environmental impacts of flooding case study

The Many Effects of Flooding

Floods can be destructive to humans and the natural environment, but they also help to drive biodiversity and are essential to the functioning of many ecosystems.

Earth Science, Climatology, Geography, Physical Geography

1931 Yangtze River Flood

In 1931, water overwhelmed the banks of the Yangtze and Huai Rivers, resulting in the Central China flood. Killing at least hundreds of thousands and potentially millions of people, it was one of the worst flooding events in recorded history. Here, people near the Yangtze River are shown.

Photograph from Adrienne Livesey, Elaine Ryder, and Irene Brien

It is hardly surprising that rivers have been an important part of human history: They provide food, freshwater, and fertile land for growing crops. While water is essential to life, it can be a destructive force too. When rivers flood, the effects can be catastrophic. Flooding is one of the most common types of natural disaster, and the results are often fatal. The Central China flood of 1931, for example, was one of the worst flooding events in recorded history. The Yangtze and Huai Rivers broke their banks, killing as many as several million people. The aftermath was devastating; deadly waterborne diseases like dysentery and cholera spread quickly, and those who survived faced the threat of starvation. The human cost of flooding can be large, but events like this have a big impact on the natural world too, and the effects are not always negative. In fact, some ecosystems rely on seasonal flooding to drive ecological processes. Floods Can Harm Wildlife Flooding can have a negative effect on wildlife, causing drowning, disease proliferation, and habitat destruction. In 2012, hundreds of animals, including many vulnerable one-horned rhinos ( Rhinoceros unicornis ), were killed in floods that swamped Kaziranga National Park in the Indian state of Assam. Unpredictable floods can be harmful even to aquatic life. For example, fish can be displaced and their nests destroyed.

Floods Cause Sedimentation and Erosion Floodwater can also alter the landscape, for instance, by eroding riverbanks and causing them to collapse. As floodwater carries material from the eroded banks, it suspends sediment in the water, which can degrade water quality and lead to harmful blooms of algae. Suspended sediment eventually settles out of the water in a process called sedimentation, which can clog riverbeds and streams, smother aquatic organisms, and destroy habitats. Erosion and sedimentation have a more negative impact on ecosystems that are already degraded or heavily modified. Floods Carry Contamination Floodwater can be contaminated with pollutants such as agricultural pesticides , industrial chemicals, debris, and sewage. If contaminated floodwater enters the ocean it can affect water quality and disrupt delicate ecosystems, such as coral reefs. In February 2019, marine biologists feared for the safety of the Great Barrier Reef off the coast of Queensland, a state in Australia, after it was inundated with polluted floodwater. Floods Spread Diseases Floods are the leading cause of weather-related infectious disease outbreaks. Flooding events increase the chance of spreading waterborne diseases, such as hepatitis A and cholera. Receding floodwater can create stagnant pools of water, which provide the perfect breeding ground for mosquitoes, which can transmit malaria and other diseases. Flood events also lead to an increase in some forms of  zoonosis , such as leptospirosis. Floods Carry Nutrients While floods bring hazards, they also bring nutrients and essential components for life. Seasonal floods can renew ecosystems, providing life-giving waters in more ways than one. Floods transport vital nutrients, such as nitrogen, phosphorus, and organic material, to the surrounding land. When the water recedes, it leaves sediment and nutrients behind on the floodplain. This rich, natural fertilizer improves soil quality and has a positive effect on plant growth, thus increasing productivity in the ecosystem. Ancient civilizations first arose along the deltas of seasonally flooded rivers, such as the Nile in Egypt, because they provided fertile soil for farmland. Floods Recharge Groundwater Floods can replenish underground water sources. Floodwater gets absorbed into the ground then percolates through layers of soil and rock, eventually reaching underground aquifers . These aquifers supply clean freshwater to springs, wells, lakes, and rivers. Ecosystems rely heavily on groundwater during dry spells when it may be the only supply of freshwater available. A good supply of groundwater has a positive impact on soil health and leads to more productive crop and pasture lands. Floods Can Trigger Breeding Events and Migrations Floods can trigger breeding events, migrations, and dispersal in some species. In 2016, thousands of water birds flocked to the Macquarie Marshes in the Australian state of New South Wales. Flooding had filled their wetland habitat for the first time in years, triggering a mass breeding event. In Cambodia, monsoon rains cause an annual flood pulse on the Mekong River that prompts migrations for some animals. The floodwaters cause the Tonle Sap river, which connects the Mekong River to Tonle Sap lake, to reverse its flow, filling the lake. When floodwater enters the lake, it triggers fish migrations, supporting one of the world’s most productive fisheries. Floods Can Boost Fish Stocks Small seasonal floods can be beneficial to native fish stocks and can help those fish outcompete invasive species that are not adapted to the river’s cycles. Sediment deposited on riverbeds during floods can provide a nursery site for small fish. Nutrients carried by floodwater can support aquatic food webs by boosting productivity. Floods Bring Life to Wetlands Wetlands are an extremely important ecosystem; approximately 40 percent of the world’s species rely on them. They filter water, mitigate flooding, and act as a carbon sink . The Okavango Delta in Botswana is a United Nations Educational, Scientific and Cultural Organization (UNESCO) World Heritage Site and one of the world’s largest, most important wetland habitats. The river captures rainfall from far to the north in the highlands of Angola. This causes a flood pulse that replenishes the wetlands at the height of the dry season, providing a lush oasis in the Kalahari Desert. National Geographic Explorer Steve Boyes, with a team of scientists and Explorers, has participated in a series of expeditions to trace the Okavango from source to sand to protect the waters of this unique habitat. Floods are a force of nature, and their consequences, both positive and negative, are strongly felt by affected ecosystems. Floods can be destructive to humans and the natural environment, but they also help to drive biodiversity and are essential to the functioning of many ecosystems. Whether you regard floods as good or bad, one thing is for certain: The world would be a very different place without them.

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When Communities Keep Flooding: A Rural Environmental Justice Case Study

December 7, 2023  • Community Strategies Group

We all want to live in places that are safe and able to respond to disasters. But right now, many rural communities and Native nations — especially communities of color and low-wealth places — experience repeated devastating flooding.

Repeated flooding is an environmental justice issue for both urban and rural communities, but rural communities need rural solutions when confronting natural disasters and associated recovery efforts, as detailed in our call to action, Through Natural Disaster to Prosperity .

The drivers of repeated flooding in rural communities are complex, including climate, unsustainable approaches to development, and structural inequity. Still, rural people across the country are working diligently and creatively on home-grown solutions.

The communities and organizations profiled in this case study are all working hard to address the causes and conditions contributing to flooding in their areas, as well as to envision and build thriving futures of equitable rural prosperity.

