , 11 and only 23% of rivers worldwide flow uninterrupted to the ocean 6 . By regulating water flow, dams generally alter the frequency, duration, and timing of annual flooding events 12 . With more than 3,700 major dams planned or under construction worldwide 13 , understanding the role of dams in climate impact studies has become increasingly important. Previous studies on flood prediction, however, have neglected the role of dams 3 , 14 due to data scarcity 15 , difficulties in parameterizing reservoir outflows, and challenges in implementing features of dams that function at a scale smaller than those accounted for by global-scale models.

Previous global-scale analyses of floods have reconstructed historical flood patterns 16 , 17 to forecast future floods considering climate change 3 , 14 and/or socio-economic development factors 18 , 19 . A key conclusion of the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) was that the number of people exposed annually to the equivalent of a historical 100-year river flood was projected to triple when compared to high and low emission scenarios. However, despite the regulation of most large rivers by dams, the extent to which their alterations of river and floodplain dynamics interacts with flooding, and the exposure of populations to floods in response to climate change remains largely unknown since dams have not been physically integrated into global flood-impact studies 3 , 14 , 15 , 20 . The few studies that have accounted for dams and/or flood protection have underscored the importance of considering dam-induced changes in streamflow characteristics in flood-hazard modelling 21 , 22 , 23 . In the contiguous United States (CONUS), dams are reported to reduce total flood exposure by 9% (protecting approximately 590 million people) owing to the medium to high dam attenuation effects on the 100-year return period discharge of 62% of CONUS hydrological units 22 .

Here, we provide the first global assessment of the role of dams in reducing future flood risk under climate change by using a modelling framework that integrates state-of-the-art global hydrological model with a new generation of global hydrodynamics model. Specifically, the modelling framework quantifies changes in the frequency of historical 100-year return period floods when dams are considered and estimates the global population at a reduced risk of flood exposure. Throughout this study, a flooding event is defined as extreme discharge associated with a 100-year return period (probability). We specifically investigated flood frequency (number of floods per year), the associated maximum flooded area, and populations exposed to these floods.

Streamflow regulation

Robust and reliable estimates of future river floods rely on two critical components: accurate reproduction of river discharge and appropriate prediction of floodplain inundation dynamics. In this study, we used two different models to simulate these critical processes globally. River discharge considering dams was simulated by H08, while flood inundation dynamics were simulated by the CaMa-Flood model. H08 is a global hydrological model that considers human interactions with the hydrological cycle. CaMa-Flood is an advanced river hydrodynamics model with an emphasis on efficient flow computation at the global scale (see Methods). Two global flood simulations were performed: one considering dams and one not considering dams. In total, four bias-corrected global circulation models (GCMs) combined with three radiative forcing scenarios (historical, RCP2.6, and RCP6.0) were used to force the models (see Methods).

The H08 model has been widely used and validated in global studies and accurately reproduces monthly river discharge in basins heavily affected by anthropogenic activities 24 . At the global scale, the H08 model has been benchmarked against other global hydrological models (GHMs) and has performed relatively well for reproducing the magnitude of high flows associated with different return periods 25 . H08 has also been calibrated and validated at finer spatial and temporal resolutions in multiple regional analyses, including the Chao-Phraya basin, the Ganges–Brahmaputra–Meghna basin, and Kyushu Island, among others 26 , 27 , 28 . Critical to these faithful discharge reproductions is the scheme used for dam operations. While improvements to the dam operation scheme implemented in H08 have been recently proposed 29 , 30 , it is still regarded as the benchmark to beat, given its ability to capture observed reservoir storage variation with high accuracy 31 . CaMa-Flood has also been extensively used and validated. It is capable of faithfully reproducing historical flood patterns 32 , 33 , 34 and daily measurements at river gauging stations across the globe 33 , partly owing to the integration of satellite-based topography data 35 . While both models have been widely used for climate impact assessments, they have never been coupled to analyze global-scale floods, leaving a gap in our understanding of the potential role of dams in reducing future flood risks. While the GCMs employed in this analysis were not assimilated, and consequently do not reproduce the exact timing of historical weather events, we nevertheless confirmed that our coupling framework can satisfactorily reproduce observed monthly discharges before and after dam construction (see Supplementary Figs.  13 – 23 ) and that its predicted maximum discharges in 33 large basins were reasonably similar to available observations (see Supplementary Fig.  24 ). We further compared global patterns of future floods with a previous publication 3 (Supplementary Figs.  1 and   5a, b ). We also compared the historical and predicted populations exposed to 100-year floods with information from published literature and a public database (see Supplementary Table  2 and Supplementary Note  1 ).

Population exposure to floods

Results indicate that, driven by climate change, the risk of floods will increase in the future. However, owing to the implementation of dams in our simulations, on average (range from the first and third quartiles in bracket represent uncertainty from the GCM ensemble), populations exposed to flooding below dams decreased by 16.3% (5.7–30.7%) in the RCP2.6 scenario and 12.8% (4.2–27.5%) in the RCP6.0 scenario, respectively, compared to the RCP simulations not considering dams (over 2006–2099, see Fig.  1 ). The decrease in the number of people exposed to floods due to the implementation of dams was highest during the last decade of the 21 st century for both RCPs. On average, 9.1 (4.6–18.1) million people were exposed to river floods in RCP2.6 (no dams) compared to 7.2 (3.5–15.1) million people in the simulation with dams. In the RCP6.0 scenario, the population exposed to river floods increased considerably to 15.3 (8.3–27.2) million and 13.4 (7.3–24.3) million for the simulations without and with dams, respectively. Large differences, consistent across experiments, in the number of people exposed to floods between the GCMs were apparent (Fig.  1b ). When population growth was taken into consideration using Shared Socioeconomic Pathways (SSPs) (see Methods), accounting for dams reduced populations exposed to flooding below dams by 20.6–32.0% for RCP2.6 and 7.0–16.8% for RCP6.0 (lowest and highest values across the five SSPs).

a 5-year moving averages of the population living below dams exposed to the historical 100-year river flood for historical (grey line) and future simulations for 2 RCPs and experiments (colour lines). The uncertainty range represent the spread among GCMs. b The 95 th and 5 th range (whiskers), median (horizontal lines in each bar), and 1 st and 3 rd quartiles (height of box) and individual mean values among GCMs (markers) of the population exposed to the historical 100-year flood for grid-cells located below dams over the 2070–2099 period.

Return period of future floods

Downstream of dams, historical 100-year floods occurred less frequently in the experiment considering dams than in the experiment with no dams for: (on average and ± standard deviation across GCMs), 66.6 ± 4.2% and 60.8 ± 12.7% of the grid-cells in RCP2.6 and RCP6.0, respectively (Fig.  2 , Supplementary Fig.  5c ). These results are similar to other regional- and country-scale analyses. For example, in the US, medium or large dam-attenuation effects were reported for 62% of hydrologic units 22 . Likewise, a study in Canada revealed that dams totally prevented flows with a return period greater than the historical 10-year recurrence 36 (see additional comparison with existing studies in Supplementary Note  3 ). Particularly prominent reductions in future flood frequency were observed along major sections of rivers containing multiple high-capacity dams (e.g. the Mississippi, Danube, and Paraná; see Supplementary Fig.  2 ). Reductions in 100-year flood frequencies in the experiments involving dams decreased moving downstream, becoming relatively small (or negligible) at the river mouth (e.g. in the Amazon, Congo, and Lena; see grey cells in Fig.  2 ). In a few locations (blue cells in Fig.  2 ), the presence of dams increased the frequency of historical 100-year floods compared to experiment without dams (6.7 ± 2.4% and 4.6 ± 1.1% for RCP2.6 and RCP6.0, respectively). This behaviour was connected to sporadic overflow events referred to as the pulsing effect by Masaki et al. 37 and has been documented for some rivers in the US 23 . Although water released from dams was regulated through the majority of the simulation period, pulsing events can result in a dam failure to prevent flooding, distorting the distributions of extreme discharge, and compromising the fitting of the extreme discharge to a Gumbel distribution (see Methods). In such cases, the definition of the 100-year flood is rather ambiguous, and while great efforts are made to prevent overflow 29 , not all are reflected in the generic scheme for dam (see Methods). Note that since the lead time before major storms is generally too short for preventive dams emptying, pulsing may not be totally averted in global dam simulations.

Grid-cells belonging to Köppen–Geiger regions BWk , BWh (hot and cold desert climates, respectively), and EF (ice cap climate) and for which the 30-year return period discharge was lower than 5 m s −1 were systematically screened out (see Methods). The case for representative concentration pathway (RCP) 6.0 is shown (RCP2.6 available in Supplementary Fig.  5c ).

Evolution of future floods for individual catchments

Median changes in the occurrence of historical 100-year river floods and the maximum flooded areas in the experiment considering dams relative to the experiment not considering dams were computed over the 2070–2099 period for 14 catchments (see Methods for the selection of catchments). Figure  3 indicates that the historical 100-year floods occurred less frequently in the experiment with dams, decreasing, on average, across catchments by 36.5% (26.6–49.1%) for RCP2.6 and 35.5% (28.8–46.6%) for RCP6.0. Similarly, the maximum flooded area in the catchments shrank on average by 22.5% (19.8–40.5%) and 25.9% (12.1–34.5%), for RCP2.6 and RCP6.0, respectively. These reductions in the occurrence of 100-year floods and maximum flooded areas were robust to the choice of extreme discharge indices used for identifying flood events (see Methods), with the exception of two catchments that experienced pulsing from dams (Supplementary Fig.  7 ). We note that by employing alternative extreme discharge indices (see Methods) to identify flood events, the eventual influence of pulsing events on the occurrence of 100-year floods and maximum flooded areas was largely filtered.

a Occurrence of the historical 100-year river flood and, b annual maximum flooded area over the period 2070–2099, given two experiments (with and without dams), and tow representative concentration pathways (RCP). The box-and-whisker plots include the 95 th and 5 th range (whiskers), median (horizontal lines in each bar), and 1 st and 3 rd quartiles (height of box) of the annual values obtained for all four global circulation models.

The 100-year return extreme discharge expected in the future (2070–2099) was calculated for all combinations of RCPs and experiments (Supplementary Fig.  8 ) along the main river of the 14 catchments. Downstream of dams, the experiment considering dams always produced a lower 100-year discharge than that produced by the experiment not considering dams. For catchments located in regions where annual precipitation and/or snowmelt is forecast to decrease in the future (the Mississippi, Volga, and Euphrates; see Supplementary Figs.  1 and  8a, c, d ), the RCP2.6 simulations produced higher 100-year discharges than those in the RCP6.0. However, simulations employing the RCP6.0 scenario and the experiment not considering dams generally produced the highest 100-year discharges. For catchments containing few dams on the mainstem river, future 100-year return extreme discharges in both experiments (with and without dams) were similar at the river mouth (Supplementary Fig.  8i, k, l, m, n ). However, in other catchments, the 100-year extreme discharges were clearly reduced in the experiments considering dams (Fig.  3 and Supplementary Fig.  7 ), resulting in reduced flood exposure to populations residing downstream of dams. In addition, the reductions in 100-year extreme discharge in the Amazon, Congo, and Mekong rivers were relatively small due to the small cumulative storage capacity of the mainstem dams compared to the discharge volume generated in these basins.

Explicitly considering dams in climate-impact studies of floods significantly offsets the population size exposed to river floods. Downstream of dams at the end of the 21 st century, a 100-year flood was, on average, indicated to occur once every 107 (79–168) years for RCP2.6 and once every 79 years (55–103) in the experiments not considering dams (see Supplementary Fig.  8 ). In RCP6.0, the historical 100-year flood occurred more frequently: once every 59 years (39–110) and 46 years (33–75) for the experiments considering and not considering dams, respectively (see Supplementary Fig.  8 ). In most catchments, dams reduced both the frequency of floods and the extent of flooded areas. Our findings were robust to the selection of indices used to identify floods although the pulsing effect of dams was identified as compromising estimates in some catchments. This problem could be partially mitigated by revising the reservoir operation method used in the present study by accounting for future precipitation variabilities and cascade-dams. Since our large-scale modelling considers daily precipitation, potential dam failure due to increased extreme precipitation events 38 (resulting in downstream flooding) is not fully considered here, nor are the construction and filling phases of a dam’s life cycle. Nevertheless, neglecting the morphological, environmental, and societal impact of dams 39 , our results imply that dams significantly decrease the risk of future global floods in terms of both frequency and intensity, protecting 1.4 (0.7–3.1) and 2.3 (0.8–3.7) million people at the end of the 21 st century, for RCP2.6 and RCP6.0, respectively.

