case study about flood

Search form

• Find Stories
• For Journalists

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 .

To read all stories about Stanford science, subscribe to the biweekly Stanford Science Digest .

• 2023 Lancet Countdown U.S. Launch Event
• 2023 Lancet Countdown U.S. Brief
• PAST BRIEFS

2020 CASE STUDY 2

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

Flood risk already affects 1.81 billion people. Climate change and unplanned urbanization could worsen exposure

Jun rentschler, melda salhab, bramka arga jafino.

Flooding is among the leading climatic threats to people’s livelihoods, affecting development prospects worldwide – and floods can also reverse years of progress in poverty reduction and development.  While the threat is already substantial, climate change and rapid urbanization in flood zones are likely to further drive up flood risks. The latest Intergovernmental Panel on Climate Change report affirms the urgency of addressing the intensifying impacts of climate change and ensuring the adaptation and resilience of the most vulnerable.

In October 2020, we presented a working paper that offered insight into global flood risk exposure and its intersection with poverty. Now, using updated state-of-the-art flood data, our analysis just published in Nature Communications , estimates that 1.81 billion people face significant flood risk worldwide, substantially higher than the 1.47 billion estimated in our initial study . Our updated study uses more accurate data on fluvial, pluvial , and coastal hazards, as well as subnational poverty . It also estimates that 170 million extremely poor people are facing flood risk and its devastating long-term consequences. Together, these findings provide alarming insights into the scale of people’s exposure and their vulnerabilities to flood hazards. A few of our key findings:

1. Exposure to flood risk is substantial, particularly in low and middle-income countries. Our estimates show that 1.81 billion people, or 23% of the world population, are directly exposed to flood depths greater than 0.15 meters in a 1-in-100-year flood event, thus posing significant risk to lives and livelihoods.  Of these, 89% live in low- and middle-income countries. Moreover, 780 million flood-exposed people live on less than $5.50 a day, and 170 million flood-exposed people live in extreme poverty (on less than $1.90 a day). In short, 4 in every 10 people exposed to flood risk globally live in poverty.

2. Flood risk is global, but the most flood-exposed people live in South and East Asia.  Flood risks are a near universal threat, affecting people in all 188 countries covered in this study.  At 668 million people, East Asia has the highest number of flood-exposed people, corresponding to about 28% of its total population. Across Sub-Saharan Africa, Europe, Central Asia, the Middle East, North Africa, Latin America, and the Caribbean flood exposure ranges between 9% to 20% of the population. And of the 2,084 subnational regions we analyzed, only 9 have less than 1% of their population exposed to flood risks. Almost 70% (1.24 billion) of flood-exposed people live in South and East Asia, with China and India alone accounting for over one-third of global exposure. And in several South and East Asian subnational areas, more than two-thirds of the population are exposed to significant flood risk.

3. When flood exposure and poverty coincide, the risk to livelihoods is most severe.  With next to no savings and limited access to support systems, the poorest households often experience the most devastating long-term consequences of floods . In our study we systematically assess where high flood risks and poverty coincide. We find that flooding is likely to cause the most detrimental impacts on livelihoods and well-being in Sub-Saharan Africa and South Asia, where high poverty persists. And within individual countries, risks are often concentrated in certain regions, including low-lying river basins or coastlines. 

4. Relying on monetary risk estimates risks overlooking the areas most in need of protection.  Despite facing substantial vulnerabilities, monetary measures of flood risk typically overlook poorer regions and countries.  When prioritizing flood protection investments, focusing on the monetary exposure of assets and economic activity skews attention towards high-income countries and economic hubs. This can mean that areas with high socioeconomic vulnerability are neglected, where flood risk mitigation measures are most urgently need to protect lives and livelihoods. Our results show that flood hazards and poverty coincide in regions were socio-economic vulnerabilities and political instability are already high.

Evolving risks require urgent action Systematic risk mitigation measures are crucial to prevent the loss of lives and livelihoods and reversal of development progress. Climate change and risky urbanization patterns are expected to further aggravate flood risk.  With safe areas already occupied, new settlements and developments are increasingly spilling into high-risk areas. As spatial planning and infrastructure investments struggle to keep up with the pace of urbanization, risks build up and are locked in.  Our study shows that low-income countries are disproportionately exposed to flood risks, and more vulnerable to disastrous long-term impacts. By highlighting the scale of the needs and priority regions for flood risk mitigation measures, our findings should facilitate prioritization and comprehensive action to safeguard livelihoods and prevent prolonged adverse impacts on development.   Download the study: Rentschler, J, Salhab, M and Jafino, B. 2022. Flood Exposure and Poverty in 188 Countries. Nature Communications .

This study was supported by the Global Facility for Disaster Reduction and Recovery (GFDRR) .

• Climate Change
• The World Region

Senior Economist

Doctoral Researcher, Bartlett Centre for Advanced Spatial Analysis

Consultant/advisor at Deltares, The Netherlands

Join the Conversation

• Share on mail
• comments added

Can coastal cities turn the tide on rising flood risk?

Climate change is increasing the destructive power of flooding from extreme rain and rising seas and rivers. Many cities around the world are exposed. Strong winds during storms and hurricanes can drive coastal flooding through storm surge. As hurricanes and storms become more severe, surge height increases. Changing hurricane paths may shift risk to new areas. Sea-level rise amplifies storm surge and brings in additional chronic threats of tidal flooding. Pluvial and riverine flooding becomes more severe with increases in heavy precipitation. Floods of different types can combine to create more severe events known as compound flooding. With warming of 1.5 degrees Celsius , 11 percent of the global land area is projected to experience a significant increase in flooding, while warming of 2.0 degrees almost doubles the area at risk.

When cities flood, in addition to often devastating human costs, real estate is destroyed, infrastructure systems fail, and entire populations can be left without critical services such as power, transportation, and communications. In this case study we simulate floods at the most granular level (up to two-by-two-meter resolution) and explore how flood risk may evolve for Ho Chi Minh City (HCMC) and Bristol (See sidebar, “An overview of the case study analysis”). Our aim is to illustrate the changing extent of flooding, the landscape of human exposure, and the magnitude of societal and economic impacts.

An overview of the case study analysis

In Climate risk and response: Physical hazards and socioeconomic impacts , we measured the impact of climate change by the extent to which it could affect human beings, human-made physical assets, and the natural world. We explored risks today and over the next three decades and examined specific cases to understand the mechanisms through which climate change leads to increased socioeconomic risk.

In order to link physical climate risk to socioeconomic impact, we investigated cases that illustrated exposure to climate change extremes and proximity to physical thresholds. These cover a range of sectors and geographies and provide the basis of a “micro-to-macro” approach that is a characteristic of McKinsey Global Institute research. To inform our selection of cases, we considered over 30 potential combinations of climate hazards, sectors, and geographies based on a review of the literature and expert interviews on the potential direct impacts of physical climate hazards. We found these hazards affect five different key socioeconomic systems: livability and workability, food systems, physical assets, infrastructure services, and natural capital.

We ultimately chose nine cases to reflect these systems and to represent leading-edge examples of climate change risk. Each case is specific to a geography and an exposed system, and thus is not representative of an “average” environment or level of risk across the world. Our cases show that the direct risk from climate hazards is determined by the severity of the hazard and its likelihood, the exposure of various “stocks” of capital (people, physical capital, and natural capital) to these hazards, and the resilience of these stocks to the hazards (for example, the ability of physical assets to withstand flooding). We typically define the climate state today as the average conditions between 1998 and 2017, in 2030 as the average between 2021 and 2040, and in 2050 between 2041 and 2060. Through our case studies, we also assess the knock-on effects that could occur, for example to downstream sectors or consumers. We primarily rely on past examples and empirical estimates for this assessment of knock-on effects, which is likely not exhaustive given the complexities associated with socioeconomic systems. Through this “micro” approach, we offer decision makers a methodology by which to assess direct physical climate risk, its characteristics, and its potential knock-on impacts.

Climate science makes extensive use of scenarios ranging from lower (Representative Concentration Pathway 2.6) to higher (RCP 8.5) CO 2 concentrations. We have chosen to focus on RCP 8.5, because the higher-emission scenario it portrays enables us to assess physical risk in the absence of further decarbonization. (We also choose a sea-level rise scenario for one of our cases that is consistent with the RCP 8.5 trajectory). Such an "inherent risk" assessment allows us to understand the magnitude of the challenge and highlight the case for action. For a detailed description of the reason for this choice see the technical appendix of the full report.

Our case studies cover each of the five systems we assess to be directly affected by physical climate risk, across geographies and sectors. While climate change will have an economic impact across many sectors, our cases highlight the impact on construction, agriculture, finance, fishing, tourism, manufacturing, real estate, and a range of infrastructure-based sectors. The cases include the following:

• For livability and workability, we look at the risk of exposure to extreme heat and humidity in India and what that could mean for that country’s urban population and outdoor-based sectors, as well as at the changing Mediterranean climate and how that could affect sectors such as wine and tourism.
• For food systems, we focus on the likelihood of a multiple-breadbasket failure affecting wheat, corn, rice, and soy, as well as, specifically in Africa, the impact on wheat and coffee production in Ethiopia and cotton and corn production in Mozambique.
• For physical assets, we look at the potential impact of storm surge and tidal flooding on Florida real estate and the extent to which global supply chains, including for semiconductors and rare earths, could be vulnerable to the changing climate.
• For infrastructure services, we examine 17 types of infrastructure assets, including the potential impact on coastal cities such as Bristol in England and Ho Chi Minh City in Vietnam.
• Finally, for natural capital, we examine the potential impacts of glacial melt and runoff in the Hindu Kush region of the Himalayas; what ocean warming and acidification could mean for global fishing and the people whose livelihoods depend on it; as well as potential disturbance to forests, which cover nearly one-third of the world’s land and are key to the way of life for 2.4 billion people.

We chose these cities for the contrasting perspectives they offer: Ho Chi Minh City in an emerging economy, Bristol in a mature economy; Ho Chi Minh City in a regular flood area, Bristol in an area developing a significant flood risk for the first time in a generation.

We find the metropolis of Ho Chi Minh City can survive its flood risk today, but its plans for rapid infrastructure  expansion and continued economic growth could be incompatible with an increase in risk. The city has a wide range of adaptation options at its disposal but no silver bullet.

In the much smaller city of Bristol, we find a risk of flood damages growing from the millions to the billions, driven by high levels of exposure. The city has fewer adaptation options at its disposal, and its biggest challenge may be building political and financial support for change.

How significant are the flood risks facing Ho Chi Minh City and what can the city do?

Flooding is a common part of life in Ho Chi Minh City. This includes flooding from monsoonal rains, which account for about 90 percent of annual rainfall, tidal floods and storm surge from typhoons and other weather events. Of the city’s 322 communes and wards, about half have a history of regular flooding with 40 to 45 percent of land in the city less than one meter above sea level.

In our analysis, we quantify the possible impact on the city as floods hit real estate and infrastructure assets. 1 Flood modeling and expert guidance were provided by an academic consortium of Institute for Environmental Studies, Vrije Universiteit Amsterdam, and Center of Water Management and Climate Change, Vietnam National University. Infrastructure assets covered include both those currently available and those under construction, planned, or speculated. Knock-on effects are adjusted for estimates of economic and population growth. We simulate possible 1 percent probability flooding scenarios for the city for three periods: today, 2050, and a longer-term scenario of 180 centimeters of sea-level rise, which some infrastructure assets built by 2050 may experience as a worse-case in their lifetime (Exhibit 1).

• Today: We estimate that 23 percent of the city could flood, and a range of existing assets would be taken offline; infrastructure damage may total $200 million to $300 million. Knock-on effects would be significant, and we estimate could total a further $100 million to $400 million. Real estate damage may total $1.5 billion.
• 2050: A flood with the same probability in 30 years’ time would likely do three times the physical damage and deliver 20 times the knock-on effects. We estimate that 36 percent of the city becomes flooded. In addition, many of the 200 new infrastructure assets are planned to be built in flooded areas. As a result, the damage bill would grow, totaling $500 million to $1 billion. Increased economic reliance on assets would amplify knock-on effects, leading to an estimated $1.5 billion to $8.5 billion in losses. An additional $8.5 billion in real estate damages could occur.
• A 180 centimeters sea-level rise scenario: A 1 percent probability flood in this scenario may bring three times the extent of flood area. About 66 percent of the city would be underwater, driven by a large western area that suddenly pass an elevation threshold. Under this scenario, damage is critical and widespread, totaling an estimated $3.8 billion to $7.3 billion. Much of the city’s functionality may be shut down, with knock-on effects costing $6.4 billion to $45.1 billion. Real estate damage could total $18 billion.

While “tail” events may suddenly break systems and cause extraordinary impact, extreme floods will be infrequent. Intensifying chronic events are more likely to have a greater effect on the economy, with a mounting annual burden over time. We estimate that intensifying regular floods may rise from about 2 percent today to about 3 percent of Ho Chi Minh City’s GDP annually by 2050 (Exhibit 2).

Ho Chi Minh City has time to adapt, and the city has many options to avert impacts because it is relatively early in its development journey. As less than half of the city’s major infrastructure needed for 2050 exists today, many of the potential adaptation options could be highly effective. We outline three key steps:

• Better planning to reduce exposure and risk
• Investing in adaptation through hardening and resilience
• Financial mobilization to mitigate impacts on lower-income populations

For additional details on these actions, download the case study, Can coastal cities turn the tide on rising flood risk? (PDF–4MB).

Could Bristol’s flood risk grow from a problem to a crisis by 2065?

Bristol is facing a new flood risk. The river Avon, which runs through the city, has the second largest tidal range in the world, yet it has not caused a major flood since 1968, when sea levels were lower, and the city was smaller and less developed. During very high tides, the Avon becomes “tide locked” and limits/restricts land drainage in the lower reaches of river catchment area. As a result, the city is vulnerable to combined tidal and pluvial floods, which are sensitive to both sea-level rise and precipitation increase. Both are expected to climb with climate change . While Bristol is generally hilly and most of the urban area is far from the river, the most economically valuable areas of the city center and port regions are on comparatively low-lying land.

With the city’s support, we have modeled the socioeconomic impacts of 200-year (0.5 percent probability) combined tidal and fluvial flood risk, for today and for 2065. This considers the flood defenses in existence today; some of these were built after the 1968 flood, and many assumed a static climate would exist for their lifetime (Exhibit 3).

• Today: The consequences of a major flood today in Bristol would be small but are still material. We find that the flood area would be relatively minor, with small overflows on the edges of the port area and isolated floods in the center of the city. Our model estimates that damage to the city’s infrastructure could amount to $10 million to $25 million, real estate damage to $15 million to $20 million, and knock-on effects of $20 million to $150 million.
• 2065: In contrast, by 2065, an extreme flood event could be devastating. Water would exceed the city’s flood defenses at multiple locations, hitting some of its most expensive real estate, damaging arterial transportation infrastructure, and destroying sensitive critical energy assets. Our model estimates that damages to the city’s infrastructure could amount to between $180 million and $390 million. It may also cause $160 million to $240 million of property damage. Overall, considering economic growth, knock-on effects could total $500 million to $2.8 billion, and disruptions could last weeks or months (Exhibit 4).

Unlike many small and medium-size cities, Bristol has invested in understanding this risk. It has undertaken a detailed review of how the scale of flooding in the city will change in the future under different climate scenarios. This improved understanding of the risks is an example that other cities could learn from.

However, adaptation is unlikely to be straightforward. It is difficult to imagine Bristol’s infrastructure assets being in a position more exposed to the city’s flood risk. Yet the center of the city, formed in the 1400s, cannot be shifted overnight, nor would its leafy reputation be the same today if the city had not oriented the growth of the past 20 years to harness its existing Edwardian and Victorian architecture. Unlike in Ho Chi Minh City, most of the infrastructure the city plans to have in place in 2065 has already been built.

In the immediate future, Bristol’s hands are likely largely tied, and hard adaptation may be the most viable short-term solution. In the medium term, however, Bristol may be able to act to improve resilience through measures such as investing in sustainable urban drainage that may reduce the depth and duration of an extreme flood event.

Bristol is already taking a proactive approach to adaptation. A $130 million floodwall for the defense of Avonmouth was planned to begin in late 2019. The city is still scoping out a range of options to protect the city. As an outside-in estimate, based on scaling costs to build the Thames Barrier in 1982, plus additional localized measures that might be needed, protecting the city to 2065 may cost $250 million to $500 million (roughly 0.5 to 1.5 percent of Bristol’s GVA today compared to the possible flood impact we calculate of between 2 to 9 percent of the city’s GVA in 2065). However, the actual costs will largely depend on the final approach. 

