case study of recent natural disasters in india

• INSIDER REVIEWS
• TECH BUYING GUIDES
• Environment

Looking back at the natural disasters that took place in India in 2021

Vaamanaa sethi   .

• Between 1970 to 2019, weather, climate and water hazards accounted for 50% of all disasters, 45% of all reported deaths and 74% of all reported economic losses, according to World Meteorological Organization (WMO).
• According to the World Economic Forum, disaster events have been recorded in the past 20 years, which have claimed the lives of 1.23 million people.
• A flashback at the natural disasters that hit different parts of India this year.

Tamil Nadu floods

The Indian Meteorological Department (IMD) had predicted heavy rainfall in parts of Tamil Nadu, and it came true from November 1. The flooding was caused by extremely heavy downpours, killing at least 41 people.

Several red alerts were issued for many areas in Tamil Nadu, including Cuddalore, Sivaganga, Ramanathapuram, Karaikal, Tiruvallur, Chennai, Kanchipuram, Chengalpattu, Viluppuram, and Tiruvannamalai for November 10-11. Over 11,000 were displaced due to the incessant rainfall.

Maharashtra floods

Starting on 22 July, Maharashtra saw heavy rainfall in many of its western districts and recorded the highest rainfall in the month of July in 40 years.

Around 251 people died and over 100 were missing due to floods and landslides in Maharashtra.

Its neighbouring state Goa also witnessed the worst floods in decades.

Kerala floods

Between October 12 and 20, after heavy rains caused rivers to overflow, cutting off towns and villages, 42 people died and 217 houses were destroyed. Out of the 42 people who lost their lives in the floods, five were children.

Kottayam and Idukki were two of the worst affected districts in the state, where days of heavy rainfall had caused deadly landslides.

Cyclone Tauktae

It was a powerful, deadly and damaging tropical cyclone in the Arabian Sea that became the strongest tropical cyclone to make landfall in the Indian state of Gujarat since the 1998 Gujarat cyclone and one of the strongest tropical cyclones to ever affect the west coast of India.

Started on May 14, the storm displaced over 200,000 people in Gujarat and killed 174 people with 80 people still missing.

Tauktae brought heavy rainfall and flash floods to areas along the coast of Kerala and Lakshadweep. There were reports of heavy rain in the states of Goa, Karnataka and Maharashtra as well.

Cyclone Yaas

It was a relatively strong and very damaging tropical cyclone that made landfall in Odisha and brought significant impact to West Bengal in May. Yaas formed from a tropical disturbance that the Indian Meteorological Department first monitored on May 23.

Around 20 people across India and Bangladesh died due to the cyclone and West Bengal was one of the most impacted states in India due to Yaas, with a loss of approximately $2.76 billion, according to several media reports.

Cyclone Gulab

The third storm in India that impacted eastern India, was formed on September 24 in Bay of Bengal. On September 26, Gulab made landfall in India's Andhra Pradesh, but weakened over land. The storm overall brought heavy rains and strong winds throughout India and the Middle East, killing at least 39 people.

Over 30,000 individuals were evacuated into safety as a result of the cyclone. This number further increased to 46,075 people as the storm further moved inland.

Assam earthquake

On April 28, a 6.4 magnitude earthquake jolted Assam. The quake resulted in two fatalities and at least 12 people were injured. The quake struck at a depth of 34 kilometres and 140 kilometres north of Guwahati.

The earthquake occurred as a result of oblique-slip faulting at a shallow depth just at the foothills of the Himalayas. Analysis by India's National Centre for Seismology revealed that the earthquake involved a slip along the Kopili Fault, near the Main Frontal Thrust.

READ MORE ARTICLES ON

• Natural Disasters
• Natural Disasters In India
• Natural Disasters In 2021
• Natural Disasters In India In 2021

Popular Right Now

Advertisement

An official website of the United States government

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

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

• Publications
• Account settings

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

• Advanced Search
• Journal List
• Indian J Community Med
• v.37(3); Jul-Sep 2012

Disaster Management in Flash Floods in Leh (Ladakh): A Case Study

Preeti gupta.

Regimental Medical Officer, Leh, Ladakh, India

Anurag Khanna

1 Commanding Officer, Army Hospital, Leh, India

2 Registrar, Army Hospital, Leh, India

Background:

On August 6, 2010, in the dark of the midnight, there were flash floods due to cloud burst in Leh in Ladakh region of North India. It rained 14 inches in 2 hours, causing loss of human life and destruction. The civil hospital of Leh was badly damaged and rendered dysfunctional. Search and rescue operations were launched by the Indian Army immediately after the disaster. The injured and the dead were shifted to Army Hospital, Leh, and mass casualty management was started by the army doctors while relief work was mounted by the army and civil administration.

The present study was done to document disaster management strategies and approaches and to assesses the impact of flash floods on human lives, health hazards, and future implications of a natural disaster.

Materials and Methods:

The approach used was both quantitative as well as qualitative. It included data collection from the primary sources of the district collectorate, interviews with the district civil administration, health officials, and army officials who organized rescue operations, restoration of communication and transport, mass casualty management, and informal discussions with local residents.

234 persons died and over 800 were reported missing. Almost half of the people who died were local residents (49.6%) and foreigners (10.2%). Age-wise analysis of the deaths shows that the majority of deaths were reported in the age group of 25–50 years, accounting for 44.4% of deaths, followed by the 11–25-year age group with 22.2% deaths. The gender analysis showed that 61.5% were males and 38.5% were females. A further analysis showed that more females died in the age groups <10 years and ≥50 years.

Conclusions:

Disaster preparedness is critical, particularly in natural disasters. The Army's immediate search, rescue, and relief operations and mass casualty management effectively and efficiently mitigated the impact of flash floods, and restored normal life.

Introduction

In the midnight of August 6, 2010, Leh in Ladakh region of North India received a heavy downpour. The cloud burst occurred all of a sudden that caught everyone unawares. Within a short span of about 2 h, it recorded a rainfall of 14 inches. There were flash floods, and the Indus River and its tributaries and waterways were overflowing. As many as 234 people were killed, 800 were injured, and many went missing, perhaps washed away with the gorging rivers and waterways. There was vast destruction all around. Over 1000 houses collapsed. Men, women, and children were buried under the debris. The local communication networks and transport services were severely affected. The main telephone exchange and mobile network system (BSNL), which was the lifeline in the far-flung parts of the region, was completely destroyed. Leh airport was flooded and the runway was covered with debris, making it non-functional. Road transport was badly disrupted as roads were washed away and blocked with debris at many places. The civil medical and health facilities were also severely affected, as the lone district civil hospital was flooded and filled with debris.

Materials and Methods

The present case study is based on the authors’ own experience of managing a natural disaster caused by the flash floods. The paper presents a firsthand description of a disaster and its prompt management. The data was collected from the records of the district civil administration, the civil hospital, and the Army Hospital, Leh. The approach used was both quantitative as well as qualitative. It included data collection from the primary sources of the district collectorate, interviews with the district civil administration and army officials who organized rescue operations, restoration of communication, and transport, mass casualty management, and informal discussions with local residents.

Disaster management strategies

Three core disaster management strategies were adopted to manage the crisis. These strategies included: i) Response, rescue, and relief operations, ii) Mass casualty management, and iii) Rehabilitation.

Response, rescue, and relief operations

The initial response was carried out immediately by the Government of India. The rescue and relief work was led by the Indian Army, along with the State Government of Jammu and Kashmir, Central Reserve Police Force (CRPF), and Indo-Tibetan Border Police (ITBP). The Indian Army activated the disaster management system immediately, which is always kept in full preparedness as per the standard army protocols and procedures.

There were just two hospitals in the area: the government civil hospital (SNM Hospital) and Army Hospital. During the flash floods, the government civil hospital was flooded and rendered dysfunctional. Although the National Disaster Management Act( 1 ) was in place, with the government civil hospital being under strain, the applicability of the act was hampered. The Army Hospital quickly responded through rescue and relief operations and mass casualty management. By dawn, massive search operations were started with the help of civil authorities and local people. The patients admitted in the civil hospital were evacuated to the Army Hospital, Leh in army helicopters.

The runway of Leh airport was cleared up within a few hours after the disaster so that speedy inflow of supplies could be carried out along with the evacuation of the casualties requiring tertiary level healthcare to the Army Command Hospital in Chandigarh. The work to make the roads operational was started soon after the disaster. The army engineers had started rebuilding the collapsed bridges by the second day. Though the main mobile network was dysfunctional, the other mobile network (Airtel) still worked with limited connectivity in the far-flung areas of the mountains. The army communication system was the main and the only channel of communication for managing and coordinating the rescue and relief operations.

Mass casualty management

All casualties were taken to the Army Hospital, Leh. Severely injured people were evacuated from distant locations by helicopters, directly landing on the helipad of the Army Hospital. In order to reinforce the medical staff, nurses were flown in from the Super Specialty Army Hospital (Research and Referral), New Delhi, to handle the flow of casualties by the third day following the disaster. National Disaster Cell kept medical teams ready in Chandigarh in case they were required. The mortuary of the government civil hospital was still functional where all the dead bodies were taken, while the injured were handled by Army Hospital, Leh.

Army Hospital, Leh converted its auditorium into a crisis expansion ward. The injured started coming in around 0200 hrs on August 6, 2010. They were given first aid and were provided with dry clothes. A majority of the patients had multiple injuries. Those who sustained fractures were evacuated to Army Command Hospital, Chandigarh, by the Army's helicopters, after first aid. Healthcare staff from the government civil hospital joined the Army Hospital, Leh to assist them. In the meanwhile, medical equipment and drugs were transferred from the flooded and damaged government civil hospital to one of the nearby buildings where they could receive the casualties. By the third day following the disaster, the operation theatre of the government civil hospital was made functional. Table 1 gives the details of the patients admitted at the Army Hospital.

Admissions in the Army Hospital, Leh

The analysis of the data showed that majority of the people who lost their lives were mainly local residents (49.6%). Among the dead, there were 10.3% foreign nationals as well [ Table 2 ]. The age-wise analysis of the deaths showed that the majority of deaths were reported in the age group 26–50 years, accounting for 44.4% of deaths, followed by 11–25 year group with 22.2% deaths.

Number of deaths according to status of residence

The gender analysis showed that 61.5% were males among the dead, and 38.5% were females. A further analysis showed that more females died in <10 years and ≥50 years age group, being 62.5% and 57.1%, respectively [ Table 3 ].

Age and sex distribution of deaths

Victims who survived the disaster were admitted to the Army Hospital, Leh. Over 90% of them suffered traumatic injuries, with nearly half of them being major traumatic injuries. About 3% suffered from cold injuries and 6.7% as medical emergencies [ Table 4 ].

Distribution according to nature of casualty among the hospitalized victims

Rehabilitation

Shelter and relief.

Due to flash floods, several houses were destroyed. The families were transferred to tents provided by the Indian Army and government and non-government agencies. The need for permanent shelter for these people emerged as a major task. The Prime Minister of India announced Rs. 100,000 as an ex-gratia to the next of kin of each of those killed, and relief to the injured. Another Rs. 100,000 each would be paid to the next of kin of the deceased from the Chief Minister's Relief Fund of the State Government.

Supply of essential items

The Army maintains an inventory of essential medicines and supplies in readiness as a part of routing emergency preparedness. The essential non-food items were airlifted to the affected areas. These included blankets, tents, gum boots, and clothes. Gloves and masks were provided for the persons who were working to clear the debris from the roads and near the affected buildings.

Water, sanitation, and hygiene

Public Health is seriously threatened in disasters, especially due to lack of water supply and sanitation. People having lost their homes and living in temporary shelters (tents) puts a great strain on water and sanitation facilities. The pumping station was washed away, thus disrupting water supply in the Leh Township. A large number of toilets became non-functional as they were filled with silt, as houses were built at the foothills of the Himalayan Mountains. Temporary arrangements of deep trench latrines were made while the army engineers made field flush latrines for use by the troops.

Water was stagnant and there was the risk of contamination by mud or dead bodies buried in the debris, thus making the quality of drinking water questionable. Therefore, water purification units were installed and established. The National Disaster Response Force (NDRF) airlifted a water storage system (Emergency Rescue Unit), which could provide 11,000 L of pure water. Further, super-chlorination was done at all the water points in the army establishments. To deal with fly menace in the entire area, anti-fly measures were taken up actively and intensely.

Food and nutrition

There was an impending high risk of food shortage and crisis of hunger and malnutrition. The majority of food supply came from the plains and low-lying areas in North India through the major transport routes Leh–Srinagar and Leh–Manali national highways. These routes are non-functional for most part of the winter. The local agricultural and vegetable cultivation has always been scanty due to extreme cold weather. The food supplies took a further setback due to the unpredicted heavy downpour. Food storage facilities were also flooded and washed away. Government agencies, nongovernmental organizations, and the Indian Army immediately established food supply and distribution system in the affected areas from their food stores and airlifting food supplies from other parts of the country.

There was a high risk of water-borne diseases following the disaster. Many human bodies were washed away and suspected to have contaminated water bodies. There was an increased fly menace. There was an urgent need to prevent disease transmission due to contaminated drinking water sources and flies. There was also a need to rehabilitate people who suffered from crush injuries sustained during the disaster. The public health facilities, especially, the primary health centers and sub-health centers, were not adequately equipped and were poorly connected by roads to the main city of Leh. Due to difficult accessibility, it took many hours to move casualties from the far-flung areas, worsening the crisis and rescue and relief operations. The population would have a higher risk of mental health problems like post-traumatic stress disorder, deprivation, and depression. Therefore, relief and rehabilitation would include increased awareness of the symptoms of post-traumatic stress disorder and its alleviation through education on developing coping mechanisms.