The aging dam landscape faces new temperature, snow, discharge, and floods patterns that increase the risk of hydrological failure 40 , 41 . To maintain historical levels of flood protection in the face of climate change, new dam release operations will be required. In addition, precise and reliable hydro-meteorological forecasts will be invaluable for maximizing flood protection and avoiding untimely and excessive outflows. By focusing solely on the role of dams in reducing global flood exposure under climate change, the results of this study are perceived as over emphasizing the benefits of dams (see Supplementary Note  2 ). However, given the many negative environmental and social impacts of dams 39 , comprehensive assessments that consider both potential benefits and adverse effects are necessary for the sustainable development of water resources. Furthermore, future analyses of global flood risks would benefit from: addressing the disparities and uncertainties associated with global dam and river datasets (e.g. location, characteristics, networks); developing realistic future population projections that account for population behaviour; enhancing historical GCM scenarios by assimilating past observations; and archiving and referencing historical reservoir operations, streamflow, and inundation for robust model validation.

Two hydrological models were used in this study. H08 is an open-source global hydrological model (GHM) that explicitly considers human water abstraction from six major water sources including dams 24 . The reservoir operation scheme in H08 is a generic one; that is, it is not tailored to a specific site. A detailed description can be found in Hanasaki et al. 31 . Outflow from dams is computed in two steps: considering the water currently available in the reservoir, a provisional annual total release is computed, and is then adjusted every month according to changes in storage, inflow, and water demand below the dams. The algorithm distinguishes two classes of dams: irrigation and non-irrigation dams, which influences the computation of monthly water release. It should be noted that, while the storage capacity used in the simulations corresponded to that reported in the Global reservoirs and Dams database (GRanD), the actual storage capacity of dams is expected to be lower due to the allocation of dead and surcharge storages. As a result, the allocated dam storage in the present simulations is likely to have been overestimated. The most recent version of the H08 model, which participated in ISIMIP2b, was employed 24 . Simulations were carried out at a spatial resolution of 0.5° by 0.5°, and a 1-day interval.

CaMa-Flood is a new generation of global river routing model that relies on HydroSHEDS 42 topography to simulate floodplain dynamics and backwater effects by explicitly solving the local inertia equation 33 . The model was reported to outperform other GHMs for reproducing historical discharge 43 . The CaMa-Flood model requires only daily runoff as an input, and by computing the inflow from upstream cells and outflow to downstream, the evolution of water storage can be predicted. In this study, three output variables were used: the total discharge exiting a grid-cell (sum of river discharge and floodplain flow), the flooded area, and the flooded fraction of a grid-cell. To output the latter two variables, CaMa-Flood assesses whether water currently stored in a grid-cell exceeds the total storage of the river section. When this is the case, excess water is then stored in the floodplain, for which topography (dictated by HydroSHEDS) controls the flood stage (water level and flooded area).

To simulate the effects of water regulation due to anthropogenic activities on floodplain dynamics, the H08 and CaMa-Flood models were coupled because, in its current global version (v3.62), the global version of CaMa-Flood cannot simulate dam operations despite being essential for assessing flood risk. Hence, the H08 model is required for accurate forecasts of dam outflow. To ensure compatibility between the models, the river network originally used in CaMa-Flood was employed in both models. The coupling procedure is as follows: simulations with the H08 model are conducted; the daily runoff predicted by H08 is used as a forcing input in CaMa-Flood; in grid-cells containing major dam(s), 44 the river discharge produced by H08 (following the reservoir operating rule) is imposed onto the CaMa-Flood model (Supplementary Fig.  3a ); the difference in daily discharge between the two models due to water regulation is added to the hypothetical storage associated with every dam but without interacting with the river or floodplain to close the water balance.

For grid cells that are neither downstream nor upstream of dams (light blue locations in Supplementary Fig.  3 ), experiments considering and not considering dams produced the same discharge outputs. In contrast, for grid cells located below and above dams, the daily discharge simulated by the experiments considering dams can change compared to the experiments not considering dams due to water regulation (below dams) and the impossibility of the backwater effect and its propagation (above dams).

The four general circulation models (GCMs; GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5) implemented in the ISIMIP2b protocol participated in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). The forcing data consisted of precipitation, temperature, solar radiation (short and long wave downward), wind speed, specific humidity, and surface pressure which were bias corrected 45 and downscaled to a 0.5° by 0.5°-grid resolution. Here we used three radiative forcing scenarios: historical climate (1861–2005), and two future scenarios consisting of a low greenhouse gas concentration emission scenario (RCP2.6; 2006–2099) and a medium–high greenhouse gas concentration (RCP6.0; 2006–2099). Note that the historical climate scenario does not attempt to reproduce the exact day-to-day historical climate but rather gives a consistent evolution of the climate under a given climatic forcing.

Dam specifications (location, storage capacity, and construction year) are provided in GRanD 44 , 46 . The dams were georeferenced to the river network employed in CaMa-Flood, iteratively adjusting dam locations when necessary until the catchment areas of each dam reported in GRanD corresponded to ± 10% of the catchment area in CaMa-Flood 47 .

Experiments

For the future scenarios (RCP2.6 and RCP6.0), two experiments were considered. In the first experiment, dams were not implemented, therefore this simulation is analogous to the simulations conducted in previous studies 3 , 14 . In contrast, in the second experiment, the effect of major global dams on water regulation, hence floodplain dynamics, were considered. Due to water regulation, the future return period (in years) associated with the historical 100-year extreme discharge might change compared to that obtained for the experiment not considering dams (Supplementary Fig.  8 ). These potential differences were used to quantify the effect of dams on the potential reduction in the future return period of the historical 100-year flood.

The H08 model has been extensively validated in catchments located in India, the US, China, Europe, and South America for predicting river discharge, total water storage anomalies, groundwater, and water transfer 24 . Across these major catchments, the average Nash–Sutcliffe efficiency ( NSE ) obtained when comparing daily observed and simulated discharge was positive. Benchmarked against GHMs, H08 was reported to perform relatively well for reproducing historical daily discharges 25 . More relevant to the context of this study, the same study 25 highlighted that the H08 model was among the top four GHMs best able to reproduce the magnitude of extreme discharge and the maximum flows associated with different return periods.

The ability of the CaMa-Flood model to reproduce floodplain inundation was reported in the Amazon basin, where it performed well 33 . In addition, the discharges produced by CaMa-Flood have been evaluated against gauge observations in 30 major river basins 33 . CaMa-Flood has also been benchmarked against nine GHMs, including the H08 model, at 1701 gauge locations 43 . Generally, discharge simulations using CaMa-Flood produce lower and later peak discharges compared to those predicted by other GHMs, resulting in more accurate reproduction of observations 43 .

The quality of discharge data produced by nine GHMs, including the H08 model used in this study, was evaluated and compared against calibrated regional hydrological models in 11 large river basins 48 . While regional models generally outperformed GHMs in most regions, GHMs reproduced the intra-annual variability of water discharge reasonably well. Extreme discharges are strongly related to floods, 5 and the inclusion of human activity in hydrological simulations, such as in H08 has been reported to greatly improve the reproduction of hydrological extremes 49 . The predicted return period for the historical 100-year discharge obtained in the experiment not considering dams was compared to the literature. Global estimates of populations exposed to river floods were also compared to those reported in the literature (Supplementary Table  2 ). We evaluated how the coupled model reproduced river discharges before and after the implementation of dams at key locations. We separated our observation dataset into two parts: pre- and post-dam construction. We then compared our dam and no-dam simulations to the relevant observations. Supplementary Table  3 lists the dam locations of the dams and their key characteristics.

Definition of flood event and extreme discharge

We compared the frequency of historical (1975–2004) and predicted future (RCP2.6 and RCP6.0; 2070–2099) flood events using given two experiments: an experiment in which no dams were considered (analogous to previous studies 3 , 4 , 5 ), and an experiment considering global dams (Supplementary Fig.  2 ) 50 . Flood events were defined as the historical 100-year return extreme discharge, that is, the extreme discharge with a probability of exceeding 1/100 in any given year.

Two annual-extreme discharge indices were used in this analysis to assess the robustness of our findings expressed by the spread (or consistency) of results from multiple GCMs and extreme indices. We primarily focused on the maximum annual daily discharge ( P max ) since it is the preferred index used in the literature 3 , 4 , 5 , 14 . The alternative indicator is the annual 5 th percentile ( P 05 ) of daily discharge.

Before fitting the Gumbel distribution to estimate the 100-year river discharge, we initially compared the two series of extreme discharges in the dam and no-dam experiments. Run-of-the-river dams tend to alter the natural flow regimes only negligibly. For such locations, the fitted Gumbel distribution should be identical in both experiments. In contrast, in rivers heavily regulated by dams, it is possible that the extreme discharge series obtained for the experiment considering dams included many identical or tied values. We initially computed the absolute difference between the annual discharge extremes obtained by the simulation not considering dams minus the simulation considering dams and compared that difference to a given threshold (150 m 3  s −1 , or an annual difference of 5 m 3  s −1 between the extreme discharge generated for the experiments with and without dams). When the threshold was exceeded, the extreme discharge series were considered dissimilar and therefore treated separately. In contrast, when the threshold was not exceeded, the two extreme discharge series were considered similar and all data were pooled before moving to the fitting phase. We assessed the sensitivity of our results to alternative thresholds, with those results reported in Supplementary Table  1 .

Fitting of Gumbel distribution

The extreme discharges were first ranked in ascending order and fitted to a Gumbel distribution using the L-moment method 51 . As a result of the comparison protocol, the number of data to fit was either 60 (experiments with and without dams produced similar extreme discharges and were pooled) or 30 (experiments with and without dams produced different extreme discharges). The fitting process is identical to that described in detail in the Supplementary Note  2 of Hirabayashi et al. 3 .

Assessment of goodness of fit

The goodness of fit of the annual extreme discharge to the Gumbel distribution was assessed using the probability plot correlation coefficient test (PPCC) 52 . While other methods can be used to assess the goodness of fit of the Gumbel distribution, the PPCC has been reported to outperform most of them in terms of rejection performance 53 . The PPCCs were computed for all historical simulations and are reported in Supplementary Fig.  9 . A PPCC score close to 1 indicates that the distribution of the extreme series is well fitted by the Gumbel distribution. For a sample size of 30, the critical PPCC score at the 95 th level of significance was reported 52 to be approximately 0.96.

A bootstrap methodology was used to assess the influence of the 30-year samples on the fitted Gumbel distribution 54 . We generated 1000 bootstrap estimates for every GCM and all experiments. We did not explore all combinations of bootstrap estimates and GCMs due to the high computational cost (1012 estimates for a given year and a single experiment). Instead, we ranked the estimates in descending order before taking the average across GCMs (1000 estimates for a given year and a single experiment). While simple, this method has the advantage of reporting the broadest confidence intervals since the lowest and highest estimates among GCMs are averaged.

In the reported global maps, we masked grid-cells belonging to the Köppen–Geiger regions BWk (hot desert climates), BWh (cold desert climates), and EF (ice cap climates) which discharge corresponding to the historical 30-year return period was less than 5 m 3  s −1 (Supplementary Fig.  4 ). In such grid cells, flooding is not a problem due to the low volume of water discharge. As a result, the goodness of fit of the Gumbel distributions was generally low (as indicated by a low PPCC score in Supplementary Fig.  9 ).

Population exposure

The population dataset, created by the Socioeconomic Data and Applications Center (SEDAC), consists of the Gridded Population of the World (GPW, v4.11) for the year 2010 55 . The population was fixed at 2010 to assess only the effect of climate change on population exposure to floods. To increase the accuracy of our exposure assessment, the original 0.5° resolution flooding depths were downscaled to a resolution of 0.005°. The file containing flooding depth resulting from historical 100-year floods was constructed annually following a two-step procedure. First, we determined the 0.5° grid cells experiencing a 100-year flood as indicated by the annual discharge extreme exceeding the 100-year historical discharge extreme. Second, for such grid cells, we extracted the maximum annual flooding depth, while the flooding depth of other grid cells was set to zero. The files were then downscaled to a 0.005° resolution using routines implemented in CaMa-Flood 33 (see model description). Population exposure to river floods was assessed by overlaying the population and flooding-depth datasets. When flooding water was present in a 0.005° cell, the population within that cell was considered exposed to flooding.

We accounted for population growth in a separate analysis using population projections from 2006 to 2099 based on shared socioeconomic pathways (SSPs) 1 to 5 provided in the ISIMIP2b framework. The time-varying population datasets were first downscaled to a 0.005° resolution. Population exposure to flooding was then determined using the procedure described above.