Bristol has gotten ahead of the game by improving its own understanding of risk. Many other small cities are at risk of entering unawares into a new climatic band for which they and their urban areas are ill prepared. While global flood risk is concentrated in major coastal metropolises, a long tail of other cities may be equally exposed, less prepared, and less likely to bounce back.

For additional details, download the case study, Can coastal cities turn the tide on rising flood risk? (PDF–4MB).

About this case study:

In January 2020, the McKinsey Global Institute published Climate risk and response: Physical hazards and socioeconomic impacts . In that report, we measured the impact of climate change by the extent to which it could affect human beings, human-made physical assets, and the natural world over the next three decades. In order to link physical climate risk to socioeconomic impact, we investigated nine specific cases that illustrated exposure to climate change extremes and proximity to physical thresholds.

Explore a career with us

Related articles.

Climate risk and response: Physical hazards and socioeconomic impacts

Earth to CEO: Your company is already at risk from climate change

Thomas L. Friedman: The three climate changes

Understanding flash flooding in the Himalayan Region: a case study

Affiliations.

• 1 Centre for Remote Sensing and Geoinformatics, Sathyabama Institute of Science and Technology, Chennai, India.
• 2 Office of Director General of Meteorology, India Meteorological Department, Ministry of Earth Science, SATMET Division, Mausam Bhavan, New Delhi, 110003, India.
• 3 Centre for Remote Sensing and Geoinformatics, Sathyabama Institute of Science and Technology, Chennai, India. [email protected].
• 4 Department of Geography, Rampurhat College, PO- Rampurhat, Birbhum, 731224, India.
• PMID: 38528024
• PMCID: PMC10963777
• DOI: 10.1038/s41598-024-53535-w

The Himalayan region, characterized by its substantial topographical scale and elevation, exhibits vulnerability to flash floods and landslides induced by natural and anthropogenic influences. The study focuses on the Himalayan region, emphasizing the pivotal role of geographical and atmospheric parameters in flash flood occurrences. Specifically, the investigation delves into the intricate interactions between atmospheric and surface parameters to elucidate their collective contribution to flash flooding within the Nainital region of Uttarakhand in the Himalayan terrain. Pre-flood parameters, including total aerosol optical depth, cloud cover thickness, and total precipitable water vapor, were systematically analyzed, revealing a noteworthy correlation with flash flooding event transpiring on October 17th, 18th, and 19th, 2021. Which resulted in a huge loss of life and property in the study area. Contrasting the October 2021 heavy rainfall with the time series data (2000-2021), the historical pattern indicates flash flooding predominantly during June to September. The rare occurrence of October flash flooding suggests a potential shift in the area's precipitation pattern, possibly influenced by climate change. Robust statistical analyses, specifically employing non-parametric tests including the Autocorrelation function (ACF), Mann-Kendall (MK) test, Modified Mann-Kendall, and Sen's slope (q) estimator, were applied to discern extreme precipitation characteristics from 2000 to 201. The findings revealed a general non-significant increasing trend, except for July, which exhibited a non-significant decreasing trend. Moreover, the results elucidate the application of Meteosat-8 data and remote sensing applications to analyze flash flood dynamics. Furthermore, the research extensively explores the substantial roles played by pre and post-atmospheric parameters with geographic parameters in heavy rainfall events that resulted flash flooding, presenting a comprehensive discussion. The findings describe the role of real time remote sensing and satellite and underscore the need for comprehensive approaches to tackle flash flooding, including mitigation. The study also highlights the significance of monitoring weather patterns and rainfall trends to improve disaster preparedness and minimize the impact of flash floods in the Himalayan region.

© 2024. The Author(s).

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• 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

23k Accesses

121 Citations

189 Altmetric

Metrics details

• 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.

Similar content being viewed by others

Floods and rivers: a circular causality perspective

G. Sofia & E. I. Nikolopoulos

The impacts of tidal wetland loss and coastal development on storm surge damages to people and property: a Hurricane Ike case-study

Zaid Al-Attabi, Yicheng Xu, … Siddharth Narayan

Extreme Coastal Water Levels Exacerbate Fluvial Flood Hazards in Northwestern Europe

Poulomi Ganguli & Bruno Merz

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 ).

Prein, A. F. et al. The future intensification of hourly precipitation extremes. Nat. Clim. Change 7 , 48 (2016).

Article   ADS   Google Scholar  

Milly, P. C. D., Wetherald, R. T., Dunne, K. A. & Delworth, T. L. Increasing risk of great floods in a changing climate. Nature 415 , 514–517 (2002).

Article   CAS   PubMed   Google Scholar  

Hirabayashi, Y. et al. Global flood risk under climate change. Nat. Clim. Change 3 , 816 (2013).

Jongman, B., Ward, P. J. & Aerts, J. C. J. H. Global exposure to river and coastal flooding: long term trends and changes. Glob. Environ. Change 22 , 823–835 (2012).

Article   Google Scholar  

Ward, P. J. et al. Assessing flood risk at the global scale: model setup, results, and sensitivity. Environ. Res. Lett. 8 , 044019 (2013).

Grill, G. et al. Mapping the world’s free-flowing rivers. Nature 569 , 215–221 (2019).

Article   ADS   CAS   PubMed   Google Scholar  

Chao, B. F., Wu, Y. H. & Li, Y. S. Impact of artificial reservoir water impoundment on global sea level. Science 320 , 212 (2008).

Oki, T. & Kanae, S. Global hydrological cycles and world water resources. Science 313 , 1068 (2006).

Abbott, B. W. et al. Human domination of the global water cycle absent from depictions and perceptions. Nat. Geosci. 12 , 533–540 (2019).

Article   ADS   CAS   Google Scholar  

Dynesius, M. & Nilsson, C. Fragmentation and flow regulation of river systems in the northern third of the world. Science 266 , 753 (1994).

Poff, N. L. & Schmidt, J. C. How dams can go with the flow. Science 353 , 1099 (2016).

Voeroesmarty, C. J. et al. The storage and aging of continental runoff in large reservoir systems of the world. Ambio 26 , 210–219 (1997).

Google Scholar  

Zarfl, C., Lumsdon, A. E., Berlekamp, J., Tydecks, L. & Tockner, K. A global boom in hydropower dam construction. Aquat. Sci. 77 , 161–170 (2015).

Dottori, F. et al. Increased human and economic losses from river flooding with anthropogenic warming. Nat. Clim. Change 8 , 781–786 (2018).

Sampson, C. C. et al. A high-resolution global flood hazard model. Water Resour. Res. 51 , 7358–7381 (2015).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Ward, P. J. et al. Usefulness and limitations of global flood risk models. Nat. Clim. Change 5 , 712 (2015).

Ward, P. J. et al. Strong influence of El Niño Southern Oscillation on flood risk around the world. Proc. Natl Acad. Sci. 111 , 15659–15664 (2014).

Winsemius, H. C. et al. Global drivers of future river flood risk. Nat. Clim. Change 6 , 381 (2015).

Jongman, B. et al. Declining vulnerability to river floods and the global benefits of adaptation. Proc. Natl Acad. Sci. 112 , E2271–E2280 (2015).

Pappenberger, F., Dutra, E., Wetterhall, F. & Cloke, H. L. Deriving global flood hazard maps of fluvial floods through a physical model cascade. Hydrol. Earth Syst. Sci. 16 , 4143–4156 (2012).

Lim, W. H. et al. Long-term changes in global socioeconomic benefits of flood defenses and residual risk based on CMIP5 climate models. Earth’s Future 6 , 938–954 (2018).

Zhao, G., Bates, P. & Neal, J. The impact of dams on design floods in the conterminous US. Water Resour. Res. 56 , e2019WR025380 (2020).

Mei, X., Van Gelder, P. H. A. J. M., Dai, Z. & Tang, Z. Impact of dams on flood occurrence of selected rivers in the United States. Front. Earth Sci. 11 , 268–282 (2017).

Hanasaki, N., Yoshikawa, S., Pokhrel, Y. & Kanae, S. A global hydrological simulation to specify the sources of water used by humans. Hydrol. Earth Syst. Sci. 22 , 789–817 (2018).

Zaherpour, J. et al. Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts. Environ. Res. Lett. 13 , 065015 (2018).

Mateo, C. M. et al. Assessing the impacts of reservoir operation to floodplain inundation by combining hydrological, reservoir management, and hydrodynamic models. Water Resour. Res. 50 , 7245–7266 (2014).

Hanasaki, N., Fujiwara, M., Maji, A. & Seto, S. On the applicability of the H08 global water resources model to the Kyusyu Island. J. Jpn. Soc. Civ. Eng., Ser. B1 74 , I_109–I_114 (2018).

Masood, M., Yeh, P. J.-F., Hanasaki, N. & Takeuchi, K. Model study of the impacts of future climate change on the hydrology of Ganges–Brahmaputra–Meghna basin. Hydrol. Earth Syst. Sci. 19 , 747–770 (2015).

Rougé, C. et al. Coordination and control: limits in standard representations of multi-reservoir operations in hydrological modeling. Hydrol. Earth Syst. Sci. Discuss. 2019 , 1–37 (2019).

Shin, S., Pokhrel, Y. & Miguez-Macho, G. High-resolution modeling of reservoir release and storage dynamics at the continental scale. Water Resour. Res. 55 , 787–810 (2019).

Hanasaki, N., Kanae, S. & Oki, T. A reservoir operation scheme for global river routing models. J. Hydrol. 327 , 22–41 (2006).

Yamazaki, D. et al. Analysis of the water level dynamics simulated by a global river model: a case study in the Amazon river. Water Resour. Res. 48 , W09508 (2012).

Yamazaki, D., Kanae, S., Kim, H. & Oki, T. A physically based description of floodplain inundation dynamics in a global river routing model. Water Resour. Res. 47 , W04501 (2011).

Yamazaki, D., Sato, T., Kanae, S., Hirabayashi, Y. & Bates, P. D. Regional flood dynamics in a bifurcating mega delta simulated in a global river model. Geophys. Res. Lett. 41 , 3127–3135 (2014).

Yamazaki, D. et al. Development of the global width database for large rivers. Water Resour. Res. 50 , 3467–3480 (2014).

Assani, A. A., Stichelbout, É., Roy, A. G. & Petit, F. Comparison of impacts of dams on the annual maximum flow characteristics in three regulated hydrologic regimes in Québec (Canada). Hydrological Process. 20 , 3485–3501 (2006).

Masaki, Y., Hanasaki, N., Takahashi, K. & Hijioka, Y. Consequences of implementing a reservoir operation algorithm in a global hydrological model under multiple meteorological forcing. Hydrological Sci. J. 63 , 1047–1061 (2018).

Hollins, X. L., Eisenberg, A. D. & Seager, P. T. Risk and resilience at the Oroville dam. Infrastructures 3 , 49–65 (2018).

Best, J. Anthropogenic stresses on the world’s big rivers. Nat. Geosci. 12 , 7–21 (2019).

Mallakpour, I., AghaKouchak, A. & Sadegh, M. Climate-induced changes in the risk of hydrological failure of major dams in California. Geophys. Res. Lett. 46 , 2130–2139 (2019).

Ehsani, N., Vörösmarty, C. J., Fekete, B. M. & Stakhiv, E. Z. Reservoir operations under climate change: storage capacity options to mitigate risk. J. Hydrol. 555 , 435–446 (2017).

Lehner, B., Verdin, K. & Jarvis, A. New global hydrography derived from spaceborne elevation data. Eos, Trans., Am. Geophys. Union 89 , 93–94 (2008).

Zhao, F. et al. The critical role of the routing scheme in simulating peak river discharge in global hydrological models. Environ. Res. Lett. 12 , 075003 (2017).

Lehner, B. et al. High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Front. Ecol. Environ. 9 , 494–502 (2011).

Lange, S. Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0). Geosci. Model Dev. 12 , 3055–3070 (2019).

Lehner, B. et al. Global Reservoir and Dam Database, Version 1 (GRanDv1): Dams, Revision 01. (2011).

Masaki, Y. et al. Intercomparison of global river discharge simulations focusing on dam operation—multiple models analysis in two case-study river basins, Missouri–Mississippi and Green–Colorado. Environ. Res. Lett. 12 , 055002 (2017).

Hattermann, F. F. et al. Cross‐scale intercomparison of climate change impacts simulated by regional and global hydrological models in eleven large river basins. Clim. Change 141 , 561–576 (2017).

Veldkamp, T. I. E. et al. Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multi-model validation study. Environ. Res. Lett. 13 , 055008 (2018).

Lehner, B., Döll, P., Alcamo, J., Henrichs, T. & Kaspar, F. Estimating the impact of global change on flood and drought risks in Europe: a continental, integrated analysis. Clim. Change 75 , 273–299 (2006).

Hosking, J. R. M. L-Moments: analysis and estimation of distributions using linear combinations of order statistics. J. R. Stat. Soc. Ser. B 52 , 105–124 (1990).

MathSciNet   MATH   Google Scholar  

Vogel, R. M. The probability plot correlation coefficient test for the normal, lognormal, and gumbel distributional hypotheses. Water Resour. Res. 22 , 587–590 (1986).

Heo, J.-H., Kho, Y. W., Shin, H., Kim, S. & Kim, T. Regression equations of probability plot correlation coefficient test statistics from several probability distributions. J. Hydrol. 355 , 1–15 (2008).

James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning: with Applications in R . (Springer Publishing Company, Incorporated, 2014).

Center for International Earth Science Information Network - CIESIN - Columbia University. Gridded Population of the World, Version 4 (GPWv4): Basic Demographic Characteristics, Revision 11. (2018).

Download references

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).

Author information

Authors and affiliations.

National Institute for Environmental Studies (NIES), Tsukuba, Japan

Julien Boulange & Naota Hanasaki

Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

Dai Yamazaki

Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI, USA

Yadu Pokhrel

You can also search for this author in PubMed   Google Scholar

Contributions

J.B. carried out the simulation and analysis. J.B., N.H., D.Y., and Y.P. commented on and edited the manuscript.

Corresponding author

Correspondence to Julien Boulange .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Peer review information Nature Communications thanks Brenden Jongman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, peer review file, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

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

Download citation

Received : 23 March 2020

Accepted : 04 December 2020

Published : 18 January 2021

DOI : https://doi.org/10.1038/s41467-020-20704-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Higher emissions scenarios lead to more extreme flooding in the united states.

• Hanbeen Kim
• Gabriele Villarini

Nature Communications (2024)

Climate control on the channel morphodynamics of the Sittaung River, Myanmar

• Luke Stefan Bisson
• Kyungsik Choi

Scientific Reports (2024)

Population structure and demographic history for year cohort dynamics of landlocked ayu Plecoglossus altivelis altivelis in dam reservoir of Japan

• Dalia Khatun
• Tomomi Tanaka
• Futoshi Aranishi

Environmental Science and Pollution Research (2024)

Climate-resilience of dams and levees in Canada: a review

• M. R. Islam
• M. Fereshtehpour

Discover Applied Sciences (2024)

The impact of short-duration precipitation events over the historic Cauvery basin: a study on altered water resource patterns and associated threats

• Satyajit Ghosh
• Dillip Kumar Barik
• Srichander Akunuri

Scientific Reports (2023)

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

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.

Welcome to OpenBook!

You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

Do you want to take a quick tour of the OpenBook's features?

Show this book's table of contents , where you can jump to any chapter by name.

...or use these buttons to go back to the previous chapter or skip to the next one.

Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

Switch between the Original Pages , where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

To search the entire text of this book, type in your search term here and press Enter .

Share a link to this book page on your preferred social network or via email.

View our suggested citation for this chapter.

Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

Get Email Updates

Do you enjoy reading reports from the Academies online for free ? Sign up for email notifications and we'll let you know about new publications in your areas of interest when they're released.

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.