They generously shared their thoughts, focusing on two key questions:

• What structural challenges keep rural communities from addressing repeated flooding?
• What will it take for rural communities to drive their own solutions to repeated flooding?

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April 26, 2024 Energy and Environment Program & 1 more

April 3, 2024 Community Strategies Group

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• Open access
• Published: 24 February 2022

Environmental and economic impact of cloudburst-triggered debris flows and flash floods in Uttarakhand Himalaya: a case study

• Vishwambhar Prasad Sati   ORCID: orcid.org/0000-0001-6423-3119 1 &
• Saurav Kumar 1  

Geoenvironmental Disasters volume  9 , Article number:  5 ( 2022 ) Cite this article

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This paper examines the environmental and economic impact of cloudburst-triggered debris flow and flash flood in four villages of Uttarkashi district, Uttarakhand Himalaya. On 18th July 2021 at 8:30 p.m., a cloudburst took place on the top of the Hari Maharaj Parvat, which triggered a huge debris flows and flash floods, affecting 143 households of four villages of downstream areas. Immediately after the cloudburst occurred, the authors visited four affected villages—Nirakot, Mando, Kankrari, and Siror. A structured questionnaire was constructed and questions were framed and asked from 143 heads of affected households on the impact of debris flows and flash floods on people’s life, settlements, cowsheds, bridges, trees, forests, and arable land in and around the villages. The volume of debris, boulders, pebbles, gravels, and mud was assessed. It was noticed that all four villages got lots of destructions in terms of loss of life—people and animals, and property damage—land, crops, and infrastructural facilities. This study shows that the location of the settlements along with the proximity of the streams, which are very violent during the monsoon season, has led to the high impact of debris flow on the affected villages. We suggest that the old inhabited areas, which are located in the risk zones, can be relocated and the new settlements can be constructed in safe places using suitability analyses.

Introduction

Cloudburst, a geo-hydrological hazard, refers to a sudden and heavy rainfall that takes place within a short span of time and a particular space (Sati 2013 ). The intensity of rainfall is often more than 100 mm/h (Das et al. 2006 ). The disruptive events, cloudbursts occur during the monsoon season in the Himalaya and trigger debris flows, flash floods, landslides, and mass movements (Fig.  1 ). Fragile landscape, rough and rugged terrain, and precipitous slope accentuate the magnitude of geo-hydrological hazards. Cloudburst-triggered debris flows, flash floods, landslides, and mass movements have become more intensive and frequent worldwide, mainly in the mountainous regions, causing large-scale destruction of people, land, and property (Houghton et al. 1996 ; Wang et al. 2014 ; Mayowa et al. 2015 ; Malla et al. 2020 ; Sim et al. 2022 ). Similarly, the Himalayan region is prone to the occurrences of cloudburst-triggered hazards, causing huge loss of life and property and degradation of forest and arable lands (Bohra et al. 2006 ; Allen et al. 2013 ; Balakrishnan 2015 ; Ruiz-Villanueva et al. 2017 ).

Cloudburst-triggered hazards in the Uttarakhand Himalaya

The Uttarakhand Himalaya, one of the integrated parts of the Himalaya, is the most fragile landscape and prone to geo-hydrological hazards—cloudbursts, avalanches, and glacier bursts (Sati 2019 ). It receives many hazards mainly cloudburst-triggered debris flows, flash floods, landslides, and mass movements during the monsoon season every year. The intensity, frequency, and severity of these hazards have been observed to increase during the recent past. Devi ( 2015 ) stated that the changing monsoon patterns and increasing precipitation in the Himalaya are associated with catastrophic natural hazards. However, these hazards are the least understood because of the remoteness of the areas and lacking meteorological stations (Thayyen et al. 2013 ).

The Uttarakhand Himalaya has many eco-sensitive zones, vulnerable to natural hazards mainly for geo-hydrological hazards. Every year, many cloudburst events occur here, cause to roadblocks, land degradation, forest and cropland loss, and losses of life and infrastructural facilities. One of the most devastating cloudburst-triggered debris flow events of this century occurred on the night of 16th and 17th June 2013 in the famous Hindu pilgrimage ‘Kedarnath’, which killed more than 10,000 people and devastated the entire Mandakini and Alaknanda river valleys (Upadhyay 2014 ; Sati 2013 ). The entire region had received 16 major geo-hydrological and terrestrial hazards within the last 50 years (Bhambri et al. 2016 ). Some of the devastating cloudburst-triggered debris flows and flash floods that occurred in the Uttarakhand Himalaya are Rudraprayag on 14th September 2012, Munsiyari on 18th August 2010, Kapkot on 19th August 2010, Nachni on 7th August 2009, Malpa and Ukhimath on 17th August 1998, Badrinath on 24th July 2004, and the Alaknanda River valley on 1970. About 20,000 people died and a huge loss of property took place due to these calamities (Das 2015 ). It has been noticed that these catastrophic events occurred mainly during the three months of the monsoon season—July, August, and September.

Debris flows and flash floods caused by glacier-bursts incidences were although not much frequent and intensive yet, during the recent past, their number has increased owing to changes in the climatic conditions. The increasing number of infrastructural facilities on the valley bottom has accelerated damages owing to exposed elements in risk-prone areas (Sati 2014 ; ICIMOD 2007a , b ; Chalise and Khanal 2001 ; Bhandari 1994 ; Uttarakhand 2017 ). Many drivers exist, which affect the severity of cloudburst-triggered hazards in the Uttarakhand Himalaya. Growing population and the construction of settlements and infrastructural facilities on the fragile slopes and along the river valleys have also caused severe hazards. The Uttarakhand region is home to world-famous pilgrimages and natural tourism. Mass tourism during the rainy season enhances the intensity of disasters.

Several studies have been carried out on glacier-bursts and cloudburst-triggered debris flows and flash floods in the Himalaya (Shugar et al. 2021 ; Byers et al. 2018 ; Cook et al. 2018 ; Asthana and Sah 2007 ; Bhatt 1998 ; Joshi and Maikhuri 1997 ; NIDM 2015 ; IMD 2013 ; Khanduri et al. 2018 ; Sati 2006 , 2007 , 2009 , 2011 , 2018a , b , 2020 ; Naithani et al. 2011 ). These studies were conducted from broader perspectives, mostly covering the entire Himalaya. However, the present paper looks into the case study of four villages of the Uttarakhand Himalaya, which were severely affected and damaged by cloudburst-triggered debris flows and flash floods, which occurred on July 18th, 2021. It analyses the environmental impact of cloudbursts in terms of forest and fruit trees dislocation, land degradation, and soil erosion—arable, forests, and barren land of the four affected villages. It also evaluates the human and economic losses like the killing of people, loss of existing crops, and damage of houses and cowsheds, respectively. The study suggests policy measures to risk reduction and rehabilitation of settlements from danger zones to safer areas after suitability analysis.