Catchment selection

Catchments were selected by ensuring that downstream areas were wide, densely populated, and contained major dams. More specifically, the following criteria were used: at least 10 grid cells below dams, a population of at least 5 million residing on the entire main river channel, and the capacity of dams divided by their annual inflow averaged over the number of dams present on the main river channel had to be higher than 0.1. While 15 catchments initially fulfilled these criteria, the Nile catchment was removed from our analysis since a significant portion of its upper section falls within the Köppen–Geiger region BWh (Supplementary Fig.  4 ), which was (partially) screened out of the analysis. The locations of the remaining 14 catchments are given in Supplementary Fig.  6 .

Catchment flood analysis

The analysis consisted of two parts: identifying in which grid cells a flood occurred and extracting the corresponding flooded area for those cells. First, daily discharge, collected annually for the 2070–2099 period, in all grid-cells composing the catchments was converted to annual extreme discharges (considering two indices) and compared to the 100-year return extreme discharge. When the annual extreme discharge was higher than that of the historical 100-year return discharge, a flood was considered to occur in that year. Second, for grid cells where a flood occurred, the maximum flooded area of the grid cell was collected. Finally, we presented the aggregated sum of flood occurrence and flooded area of grid-cells located downstream of dams.

Data availability

The H08 model is open source and its source code is available online ( http://h08.nies.go.jp/h08/index.html ). The source code of the CaMa-Flood model can be requested from D.Y. All input data are available through the ISIMIP2b protocol which is freely accessible ( https://www.isimip.org/ ). Detail explanations regarding the coupling procedure, including the new variables introduced in the model and the source file to edit, are available online ( https://zenodo.org/record/3701166 ).

Code availability

Computer code used for analysis and graphic preparation is available online with explanation ( https://zenodo.org/record/3701166 ).

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Acknowledgements

This work was mainly supported by Environment Research and Technology Development Fund (2RF-1802) of the Environmental Restoration and Conservation Agency (grant number JPMEERF20182R02), Japan. It was partially supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI grant number 16H06291. Y.P. acknowledges the support from the National Science Foundation (CAREER Award, grant number 1752729).

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Boulange, J., Hanasaki, N., Yamazaki, D. et al. Role of dams in reducing global flood exposure under climate change. Nat Commun 12 , 417 (2021). https://doi.org/10.1038/s41467-020-20704-0

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Case Studies

In the first phase of the AFPM, a number of case studies on flood management were collected from various regions, based on the experiences of organizations active in flood management. These case studies were essential in formulating the Integrated Flood Management concepts, as they helped to:

• Identify the extent to which integrated flood management has been carried out;
• Understand shortcoming in flood management practices worldwide;
• Extract lessons learned and good practices in flood management;
• Catalogue the policy changes required to support IFM; and
• Identify the institutional changes required to achieve IFM.

The case studies are presented here for “historical purposes”: having been compiled almost 20 years ago, they are reflecting national situations that might since have developed. As such, the case studies might be used as baseline or reference material for studies that aim to check the improvements in flood management since the beginning of the century.

The Overview Situation Paper on flood management practices extracts the essence of each case study, emphasizes findings and recommendations with relevance to the aspects of Integrated Flood Management and the potential for practices to be replicated in other locations. Download the Overview Situation Paper  here .

Risk Analysis and Uncertainty in Flood Damage Reduction Studies (2000)

Chapter: case studies, case studies.

This chapter illustrates the Corps of Engineers's application of risk analysis by reviewing two Corps flood damage reduction projects: Beargrass Creek in Louisville, Kentucky, and the Red River of the North in East Grand Forks, Minnesota, and Grand Forks, North Dakota. The Beargrass Creek case study describes the entire procedure of risk-based engineering and economic analysis applied to a typical Corps flood damage reduction project. The Red River of the North case study focuses on the reliability of the levee system in Grand Forks, which suffered a devastating failure in April 1997 that resulted in more than $1 billion in flood damages and related emergency services.

The Corps of Engineers has used risk analysis methods in several flood damage reduction studies across the nation, any of which could have been chosen for detailed investigation. Given the limits of the committee's time and resources, the committee chose to focus upon the Beargrass Creek and Red River case studies for the following reasons: committee member proximity to Corps offices, a high level of interest in these two studies, and the availability of documentation from the Corps that adequately described their risk analysis applications.

Differences in approaches taken at Beargrass Creek and along the Red River of the North to reducing flood damages are reflected in these studies. At Beargrass Creek, the primary flood damage reduction measures were detention basins; at the Red River of the North, the primary measures were levees. The Corps uses rainfall-runoff models in nearly all of its flood damage reduction studies to simulate streamflows needed for flood-frequency analysis, and a rainfall-runoff model was employed in the Beargrass Creek study. In the Red River study, however, the goal

was to design a system that would, with a reasonable degree of reliability, contain a flood of the magnitude of 1997's devastating flood. The Corps focused on traditional flood–frequency analysis and manipulated the frequency curve at a gage location to derive frequency curves at other locations (vs. using a rainfall-runoff model to derive those curves).

BEARGRASS CREEK

In 1997 the Corps held a workshop (USACE, 1997b) at which experience accumulated since 1991 in risk analysis for flood damage reduction studies was reviewed. O'Leary (1997) described how the new procedures had been applied in the Corps's Louisville, Kentucky, district office. In particular, O'Leary described an application to a flood damage reduction project for Beargrass Creek, economic analyses for which were done both under the old procedures without risk and uncertainty analysis and under the new procedures that include those factors. Conclusions of the Beargrass Creek study are summarized in two volumes of project reports (USACE, 1997c,d). These documents, plus a site visit to the Louisville district by a member of this committee, form the basis of this discussion of the Beargrass Creek study. The Beargrass Creek data are distributed with the Corps's Hydrologic Engineering Center Flood Damage Assessment (HEC-FDA) computer program for risk analysis as an example data set. The Beargrass Creek study is also used for illustration in the HEC-FDA program manual and in the Corps 's Risk Training course manual. Although there are variations from study to study in the application of risk analysis, Beargrass Creek is a reasonably representative case with which to examine the methodology.

As shown Figure 5.1 , Beargrass Creek flows through the city of Louisville, Kentucky, and into the Ohio River on its south bank. The Beargrass Creek basin has a drainage area of 61 square miles, which encompasses about half of Louisville. The basin currently (year 2000) has a population of about 200,000. This flood damage reduction study's focal point is the lower portion of the basin shown in Figure 5.1 —the South Fork of Beargrass Creek and Buechel Branch, a tributary of the South Fork.

Locally intense rainstorms (rather than regional storms) cause flooding in Beargrass Creek. A 2-year return period storm causes the creek to overflow its banks and produces some flood damage. Under existing conditions, the Corps estimates that a 10-year flood will impact

FIGURE 5.1 The Beargrass Creek basin in Louisville, Kentucky. SOURCE: USACE (1997a) (Figure II-1).

about 300 buildings and cause about $7 million in flood damages, while a 100-year flood will impact about 750 buildings and cause about $45 million in flood damages (USACE, 1997c). The expected annual flood damage under existing conditions is approximately $3 million per year.

Flood Damage Reduction Measures

Beargrass Creek has several flood damage reduction structures, the most notable of which is a very large levee at its outlet on the Ohio River ( Figure 5.2a ). This levee was built following a disastrous flood on the Ohio in January 1937, and the levee crest is an elevation of 3 feet above the 1937 flood level on the Ohio River. During the 1937 flood it was reported that “at the Public Library, the flood waters reached a height such that a Statue of Lincoln appeared to be walking on water!” (USACE, 1997b, p. III-2). Near the mouth of Beargrass Creek, a set of

gates can be closed to prevent water from the Ohio River from flowing back up into Louisville. In the event of such a flood, a massive pump station with a capacity of 7,800 cubic feet per second (cfs) is activated to discharge the flow of Beargrass Creek over the levee and into the Ohio River.

Between 1906 and 1943, a traditional channel improvement project was constructed on the lower reaches of the South Fork of Beargrass Creek. It consists of a concrete lined rectangular channel with vertical sides, with a small low-flow channel down the center ( Figure 5.2b ). The channel's flood conveyance capacity is perhaps twice that of the natural channel it replaced, but the concrete channel is a distinctive type of landscape feature that environmental concerns will no longer permit. Other structures have been added since then, including a dry bed reservoir completed in 1980, which functions as an in-stream detention basin during floods.

The proposed flood damage reduction measures for Beargrass Creek form an interesting contrast to traditional approaches. The emphasis of the proposed measures is on altering the natural channel as little as possible and detaining the floodwaters with detention basins. These basins are either located on the creek itself or more often in flood pool areas adjacent to the creek into which excessive waters can drain, be held for a few hours until the main flood has passed, and then gradually return to the creek. Figure 5.2c shows a grassed detention pond area with a concrete weir (in the center of the picture) adjacent to the creek. Figure 5.2d shows Beargrass Creek at this location (a discharge pipe from the pond is visible on the right side of the photograph). Water flows from the creek into the pond over the weir and discharges back into the creek through the pipe. The National Economic Development flood damage reduction alternative on Beargrass Creek called for a total of eight detention basins, one flood wall or levee, and one section of modified channel. Other alternatives such as flood-proofing, flood warning systems, and enlargement of bridge openings were considered but were not included in the final plan.

The evolution of flood damage reduction on Beargrass Creek represents an interesting mixture of the old and the new—massive levees and control structures on the Ohio River, traditional approaches (the concrete-lined channel) in the lower part of the basin, more modern instream and off-channel detention basins in the upstream areas, and local channel modifications and floodwalls. Maintenance and improvement of stormwater drainage facilities in Beargrass Creek are the responsibility of the Jefferson County Metropolitan Sewer District, which is the principal local partner working with the Corps to plan and develop flood damage reduction measures.

(a) Levee on the Ohio River

(b) Concrete-lined channel

(c) Detention pond

(d) Beargrass Creek at the detention pond

FIGURE 5.2 Images of Beargrass Creek at various locations: (a) the levee on the Ohio River, (b) a concrete-lined channel, (c) a detention pond, and (d) the Beargrass Creek at the detention pond.

In some locations, development has been prohibited in the floodway; but in other places, buildings are located adjacent to the creek. The Corps's feasibility report includes the following comments: “Urbanization continues to alter the character of the watershed as open land is converted to residential, commercial and industrial uses. The quest for open area residential settings in the late 1960s and early 1970s caused a tremendous increase in urbanization of the entire basin. Several developers have utilized the aesthetic beauty of the streambanks as sites for residential as well as commercial developments. This has resulted in increased runoff throughout the drainage area as development has occasionally encroached on the floodplain and, less frequently, the floodway” (USACE, 1997b, p. II-2).

Damage Reaches

To conduct the flood damage assessment, the two main creeks— South Fork of Beargrass Creek and Buechel Branch—are divided into damage reaches. Flood damage and risk assessment results are summarized for each damage reach, and the expected annual damage for the project as a whole is found by summing the expected annual damages for each reach. As shown in Figure 5.3 , the South Fork was divided into 15 damage reaches and the Buechel Branch into 5 reaches (a sixth damage reach on Buechel Branch is not shown in this figure). Approximately 12 miles of Beargrass Creek, and 2.2 miles of Buechel Branch are covered by the these damage reaches. The average length of a damage reach is thus 0.8 miles for the South Fork of the Beargrass Creek, and the average length for Buechel Branch is 0.4 miles. The shorter reaches on Buechel Branch are adjacent to similarly short, upstream reaches in Beargrass Creek where most flood damage occurs. Longer damage reaches are used downstream on Beargrass Creek where less damage occurs.

The highest expected annual flood damage is on Reach SF-9 on the upper portion of the South Fork of Beargrass Creek. Results from this damage reach are used for illustrative purposes at various points in this chapter.

FIGURE 5.3 Damage reaches on the South Fork of Beargrass Creek and Buechel Branch. SOURCE: USACE (1997a) (Figure III-3).

Flood Hydrology

Most of the flood damage reduction measures being considered are detention basins, which diminish flood discharge by temporarily storing floodwater. It follows that the study's flood hydrology component has to be conducted using a time-varying rainfall–runoff model because this allows for the routing of storage water through detention basins. In this case, the HEC-1 rainfall–runoff model from the Corps's Hydrologic Engineering Center (HEC) was used to quantify the flood discharges. The Hydrologic Engineering Center has subsequently released a successor rainfall-runoff model to HEC-1, called HEC-HMS (Hydrologic Modeling System), which can also be used for this type of study (HEC, 1998b).