Economic impact

Although it would be too early to estimate the impact on economy, the economy of the region would be severely affected due to the disaster. The scanty local vegetable and grain cultivation was destroyed by the heavy rains. Many houses were destroyed where people had invested all their savings. Tourism was the main source of income for the local people in the region. The summer season is the peak tourist season in Ladakh and that is when the natural disaster took place. A large number of people came from within India and other countries for trekking in the region. Because of the disaster, tourism was adversely affected. The disaster would have a long-term economic impact as it would take a long time to rebuild the infrastructure and also to build the confidence of the tourists.

The floods put an immense pressure and an economic burden on the local people and would also influence their health-seeking behavior and health expenditure.

Political context

The disaster became a security threat. The area has a high strategic importance, being at the line of control with China and Pakistan. The Indian Army is present in the region to defend the country's borders. The civil administration is with the Leh Autonomous Hill Development Council (LAHDC) under the state government of Jammu and Kashmir.

Conclusions

It is impossible to anticipate natural disasters such as flash floods. However, disaster preparedness plans and protocols in the civil administration and public health systems could be very helpful in rescue and relief and in reducing casualties and adverse impact on the human life and socio economic conditions.( 2 ) However, the health systems in India lack such disaster preparedness plans and training.( 3 ) In the present case, presence of the Indian Army that has standard disaster management plans and protocols for planning, training, and regular drills of the army personnel, logistics and supply, transport, and communication made it possible to immediately mount search, rescue, and relief operations and mass casualty management. Not only the disaster management plans were in readiness, but continuous and regular training and drills of the army personnel in rescue and relief operations, and logistics and communication, could effectively facilitate the disaster management operations.

Effective communication was crucial for effective coordination of rescue and relief operations. The Army's communication system served as an alternative communication channel as the public communication and mobile network was destroyed, and that enabled effective coordination of the disaster operations.

Emergency medical services and healthcare within few hours of the disaster was critical to minimize deaths and disabilities. Preparedness of the Army personnel, especially the medical corps, readiness of inventory of essential medicines and medical supplies, logistics and supply chain, and evacuation of patients as a part of disaster management protocols effectively launched the search, rescue, and relief operations and mass casualty reduction. Continuous and regular training and drills of army personnel, health professionals, and the community in emergency rescue and relief operations are important measures. Emergency drill is a usual practice in the army, which maintains the competence levels of the army personnel. Similar training and drill in civil administration and public health systems in emergency protocols for rescue, relief, mass casualty management, and communication would prove very useful in effective disaster management to save lives and restore health of the people.( 2 – 4 )

Lessons learnt and recommendations

Natural disasters not only cause a large-scale displacement of population and loss of life, but also result in loss of property and agricultural crops leading to severe economic burden.( 3 – 6 ) In various studies,( 3 , 4 , 7 , 8 ) several shortcomings have been observed in disaster response, such as, delayed response, absence of early warning systems, lack of resources for mass evacuation, inadequate coordination among government departments, lack of standard operating procedures for rescue and relief, and lack of storage of essential medicines and supplies.

The disaster management operations by the Indian Army in the natural disaster offered several lessons to learn. The key lessons were:

• Response time is a critical attribute in effective disaster management. There was no delay in disaster response by the Indian Army. The rescue and relief operations could be started within 1 h of disaster. This was made possible as the Army had disaster and emergency preparedness plans and protocols in place; stocks of relief supplies and medicines as per standard lists were available; and periodic training and drill of the army personnel and medical corps was undertaken as a routine. The disaster response could be immediately activated.
• There is an important lesson to be learned by the civil administration and the public health system to have disaster preparedness plans in readiness with material and designated rescue officers and workers.
• Prompt activation of disaster management plan with proper command and coordination structure is critical. The Indian Army could effectively manage the disaster as it had standard disaster preparedness plans and training, and activated the system without any time lag. These included standard protocols for search, rescue, and evacuation and relief and rehabilitation. There are standard protocols for mass casualty management, inventory of essential medicines and medical supplies, and training of the army personnel.
• Hospitals have always been an important link in the chain of disaster response and are assuming greater importance as advanced pre-hospital care capabilities lead to improved survival-to-hospital rate.( 9 ) Role of hospitals in disaster preparedness, especially in mass casualty management, is important. Army Hospital, Leh emergency preparedness played a major role in casualty management and saving human lives while the civil district hospital had become dysfunctional due to damage caused by floods. The hospital was fully equipped with essential medicines and supplies, rescue and evacuation equipments, and command and communication systems.
• Standard protocols and disaster preparedness plans need to be prepared for the civil administration and the health systems with focus on Quick Response Teams inclusive of healthcare professionals, rescue personnel, fire-fighting squads, police detachments, ambulances, emergency care drugs, and equipments.( 10 ) These teams should be trained in a manner so that they can be activated and deployed within an hour following the disaster. “TRIAGE” has to be the basic working principle for such teams.
• Effective communication system is of paramount importance in coordination of rescue and relief operations. In the present case study, although the main network with the widest connectivity was extensively damaged and severely disrupted, the army's communication system along with the other private mobile network tided over the crisis. It took over 10 days for reactivation of the main mobile network through satellite communication system. Thus, it is crucial to establish the alternative communication system to handle such emergencies efficiently and effectively.( 2 , 11 )
• Disaster management is a multidisciplinary activity involving a number of departments/agencies spanning across all sectors of development.( 2 ) The National Disaster Management Authority of India, set up under National Disaster Management Act 2005,( 1 ) has developed disaster preparedness and emergency protocols. It would be imperative for the civil administration at the state and district levels in India to develop their disaster management plans using these protocols and guidelines.
• Health system's readiness plays important role in prompt and effective mass casualty management.( 2 ) Being a mountainous region, the Ladakh district has difficult access to healthcare, with only nine Primary Health Centers and 31 Health Sub-Centers.( 12 ) There is a need for strengthening health systems with focus on health services and health facility network and capacity building. More than that, primary healthcare needs to be augmented to provide emergency healthcare so that more and more lives can be saved.( 7 )
• Training is an integral part of capacity building, as trained personnel respond much better to different disasters and appreciate the need for preventive measures. Training of healthcare professionals in disaster management holds the key in successful activation and implementation of any disaster management plan. The Army has always had standard drills in all its establishments at regular intervals, which are periodically revised and updated. The civil administration and public health systems should regularly organize and conduct training of civil authorities and health professionals in order to be ready for action.( 1 – 4 )
• Building confidence of the public to avoid panic situation is critical. Community involvement and awareness generation, particularly that of the vulnerable segments of population and women, needs to be emphasized as necessary for sustainable disaster risk reduction. Increased public awareness is necessary to ensure an organized and calm approach to disaster management. Periodic mock drills and exercise in disaster management protocols in the general population can be very useful.( 1 , 3 , 4 )

Source of Support: Nil

Conflict of Interest: None declared.

• 0 Shopping Cart

Kerala flood case study

Kerala flood case study.

Kerala is a state on the southwestern Malabar Coast of India. The state has the 13th largest population in India. Kerala, which lies in the tropical region, is mainly subject to the humid tropical wet climate experienced by most of Earth’s rainforests.

A map to show the location of Kerala

Eastern Kerala consists of land infringed upon by the Western Ghats (western mountain range); the region includes high mountains, gorges, and deep-cut valleys. The wildest lands are covered with dense forests, while other areas lie under tea and coffee plantations or other forms of cultivation.

The Indian state of Kerala receives some of India’s highest rainfall during the monsoon season. However, in 2018 the state experienced its highest level of monsoon rainfall in decades. According to the India Meteorological Department (IMD), there was 2346.3 mm of precipitation, instead of the average 1649.55 mm.

Kerala received over two and a half times more rainfall than August’s average. Between August 1 and 19, the state received 758.6 mm of precipitation, compared to the average of 287.6 mm, or 164% more. This was 42% more than during the entire monsoon season.

The unprecedented rainfall was caused by a spell of low pressure over the region. As a result, there was a perfect confluence of the south-west monsoon wind system and the two low-pressure systems formed over the Bay of Bengal and Odisha. The low-pressure regions pull in the moist south-west monsoon winds, increasing their speed, as they then hit the Western Ghats, travel skywards, and form rain-bearing clouds.

Further downpours on already saturated land led to more surface run-off causing landslides and widespread flooding.

Kerala has 41 rivers flowing into the Arabian Sea, and 80 of its dams were opened after being overwhelmed. As a result, water treatment plants were submerged, and motors were damaged.

In some areas, floodwater was between 3-4.5m deep. Floods in the southern Indian state of Kerala have killed more than 410 people since June 2018 in what local officials said was the worst flooding in 100 years. Many of those who died had been crushed under debris caused by landslides. More than 1 million people were left homeless in the 3,200 emergency relief camps set up in the area.

Parts of Kerala’s commercial capital, Cochin, were underwater, snarling up roads and leaving railways across the state impassable. In addition, the state’s airport, which domestic and overseas tourists use, was closed, causing significant disruption.

Local plantations were inundated by water, endangering the local rubber, tea, coffee and spice industries.

Schools in all 14 districts of Kerala were closed, and some districts have banned tourists because of safety concerns.

Maintaining sanitation and preventing disease in relief camps housing more than 800,000 people was a significant challenge. Authorities also had to restore regular clean drinking water and electricity supplies to the state’s 33 million residents.

Officials have estimated more than 83,000km of roads will need to be repaired and that the total recovery cost will be between £2.2bn and $2.7bn.

Indians from different parts of the country used social media to help people stranded in the flood-hit southern state of Kerala. Hundreds took to social media platforms to coordinate search, rescue and food distribution efforts and reach out to people who needed help. Social media was also used to support fundraising for those affected by the flooding. Several Bollywood stars supported this.

Some Indians have opened up their homes for people from Kerala who were stranded in other cities because of the floods.

Thousands of troops were deployed to rescue those caught up in the flooding. Army, navy and air force personnel were deployed to help those stranded in remote and hilly areas. Dozens of helicopters dropped tonnes of food, medicine and water over areas cut off by damaged roads and bridges. Helicopters were also involved in airlifting people marooned by the flooding to safety.

More than 300 boats were involved in rescue attempts. The state government said each boat would get 3,000 rupees (£34) for each day of their work and that authorities would pay for any damage to the vessels.

As the monsoon rains began to ease, efforts increased to get relief supplies to isolated areas along with clean up operations where water levels were falling.

Millions of dollars in donations have poured into Kerala from the rest of India and abroad in recent days. Other state governments have promised more than $50m, while ministers and company chiefs have publicly vowed to give a month’s salary.

Even supreme court judges have donated $360 each, while the British-based Sikh group Khalsa Aid International has set up its own relief camp in Kochi, Kerala’s main city, to provide meals for 3,000 people a day.

International Response

In the wake of the disaster, the UAE, Qatar and the Maldives came forward with offers of financial aid amounting to nearly £82m. The United Arab Emirates promised $100m (£77m) of this aid. This is because of the close relationship between Kerala and the UAE. There are a large number of migrants from Kerala working in the UAE. The amount was more than the $97m promised by India’s central government. However, as it has done since 2004, India declined to accept aid donations. The main reason for this is to protect its image as a newly industrialised country; it does not need to rely on other countries for financial help.

Google provided a donation platform to allow donors to make donations securely. Google partners with the Center for Disaster Philanthropy (CDP), an intermediary organisation that specialises in distributing your donations to local nonprofits that work in the affected region to ensure funds reach those who need them the most.

Google Kerala Donate

Tales of humanity and hope

Check your understanding.

Premium Resources

Please support internet geography.

If you've found the resources on this page useful please consider making a secure donation via PayPal to support the development of the site. The site is self-funded and your support is really appreciated.

Related Topics

Use the images below to explore related GeoTopics.

River flooding and management

Topic home, wainfleet floods case study, share this:.

• Click to share on Twitter (Opens in new window)
• Click to share on Facebook (Opens in new window)
• Click to share on Pinterest (Opens in new window)
• Click to email a link to a friend (Opens in new window)
• Click to share on WhatsApp (Opens in new window)
• Click to print (Opens in new window)

If you've found the resources on this site useful please consider making a secure donation via PayPal to support the development of the site. The site is self-funded and your support is really appreciated.

Search Internet Geography

Latest Blog Entries

Pin It on Pinterest

• Click to share
• Print Friendly

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

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• 13 December 2023

The climate disaster strikes: what the data say

• Shannon Hall 0

Shannon Hall is a freelance science journalist in Colorado.

You can also search for this author in PubMed   Google Scholar

You have full access to this article via your institution.

Floods similar to this one at Gurugram in August are becoming more common in India. Credit: Parveen Kumar/Hindustan Times via Getty

India is facing a harsh new reality. In Kerala, near the southern tip of the subcontinent, floods frequently inundate farmlands and pour into households. In the sea off Mumbai, fewer fish are caught owing to an increase in cyclones and heavier rainfall. Farther north in Rajasthan, cattle farmers face rising temperatures and water shortages. In the northern state of Uttarakhand, forest fires are more frequent today than in the past. And in Satabhaya, a coastal village off the Bay of Bengal, rising sea levels and coastal erosion have forced hundreds of families to leave. All these problems have, of course, been exacerbated by climate change.

Since the pre-industrial period, India’s rising temperature has caused the Himalayan glaciers to retreat, droughts to worsen, flash floods and landslides to increase and cyclones to intensify across the country’s 7,500-kilometre-long coastline. These events are now occurring on a regular basis (see ‘India’s extreme weather events’). In the first nine months of 2022, for example, India had an extreme weather event nearly every single day. “It can be a year-long swaying of the climate sledgehammer,” says Raghu Murtugudde, a climate researcher at the Indian Institute of Technology Bombay. “We now have disasters in pretty much every season.”

Source: Centre for Science and Environment

Worrying change

In 2020, India’s Ministry of Earth Sciences published the first climate-change assessment report for the country 1 . It was based on data from 1901 to 2018 and showed that the country’s average temperature in that period had risen by roughly 0.7 °C, bringing with it extreme weather patterns.