The Uttarakhand Himalaya is located in the north of India and south of the Himalaya. It is also called the Indian Central Himalayan Region. Out of the total 93% mountainous area, 16% is snow-capped, called the Greater Himalaya. The terrain is undulating and precipitous and the landscape is fragile, vulnerable to natural hazards. This catastrophic event occurred in the four villages of Uttarkashi district. The Uttarkashi town lies about 10 km downstream of the affected villages. A National Highway number 108, connecting Haridwar and Gangotri, is passing through Uttarkashi town. The four affected villages—Nirakot, Mando, Kankrari, and Siror are located in the upper Bhagirathi catchment, which is prone to geo-hydrological hazards. The slope gradient of these villages varies from 15° to 70°. Indravati is a perennial stream, a tributary of the Bhagirathi River that meets Bhagirathi from its left bank. All three Gadheras (streams)—Mando, Diya, and Siror are seasonal but violent during the monsoon season. Nirakot (1530 m) village is located in the middle altitude of the Hari Maharaj Parvat (2350 m) in a steep slope, Mando village (1180 m) is located on the left bank of the Bhagirathi River along the Mando Gadhera with gentle to a steep slope, Kankrari (1620 m) village is located on the moderate to the gentle slope on the bank of the Diya Gadhera, and Siror village (1280 m) is situated on the left bank of both Bhagirathi and Siror Gadhera with gentle to the steep slope (Fig.  2 ). One of the prominent eco-sensitive zones of the Uttarakhand Himalaya, the ‘Bhagirathi Eco-Sensitive Zone’ is 120 km long, spanning from Uttarkashi to Gaumukh, along the Bhagirathi River valley (Sati 2018a , b ). The rural people depend on the output of the traditional farming systems, often face intensive natural hazards. The settlements are located either on the fragile and steep slopes or on the banks of streams, which are very violent during the monsoon season when a heavy downpour occurs. Therefore, heavy losses of life and property in these areas are common, taking place every year.

Location map of cloudburst source and hit areas and their surroundings

Methodology

This study was empirically tested and a qualitative approach was employed to describe data. A structured questionnaire was constructed. The main questions framed and asked from the heads of households were—human and animal death, damage to self property—houses and cowsheds, and existing crops—cereals, fruits, and vegetables. Loss to public properties such as bridges, public institutions, and forest land was assessed. Based on the questions framed, we surveyed 143 heads of households of four villages, which were partially or fully affected due to cloudburst-triggered debris flow. These villages are Nirakot, Mando, Kankrari, and Siror. To assess the debris and the damaging areas, the authors travelled from the source areas to the depositional zones and measured the volume of debris—boulders, pebbles, sands, and soils using a formula; circumference = 2πR and area = π * R 2 . The slope gradient, accessibility, economic conditions, and climate of the villages were assessed and based on which, the susceptibility analysis of the villages was carried out. The villages were divided into very high susceptibility, high susceptibility, and moderate susceptibility levels. Both environmental degradation and economic losses in four villages were assessed. We used Geographical Positioning System (GPS) to obtain the data of altitude, longitude, and latitude. Two maps—case study villages and the major cloudburst incidences—2020 and 2021 were prepared and data were also presented using graphs. Photographs of four villages were used to present the destruction of villages due to the cloudburst event.

Results and analysis

Major cloudburst incidences in the uttarakhand himalaya.

Past incidences depict that the Uttarakhand Himalaya suffered tremendously due to cloudburst-triggered calamities. We gathered data on the major cloudburst incidences in Uttarakhand in the monsoon seasons of 2020 and 2021 from the state disaster relief force (SDRF), Dehradun. From May to September 2020, 13 major cloudburst incidences were noticed in Uttarakhand (Table 1 ). These incidences resulted in the death of 22 people and 77 animals, and 19 houses were fully damaged. Similarly, from May to September 2021, 17 major cloudburst incidences were occurred in the Uttarakhand Himalaya, resulting in the death of 34 people and 144 animals, and 106 houses were buried. Besides, it caused a huge loss to public property and landscape degradation.

The economic losses in 2021 were much higher than the losses in 2020 (Fig.  3 ). In 2021, the frequency and intensity of cloudburst-triggered calamities were also higher. The loss of animals was quite high both the years. Houses that collapsed due to calamity were six times higher in 2021 than in 2020. The loss of human life was substantial in both years. Several bridges were washed away.

Loss of human lives, livestock, houses and bridges due to cloudburst in Uttarakhand during the 2020 and 2021

District-wise major cloudburst events of 2020–2021 are shown in the map of the Uttarakhand Himalaya (Fig.  4 ). A total of 30 major cloudburst incidences were recorded, out of which, 17 occurred in 2021. The Uttarkashi district received the highest incidences (07), followed by the Chamoli district (05). Dehradun and Pithoragarh districts have recorded 04 incidences each. Rudraprayag 03 and Tehri, Almora, Bageshwar have recorded 01 each. It has been observed that cloudburst-triggered incidences mainly occurred in remote places along the fragile river valleys and middle slopes.

Location map of cloudbursts hit areas in 2020 and 2021

Case study of affected villages

On July 18, 2021, a cloudburst hits the Hari Maharaj Parvat (hilltop) at an altitude of 2350 m at 8:30 p.m., which triggered huge debris flows and flash floods. The four villages—Nirakot, Mando, Kankrari, and Siror of Uttarkashi district, located down slopes of the hilltop and close to the Uttarkashi town, were severely affected due to debris flow (Table 2 ). At the cloudburst hit area, it formed three gullies, which later on merged into three streams, along which these villages are located. Debris, from the source i.e. hilltop of Hari Maharaj Parvat, equally flew in three directions. Since the cloudburst event occurred at 8:30 p.m., the people did not have time to move with their movable property and therefore, the magnitude of damage was enormous.

The villages are located from the altitudes of 1180 m (lowest) to 1620 m (highest). Mando village is located at 1180 m, Kankrari village at 1620 m, Nirakot at 1530 m, and Siror has 1280 m altitude. The two villages—Nirakot and Mando have west-facing slopes, Kankrari has a south-facing slope, and Siror has a north-facing slope. These villages are located along the tributaries of the Bhagirathi River, with 2 to 5 km distance from the road. The intensity and volume of debris were different in different villages, therefore, the casualties and losses were also varied. The villages are surrounded by agricultural and forestlands. The farmers mainly grow subsistence cereal crops—paddy, wheat, pulses, oilseeds, fruits, and vegetables. Forest types comprise pine (sub-tropical) and oak and coniferous forests (temperate), used for fodder, firewood, and wild fruits.