In each damage reach, and for each alternative plan considered, the risk analysis procedure for flood damage assessment requires a flood – frequency curve defining the annual maximum flood discharge at that location which is equaled or exceeded in any given year with a given probability. In this study all these flood–frequency curves were produced through rainfall–runoff modeling. In other words, a storm of a given

return period was used as input to the HEC-1 model, the water was routed through the basin, and the magnitude of the discharge at the top end of each damage reach was determined (Corps hydrologists have assumed, based on experience in the basin, that storms of given return periods produce floods of the equivalent return period). By repeating this exercise for each of the annual storm frequencies to be considered, a flood–frequency curve was produced for each damage reach. There are eight standard annual exceedance probabilities normally used to define this frequency curve: p = 0.5, 0.2, 0.1, 0.04, 0.02, 0.01, 0.004, and 0.002, corresponding to return periods of 2, 5, 10, 25, 50, 100, 250, and 500 years, respectively. In this study, because even small floods cause damage, a 1-year return period event was included in the analysis and assigned an exceedance probability of 0.999.

Considering that there are 21 damage reaches in the study area and 8 annual frequencies to be considered, each alternative plan considered requires the development of 21 flood–frequency curves involving 168 discharge estimates. During project planning, as dozens of alternative components and plans were considered, the sheer magnitude of the tasks of hydrologic simulation and data assembly becomes apparent.

The hydrologic analysis is further complicated by the fact that the design of detention basins is not simply a cut-and-dried matter. A basin designed to capture a 100-year flood requires a high–capacity outlet structure. Such a basin will have little impact on smaller floods because the outlet structure is so large that smaller events pass through almost unimpeded. If smaller floods are to be captured, a more confined outlet structure is needed, which in turn increases the required storage volume for larger floods. This situation was resolved in the Beargrass Creek study by settling on a 10-year flood as the nominal design event for sizing flood ponds and outlet works. The structures designed in this manner were then subjected to the whole range of floods required for the economic analysis.

Rainfall–Runoff Model

The HEC-1 model was validated by using historical rainfall and runoff data for four floods (March 1964, April 1970, July 1973, February 1990). Modeling results were within 5 percent to 10 percent of observed flows at two U.S. Geological Survey (USGS) streamflow gaging stations: South Fork of Beargrass Creek at Trevallian Way and Middle Fork

of Beargrass Creek at Old Cannons Lane, which have flow records beginning in 1940 and 1944, respectively, and continuing to the present. A total of 42 subbasins were used in the HEC-1 model, and runoff was computed using the U.S. Soil Conservation Service (renamed the Natural Resources Conservation Service in 1994) curve number loss rates and unit hydrographs. The Soil Conservation Service curve numbers were adjusted to allow the matching of observed and modeled flows for the historical events. A 6-hour design storm was used, which is about twice the time of concentration of the basin. The design storm duration chosen is longer than the time of concentration of the basin so that the flood hydrograph has time to rise and reach its peak outflow at the basin outlet while the storm is still continuing. If the design storm is shorter than the time of concentration, rainfall could have ceased in part of the basin before the outflow peaks at the basin outlet. The storm rainfall hydrograph was based on National Weather Service 1961 Technical Paper 40 (NWS, 1961) and on a Soil Conservation Service storm hydrograph, and a 5-minute time interval of computation was used for determining the design discharges.

There is a long flood record of 56 years of data (1940–1996) available in the study area (USGS gage on the South Fork of Beargrass Creek at Trevallian Way). A comparison was made of observed flood frequencies at this site with those simulated by HEC-1, with some adjustment of the older flood data to allow for later development. Traditional flood frequency analysis of observed flow data had little impact in the study. This may have been the case because there was only one gage available within the study area, or because the basin has changed so much over time that the flood record there does not represent homogeneous conditions. Furthermore, the alternatives mostly involve flood storage, which requires computation of the entire flood hydrograph, not just the peak discharge.

Uncertainty in Flood Discharge

Uncertainty in flood hydrology is represented by a range in the estimated flood–frequency curve at each damage reach. In the HEC-FDA program, there are two options for specifying this uncertainty: an analytical method based on the log-Pearson distribution and a more approximate graphical method. The log-Pearson distribution is a mathematical function used for flood–frequency analysis, the parameters of which are determined from the mean, standard deviation, and coefficient

of skewness of the logarithms of the annual maximum discharge data. The graphical method is a flood frequency analysis performed directly on the annual maximum discharge data without fitting them with a mathematical function. In this case the graphical method was used with an equivalent record length of 56 years of data, the length of the flood record of the USGS gage station at Trevallian Way at the time of the study. Figure 5.4 shows the flood–frequency curve for damage reach SF-9 on the South Fork of Beargrass Creek, with corresponding confidence limits based on ± 2 standard deviations about the mean curve.

The confidence limits in this graph are symmetric about the mean when the logarithm to base 10 of the discharge is taken, rather than the discharge itself. This can be expressed mathematically as:

where Q is the discharge value at the confidence limit, log Q is the expected flood discharge, σ log Q is the standard deviation (shown in the rightmost column of Table 5.1 ), and K is the number of standard deviations above or below the mean that the confidence limit lies. Because these confidence limits are defined in the log space, it follows that they are not symmetric in the real flood discharge space. As Table 5.1 shows, the expected discharge for the 100-year flood ( p = 0.01) is 4,310 cfs, the upper confidence limit is 6,176 cfs, and the lower limit is 3,008 cfs. The difference between the mean and the upper confidence limit is thus about 40 percent larger than the difference between the mean and the lower confidence limit. The confidence limits for graphical frequency analysis are computed using a method based on order statistics, as described in USACE (1997d). In this method, a given flood discharge estimate is considered a sample from a binomial distribution, whose parameters p and n are the nonexceedance probability of the flood and the equivalent record length of flood observations in the area, respectively. In this case, n = 56 years, since this is the record length of the Trevallian Way gage.

River Hydraulics

Water surface profiles for all events were determined using the HEC-2 river hydraulics program from the Corps's Hydrologic Engineering Center in Davis, California. Field-surveyed cross sections were obtained

FIGURE 5.4 The flood–frequency curve and its uncertainty at damage reach SF-9 on the South Fork of Beargrass Creek.

at all bridges and at some stream sections near bridges. Maps with a scale of 1 inch = 100 feet with contour intervals of 2 feet were used to define cross sections elsewhere on the stream reaches and were used for measuring the distance between cross sections on the channel and in the left and right overbank areas. Manning's n values for roughness were based on field inspection, on reproduction of known high-water marks from the March 1964 flood on Beargrass Creek, and on reproduction of the rating curve of the USGS gage at Trevallian Way. Manning's equation relates the channel velocity to the channel's shape, slope, and roughness. Manning's n is a numerical value describing the channel roughness. Manning's n values in the concrete channel ranged from 0.015 at the channel invert to 0.027 near the top of the bank. In the natural channels, Manning 's n values ranged from 0.035 to 0.050. In the overbank areas, these values ranged from 0.045 to 0.065. Where buildings blocked the flow, the cross sections were cut off at the effective

TABLE 5.1 Uncertainties in Estimated Discharge Values at Reach SF-9

flow limits. A total of 201 cross sections were used for the South Fork of Beargrass Creek, and 61 cross sections were used for Buechel Branch. The average distance between cross sections was 330 feet on the South Fork of Beargrass Creek and 245 feet on Buechel Branch. Cross sections are spaced more closely than this near bridges and more sparsely in reaches where the cross section is relatively constant.

Figure 5.5 shows the water surface profiles along Beargrass Creek for the eight flood frequencies considered, under existing conditions without any planned control measures. The horizontal axis of this graph is the distance in miles upstream from Beargrass Creek's outlet on the Ohio River. The vertical axis is the elevation of the water surface in feet above mean sea level. The bottom profile in this graph is the channel invert or channel bottom elevation. The top profile is for p = 0.002—the 500-year flood. This particular profile shows a sharp drop near the bottom end of the channel, caused by a bridge at that location that constricts the flow. The flat water surface elevation upstream of the bridge is a backwater effect produced by the inadequate capacity of the bridge opening to convey the flow that comes to it.

For each flood profile computed, the number of structures flooded and the degree to which they are flooded must be assessed. Figure 5.6 shows the locations of the first-floor elevations of structures affected by flooding on the South Fork of Beargrass Creek in relation to several flood water surface profiles under existing conditions. Damage reach SF-9 is located between river miles (RM) 9.960 and 10.363, near the point where there is a sharp drop in the channel bed and water surface elevation on Beargrass Creek. It can be seen that the density of development varies along the channel. Flood damage reduction measures are most effective when they are located close to damage reaches with significant numbers of structures, and they are least effective when they are distant from such reaches.

FIGURE 5.5 Water surface profiles for design floods in Beargrass Creek under existing conditions.

Each damage reach has an index location, which is an equivalent point at which all of the damages along the reach are assumed to occur. On reach SF-9, this index location is at river mile 10.124. To assess damages to structures within each reach, an equivalent elevation is found for each structure at the index location such that its depth of flooding at that location is the same as it would have been at the correct location on the flood profile, as shown in Figure 5.7 .

The technique of assigning an elevation at the index location can be far more complex than Figure 5.7 implies, because allowance is made in the HEC-FDA program for the various flood profiles to be nonparallel and also to change in gradient upstream of the index location compared to downstream. In the Beargrass Creek study, a single flood profile for the p = 0.01 event was chosen, and all other profiles were assumed parallel to this one. One damage reach on Beargrass Creek was subdivided into three subreaches to make this assumption more nearly correct. A spatial distribution of buildings over the damage reach is thus converted

FIGURE 5.6 Locations of structures on floodwater surface profiles along the damage reaches of the South Fork of Beargrass Creek. SOURCE: USACE, 1997c.

FIGURE 5.7 Assignment of structures to an index location.

into a probability distribution of buildings at the index location, where the uncertainty in flood stage is quantified.

Uncertainty in Flood Stage

The uncertainty in the water surface elevation was quantified by assuming that the standard deviation of the elevation at the index location for the 100-year discharge is 0.5 feet. The 100-year discharge at reach SF-9 is 4,310 cfs, which is the next to last set of points in Fugure 5.8 . To the right of these points, between the 100-year and 500-year flood discharges, the uncertainties are assumed to be constant. For discharges lower than the 100-year return period, the uncertainties in stage height are reduced linearly in proportion to the depth of water in the channel. The various lines shown in Figure 5.8 are drawn as the expected water surface elevation ± 1 or 2 standard deviations determined in this manner.

Economic Analysis

The Corps's analysis of a flood damage reduction project's economic costs and benefits is guided by the Principles and Guidelines ( Box 1.1 provides details on the P&G's application to flood damage reduction

FIGURE 5.8 Uncertainty in the flood stage for existing conditions at reach SF-9 of the South Fork of Beargrass Creek.

studies). According to the P&G , the economic analysis of damages avoided to floodplain structures because of a flood damage reduction project is restricted to existing structures (i.e., federal policy does not allow damages avoided to prospective future structures to be counted as benefits). The P&G do, however, call for the benefits of increased net income generated by floodplain activities after a project has been constructed (so-called “intensification benefits”) to be included in the economic analysis.

Economic analysis of flood damages considers various sorts of flood damage, principal among them being the damage to flooded structures. Information about the structures is quantified using a “structure inventory,” an exhaustive tabulation of every building and other kind of structure subjected to flooding in the study region. A separate computer program called Structure Inventory for Damage Analysis (SID) was used

to evaluate the number of structures flooded as a function of water surface elevation. Structures are divided into four categories: single-family residential, multifamily residential, commercial, and public. A structure is considered to be flooded if the computed flood elevation is above its first-floor elevation. The amount of damage D is a function of the depth of flooding h and the type of structure, and is expressed by a factor, r ( h ), which is equal to a percentage of the value of the structure ( V ) and of its contents (C). This analysis can be expressed as

D = r 1 ( h ) V + r 2 ( h ) C . (5.2)

For residential structures, these damage factors were quantified in 1995 by the Federal Emergency Management Agency (FEMA) using data from flood damage claims. For example, for a one-story house without a basement flooded to a depth of 3 feet, the FEMA estimate is that the damage factors are r 1 = 27% of the value of the structure and r 2 = 35% of the value of the contents. For the same house flooded to a depth of 6 feet, the corresponding damage factors are r 1 = 40% for the structure, and r 2 = 45% for the contents, respectively. The Marshall and Swift Residential Cost Handbook (Marshall and Swift, 1999) was used to estimate the value of single- and multi-family structures (it bears mentioning that the use of standard references such as the Marshall and Swift handbook may potentially represent another source of “knowledge uncertainty ”). The values of their contents were assumed to be 40 percent to 44 percent of the value of the structure. For commercial and public buildings, the values of the structures and their contents were established through personal interviews by Corps personnel. About 85 percent of the structures subject to flood damage are residential buildings.