Web page reproduced with the kind permission of  the Met Office

Start exploring

• Search Resources
• All Levels Primary Secondary Geography Secondary Maths Secondary Science
• Choose a topic All Topics Air Masses Anticyclones Carbon Cycle Climate Climate Change Climate Zones Clouds Contour Coriolis Depressions El Nino Extreme Weather Flooding Front Global Atmospheric Circulation Hydrological Cycle Microclimates Past Climate Snow Synoptic Charts Tropical Cyclones Urban Heat Island Water Cycle Weather Weather Forecast Weather Map Weather System
• All Climate Change Adaptation Afforestation Agriculture air quality aircraft Albedo Anthropocene anthropogenic Arctic/ Antarctic Carbon cycle Carbon dioxide removal/ sequestration Carbon footprint Carbon sinks Causes Climate Climate Crisis/ Emergency Climate justice Climate literacy climate stripes/ visualisation Climate zone shift clouds CO2/ carbon dioxide emissions COP Cryosphere Deforestation Desertification Ecosystems Electric vehicles Energy Equity Evidence Extreme weather Feedback loops Fire weather Flood defences Flooding/ flood risk Fossil fuels Fuel security Global energy budget/ balance Green climate fund Greenhouse effect Greenhouse gas concentrations Heatwaves/ extreme heat Hydroelectric power Ice sheets Impacts IPCC Keeling curve Mitigation Modelling Natural variability Nature based solutions Negotiations Net zero Nuclear power Observations Ocean acidification Ocean warming Proxy records Regional climate change Renewable/ non fossil fuel energy Scenarios Schools Strike for Climate Society Solar energy Solutions Storms Temperature/ global warming Tipping points Transport Tropical cyclones Uneven impacts UNFCCC/ governance Urban green infrastructure Urban heat Urbanisation Water cycle Water security Water vapour (H2O) concentrations Weather Wild fires Wind power
• Choose an age range All ages Primary    0 to 7    7 to 11 Secondary    11 to 14    14 to 16    16 to 18

Latest from blog

A new climate for design education, book review: a climate in chaos, new maths lessons with climate contexts, climate change and the natural history gcse, related resources ….

Country-by-Country Emissions of Greenhouse Gases

6. Past Climate Change

UK Climate: Data

Subscribe to MetLink updates

Weather and climate resources and events for teachers.

© 2024 Royal Meteorological Society RMetS is a registered charity No. 208222

About MetLink   Cookies Policy   Privacy Policy

• We use cookies on this site to enhance your user experienceBy clicking any link on this page you are giving your consent for us to set cookies. More info
• Strictly Necessary Cookies

By clicking any link on this page you are giving your consent for us to set cookies. More info

Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.

If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.11(1); 2019

Household disaster awareness and preparedness: A case study of flood hazards in Asamankese in the West Akim Municipality of Ghana

Frank j. glago.

1 Akatsi College of Education, Akatsi, Akatsi South District, Ghana

Associated Data

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Increasing disasters and their associated devastating impacts on society have called into question the capacity of countries to address disaster occurrences. Hitherto, primary disaster management institutions have addressed disaster in a piecemeal manner, commonly through the distribution of relief items after occurrence of disasters. Considering this shortfall and as a contribution to the current discourse of disaster management, this study investigated households’ awareness and preparedness for flood disasters in Asamankese, a rapidly developing township, which has also seen increase in flood disasters in recent times. To this end, a mixed research method approach was used in both data collection and analysis. A survey was conducted to collect data from 200 households in the township. Two focus group discussions were also organised to gather in-depth insights. The study found that households’ awareness of flood disaster risks was very high in both flood-prone and non-flood-prone ecological zones of Asamankese. Also, notable from the study was that whereas level of awareness was high among residents, preparedness levels were generally low, especially in terms of financial preparedness. Several recommendations were proposed, which include improving public education and sensitisation on flood disaster preparedness strategies, creating financial support scheme for residents to increase their financial preparedness as well as encouraging residents to increase their social capital support and participate in community gatherings.

Introduction

While disaster events in the past three decades have increased in frequency, their spatial distribution has made them a global phenomenon (Amoako & Boamah 2015 ). In addition, the impact of disasters has been quite devastating, claiming lots of lives daily (Oteng-Ababio 2013 ). Disasters can result from forces of nature, which may be aided by activities of man such as the construction of roads, irrigation and building of other infrastructure (Ogbanga 2015 ; Pokhrel 2015 ). Disasters may occur in the form of drought, fire outbreak, earthquake, tsunami, windstorm, flood, among others. What these events share in common is their ability to cause widespread community disruption, displacement, economic loss, property damage, deaths and injury as well as profound emotional suffering (Gillis, Shoup & Sicat 2001 ; Ogbanga 2015 ). According to the Internal Displacement Monitoring Centre (IDMC, 2015 ), disasters caused by natural hazards have displaced on average 26.4 million people annually between 2008 and 2015, which is equivalent to one person per second (International Federation of Red Cross and Red Crescent Societies [IFRC] 2016 ).

According to Fara ( 2001 ), there is no such thing as natural disaster. Events such as earthquakes, cyclones, tsunamis, volcanic eruptions, landslides, storms, fires, droughts and floods by themselves are not considered disasters. Rather, they become disasters when they adversely affect human life, livelihoods and property (IFRC 2007 ; Sinnakaudan et al. 2003 ; White 1945 ). While disaster events are not limited to a geographical space, their impact and the ability to recover from them varies significantly across space, with developing countries being the most affected areas (ActionAid 2006 ; World Bank 2010 ). Climate change, environmental degradation, population growth, increasing urbanisation, unsustainable development in hazard-prone environments, risky technologies, growing social and economic inequalities have all contributed to a dramatic increase in disaster events (Kötter 2003 ; Perrow 2007 ).

The persistent increase in the occurrence of disasters poses a substantive danger to the achievement of both sustainable development and poverty reduction initiatives (United Nations Office for Disaster Risk Reduction [UNISDR] 2009 ). The UNISDR ( 2009 ) defines disaster as follows:

[ A ] serious disruption of the functioning of a community or a society causing widespread human, material, economic or environmental losses which exceed the ability of the affected community or society to cope using its own resources. ( https://www.eird.org/eng/terminologia-eng.htm ). (n.p.)

The Oxford Reference Dictionary (ORD) defines flood as an overflowing or influx of water beyond its normal confines. Floods usually occur when the volume of water within a water body or water channel exceeds its carrying capacity, and as a result flows outside the normal perimeter of the water body (Adams 2008 ). Impacts of disasters may include loss of life, injury, disease and other negative effects on human, physical, mental and social well-being, together with damage to property, destruction of assets, loss of services, social and economic disruption and environmental degradation (UNISDR 2009 ).

Risk is usually associated with the human inability to cope with a particular situation. It comprises exposure to danger, adverse or undesirable prospects and conditions that contribute to danger (Hewett 1997 ). Sayers, Hall and Meadowcroft ( 2002 ) define risk as the probability of an event’s occurrence linked to its possible consequences. Disaster risks therefore denote the probability of disaster occurrence. Individuals, cities, and government, social and civil groups from various disciplines take into account the significance of sustained efforts to mitigate social, environmental, economic and emotional cost of disaster by addressing disaster risks (UNISDR 2002 ).

Among disaster events that have gained significant attention in recent times are those caused by floods. Flood disasters are vicious threats, rather than a natural occurrence when humans interfere with flood plains, and their management requires appropriate action at various scales and local community involvement (Anderson 1991 ; Douglas 2017 ). Although national and international institutions across the globe have developed and implemented programmes intended to control flood disasters, the phenomena persist (Bichard & Thurairajah 2014 ). On the global scale, flood disaster occurrences are phenomenal, and are probably the widest spread disasters that occur in most countries and cause maximum deaths (IFRC 2016 ). According to the United Nations Regional Coordinator in Dakar (October 2007), the worst flooding in 30 years, that battered West Africa in July 2007, caused more than 210 deaths and affected more than 785 000 people (Oppong 2011 ).

Disaster risk awareness, which denotes the extent of common knowledge about disaster risks, and the factors that lead to disasters, influence the actions that could be taken individually or collectively to address exposure and vulnerability to hazards. Awareness is a very crucial element for a society to effectively adapt to a flood risk. As stated by Shen ( 2009 ), awareness is diminished when the provision of an appropriate information is minimal or when memories of past experiences or events are diminished. Awareness can generally be uplifted through efforts that are centred on local issues, contain simple solutions to reduce flood risk and are repeated on regular basis (Poortinga, Bronstering & Lannon 2011 ).

United Nations Disaster Relief Organisation (UNDRO 1991 ) defines disaster preparedness as the state of taking direct and indirect measures to reduce damages that accompany disaster events to the minimum level possible. The objectives of preparedness are to ensure that appropriate mechanisms and resources are in place to assist those afflicted by the disaster and enable them to help themselves (United Nations Development Programme [UNDP] 1992 ).

Awareness and preparedness towards disasters vary depending on the characteristics of individuals within the community and characteristics of communities across space (Gerdan 2014 ; IFRC 2011 ). For instance, Gerdan ( 2014 ) has suggested that there is a direct link between education or sensitisation and awareness. Using educational levels of respondents, Gerdan ( 2014 ) found that higher levels of education contributed to producing positive awareness. In addition to this, the Regional Office for the Arab States of the United Nations Office for Disaster Risk Reduction (formerly UNISDR-ROAS) (USAID 2011 ) have indicated that depending on the type of community, access to information may vary depending on the social grouping and therefore one’s awareness of disaster risks. These groups may include gender, ethnic grouping and social status. Lastly, IFRC ( 2011 ) suggests that most people become disaster-aware based on their own personal experiences with disaster events over time.

The link between preparedness and awareness is well understood (Gerdan 2014 ; Sinclair & Pegram 2003 ), and as suggested by Gerdan ( 2014 :159): ‘It is possible to increase the capacity to cope with the disasters, by raising the awareness of all components, all individuals and communities in line with this common cause’.

The aftermaths of flood disasters in Ghana are the large-scale destruction of infrastructure, displacement of people, loss of human lives, outbreak of diseases, huge loss of investments, among other things. Over the years, the government and disaster management agencies of Ghana have mainly focused on disaster relief activities after the occurrence of disasters (Oteng-Ababio 2013 ).

Ghana, similar to other African countries, has had a fair share of flood disasters in recent times, with urban areas having a disproportionate share of floods (Global Facility for Disaster Reduction and Recovery [GFDRR] 2014 ; Okyere, Yacouba & Gilgenbach 2012 ). By way of example, in 2007, a catastrophic flood in the northern region of Ghana affected more than 325 000 people, with approximately 100 000 people requiring assistance for the restoration of their livelihoods (GFDRR 2014 ). In addition, a more recent and perhaps the most devastating flood in the history of Ghana occurred in Accra on 03 June 2015 where 159 people lost their lives and several others were rendered homeless (Daily Graphic 2015 ). National Disaster Management Organisation (NADMO 2010 ) suggests that although Ghana is vulnerable to certain disasters, floods have been the major disaster that the country has faced in recent years, especially in urban areas (Kordie 2013 ).

The West Akim Municipality of Ghana is generally considered a flood-prone area. The municipality experiences serious perennial floods that cause loss of lives and destruction to properties. As a result, some residents of Asamankese township in the municipality abandon their homes at the slightest rainfall (Golden Gazette 2014 ). In early October of 2018, a mother and child died when their house carved in after five hours of continuous downpour that caused flooding in several communities of Asamankese township. Thousands of other residents in various communities of the municipality were also displaced (Ansah 2018 ). According to the West Akim municipal office of NADMO, 33 major flood events have been recorded in Asamankese township between 2009 and 2018, which means on average three major floods in the township annually. This resulted in distracting movements of residents, hindering pursuance of vital economic activities and rendering many residents homeless. Properties worth GH₵72 550.00 (about $20 000.00) were directly damaged during this period. In February 2015, for instance, torrential rains rendered some 50 families homeless in Asamankese, destroying properties worth thousands of cedis (Ghana News 2015 ).

In many countries such as the Netherlands, Germany, Italy, Japan and Bangladesh, extensive research has been done to access households’ preparedness for flood disasters (see Mallick et al. 2005 ; Motoyoshi 2006 ; Takao et al. 2004 ; Thieken et al. 2007 ). Takao et al. ( 2004 ) conducted a survey on residents’ awareness and preparedness to tackle floods in Nagoya City of Japan in 2002; the authors revealed that residents’ preparedness was not dependent on anticipation of floods, rather on ownership of home and amount of damage experienced during previous floods. Such insightfulness becomes relevant in attempts to comprehensively manage flood disasters.

However, limited research is done to proffer nuanced understanding of awareness and, more importantly, preparedness of individual households towards disaster prevention in Ghana. Studies of preparedness on disasters have disproportionately looked at institutional preparedness (e.g. see Oteng-Ababio 2013 ). Little focus is given to flood issues in small and medium towns and, more importantly, on the level of preparedness in these areas to confront floods. It is based on this understanding that the present research has tried to improve knowledge about the awareness and preparedness of individual households towards flood disaster risk prevention in Asamankese township. The research further interrogated some important factors that affect households’ level of awareness and preparedness to flood disasters in the study area. Level of awareness about flood disaster risk among residents was checked from knowledge of both physical and human-induced factors that contribute to floods in the area. In a similar fashion, level of individual households’ preparedness to flood disasters was also looked at from financial as well as social preparedness perspective. This study is therefore critical to understanding and empowering individual households on disaster management.

Data and methods

Profile of study area.

Asamankese township is the capital town of West Akim Municipality. The topography of the municipality is generally mountainous and undulating. The municipality can be categorised as both lowland and highland area. The highest point is found around the Atiwa range, which is about 1250 ft. above sea level and is located between Pabi, Wawase and Asamankese in the northern part of the municipality (Ampadu-Agyei 2009 ). These conditions place Asamankese township in a valley-like landscape. A medium range, rising gradually between 500 ft and 1200 ft above sea level can be found in the eastern part of the municipality. The rest of the municipality is characterised by relatively lowland areas. The general elevation of Asamankese is about 500 ft. above the sea level (Ampadu-Agyei 2009 ; http://www.floodmap.net) .

The West Akim Municipality falls within the semi-equatorial climatic zone. The municipality, similar to many parts of southern Ghana, is characterised by a double maxima rainfall regime and a period of dry spell (Ampadu-Agyei 2009 ). The mean annual rainfall is between 1238 mm and 1660 mm. Temperature is mostly high throughout the year, yielding an average of 26.1 °C (Ampadu-Agyei 2009 ).

Asamankese township is traversed by several streams which take their sources from highland areas and are seasonal in nature. The major rivers that traverse the township include Abukyen, Ayensu and Supon. These water bodies provide good opportunities of crop cultivation. Rivers and wells constitute the major sources of water, respectively providing approximately 33.1% and 35.1% of total water supply to Asamankese township (Karim et al. 2015 ).

Taking into consideration the topography and drainage as well as past experiences with flood events, the Asamankese township has been divided into flood-prone zone (hereafter referred as FPZ) situated on the eastern side to the township bordering river Abukyen, and non-flood-prone zone (hereafter referred as NFPZ) which covers mostly the western side of the municipality. The FPZ comprises the Old Zongo community, the Estate and the Abaase community (see Figure 1 ), whereas the NFPZ of the Asamankese township comprises the Anum, Asamanketewa and Beposo communities (see Figure 1 ).

Map of Asamankese township.

With respect to population growth and physical expansion, Asamankese has been regarded as one of the fastest growing townships in the eastern region of Ghana (Ghana Statistical Service [GSS] 2010 ). The population of this township has increased from about 16 905 in 1970 to about 39 435 in 2010. Thus, within 40 years (from 1970 to 2010), Asamankese has recorded an increase of about 22 530 in population, thus representing about 133% increase between 1970 and 2010. This increase in population is partly attributed to the town’s strategic location and favourable soil conditions suitable for commercial agriculture, which has recorded an annual growth rate of about 2.5% (Danso-Wiredu 2011 ).

Sources of data collection

The primary quantitative data for this research were collected through a survey. The survey was aimed at soliciting the perceptions of residents on their level of awareness and preparedness towards flood disasters as well as factors that influence their awareness and preparedness levels. A five-item Likert scale ranging from ‘very high’ as the highest level of awareness to ‘very low’ being the lowest level of awareness was employed in the survey. Using this yardstick, as well as experiences from previous flood disasters, respondents were asked to rate their level of awareness of flood disaster risk. Two household heads were identified as key informants and were selected for both interviews and focus group discussions. Two focus group discussions were held with the residents, one with the residents from FPZ and another with the residents of NFPZ. This gave insights into the differences regarding the awareness and preparedness levels of the residents of different ecological zones of the municipality. Secondary data in the form of existing academic literature, magazines, print media and reports from various stakeholder institutions in disaster management were used to broaden the understanding of research area.