Located at the high-risk zones, these villages face several disaster incidences every year. Out of the total 143 heads of households surveyed, more than 80% of heads were in favour of rehabilitating them in the safer areas. They wanted to relocate their houses and cowshed within the village territory with financial assistance from the state government. The streams, along which the settlements are constructed, are fragile and highly vulnerable to landslide hazards. Further, the cloudburst incidences are increasing due to climate change, the heads of households perceived.

Figure  5 shows four villages—Nirakot, Mando, Kankrari, and Siror, which were severely affected by cloudburst-triggered debris flow and flash flood. The volume of debris and boulders can be seen in all the villages. These villages are surrounded by dense sub-tropical and temperate forests that vary from pine to mixed-oak and deodar. Kharif crops were growing in the arable land whereas a large cropland has been washed away.

Cloudburst affected villages a Nirakot, b Mando, c Kankrari, d Siror; Photo: by authors

Impact of cloudburst-triggered debris flow and flash flood

Environmental impact.

The environmental impact of cloudburst-triggered debris flow and flash flood in four villages of Uttarkashi district was analyzed (Table 3 ). The major variables were the number of forest trees dislocated, total land degradation, land degradation under existing crops, number of fruit trees dislocated, land degradation under arable land, number of buildings were damaged, number of bridges damaged, and boulders’ volume. Forest trees, which dislocated were pine in the middle altitude and mixed-oak and deodar in the higher altitude. A total of 770 forest trees were dislocated from all four villages, out of which, 500 were from the Kankrari village (highest). The lowest trees dislocated were from Siror village (70). The total land degradation from the cloudburst hit areas to the affected areas was huge, however, we have measured the land which was within and surrounding each village. The total land degradation was 52.5 acres with the highest in Kankrari (45 acres) and the lowest in Siror (0.5 acres). The land degradation under existing crops was 22.6 acres in all four villages, varying from 0.1 acres in Siror to 20.6 acres in Kankrari. The total number of fruit trees dislocated was 486. Land degradation under arable land was 22.6 acres. It includes the area under existing crops both agriculture and horticulture. A total of 19 buildings were damaged whereas a total of 14 bridges, connecting the affected villages were washed away.

Economic impact

The economic impact due to cloudburst calamity was tremendous in the forms of a household affected, loss of human and animal life, building loss, forest loss, loss of existing crops including fruits, loss of arable land, and loss of bridges (Table 4 ). The value of all these assets was calculated in Indian Rupees (INR) at the current price. The total number of households affected was 143, of which, 100 households belonged to the Kankrari village (highest) and three households (lowest) were from Siror village. Four people died due to the calamity—three women from Mando village and 1 man from Kankrari village. Two cows from Mando village died. The total loss from the collapse of the building was 1.7 million INR, with the highest (1.1 million INR) from Kankrari village. A total of 0.77 million INR was lost due to forest loss, and the loss from existing crops was 3.35 million INR. Loss from dislocation of fruit trees was noted high, which was about 0.5 million INR. A large portion of arable land was flown which value was 11.3 million INR. About 14 million INR was lost due to the collapse of bridges. As a whole, about 31.62 million INR was lost due to cloudburst calamity. Per household loss by the cloudburst calamity was noted 0.22 million INR.

Average circumference, area, and volume of boulders

We calculated the average circumference, area, and volume of boulders in the case study villages using a formula: circumference = 2πR; Area = π * R 2 ; volume = length × width × depth (Table 5 ). We noticed that the highest average area of boulders was in Mando village, which is 28.3 m 2 followed by Kankrari 19.6 m 2 , Nirakot 12.57 m 2 , and Siror 7.1 m 2 . In terms of the total volume of debris, it was the highest in Kankrari village, followed by Mando, Nirakot, and Siror villages.

Figure  6 shows the average diameter of boulders in the cloudburst-affected villages. We drew the figure with a scale of 1 cm is equal to 1 m. The average biggest diameter of boulders was found in Mando village (6 m), followed by Kankrari (5 m) and Nirakot (4 m) villages. The average smallest diameter of boulders was found in Siror village (3 m).

Village-wise average diameter of boulders

Susceptibility analysis

Based on the above description, susceptibility analysis of the case study villages was carried out (Table 6 ). The main variables of susceptibility were slope gradient, accessibility of villages, economic conditions of households, and climatic conditions. We noticed that Nirakot village has very high susceptibility, Kankrari has high, and Siror and Mando have moderate susceptibility.

The Uttarakhand Himalaya is highly vulnerable to geo-hydrological disasters because of its geological formation (Vaidya 2019 ). It is an ecologically fragile, geologically sensitive, and tectonically and seismically very active mountain range (Sati 2019 ). The geo-hydrological events—cloudbursts and glacier bursts-triggered catastrophes are very common and devastating. The monsoon season poses severe threats to natural hazards because of heavy downpours. About 93% of the Uttarakhand Himalaya is mountainous mainland, of which 16% is snow-capped. The undulating and precipitous terrain and remoteness are the most vulnerable for disaster risks.

This study reveals that most of the cloudbursts incidences in 2020–21 occurred mainly in the remote mountainous districts of the Uttarakhand Himalaya. The villages in the Uttarakhand Himalaya are located on the sloppy land and along the river valleys, which are fragile and very vulnerable to disasters. The rivers flow above danger marks during the monsoon season cause threats to rural settlements. The roads of Uttarakhand are constructed along the river banks and on fragile lands. These roads lead to the highland and river valley pilgrimages where the number of tourists and pilgrims visit every year mainly during the monsoon season. There are many locations along the river valleys where the houses are constructed on the debris, deposited by rivers during debris flow events. Therefore, the environmental and economic losses due to debris flows and flash floods are high. The construction of hydropower projects along the river valleys without using sufficient technology further accentuates the vulnerability of debris flows and flash floods. One of the recent examples is the Rishi Ganga tragedy in Chamoli district where more than 200 people died with a huge loss to property (Sati 2021 ). We observed that the cloudburst triggered calamity in 2021 was higher than in 2020. The trend of occurring natural hazards has been increasing. Similarly, the intensity and frequency of natural hazards were observed high.

The present study shows that the environmental and economic loss in the four villages of the Bhagirathi River valley was huge due to cloudburst-triggered debris flows and flash floods. Almost every household of the villages were affected by cloudburst calamity. There were large forest and arable land degradation, forest and fruit trees were dislocated, loss of life—human and animal, and the houses and bridges were collapsed. The calamity also poses threat to the future, in terms of, the large deposition of debris including boulders, pebbles, and gravels in the villages along the streams and gullies. The rural people are poor and their livelihood is dependent on practicing subsistence agriculture. Many of them are living below the poverty line in these villages. Because the existing crops have been lost, they are facing food insecurity. Further, the psychological problems are immense. The fear of another calamity is always there in the mind of people as all villages are situated in very high to moderate susceptible areas. The national highway is passing through the right bank of the Bhagirathi River and the affected villages are situated on the left bank. The connectivity problem is immense all the time in these villages. The entire rural areas of the Uttarakhand Himalaya are facing similar problems.