Types of flood damages beyond those to structures were also considered. For instance, there are several automobile sales lots in the floodplain, and prospective damages to cars parked there during a flood were estimated. Nonphysical damage costs include the costs of emergency services and traffic diversion during flooding. Damage to roads and utilities were also considered.

Uncertainty in Flood Damage

The economic analysis has three sources of uncertainty:

the elevation of the first floor of the building,

the degree of damage given the depth of flooding within the building, and

the economic value of the structure and its contents.

For most structures in Beargrass Creek, the first-floor elevation was estimated from the ground elevation on maps with a scale of 1 inch = 100 feet and with contour intervals of 2 feet. For a sample of 195 structures (16% of the total number), the first-floor elevations were surveyed. It was found that the average difference between estimated and surveyed first-floor elevations of these structures was 0.62 feet.

Corps Engineering Manual (EM) 1110-2-1619 (USACE, 1996b) was used to estimate values for the uncertainties in economic analysis. A standard deviation of 0.2 feet was used to define the uncertainty in first-floor elevations. The uncertainty in the degree of damage given a depth of inundation was estimated by varying the percent damage factor described previously. For residential structures the value of the structure was assigned a standard deviation of 10 percent of the building value, and the ratio of the value of the contents to the structure was allowed to vary with a standard deviation of 20 percent to 25 percent.

For commercial property a separate damage estimate, based on interviews with the owners, was made for each significant property and was expressed as a triangular distribution with a minimum, expected, and maximum damage value for the property. Because every individual structure potentially affected by flooding is inventoried in the damage estimate data, the amount of work required to collect all these damage data was extensive.

The end result of these estimates at each damage reach and damage category is a damage–stage curve (such as Figure 5.9 ) that accumulates the damage to all multifamily structures in this damage reach for various water surface elevations at the index location, denoted by stage on the horizontal axis. This curve is prepared by first dividing the range of the stage (476–486 feet) into increments —increments of 0.5 feet in this case. For each structure, a cycle of 100 Monte Carlo simulations is carried out in which the first-floor elevation and the values of the structure and contents are randomly varied. From these simulations estimates are formed for each 0.5-foot stage height increment of what the expected damage and standard deviation of the damage to that structure would be if the flood stage were to rise to that elevation. For each stage increment, these means and standard deviations are accumulated over all structures in the

reach to form the estimate of the mean and standard deviation of the reach damage ( Figure 5.9 ).

A similar function is prepared for each of the damage categories. At any flood stage, the sum of the damages across all categories is the total flood damage for that reach.

Project Planning

The discussion of the Beargrass Creek study reviewed the technical means by which a particular flood damage reduction plan is evaluated. A plan consists of a set of flood damage reduction measures, such as detention ponds, levees or floodwalls, and channel modifications, implemented at particular locations on the creek. The base plan against which all others are considered is the “without plan,” which means a plan that considers existing conditions in the floodplain and the development expected to occur even in the absence of a flood damage reduction plan. Such development must meet floodplain management policies and have structures elevated out of the 100-year floodplain. A base year of 1996 was chosen for the Beargrass Creek study.

In carrying out project planning, the spatial location of the principal damage reaches is important because flood damage reduction measures located just upstream of or within such reaches have greater economic impact than do flood damage reduction measures located in areas of low flood damage. Project planning also involves a great deal of interaction with local and state agencies, in this case principally the Jefferson County Metropolitan Sewer District.

The Beargrass Creek project planning team consisted primarily of three individuals in the Corps's Louisville district office: a project planner from the planning division, a hydraulic engineer from the hydrology and hydraulics design section, and an economic analyst from the economics branch. The HEC-FDA computer program with risk analysis was carried out by the economic analyst using flood–frequency curves and water surface profiles supplied by the hydrology and hydraulics section and using project alternatives defined by the project planner. The hydrology and hydraulics section was also responsible for the preliminary sizing of potential project structures being considered as plan components. The bulk of the work of implementing the risk analysis aspects of flood damage assessment thus fell within the domain of the Corps economic analyst.

The HEC-FDA program is applied during the feasibility phase of

FIGURE 5.9 The damage–stage curve with uncertainty for multifamily residential property in Reach SF-9 of the South Fork of Beargrass Creek.

flood damage reduction planning. This had been preceded by a reconnaissance phase, a preliminary assessment of whether reasonable flood damage reduction planning can be done in the area. As explained in Chapter 2 , the reconnaissance phase is fully funded by the federal government, but the feasibility phase must have half the costs met by a local sponsor. Assuming the feasibility phase yields an acceptable plan and additional funds are authorized, the project proceeds to a detailed design and construction phase, which also requires local cost sharing. The Beargrass Creek project is now (as of May 2000) in the detailed design phase.

Evaluation of Project Alternatives

Expected annual flood damages in Beargrass Creek under existing conditions are estimated to be $3 million. Project benefits are calculated as the difference between this figure and the lower expected annual damages that result with project components in place. Project costs are annualized values of construction costs discounted over a 50-year period using an interest rate of 7.625 percent. Project net benefits are the differ-

ence between project benefits and costs. For components to be included in the project, they must have positive net benefits.

The first step in evaluating project alternatives is to consider each component flood damage reduction measure by itself to see if it yields positive net benefits. A total of 22 components were examined individually, 11 on the South Fork of Beargrass Creek and 11 on Buechel Branch. All 11 of the South Fork components were economically justified on a stand-alone basis. Only 3 of the 11 components on Buechel Branch were justified individually: the other 8 components were thus deleted from further consideration.

The next step is to formulate the National Economic Development (NED) plan. In theory, this is supposed to proceed by selecting first the component with the largest net benefits, adding the component with the next largest net benefits, evaluating them together, and continuing to add more components until the combined set of components has the largest overall net benefits. It turned out that this idealized approach could not be used at the South Fork of Beargrass Creek because of economic and hydraulic interactions among the components. The study team commented: “Therefore, the formulation process was different and more complicated than originally anticipated. The study team could not follow the incremental analysis procedure to build up the NED plan because the process became a loop of H&H computer runs. Our component with the greatest net benefits is located near the midpoint of the stream; thus, each time we would add a component upstream it would affect all components downstream and vice versa. We could never truly optimize or identify the plan which produces the greatest net benefits” (USACE, 1997c, p. IV-62).

The problems were further complicated by the fact that there are three separate sections of the study region: the South Fork of Beargrass Creek and Buechel Branch upstream of their junction and the South Fork downstream of this junction ( Figure 5.3 ). In the downstream region, flood damage reduction measures on the upper South Fork and Buechel Branch compete for project benefits by reducing flood damages. The result of these complications is that the plan was built up incrementally by separately considering the three sections of the region. First, the most upstream control structure in each section was selected, then structures downstream were added. At the end—when the components from the three sections had been aggregated into a single overall plan—it was determined whether the plan could be improved by omitting individual marginal components. The end result of this iterative process was a recommended plan with 10 components: 8 detention basins, 1 floodwall,

and 1 channel improvement.

Each plan has to be evaluated using the Monte Carlo simulation process. The number of simulations varies by reach, with 10,000 required for Reach SF-9 and with a range of 10,000–100,000 required for the other reaches. On a 300 MHz Pentium computer, evaluation of a single plan takes about 25 minutes of computation time.

Risk of Flooding

The HEC-FDA program also produces a set of statistics that quantify the risk of being flooded in any reach for a given plan, as shown in Table 5.2 . For reach SF-9, the target elevation is 477.2 feet, which is the elevation of the overbank area in this reach. The probability estimates shown are annual exceedance probability and conditional nonexceedance probability. The annual exceedance probability refers to the risk that flooding will occur considering all possible floods in any year. The conditional nonexceedance probability describes the likelihood that flooding will not occur during a flood of defined severity, such as the 100-year (1 percent chance) flood.

There is a subtle but important distinction between these two types of risk measures. The annual exceedance probability accumulates all the uncertainties into a single estimate both from the natural variability of the unknown severity of floods and from the knowledge uncertainty in estimating methods and computational parameters. The conditional non-exceedance probability estimate divides these two uncertainties, because it is conditional on the severity of the natural event and thus represents only the knowledge uncertainty component. In this sense, the conditional nonexceedance probability corresponds most closely to the traditional idea of adding 1 foot or 3 feet on the 100-year base flood elevation, while the annual exceedance probability corresponds more closely to the goal of ensuring that the chance of being flooded is less than a given value, such as 1 percent, considering all sources of uncertainty.

The “target stage annual exceedance probability” values in Table 5.2 are the median and the expected value or mean of the chance that flooding will occur in any given year for the various reaches. Thus, for reach SF-9, there is approximately a 36 percent chance that flooding will occur beyond the target stage in any given year, while in reach SF-14 upstream, that chance is only about 9 percent. The “long term risk” values in the

TABLE 5.2 Risk of Flooding in Damage Reaches Calculated Uncertainty for 1996 at Beargrass Creek

figure refer to the chance (Rn) that there will be flooding above the target stage at least once in n years, determined by the formula

R n = 1− (1− p e ) n , (5.3)

where p e is the expected annual exceedance probability. For example, for reach SF-9, where p e = 0.3640, for n = 10 years, R 10 = 1− (1 − 0.3640) 10 = 0.9892, as shown in Table 5.2 .

The conditional nonexceedance probability values shown on the right-hand side of Table 5.2 are conditional risk values that correspond to the reliability that particular floods can be conveyed without causing damage in this reach. Thus, in reach SF-9, a 10 percent chance event (10-year flood) has about a 0.27 percent chance of being conveyed without exceeding the target stage, while for a 1 percent chance event (100-year flood), there is essentially no chance that it will pass without exceeding the target stage. By contrast, in Reach SF-14 at the upstream end of the study area, the conditional nonexceedance probability of the reach passing the 10-year flood is about 52 percent; that of the reach passing the 100-year flood is about 100 percent. As the flood severity increases, the chance of a reach being passed without flooding diminishes.

Effect on Project Economics of Including Risk and Uncertainty

The HEC-FDA program that includes risk and uncertainty factors in project analysis became available to the Beargrass creek project team late in the study period. Before then, the team used an earlier economic analysis program (Expected Annual Damage, or EAD) which computed expected annual damages without these uncertainties. O' Leary (1997) presented the data shown in Table 5.3 to compare the two approaches. It is evident that including risk and uncertainty increases the expected annual damage both with and without flood damage reduction plans. The net effect of their inclusion on the Beargrass Creek project is to increase the annual flood damage reduction benefits from $2.078 million to $2.314 million. The study team made a comparison between the components included in the National Economic Development plan in the two computer programs and found that there was no change. Hence, although the inclusion of risk and uncertainty increased project benefits, it did not result in changing the flood damage reduction components included in the National Economic Development plan.

O'Leary (1997) also presented statistics of the project benefits derived from the HEC-FDA program for the National Economic Development plan. The expected annual benefits of the National Economic Development plan—$2.314 million—are the same in Table 5.3 and Table 5.4 . The net benefits in the fourth column of Table 5.4 are found by subtracting the annual project costs from the expected annual benefits; the benefit-to-cost ratio is the ratio of the expected benefits to costs.

The 25 th percentile, median (50 th percentile), and 75 th percentile of the expected annual benefits are also shown. The project net benefits are positive at all levels of assessment, and all benefit-to-cost ratios are greater than 1.00. It is interesting to see that the median expected annual benefits ($2.071 million) are nearly the same as the expected value of these benefits without considering uncertainty ($2.078 million). Moreover, the expected value ($2.314 million) is greater than the median, and the difference between the 75 th percentile and the median is greater than the difference between the median and the 25 th percentile. All these characteristics point to the fact that the distributions of flood damages and of expected annual benefits are positively skewed when uncertainties in project hydrology, hydraulics, and economics are considered. This is why the project benefits increase when these uncertainties are considered. The project benefits for the 25 th percentile, 50 th percentile, and 75 th percentile in Table 5.4 should be read with caution because they are compiled for the project by adding together the corresponding values for all the damage reaches. The percentile value of a sum of random variables is not necessarily equal to the sum of the percentile values of each variable.