March 2022, for example, was the hottest March on the subcontinent since records began in 1901, with temperatures of more than 40 °C combined with a drought as rainfall slowed to about one-third of normal rates. The two placed enormous stress on agriculture, with some regions experiencing a 30% reduction in their harvests, forcing the country to restrict its wheat exports. The increased demand on the electricity grid, partly caused by the increased use of air-conditioning units, caused the worst electricity shortage in more than six years. At least two states, Tripura in the northeast and West Bengal in the east, ordered schools to close. The extreme temperatures led to the deaths of at least 90 people. “It really shocked the country,” says Chandni Singh, who works on climate-change adaptation at the Indian Institute for Human Settlements, a national education institution based in Bengaluru.

Nature Spotlight: India

Heatwaves often have cascading effects on other hazards, leading to wildfires that destroy crops and release dangerous smoke, as well as accelerating the spread of infectious diseases and causing death. They can also lead to water shortages. Warmer air can hold more moisture, leading to lengthy dry periods before all that moisture gets dumped in a matter of days or even hours.

The 2020 assessment 1 shows that the total seasonal rainfall had dropped, but when it does rain, it pours, causing floods and landslides. In July this year, for example, torrential rain tore across northern India, causing landslides and flash floods, killing at least 22 people. And in August, heavy monsoon rains triggered landslides in India’s Himalayan region, leaving nearly 50 people dead.

Events such as these have become the new normal. A climate assessment report published in November 2022 by the Centre for Science and Environment, a public-interest research and advocacy organization based in New Delhi, analysed the first nine months of 2022. It found that India experienced extreme weather events, ranging from heatwaves to cyclones, for 88% of that time period 2 . These disasters claimed 2,755 lives, affected 1.8 million hectares of crops, destroyed around 400,000 homes and killed almost 70,000 livestock.

“This is the watermark of climate change,” the report states 2 . “It is not about the single event but about the increased frequency of the events — an extreme event we saw once every 100 years has now begun to occur every five years or less. Worse, it is now all coming together — each month is breaking a new record. This in turn is breaking the backs of the poorest who are worst impacted and are fast losing their capacities to cope with these repeated and frequent events.”

Vulnerability

Part of the problem is that India is positioned between the melting Himalayas and three rapidly warming bodies of water. The Arabian Sea warmed by 2 °C from 1982 to 2019, pumping moisture into northwest India. But the mountains squeeze the moisture from these damp winds as if they were a sponge, dumping rain and causing floods and landslides. The temperature of the sea now hovers at around 28 °C, which is warm enough for cyclones to form. A 2021 study shows that there has been a 50% increase in the number of cyclones in the past 40 years from the Arabian Sea 3 .

There is “one extreme weather event after another — whether it’s the monsoon or the tropical cyclones”, says Roxy Mathew Koll, an oceanographer at the Indian Institute of Tropical Meteorology in Pune.

To make matters worse, those cyclones now intensify rapidly, strengthening to dangerous storms in a matter of hours. Cyclone Amphan, for example, which caused widespread damage in eastern India in May 2020, initially had wind speeds of 140 kilometres per hour but quickly strengthened to 215 kilometres per hour, switching from a category 1 cyclone to a category 4 cyclone in less than a day. “We go to sleep thinking that it’s a big cyclone and by the time we wake up, our roofs are gone,” Koll says. “There is no time to respond.”

Women in Maharashtra state draw water from a well that is almost empty. Credit: Ritesh Shukla/Getty

It is clear that India’s unique locale places it at the mercy of climate change, but Singh points out that it is societal and economic factors that leave the Indian population with little protection. For example, in a heatwave, someone who can remain indoors with a cooling system is much less exposed than is a street vendor who needs to work outside — but fewer than 10% of Indians own air-conditioning. Furthermore, India’s booming population will place further demand on food and water, and the sheer number of people living in vulnerable areas (particularly in coastal regions) will rise, meaning climate change will take an even greater toll.

Mumbai is particularly vulnerable, Koll says. It is already one of the most populous cities in the world and its population is likely to double to around 40 million by 2050. By that time, climate projections suggest a global temperature increase of 2 °C, causing worse monsoons, cyclones, storm surges, heatwaves and increased humidity in the city. “The impact at that time is unimaginable, even for climate scientists like me,” he says.

Managing the fallout

The 2020 assessment 1 projected that the average temperature in India will increase by 4.4 °C by the end of the century. That will cause summer heatwaves to be 3–4 times more common and twice as long. A study by the Climate Impact Lab at the Tata Centre for Development, University of Chicago, predicts that there will be one million deaths a year from extreme heat in India by the end of the century if greenhouse emissions continue at their current level 4 .

This means India needs to adapt to climate change. “We are headed to a very, very warm and hot world,” says Karthik Ganesan, a fellow at the Council on Energy, Environment and Water, a non-profit policy-research institution in New Delhi. “India’s effort must go towards figuring out solutions and managing the fallout of climate change.”

In 2008, the Indian government launched the National Action Plan for Climate Change in an effort to do just that. The country has since poured money into hundreds of different adaptation schemes, from more-efficient irrigation systems to early-warning systems. The latter are crucial. If the government knows a cyclone is coming, for example, it can arrange an evacuation that can save lives. Before a drought, farmers can plan their irrigation effectively. And hospitals will have time to prepare for an influx of patients before a heatwave.

Some of these adaptation schemes have already made an impact. In 2013, Ahmedabad, a city north of Mumbai in western India, implemented the first heat action plan across India and south Asia. After a six-day heatwave in 2010 that reached 46.8 °C and caused an extra 1,344 deaths (a 43.1% jump above the baseline death rate), the plan’s main goal was to alert the populations most at risk. But it also alerted government agencies, health officials, emergency responders, local community groups and media outlets. And it provided training to health-care professionals to help them prevent and manage heat-related cases. A later study estimated that 2,380 deaths were avoided in the two years after the plan was put into action 5 . Today, 30 similar plans are in place across the country.

Heat action plans are not the only success story. In 1999, the Odisha cyclone, which hit the east coast south of Kolkata, peaked with winds of 260 kilometres per hour and killed nearly 10,000 people (although some estimates suggest there were as many as 30,000 fatalities). With the help of improved forecasts today, cyclones of similar intensity kill dozens of people, not thousands, Koll says.

The study that analysed the first nine months of 2022 also found that fatalities from cyclones are increasingly rare 2 . Cyclones that devastated nearly 100,000 hectares of land resulted in only two deaths. “This is because of the laudable work” done by the India Meteorological Department in cyclone forecasting, so “there is adequate warning to governments”, the report states.

Despite these advancements, Singh argues that many of the adaptation schemes are insufficient, mainly because the country is so large with so many different hazards. It also struggles to think holistically about many of these issues, she says. For example, Indian farmers have started using drought-resilient seed, but the government has not reformed the agrarian system as a whole. This means that farmers will simply run into other problems down the line, such as a lack of refrigerated trucks to ensure that the produce reaches the consumer in a decent condition.

Singh says more fundamental change is needed. “We cannot be thinking of endlessly adapting to heatwaves or endlessly raising houses on stilts. We need to make deep changes in how we run the world.”

India has invested in solar and wind power . It has committed to the Paris agreement to reduce its carbon intensity. And it has increased forest regeneration. These steps should help to mitigate the worst effects of climate change, although it needs other countries to take similar steps as well. As Singh says: “This is just a horrific trailer of what’s to come.”

Nature 624 , S26-S28 (2023)

doi: https://doi.org/10.1038/d41586-023-03910-w

This article is part of Nature Spotlight: India , an editorially independent supplement. Advertisers have no influence over the content.

Krishnan, R. et al. (eds) Assessment of Climate Change Over the Indian Region (Springer, 2020).

Google Scholar  

Pandey, K. & Sengupta, R. Climate India 2022: An Assessment of Extreme Weather Events (Centre for Science and Environment, 2022).

Deshpande, M. et al. Clim. Dyn. 57 , 3545–3567 (2021).

Article   Google Scholar  

Climate Impact Lab. Climate Change and Heat-Induced Mortality in India (Tata Centre for Development, 2019).

Hess, J. J. et al. J. Environ. Public Health 2018 , 7973519 (2018).

Article   PubMed   Google Scholar  

Download references

Related Articles

• Climate change
• Developing world

Academics say flying to meetings harms the climate — but they carry on

News 13 SEP 24

How to change people’s minds about climate change: what the science says

News 06 SEP 24

Massive Attack’s science-led drive to lower music’s carbon footprint

Career Feature 04 SEP 24

How to support Indigenous Peoples on biodiversity: be rigorous with data

Editorial 10 SEP 24

No basis for claim that 80% of biodiversity is found in Indigenous territories

Comment 04 SEP 24

African scientists must not be priced out of mental-health research

World View 03 SEP 24

Energy storage overcapacity can cause power system instability and blackouts, too

Correspondence 10 SEP 24

Live music is a major carbon sinner — but it could be a catalyst for change

Editorial 04 SEP 24

Inside China’s race to lead the world in nuclear fusion

News Feature 28 AUG 24

Faculty Positions at SUSTech School of Medicine

SUSTech School of Medicine offers equal opportunities and welcome applicants from the world with all ethnic backgrounds.

Shenzhen, Guangdong, China

Southern University of Science and Technology, School of Medicine

Postdoctoral Fellowships Worldwide

IBSA Foundation for scientific research offers 6 fellowships offers of € 32.000 to young researchers under 40 years.

The call is open to people from research institutes and universities from all over the world.

IBSA Foundation for scientific research

Staff Scientist - Immunology

Staff Scientist- Immunology

Houston, Texas (US)

Baylor College of Medicine (BCM)

Institute for Systems Genetics, Tenure Track Faculty Positions

The Institute for Systems Genetics at NYU Langone Health has tenure track faculty positions (assistant professor level) at the new SynBioMed Center.

New York City, New York (US)

NYU Langone Health

Faculty Position

The Institute of Cellular and Organismic Biology (ICOB), Academia Sinica, Taiwan, is seeking candidates to fill multiple tenure-track faculty position

Taipei (TW)

Institute of Cellular and Organismic Biology, Academia Sinica

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

Quick links

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

• News, Stories & Speeches
• Get Involved
• Structure and leadership
• Committee of Permanent Representatives
• UN Environment Assembly
• Funding and partnerships
• Policies and strategies
• Evaluation Office
• Secretariats and Conventions
• Asia and the Pacific
• Latin America and the Caribbean
• New York Office
• North America
• Climate action
• Nature action
• Chemicals and pollution action
• Digital Transformations
• Disasters and conflicts
• Environment under review
• Environmental law and governance
• Extractives
• Fresh Water
• Green economy
• Ocean, seas and coasts
• Resource efficiency
• Sustainable Development Goals
• Youth, education and environment
• Publications & data

The devastating impact of floods in India—and what can be done

The COVID-19 pandemic has revealed the vulnerability of our global systems when it comes to  environmental, health and economic issues. As the crisis continues, there is an increasing recognition of how multiple economic, social and institutional drivers exacerbate environment risks, including global heating, resilience and human health.

India is one of the most disaster-prone countries in the world , with hydrological (water-related) disasters being among the most frequent and having high mortality and damage costs.

Nature-based solutions offer some of the best ways to mitigate the impacts of flooding.

Ecosystem-based Disaster Risk Reduction (Eco-DRR) is an approach where the regulatory functions of ecosystems (such as forests, wetlands and mangroves) are systematically harnessed to mitigate, prevent, or buffer against disasters.

Eco-DRR recognizes that ecosystems can provide disaster risk reduction services as well as offer other ecosystem services of productive and cultural value, which also contribute to building local resilience to disasters and climate change.

Thanks to funding from the European Commission, the United Nations Environment Programme (UNEP), in collaboration with Partners for Resilience , is focusing on scaling-up Eco-DRR interventions and promoting large-scale implementation of Eco-DRR in Kerala, southern India.

The focus is on developing capacity to undertake ecosystem restoration for DRR as part of the Mahatma Gandhi Rural Employment Guaranteed Scheme, a nationwide programme which employs 2.6 million women in Kerala. The project entails developing training modules, a handbook and undertaking training on ecosystem restoration for DRR with local government technical staff, elected officials and local technical staff.

The Kerala State Disaster Management Authority will act as the main institutional counterpart for UNEP. The Kerala Institute of Local Administration will lead the development of the training materials, handbook and training workshops, drawing on the expertise of national and international experts.

The main aim of this project is to develop different models for demonstrating implementation of Eco-DRR, which can be scaled up using existing programmes, which advance implementation of the Sendai Framework for Disaster Risk Reduction and the Sustainable Development Agenda.

The project, which runs from 2019 to 2021, will be managed by UNEP’s Crisis Management Branch which also houses the secretariat of the Partnership for Environment and Disaster Risk Reduction , a global alliance of 24 international agencies, non-government organizations, and specialist institutes.

“One of the project’s aims is to catalyse public and private investment for scaling up Eco-DRR approaches for poverty alleviation, development, risk reduction and climate change mitigation/ adaptation,” says UNEP’s Country Head Atul Bagai.

Floods account for more than half of climate-related disasters in India and have cost the country over US$50 billion since 1990, according to new research by the Asian Development Bank.

The country had 278 floods from 1980 to 2017 affecting more than 750 million people and causing about US$58.7 billion in losses, according to the International Disasters Database, EM-DAT, 2018 .

“Extreme precipitation and flooding cause large-scale impacts on people, and are further intensified by rapid urbanization, infrastructure expansion, and large numbers of people residing in informal settlements in destitute conditions,” says the study Impacts Of Natural Disasters On Households And Small Businesses In India .

The research analyses the impacts of extreme precipitation on vulnerable households and small and medium-sized enterprises in Mumbai, Chennai, and Puri district and highlights the heterogeneity of flood impacts and their potential to push the poor into a debt trap and further poverty.

“Investments in climate-friendly actions, such as in Kerala, stimulate economies, create employment opportunities, and increase resilience to recurrent environmental and health threats,” says Bagai.