The target population for the study was drawn from both FPZ and NFPZ ecological regions of Asamankese. The reason was to ascertain whether significant variations exist in the levels of awareness and preparedness towards flood disasters in different locations. Heads of households were purposely selected as points of contact from each household interviewed; they were selected because of their vital decision-making roles regarding their wellness and preparedness within the communities. These household heads mostly include men and women who have lived in their respective areas for more than two decades.

A total of 200 households from six communities in Asamankese township were selected to participate in the study. Using a stratified sampling method, the 200 households selected were divided into 120 households from FPZ (Old Zongo, Estate and Abaase communities) and 80 households from NFPZ (Anum, Asamanketewa and Beposo communities). This sampling was purported to highlight more issues of residents in FPZ and to be able to make substantial recommendations based on their responses to alleviate their challenges. Respondents sampled in various communities were proportional to the overall sample size of 200 respondents. Therein, respondents from Old Zongo (with a population of about 450 people) accounted for 24% of the entire 200 households visited. The Estate (with a population of about 300 people) and Abaase (with a population of about 250 people) communities respectively represented 21%, and 15% of the entire 200 respondents. In NFPZ, on the other hand, respondents from Anum (having a population of about 360 people) and Beposo (a population of about 350 people) represented 14% each of the entire selected households, whiles Asamanketewa (with a population of about 250 people) communities accounted for 12% of total households visited. After the strata were deduced, simple random sampling method was used to reach the required number of households in each of the communities. The choice of the simple random sampling at this stage was because population sizes of various communities were small, and such smaller communities had significant homogeneity within the population. Two five-member, mixed-gendered focus group discussions were held, which represented residents from both FPZ and NFPZ. The focus group members were selected among the households’ heads interviewed during the survey, so that one focus group discussion was held in each of the ecological zones with each member residing in the community for not less than 10 years.

Ethical considerations

This article followed all ethical standards for a social science research and maintains the anonymity of its direct informants.

Results and discussion

Level of flood disaster awareness in asamankese.

Using the five-item Likert scale, the result, as shown in Figure 2 , indicates that 37.5% of the respondents within FPZ recognised flood disaster as very high, 27.5% ranked it high and 30.8% recognised flood disaster as medium risk. The cumulative response for ‘very high and high’ gave an indication that indeed people were aware of floods as a serious disaster challenge, especially in FPZ. In the other ecological zone, respondents’ perception of flood disaster risk appeared to be equal as 38.8% of the respondents indicated flood disaster as very high. In addition, 35.0% and 23.8% of respondents, respectively, recognised high and medium levels of awareness to flood disaster risk. It is observed that there is an established high level of awareness to the flood disaster risks among residents of Asamankese. In addition, the findings show that residents’ high level of awareness to flood disaster risk is not dependent on the ecological zone in which they reside in Asamankese township.

Level of awareness of flood disaster risks among residents of Asamankese.

Means of flood disaster awareness

There are different modes through which communications regarding disaster risks are formed, disseminated and applied among various target groups (Ardaya, Evers & Ribbe 2017 ). Hence, this study sought to find sources through which residents of Asamankese were informed about flood disaster risks. In resonance with Shen ( 2009 ) and Takoa et al. ( 2004 ), which relate that memories of past experiences are central to shaping people’s awareness, the findings indicated that most people (about 61%) became aware of flood risks in Asamankese through their personal experiences with disaster events over time. As inferred from Table 1 , majority of the respondents, about 68.3% and 51.2% from FPZ and NFPZ respectively, indicated that their awareness of flood disaster risk was the result of their personal experiences with flood events. Announcements on radio and occasional community meetings also accounted as other sources of flood disaster risks awareness within the two ecological zones.

Source of flood disaster awareness among residents of Asamankese.

Respondents’ awareness of the effect of their physical environment and geography in flood disasters

Environmental factors that lead to disaster occurrences tend to increase one’s exposure and vulnerability to pending hazards. In view of this, this study sought to find out the level of respondents’ knowledge of the geography of the areas of their residence. When asked about why the area tends to experience much flooding, respondent in FPZ unsurprisingly asserted to the valley-like cosmos of the township, which makes it swampy. Proximity of settlements to river bodies that often overflow their banks was another issue raised. Statistically, 69.5% of the 120 respondents from FPZ alluded to the area being valley-like and hence swampy. It is therefore apparent that the residents of Asamankese do have fair knowledge of the areas’ natural vulnerability to flood.

Resident’s awareness of human-induced factors contributing to flood disasters

Human-induced factors often heighten the exposure of communities in a natural flood-conducive environment to rampant flood disasters. This research therefore sought to investigate some human-induced factors that increase exposure of resident’s in Asamankese Township to frequent flood disasters. Factors highlighted from residents’ own perspectives are summarised in Table 2 .

Contribution of human-induced factors to flooding.

As seen in Table 2 , significant proportion of residents in FPZ (39.2%) ranked poor drainage systems, 36.7% of respondents highlighted the development of slum and the activities from these areas such as improper disposal of waste, while 15.8% of respondents in FPZ also highlighted the building of houses on water ways as the human-induced factors heightening the exposure of the settlement to rampant flood disasters. Other factors raised include dumping of refuse in drains by some residents and destruction of vegetative cover which tends to enhance the free flow of surface water and reduces its rate of percolation, hence increasing floods’ susceptibility. These factors were equally shared by the NFPZ residents as being responsible for rampant floods occurring in the alternative ecological zone. On 17 October 2015, a 47-year-old head of a household residing in Anum, Asamankese township, recounted in a focus group discussion the following:

[ T ]he main problem in Old Zongo and Abaase areas is the gutters. The gutters are not enough to carry the water when it rains heavily, and secondly, they pour so much rubbish in the gutters, so some of the gutters are also full of rubbish. So, when it rains heavily, where will the water go, it must flood the area … the way we build in this area too is a problem. I even think government is not hard on people so we just build anyhow in the waterways. We in this area also experience floods but it is not serious like in Old Zongo areas, that is why we are always trying to tell people here not to build in the waterway, because of what is going on in Old Zongo and Abaase areas.

This narrative is not far from narratives related in previous literature. For instance, Karley (2009, cited in Amoako & Boamah 2015 ), related that:

[ A ]vailable evidence does not support the fact that, flooding in most parts of the country is as a result of unusual rainfall, rather, the problem results from the lack of drainage facilities to collect the storm water for safe disposal. (p. 25)

Braimah et al. ( 2014 ) also added that as many as 82% of their respondents indicated lack of drainage system, whereas 70% indicated improper disposal of waste or refuse.

Individual level factors and concomitant level of flood disaster awareness

Setting aside physical geography, myriad other factors may also affect individual’s level of awareness of its environment (UNISDR-ROAS 2005). With that disposition, the study sought to analyse the relationship between individual level factors (that is, level of formal education, type of occupation and individual’s gender) and residents’ level of awareness of flood disaster risks. Consequently, a chi-square ( χ 2 ) test of independence was run at 95% confidence level (95% CI) (0.05) to ascertain the significance of these individual characteristics of their awareness levels.

Within FPZ, respondents with basic formal education and those with secondary education (39.4% and 33.3% respectively) constitute the majority of those who ranked flood disasters as the greatest disaster risks in their communities, compared to those with no formal education (15.2%) and tertiary education (12.1%) (see Table 3 ). Surprisingly, these two groups of residents (residents with basic formal education and secondary education) at the same time constitute the majority (20% of respondents with basic education and 60% with secondary school level education) of residents who claimed flood disasters were not a major risk in the township. Similar pattern was observed in the NFPZ of Asamankese township, where respondents with secondary school level education (53.6%) and tertiary level education (35.7%) significantly ranked flood disasters as a serious concern, but at the same time respondents with secondary school level education (38.7%) and tertiary level education (45.2%) ranked floods as not a serious disaster risk. Even though some variations were identified during cross tabulation, the chi-square test of association, in sync with Wang et al. ( 2018 ), showed no significant relationship existing between residents’ level of education and flood awareness in both ecological areas, as the p -values of 0.226 (FPZ) and 0.638 (NFPZ) were higher than the chosen 0.05 level of significance (see Table 3 ).

Relationship between level of education and flood disaster awareness.

Note: Flood-prone zone – c 2 statistic = 11.772, df = 9; p = 0.226 > 0.05. Non-flood-prone zone – c 2 statistic = 6.897, df = 9; p = 0.648 > 0.05.

Regarding respondents’ occupation and their level of awareness of flood disaster risks in FPZ of Asamankese township, the private informal workers category had the highest level of awareness (63.7%) towards flood disaster risks (see Table 4 ). This supports the findings of Wang et al. ( 2018 ) on flood risk perception in Jingdezhen, China. Accordingly, self-employed residents (in this study as private informal workers) had the highest flood risk awareness reached using a range of 1–4 for the corresponding ‘very high’, ‘high’, ‘low’ and ‘very low’ levels of awareness. This suggests a more frequent experience with flood disasters among private informal workers, compared with formal government workers, private formal workers and farmers. However, as seen in Table 4 , the private informal working class was represented significantly across various levels of perception on flood disaster risk in both ecological zones, compared with formal public servants, private formal workers and farmers. This skewed representation reflects the overall occupational constitution of residents in the township. Asamankese being the economic capital of West Akim municipality (Ministry of Finance and Economic Planning 2016 ), commerce is the major occupation of vast private informal traders and artisans and a minority population of formal public servants (Abdulai 2015 ). Private informal workers’ high awareness of flood disaster risks, as shown in Wang et al. ( 2018 ), might be explained by the fact that damages from flood disasters were solely borne by them at personal level, while private formal workers and public servants might receive some insurance cover from their place of work. The results of the chi-square test performed showed a significant relationship between occupation type and awareness of flood events in FPZ, given that the p -value obtained was less than 0.05 (0.047). Thus, residents’ occupation in FPZ somewhat influenced their level of flood disaster risk awareness in Asamankese.

Relationship between individual’s type of occupation and rating of flood awareness.

Note: Flood-prone zone – c 2 statistic=17.080, df = 9; p = 0.047 < 0.05. Non-flood-prone zone – c 2 = 15.729, df = 9; p = 0.073 > 0.05.

The results of chi-square test performed for association in NFPZ, however, depict no significant relationship between residents’ occupation and flood awareness as p = 0.073 obtained is greater than the chosen level of significance. Hence, this indicates that generally one’s level of awareness of flood disaster risks, as concurred by Takao et al. ( 2004 ), is not dependent on one’s occupation.

The study again reached out to establish relationship between respondents’ gender and their rating of flood awareness using a range of 1–4 with the corresponding values of ‘very high’, ‘high’, ‘low’ and ‘very low’. The data indicated that a significant percentage of females were aware of floods within the study area. Specifically, a cross tabulation revealed that 63.6% and 64.3% of female respondents in FPZ AND NFPZ respectively were ranked very high for their flood disaster risk awareness as compared to 36.4% and 35.7% of male respondents in the corresponding zones as shown in Table 5 . Although this high level of female awareness is reinforced by Wang et al. ( 2018 ), results of chi-square test about association indicated that there is no significant relationship between gender of respondents and their awareness of flood events in both zones as the respective p -values of 0.081 and 0.959 for FPZ and NFPZ were higher than the 0.05 level of significance.

Relationship between individual’s gender and rating of flood awareness.

Note: Flood-prone zone – c 2 statistic = 6.687, df = 3; p = 0.081 > 0.05. Non-flood-prone zone – c 2 statistic = 0.306, df = 3; p = 0.959 > 0.05.

In sum, with reference to the data obtained from the West Akim Municipal Office of the NADMO, it is evident that flood is a common disaster in Asamankese township. Again, it is also revealing that, aside residents’ occupation and level of awareness in FPZ, individual dynamic factors such as level of education, gender and occupation to a great extent, had no significant influence on the level of awareness of flood disaster risks, and thus, the latter, in congruence with Takao et al. ( 2004 ), was informed primarily by residents’ personal experiences with past flood events.

Preparedness strategies adopted by households to confront flood disasters

Financial preparedness and resilience.

As argued by Cannon ( 1994 ), awareness of flood disaster risks and one’s vulnerabilities are insufficient in reducing their impacts, lest it is coupled with an understanding of different economic systems and economic capacities of people to withstand and recover from disasters. The study therefore sought to find out how prepared were residents in terms of their economic resilience. For this, the respondents were asked their assured means of sustenance (if any at all) should they become victims of flood disaster. The results summarised in Table 6 show that of the 120 respondents in FPZ, only 28.3% had some means of sustaining themselves, while 71.7% indicated that they had no guaranteed means of sustenance should they become victims of floods. Similarly, majority of respondents (about 71.2%) in NFPZ had no assured means of sustenance in case of becoming flood victims. Only about 28.8% of respondents (see Table 6 ) indicated that they had an assured means of sustenance.

Economic and social resilience to floods in Asamankese township.

The result brings to the fore the issue pointed out by Flooding Issues Advisory Forum (FIAC, 2007 ) that sustainable flood management involves both social and economic resilience. Moreover, it also suggested that sensitisation should not only include communication of hazards or announcing impending floods but also education on the issues such as better economic planning within a catchment area.

In the case of those who had some means of sustenance, they were further inquired about the specific means of sustenance in anticipation of flood disasters. As summarised in Table 6 , out of the 34 respondents in FPZ, 91.2% indicated that they had financial savings (referred to as Susu in Ghanaian parlance) as a recovery plan, while only 8.8% indicating that they rely on insurance. On the other hand, out of 23 respondents in NFPZ, 78.3% indicated that they had financial savings, while 21.7% indicated that they depend on insurance. The results thus show that a large proportion of respondents (about 71.7% and 71.2% in FPZ and NFPZ respectively) had no means of sustaining themselves to face flood disasters. For those who had some means of sustenance, they largely depended on their financial savings, and this was the situation across both ecological zones.

According to Grothmann and Reusswig (2004), self-protective behaviours by residents of flood-prone urban areas could help to scale down flood damage monetarily by 80%, which thus reduces the need for public flood risk management. Hence, this research further probed into the sufficiency of financial savings for the full recovery of residents from flood catastrophes. During the focus group discussions in FPZ, participants opined that their financial savings were often inadequate, and thus unlike Grothmann and Reusswig’s (2004) claim, couldn’t be sufficient to resuscitate them from flood catastrophes. A 62-year-old former teacher who participated in the focus group discussion held on 03 October 2015 in Old Zongo related as follows:

Some of the people here can do Susu, a lot of people too cannot do it because they don’t have the money. Even many people here pay their children’s school fees, they go to hospital when they are sick, and it is this same small ‘Susu’ that they take to do these things. So, when serious floods happen, and affects them, how much more will they have to cater for themselves from the small susu. They can buy food for some few months then the money finish. If their families too cannot contribute much, then even the small children may have to go to the market side to get something to do so they can survive.

This insufficiency of personal financial protection in times of flood disasters owes to low incomes earned by majority of the residents who were private informal workers (see Table 4 ), particularly in FPZ. Respondents were then asked how they would regain some basic possessions they lost in floods. The results presented in Table 6 show that just 35.8% and 25.0% of respondents in FPZ and NFPZ respectively could regain their possessions through savings. A larger number of respondents indicated that they would be able to regain their lost possessions in floods through the extended family social support. The results, therefore, suggest that although individual financial savings are crucial in meeting the very basic needs of food and clothing immediately after floods, the extended family support is essential to regain some lost possessions. In this regard, this study suggests that the focus should be on preparing of both areas of personal savings and insurance schemes for building resolute capacity to recover from flood disasters.

Social preparedness and resilience

Continuing with the factors that are critical to enhance resilience, respondents were asked about the social structures that enhance their preparedness or serve as a conduit to improve their preparedness for floods. As presented in Figure 3 , 36.7% and 33.8% of respondents in FPZ and NFPZ indicated that the family social structure was critical to prepare for floods. The result again shows that about a quarter (23.3% and 25.0% respectively) of respondents in FPZ and NFPZ asserted that the church was crucial to their preparedness for floods. This is because occasional announcements are made in the church regarding anticipated rainfall. The church then organises some cleanup exercises to help de-silt some choked gutters, especially around the church premises. Attending members then replicate the cleanup exercise in their homes in anticipation of heavy rains.

Social structures that enhance preparedness.