TABLE 5.3 Expected Annual Damages (EAD) With and Without Uncertainty in Damage Computations (millions of dollars per year)

TABLE 5.4 Statistics of project benefits under the NED plan using the HEC-FDA Program

RED RIVER OF THE NORTH AT EAST GRAND FORKS, MINNESOTA, AND GRAND FORKS, NORTH DAKOTA

A devastating flood occurred at East Grand Forks, Minnesota, and Grand Forks, North Dakota, in April 1997. After the flood, flood damage reduction studies previously done for the two cities were combined into a joint study, and risk analysis was performed to evaluate the reliability of the proposed alternatives and to evaluate their economic impacts. A risk analysis study performed before the flood was presented in a paper at the Corps's 1997 Pacific Grove, California, workshop (Lesher and Foley, 1997). This paper and subsequent analysis (USACE, 1998a, b, c), as well as a visit to the Corps's St. Paul district office by a member of this committee, form the basis of this discussion of the East Grand Forks–Grand Forks study.

East Grand Forks, Minnesota, and Grand Forks, North Dakota, are located on opposite banks of the Red River of the North and are approximately 300 miles above the river's mouth at Lake Winnipeg, Manitoba, Canada ( Figure 5.10 ). The East Grand Forks–Grand Forks metropolitan area has a population of approximately 60,000 and is located about 100 miles south of the U.S.–Canadian border. The total drainage area of the East Grand Forks–Grand Forks basin is 30,100 square miles. Included in this drainage area is the Red Lake River subbasin that effectively drains about 3,700 square miles in Minnesota and joins the mainstream of the Red River at East Grand Forks. The study area of East Grand Forks–Grand Forks lies in the middle of the Red

FIGURE 5.10 Schematic of the Red River of the North (RRN) and Red Lake River (RLR) at the East Grand Forks, Minnesota and Grand Forks, North Dakota study area. Numbers indicate USGS stream gages.

River Valley. The valley is exceptionally flat with a gradient that slopes 3–10 feet per mile toward the river with the north–south axis having a gradient of about three-quarters of a foot per mile. The valley extends approximately 23 miles west and 35 miles east of East Grand Forks– Grand Forks and is a former glacial lake bed.

Both cities have a long history of significant flooding from the Red River of the North and the Red Lake River. The most damaging flood of record occurred in April 1997 (see Table 5.5 ), when the temporary levee systems and flood-fighting efforts of both communities could not hold back the floodwaters of the Red River. The resulting damages were disastrous and affected both cities dramatically. Total damages to existing structures and contents during the 1997 flood were estimated to exceed $800 million. An additional $240 million was spent for emergency-related costs.

TABLE 5.5 Maximum Recorded Instantaneous Peak Flows; Red River of the North at Grant Forks, North Dakota

Risk Analysis

A risk analysis for the proposed flood damage reduction project for the Red River of the North at East Grand Forks, Minnesota, and Grand Forks, North Dakota, used a Latin Hypercube analysis to sample interactions among uncertain relationships associated with flood discharge and elevation estimation. Latin Hypercube is a stratified sampling technique used in simulation modeling. Stratified sampling techniques, as opposed to Monte Carlo-type techniques, tend to force convergence of a sampled distribution in fewer samples. Because the Hydrologic Engineering Center Flood Damage Analysis program (HEC-FDA) was new at the time, and in the interest of saving time, the analysis was performed using a spreadsheet template. The flood damage reduction alternatives analyzed included levees of various heights and a diversion channel in conjunction with levees. The project reliability option in the HEC risk spreadsheet was used to determine the reliability of the alternative levee heights and of the diversion channel in conjunction with levees. The following sections discuss the sensitivity in quantifying the uncertainties and the representation of risk for the alternatives.

Discharge–Frequency Relationships

The log-Pearson Type III distribution, recommended in the Water Resource Council's Bulletin 17B (IACWD, 1981) and incorporated

within the Corps's HEC Flood Frequency Analysis (HEC-FFA) computer program, was used for frequency analysis of maximum annual streamflows, and the noncentral t distribution was used for the development of confidence limits. Discharge–frequency relationships were needed for both the levees and the diversion channel in combination with levees. An analysis (coincidental frequency) was performed to develop the discharge– frequency curves for the Red River of the North downstream and upstream of the Red Lake River for the levees only condition. A graphical method was used to develop the discharge–frequency curves for the diversion channel in combination with levees. Details of these procedures can be found in a Corps instruction manual from the St. Paul district (USACE, 1998a). A brief discussion of these procedures is provided below.

The Grand Forks USGS stream gage (XS 44) is currently located 0.4 miles downstream from the Red Lake River in Grand Forks, North Dakota ( Figure 5.10 ). The discharge–frequency curve for this station along with the 95 percent and 5 percent confidence limits (90% confidence band) are plotted in Figure 5.11 . An illustration of the noncentral t probability density function for the 1 percent event is also shown in that figure. Selected quantities of that discharge–frequency relationship are shown in column 2 of Table 5.6 . The coincidental discharge–frequency relationship for the Red River just upstream of the mouth of the Red Lake River (column 3 of Table 5.6 ) was computed with the HEC-FFA computer program. The basic flow values were obtained by routing the 96 years of available data on Red Lake River flows from Crookston (55 miles upstream of the mouth) downstream to Grand Forks. The resulting flows were subtracted from the Red River at Grand Forks flows to obtain coincident discharges on the Red River upstream of Red Lake River. The two-station comparison method of Bulletin 17B was used to adjust the logarithmic mean and standard deviation of this short record (96 years) based on regression analysis with the long-term record at the Grand Forks station (172 years). Correlation of coincident flows for the short record with concurrent peak flows for the long record produced a correlation coefficient of 0.975.

Adjustment of the statistics yielded an equivalent record length of 165 years. The adopted coincidental discharge–frequency curve for the Red River upstream of the Red Lake River is shown in column 3 of Table 5.6 for selected annual exceedance probabilities. The coincidental discharge –frequency curve for the Red Lake River at the mouth was determined by computing the difference in Red River flows both upstream and downstream of Red Lake River (see column 4 in Table 5.6 ). Statistics for the adopted relationship were approximated by synthetic methods presented in Bulletin 17B (for more details, see USACE (1998a)).

FIGURE 5.11 Flood (discharge) frequency curve for the Red River at Grand Forks.

TABLE 5.6 Instantaneous Annual Peak Discharges (cfs) and their Annual Exceedance Probabilities (%) — Existing Conditions

and downstream of Red Lake River (see column 4 in Table 5.6). Statistics for the adopted relationship were approximated by synthetic methods presented in Bulletin 17B (for more details, see USACE (1998a)).

The Plan Comparison Letter Report developed in February 1998 for flood damage reduction studies for East Grand Forks, Minnesota, and Grand Forks, North Dakota, evaluated an alternative flood damage reduction plan that included a split-flow diversion channel along with permanent levees. The discharge–frequency relationships for the modified conditions, shown in Table 5.7 , were developed as follows. The modified-condition discharge–frequency curve for the Red River upstream of Red Lake River was graphically developed based upon the operation of the diversion channel inlet. Red River flows are not diverted until floods start to exceed those having return periods of 5 years (20% annual exceedance probability). The channel is designed to continue to divert Red River flows at a rate that allows the design flood (0.47%) discharge of 102,000 cfs (upstream of the diversion) to be split such that 50,500 cfs is diverted and 51,500 cfs is passed through the cities. This operation is reflected in the modified discharge–frequency relationship shown in Table 5.7 for the Red River upstream of Red Lake River (columns 2 and

TABLE 5.7 Instantaneous Annual Peak Discharges (cfs) and their Annual Exceedance Probabilities (%)—Condition with Diversion Channels

3).Synthetic statistics (mean, standard deviation, and skewness) in accordance with methodology presented in Bulletin 17B were computed for the discharge-frequency relationships of the below-diversion flows.

The modified-condition discharge–frequency curve for the Red River downstream of Red Lake River was graphically computed based upon the operation of the diversion channel. The modified-condition Red River discharges upstream of Red River were added to the coincident flows on Red Lake River (column 4). The resulting discharges were plotted for graphical development of the modified-condition discharge– frequency relationship for the Red River downstream of Red Lake River and are summarized in Table 5.7 (column 5). Synthetic statistics for this discharge–frequency relationship were computed for use in the risk analysis.

Elevation–Discharge Relationships

The water surface elevations computed using the HEC-2 computer program are shown in Table 5.8 for three cross sections (7790, 7800, and 7922) corresponding to the previous USGS gage locations and for cross

section 44, which corresponds to the current USGS gage location (see Figure 5.10 for the cross section locations). These computed water surface elevations (CWSE) were based on the expected discharge quantities from the coincidental frequency analysis performed in June 1994 for the Grand Forks Feasibility Study. These data were used to transfer observed elevations from previous USGS gage sites to the current site (cross section 44) at river mile 297.65, and they were used in determining the elevation –discharge uncertainty. The water surface profile analysis was performed using cross-sectional data obtained from field surveys. Data were also obtained from field surveys and from USGS topographic maps. The HEC-2 model was calibrated to the USGS stream gage data and to high-water marks for the 1969, 1975, 1978, 1979 and 1989 flood events throughout the study area. Note that these water surface elevations assume the existing East Grand Forks and Grand Forks emergency levees are effective. The levees were assumed effective because through extraordinary efforts, they have generally been effective for past floods with the exception of the 1997 flood.

Ratings at stream gage locations provide an opportunity to directly analyze elevation–discharge uncertainty. The measured data are used to derive the “best fit” elevation-discharge rating at the stream gage location, which generally represents the most reliable information available. In this study, the adopted rating curve for computing elevation uncertainty is based on the computed water surface elevations from the calibrated HEC-2 model shown in Table 5.8 .

This adopted rating curve for cross section 44 at the current USGS gage is shown in Figure 5.12 . Measurements at the gage location were used directly to assess the uncertainty of the elevation–discharge relationship. The normal distribution was used to describe the distribution of error from the “best-fit” elevation–discharge rating curve. The observed gage data (for the four cross sections presented in Table 5.8 ) were transferred to the current gage site at river mile 297.65 based on the gage location adjustments presented in Table 5.9 , which were computed from the water surface elevations in Table 5.8 . These adjustments were plotted against the corresponding discharge below the Red Lake River, and curves were developed to obtain adjustments for other discharges.

The deviations of the observed elevations from the fitted curve were used to estimate the uncertainty of the elevation–discharge rating curve shown in Figure 5.11 . The deviations reflect the uncertainty in data values as a result of changes in flow regime, bed form, roughness/resistance to flow, and other factors inherent to flow in natural streams. Errors also

TABLE 5.8 Computed Water Surface Elevations of the Red River of the North at Grand Forks, North Dakota (units in feet above sea level)

FIGURE 5.12 Rating curve (water elevation vs. discharge)for the Red River at Grand Forks.

TABLE 5.9 Adjustments Used in Transferring Observed Elevations from Previous USGS Gage Sites to Current Gage Site at RM 297.65 (XS 44)

result from field measurements or malfunctioning equipment. A minimum of 8–10 measurements is normally required for meaningful results. The measure used to define the elevation–discharge relationship uncertainty is the standard deviation:

Where X = observed elevation adjusted to current gage location (if 5.12 necessary), M = computed elevation from adopted rating curve, and N = number of measured discharge values (events).

The elevation uncertainty was computed for two different discharge ranges for this analysis. Based on the observed elevations plotted on the adopted rating curve, it appeared that there was greater uncertainty for discharges less than about 10% of annual exceedance probability event due to ice effects on flow. Therefore, the standard deviation was computed for discharges greater than between 22,000 cfs, which corresponds approximately to the zero damage elevation based on the adopted rating curve, and 44,000 cfs, which is slightly greater than the 10 percent annual exceedance probability. The standard deviation was also computed for discharges greater than 50,000 cfs. During the period of record, there were 25 events with a discharge between 22,000 and 44,000 cfs and 10 events with a discharge greater than 50,000 cfs. The standard deviation was 1.66 feet for discharges between 22,000 and 44,000 cfs and was 1.55

feet for discharges greater than 50,000 cfs. In the risk and uncertainty simulations, the standard deviation was linearly interpolated between 1.66 and 1.55 feet for discharges between 44,000 and 50,000 cfs. (See USACE (1998b) for more details.)

In an earlier risk analysis that was performed for the Grand Forks Feasibility Study, a much lower standard deviation of 0.50 feet was used for discharges greater than 50,000 cfs. However, adding the 1997 flood to the analysis resulted in a standard deviation of 1.55 feet, which is similar to that computed for discharges less than 44,000 cfs. It should be noted that the discharge and elevation used in this analysis for the 1997 flood was the peak discharge of 136,900 cfs occurring on April 18, 1997 (see Table 5.4 ), and an elevation of 831.21 feet (Stage 52.21). The peak elevation of 833.35 feet (Stage 54.35) occurred on April 22, 1997 at a discharge of 114,000 cfs. The elevation of 831.21 feet was almost 5 feet below the rating curve at a discharge of 136,900 cfs; however, the peak elevation of 833.35 feet at a discharge of 114,000 cfs was essentially on the adopted rating curve. Both of these points are plotted on the rating curve in Figure 5.12 . Lines representing ± 2 standard deviations for the normal distribution, which encompasses approximately 95 percent of all possible outcomes, are also shown on the rating curve. An illustration of the normal distribution at the 1 percent (100-year) event for the project levee condition is also shown in Figure 5.12 .