The UN Decade on Ecosystem Restoration 2021–2030 , led by the United Nations Environment Programme, the Food and Agriculture Organization of the United Nations and partners such as the Africa Restoration 100 initiative, the Global Landscapes Forum and the International Union for the Conservation of Nature, covers terrestrial as well as coastal and marine ecosystems. A global call to action, it will draw together political support, scientific research and financial muscle to massively scale up restoration. Help us shape the Decade .

For more information, please contact Atul Bagai: [email protected]

• Fresh water
• Capacity building
• Biodiversity

Further Resources

• What the world can learn from Kerala about how to fight covid-19
• COVID-19 updates from the United Nations Environment Programme

Related Content

Related Sustainable Development Goals

© 2024 UNEP Terms of Use Privacy   Report Project Concern Report Scam Contact Us

• Share full article

Advertisement

Landslide in India Buries Dozens, Killing at Least 25

Days of heavy rain had loosened the soil. India and neighboring Bangladesh have had record rainfall and severe flooding in the past two months.

By Karan Deep Singh

• Published July 1, 2022 Updated July 2, 2022

At least 25 people were killed and more feared dead, after days of heavy rainfall set off a landslide in India’s remote and mountainous northeastern state of Manipur.

It is the latest tragedy in a country that has been plagued by catastrophic rainfall and flooding in recent months. The extreme weather has destroyed communities, forced evacuations and threatened lives.

On Saturday, rescue workers in Manipur were still looking for dozens of people, who were instantly buried under layers of mud and rocks overnight Wednesday, when the landslide occurred in the Noney District. Indian television stations showed rescue personnel carrying mud-covered bodies on stretchers.

More rainfall has made rescue efforts even more challenging, Nongthombam Biren Singh, the chief minister of Manipur State, said on Twitter . He said 25 bodies had been recovered and 18 injured people had been rescued. “38 persons are still missing,” he added.

Many of the people who died and those still trapped under the rubble had been in the area to work on the construction of a railroad station deep in the mountains. Some were soldiers in the Indian Army. Others were railway workers, local villagers and laborers.

“The entire country is deeply saddened by loss of lives,” Mr. Singh said on Friday.

Prime Minister Narendra Modi said on Twitter that he had reviewed the situation in Manipur and had assured Mr. Singh of “all possible support” from the central government. “I pray for the safety of all those affected,” he said. “My thoughts are with the bereaved families.”

Weeks of heavy rainfall from the monsoons have already killed more than 100 people and left millions homeless in India’s northeast and in neighboring Bangladesh. More than 60 people were killed in May during days of flooding, landslides and thunderstorms that left many people without food and drinking water and isolated them by cutting off the internet.

Tying climate change to an extreme weather event requires extensive scientific analysis. But climate change is often a contributing factor.

Scientists have said that India and Bangladesh are particularly vulnerable to climate change because of their proximity to the warm tropical waters of the Indian Ocean and the Bay of Bengal, which are increasingly experiencing heat waves . The rising sea temperatures have led to dry conditions in some parts of the Indian subcontinent and a significant increase in rainfall in other areas, according to a study published in January by the Indian Institute of Tropical Meteorology in Pune.

In India’s northeastern state of Assam, one of the worst affected areas during the pre-monsoon and monsoon season, a paramilitary camp was inundated by floodwaters on Friday after persistent rain over the last three days.

Mr. Singh, the chief minister, said the authorities were expecting bad weather to persist in Manipur. “The situation in the landslide affected area,” he said, “is still serious.”

The India Meteorological Department forecast heavy rainfall on Sunday in at least 14 states, including Assam, Manipur, Meghalaya, Tripura and Nagaland, all in the northeast. The heavy rains delayed flights and submerged roads in India’s capital, New Delhi, on Thursday.

Karan Deep Singh is a reporter and visual journalist based in New Delhi, India. He previously worked for The Wall Street Journal, where he was part of a team that was named a finalist for the 2020 Pulitzer Prize in Investigative Reporting and nominated for a national Emmy Award. More about Karan Deep Singh

• डाउन टू अर्थ
• Print Edition

• Agriculture
• Data Centre
• Young Environmentalist
• Newsletters

A month after India’s deadliest landslide ever, Wayanad villages begin to recover

On July 30, 2024, Wayanad in Kerala witnessed India’s worst-ever landslide, devastating the villages of Punchirimattam, Chooralmala and Mundakkai. The scale of destruction was unimaginable, with over 231 people confirmed dead, while body parts of 218 others have been recovered. After this massive tragedy, life struggles to return to normal, with many areas abandoned and debris still scattered across a once-thriving landscape.

In Chooralmala, the local post office is one of the few structures still operating. Packages pile up daily, mostly addressed to the missing or deceased. Outside, the police patrol to prevent curious disaster tourists from taking selfies, while another team guards ruined homes against theft.

A village shattered

At 2 am on that fateful night, two massive landslides wiped out an eight-kilometre stretch, leaving behind a trail of destruction. Homes are buried under loose soil and rocks, and survivors have been relocated to shelters or rented homes. Many occasionally return to their damaged properties, hoping to salvage belongings. Still, a civil defence official said chances of finding more survivors are slim, after weeks of rescue operations.

Chooralmala postmaster G Shalini reflected on the stack of undelivered packages to this reporter. “Most of these parcels will never reach their recipients — they are either deceased or still missing,” she said. Shalini and her husband, who also serves as a postmaster in Mundakkai, narrowly escaped the disaster as their home flooded. “We were lucky to survive,” she said.

Scattered across three villages lie remnants of lives interrupted: Kitchen utensils, school bags and furniture lie buried under mud and debris. Mud also covers houses, schools and other buildings, with half-cut pillars, muck reaching the bedrooms, and bikes and four-wheelers buried under slush.

Locals report thefts in the aftermath, with one incident involving the recovery of Rs 4 lakh beneath rubble. To deter such activities, police patrols have been increased and only residents with valid identity cards are permitted to return to their properties.

The government’s incident command centre has become the focal point of recovery, verifying the credentials of locals and issuing duplicate documents like Aadhaar and PAN cards, lost to the disaster. Meanwhile, rescue teams and earthmovers remain on standby as heavy rain continues, raising fears of further landslides.

The landslides have severely damaged agriculture in the region — large areas of cardamom, coffee, pepper, tea, coconut, areca nut and banana plantations have been destroyed, resulting in significant economic losses for the region, according to a senior official in the district administration.

Largest landslide in Indian history

The Kerala State Disaster Management Authority recently confirmed the Wayanad landslide as the largest in India’s recorded history. Research showed the event triggered a debris flow of approximately six million cubic meters — enough to fill 2,400 Olympic-sized swimming pools.

The Wayanad disaster was five times larger than the Malpa landslide in Uttarakhand in 1998, which had previously held the record for the biggest debris flow in the country. It was also three hundred times bigger than the 2020 landslide in Pettimudi, near Munnar, Kerala.

Researchers from the Kerala University and the Kerala University of Fisheries and Ocean Studies, in collaboration with the Indian Institute of Science Education and Research-Mohali, conducted a study using photogrammetry and LiDAR technology. Their findings revealed that the landslide originated upstream of the Punnapuzha River, deep within the forested eastern slopes of the Western Ghats.

“The debris avalanche travelled eight kilometres from the landslide’s crown,” explained lead researcher KS Sajinkumar. “This was no ordinary landslide — it was a rock slide that transformed into a debris flow, blocked by Seethamma Kund before unleashing its force as a deadly avalanche.”

The study found that the disaster displaced rocks the size of vehicles, which had been worn smooth by rivers 250 million years ago. These rocks, exposed by previous landslides, had become vulnerable and the extreme rainfall in Wayanad this season only hastened their destruction.

Landslides usually stop once they reach the surface of the rock. In the case of the Chaliyar river, Adin Ishan from Indian Institutes of Science Education and Research, Mohali discovered that weathered rocks beneath the riverbed, which were orientated against the flow of water, allowed water to seep in and accelerate the weathering process. This led to a significant 185 per cent increase in sediment levels in the river during the incident, indicating the large size of the landslip.

Ignored warnings

Adding to the tragedy is the revelation that this disaster could have been prevented. The Hume Centre for Ecology and Wildlife Biology in Kalpetta, which operates over 200 meteorological stations in Wayanad, had issued a landslide warning 16 hours before the event. However, it appears the district collector’s office failed to act on the alert.

Activist MT Thomas filed a Right to Information request, questioning whether the office had received the alert. According to Thomas, the centre used data from its local weather monitoring systems to alert district officials to the possibility of landslides in Mundakkai and the surrounding villages 16 hours before the disaster.

The State Public Information Officer of the Disaster Management Wing at Wayanad Collectorate, however, has denied receiving official warnings, though previous alerts from the Hume Centre had led to timely evacuations, saving lives in earlier disasters.

The nearest weather station in Puthumala had recorded over 200 millimetres of rainfall on July 28 and another 130 mm overnight — significant enough to trigger landslides.

On August 29, at 9 am, the Hume Centre issued a landslide alert due to the risk posed by approximately 600 millimetres of rainfall. In total, the region had received 572 millimetres of rain within 48 hours.

While the Wayanad administration claimed to be unaware of the report, they acknowledged that in the past, they have acted on the Hume Centre’s warnings. In 2020, a prompt response to an alert from the centre saved lives in Mundakkai.

CK Vishnudas, the centre’s director, had shared the information with the District Emergency Operating Cell (DEOC), emphasising the urgency of evacuating residents from Mundakkai and two nearby villages. However, the district administration issued its own warning 14 hours later, on the same day as the Hume Centre's alert, but it did not mention the need for evacuation.

Thomas stated that he will file an appeal over the issue.

Schools reopen

A month after the disaster, life attempts to move forward. More than 600 students have returned to school, though their classes have been relocated to safer areas. Both the Government Vocational Higher Secondary School in Chooralmala and the Government Lower Primary School in Mundakkai were destroyed and at least 53 children from the two schools died.

Pupils now attend classes in Meppadi, with many receiving new uniforms and study materials to replace what was lost.

For these children, education is a welcome distraction from the trauma. “We won’t talk about the landslides in the classroom,” said headmaster K Unnikrishnan. “Our focus is on helping them adjust.”

Teachers have been trained in counselling techniques and the children are receiving ongoing therapy, according to district educational officer BC Bijesh.

One of the most heartbreaking stories is that of Avanthika Prashob, a student who survived but lost her entire family in the landslides. Her counsellors have advised not to tell her the full extent of her loss just yet, as she continues to recover from her injuries while living with her uncle.

Rehabilitation and delayed assistance

Prime Minister Narendra Modi visited the landslide-hit regions shortly after the disaster and the state government submitted a detailed memorandum for central assistance. However, despite promises, aid has yet to reach the victims.

In the meantime, the state government is pushing forward with plans to build new townships for the displaced families. These will include 1,000 square-foot homes and small plots of land for cultivation. Philanthropists have also offered support, donating land and financial aid.

As Wayanad struggles to rebuild, its residents are left grappling with loss — of loved ones, homes and livelihoods. And while efforts to restore normalcy continue, the scale of the disaster will be felt for years to come.

Related Stories

• Brief Report
• Open access
• Published: 13 July 2022

The tale of three landslides in the Western Ghats, India: lessons to be learnt

• R. S. Ajin 1 ,
• D. Nandakumar 2 ,
• A. Rajaneesh 3 ,
• T. Oommen 4 ,
• Yunus P. Ali 5 &
• K. S. Sajinkumar 3  

Geoenvironmental Disasters volume  9 , Article number:  16 ( 2022 ) Cite this article

9107 Accesses

18 Citations

12 Altmetric

Metrics details

In recent years, landslides have become a typical monsoon calamity in the Western Ghats region of Kerala, India. In addition to property damage, heavy rainfall (36% above normal) and multiple landslides (4728) killed 48 people in 2018. This tendency continued throughout the monsoon seasons of 2019, 2020, and 2021, resulting in the deaths of over 100 people. Anomalous precipitation is ascribed to the frequent development of low-pressure in the surrounding oceans. Using ground real data and satellite imagery, we evaluated the features of three large landslides in the state of Kerala, which occurred during the monsoon season of 2021. Our investigation found that the Kokkayar landslide was triggered by anthropogenic-related agricultural activities, the Plappally landslide by geomorphic and tectonic processes as well as human involvement, and the Kavali landslide by forest fragmentation with dense vegetation on thin soil. The triggering mechanism for all three of these landslides, however, is the intense rainfall of 266 mm in less than 24 h. Thus, an accurate and precise forecast of rainfall can be used to define a threshold for an early warning, which will be vital for saving lives.

Introduction

Catastrophic landslides have become a common monsoonal phenomenon in India’s southwest state of Kerala, which is located in the foothills of the prominent mountain chain, the Western Ghats. The anomalous rainfall of 2018, which was about 36% more than the normal rainfall (Vishnu et al. 2019 ), triggered 4728 landslides (Hao et al. 2020 ) and killed 48 people. These landslides occurred in a single storm-event i.e., 16th August 2018. The following years saw further landslides, with the monsoon season of 2019 witnessing disastrous landslides such as the one at Puthumala, which killed 17 people, and the Kavalappara, which killed another 59 people (Sajinkumar and Oommen 2020 ; Wadhawan et al. 2020 ). Both these landslides occurred on 8th August 2019. The Pettimudi landslide of 6th August 2020 was the most tragic one that killed 70 people and devastated several hutments in a tea plantation region (Achu et al. 2021 ; Sajinkumar and Oommen 2021 ). Year 2021 also experienced cataclysmic landslides on 16th October with the most disastrous ones being at Kokkayar in Idukki district and Plappally and Kavali, near Koottickal in Kottayam district. All these devastating landslides that occurred since 2018 showed an uneven geographic distribution (Fig.  1 a, b), pointing to the possibility that many parts of the Western Ghats are susceptible to landslides, though these landslides are located along the same valley (Fig.  1 c). In this study, we narrate the ground real data and interpretation of high-resolution remotely sensed images of the three landslides- Kokkayar, Plappally and Kavali (Fig.  2 , a, b, c) that occurred in 2021. We also employed ethnographic techniques, such as in-depth interviews with elderly impacted individuals, to learn about their shared experiences. These three landslides are amongst the tens of landslides in the vicinity of the study area (Fig.  3 ). The reason for selecting these three landslides is because of their catastrophic nature resulting in many human casualties. We believe that the narrative of these three landslides applies to other landslides that occurred in the immediate vicinity of this area.