Cloudburst-triggered debris flows and flash floods are natural calamities in the Himalayan regions. They occur naturally and cannot be stopped. The losses—environmental and economic are also huge. However, the severity of these natural calamities can be minimized. For example, the high impact of cloudburst-triggered debris flow on the four study villages was mainly due to their location along the streams and on the fragile slopes. This can be avoided by constructing the settlements in safer places generally away from the violent streams. In the disaster risk zones, scenario analysis can be carried out under which, identifying driving forces of disaster risks is the first step. Then, the critical uncertainties are to be identified, and finally, a possible scenario can be developed. Nature-based eco-disaster risk reduction can be adopted to prevent further disaster risks. A large-scale plantation drive in the degraded land will restore the fragile landscape. Both pre and post-disaster risk reduction measures can be adopted to reduce the economic and environmental impact of debris flows. There must be policies implementation programmes for providing immediate relief packages for the affected people in terms of food and shelters. In a long run, susceptibility analyses should be carried out to understand the risk to the settlements so that the settlements can be replaced on the safer side if needed. A special budget can be allocated to hazard-prone villages during adverse situations.

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Sati, V.P., Kumar, S. Environmental and economic impact of cloudburst-triggered debris flows and flash floods in Uttarakhand Himalaya: a case study. Geoenviron Disasters 9 , 5 (2022). https://doi.org/10.1186/s40677-022-00208-3

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Assessment of the 2021 summer flood in Central Europe

• Frank Lehmkuhl 1 ,
• Holger Schüttrumpf 2 ,
• Jan Schwarzbauer 3 ,
• Catrina Brüll 2 ,
• Michael Dietze 4 ,
• Peter Letmathe 5 ,
• Carolin Völker 6 &
• Henner Hollert 7  

Environmental Sciences Europe volume  34 , Article number:  107 ( 2022 ) Cite this article

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The flood event in July 2021 in the uplands of the Eifel-Ardennes mountains in Germany, Belgium and The Netherlands and their foreland was caused by heavy rainfall and resulted in one of the largest flood disasters in Western Europe for decades. Due to climate change, it can be assumed that such events will become more frequent in future. Even though such extreme flood can happen at any time, the consequences and impacts can be significantly reduced by appropriate technical and non-technical measures. However, such measures always require a comprehensive understanding and knowledge of previous events and comparable processes. Therefore, this special issue aims at collecting the scientific evaluation and its implications of the 2021 extreme summer flood. This editorial serves as an introduction for an article collection published in the journal Environmental Sciences Europe , providing an overview of the current state of integrative assessment of the 2021 summer flood in Central Europe.

In summer 2021, parts of Germany, Belgium and The Netherlands were hit by an extreme flood event, specifically the uplands of the Eifel-Ardennes mountains. In Germany, effects were focused on the catchments of the rivers Ahr, Erft, Inde and Rur, with the most severe damage in the Ahr valley. Entire villages and small-to-medium cities were several meters inundated, buildings were destroyed and even some entire properties were no longer available (see e.g., Korswagen et al. [ 1 ]). More than 180 people lost their lives, more than 750 people were injured and thousands were economically affected. A disproportionate number of people from particularly vulnerable population groups, such as elder or disabled persons, were among the victims of the flood. Many people still suffer the physical, psychological and material consequences of the flood disaster in March 2022 [ 2 , 3 ].

Even though it is impossible to avoid such extreme flood events, the consequences and impacts can be significantly reduced by appropriate technical and non-technical measures. Generally, the objective of all measures must be to ensure that such extreme events do not lead to a flood disaster on the scale observed. However, such measures always require a comprehensive understanding and knowledge of previous events and comparable processes. Therefore, this special issue aims at collecting the scientific evaluation and its implications of the 2021 extreme summer flood.

The flood disaster was caused by an atmospheric low named Bernd, which brought abundant precipitation of up to 200 mm in the Eifel between 13 and 15 July 2021 ([ 4 , 5 ]; Fig.  1 ). A peak discharge of approx. 1000 to 1300 m 3 /s was estimated at the Altenahr gauge [ 6 ]. Reconstructions of two historical floods, on 21. July 1804 and 13 June 1910 by Roggenkamp and Herget [ 7 , 8 ], show a comparable discharge at Dernau of 1,208 and 549 m 3 /s, respectively. However, these historical events have not been considered in the flood hazard maps of Rhineland-Palatinate, as they occurred outside the gauge measurement periods, resulting in a misinterpretation of the design water levels for a given return period of such large floods. Despite the overall indicative role of reconstructed historical floods, frequent arguments for neglecting them are inter alia uncertainties in water levels, and unconstrained changes in land use and runoff characteristics. The economic consequences are also systematically underestimated. In the light of post-event evidence estimations should use such historical information as well as mounting evidence that climate change further increases the risk of extreme flood events, both allowing to assessed future flood hazard and flood risks more thoroughly.

Relief and precipitation for selected catchments of rivers affected by the flood in summer 2021 based on Radar data (adapted from Döscher et al. [ 5 ]) at the northern margin of the Eifel mountains

Due to their flash flood characteristics, the geomorphological effects of pluvially triggered floods in the Central European uplands differ clearly from the effects of lowland river floods. Narrow and often steep valleys in combination with thin and saturated cover beds and thus reduced retention potential promote rapid surface runoff on slopes and hence a fast-rising flood hydrograph, further enhanced by mostly narrow floodplains. This phenomenon has been known in the literature for some time and was first described as the "partial drainage area effect" by Dune and Leopold [ 9 ]. Figure  2 provides an example of enormous erosion in a natural bend of the Ahr River.

Bend of the Ahr River close to Laach before (left, Google satellite 2015) and after (right, unoccupied aerial vehicle, UAV. Picture by Physical Geography and Geoecology). The red line indicates the area of UAV coverage. Notice the trees on the left picture and the broad gravel floor on the right

Arguably, the 14–15 July 2021 event activated more than just a fluvial process domain, with the most important impacts on the affected landscape being caused by other than just hydrological dynamics. Dietze et al. [ 10 ] reviewed the boundary conditions of the flood and discuss the emerging features that made this event different from previous ones. They address rain-triggered gravitational and hyper-concentrated hillslope processes interacting with the river systems, aspects of large woody and anthropogenic debris mobilization, the legacy of sustained human land use amplifying the swiftness and magnitude of the flood, and previously not anticipated though emerging process connections and feedbacks as critical non-hydrological dimensions of the flood. Accounting for those and perhaps further non-hydrological dimensions of rain-triggered extreme events would be a further laudable step to take for increasing the preparedness for future events, also in other regions.