An equally important area noted was friends from the market. It is observed in Figure 3 that 16.7% of respondents from FPZ and 18.8% from NFPZ highlighted the importance of information from friends from market centres. Hence, it is imperative to realise the importance and significance of social capital and social institutions such as friends and family towards resilience-building and disaster preparedness of communities. It therefore goes without saying that in the adoption of any sustainable flood management strategy, social structures (such as families and community commercial activity centres) should be seriously considered. Therefore, it is important that churches and market centres should become places where information on imminent flood disasters from reliable sources be communicated. In addition, by improving individual economic resilience, the study shows that it would invariably strengthen not just one’s immediate nuclear family but also improve the preparedness and ability of other members of the extended family to recover from disasters.

Flood disasters are major environmental challenges faced by residents of Asamankese, the capital of the West Akim Municipality in Ghana. Residents’ level of awareness of flood disaster risks tends to be high because of their own experiences irrespective of their individual level of education, occupation and gender. The rather rampant flood disasters in Asamankese are the combined result of natural environment and inappropriate human behaviours. Despite the high level of awareness of flood disaster risks, there appears to be an incommensurate level of flood disaster preparedness in the settlement. This is mainly because of low level of individual economic capacity to withstand and recover from these disasters.

In times of devastating floods, it is equally important to note the influence of strong social capital in one’s preparedness and ability to recover from flood disasters in the township. This strong element of social capital and institutions was the major means by which many people could recover their possessions that were lost in floods. Moreover, while an early warning system might be in place (partly through church gatherings and market centres), the fact is that preparedness for flood disasters in Asamankese is low, which means that people might not be able to respond properly to and recover fully from the impact of floods.

Recommendations

The following recommendations are made from the findings of this study for promoting local and institutional reforms for awareness and preparedness towards flood disasters.

• Relevant institutions, such as private radio stations, in Asamankese should collaborate with state institutions to further organise these communities, educate them and support them to adopt appropriate adaptive skill-building techniques such as building on stilts and adherence to appropriate building codes. This is important because some of these institutions are already serving as sources of awareness of imminent floods to some sections of Asamankese residents.
• In addition, relevant state institutions should collaborate with social institutions such as churches in Asamankese to set up financial support schemes for residents. These schemes should collect financial contributions from local residents, institutions as well as government’s financial support. Such schemes if managed properly by local residents and existing institutions could become a reliable financial support to assist victims of floods in the township.
• Community-initiated mitigation measures, such as construction of new drains, de-silting of existing drains as well as expanding individual’s social capital by participating in social gatherings, should be vehemently encouraged by local community members to build a more socially resilient community.

Acknowledgements

Competing interests.

The author declares that he has no financial or personal relationships that have inappropriately influenced him in the writing of this article.

Authors’ contributions

I declare that I am the sole author of this research article.

Funding information

No grant or financial support was solicited from any organisation for this work.

Data availability statement

The author declares that this article is the result of his original research work, and the views expressed in this article are his personal opinions emanating from field data and views of respondents of Asamankese, and do not represent an official position of the author’s affiliate institution.

How to cite this article: Glago, F.J., 2019, ‘Household disaster awareness and preparedness: A case study of flood hazards in Asamankese in the West Akim Municipality of Ghana’, Jàmbá: Journal of Disaster Risk Studies 11(1), a789. https://doi.org/10.4102/jamba.v11i1.789

• Abdulai A.R., 2015, Abbreviated resettlement action plan on the rehabilitation of the day-by-day road in Asamankese , Researchgate, Ghana, viewed August 2015, from https://www.researchgate.net/publication/277312940_ABBREVIATED_RESETTLEMENT_ACTION_PLAN_ON_THE_REHABILITATION_OF_THE_DAY-BY-DAY_ROAD_IN_ASAMANKESE_GHANA/citation/download . [ Google Scholar ]
• ActionAid , 2006, Climate change, urban flooding and the rights of the urban poor in Africa. Key findings from six African cities , a report by ActionAid International, Johannesburg. [ Google Scholar ]
• Adams A.G., 2008, ‘ Perennial flooding in the Accra Metropolis: The human factor ’, unpublished Master of Science thesis, Kwame Nkrumah University of Science and Technology, Kumasi. [ Google Scholar ]
• Amoako C. & Boamah E.F., 2015, ‘ The three-dimensional causes of flooding in Accra, Ghana ’, International Journal of Urban Sustainable Development 7 ( 1 ), 109–129. 10.1080/19463138.2014.984720 [ CrossRef ] [ Google Scholar ]
• Ampadu-Agyei B.K.N., 2009, The impact of job satisfaction on staff attitude towards patients/clients in the public health institutions. A case study of west Akim municipality , KNUST Press, Asamankese. [ Google Scholar ]
• Anderson M.B., 1991, ‘Which costs more: Prevention or recovery’, Managing natural disasters and the environment , pp. 17–27, World Bank, Washington, DC. [ Google Scholar ]
• Ansah M., 2018, ‘ Mother and child feared dead in Asamankese floods ’, Citi Newsroom , viewed 27 July 2015, from https://citinewsroom.com/2018/10/17/mother-and-child-feared-dead-in-asamankese-floods .
• Ardaya A.B., Evers M. & Ribbe L., 2017, ‘ What influences disaster risk perception? Intervention measures, flood and landslide risk perception of the population living in flood risk areas in Rio de Janeiro state, Brazil ’, International Journal of Disaster Risk Reduction 25 , 227–237. 10.1016/j.ijdrr.2017.09.006 [ CrossRef ] [ Google Scholar ]
• Bichard E. & Thurairajah N., 2014, ‘ Trialling behaviour change strategies to motivate interest in property level flood protection ’, International Journal of Disaster Resilience in the Built Environment 5 ( 2 ), 130–143. 10.1108/IJDRBE-02-2012-0008 [ CrossRef ] [ Google Scholar ]
• Braimah M.M., Abdul-Rahaman I., Oppong-Sekyere D., Momori P.H., Abdul-Mohammed A. & Dordah G.A., 2014, ‘ A study into the causes of floods and its socio-economic effects on the people of Sawaba in the Bolgatanga Municipality, Upper East, Ghana ’, International Journal of Pure & Applied Bioscience 2 ( 1 ), 189–195. [ Google Scholar ]
• Cannon T., 1994, ‘ Vulnerability analysis and the explanation of “natural disasters” ’, Disasters, Development and Environment 1 , 13–30. [ Google Scholar ]
• Daily Graphic , 2015, ‘ Ghana among the worst hit by torrential rains ’, Daily Graphic , 28 July 2015, p. 3. [ Google Scholar ]
• Danso-Wiredu E.Y., 2011, ‘ Mobility and access for off-road rural farmers in West Akim District ’, Ghana Journal of Geography 3 , 230–249. [ Google Scholar ]
• Douglas I., 2017, ‘ Flooding in African cities, scales of causes, teleconnections, risks, vulnerability and impacts ’, International Journal of Disaster Risk Reduction 26 , 34–42. 10.1016/j.ijdrr.2017.09.024 [ CrossRef ] [ Google Scholar ]
• Fara K., 2001, ‘ How natural are natural disasters? Vulnerability to drought of communal farmers in Southern Namibia ’, Risk Management 3 ( 3 ), 47–63. 10.1057/palgrave.rm.8240093 [ CrossRef ] [ Google Scholar ]
• Flooding Issues Advisory Forum (FIAC) , 2007, Sustainable flood management report , FIAC, Scotland, viewed 27 July 2015, from http://reliefweb.int/report/mozambique/cws-situation-report-2008-mozambique-floods . [ Google Scholar ]
• Gerdan S., 2014, ‘ Determination of disaster awareness, attitude levels and individual priorities at Kocaeli University ’, Eurasian Journal of Educational Research 55 , 159–176. 10.14689/ejer.2014.55.10 [ CrossRef ] [ Google Scholar ]
• Ghana News , 2015, ‘ Torrential rains render 50 families homeless at Asamankese ’, Ghana News , viewed 11 August 2017, from https://www.newsghana.com.gh/torrential-rains-render-50-families-homeless-at-asamankese .
• Ghana Statistical Service (GSS) , 2010, 2010 population and housing census report on District Assemblies , Ghana Statistical Service, Accra. [ Google Scholar ]
• Gillis M., Shoup C. & Sicat G.P., 2001, World development report 2000/2001 – Attacking poverty , The World Bank, Washington, DC. [ Google Scholar ]
• Global Facility for Disaster Reduction and Recovery (GFDRR) , 2014, Bringing resilience to scale, annual report , viewed 21 September 2015, from https://sustainabledevelopment.un.org/content/documents/1948GFDRR%20ANNUAL%20REPORT%202014.pdf .
• Golden Gazette , 2014, ‘ Asamankese drainage, special report ’, The Gazette , viewed 10 September 2018, from http://jerryjohnakornor1.blogspot.com/2014/08/asamankese-drainage-special-report.html .
• Grothmann T. & Reusswig F., 2006, ‘ People at risk of flooding: Why some residents take precautionary action while others do not ’, Natural Hazards 38 ( 1–2 ), 101–120. 10.1007/s11069-005-8604-6 [ CrossRef ] [ Google Scholar ]
• Hewett K., 1997, Regions of risks: A geographical introduction to disasters , Longman, Routledge, Boston, MA. [ Google Scholar ]
• International Displacement Monitoring Centre (IDMC) , 2015, Global Estimates 2015: People displayed by disasters , viewed 21 September 2017, from http://www.internal-displacement.org/publications/global-estimates-2015-people-displaced-by-disasters .
• International Federation of Red Cross and Red Crescent Societies (IFRC) , 2007, Disaster response and contingency planning guide , IFRC, Geneva. [ Google Scholar ]
• International Federation of Red Cross and Red Crescent Societies (IFRC) , 2011, World disasters report: Hunger and malnutrition , viewed 12 August 2016, from www.ifrc.gov/global/publications/disasters/wdr/wdr2011 .
• International Federation of Red Cross and Red Crescent Societies (IFRC) , 2016, World disasters report 2016 – Resilience: Saving lives today, investing for tomorrow , IFRC, Geneva. [ Google Scholar ]
• Karim A.U., Ntiamoa-Baidu Y., Ampofo J.A. & Kingsford-Adaboh R., 2015, ‘ Nitrate, chloride and calcium contamination of hand-dug well water from household pit-latrine in Asamankese, Eastern Ghana ’, Universal Journal of Chemistry 3 ( 1 ), 1–9. [ Google Scholar ]
• Kordie A.G., 2013, ‘Social support and perceived vulnerability to flooding among urban poor dwellers in Accra’, Regional Institute for Population Studies, University of Ghana, Legon. [ Google Scholar ]
• Kötter T., 2003, ‘ Prevention of environmental disasters by spatial planning and land management ’, 2nd FIG Regional Conference, Marrakesh, Morocco, pp. 2–5. [ Google Scholar ]
• Mallick D.L., Rahman A., Alam M., Juel A.S.M., Ahmad A.N. & Alam S.S., 2005, ‘ Case study 3: Bangladesh floods in Bangladesh: A shift from disaster management towards disaster preparedness ’, IDS Bulletin 36 ( 4 ), 53–70. 10.1111/j.1759-5436.2005.tb00234.x [ CrossRef ] [ Google Scholar ]
• Ministry of Finance and Economic Planning , (2016). The Composite Budget for the West Akim Municipal Assembly for the 2016 fiscal year , viewed 06 June 2018, from https://www.mofep.gov.gh/sites/default/files/composite-budget/2016/ER/West-Akyem.pdf .
• Motoyoshi T., 2006, Public perception of flood risk and community-based disaster preparedness, a better integrated management of disaster risks: Toward resilient society to emerging disaster risks in megacities , Terrapub, Tokyo, pp. 121–134. [ Google Scholar ]
• National Disaster Management Organisation (NADMO) , 2010, Unpublished annual report 2010 , NADMO, Swedro. [ Google Scholar ]
• Ogbanga M.M., 2015, ‘ Impacts of flooding disaster on housing and health in two communities of Ahoada East and West Local Government areas of rivers state ’, Nigerian Journal of Agriculture, Food and Environment 11 ( 1 ), 44–50. [ Google Scholar ]
• Okyere Y.C., Yacouba Y. & Gilgenbach D., 2012, ‘ The problem of annual occurrences of floods in Accra: An integration of hydrological, economic and political perspectives ’, Interdisciplinary term paper, Zef Doctoral Studies Program, Universitat Bonn, pp. 1–50, Theoretical and Empirical Researches in Urban Management, Bonn. [ Google Scholar ]
• Oppong B., 2011, ‘ Environmental hazards in Ghanaian cities: The incidence of annual floods along the Aboabo river in the Kumasi metropolitan area (KMA) of the Ashanti region of Ghana ’, MPhil thesis, Department of Geography and Rural Development, Kwame Nkrumah University of Science and Technology, Kumasi. [ Google Scholar ]
• Oteng-Ababio M., 2013, ‘ Prevention is better than cure: Assessing Ghana’s preparedness (capacity) for disaster management ’, Jamba. Journal of Disaster Risk Studies 5 ( 2 ), 75–86. 10.4102/jamba.v5i2.75 [ CrossRef ] [ Google Scholar ]
• Perrow C., 2007, ‘Disasters ever more? Reducing US vulnerabilities’, Handbook of disaster research , pp. 521–533, Springer, New York. [ Google Scholar ]
• Pokhrel S., 2015, ‘ Return on investment (ROI) modelling in public health: Strengths and limitations ’, The European Journal of Public Health 25 ( 6 ), 908–909. 10.1093/eurpub/ckv136 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Poortinga,W., Bronstering K. & Lannon S., 2011, ‘ Awareness and perceptions of the risks of exposure to indoor radon: A population-based approach to evaluate a radon awareness and testing campaign in England and Wales ’, Risk Analysis 31 ( 11 ), 1800–1812. 10.1111/j.1539-6924.2011.01613.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sayers P.B., Hall J.W. & Meadowcroft I.C., 2002, ‘ Towards risk-based flood hazard management in the UK. Proc. of ICE ’, Journal of Civil Engineering 150 ( 5 ), 36–42. 10.1680/cien.2002.150.5.36 [ CrossRef ] [ Google Scholar ]
• Shen X., 2009, ‘ Flood risk perception and communication in different cultural contexts – A comparative case study between Wuhan, China and Cologne, Germany ’, PhD dissertation, University of Bonn, Bonn. [ Google Scholar ]
• Sinclair S. & Pegram G., 2003, A flood now-casting system for the eThekwini metro, urgent now-casting using Radar – An integrated pilot study , p. 1, Water Research Commission, Silowa Printers, Pretoria. [ Google Scholar ]
• Sinnakaudan S.K., Ghani A.A., Ahmed M.S.S. & Zakaria N.A., 2003, ‘ Floods risk mapping for Pari river incorporating sediment transport ’, Journal of Environmental Modeling and Software 18 ( 2 ), 119–130. 10.1016/S1364-8152(02)00068-3 [ CrossRef ] [ Google Scholar ]
• Takao K., Motoyoshi T., Sato T., Fukuzondo T., Seo K. & Ikeda S., 2004, ‘ Factors determining residents’ preparedness for floods in modern megalopolises: The case of the Tokai flood disaster in Japan ’, Journal of Risk Research 7 ( 7–8 ), 775–787. 10.1080/1366987031000075996 [ CrossRef ] [ Google Scholar ]
• Thieken A.H., Kreibich H., Müller M. & Merz B., 2007, ‘ Coping with floods: Preparedness, response and recovery of flood-affected residents in Germany in 2002 ’, Hydrological Sciences Journal 52 ( 5 ), 1016–1037. 10.1623/hysj.52.5.1016 [ CrossRef ] [ Google Scholar ]
• United Nations Development Programme (UNDP) , 1992, An overview of disaster management , Disaster Management Training Programme , UNDP, viewed 15 August 2015, from http://www.pacificdisaster.net/pdnadmin/data/original/dmtp_02_an_overview_dm_8.pdf . [ Google Scholar ]
• United Nations Disaster Relief Organisation (UNDRO) , 1991, Mitigation of natural disasters phenomena, effects and options. A manual for planner , Office of the UN Disaster Relief Coordinator , Geneva, p. 157, viewed 15 August 2015, from http://cidbimena.desastres.hn/pdf/eng/doc1028/doc1028-indice.pdf . [ Google Scholar ]
• United Nations Office for Disaster Risk Reduction (UNISDR) , 2002, ‘Living with risk’: A global review of disaster reduction initiatives , UNISDR Secretariat, Geneva. [ Google Scholar ]
• United Nations Office for Disaster Risk Reduction (UNISDR) , 2009, Global assessment report on disaster risk reduction: Risk and poverty in a changing climate , UNISDR Secretariat, Geneva. [ Google Scholar ]
• USAID , 2011, Introduction to Disaster Risk Reduction , viewed 18 October 2019, from https://www.preventionweb.net/files/26081_kp1concepdisasterrisk1.pdf .
• Wang Z., Wang H., Huang J., Kang J. & Han D., 2018, ‘ Analysis of the public flood risk perception in a flood-prone city: The case of Jingdezhen city in China ’, Water 10 ( 11 ), 1577 10.3390/w10111577 [ CrossRef ] [ Google Scholar ]
• White G.F., 1945, ‘Human adjustment to floods, a geographical approach to floods problem in the United States’, Department of Geography Research Paper No. 29, University of Chicago, Chicago, IL. [ Google Scholar ]
• World Bank , 2010, Response to Pakistan floods: Evaluating lessons and opportunity , The World Bank, Washington, DC. [ Google Scholar ]

Land under water: What causes extreme flooding?