(Source: Google Earth) ( b ) Study area with elevation map draped over hill shade map showing major landslides since 2018 (Elevation data is ALOS PALSAR) ( c ) Google Earth image showing the spatial distribution of these three landslides along a valley

Location map ( a ) South India

Field photos of ( a ) Kokkayar landslide ( b ) Plappally landslide ( c ) Kavali landslide

A distant view of the hills in the study area showing several landslides

Site and situation of the landslides

Kokkayar landslide.

Kokkayar landslide (9°34′21''N; 76°53′13''E) of Peermade taluk in the Idukki district of Kerala has killed seven people and completely destroyed seven houses. The dimension of this landslide is 500 m (length) × 40 m (avg. width) × 1 m (avg. thickness). Rubber plantations predominantly occupy the area with intermittent clusters of mixed vegetation. The area is utilized for agriculture through terrace cultivation with the cut slope protected by rubble masonry wall. Rain pits were constructed on this slope. Houses are constructed by the cut and fill method but without any support in the cut slope. Most of the houses have dug wells and the depth to water level is shallow (< 2 m) whereas during the landslide these were found overflowing (as per local witness), pointing to the fully saturated column of soil. Numerous springs spout from this area (Fig.  4 a). This spouting phenomenon existed before landslides because dwellings have drains to flush away storm water (Fig.  4 b). These observations indicate that a seasonal first and/or second-order stream flows through this area, which might have been modified during the course of agriculture and/or habitation. A few fresh gullies have been formed, to which water is now confined.

a Spouting of spring at Kokkayar landslide ( b ) A demolished house having provisions for draining storm water ( c ) A highly-weathered joint in the country hornblende biotite gneiss ( d ) Soil profile showing dislodged material, lateritic soil, saprock and weathered bedrock

The in-depth interviews with the local people revealed that the vegetation, mainly rubber trees were clear-felled after slaughter tapping a few years prior to the event. Contour bunding and rain-pits were made prior to replanting the rubber saplings. These interventions seem to have taken place ignoring the natural hydrological requirement of letting the first/second order streams to have its free flow channels. Such interventions may have contributed to destabilizing of soil on the slopes.

The area is characterized by outcrops of hornblende biotite gneiss. The general trend of this foliated rock is 173°/35 W. The preponderance of feldspar in this rock and its subsequent alteration through weathering has resulted in the formation of clay. The rock is highly jointed, and weathering is found to be extensive along these joints (Fig.  4 c). The crown of the landslide is occupied by bouldery outcrops of this rock with no soil cover. Hence, during monsoon, all the water in the crown part has surcharged the immediately downslope column of lateritic soil causing an increase in pore-water pressure. Near the flanks of the landslide, the soil profile shows dislodged soil followed by lateritic soil of 1 m thickness and another 1 m thick saprolite (Fig.  4 d). This is further followed by bedrock. The dislodged material was finally dumped into the Pullakayar, a tributary of Manimala River.

Plappally landslide

Plappally landslide (9°37′3''N; 76°52′21''E) in Kanjirapally taluk of Kottayam district has killed four people and demolished two buildings. This landslide of 500 m (length) × 20 (avg. width) × 1 m (avg. thickness) was initiated in a rubber plantation whereas its runout stretches through areas of different land use types. In the Google Earth image (before landslide), the upslope in which the landslide occurred is confined is a truncated spur and its right boundary is marked by a straight lower-order river course, indicating a lineament (Fig.  5 a). Due to the broader surface area of this spur, the run-off zone is more extensive. The storm water when crossing the barren rock outcrop, situated downslope, facilitates sudden surcharge to the thin veneer of soil lying immediately downslope. It is in this zone the recent landslide was initiated. The surcharge zone can be well seen in the high-resolution (3 m) False Colour Composite (FCC) of Planet Lab (Fig.  5 b). The truncated spur together with the bulged foothill suggests this as a paleo-landslide, within which the recent landslide occurred.

a Google Earth image showing a distant view of Plappally landslide showing a suspected lineament, remnants of paleolandslide and its associated truncated spur ( b ) 3 m resolution FCC of Planet Lab image showing the landslide runout and its surcharge area ( c ) Storm water gushing through the uprooted house location ( d ) The ruins of the devastated house, which was constructed along the course of a lower-order stream ( e ) Seepage along the joints of hornblende biotite gneiss

This landslide is also confined to a lower-order stream course. The two buildings, which were destroyed, were constructed precisely on the river course. Water gushes through this during the monsoon (Fig.  5 c), whereas it is dry during the non-monsoon season (Fig.  5 d) showing its seasonal nature. But seepage can be seen along the joints of the country rock, hornblende biotite gneiss (Fig.  5 e). Here again, in the upper slope, where the houses stood before the landslide, plantation with young rubber trees existed, which indicates a similar influencing factor like at Kokkayar.

Kavali landslide

Six people died and one house was demolished by the Kavali landslide, which is 250 m (length) × 15 (avg. width) × 2 m (avg. thickness) in dimension. Hornblende biotite gneiss is the country rock, which is highly weathered and jointed. The attitude of this highly foliated rock is 315°/80NE. Here too, spring water is tapped for domestic purposes. The destroyed house was constructed in a cut-slope, but the cut-slope is still retained after the landslide. The cut-slope profile exhibits lateritic soil, saprolite, and weathered bedrock. The area is characterized by thick vegetation when compared to the sparse vegetation in the adjacent area. This thick mixed vegetation with rubber plantation is the major crop, followed by nutmeg, arecanut, and teak. Google Earth image (Fig.  6 a) also revealed thick vegetation. A Normalized Difference Vegetation Index (NDVI) map was created using the high-resolution Planet Lab image to understand the area’s land use. NDVI revealed that the landslide occurred in a densely vegetated area when compared to other areas consisting of a wide variety of land uses like moderate vegetation, grassland, barren outcrop, and built-up. Usually, landslides are less reported in densely vegetated areas (cf. Alcantara-Ayala et al. 2006 ; Reichenbach et al. 2014 ). In contrast to this, a recent study by Lan et al. ( 2020 ) suggests that a densely vegetated slope decreases its stability. This study has been concurred with by the recent findings of Hao et al. ( 2022 ) wherein most of the landslides that occurred in Kerala during 2018 are spatially associated with forest land. However, a closer look at Fig.  6 a, b reveals forest fragmentation and breaking-off of the contiguity of the forest canopy, creating scattered and fragmented forest islands. Studies reveal that such a process could compromise landscape integrity (Ramachandra and Kumar 2011 ; Batar et al. 2021 ).

a Google Earth image showing a distant view of Kavali landslide and forest fragmentation ( b ) NDVI of Kavali area depicting dense vegetation in landslide occurred area

The Western Ghats, especially its southern part encompassing the entire state of Kerala, witness landslides often during monsoon season. Since 2018, the noteworthy feature of the monsoon has been that it triggers landslides during the sporadic high-intensity rainfall (cf. Vishnu et al. 2019 , 2020 ; Yunus et al. 2021 ; Sajinkumar et al. 2022 ). Though several studies have been conducted in this region, and measures suggested were not adopted, we present here specific omnipresent reasons that facilitate landslides in this region.

Introspection of land use policy

The recent landslide susceptibility map of Kerala (cf. Sajinkumar and Oommen 2021 ; Escobar-Wolf et al. 2021 ) shows an area of 3300 and 2886 km 2 as highly and moderately susceptible to landslides, respectively. It will be an arduous task to implement stringent measures such as habitation- and construction-free zones in these areas. However, some of the landslide-facilitating practices that are common, may be inadvertently so, can be averted. Kerala is predominantly an agrarian state, and the general agricultural land use seen are cash crops, with rubber plantations occupying the midlands and tea, coffee and cardamom in the highlands. All the three landslides occurred in the midlands, especially where rubber plantation dominates the land use. The construction of rain pits is a common practice in almost all rubber estates. Major disturbance to the slope stability occurs when fully matured rubber trees are slaughtered after their life span of ~ 20 years, and fresh saplings are planted in a broad pit of 1 m 3 size. Rain pits are also dug here. The method of stubble mulching is not practiced here and large area of land will be disturbed when the trees are uprooted using machinery. Hence, avoiding rain pits, planting pits, and promoting stubble mulching practice will help reduce the probability of landslide occurrences. Avoiding rain pits and planting pits in susceptible landslide areas will help increase run-off rather than infiltration. In addition, all agricultural techniques on the hilly slope affects the lower-order drainage, by obstructing it with rubble-masonry walls, redirecting it to a more hazardous slope, or by constructing houses. These lower-order courses, except in thickly vegetated forest areas, are usually seasonal, and during monsoon season, the normal flow of water is thus disturbed by these practices. Hence, a stringent land use policy to avoid such practices in agricultural fields is a pressing requirement.

Rainfall- the sole triggering factor

As mentioned, these three landslides were also triggered by a sporadic-high intensity rainfall of > 266 mm in a single day (Fig.  7 ) but with a 5-day antecedent rainfall of only 109.9 mm. The comparatively higher rainfall of 48.8 (2nd October), 45.4 (8th October and 69.6 mm (11th October) might have saturated the soil column and the 16th October anomalous event was sufficient enough to trigger landslides. In order to limit the risk of rainfall-induced landslides, an accurate and exact rainfall forecast that allows for the issuance of early warnings based on the rainfall threshold of the area is essential (Weidner et al. 2018 ). The sparse density of rain gauges and manual operation methods make things difficult. For e.g., the rain gauge station nearest to these three landslides is Kanjirapally, approximately 10 km away from this landslide, which is grossly inadequate to capture the micro-climatic conditions of the susceptible areas. Moreover, this rain gauge station is a manual one with daily rainfall recording on the succeeding day at 8.30 am ( www.imd.gov.in ). Having automated rain gauges that report rain information near real-time will be critical for developing early warning systems.

Hyetograph of Kanjirappally rain gauge, which is the nearest to the landslide affected area. Note the prominent 266 mm rainfall on the landslide day

Soil thickness and soil-rock interface plane

The hilly area of the entire state of Kerala is characterized by a thin veneer of unconsolidated soil, resting above the massive Precambrian crystalline rock except for plateau regions like Munnar and Nelliyampathy (Sajinkumar and Anbazhagan 2015 ). Usually, the glide plane of the landslides will be the contact plane of these two litho-units (cf. Istiyanti et al. 2021 ). Thus, wherever the landslide occurs, the bedrock will be exposed, which can be seen in all these three landslides. Hence, along with the understanding of landslide susceptibility, the soil thickness of the area and the saturation capacity of that soil column have to be investigated. The contact between these two litho-units is stable in a plain or gentler slope (Fig.  8 a) whereas it will be in a meta-stable position when in a steep slope (cf. Getachew and Meten 2021 ; Puente-Sotomayor et al. 2021 ) (Fig.  8 b). This equilibrium will be lost when the soil column is saturated by water during the monsoon season (Fig.  8 c).

Sketch depicting the contact between unconsolidated soil and massive crystalline Precambrian rocks along the Western Ghats part of Kerala. ( a–c ) shows the different stages of stability of these two lithounits

The three landslides that occurred on 16th October 2021 are located in the same valley, and were triggered by a high-intensity rainfall of 266 mm in one day. These similarities are never the same when conditioning factors are analyzed. The steep slopes of the hilly regions where all three landslides occurred originally contained natural contiguous forests that may have held the thin soil and regolith layer together. The modern landscape, however, is dominated by human interventions such as the replacement of natural vegetation with plantations, highways, and human settlements. These measures facilitated the triggering of the landslides by a sudden storm of intense rainfall (cf. Lahai et al. 2021 ). However, a closer check using ground reality and satellite photographs revealed that the Kokkayar landslide was completely caused by humans, whereas the Plappally landslide was also affected by geomorphic and tectonic causes. The third site, the Kavali landslide, was caused by forest fragmentation on the forest island. Consequently, regardless of the contributing components, the common and vital feature to be researched is the rainfall dynamics, which can be converted into early warning systems, thereby saving countless lives.