The fast discharge and transport of coarser debris of high gradient valley confined floods produces greater effects on lateral erosion and damages of infrastructure, such as bridges, and houses as well as on incision and backward erosion. In contrast, lowland river floods and flooding often spread over a wide area and can thus inundate large areas, but the damage to infrastructure and buildings is usually less due to the lower flow velocities. Instead, portions of the suspension loads are accumulated as fine sediment and silt on the floodplains.

In general, an often-neglected aspect of flood events is the emission, remobilization, dispersion and accumulation of pollutants and contaminants transported as or adhering to suspension load (see Fig.  3 ). Different flood-induced emission sources and pathways can be distinguished especially from urban areas, comprising waste water from destroyed sewage treatment plants, oil, fuel and other pollutants from industrial areas, waste disposal sites, houses and (former) mining areas including their tailings. The corresponding contaminated sediments and compounds are transported and accumulated in sediments downstream or the floodplains of the middle and lower reaches. Wherever the transport energy of the water decreases sufficiently, the contaminants are deposited: in floodplains, gardens, playgrounds, streets and other flooded areas (e.g., basements). Ultimately, this can lead to a significant accumulation of contaminants in the various sedimentation areas provoking environmental and health risks, especially as the July flood affected larger areas than previous floods in the region. The review of Crawford et al. [ 11 ] addresses the general problem of remobilization of pollutants during extreme flood events which poses severe risks to human and environmental health. The role of sediment re-suspension, e.g., during flood events, and possible ecotoxicological effects of re-mobilized particle-bound contaminants to aquatic organisms have scarcely been investigated. Effect-based methods are very well suited for investigating the ecotoxicological impact of sediments and suspended matter in floods with regard to their adverse effects on humans and ecosystems [ 12 ]. Within the last years several studies were published addressing the ecotoxicological impact of flood events [ 13 , 14 , 15 ] or using combined approaches for evaluating flood events and the risk of erosion [ 12 ]. Both inorganic and organic pollutants, as potentially remobilized during flood events, can result in various adverse health effects. A range of toxic effects are known after exposure, including neuro-, immune-, haemato- and hepatotoxicity, reproductive toxicity, genotoxicity as well as carcinogenicity. Endocrine effects, e.g., effects on the human thyroid hormone system, are also receiving increasing attention, as recent studies have shown. Bioanalytical methods can be used to create a comprehensive toxicological profile of flood samples, which can be narrowed down to individual substance groups with chemical-analytical information or even partially explained with the detection of toxic individual substances in effect-directed analysis or mass balance calculations. The here presented paper series will also give insights into the ecotoxicological effects of the extreme flood in summer 2021.

Pathways of pollutant in river catchments

Lehmkuhl et al. [ 16 ] summarize the geomorphological aspects comparing uplands and lowlands for this July flood event and the resulting sediment pollution by examples from the Inde catchment in North Rhine Westphalia.

Furthermore, large-scale anthropogenic relief changes due to mining in floodplain areas have resulted in an increase of susceptibility to amplified flood effects and such landscapes are prone to catastrophic erosion events during flooding. There are two examples of major backward erosion during this flood event in western German in July 2021: the Inde River flooded the lignite mine of Inden close to the settlement of Lamersdorf and the Erft River flooded an open gravel pit near the town of Erftstadt-Blessem. Both events resulted in massive erosional processes, including the catastrophic destruction of parts of Erftstadt-Blessem. Flooded open cast mining areas and large base-level change on short distance result in a steep local gradient, which in turn promotes increased stream power that could drive intense and rapid headwall erosion eating away terrace landforms for several hundred meters. Boundary conditions are relief (base-level change) and material (flood loam and gravel). In both areas, large gully erosion occurred, resulting in the re-deposition of more than 500,000m 3 of sediments [ 16 , 17 ].

The consequences of climate change are increasingly being discussed in the public discourse as a cause of severe floods. Therefore, in addition to the natural science perspective, flood impact research should also consider social and socio-ecological issues. The added value of the interdisciplinary approach is generated by linking the natural and social science perspectives, as for example in the question of the social perception and discourse of pollutants due to extreme flooding. The overarching approach enables a systemic view of risks and the consideration of complex societal problems that cannot be captured by a single discipline. Ultimately, however, this overarching approach will only lead to appropriate action if the risks and their consequences are adequately considered in individual and policy decisions. Since many actors are economically driven, the consequences must be translated into appropriate cost estimates for remobilized pollutants during flood events. These cost estimates should include direct economic impacts such as the direct damage to soil and buildings, but also the indirect damage to human health and the provision of ecosystem services. Ideally, wise decision patterns can significantly reduce the risks of future extreme flood events as well as the economic consequences.

Future flood protection and settlement development in the catchments of upland rivers, such as the Ahr, should consider the lessons learned from the flood disaster in July 2021. That event has readjusted the bar for flood protection and shifted it to previously unknown heights. Previous approaches and methods should be questioned and revised. The emergence of an event similar to that one is possible at any time, as historical flood data have already shown. However, according to studies on the influence of climate change the return period of extreme events will rather decrease. Blöschl et al. [ 18 ] already showed that in north-western Europe, a typical 100-year event in 1960 has already become a 50- to 80-year event. Hence, due to climate change, we have to expect both, more frequent extreme events like the one in 2021 but occasionally also even larger events. Therefore, measures to improve flood protection and more adapted settlement structures and building methods are already important climate adaptation measures.

Call for papers

This article collection in Environmental Sciences Europe (ESEU) will be the place for articles from different disciplines, such as Geoecology, Geomorphology, Hydraulic engineering, Environmental chemistry, Ecotoxicology, Social ecology, Sociology and Economics. The article collection is intended to provide an overview of the current state of integrative assessment of the 2021 summer flood in Central Europe. We cordially invite all colleagues who feel they can contribute to the topic to submit a manuscript to ESEU with reference to this series.