Risk and Uncertainty Analysis Results

Four index locations were selected to evaluate project performance and project sizing. These locations are cross sections 57, 44 (current USGS gage), 27, and 15 ( Figure 5.10 ). The four locations were selected based on economic requirements for project sizing (see USACE, 1998c). The elevation–discharge rating curves (based on HEC-2 analysis) for existing and project conditions at these locations can be found in the USACE (1998b). Each of these rating curves shows three conditions, where applicable: (1) existing conditions, (2) removal of the pedestrian bridge at cross sections 7920-7922 and with project levees (“levee only”); and (3) with removal of the pedestrian bridge, with project levees, and with the diversion channel (“diversion channel”). Existing conditions means that the existing emergency levees are assumed to be effective up to and including the 5 percent (20-year) event and are ineffective for larger floods. The 5 percent (20-year) event was selected based

on comparison of water surface profiles with effective and probable failure point (PFP) levee elevations provided by the Geotechnical Design Section analysis (see USACE, 1998b, paragraph A.2.11 and Appendix B of this report). The pedestrian bridge was removed based on input from the cities of East Grand Forks and Grand Forks. The rating curves for the diversion channel alternative were based on limited information. The Red River to the North would start to divert into the diversion channel at the 20 percent (5-year) flood; therefore, up to this point the rating curve for existing conditions with levees was used.

An additional location was also selected to evaluate the performance of the levee only and diversion channel with 1 percent (100-year) levee alternatives. This location is at cross section 7700 at the downstream end of the project levees (see Figure 5.10 ). Cross section 7700 was selected based on hydraulic analysis as the least critical location—the location where the levees in combination with the diversion channel would first overtop from downstream backwater (see USACE, 1998b).

Project Reliability

The project reliability results are summarized in Table 5.10 , Table 5.11 through Table 5.12 . Table 5.9 contains the results for the levees-only alternatives. Table 5.11 contains the results for the diversion channel in combination with 1 percent (100-year) levees. Note that in Table 5.10 , three different alternative top-of-levee heights are evaluated, whereas in Table 5.11 , it is always the same alternative—diversion channel with 1 percent levees— but for the three different events. The top-of-levee elevations were computed based on a water surface elevation profile to ensure initial overtopping would occur at the least-critical location (here, cross section 7700). The downstream top-of-levee elevations were selected with the intent of having 90 percent probability of containing the specified flood and were based on previous risk analysis for the Grand Forks Feasibility Study preliminarily updated to include the 1997 flood. The 2 percent (50-year), 1 percent (100-year), and 0.47 percent (210-year/1997 flood) top-of-levee profiles are 3.2, 3.4, and 2.7 feet above their respective water surface profiles at the downstream end ( Table 5.10 ).

As seen in Table 5.10 , the intent of having 90 percent probability of containing the specified flood is generally realized. The 2 percent levees have a 92 percent probability of containing the 2 percent flood. The 1 percent levees have a 90 percent probability of containing the 1 percent

flood. The 0.47 percent levees have an 87 percent probability of containing the 0.47 percent flood.

TABLE 5.10 Reliability at Top of Levee for Three Top-of-Levee Heights

TABLE 5.11 Project Reliability at Top of Levee for Diversion Channel with 1 Percent (100-Year) Levees for Three Different Events

Reliability results for the diversion channel with 1 percent levees are summarized in Table 5.11 . Note again that the levees constructed in combination with the diversion are the same as for the 1 percent flood without the diversion channel and are the same for all three floods analyzed. As seen in the table, there is a 99 percent or greater probability of containing the flood for all three floods considered when the project includes the diversion channel.

As previously noted, the most critical location for project performance is at cross section 7700 at the downstream end of the project. Table 5.12

summarizes the results for all the alternatives considered and for numerous floods. The probability of the diversion channel in combination with 1 percent levees for the 0.2 percent event is listed in the table as greater than 95%. A more specific reliability was not cited for the 0.2 percent event for two reasons: (1) the discharge–frequency curve based on the approximate statistics starts to diverge from the graphical curve for extreme events and, (2) there was limited information available to develop the Red River to the North rating curves for the diversion alternative. These reasons are also why more extreme events were not analyzed.

TABLE 5.12 Conditional Exceedance Probability of Alternative for Various Events (based on analysis at downstream end of project—XS 7700)

Table 5.13 presents the simulated conditional exceedance probabilities from the economic project sizing analysis. The without-project condition is also included in this table for comparison purposes. The without-project condition is based on a zero damage elevation of 824.5 feet, assumes credit is given to the existing levees, and assumes all properties that were substantially damaged (50% or more damage) in the 1997 flood have been removed.

Based on the above analysis of alternative plans and further economic and environmental considerations, the recommended National

TABLE 5.13 Residual Risk Comparison

Economic Development (NED) plan consists of a permanent levee and floodwall system designed to reliably contain the 210-year flood event. This equates to an 87.7 percent reliability of containing the 210-year flood event ( Table 5.12 ) and would reliably protect against a flood of the magnitude of the 1997 flood.

The recommended plan would remove protected areas from the regulatory floodplain, increase recreational opportunities, and enhance the biological diversity in the open space created. The recommended plan anticipates the need to acquire over 250 single-family residential structures, 95 apartment or condominium units, and 16 businesses along the current levee/floodwall alignment.

The total cost of the recommended multipurpose project is $350 million including recreation features and cultural resources mitigation costs. The federal share of the project would be $176 million and the nonfederal share would be $174 million. The benefit-to-cost ratio has been calculated as 1.07 for the basic flood reduction features of the project and as 1.90 for the separable recreation features (USACE, 1998b). The recommended project has an overall benefit-to-cost ratio of 1.10.

The cities of East Grand Forks, Minnesota, and Grand Forks, North Dakota, will serve as the project's nonfederal sponsors. Through legislation, the State of Minnesota has committed to provide financial support in the form of bonds and returned sales taxes to the city of East Grand Forks. In verbal and written comments from its governor, the State of North Dakota has committed to provide financial assistance to the city of Grand Forks.

Reducing flood damage is a complex task that requires multidisciplinary understanding of the earth sciences and civil engineering. In addressing this task the U.S. Army Corps of Engineers employs its expertise in hydrology, hydraulics, and geotechnical and structural engineering. Dams, levees, and other river-training works must be sized to local conditions; geotechnical theories and applications help ensure that structures will safely withstand potential hydraulic and seismic forces; and economic considerations must be balanced to ensure that reductions in flood damages are proportionate with project costs and associated impacts on social, economic, and environmental values.

A new National Research Council report, Risk Analysis and Uncertainty in Flood Damage Reduction Studies , reviews the Corps of Engineers' risk-based techniques in its flood damage reduction studies and makes recommendations for improving these techniques. Areas in which the Corps has made good progress are noted, and several steps that could improve the Corps' risk-based techniques in engineering and economics applications for flood damage reduction are identified. The report also includes recommendations for improving the federal levee certification program, for broadening the scope of flood damage reduction planning, and for improving communication of risk-based concepts.

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Flooding Case studies

Cockermouth, UK – Rich Country (MEDC) Picture Causes: Rain A massive downpour of rain (31.4cm), over a 24-hour period triggered the floods that hit Cockermouth and Workington in Cumbria in November 2009

What caused all the rain? The long downpour was caused by a lengthy flow of warm, moist air that came down from the Azores in the mid-Atlantic. This kind of airflow is common in the UK during autumn and winter, and is known as a ‘warm conveyor’. The warmer the air is, the more moisture it can hold.

What else helped to cause the Cumbrian Floods? · The ground was already saturated, so the additional rain flowed as surface run-off straight into the rivers · The steep slopes of the Cumbrian Mountains helped the water to run very rapidly into the rivers · The rivers Derwent and Cocker were already swollen with previous rainfall · Cockermouth is at the confluence of the Derwent and Cocker (i.e. they meet there)

The effects of the flood · Over 1300 homes were flooded and contaminated with sewage · A number of people had to be evacuated, including 50 by helicopter, when the flooding cut off Cockermouth town centre · Many businesses were flooded causing long-term difficulties for the local economy · People were told that they were unlikely to be able to move back into flood-damaged homes for at least a year. The cost of putting right the damage was an average of £28,000 per house · Insurance companies estimated that the final cost of the flood could reach £100 million · Four bridges collapsed and 12 were closed because of flood damage. In Workington, all the bridges were destroyed or so badly damaged that they were declared unsafe – cutting the town in two. People faced a huge round trip to get from one side of the town to the other, using safe bridges · One man died– PC Bill Barker

Responses to the flood · The government provided £1 million to help with the clean-up and repairs and agreed to pay for road and bridge repairs in Cumbria · The Cumbria Flood Recovery Fund was set up to help victims of the flood. It reached £1 million after just 10 days · Network Rail opened a temporary railway station in Workington The ‘Visit Cumbria’ website provided lists of recovery services and trades, and people who could provide emergency accommodation

Management of future floods at Cockermouth £4.4 million pound management scheme New flood defence walls will halt the spread of the river Funding from Government and local contributors River dredged more regularly to deepen the channel New embankments raise the channel height to reduce the likelihood of extra floods New floodgates at the back of houses in Waterloo street

Pakistan, Asia – Poor Country Picture At the end of July 2010 usually heavy monsoon rains in northwest Pakistan caused rivers to flood and burst their banks. The map below shows the huge area of Pakistan affected by flooding. The floodwater slowly moved down the Indus River towards the sea.

Continuing heavy rain hampered the rescue efforts. After visiting Pakistan, the UN Secretary General, Ban Ki-moon, said that this disaster was worse than anything he’d ever seen. He described the floods as a slow-moving tsunami.

The effect of the floods · At least 1600 people died · 20 million Pakistanis were affected (over 10% of the population), 6 million needed food aid · Whole villages were swept away, and over 700,000 homes were damaged or destroyed · Hundreds of thousands of Pakistanis were displaced, and many suffered from malnutrition and a lack of clean water · 5000 miles of roads and railways were washed away, along with 1000 bridges · 160,000km2 of land were affected. That’s at least 20% of the country · About 6.5 million acres of crops were washed away in Punjab and Sindh provinces

The responses to the floods · Appeals were immediately launched by international organisation, like the UK’s Disasters Emergency Committee – and the UN – to help Pakistanis hit by the floods · Many charities and aid agencies provided help, including the Red Crescent and Medecins Sans Frontieres · Pakistan’s government also tried to raise money to help the huge number of people affected · But there were complaints that the Pakistan government was slow to respond to the crisis, and that it struggled to cope · Foreign Governments donated millions of dollars, and Saudi Arabia and the USA promised $600 million in flood aid. But many people felt that the richer foreign governments didn’t do enough to help · The UN’s World Food Programme provided crucial food aid. But, by November 2010, they were warning that they might have cut the amount of food handed out, because of a lack of donations from richer countries

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Flood resilient shelter case studies and reports.

This Collection contains reports and case studies relative to contexts of flood disaster response. The main topics are centered around how those who have been affected by floods can follow certain guidelines from various countries. The collection contains resources from multiple continents which is a way of enchaining mutually beneficial knowledge. Current guidance comes from leading global organizations: All India Disaster Mitigation Institute, ARUP, IFRC, IOM, MDPI, Netherlands Red Cross, UK aid, Practical Action, Oxfam International, Shelter / NFI Cluster, Shelter Projects, Solidarités International, UN Habitat. Please send suggestions for additional content for this Collection to [email protected] , or create your own Collection on the Library! You might find other helpful collections on humanitarian natural disaster shelter response and collections that are specific to natural disaster management below.

Resources on this Collection

Improved Shelters for Responding to Floods in Pakistan Phase 1: Study to Develop a Research Methodology

2007 Floods in South Asia: From Impact to Knowledge

Flood Resistant Housing Low-Cost Disaster-Resistant Housing in Bangladesh

Vietnam shelter: frames to loans

SRI LANKA 2017 / FLOODS AND LANDSLIDES

Kenya 2018 / floods

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Flood Resilient Shelter Reconstruction, Retrofitting and Repair Technical Guidelines

This Collection contains technical guidelines and recommendations to design proper retrofitting, repair or reconstruction interventions relatively to contests of flood disaster response. It includes documents in English and Vietnamese language.