According to ufz researchers, the more flood driving factors there are, the more extreme a flood is.

If rivers overflow their banks, the consequences can be devastating -- just like the catastrophic floods in North Rhine-Westphalia and Rhineland-Palatinate of 2021 showed. In order to limit flood damage and optimise flood risk assessment, we need to better understand what factors can lead to extreme forms of flooding and to what extent. Using methods of explainable machine learning, researchers at the Helmholtz Centre for Environmental Research (UFZ) have shown that floods are more extreme when several factors are involved in their development. The research was published in Science Advances .

There are several factors that play an important role in the development of floods: air temperature, soil moisture, snow depth, and the daily precipitation in the days before a flood. In order to better understand how individual factors contribute to flooding, UFZ researchers examined more than 3,500 river basins worldwide and analysed flood events between 1981 and 2020 for each of them. The result: precipitation was the sole determining factor in only around 25% of the almost 125,000 flood events. Soil moisture was the decisive factor in just over 10% of cases, and snow melt and air temperature were the sole factors in only around 3% of cases. In contrast, 51.6% of cases were caused by at least two factors. At around 23%, the combination of precipitation and soil moisture occurs most frequently.

However, when analysing the data, the UFZ researchers discovered that three -- or even all four -- factors can be jointly responsible for a flood event. For example, temperature, soil moisture, and snow depth were decisive factors in around 5,000 floods whilst all four factors were decisive in around 1,000 flood events. And not only that: "We also showed that flood events become more extreme when more factors are involved," says Dr Jakob Zscheischler, Head of the UFZ Department "Compound Environmental Risks" and senior author of the article. In the case of one-year floods, 51.6% can be attributed to several factors; in the case of five- and ten-year floods, 70.1% and 71.3% respectively can be attributed to several factors. The more extreme a flood is, the more driving factors there are and the more likely they are to interact in the event generation. This correlation often also applies to individual river basins and is referred to as flood complexity.

According to the researchers, river basins in the northern regions of Europe and America as well as in the Alpine region have a low flood complexity. This is because snow melt is the dominant factor for most floods regardless of the flood magnitude. The same applies to the Amazon basin, where the high soil moisture resulting from the rainy season is often a major cause of floods of varying severity. In Germany, the Havel and the Zusam, a tributary of the Danube in Bavaria, are river basins that have a low flood complexity. Regions with river basins that have a high flood complexity primarily include eastern Brazil, the Andes, eastern Australia, the Rocky Mountains up to the US west coast, and the western and central European plains. In Germany, this includes the Moselle and the upper reaches of the Elbe. "River basins in these regions generally have several flooding mechanisms," says Jakob Zscheischler. For example, river basins in the European plains can be affected by flooding caused by the combination of heavy precipitation, active snow melt, and high soil moisture.

However, the complexity of flood processes in a river basin also depends on the climate and land surface conditions in the respective river basin. This is because every river basin has its own special features. Among other things, the researchers looked at the climate moisture index, the soil texture, the forest cover, the size of the river basin, and the river gradient. "In drier regions, the mechanisms that lead to flooding tend to be more heterogeneous. For moderate floods, just a few days of heavy rainfall is usually enough. For extreme floods, it needs to rain longer on already moist soils," says lead author Dr Shijie Jiang, who now works at the Max Planck Institute for Biogeochemistry in Jena.

The scientists used explainable machine learning for the analysis. "First, we use the potential flood drivers air temperature, soil moisture, and snow depth as well as the weekly precipitation -- each day is considered as an individual driving factor -- to predict the run-off magnitude and thus the size of the flood," explains Zscheischler. The researchers then quantified which variables and combinations of variables contributed to the run-off of a particular flood and to which extent. This approach is referred to as explainable machine learning because it uncovers the predictive relationship between flood drivers and run-off during a flood in the trained model. "With this new methodology, we can quantify how many driving factors and combinations thereof are relevant for the occurrence and intensity of floods," adds Jiang.

The findings of the UFZ researchers are expected to help predict future flood events. "Our study will help us better estimate particularly extreme floods," says Zscheischler. Until now, very extreme floods have been estimated by extrapolating from less extreme floods. However, this is too imprecise because the individual contributing factors could change their influence for different flood magnitudes.

• Natural Disasters
• Snow and Avalanches
• Global Warming
• Environmental Issues
• Environmental impact assessment
• Mid-Atlantic United States flood of 2006
• Effects of global warming
• Preparations for Hurricane Katrina
• Hurricane Katrina

Story Source:

Materials provided by Helmholtz Centre for Environmental Research - UFZ . Note: Content may be edited for style and length.

Journal Reference :

• Shijie Jiang, Larisa Tarasova, Guo Yu, Jakob Zscheischler. Compounding effects in flood drivers challenge estimates of extreme river floods . Science Advances , 2024; 10 (13) DOI: 10.1126/sciadv.adl4005

Cite This Page :

Explore More

• Illuminating Oxygen's Journey in the Brain
• DNA Study IDs Descendants of George Washington
• Heart Disease Risk: More Than One Drink a Day
• Unlocking Supernova Stardust Secrets
• Why Do Some Memories Become Longterm?
• Cell Division Quality Control 'Stopwatch'
• What Controls Sun's Differential Rotation?
• Robot, Can You Say 'Cheese'?
• Researchers Turn Back the Clock On Cancer Cells
• Making Long-Term Memories: Nerve-Cell Damage

Trending Topics

Strange & offbeat.

Future Strategies

State Case Studies

Federal Review

Management Goals

• Holistic Approach to Coastal FRM

CONTENTS ≡

CONTENTS ✕

Coastal Management Program

Shoreline regulations, floodplain management, wetland management, building codes, community planning, stormwater and runoff management, erosion management, climate adaptation initiatives, state management capacity, alternatives to structural mitigation, long-term planning, balance of mitigation and disaster recovery, holistic management approach, new york coastal flood risk management case study.

Policies and Programs

The New York Coastal Management Program , established in 1982, is housed within the New York Department of State’s Office of Planning, Development, and Community Infrastructure . Much of the program’s legislative authority is drawn from the state Waterfront Revitalization of Coastal Areas and Inland Waterways law as well as the Coastal Erosion Hazard Areas law . The program pursues goals related to coastal resources protection and development, local waterfront revitalization, coordination of major activities affecting coastal resources, public awareness of coastal issues, and federal consistency with state coastal management policies. Within New York, the Department of State administers the program and coordinates its implementation in cooperation with the state Department of Environmental Conservation as well as other state agencies.

Coastal program boundaries extend along the coast of Long Island, New York City, Hudson River estuary, both Great Lakes that border New York, and the Niagara River. Specific landward boundaries of the coastal program vary by region and locality due to initial delineation proposals from local government agencies. All barrier and coastal islands on Long Island are included within program boundaries along with areas 1,000 feet landward of the shoreline, extending further in some cases. The New York City program boundary generally extends 500 to 1,000 feet inland from the shoreline, with select areas along major tributaries also extending further. Within the Hudson River Valley the landward boundary is generally 1,000 feet but may extend up to 10,000 feet in areas that possess high aesthetic, agricultural, or recreational value. In the Great Lakes region the boundary is also generally 1,000 feet, though urbanized areas or transportation infrastructure parallel to shore limit the boundary to 500 feet or less in some cases.

Coastal management program consistency reviews require federal actions in the state coastal zone to be consistent with the enforceable policies of the state program or the policies of an approved local waterfront revitalization program. The program also contains provisions to ensure consistency of state actions in coastal areas. Of the 44 coastal management program enforceable policies in New York, seven specifically address flooding and erosion hazards. These policies touch on a number of aspects of coastal flood risk management including the siting of buildings in coastal areas to minimize risk to property and human lives, protection of natural features that mitigate coastal flood risk, construction of erosion control structures to to meet long-term needs, prevention of flood level increases due to coastal activities or development, prevention of coastal mining or dredging from interfering with natural coastal processes, use of public funds for erosion protection structures, and use of non-structural mitigation measures when possible. Additional enforceable policies address coastal development, fish and wildlife resources, public access, recreation, historic and scenic resources, agricultural lands, energy and ice management, water and air resources, and wetlands management.

At the state level, aspects of New York’s Environmental Conservation Law , Local Waterfront Revitalization Program , and State Environmental Quality Review permitting program influence coastal zoning and development decisions. Article 34 of the Environmental Conservation Law requires the identification of coastal erosion hazard areas and rates of recession of coastal lands. Shoreline setbacks must then be implemented at a distance that is sufficient to minimize damage from erosion. Article 36 of the Environmental Conservation Law , the state Flood Plain Management Act, also addresses coastal hazards, requiring walled and roofed buildings to be sited landward of mean high tide and prohibiting mobile homes within coastal high hazard areas, among other restrictions. Article 15, Water Resources Law , regulates the placement of coastal structures such as docks or piers and also addresses the placement of fill in coastal areas. Together these elements of the Environmental Conservation Law provide much of the legal basis for zoning decisions that can affect coastal flood risk at the municipal and local level.

Participation in the Local Waterfront Revitalization Program can also influence a local government’s coastal zoning decisions. In the process of preparing and adopting a revitalization program, local governments provide a more specific implementation of the state Coastal Management Program, taking advantage of local regulatory powers such as zoning ordinances and site plan review. Upon approval of a Local Waterfront Revitalization Program, state actions must then be consistent with the local program. In this way the enforceable policies of the Coastal Management Program, including those that relate to coastal flooding and erosion, are incorporated into local zoning decisions. Elements of enforceable policies are also incorporated into environmental permitting through the State Environmental Quality Review Program, which requires state agencies and local governments to prepare an environmental impact statement for any action that may have a significant impact on the environment. If an action in a coastal area requires the preparation of an impact statement, it must also be determined that the action is consistent with any relevant coastal enforceable policies. Consistency reviews must also be applied to NYS SEQRA type 1 actions as well as unlisted actions.

Floodplain management activities within New York are primarily conducted through the National Flood Insurance Program . Any regulations developed by the state must be at a minimum as strict as those prescribed by FEMA. Beyond the state level communities may adopt more restrictive floodplain management regulations. Within the state, local communities largely regulate development within federally designated Special Flood Hazard Areas, with state assistance provided by the New York State Department of Environmental Conservation. Local development permits govern private development within floodplains as well as development by a county, city, town, village, school district, or public improvement district, as specified in the state Environmental Conservation Law.

State standards for floodplain development permits in all designated special flood hazard areas require adequate anchorage and use of flood resistant material for all new construction and substantial improvement to existing structures. Utilities must also be designed in a manner that minimizes or eliminates risk of damage or failure during flood events. In areas where base flood elevation data exists, new construction or substantially improved residential structures must have the lowest floor at two feet above the BFE, including basements and cellars. Nonresidential structures may employ floodproofing to provide protection. Any enclosed areas below the base flood elevation must be designed to allow for the equalization of hydrostatic forces on exterior walls during a flood event. If no base flood elevation has been determined, new construction or substantially improved residential structures must be elevated above grade to the depth specified on the corresponding flood insurance rate map or two feet if no number is specified, with nonresidential structures again able to employ floodproofing measures. All state agency activities, whether directly undertaken, funded, or approved by an agency, must also be evaluated in terms of significant environmental impacts under the State Environmental Quality Review program, which includes a substantial increase in flooding as a criteria of significance. An environmental impact statement must be prepared if it is determined that an action may have a potential significant adverse impact.

All structures must be located landward of mean high tide levels within coastal high hazard areas, and all new construction or substantially improved structures must be elevated on pilings or columns so that the bottom of the lowest horizontal structural member of the lowest flood is elevated to or above the BFE. Pilings or column foundations must be adequately anchored, and fill is prohibited for use as a structural support for any new structure or substantial improvement. Space below the lowest floor may not contain obstructions to flood flows or otherwise be enclosed with non-breakaway walls. Any such space below the lowest structural floor may not be used for human habitation. New development or substantial improvement to structures must also not affect sand dunes in any way that increases potential flood damages.

The New York State Department of Environmental Conservation is also responsible for wetland management within the state. Statutory authority for wetland regulations stems from the Tidal Wetlands Act and Freshwater Wetlands Act , part of the larger state Environmental Conservation Law . Wetlands and wetland regulations are divided into either tidal or freshwater, and wetlands are further classified within each category. State wetland inventories containing information on delineated areas and classifications are made available for public use as part of the state wetland mapping program. Activities within wetland areas are regulated through a permit system.

Tidal wetlands regulations are designed to allow uses of wetlands that are compatible with the preservation, protection, and enhancement of ecological values including flood protection and storm control. Development restrictions require that all buildings and structures in excess of 100 square feet be located a minimum of 75 feet landward from tidal wetland edges, with less stringent setbacks in place for buildings located within New York City. Similar setback requirements exist for impervious surfaces exceeding 500 square feet. On-site sewage systems must have a setback of at least 100 feet, and a minimum of two feet of soil must be between the bottom of a system and the seasonal high groundwater level.

Permit standards for activities within tidal wetlands require that any proposed activity be compatible with the overall state policy of preserving and protecting tidal wetlands, and as such any activity may not cause any undue adverse impact on the ecological value of an affected wetland area or any adjoining areas. Standards also require that any activity within tidal wetlands be compatible with public health and welfare, be reasonable and necessary, and take into account both alternative actions and the necessity of water access or dependence for the proposed action. The state also publishes compatible use guidelines for activities within wetlands based on wetland type. If any activity is presumed to be incompatible with state tidal wetland use guidelines, an applicant must overcome the presumption of incompatible use and demonstrate that the activity is compatible with the preservation, protection, and enhancement of wetland values. If a use is specifically listed as incompatible within guidelines the use is then prohibited. Permitted activities in areas adjacent to tidal wetlands must also be compatible with public health and welfare, have no undue adverse impact on wetland ecological values, and comply with use guidelines.

State flood-resistant construction requirements are listed in the International Residential Code as adopted by New York State . Regulations apply to new residential buildings and structures located fully or partially within flood hazard areas as well as any substantially improved or restored structures within flood hazard areas. Construction requirements are based on the design flood elevation, which at a minimum must be the higher of either the peak elevation of a 1% annual chance flood event or the elevation of the design flood event as adopted on community flood hazard maps. Structures within flood hazard areas must generally be designed and anchored to resist the flood forces associated with the design flood elevation, and methods and practices to minimize flood damage must also be employed.

For the purposes of determining appropriate structural elevations, the lowest floor of a structure is defined as the lowest floor of any enclosed area, including basements. Within flood hazard areas not subject to high-velocity wave action, structures must have the lowest floor elevated to two feet above the base flood elevation or design flood elevation, whichever is higher. Utility systems must also be elevated to this standard. If no depth number is specified structures must be elevated not less than three feet above the highest adjacent grade. Any enclosed area below the design flood elevation must be used only for building access, parking, or storage and must contain flood openings sufficient to equalize hydrostatic forces on exterior walls.

For buildings and structures located in coastal high-hazard areas, including both V zones and Coastal A zones, the lowest floor must be elevated so that the lowest horizontal structural members are elevated to either the base flood elevation plus two feet or the design flood elevation, whichever is higher. Any walls below the design flood elevation must be designed to break away without causing damage to the elevated portion of the building, and again may be used only for parking, building access, or storage. Structures must be elevated using adequately anchored pilings or columns, with select exceptions in Coastal A zones. The use of fill for structural support and any construction of basement floors below grade are prohibited. New buildings and any substantially improved structures in coastal high-hazard areas must be located landward of the mean high tide, and any alteration of sand dunes must not result in any increased potential for flood damage in surrounding areas.