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Acknowledgements

We thank Philipp Schulte and Wolfgang Römer for valuable comments, help during field work and discussions. L. Dörwald carried out drone flights and processed the UAV image. We would like to thank M. Formen and J. Keßels for their help with the figures. FL and HS acknowledge funding from DFG (projectnumber 496274914) and BMBF (01LR2102H, 13N16226). H.H. and C.V. have been supported by the RobustNature Cluster of Excellence Initiative (internal prefunding of Goethe University Frankfurt). HH and CB acknowledge funding from DFG (projectnumber 497800446)

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Chair of Physical Geography and Geoecology, Department of Geography, RWTH Aachen University, Wüllnerstr. 5B, 52056, Aachen, Germany

Frank Lehmkuhl

Institute of Hydraulic Engineering and Water Resources Management, RWTH Aachen University, Mies-Van-Der-Rohe-Str. 17, 52074, Aachen, Germany

Holger Schüttrumpf & Catrina Brüll

Laboratory for Organic-Geochemical Analysis, Institute of Geology and Geochemistry of Petroleum and Coal, RWTH Aachen University, Lochnerstr. 4-20, 52056, Aachen, Germany

Jan Schwarzbauer

Faculty of Geosciences and Geography, Georg-August-University, Goldschmidtstr. 3-5, D-37077, Göttingen, Germany

Michael Dietze

Faculty of Business and Economics, Chair of Management Accounting, RWTH Aachen University, 52062, Aachen, Germany

Peter Letmathe

ISOE – Institute for Social-Ecological Research, Hamburger Allee 45, 60486, Frankfurt am Main, Germany

Carolin Völker

Department Evolutionary Ecology & Environmental Toxicology (E3T), Faculty Biological Sciences (FB15), Goethe University Frankfurt, Max-Von-Laue-Str. 13, 60438, Frankfurt am Main, Germany

Henner Hollert

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FL and HS conceptualized the manuscript. JS, CB, MD, PL, CV, HH contributed specific aspects to the manuscript and improved the manuscript content. All authors read and approved the final manuscript.

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Lehmkuhl, F., Schüttrumpf, H., Schwarzbauer, J. et al. Assessment of the 2021 summer flood in Central Europe. Environ Sci Eur 34 , 107 (2022). https://doi.org/10.1186/s12302-022-00685-1

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Assessing the Environmental Effects Associated with Flood Protection Measures

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• Najlaa Ben-Lhachemi   ORCID: orcid.org/0009-0006-6749-9258 9 ,
• Mohammed Benchrifa   ORCID: orcid.org/0000-0001-8341-9576 10 ,
• Souad Nasrdine   ORCID: orcid.org/0009-0004-7021-9005 9 ,
• Jamal Mabrouki   ORCID: orcid.org/0000-0002-3841-7755 11 &
• Miloudia Slaoui 9  

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Environmental deterioration is brought on by both short-term and long-term variations in the components of the air, water, and soil levels. Air pollution is significantly impacted by construction operations. The purpose of this study is to evaluate the effects of flood protection efforts made at Oued Ikkour in the Zaouiat Cheikh area. We used the AirQ+ software, focusing on the PM10 and O 3 pollutants, to estimate the health-related impacts of air pollution. To display the distribution of pollutants and do statistical analysis on PM10 and O 3 concentrations, we again used the SPSS program. Our research sought to thoroughly assess the effects of air pollution brought on by the flood protection activities at Oued Ikkour. We thoroughly examined the effect on public health and presented a thorough statistical study of PM10 and O 3 pollutant levels by using the appropriate tools and methodology.

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Ben-Lhachemi, N., Benchrifa, M., Nasrdine, S., Mabrouki, J., Slaoui, M. (2024). Assessing the Environmental Effects Associated with Flood Protection Measures. In: Mabrouki, J., Azrour, M. (eds) Integrated Solutions for Smart and Sustainable Environmental Conservation. Studies in Systems, Decision and Control, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-031-55787-3_9

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• Published: 18 January 2021

Role of dams in reducing global flood exposure under climate change

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

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• Climate-change impacts

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

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Introduction.

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

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

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

Streamflow regulation

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

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

Population exposure to floods

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

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

Return period of future floods

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

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

Evolution of future floods for individual catchments

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

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

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

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

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

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

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

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

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

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

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

Experiments

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

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

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

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

Definition of flood event and extreme discharge

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

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

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

Fitting of Gumbel distribution

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

Assessment of goodness of fit

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

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

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

Population exposure

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

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

Catchment selection

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

Catchment flood analysis

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

Data availability

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

Code availability

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

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Acknowledgements

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

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

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

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Kenya’s devastating floods expose decades of poor urban planning and bad land management

Chartered Consultant in Hydrology and Water Resources, Visiting Research Fellow, King's College London

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Sean Avery is affiliated with: Hydrological Society of Kenya, Water Resource Associates, Kings College London, University of Gent

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Floods in Kenya killed at least 169 people between March and April 2024. The most catastrophic of these deaths occurred after a flash flood swept through a rural village killing 42 people . Death and destruction have also occurred in the capital, Nairobi, a stark reminder of the persistent failure to keep abreast of the city’s rapid urbanisation needs. Sean Avery , who has undertaken numerous flood and drainage studies throughout Africa, unpacks the problems and potential solutions.

Are floods in Kenya causing more damage? If so, why?

Floods are the natural consequence of storm rainfall and have an important ecological role . They inundate flood plains where silts settle, riverbed aquifers are recharged and nutrients are gathered. Annual rainfall in Kenya varies from 2,000mm in the western region to less than 250mm in the drylands covering over 80% of Kenya. But storm rainfalls are widespread. This means that floods can occur in any part of the country.

The impact of floods has become more severe due to a number of factors.

The first is how much water runs off. In rural areas, changes to the landscape have meant that there’s been an increase in the amount of storm runoff generated from rainfall. This is because the natural state of the land has been altered through settlement, roads, deforestation, livestock grazing and cultivation. As a result, a greater proportion of rainfall runs off. This runoff is more rapid and erosive, and less water infiltrates to replenish groundwater stores.

The East African Flood Model , a standard drainage design tool, demonstrates that by reducing a forested catchment into a field for livestock pasture, for instance, the peak flood magnitude can increase 20-fold. This form of catchment degradation leads to landslides, dams can breach, and road culverts and irrigation intakes are regularly washed away.

Land degradation in sub-Saharan rangelands is omnipresent, with over 90% rangeland degradation reported in Kenya’s northern drylands . Kenyan research has recorded dramatic increases in stormwater runoff due to overgrazing.

Second, human pressure in urban areas – including encroachment into riparian zones and loss of natural flood storage buffers through the destruction of wetlands – has increased flood risks. Riparian zones are areas bordering rivers and other bodies of water.

By 2050, half of Kenya’s population will live in urban areas. Green space is progressively being filled with buildings and pavements. A large proportion of urban population lives in tin-roofed slums and informal settlements lacking adequate drainage infrastructure. As a result, almost all of the storm rainfall is translated into rapid and sometimes catastrophic flooding.

Third, flood risks are worse for people who have settled in vacant land which is often in low-lying areas and within flood plains. In these areas, inundation by flood waters is inevitable.

Fourth, Nairobi’s persistent water supply shortages have led to a proliferation of boreholes whose over-abstraction has resulted in a dramatic decline in the underground water table’s levels. This leads to aquifer compression, which is compounded by the weight of buildings. The result is ground level subsidence , which creates low spots where stormwater floods collect.