Achu AL, Joseph S, Aju CD, Mathai J (2021) Preliminary analysis of a catastrophic landslide event at Pettimudi, Kerala state. India Landslides 18:1459–1463

Article   Google Scholar  

Alcántara-Ayala I, Esteban-Chávez O, Parrot JF (2006) Landsliding related to land-cover change: a diachronic analysis of hillslope instability distribution in the Sierra Norte, Puebla, Mexico. CATENA 65:152–165

Batar AK, Shibata H, Watanabe T (2021) A novel approach for forest fragmentation susceptibility mapping and assessment: a case study from the Indian Himalayan region. Remote Sens 13(20):4090

Escobar-Wolf RV, Sanders JD, Oommen T, Sajinkumar KS, Vishnu CL (2021) A GIS tool for infinite slope stability analysis (GIS-TISSA). Geosci Front 12(2):756–768

Getachew N, Meten M (2021) Weights of evidence modeling for landslide susceptibility mapping of Kabi-Gebro locality, Gundomeskel area central Ethiopia. Geoenviron Disasters 8(1):1–22

Hao L, Rajaneesh A, van Westen C, Sajinkumar KS, Martha TR, Jaiswal P, McAdoo BG (2020) Constructing a complete landslide inventory dataset for the 2018 Monsoon disaster in Kerala, India, for land use change analysis. Earth Syst Sci Data 12(4):2899–2918

Hao L, van Westen C, Rajaneesh A, Sajinkumar KS, Martha TR, Jaiswal P (2022) Evaluating the relation between land use changes and the 2018 landslide disaster in Kerala, India, for land use change analysis. CATENA 216:106363

Istiyanti ML, Goto S, Ochiai H (2021) Characteristics of tuff breccia-andesite in diverse mechanisms of landslides in Oita Prefecture, Kyushu Japan. Geoenviron Disasters 8(1):1–14

Lahai YA, Anderson KF, Jalloh Y, Rogers I, Kamara M (2021) A comparative geological, tectonic and geomorphological assessment of the Charlotte, Regent and Madina landslides, Western area Sierra Leone. Geoenviron Disasters 8(1):1–17

Lan H, Wang D, He S, Fang Y, Chen W, Zhao P, Qi Y (2020) Experimental study on the effects of tree planting on slope stability. Landslides 17:1021–1035

Puente-Sotomayor F, Mustafa A, Teller J (2021) Landslide susceptibility mapping of urban areas: logistic regression and sensitivity analysis applied to quito Ecuador. Geoenviron Disasters 8(1):1–26

Ramachandra T, Kumar U (2011) Characterisation of landscape with forest fragmentation dynamics. J Geogr Inf Syst 3(3):242–253

Google Scholar  

Reichenbach P, Busca C, Mondini AC, Rossi M (2014) The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy). Environ Manage 54:1372–1384

Article   CAS   Google Scholar  

Sajinkumar KS, Anbazhagan S (2015) Geomorphic appraisal of landslides on the windward slope of Western Ghats, southern India. Nat Hazards 75(1):953–973

Sajinkumar KS, Oommen T (2020) Rajamala landslide: continuation of a never-ending landslides series. J Geol Soc India 6:310

Sajinkumar KS, Arya A, Rajaneesh A, Oommen T, Ali P, Yunus RVR, Avatar R, Thrivikramji KP (2022) Migrating rivers, consequent paleochannels: the unlikely partners and hotspots of flooding. Sci Total Environ 807:150842

Sajinkumar KS, Oommen T (2021) Landslide atlas of Kerala. Geol Soc India, p 34.

Vishnu CL, Sajinkumar KS, Oommen T, Coffman RA, Thrivikramji K, Rani VR, Keerthy S (2019) Satellite-based assessment of the August 2018 flood in parts of Kerala, India. Geomat Nat Hazards Risk 10(1):758–767

Vishnu CL, Rani VR, Sajinkumar KS, Oommen T, Bonali FL, Pareeth S, Thrivikramji K, McAdoo BG, Anilkumar Y (2020) Catastrophic flood of August 2018, Kerala, India: partitioning role of geologic factors in modulating flood level using remote sensing data. Remote Sens Appl Soc Environ 2:100426

Wadhawan SK, Singh B, Ramesh MV (2020) Causative factors of landslides 2019: case study in Malappuram and Wayanad districts of Kerala. India Landslides 17:2689–2697

Weidner L, Oommen T, Escobar-Wolf RV, Sajinkumar KS, Rinu S (2018) Regional scale back-analysis using TRIGRS: An approach to advance landslide hazard modeling and prediction in sparse data regions. Landslides 15(12):2343–2356

Yunus AP, Fan X, Subramanian SS, Jie D, Xu Q (2021) Unraveling the drivers of intensified landslide regimes in Western Ghats, India. Sci Total Environ 770:145357

Download references

Acknowledgements

The authors thank Kerala State Disaster Management Authority (KSDMA) for facilitating fieldwork in these areas. Jobin Sebastian, a freelance photographer and paraglide trainer, is highly thanked for providing photos (Figs. 1 d and 2 ). The lab work was carried out at the Laboratory for Earth Resources Information System (LERIS) housed at the Department of Geology, University of Kerala. LERIS is a collaborative initiative of Indian Space Research Organization and University of Kerala.

The author declare that there is no funding in the manuscript.

Author information

Authors and affiliations.

State Emergency Operations Centre, Kerala State Disaster Management Authority (KSDMA), Thiruvananthapuram, Kerala, 695033, India

Sree Sankaracharya University of Sanskrit, Kalady, Kerala, 683574, India

D. Nandakumar

Department of Geology, University of Kerala, Thiruvananthapuram, Kerala, 695581, India

A. Rajaneesh & K. S. Sajinkumar

Department of Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, MI, 49931, USA

Indian Institute of Science Education and Research, Mohali, Punjab, 140306, India

Yunus P. Ali

You can also search for this author in PubMed   Google Scholar

Contributions

RSA-Field visit, manuscript writing DN-Field visit, manuscript writing AR-Data analysis, figure preparation TO-Data analysis, figure preparation, manuscript writing YPA-Data analysis, figure preparation, manuscript writing KSS-Field visit, Data analysis, figure preparation, manuscript writing, supervision

Corresponding author

Correspondence to K. S. Sajinkumar .

Ethics declarations

Ethical approval.

The data employed in this work is accessible to the public upon request and therefore ethical approval is not required. This paper is not being submitted to any other journal at the same time.

Competing interests

The authors declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service, and/or company that could be construed as influencing the position presented in, or the review.

Additional information

Publisher's note.

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

Rights and permissions

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

Reprints and permissions

About this article

Cite this article.

Ajin, R.S., Nandakumar, D., Rajaneesh, A. et al. The tale of three landslides in the Western Ghats, India: lessons to be learnt. Geoenviron Disasters 9 , 16 (2022). https://doi.org/10.1186/s40677-022-00218-1

Download citation

Received : 13 February 2022

Accepted : 02 July 2022

Published : 13 July 2022

DOI : https://doi.org/10.1186/s40677-022-00218-1

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Human interventions
• Western Ghats

• Español (Spanish)
• Français (French)
• Bahasa Indonesia (Indonesian)
• Brasil (Portuguese)
• India (English)
• हिंदी (Hindi)
• Feature Stories
• Explore All
• Subscribe page
• Submissions
• Privacy Policy
• Terms of Use
• Advertising
• Wild Madagascar
• Selva tropicales
• Mongabay.org
• Tropical Forest Network

Southern India’s 2016-2018 drought was the worst in 150 years

Share this article.

If you liked this story, share it with other people.

• A severe drought that hit southern India during 2016-2018 was the worst to hit the region over the past 150 years and was associated with a deficit in the northeastern monsoon.
• Drought conditions linked to northeastern monsoonal rainfall across southern India are associated with cool phases of the tropical Indo-Pacific Ocean. Cool phases of the Pacific Ocean are known as La Niña.
• If severe droughts in southern India are linked to La Niña, they could potentially be predicted, said an independent expert.

Southern India was hit by severe drought from 2016 to 2018 arising from low rainfall during the northeast monsoon, which occurs during the winter. So severe was the impact that a water crisis erupted in Chennai, India’s sixth-largest city of 11 million inhabitants, as four of the city’s major reservoirs went bone-dry and groundwater levels plummeted. In the summer of 2019, a “Day Zero” was declared and residents scrambled to obtain water from tankers. 

Now, after examining rainfall data over the past 150 years, researchers in India and the US conclude that the 2016-2018 northeast monsoon drought was unprecedented with more than 40 percent deficit in northeast monsoonal rainfall during the three years. 

The recent drought was worse than the Great Drought of 1874-1876 that led to crop failure, which in turn resulted in the Great Madras Famine of 1876 to 1878 that claimed millions of lives. The team demonstrates that cool phases in the equatorial Indian and Pacific Oceans are associated with the rainfall deficit. 

“The consecutive failure of the northeast monsoon can result in a water crisis in Southern India,” lead author Vimal Mishra, associate professor at Indian Institute of Technology, Gandhinagar, told Mongabay-India, adding that “it has considerable implications to agricultural productivity.” 

While India receives most of its annual rainfall during the Indian summer monsoon (June to September), southern India receives about 40 percent of its rainfall from October to December in what is known as the northeastern monsoon (NEM) or the winter monsoon. It is crucial for drinking water and agriculture contributing to the livelihood of millions. 

The southern Indian states of Andhra Pradesh, Karnataka and Tamil Nadu continuously declared drought from 2016 to 2018 linked to low northeast monsoonal rainfall. Over 60 percent of the rural population in southern India is engaged in agriculture and relies on rainfall from the winter monsoon. 

Failure of the northeast monsoon 

How severe was the recent drought compared to those Southern India has experienced in the past? What are the causes of the deficit in the northeast monsoon? Mishra’s team sought to answer these questions. 

To investigate the long-term history of NEM droughts in the region, the team used rainfall observations from the India Meteorology Department from 1870 to 2018. Data on total water storage was obtained from NASA’s Gravity Recovery and Climate Experiment (GRACE) satellites for April 2002 to June 2017 while the GRACE Follow-On (GRACE-FO) mission provided data for 2018 onwards.

Over the past 150 years, there were five main periods of drought with more than 29 percent deficit in rainfall (1876, 2016, 1938, 1988, and 1974 in order of severity). Looking at single year rainfalls, 1876 was the driest year with a precipitation deficit of 69 percent followed by 2016 with a deficit of 63 percent. But when considering cumulative rainfall over three years, 2016 to 2018 was the worst NEM drought with a precipitation deficit of 45 percent while the 1874 to 1876 drought, or the Great Drought as it is known, was the second-worst with a deficit of 37 percent.

The GRACE satellite indicated that total water loss in Southern India in December 2016 was 79 cubic kilometres (km3) while the GRACE-FO data showed that the loss was 46.5 km3 in June 2017 and 41.7 km3 in June 2019. Loss in total water storage likely resulted in significant depletion of groundwater in the region, say the authors.

What factors were associated with deficits in the northeast monsoon?

The team examined sea surface temperatures (SST), sea-level pressure and wind fields during the winter monsoon to understand how circulation patterns affect variability in northeast monsoonal rainfall. Sea surface temperature over the equatorial Indian and Pacific Oceans affects year-to-year variability of the northeast monsoon, explained Mishra. “SST anomalies cooler than normal are linked to a weak northeast monsoon.”

In 2016 and 2017, cool SST anomalies prevailed in the tropical Indo-Pacific Ocean and were associated with La Niña in the central Pacific, the researchers observed. La Niña is a climate pattern that occurs irregularly every two to seven years. During La Niña, the surface waters over the equatorial Pacific Ocean are cool and this affects global weather patterns.

At the same time, the researchers noted anomalous cooling was seen in the Indian Ocean. Such patterns along with those seen in sea-level pressure and surface-air temperatures gave rise to anomalous westerlies in the equatorial Indian Ocean, which weakened moisture transport from the Bay of Bengal during the northeast monsoon, explained the authors.

Interestingly, the study revealed that out of five of the major droughts that struck southern India over the past 150 years, four occurred during La Niña.

Deepti Singh, assistant professor at Washington State University, who was not connected with the study, notes that the paper “links the recent severe, multi-year drought primarily to La Niña conditions in the tropical Pacific Ocean in 2016-2017 and 2017-18.”

This finding “implies that there is potential to predict them a few months in advance since La Niña events can be predicted with some skill in the summer,” said Singh, adding that “this means that stakeholders can prepare for and mitigate their impacts.”

While the study does not explain what made the 2016-2018 drought one of the strongest on record, “it demonstrates that natural climate variability can lead to extreme events.” She stresses that a better understanding of these drivers can inform our ability to predict severe droughts in the future. “Timely predictions of such events can help better manage and potentially reduce their societal impacts,” Singh says. 

“This is particularly important since extreme La Niña conditions are projected to become more frequent with warming and if this link holds, it might mean increasing drought risks to the region, which will likely be worsened by hotter conditions. ”

Mishra, V., Thirumalai, K., Jain, S., & Aadhar, S. (2021). Unprecedented drought in South India and recent water scarcity.  Environmental Research Letters ,  16 (5), 054007.

Banner image: Climate change can increase the frequency of drought conditions in India. Photo by Christopher Michel/Flickr.

Climate Innovations

Polluting leather industry adopts sustainable practices to reduce environmental footprint

Brick kilns embrace zigzag design to cut pollution and boost efficiency

Innovating with deadstock: From dump yards to designer racks

Innovative air-to-water tech using liquid desiccant makes affordable, renewable water

If the Green Revolution rode on the strength of chemicals derived mainly from fossil fuels, now there is a shift in the thinking on how agriculture is being done in India, with a thrust on growing indigenous crop varieties and following natural farming practices. In the industrial sector, with initiatives such as ‘Make in India’, […]

Free and open access to credible information

Latest articles.

Your Environment This Week: Indian savannas, secret lives of small cats, food insecurity in the Himalayas

[Interview] Vigyan awardee Purabi Saikia on the importance of mapping forest resources

Operation Bhediya reveals gaps in human-wildlife conflict mitigation

A new remote sensing study spotlights Indian savannas

Capturing the secret lives of small cats

[Explainer] What is micro irrigation?

Multiple studies flag food insecurity as a threat in the Himalayas

New urban governance bill in Bengaluru faces criticism, takes away citizen power says analysis

• Get involved

• English pdf (0.6 MB)

Disaster Management in India- A Status Report

January 21, 2012.

India has been traditionally vulnerable to natural disasters on account of its unique geo-climatic conditions. Floods, droughts, cyclones, earthquakes and landslides have been a recurrent phenomena.

India has been traditionally vulnerableto natural disasters on account of its unique geo-climatic conditions.Floods, droughts, cyclones, earthquakes and landslides have been a recurrent phenomena. About 60% of the landmass is prone to earthquakes of various intensities; over 40 million hectares is prone to floods; about 8% of the total area is prone to cyclones and 68% of the area is susceptible todrought. Inthedecade 1990-2000, an average of about 4344 people lost their lives and about 30 million people were affected by disasters every year.The loss in terms of private, community and public assets has been astronomical.

At the global level, there has been considerable concern over natural disasters.Even as substantial scientific and material progress is made, the loss of lives and property due to disasters has not decreased. In fact, the human toll and economic losses have mounted.It wasin this background that the United Nations General Assembly, in 1989, declaredthe decade 1990-2000 as the International Decadefor Natural Disaster Reduction with the objectiveto reducelossof lives and propertyand restrict socio-economic damagethrough concerted international action, specially in developing countries.