Planning at the state level is guided by the State Smart Growth Public Infrastructure Policy Act , an article within the larger Environmental Conservation Law. The act outlines criteria for public infrastructure projects that are either approved, directly undertaken, or financed by state infrastructure agencies. Among these criteria is a requirement that future public infrastructure projects mitigate future climate risk due to sea level rise, storm surge, or flood events based on available data or predictions of future extreme weather conditions. This and other criteria must be met to a practicable extent, and if deemed impracticable an agency must provide a detailed statement of justification.

The Office of Planning, Development, and Community Infrastructure within the Department of State administers several programs involved in community planning. The New York Rising Community Reconstruction Program provides recovery and resiliency planning assistance to communities affected by severe storm events, including hurricanes Sandy and Irene. The program is operated through the Governor’s Office of Storm Recovery and involves collaborations between state teams and community members to develop reconstruction plans and strategies to increase physical, economic, and social resilience, often including elements related to mitigating future flood risk. State Waterfront Revitalization Programs are also involved in community redevelopment planning. These programs establish land and water use policies and identify revitalization projects at a local level to allow for sustainable use of coastal resources, including planning for coastal flood risk resilience. Local Waterfront Revitalization Programs can also be a conduit for technical assistance and grant funding to facilitate climate change adaptation through the New York State Environmental Protection Fund grant program , a permanent fund addressing a broad range of environmental and community development needs.

The majority of stormwater regulations in New York focus on water quality issues as part of the State Pollutant Discharge Elimination System , a state program that has been approved by the EPA as part of the National Pollutant Discharge Elimination System . The program regulates point source discharges to both groundwater and surface waters and also conducts permitting for stormwater runoff from industrial activities, municipal sewer systems in urbanized areas, and construction activities. The program is administered by the state Department of Environmental Conservation.

While water quality is the focus of stormwater programs within the state, the state stormwater design manual lists best practices that include measures to reduce overbank flooding in order to maintain pre-development peak discharge rates for two and ten-year frequency storm events following development. The design manual also addresses risks due to potential floodplain expansion following development as well as green infrastructure strategies. These green infrastructure strategies are presented as a means to meet runoff reduction standards, which require that post-development conditions replicate pre-development hydrology. Stormwater projects, like all activities undertaken, funded, or approved by state agencies, are also under the purview of the State Environmental Quality Review Act , which requires preparation of an environmental impact statement if a project is likely to cause a significant increase in flood risk.

Coastal erosion in New York is managed within designated coastal erosion hazard areas. Areas are designated as per requirements of the state Coastal Erosion Hazard Areas Act , part of the larger state Environmental Conservation Law. Regulatory programs within identified hazard areas are administered by the state Department of Environmental Conservation. Programs may also be established at a local level if minimum state standards and criteria are met. The objectives of the program, as outlined in the state administrative code, are to ensure that activities in coastal areas subject to flooding minimize or prevent damage to property and natural features, that structures are placed at a safe distance from hazard areas to prevent premature damage to both structures and natural features, that public investment likely to encourage development within erosion hazard areas is restricted, and that publicly financed structures are only used when necessary and effective. Sections of the state administrative code also describe the erosion protection functions of natural protective features in order to guide the review of permit applications.

Coastal erosion management permits are required for any regulated activity conducted within a designated coastal erosion hazard area. Coastal erosion management permit standards require that any proposed activity be reasonable and necessary, with consideration of proposed alternatives, and that an activity will not likely lead to a measurable increase in erosion at the proposed site or other locations. Standards also require activities to prevent or minimize adverse effects to natural protective features, existing erosion protection structures, or natural resources such as fish and wildlife habitat.

Regulations within structural hazard areas allow for placement of movable structures, with construction restrictions, if a permit has been granted. Construction or placement of nonmovable structures is prohibited. Any public utility systems within structural hazard areas also require a coastal erosion management permit. Additional restrictions on regulated activities are present within natural protective feature areas, including nearshore areas, beaches, bluffs, primary dunes, and secondary dunes. Construction of erosion protection structures is allowed within such areas provided the structure meets permitting requirements and is designed to prevent or minimize damage to property and natural features in a cost-effective manner. Structures must be designed to control erosion on site for a minimum of 30 years.

New York has put forth several climate adaptation measures at the state level, led primarily by the state Department of Environmental Conservation. Sea-level rise projections for threatened coastal areas are currently published within the state administrative code, a recommendation from the previously convened NYS Sea Level Rise Task Force . The projections formally establish sea-level rise levels throughout Long Island, New York City, and the Hudson River, providing information based on five risk scenarios and extending out to 2100. The Department of Environmental Conservation has also formally acknowledged its role in climate change adaptation through Commissioner’s Policy 49: Climate Change and DEC Action . The policy outlines methods by which climate change considerations may be integrated into current DEC activities and programs, including making greenhouse gas reductions a primary goal, creating specific mitigation objectives for existing and future programs, incorporating adaptation strategies into programs and activities, considering climate change implications in daily department activities, and identifying specific actions to further climate change goals and objectives as part of annual planning processes. The policy goes on to establish mitigation and adaptation objectives as well as departmental responsibilities and implementation procedures.

The 2014 Community Risk and Resiliency Act (CRRA) forms the basis for a number of climate adaptation initiatives within New York from a legislative standpoint. The previously mentioned sea-level rise projections were a product of the CRRA, as the act amended the state Environmental Conservation Law to include a requirement that the DEC adopt science-based projections. The CRRA also amended additional sections of the Environmental Conservation Law to require applicants for identified funding and permitting programs to demonstrate that risk due to sea-level rise, storm surge, and flooding have been considered in project design and requires the DEC to incorporate similar considerations into facility-siting regulations. The sea-level rise, storm surge, and flood risk mitigation components of the Smart Growth Public Infrastructure Policy Act are also tied to the CRRA. The CRRA also directs the Department of State and Department of Conservation to develop model local laws that consider data-based future risk due to sea-level rise, storm surge, and flooding as well as guidance on the use of natural resources and natural processes to enhance community resilience to such hazards.

Elements of Policy Goals/Management Principles

• State management capacity is bolstered by the New York Coastal Management Program’s federal consistency review process, which requires that federal activities within the state coastal zone be consistent with the program’s enforceable policies. The New York program has 44 enforceable policies in total, with 7 specifically addressing flood and erosion hazards.
• Local governments can implement the state Coastal Management Program at a smaller scale through the Local Waterfront Revitalization Program, extending the influence of state program goals and enforceable policies.
• The enforceable policies of the state coastal management program address the protection of natural features that mitigate coastal flood risk and the use of non-structural mitigation measures where feasible.
• Shoreline setbacks must be established within identified coastal erosion hazard areas, and setbacks must be at a distance sufficient to minimize damage from erosion considering the rate of recession of coastal lands.
• Floodplain management regulations require that any new development or substantial improvement to structures in coastal areas not affect sand dunes in any way that might increase potential flood damages.
• Wetland management regulations require that structures be located a minimum of 75 feet landward from the edges of tidal wetlands, preserving natural flood risk mitigation functions.
• Sections of the state administrative code related to erosion management include descriptions of the erosion protection functions of natural features to guide permit applications, and permit standards require that erosion management activities prevent or minimize adverse impacts on natural protective features.
• The state stormwater management design manual includes information on green infrastructure strategies, which are presented as a means to meet runoff reduction standards and maintain pre-development hydrology for project areas.
• The state building code requires structures not subject to wave action to have the lowest floor elevated a minimum of one foot above the base flood elevation. This rule applies to the lowest horizontal structural members of structures that are subject to wave action.
• State regulations require that erosion protection structures in coastal areas be designed to control erosion on site for a minimum of 30 years.
• Public infrastructure projects approved, undertaken, or financed by state agencies must account for and mitigate risk due to future climate risk factors such as sea-level rise, storm surge, and flood events. Mitigation efforts must be based on available data as well as projections of future conditions.
• The state has published sea-level rise projections for threatened coastal areas within the state administrative code, formally establishing risk based on five scenarios and extending to 2100.
• Commissioner’s Policy 49: Climate Change and DEC Action identifies ways that climate change considerations could be incorporated into current state programs and activities and defines departmental responsibilities and procedures for implementing the climate adaptation goals of the policy.
• The state Community Risk and Resiliency Act formally establishes a number of climate adaptation initiatives within the state, including the requirement that the state Department of Environmental Conservation adopt science-based sea-level rise projections and that applicants to funding and permitting programs demonstrate that climate risk has been incorporated into the siting of facilities.
• The enforceable policies of the state coastal management program address the siting of buildings in coastal areas to reduce risk and well as restrictions on the use of public funds for erosion protection structures.
• One of the objectives of the state erosion management program as described in the state administrative code is to restrict public investment that could encourage development within coastal erosion hazard areas. An additional objective is to use publicly financed erosion control structures only when necessary and effective.
• The New York Rising Community Reconstruction program works to develop reconstruction plans and strategies to increase coastal community resilience following severe storm events, often involving the mitigation of future flood risk.
• The New York Coastal Management Program lists coordination of major activities affecting coastal resources as one of the program goals, and multiple state agencies are involved in implementing the program’s broad suite of enforceable policies.
• If an action requires preparation of an environmental impact statement as part of the State Environmental Quality Review Program it must also be consistent with the enforceable policies of the state coastal program, including policies related to coastal hazards.
• State wetland regulations are based on the preservation, protection, and enhancement of ecological values as opposed to acreage, with flood control and storm protection listed among the functions provided.
• The State Environmental Quality Review Program includes the potential for a substantial increase in flooding as a criteria of significance, which then triggers the preparation on an environmental impact statement for state agency activities.
• State Waterfront Revitalization Programs establish land and water use policies that incorporate coastal resilience into revitalization projects and community redevelopment planning.

View the other State Coastal Flood Risk Management Case Studies:

share this!

March 27, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

fact-checked

peer-reviewed publication

trusted source

Researchers find the more flood driving factors there are, the more extreme a flood is

by Helmholtz Association of German Research Centres

There are several factors that play an important role in the development of floods: air temperature, soil moisture, snow depth, and the daily precipitation in the days before a flood. In order to better understand how individual factors contribute to flooding, UFZ researchers examined more than 3,500 river basins worldwide and analyzed flood events between 1981 and 2020 for each of them.

The result: precipitation was the sole determining factor in only around 25% of the almost 125,000 flood events . Soil moisture was the decisive factor in just over 10% of cases, and snow melt and air temperature were the sole factors in only around 3% of cases.

In contrast, 51.6% of cases were caused by at least two factors. At around 23%, the combination of precipitation and soil moisture occurs most frequently.

However, when analyzing the data, the UFZ researchers discovered that three—or even all four—factors can be jointly responsible for a flood event.

For example, temperature, soil moisture, and snow depth were decisive factors in around 5,000 floods while all four factors were decisive in around 1,000 flood events. And not only that: "We also showed that flood events become more extreme when more factors are involved," says Dr. Jakob Zscheischler, Head of the UFZ Department "Compound Environmental Risks" and senior author of an article published in the journal Science Advances .

In the case of one-year floods, 51.6% can be attributed to several factors; in the case of five- and 10-year floods, 70.1% and 71.3% respectively can be attributed to several factors. The more extreme a flood is, the more driving factors there are and the more likely they are to interact in the event generation. This correlation often also applies to individual river basins and is referred to as flood complexity.

According to the researchers, river basins in the northern regions of Europe and America as well as in the Alpine region have a low flood complexity. This is because snow melt is the dominant factor for most floods regardless of the flood magnitude. The same applies to the Amazon basin, where the high soil moisture resulting from the rainy season is often a major cause of floods of varying severity.

In Germany, the Havel and the Zusam, a tributary of the Danube in Bavaria, are river basins that have a low flood complexity. Regions with river basins that have a high flood complexity primarily include eastern Brazil, the Andes, eastern Australia, the Rocky Mountains up to the US west coast, and the western and central European plains.

In Germany, this includes the Moselle and the upper reaches of the Elbe. "River basins in these regions generally have several flooding mechanisms," says Jakob Zscheischler. For example, river basins in the European plains can be affected by flooding caused by the combination of heavy precipitation, active snow melt , and high soil moisture.

However, the complexity of flood processes in a river basin also depends on the climate and land surface conditions in the respective river basin. This is because every river basin has its own special features. Among other things, the researchers looked at the climate moisture index, the soil texture, the forest cover , the size of the river basin, and the river gradient.

"In drier regions, the mechanisms that lead to flooding tend to be more heterogeneous. For moderate floods, just a few days of heavy rainfall is usually enough. For extreme floods, it needs to rain longer on already moist soils," says lead author Dr. Shijie Jiang, who now works at the Max Planck Institute for Biogeochemistry in Jena.

The scientists used explainable machine learning for the analysis. "First, we use the potential flood drivers air temperature , soil moisture , and snow depth as well as the weekly precipitation—each day is considered as an individual driving factor—to predict the run-off magnitude and thus the size of the flood," explains Zscheischler.

The researchers then quantified which variables and combinations of variables contributed to the run-off of a particular flood and to which extent. This approach is referred to as explainable machine learning because it uncovers the predictive relationship between flood drivers and run-off during a flood in the trained model.

"With this new methodology , we can quantify how many driving factors and combinations thereof are relevant for the occurrence and intensity of floods," adds Jiang.

The findings of the UFZ researchers are expected to help predict future flood events. "Our study will help us better estimate particularly extreme floods," says Zscheischler.

Until now, very extreme floods have been estimated by extrapolating from less extreme floods. However, this is too imprecise because the individual contributing factors could change their influence for different flood magnitudes.

Journal information: Science Advances

Provided by Helmholtz Association of German Research Centres

Explore further

Feedback to editors

Romania center explores world's most powerful laser

8 hours ago

A cosmic 'speed camera' just revealed the staggering speed of neutron star jets in a world first

Mar 30, 2024

Saturday Citations: 100-year-old milk, hot qubits and another banger from the Event Horizon Telescope project

Curiosity rover searches for new clues about Mars' ancient water

Study says since 1979 climate change has made heat waves last longer, spike hotter, hurt more people

Scientist taps into lobsters' unusual habits to conquer the more than 120-year quest to farm them

Mar 29, 2024

Blind people can hear and feel April's total solar eclipse with new technology

Mapping the best route for a spacecraft traveling beyond the sun's sphere of influence

Researchers outline new approach in search for dark matter through future DUNE research project

Researchers reveal evolutionary path of important proteins

Relevant physicsforums posts, iceland warming up again - quakes swarming.

16 hours ago

Unlocking the Secrets of Prof. Verschure's Rosetta Stones

‘our clouds take their orders from the stars,’ henrik svensmark on cosmic rays controlling cloud cover and thus climate.

Mar 27, 2024

Higher Chance to get Lightning Strike by Large Power Consumption?

Mar 20, 2024

A very puzzling rock or a pallasite / mesmosiderite or a nothing burger

Mar 16, 2024

Earth's earliest forest discovered in SW England

Mar 8, 2024

More from Earth Sciences

Related Stories

AI could help predict floods where traditional methods struggle

Mar 21, 2024

Major floods increased in temperate climates but decreased elsewhere

Mar 10, 2021

Climate change likely to drive more floods in some parts of the US, fewer in others

Jan 9, 2024

Extreme UK flood levels are happening much more often than they used to, analysis shows

Jan 11, 2024

A new way to assess the propensity of rivers to generate extreme floods

Mar 31, 2023

The continental U.S. is experiencing more flooding, and earlier in the year

Dec 15, 2017

Recommended for you

Largest ice shelf in Antarctica lurches forward once or twice each day

Researchers find WWI and WWII bombs in the ground are becoming more volatile

Fukushima fallout transport longevity revealed by North Pacific ocean circulation patterns

Mar 28, 2024

Atmospheric observations in China show rise in emissions of a potent greenhouse gas

Study finds landfill point source emissions have an outsized impact and present opportunity to tackle US waste methane

Let us know if there is a problem with our content.

Use this form if you have come across a typo, inaccuracy or would like to send an edit request for the content on this page. For general inquiries, please use our contact form . For general feedback, use the public comments section below (please adhere to guidelines ).

Please select the most appropriate category to facilitate processing of your request

Thank you for taking time to provide your feedback to the editors.

Your feedback is important to us. However, we do not guarantee individual replies due to the high volume of messages.