Current guidance comes from local and leading global organizations: ARUP, Asian Disaster Preparedness Center (ADPC), CECI, CRS, IOM, National Disaster Management Authority (NDMA), USAID.

Please send suggestions for additional content for this Collection to [email protected] , or create your own Collection on the Library! You might find other helpful collections on humanitarian natural disaster shelter response and collections that are specific to natural disaster management below.

Storm Resilient Shelter Case Studies and Reports

This Collection contains relevant reports and case studies relative to the contest of a post-storm disaster response. Current guidance comes from leading global organizations: Asian Disaster Preparedness Center (ADPC), CARE International, CRS, Eindhoven University of Technology, Habitat For Humanity, Humanitarian Practice Network (HPN), ICRC, IOM, MDPI, REACH, Shelter Case Studies, Shelter Projects, USAID, World Habitat Award.

Please send suggestions for additional content for this Collection to [email protected] , or create your own Collection on the Library!

You might find other helpful collections on humanitarian Post-Storm shelter response and events and collections that are specific to Natural Disaster Management below.

Post Disaster Engineering - Reports and Case Studies

Proposed Description:This Collection is part of the 'Post-Disaster Engineering Channel', aimed at improving post-disaster shelter outcomes. It contains resources including practical approaches, case studies and reports. The collection offers guidance to different stakeholders on key considerations to take in post-disaster reconstruction. It adopts a self-recovery approach to rebuilding and in doing so embraces the concept of ‘self-recovery’ efforts process where disaster-affected households repair, build or rebuild their shelter themselves or through local builders. Please send suggestions for additional content for this Channel to [email protected] , or create your own Collection on the Library!

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Case Study – Floods

Floods and flooding.

Floods can be devastating — costing the lives of people and animals, as well as destroying crops, homes and businesses.

The east coast of England and the Netherlands have always been prone to flooding as storms track off the North Sea, bringing water surges and huge waves with them.

The devastation floods can cause

Flooding caused by surges, the surge of 1953, storm tide warnings.

What happened to cause this storm?

Surges still causing damage

Flood defences.

About 10,000 people died in a single flood in the Netherlands in 1421. Water from the North Sea flooded inland and swept through 72 villages, leaving a trail of destruction.

Further severe floods struck the region in 1570, 1825, 1894, 1916 and 1953. All of them occurred despite the area having extensive flood defence systems — sometimes nature’s power is just too strong. These defences are vital for the Netherlands, where 40% of the country is below sea level.

Along the coast of eastern England there have also been many failures of coastal defences. Even London has seen disastrous flooding. In January 1928 a northerly gale raised water levels in the Thames Estuary. Water overtopped embankments and low-lying riverside districts were flooded in the city, drowning 14 people.

Tides affect sea levels, but sometimes the weather will also play its part in raising or lowering water height. This is called a surge and is measured by how much higher or lower the sea is than expected on any given tide. A surge is positive if the water level is higher than the expected tide, and negative if lower. Positive surges happen when water is driven towards a coast by wind and negative when it is driven away.

While wind is the main cause of surges, barometric pressure – the pressure in the air — also plays its part. When pressure decreases by one millibar, sea level rises by one centimetre. Therefore, a deep depression with a central pressure of about 960 mb causes sea level to rise half a metre above the level it would have been had pressure been about average (1013 mb). When pressure is above average, sea level correspondingly falls.

When strong winds combine with very low pressure they can raise the sea level in eastern England by more than two metres. Fortunately such surges normally occur at mid-tide levels — so do not cause as much damage. If they were to coincide with high tide it could be a very different story.

Surges travel counter-clockwise around the North Sea — first southwards down the western half of the sea, then northwards up the western side. They take about 24 hours to progress most of the way around.

Waves, generated by strong winds, are another flooding factor. While coastal defences are designed to deal with high tides, these defences can be badly damaged by a pounding from large and powerful waves. Some waves are so large that they simply break over coastal defences, sending water flooding in and undermining sea-wall foundations until they collapse.

More than 2,000 people drowned at the end of January 1953 when the greatest surge on record, happened in the North Sea. The surge measured nearly three metres in Norfolk and even more in the Netherlands. About 100,000 hectares of eastern England were flooded and 307 people died. A further 200,000 hectares were flooded in the Netherlands, and 1,800 people drowned.

The storm that caused this disastrous surge was among the worst the UK had experienced.

• Hurricane force winds blew down more trees in Scotland than were normally felled in a year.
• A car ferry, the Princess Victoria, sank with the loss of 133 lives — but 41 of the passengers and crew survived.
• From Yorkshire to the Thames Estuary, coastal defences were pounded by the sea and gave way under the onslaught.

As darkness fell on 31 January, coastal areas of Lincolnshire bore the brunt of the storm.

• Sand was scoured from beaches and sand hills
• Timber-piled dunes were breached
• Concrete sea walls crumbled
• The promenades of Mablethorpe and Sutton-on-Sea were wrecked.
• Salt water from the North Sea flooded agricultural land

Later that evening, embankments around The Wash were overtopped and people drowned in northern Norfolk. At Wells-next-the-Sea, a 160-ton vessel was left washed up on the quay after waves pounded it ashore.

In 1953, because many telephone lines in Lincolnshire and Norfolk were brought down by the wind, virtually no warnings of the storm’s severity were passed to counties farther south until it was too late. Suffolk and Essex suffered most.

By midnight, Felixstowe, Harwich and Maldon had been flooded, with much loss of life. Soon after midnight, the sea walls on Canvey Island collapsed and 58 people died. At Jaywick in Clacton, the sea rose a metre in 15 minutes and 35 people drowned.

The surge travelled on. From Tilbury to London’s docklands, oil refineries, factories, cement works, gasworks and electricity generating stations were flooded and brought to a standstill.

In London’s East End, 100 metres of sea wall collapsed, causing more than 1,000 houses to be inundated and 640,000 cubic metres of Thames water to flow into the streets of West Ham. The BP oil refinery on the Isle of Grain was flooded, and so was the Naval Dockyard at Sheerness.

The disastrous surge of 1953 was predicted successfully by the Met Office and the Dutch Surge Warning Service. Forecasts of dangerously high water levels were issued several hours before they happened. An inquiry into the disaster recommended, however, that a flood warning organisation should be set up. This led to the setting up of the Storm Tide Warning Service.

In the early hours of 30 January 1953, the storm that was to cause the havoc was a normal looking depression with a central pressure of 996 mb, located a little to the south of Iceland. While it looked normal, during the day the pressure rapidly deepened and headed eastwards.

By 6 p.m. on 30 January, it was near the Faeroes, its central pressure 980 mb. By 12p.m. (midday) on 31 January, it was centred over the North Sea between Aberdeenshire and southern Norway and its central pressure was 968 mb.

Meanwhile, a strong ridge of high pressure had built up over the Atlantic Ocean south of Iceland, the pressure within being more than 1030 mb. In the steep pressure gradient that now existed on the western flanks of the depression, there was a very strong flow from a northerly point. Winds of Force 10 were reported from exposed parts of Scotland and northern England. The depression turned south-east and deepened to 966 mb before filling. By midday on 1 February, it lay over northern Germany, its central pressure 984 mb.

All day on 31 January, Force 10/11 winds blew from the north over western parts of the North Sea. They drove water south, and generated waves more than eight metres high. The surge originated in the waters off the north-east coast of Scotland and was amplified as it travelled first southwards along the eastern coasts of Scotland and England, and then north-east along the coast of the Netherlands. It reached Ijmuiden in the Netherlands around 4 a.m. on 1 February.

Since 1953, there have been other large surges in the North Sea. Among them one, on 12 January 1978, caused extensive flooding and damage along the east coast of England from Humberside to Kent. London came close to disaster, escaping flooding by only 0.5 m, and the enormous steel and rubber floodgates designed to protect the major London docks were closed for the first time since their completion in 1972.

Concern over rising sea levels, and the potential catastrophe if London were to be flooded, led the Government to build the Thames Flood Barrier. Based at Woolwich and finished in 1982, it is the world’s second largest movable flood barrier. It is designed to allow ships to pass in normal times, but flood gates come down to stop storm surges in times of need. The barriers are closed about four times a year, on average.

Over the years, coastal defences in the Netherlands and eastern England have been raised and strengthened continually to protect against storm surges. Our coasts and estuaries are safer now than they have ever been. Nevertheless, surges remain a threat, as complete protection against the most extreme can never be guaranteed.

The likelihood of being taken by surprise is now lower, because weather and surge forecasting systems have improved greatly in recent years, and the Storm Tide Forecasting Service has established clear and effective procedures for alerting the authorities when danger threatens.

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Tewkesbury Floods 2007 Case Study

Home > Geotopics > Tewkesbury Floods 2007 Case Study

The historic town of Tewkesbury in Gloucestershire, UK, faced a catastrophic flooding event in July 2007. This incident, part of a broader pattern of severe floods across the UK during that summer, offers a vital case study for understanding the dynamics of flooding, particularly the intertwining of natural and human-induced factors.

Tewkesbury on the River Severn

The Causes of the 2007 Tewkesbury Floods

The flooding in Tewkesbury was the result of both natural and human factors. The primary natural cause was the extreme and persistent rainfall during the summer, which led to the rivers Severn and Avon converging near Tewkesbury, overflowing their banks. The town’s geographical setting made it inherently susceptible to flooding. Additionally, the surrounding hills accelerated the run-off process, leading to an even greater influx of water into the river systems.

On the human side, the increased urban development in Tewkesbury and its surrounding areas contributed significantly to the flooding. Expanding impermeable surfaces like roads and buildings meant less rainwater could be absorbed into the ground, increasing the volume of run-off. Furthermore, the existing flood defence mechanisms were inadequate for such an extraordinary event. Changes in land use, including agricultural practices in the catchment area, also altered the natural water absorption and drainage patterns.

The Impacts of the Flood

The social impacts of the Tewkesbury floods were profound and multifaceted. Thousands of residents were displaced as over 3,500 homes were evacuated. The health risks posed by the floodwaters were significant, including threats of waterborne diseases and limited access to healthcare facilities due to the inundated infrastructure. The community faced considerable disruption, with schools closing down and local events being cancelled, affecting the town’s social fabric.

Economically, the floods inflicted substantial damage. The cost of damages to properties and infrastructure amounted to millions of pounds, heavily straining financial resources. Local businesses, especially those reliant on tourism , faced severe interruptions, leading to significant economic losses. The flood’s aftermath saw a surge in insurance claims and a need for considerable investment in reconstruction and recovery efforts.

Environmentally, the floods had far-reaching impacts. The local ecosystems experienced significant disruption, affecting both wildlife and plant life. Water pollution levels increased, with run-off from agricultural lands and overflowing sewage systems contaminating the waterways. The severity of the flooding potentially led to long-term changes in the landscape , including alterations in the courses of local rivers.

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Assessing future changes in flood frequencies under CMIP6 climate projections using SWAT modeling: a case study of Bitlis Creek, Turkey

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Emrah Yalcin; Assessing future changes in flood frequencies under CMIP6 climate projections using SWAT modeling: a case study of Bitlis Creek, Turkey. Journal of Water and Climate Change 2024; jwc2024646. doi: https://doi.org/10.2166/wcc.2024.646

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Climate change is altering flood risk globally, with local variations prompting the necessity for regional assessments to guide the planning and management of water-related infrastructures. This study details an integrated framework for assessing future changes in flood frequencies, using the case of Bitlis Creek (Turkey). The precipitation and temperature simulations of 21 global circulation models (GCMs) from the coupled model intercomparison project phase 6 (CMIP6) are used to drive the developed soil and water assessment tool (SWAT) model in generating daily streamflow projections under the CMIP6 historical experiment and the shared socio-economic pathway (SSP) scenarios of SSP245 and SSP585. Five probability distribution functions are considered to calculate the 5-, 10-, 25-, 50-, 100-, and 500-year flood discharges for the historical period 1955–2010 and the future periods 2025–2074 and 2025–2099. The quantification of climate change impacts on the design discharges is based on the medians of the flood discharges obtained for the climate data of each GCM, using the best-fitted distribution functions according to the Kolmogorov–Smirnov test results. The findings illustrate significant increases in discharge rates, ranging from 21.1 to 31.7% for the 2025–2099 period under the SSP585 scenario, highlighting the necessity of considering changing climate conditions in designing water-related infrastructures.

A framework is proposed for assessing possible changes in flood frequencies under the climate projections of CMIP6 GCMs using the SWAT model.

The quantification relies on the medians of flood discharges obtained from the climate projections of each GCM.

The methodology applied to Bitlis Creek indicates that the changing climate may lead to notable increases in flood discharges, emphasizing the need for adaptive measures.

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