The super cyclone in OrissainOctober, 1999 and theBhuj earthquake in Gujarat inJanuary, 2001 underscored the need to adopt a multi dimensional endeavour involvingdiversescientific, engineering, financial and social processes; the need to adopt multidisciplinary and multi sectoralapproach and incorporationof risk reduction in the developmental plans and strategies.

Over the past couple of years, the Government of India have brought about a paradigm shift in the approach to disaster management.The new approach 4 proceeds from the conviction that development cannot be sustainable unless disaster mitigation is built into the development process.Another corner stone of the approach is that mitigation has to be multi-disciplinary spanning across all sectors of development.The new policy also emanates from the belief that investments in mitigation are much more cost effective than expenditure on relief and rehabilitation.

Disaster management occupies an important place in this country’s policy framework as it is the poor and the under-privileged who are worst affected on account of calamities/disasters. Disasters retard socio-economic development, further impoverish the impoverished and lead to diversion of scarce resources from development to rehabilitation and reconstruction.

The steps being taken by the Government emanatefrom the approach outlined above.The approach has been translated into a National Disaster Framework [a roadmap] covering institutional mechanisms, disaster prevention strategy, early warning system, disaster mitigation, preparedness and response and human resource development.The expected inputs, areas of intervention and agencies to be involved at the National, State and district levels have been identified and listed in the roadmap.This roadmap has been shared with all the State Governments and Union Territory Administrations.Ministries and Departments of Government of India, and the State Governments/UT Administrations have been advised to develop their respective roadmaps taking the national roadmap as a broad guideline.There is, therefore, now a common strategy underpinning the action being taken by all the participating organisations/stakeholders.

The changed approach is being put into effect through:

(a) Institutional changes

(b) Enunciationof policy

(c) Legal and techno-legal framework

(d) Mainstreaming Mitigation into Development process

(e) Funding mechanism

(f) Specific schemes addressing mitigation

(g) Preparedness measures

(h) Capacity building

(i) Human Resource Development

and, above all, community participation. These are detailed in the following

Document Type

Regions and countries, related publications.

Publications

Nasha mukt bharat abhiyaan - assessment report.

In 2020, the  Ministry of Social Justice and Empowerment (MoSE) launched the Nasha Mukt Bharat Abhiyaan, a country-wide campaign focusing on substance abuse pre...

SDG India Index 2023-2024

As the most populous country, India will play a leading role in determining the achievement of the SDGs. In recognition of this responsibility, India has active...

Understanding Generative AI's Implications on Gender Usin...

This report was produced by Aapti Institute, commissioned by the United Nations Development Programme (UNDP) under the Business and Human Rights in Asia program...

Trans Employment Mela Report 2023

This is an impact report of the Transgender Employment Mela - a one of its kind opportunity to enable members of the Transgender community get employed with Cor...

Japan and UNDP in Asia and the Pacific: Investing togethe...

The Government of Japan has been a longstanding and key partner of UNDP in Asia and the Pacific region. This report provides a snapshot of results achieved toge...

Global Infrastructure Resilience: Capturing the Resilienc...

Infrastructure resilience is a critical global challenge. In lower income countries, a huge and widening infrastructure deficit conspires against social and eco...

Unnatural Disaster: How Global Warming Helped Cause India’s Catastrophic Flood

Heat of the Moment

Planet Earths average temperature has risen about one degree Fahrenheit in the last fifty years. By...

Two years ago this month, a flood devastated the Himalayan village of Kedarnath, India, the destination of half a million Hindu pilgrims annually. The town sits 11,500 feet up in a tight valley. Sharp, snowy peaks tower on three sides and a stone temple sits at one end. The flood — which occurred on June 17, 2013 — was India's worst disaster in a decade. Several thousand people drowned. The deluge tore apart dozens of bridges, swept away miles of paved roads, and carried off herds of livestock.

Government officials, scientific researchers, and media commentators soon speculated about the cause of the flood and about why so many people had died. They pointed to the early and heavy monsoon rains. They railed against poorly built homes, unregulated development along the Mandakini River that runs through Kedarnath, and soil erosion caused by thousands of pilgrims trekking on foot and on donkeys to reach this remote town in the northern Indian state of Uttarakhand. All these factors contributed. Yet in the two years since the flood, scientists studying with the care and intensity of forensic investigators have added another key cause: global warming. In recent papers, they conclude that melting glaciers and shifting storm tracks may soon set off more catastrophic floods in mountainous regions of India and adjacent countries. Atmospheric scientists say that in northern India the intense rains that preceded the disaster are extremely rare. But they have discovered that an unusual collision of weather systems steered storms over Uttarakhand and locked them in place, pouring rain down for days. Long-term changes in weather patterns are making such collisions more likely, a development that some scientists believe is caused by global warming. Global warming has is also melting glaciers all over the Himalayas, including one perched above Kedarnath. Some researchers say that had the glacier remained healthy, heavy rain alone would not have destabilized a gravel bank that collapsed, releasing a destructive pulse of debris-filled water. Sitting on the carpet in his father's living room, in New Delhi, 200 miles southwest of Kedarnath, Vaibhav Kaul, a young geographer, watched reports of the disaster on TV. He remembers thinking to himself, "that was exactly the kind of scenario I'd been studying for the year." He'd recently completed a Masters degree in Environmental Change and Management at Oxford University in the United Kingdom. His thesis studied how Himalayan communities could better prepare for catastrophic floods from the lakes above them. He'd briefly considered making Kedarnath the subject of his research. A devout Hindu and descended from Kashmiri Pandits — an elite caste that has produced many of India's ruling class of scholars, administrators and politicians — Kaul had made the religious excursion to Kedarnath once before and hiked two hours above the town to Lake Chorabari Tal. But when he later read a report cataloging the Himalayan lakes most likely to flood and endanger communities below, Chorabari Tal was not among those listed as "potentially dangerous." He decided to study towns elsewhere in the Himalayas instead. But his instincts proved to be correct. Four months after the flood, Kaul set off on a scientific pilgrimage to the disaster site, determined to learn why a town considered relatively safe had flooded. He wore a traditional robe, hanging to below his knees, and the sort of dark wool vest favored by men in Kashmir, the mountainous region of his ancestors. The flood had severed the eight-mile footpath to Kedarnath from the rest of India. Kaul took a bus to Guptkashi, the closest town with public transport, but nearly 25 miles short of Kedarnath. He continued on foot, astonished at the scale of destruction even so far downstream. The flood had passed through Kedarnath and surged down the Mandakini, joined by swollen tributaries, gathering force and debris. Kaul saw bare abutments where bridges had stood and foundationless houses dangling above landslide scars. Thirty hydroelectric plants had been damaged or destroyed. About four miles shy of Kedarnath, he came to the former site of Rambara, a way station that once had about 100 seasonal shopping stalls and several small hotels. Pilgrims had rested there over sweet, milky tea and fried flatbreads and bought camping supplies and religious trinkets. Kaul saw only an empty shelf of bedrock strewn with boulders. "One couldn't imagine there'd been anything there," he said later. Some of of Kedarnath's steel-reinforced concrete guesthouses and stuccoed fieldstone homes survived better. Still, nearly three quarters of its 259 buildings had been damaged. More than half had been battered and washed away. The flood took most of its victims in Kedarnath, the season's first pilgrims. "They were still finding dead people," Kaul recalled, noting that he had smelled rotting flesh and watched relief workers excavate a severed leg. Kaul climbed steep hills to an overlook about 2,000 feet above the town. The top of a hulking mountain, nearly 23,000 feet tall and crowned by Chorabari glacier, appeared. It blocked the sky at the head of the valley. At an inflection point, where the slope leveled off, a vast tongue of ice stretched out for a mile. Kaul looked for Chorabari Tal. It should have been visible below him, near the tongue's tip. But there was no lake to be seen. Titanic geologic forces had forged Chorabari Tal during the widespread cold spell that lasted from about 1300 to 1870 and is known as the Little Ice Age. The glacier had bulldozed stone into linear piles — moraines — jammed between the advancing tongue and the valley's bedrock rim. The ice had then receded, leaving the lake's lens-shaped basin, a depression with no outlet. Rain and melted snow filled it every spring and summer. At times, water drained out through the porous moraine, and the water level dropped. Now, as Kaul looked down, he saw that the basin was empty. He knew what had occurred: The moraine had ruptured, letting loose the lake's entire contents in a catastrophic spasm. Kaul surveyed the town sprawling in the valley below him. It was built on a bed of gravel shaped like the prow of a ship sailing toward the glacier. Metal roofs sparkled. Unscathed by the flood, the Kedarnath Hindu temple stood at the narrow end of town. The shrine, stately, gabled and thick-walled, was built of huge stacked stone blocks. Priests probably constructed it in the 8th or 9th century, on the site of an even older temple, and dedicated it to the Hindu god Lord Shiva the Destroyer. From his overlook, Kaul saw the boulder that had saved the temple from destruction. Miraculously, the deluge had scooped up this 30-foot-long rock and dropped it, perpendicular to the current, just steps short of the temple, where it had deflected the churning waters around the historic building. Kaul snapped a set of pictures. A few weeks later, British geographer Dave Petley, at the University of East Anglia, published some of Kaul's photos on a blog widely read by landslide researchers. These photos proved what Petley and other scientists had suspected from blurry satellite images released by India's space agency. They showed a V-shaped cut in the natural dike that had dammed Chorabari Tal for centuries. An 18-wheeler could fit through the break in the gravel wall. Using Kaul's photos, Petley explained in blog posts the chain of events implied by the breached embankment in the days before the flood. The lake probably had swelled to capacity during the heavy rain and accompanying snowmelt. The weight of all that water could have punched through the dike. When the wall broke apart, an immense wave loaded with boulders raced a mile downhill straight for Kedarnath. Terrified pilgrims and inhabitants had huddled inside any shelter they found. Survivors describe hearing a tremendous boom. The torrent poured into the Mandakini River, already raging above the town with the downpours and snowmelt. Chandi Prasad Tiwari, a shopkeeper, saw a wave crest over a three-story building. "God, please help us, please help us," he recalls sobbing. He felt sure he'd die. The Chorabari Tal's basin is not huge. "Just a puddle," Kaul says. But it probably released its entire contents, about 100 million gallons, in a quarter of an hour, say scientists at the University of Calcutta. The team estimated that for several minutes the torrent pounded Kedarnath with half the flow of Niagara Falls. If Chorabari Tal's 100 million gallons were the explosive blow that hit Kedarnath and its occupants, what set it in motion? Why did the lake basin, intact for hundreds of years, burst now? Researchers have spent the two years since the catastrophe trying to find out. They suspect two factors: the unusually heavy rainfall, and the degraded condition of Chorabari Glacier. Monsoon rain poured torrentially throughout India the week prior to the flood. Twice as much rain fell in the first two weeks of June, 2013 as had fallen in the same fortnight in any of the prior 60 years. It fell with ferocity in the mountains of Uttarakhand. Just before the storm washed it away on June 16, a rain gauge at Chorabari Tal set up by Indian researchers recorded 13 inches of rainfall in a 24-hour period. Despite scant long-term weather records for the region, studies show that such a downpour was rare, and perhaps unprecedented. One research paper calculated that an equally wet month probably occurs less often than once a century. Indian researchers attribute the abrupt, intense rainstorms that sometimes drench Himalayan states to a type of weather system called a Western Disturbance, in which moisture travels to India on high altitude winds from the Mediterranean Sea, over the Arabian Peninsula, past Iran, Afghanistan, and Pakistan. There the wet wind hits the Himalayas and drops its moisture, showering northern India several times a month during the winter. Western Disturbance storms are less common after mid-spring. Then, the summer monsoon begins, bearing moisture from the Bay of Bengal and spreading north and west from near Kolkata on India's eastern coast. In an interview at Jawaharlal Nehru University in New Delhi, A. P. Dimri, a specialist in Himalayan weather patterns, says that a freak collision of a Western Disturbance and the summer monsoon combined in the extreme rains of June, 2013 ( see this paper he coauthored ). The monsoon struck southeastern India as usual, in the second week of the month. But clouds raced north and west with extraordinary speed. The rain arrived in Uttrakhand two weeks ahead of schedule, crashing into that Western Disturbance. Dimri says the two opposing systems of moisture-laden air "smushed together," into a Frankenstorm that stayed pinned for two days to the southern flank of the Himalayas. Some researchers say similar conditions may have loosed floods that killed 3,000 people in Pakistan and northern India in the summer of 2010. Several recent research papers say that global warming may have set up Kednarath for the disaster, by transforming regional weather patterns and eroding Chorabari Glacier. Western Disturbance storms are becoming more frequent and lasting longer. The monsoon is launching its march across India earlier and traveling faster. Dimri says that global warming may be responsible for this transformation of India's weather. In a 2014 paper , scientists at the Indian Institute of Tropical Meteorology conclude that rapid heating of the high-altitude Tibetan Plateau north of the Himalayas, caused by global warming, is rerouting Western Disturbance storms. The Tibetan Plateau has heated up faster than nearby lowlands. When patterns of heat over Earth's surfaces changes, so do the winds they drive. A team led by researchers at Stanford University has the studied whether global warming made Uttrakhand's drenching month of June 2013 more likely. Using 11 leading computer models, they compared simulations of June rain with and without the last 150 years of burning coal and oil and natural gas. They published their results in a special extreme weather issue of the Bulletin of the American Meteorological Society . The paper's lead author, Deepti Singh, a Stanford graduate student, says that of the three models that they trusted most, two showed that the June event was "more likely than it would have been," had humans not heated the planet. Dimri says that though changes in India's weather are undeniable, it's hard prove conclusively that global warming caused them. Weather patterns in the Himalayas are just too complicated. He points to the well-known difficulty of constructing a realistic computer model of the complex and interactive monsoon. However, a study published earlier this year offers new and more certain, evidence of global warming's role in the Kedarnath flood. According to an in the May 2015 issue of the journal Landslides , the heavy rain and melting snow probably wouldn't have breached the lake's bank had the tongue of ice that lays alongside the moraine not receded in recent decades. Chorabari Glacier has been retreating rapidly for at least 50 years. It has lost 11 percent of its surface area, and its tongue has contracted by about one-quarter of a mile since 1962. Other nearby glaciers and many glaciers around the world are in even faster retreat. Indian glaciologists say without hesitation that global warming is responsible for Chorabari's decline. Simon Allen, a researcher at Zurich University and lead author of the Landslides paper says if buttressed by the bigger, healthier tongue of prior decades, the moraine could have withstood more pressure, Chorabari Tal might have survived the storm, and Kedarnath might have suffered far less destruction. Chorabari Tal is only one of scores of lakes that may have been destabilized by receding glaciers in the Himalayas, he says. "We're going to have more of these things."