What should be done to minimise the risks?

Rural areas require a different set of solutions.

Natural watercourses throughout Kenya are being scoured out by larger floods due to land use pressures. These watercourses are expanding and riparian vegetation cover is disappearing. The flood plains need space to regenerate the natural vegetation cover as this attenuates floods, reducing the force of runoff and erosion.

There are existing laws to protect riverbanks, and livestock movements in these areas must also be controlled. Any building or informal settlement within riparian areas is illegal and would otherwise be exposed to the dangers of floods. Enforcement is a challenge, however, as these areas are favoured by human activities and often these people are among the poorest.

Urban areas have a host of particular challenges that need to be addressed.

Take Nairobi, Kenya’s capital city. The physical planning process is hindered by corruption . Inappropriate and unsafe developments proliferate alongside inadequate water supply, wastewater and solid waste disposal infrastructure. Sewage effluent is often discharged into stormwater drains, even in high-class areas of the city. And there is little control of development in the growing urban centres bordering Nairobi, with transport corridors being congested. Throughout the country, laws that protect riparian zones are flouted.

None of this is sustainable.

Each municipality is obliged to provide infrastructure that includes an effective engineered stormwater drainage network. And in parallel, wastewater and solid wastes must be separately managed.

The typical stormwater drainage network comprises adequately sized earth and lined channels, and pipes and culverts that convey the stormwater to the nearest watercourse. Constant maintenance is essential, especially before the onset of rains, to avoid blockage by garbage and other human activities.

Modern-day urban flood mitigation measures include the provision of flood storage basins. Unfortunately this is impossible in Nairobi where developments are built right up to the edge of watercourses. Constrained channels thereby cause upstream flooding as there is nowhere else for the water to go.

Attempts have been made to reverse urban riparian zone encroachments , but these efforts faltered due to legal repercussions. To this day, unscrupulous developers encroach with impunity .

It’s essential that the authorities demarcate riparian boundaries and set aside buffer zones that cannot be “developed”.

• Urbanisation
• Deforestation
• Groundwater
• Water catchment
• East Africa
• Sub-Saharan Africa

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An economic analysis of flooding in the Caribbean: The case of Jamaica and Trinidad and Tobago

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Description

Flooding as an extreme event has become progressively evident in the Caribbean sub-region, as a result of an increased number of intense rainfall events, and storm surges from hurricanes. Such events in turn, have been linked to the impacts of global climate change, which has been shown to be the cause for several specific events including sea-level rise; global temperature rise, ocean warming and acidification, and the melting of glaciers. In the specific instance of the Caribbean subregion, flooding events often result in significant disruptions of economic and social life. This study uses a case-study approach of selected areas in Trinidad and Tobago and Jamaica to investigate the potential economic impacts of recurrent flood events and compare with a potential cost saving benefit of specific flood control interventions.

Table of contents

Abstract .-- Introduction .-- I. Flooding in the Caribbean .-- II. Methodological approach .-- III. Selected areas of study. Socioeconomic profiles .-- IV. Potential effects and mitigation measures .-- V. conclusions.

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How does flooding affect humans and the environment?

Impact of flooding on humans

Flooding has a range of impacts on humans, including:

• people can be injured or killed by flooding
• floodwater is often contaminated with sewage, which can lead to illness and affect clean drinking water
• power supplies can be disrupted
• businesses can be forced to shut down
• services such as hospitals and schools can close
• transport networks can be affected, such as flood damage to bridges, railways and roads
• homes and properties can be flooded
• people may have to move out of their properties until flood damage is repaired
• possesions can be damaged and washed away

Impact of flooding on the environment

Flooding has a range of impacts on the environment, including:

• wildlife habitats can be destroyed by floodwater
• contaminated floodwater can pollute rivers and habitats
• silt and sediment can destroy crops on farms
• river banks and natural levées can be eliminated as rivers reach bankfull capacity
• rivers can be widened, and deposition can increase downstream
• trees can be uprooted by high-velocity water flow
• plants that survive the initial flood may die due to being inundated with water

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Floods and cause-specific mortality in the UK: a nested case-control study

Affiliations.

• 1 School of Public Health and Preventive Medicine, Monash University, Level 2, 553 St Kilda Road, Melbourne, VIC, 3004, Australia.
• 2 Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
• 3 Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Melbourne, VIC, 3800, Australia.
• 4 Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China. [email protected].
• PMID: 38715068
• PMCID: PMC11077877
• DOI: 10.1186/s12916-024-03412-0

Background: Floods are the most frequent weather-related disaster, causing significant health impacts worldwide. Limited studies have examined the long-term consequences of flooding exposure.

Methods: Flood data were retrieved from the Dartmouth Flood Observatory and linked with health data from 499,487 UK Biobank participants. To calculate the annual cumulative flooding exposure, we multiplied the duration and severity of each flood event and then summed these values for each year. We conducted a nested case-control analysis to evaluate the long-term effect of flooding exposure on all-cause and cause-specific mortality. Each case was matched with eight controls. Flooding exposure was modelled using a distributed lag non-linear model to capture its nonlinear and lagged effects.

Results: The risk of all-cause mortality increased by 6.7% (odds ratio (OR): 1.067, 95% confidence interval (CI): 1.063-1.071) for every unit increase in flood index after confounders had been controlled for. The mortality risk from neurological and mental diseases was negligible in the current year, but strongest in the lag years 3 and 4. By contrast, the risk of mortality from suicide was the strongest in the current year (OR: 1.018, 95% CI: 1.008-1.028), and attenuated to lag year 5. Participants with higher levels of education and household income had a higher estimated risk of death from most causes whereas the risk of suicide-related mortality was higher among participants who were obese, had lower household income, engaged in less physical activity, were non-moderate alcohol consumers, and those living in more deprived areas.

Conclusions: Long-term exposure to floods is associated with an increased risk of mortality. The health consequences of flooding exposure would vary across different periods after the event, with different profiles of vulnerable populations identified for different causes of death. These findings contribute to a better understanding of the long-term impacts of flooding exposure.

Keywords: Floods; Long-term; Mortality; Natural disaster.

© 2024. The Author(s).

Publication types

• Research Support, Non-U.S. Gov't
• Case-Control Studies
• Cause of Death
• Floods* / mortality
• Middle Aged
• Risk Factors
• United Kingdom / epidemiology

Grants and funding

• GNT1163693/National Health and Medical Research Council
• GNT2008813/National Health and Medical Research Council
• GNT2000581/National Health and Medical Research Council
• GNT2009866/National Health and Medical Research Council
• DP210102076/Australian Research Council
• 202006010044/China Scholarship Council
• 202006010043/China Scholarship Council
• 201906210065/China Scholarship Council