E-mail the story

Your email address is used only to let the recipient know who sent the email. Neither your address nor the recipient's address will be used for any other purpose. The information you enter will appear in your e-mail message and is not retained by Phys.org in any form.

Newsletter sign up

Get weekly and/or daily updates delivered to your inbox. You can unsubscribe at any time and we'll never share your details to third parties.

More information Privacy policy

Donate and enjoy an ad-free experience

We keep our content available to everyone. Consider supporting Science X's mission by getting a premium account.

E-mail newsletter

Advertisement

Sensitivity analysis of a 2D flood inundation model. A case study of Tous Dam

• Original Article
• Published: 25 March 2024
• Volume 83 , article number  213 , ( 2024 )

Cite this article

• Aftab Ullah 1 ,
• Sajjad Haider 1 &
• Rashid Farooq 2  

90 Accesses

Explore all metrics

Floods are one of the most common natural hazard having far reaching environmental implications such as soil and bank erosion, pollution of ground and surface water and landslides etc. Flood inundation modeling is frequently simulated through the numerical solution of 2D shallow water equations. These equations are a potent tool that can be used to obtain satisfactory solutions in complex riverine geometry. The usage of Sensitivity Analysis (SA) is becoming more prevalent in environmental modeling for various purposes such as uncertainty assessment, model calibration, and robust decision-making. This research is a case study of the Tous Dam break in Spain and the resultant urban flooding which was assessed for SA using the method of Morris. In all, five parameters were assessed, namely, river Manning, large roughness, time step, turbulent coefficient, and downstream boundary condition for their influence on the model output. The output, comprising depth, flood extent, bed shear stress, and time of initial inundation among others was assessed spatially as well as temporally. The temporal disaggregation revealed that the dominant factors changed position as the flood regime changed from rising to peak and then to recession. The peak flow stage had the large roughness as the most influential parameter and river Manning in the first 2 h of the rising phase while downstream boundary condition predominated during both the rising and receding phases. Curiously, temporal disaggregation revealed that the model time step, which was a marginally important parameter throughout, was able to assume the No. 1 ranking during the phase of decreasing flow i.e. the onset of the recession phase, when the model time step has to adjust from high flow velocity to slower velocities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Dam breach analysis and flood inundation mapping of Dire Dam, using HEC-HMS and HEC-RAS models

Belete Hailemariam Tessema, Abrha Ybeyn Gebremedhn & Yitea Seneshaw Getahun

Modeling Approach to Study the Riverine Flood Hazard of Lower Damodar River

HEC-RAS One-Dimensional Hydrodynamic Modelling for Recent Major Flood Events in Pahang River

Data availability.

Data is available on the following link  https://doi.org/10.1080/00221686.2007.9521832 .

Abily M, Bertrand N, Delestre O et al (2016) Spatial global sensitivity analysis of high resolution classified topographic data use in 2D urban flood modelling. Environ Model Softw 77:183–195. https://doi.org/10.1016/j.envsoft.2015.12.002

Article   Google Scholar  

Ahmadi M, Ascough JC, DeJonge KC, Arabi M (2014) Multisite-multivariable sensitivity analysis of distributed watershed models: Enhancing the perceptions from computationally frugal methods. Ecol Modell 279:54–67. https://doi.org/10.1016/j.ecolmodel.2014.02.013

Article   CAS   Google Scholar  

Alcrudo F, Mulet J (2007) Description of the Tous Dam break case study (Spain). J Hydraul Res 45:45–57. https://doi.org/10.1080/00221686.2007.9521832

Alipour A, Jafarzadegan K, Moradkhani H (2022) Global sensitivity analysis in hydrodynamic modeling and flood inundation mapping. Environ Model Softw 152:105398. https://doi.org/10.1016/J.ENVSOFT.2022.105398

Bellos V, Papageorgaki I, Kourtis I et al (2020) Reconstruction of a flash flood event using a 2D hydrodynamic model under spatial and temporal variability of storm. Nat Hazards 101:711–726. https://doi.org/10.1007/S11069-020-03891-3/FIGURES/6

Brockmann D, Morgenroth E (2007) Comparing global sensitivity analysis for a biofilm model for two-step nitrification using the qualitative screening method of Morris or the quantitative variance-based Fourier Amplitude Sensitivity Test (FAST). Water Sci Technol 56:85–93. https://doi.org/10.2166/WST.2007.600

Cacuci (2003) Sensitivity and Uncertainty Analysis, Volume I: Theory. In: CRC Press. Raton, Florida. https://scholar.google.com/scholar_lookup?title=Sensitivity and Uncertainty Analysis%2C Volume I%3A Theory&author=D.G. Cacuci&publication_year=2003. Accessed 26 Jun 2022

Campolongo F, Braddock R (1999) Sensitivity analysis of the IMAGE Greenhouse model. Environ Model Softw 14:275–282. https://doi.org/10.1016/S1364-8152(98)00079-6

Campolongo F, Saltelli A (1997) Sensitivity analysis of an environmental model: an application of different analysis methods. Reliab Eng Syst Saf 57:49–69. https://doi.org/10.1016/S0951-8320(97)00021-5

Campolongo F, Cariboni J, Saltelli A (2007) An effective screening design for sensitivity analysis of large models. Environ Model Softw 22:1509–1518. https://doi.org/10.1016/J.ENVSOFT.2006.10.004

Cesare MA (1991) Firstorder analysis of openchannel flow. J Hydraul Eng 117:242–247. https://doi.org/10.1061/(ASCE)0733-9429(1991)117:2(242)

Chaudhry MH (2007) Open-channel flow: Second Edition. Open-Channel Flow. Springer, pp 1–523.

Ciric C, Ciffroy P, Charles S (2012) Use of sensitivity analysis to identify influential and non-influential parameters within an aquatic ecosystem model. Ecol Modell 246:119–130. https://doi.org/10.1016/J.ECOLMODEL.2012.06.024

Confalonieri R, Bellocchi G, Donatelli M (2010) A software component to compute agro-meteorological indicators. Environ Model Softw 25:1485–1486. https://doi.org/10.1016/J.ENVSOFT.2008.11.007

Costabile P, Costanzo C, Kalogiros J, Bellos V (2023) Toward street-level nowcasting of flash floods impacts based on HPC hydrodynamic modeling at the watershed scale and high-resolution weather radar data. Water Resour Res 59:034599. https://doi.org/10.1029/2023WR034599

Dimitriadis P, Tegos A, Oikonomou A et al (2016) Comparative evaluation of 1D and quasi-2D hydraulic models based on benchmark and real-world applications for uncertainty assessment in flood mapping. J Hydrol 534:478–492. https://doi.org/10.1016/J.JHYDROL.2016.01.020

Franczyk A (2019) Using the Morris sensitivity analysis method to assess the importance of input variables on time-reversal imaging of seismic sources. Acta Geophys 67:1525–1533. https://doi.org/10.1007/S11600-019-00356-5

Haider S, Saeed U, Shahid M (2020) 2D numerical modeling of two dam-break flood model studies in an urban locality. Arab J Geosci 13:1–15. https://doi.org/10.1007/S12517-020-05709-9/FIGURES/13

Hall JW, Tarantola S, Bates PD, Horritt MS (2005) Distributed sensitivity analysis of flood inundation model calibration. J Hydraul Eng 131:117–126

Herman JD, Kollat JB, Reed PM, Wagener T (2013) Technical note: method of morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models. Hydrol Earth Syst Sci 17:2893–2903. https://doi.org/10.5194/HESS-17-2893-2013

Huang YT, Liu L (2008) A hybrid perturbation and Morris approach for identifying sensitive parameters in surface water quality models. J Environ Informatics 12:150–159. https://doi.org/10.3808/JEI.200800133

Ishigaki T, Nakagawa H, Baba Y (2004) Hydraulic model test and calculation of flood in urban area with underground space. Environ Hydraul Sustain Water Manag Two Vol Set. https://doi.org/10.1201/B16814-232

Jung Y, Merwade V (2015) Estimation of uncertainty propagation in flood inundation mapping using a 1-D hydraulic model. Hydrol Process 29:624–640. https://doi.org/10.1002/HYP.10185

King DM, Perera BJC (2013) Morris method of sensitivity analysis applied to assess the importance of input variables on urban water supply yield—a case study. J Hydrol 477:17–32. https://doi.org/10.1016/j.jhydrol.2012.10.017

Lai YG (2008) SRH-2D version 2: Theory and user’s manual, Sedimentation and River Hydraulics Group, Technical Service Center, Bureau of Reclamation, Denver.  https://www.usbr.gov/pmts/sediment/

Lai YG (2009) Two-dimensional depth-averaged flow modeling with an unstructured hybrid mesh. J Hydraul Eng 136:12–23. https://doi.org/10.1061/(ASCE)HY.1943-7900.0000134

Lai E, Knowles B, Hogan S, Lai Y (2021) Flood simulation and assessment due to dam breaching of the Cherry Creek Reservoir, Colorado. World Environ Water Resour Congr 2021 Plan a Resilient Futur along Am Freshwaters—Sel Pap from World Environ Water Resour Congr. 2021. https://doi.org/10.1061/9780784483466.012

Lavoie B, Mahdi TF (2017) Comparison of two-dimensional flood propagation models: SRH-2D and Hydro_AS-2D. Nat Hazards 86:1207–1222. https://doi.org/10.1007/S11069-016-2737-7/FIGURES/12

Mohr J (1998) Dam-break flood analysis: Review and recommendations. Volume 111 of Bulletin (International Commission on Large Dams)

Molinaro P, Natale L (1994) Modelling of Flood Propagation Over Initially Dry Areas: Proceedings of the Specialty Conference Held in Milan, Italy at ENEL-DSR-CRIS, 29 June–1 July 1994

Morris MD (1991) Factorial sampling plans for preliminary computational experiments. Technometrics 33:161–174. https://doi.org/10.1080/00401706.1991.10484804

Morris DJ, Speirs DC, Cameron AI, Heath MR (2014) Global sensitivity analysis of an end-to-end marine ecosystem model of the North Sea: Factors affecting the biomass of fish and benthos. Ecol Modell 273:251–263. https://doi.org/10.1016/j.ecolmodel.2013.11.019

Neal J, Keef C, Bates P et al (2013) Probabilistic flood risk mapping including spatial dependence. Hydrol Process 27:1349–1363

Oubennaceur K, Chokmani K, Nastev M et al (2019) New sensitivity indices of a 2D flood inundation model using gauss quadrature sampling. Geosci. https://doi.org/10.3390/geosciences9050220

Papaioannou G, Loukas A, Vasiliades L, Aronica GT (2016) Flood inundation mapping sensitivity to riverine spatial resolution and modelling approach. Nat Hazards 83:117–132. https://doi.org/10.1007/s11069-016-2382-1

Pappenberger F, Beven KJ, Ratto M, Matgen P (2008) Multi-method global sensitivity analysis of flood inundation models. Adv Water Resour 31:1–14. https://doi.org/10.1016/j.advwatres.2007.04.009

Paquier A, Bazin PH, El Kadi AK (2020) Sensitivity of 2D hydrodynamic modelling of urban floods to the forcing inputs: lessons from two field cases. Urban Water J 17:457–466. https://doi.org/10.1080/1573062X.2019.1669200

Pianosi F, Sarrazin F, Wagener T (2015) A Matlab toolbox for global sensitivity analysis. Environ Model Softw 70:80–85. https://doi.org/10.1016/j.envsoft.2015.04.009

Pilotti M, Maranzoni A, Milanesi L et al (2014) Dam-break modeling in alpine valleys. J Mt Sci 11:1429–1441. https://doi.org/10.1007/S11629-014-3042-0/METRICS

Pilotti M, Milanesi L, Bacchi V et al (2020) Dam-break wave propagation in alpine valley with HEC-RAS 2D: experimental cancano test case. J Hydraul Eng 146:05020003. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001779/SUPPL_FILE/SUPPLEMENTAL_DATA_HY.1943-7900.0001779_PILOTTI.ZIP

Quirogaa VM, Kurea S, Udoa K, Manoa A (2016) Application of 2D numerical simulation for the analysis of the February 2014 Bolivian Amazonia flood: application of the new HEC-RAS version 5. Ribagua 3:25–33. https://doi.org/10.1016/J.RIBA.2015.12.001

Ralston DC (1987) Mechanics of embankment erosion during overflow. In: Hydraulic engineering. ASCE. pp 733–738.

Ren J, Zhang W, Yang J (2019) Morris sensitivity analysis for hydrothermal coupling parameters of embankment dam: a case study. Math Probl Eng. https://doi.org/10.1155/2019/2196578

Saltelli A (2004) Sensitivity analysis in practice: a guide to assessing scientific models. Wiley, New York, p 219

Google Scholar  

Saltelli A (2008) Global sensitivity analysis: the primer. Wiley, New York

Saltelli A, Chan K, Scott EM (2000) Sensitivity analysis. Wiley, New York, p 475

Saltelli A, Ratto M, Tarantola S, Campolongo F (2006) Sensitivity analysis practices: strategies for model-based inference. Reliab Eng Syst Saf 91:1109–1125

Singh VP, Scarlatos PD (1988) Analysis of gradual earthdam failure. J Hydraul Eng 114:21–42. https://doi.org/10.1061/(ASCE)0733-9429(1988)114:1(21)

Soares Frazao S, Zech Y (2005) Simulation of the IMPACT case study on the Tous dam-break flow. Proceedings of the 31st IAHR World Congress, Seoul, 2005

Sun XY, Newham LTH, Croke BFW, Norton JP (2012) Three complementary methods for sensitivity analysis of a water quality model. Environ Model Softw 37:19–29. https://doi.org/10.1016/J.ENVSOFT.2012.04.010

Thomas Steven Savage J, Pianosi F, Bates P et al (2016) Quantifying the importance of spatial resolution and other factors through global sensitivity analysis of a flood inundation model. Water Resour Res 52:9146–9163. https://doi.org/10.1002/2015WR018198

Vetsch D., Siviglia A. FR 2018. (2018) System Manuals of BASEMENT, Version 2.8. Lab Hydraul Glaciol Hydrol (VAW) ETH Zurich.

Willis TDM (2014) Systematic analysis of uncertainty in flood inundation modelling. PhD thesis, University of Leeds. https://etheses.whiterose.ac.uk/7493/

Xing Y, Shao D, Ma X et al (2021) Investigation of the importance of different factors of flood inundation modeling applied in urbanized area with variance-based global sensitivity analysis. Sci Total Environ 772:145327. https://doi.org/10.1016/J.SCITOTENV.2021.145327

Xu Y, Zhang LM (2009) Breaching parameters for earth and rockfill dams. J Geotech Geoenvironmental Eng 135:1957–1970. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000162

Yi X, Zou R, Guo H (2016) Global sensitivity analysis of a three-dimensional nutrients-algae dynamic model for a large shallow lake. Ecol Modell 327:74–84. https://doi.org/10.1016/j.ecolmodel.2016.01.005

Download references

Acknowledgements

I would like to extend my wholehearted gratitude towards my supervisor Dr. Sajjad Haider, of the Department of Water Resources Engineering and Management, NUST Institute of Civil Engineering (NICE), National University of Sciences and Technology, Islamabad, Pakistan for all the time and support he provided me technically and morally.

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and affiliations.

School of Civil and Environmental Engineering (SCEE), NUST Institute of Civil Engineering (NICE), National University of Sciences and Technology (NUST), Sector H-12, Islamabad, 44000, Pakistan

Aftab Ullah & Sajjad Haider

Department of Civil Engineering, International Islamic University Islamabad (IIUI), Sector H-10, Islamabad, 44000, Pakistan

Rashid Farooq

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization: SH; methodology: AU, SH, RF; data curation: AU; formal analysis and investigation: AU, SH; software: AU; validation and visualization: AU; writing—original draft preparation: AU; writing—review and editing: SH, RF; supervision: SH.

Corresponding author

Correspondence to Aftab Ullah .

Ethics declarations

Conflict of interest.

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Ullah, A., Haider, S. & Farooq, R. Sensitivity analysis of a 2D flood inundation model. A case study of Tous Dam. Environ Earth Sci 83 , 213 (2024). https://doi.org/10.1007/s12665-024-11500-w

Download citation

Received : 27 October 2023

Accepted : 07 February 2024

Published : 25 March 2024

DOI : https://doi.org/10.1007/s12665-024-11500-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• 2D flood modeling
• Urban flash flood
• Sensitivity analysis
• Morris method
• Find a journal
• Publish with us
• Track your research