Heat of the Moment: Daniel Grossman on Climate Change

Related content, as himalayan glaciers melt, two towns face the fallout.

March 24, 2015

Indian Cyclone Reflects Carnage Posed by Climate Change to Those in Poverty

July 22, 2015

RELATED TOPICS

Environment and Climate Change

Support our work

Your support ensures great journalism and education on underreported and systemic global issues

• Create new account
• Reset your password
• Translation
• Alternative Standpoint
• HT Parekh Finance Column
• Law and Society
• Strategic Affairs
• Perspectives
• Special Articles
• The Economic Weekly (1949–1965)
• Economic and Political Weekly
• Open Access
• Notes for contributors
• Style Sheet
• Track Your Submission

• Resource Kits
• Discussion Maps
• Interventions
• Research Radio

Advanced Search

A + | A | A -

Heavy Landslides in Wayanad, Kerala

The recent landslide disaster in Wayanad, Kerala on 30 July 2024 killed 231 people and 119 still missing, leaving behind a trail of great destruction. The Wayanad landslide tragedy has reignited an intense discussion over how the Western Ghats have been severely affected by climate change. The increasing frequency and intensity of landslides, driven by climate change, point out the critical need for effective adaptation and mitigation strategies.

Being a vast country, India is susceptible to many natural disasters. A natural disaster can happen because of floods, earthquakes, landslides, hurricanes, cyclones, volcanic eruptions, drought, and similar earth or weather-related events. These are large-scale meteorological or geological events, often intensified by human actions, causing property loss, human casualties, and profound environmental effects. India is also prone to extreme weather events, but these are becoming more frequent and severe, intensified by global warming. The case of landslides is alarming for the mountainous regions of India, as their frequency and severity have increased in recent decades. 

Dear Reader,

To continue reading, become a subscriber.

Explore our attractive subscription offers.

To gain instant access to this article (download).

(Readers in India)

(Readers outside India)

Your Support will ensure EPW’s financial viability and sustainability.

The EPW produces independent and public-spirited scholarship and analyses of contemporary affairs every week. EPW is one of the few publications that keep alive the spirit of intellectual inquiry in the Indian media.

Often described as a publication with a “social conscience,” EPW has never shied away from taking strong editorial positions. Our publication is free from political pressure, or commercial interests. Our editorial independence is our pride.

We rely on your support to continue the endeavour of highlighting the challenges faced by the disadvantaged, writings from the margins, and scholarship on the most pertinent issues that concern contemporary Indian society.

Every contribution is valuable for our future.

• About Engage
• For Contributors
• About Open Access
• Opportunities

Term & Policy

• Terms and Conditions
• Privacy Policy

Circulation

• Refund and Cancellation
• User Registration
• Delivery Policy

Advertisement

• Why Advertise in EPW?
• Advertisement Tariffs

Connect with us

320-322, A to Z Industrial Estate, Ganpatrao Kadam Marg, Lower Parel, Mumbai, India 400 013   

Phone: +91-22-40638282   |   Email: Editorial - [email protected]  |  Subscription - [email protected]   |   Advertisement - [email protected]     

Designed, developed and maintained by  Yodasoft Technologies Pvt. Ltd.

A comparative evaluation of statistical and machine learning approaches for debris flow susceptibility zonation mapping in the Indian Himalayas

• Published: 14 September 2024

Cite this article

• Rajesh Kumar Dash   ORCID: orcid.org/0000-0002-5486-5697 1 ,
• Neha Gupta   ORCID: orcid.org/0000-0003-0207-214X 2 ,
• Philips Omowumi Falae   ORCID: orcid.org/0000-0001-9407-1229 3 ,
• Rajashree Pati   ORCID: orcid.org/0009-0006-1439-8568 1 , 4 &
• Debi Prasanna Kanungo   ORCID: orcid.org/0000-0001-5106-1055 1 , 4  

Spatial prediction of debris flows in terms of susceptibility mapping is the first and foremost requirement for disaster mitigation. In the present study, a comparative evaluation of machine learning and statistical approaches for debris flow susceptibility zonation (DFSZ) mapping has been attempted using 10 causative thematic layers (slope, aspect, elevation, plan curvature, profile curvature, topographic wetness index, stream power index, geology, proximity to streams, normalized difference vegetation index) and a debris flow inventory containing 85 debris flow locations. The employed machine learning (ML) approaches include random forest (RF), naïve Bayes (NB), and extreme gradient boosting (XGBoost) models whereas statistical models include the weight of evidence (WoE) and index of entropy (IoE). The results indicated that in all 5 DFSZ maps, about 21.20–47.98% of the area is very highly and highly susceptible to debris flows. It is observed that the major debris flows as well as high susceptible zones are distributed along the river Alakananda and its tributaries and at the vicinity of the NH-58. Among the statistical models, the DFSZ map prepared using the weight of evidence (WoE) model provides higher accuracy in terms of the success rate and the prediction rate compared to that prepared using the index of entropy model (IoE). Among the machine learning-based models, both the extreme gradient boosting (XGBoost) and random forest (RF) models give better accuracy and are more efficient than the Naïve Bayes (NB) model. It is also observed that the ML models perform better than the statistical models for a part of Chamoli district, Uttarakhand state (India). The novelty of the present study lies in the spatial prediction of one of the most destructive forms of mass movement (debris flow) in the Indian Himalayas using statistical and ML models and establishing the superiority of the ML Random Forest & XGBoost model over other ML and statistical models for the present case. This study will help make decisions on the suitability of developmental activities and human settlement in the area under consideration. The present study is one among the few studies focused on the DFSZ mapping in Indian Himalayas and can be replicated in other debris flow prone regions worldwide.

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

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Explore related subjects

• Artificial Intelligence

Data availability

All data generated or analysed during this study are included in this published article.

Abbreviations

One-dimensional convolutional neural network

Advanced land observing satellite

Area under curve

Area under prediction rate curve

Area under success rate curve

Bayes discriminant analysis

Back propagation neural network

Boosted regression trees

Certainty factor

Digital elevation model

Debris flow susceptibility index

Debris flow susceptibility zonation

Decision tree

False negative

False positive

Frequency ratio

High susceptibility

Index of entropy

Information value

Logistic regression

Low susceptibility

Landslide susceptibility mapping

Landslide susceptibility zonation

Moderate susceptibility

Machine learning

Naïve Bayes

Normalized difference vegetation index

National highway

Negative predictive value

National remote sensing centre

Phased array L-band synthetic aperture radar

Positive predictive value

Random forest

Receiver operating characteristics

Stream power index

Support vector machine

True negative

True positive

Topographic wetness index

Union territory

Very high susceptibility

Variance inflation factor

Very low susceptibility

Weight of evidence

Extreme gradient Boosting

Abdo, H. G., Almohamad, H., Al Dughairi, A. A., Ali, S. A., Parvin, F., Elbeltagi, A., Costache, R., Mohammed, S., Al-Mutiry, M., & Alsafadi, K. (2022). Spatial implementation of frequency ratio, statistical index and index of entropy models for landslide susceptibility mapping in Al-Balouta river basin, Tartous Governorate, Syria. Geoscience Letters, 9 (1), 1–24.

Article   Google Scholar  

Achour, Y., & Pourghasemi, H. R. (2020). How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geoscience Frontiers, 11 (3), 871–883.

Aggarwal, A., Rani, A., & Kumar, M. (2020). A robust method to authenticate car license plates using segmentation and ROI based approach. Smart and Sustainable Built Environment, 9 (4), 737–747.

Akgun, A. (2012). A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: A case study at İzmir, Turkey. Landslides, 9 (1), 93–106.

Bednarik, M., Magulová, B., Matys, M., & Marschalko, M. (2010). Landslide susceptibility assessment of the Kraľovany-Liptovský Mikuláš railway case study. Physics and Chemistry of the Earth, Parts a/b/c, 35 (3–5), 162–171.

Bhagat, R. C., & Patil, S. S. (2015). Enhanced SMOTE algorithm for classification of imbalanced big-data using random forest. In 2015 IEEE international advance computing conference (IACC) , 403–408.

Bourenane, H., Meziani, A. A., & Benamar, D. A. (2021). Application of GIS-based statistical modeling for landslide susceptibility mapping in the city of Azazga, Northern Algeria. Bulletin of Engineering Geology and the Environment, 80 (10), 7333–7359.

Breiman, L. (2001). Random forests. Machine Learning, 45 , 5–32.

Can, R., Kocaman, S., & Gokceoglu, C. (2021). A comprehensive assessment of XGBoost algorithm for landslide susceptibility mapping in the upper basin of Ataturk dam, Turkey. Applied Sciences, 11 (11), 4993.

Cao, J., Qin, S., Yao, J., Zhang, C., Liu, G., Zhao, Y., & Zhang, R. (2023). Debris flow susceptibility assessment based on information value and machine learning coupling method: From the perspective of sustainable development. Environmental Science and Pollution Research, 30 (37), 87500–87516.

Cervi, F., Berti, M., Borgatti, L., Ronchetti, F., Manenti, F., & Corsini, A. (2010). Comparing predictive capability of statistical and deterministic methods for landslide susceptibility mapping: A case study in the northern Apennines (Reggio Emilia Province, Italy). Landslides, 7 , 433–444.

Chattoraj, S. L., Champati Ray, P. K., & Kannaujiya, S. (2019). Simulation outputs of major debris flows in garhwal Himalaya: A geotechnical modeling approach for hazard mitigation. In R. R. Navalgund, A. Senthil Kumar, & S. Nandy (Eds.), Remote sensing of northwest Himalayan ecosystems. Springer.

Google Scholar  

Chen, W., & Yang, Z. (2023). Landslide susceptibility modeling using bivariate statistical-based logistic regression, naïve Bayes, and alternating decision tree models. Bulletin of Engineering Geology and the Environment, 82 (5), 190.

Chen, W., Zhang, S., Li, R., & Shahabi, H. (2018). Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. Science of the Total Environment, 644 , 1006–1018.

Chithaluru, P., Al-Turjman, F., Kumar, M., & Stephan, T. (2021). MTCEE-LLN: Multilayer threshold cluster-based energy-efficient low-power and lossy networks for industrial internet of things. IEEE Internet of Things Journal, 9 (7), 4940–4948.

Chithaluru, P., Al-Turjman, F., Kumar, M., & Stephan, T. (2023). Energy-balanced neuro-fuzzy dynamic clustering scheme for green & sustainable IoT based smart cities. Sustainable Cities and Society, 90 , 104366.

Cruden, D. M., & Varnes, D. J. (1996). Landslides: Investigation and mitigation. Chapter 3—Landslides Types and Processes.  Transportation research board special report , 247.

Dahiya, N., Pandit, K., & Sarkar, S. (2022). A comparison of slope stability assessment techniques using different rock mass classification systems and finite element method (FEM): A case study from the Garhwal Himalayas, India. Journal of Earth System Science, 131 (4), 242.

Dam, N. D., Amiri, M., Al-Ansari, N., Prakash, I., Le, H. V., Nguyen, H. B. T., & Pham, B. T. (2022). Evaluation of Shannon entropy and weights of evidence models in landslide susceptibility mapping for the Pithoragarh district of Uttarakhand State . Advances in Civil Engineering. https://doi.org/10.1155/2022/6645007

Book   Google Scholar  

Das, S., Sarkar, S., & Kanungo, D. P. (2022). GIS-based landslide susceptibility zonation mapping using the analytic hierarchy process (AHP) method in parts of Kalimpong Region of Darjeeling Himalaya. Environmental Monitoring and Assessment, 194 (4), 234.

Das, S., Sarkar, S., & Kanungo, D. P. (2024). An ensemble approach of bi-variate statistical models with soft-computing techniques for GIS-based landslide susceptibility zonation in the Kalimpong region of Darjeeling Himalaya, India. Environmental, Development and Sustainability . https://doi.org/10.1007/s10668-024-04592-8

Dash, R. K., Falae, P. O., & Kanungo, D. P. (2022). Debris flow susceptibility zonation using statistical models in parts of Northwest Indian Himalayas—Implementation, validation, and comparative evaluation. Natural Hazards, 111 (2), 2011–2058.

Dash, R. K., Kanungo, D. P., & Malet, J. P. (2021). Runout modelling and hazard assessment of Tangni debris flow in Garhwal Himalayas, India. Environmental Earth Sciences, 80 (9), 1–19.

Dash, R. K., Samanta, M., & Kanungo, D. P. (2023). Debris flow hazard in India: Current status, research trends, and emerging challenges. In P. Thambidurai & T. N. Singh (Eds.), Landslides: Detection, prediction and monitoring: Technological developments (pp. 211–231). Cham: Springer International Publishing.

Chapter   Google Scholar  

Daud, H., Tanoli, J. I., Asif, S. M., Qasim, M., Ali, M., Khan, J., Bhatti, Z. I., & Jadoon, I. A. K. (2024). Modelling of debris-flow susceptibility and propagation: A case study from Northwest Himalaya. Journal of Mountain Science, 21 (1), 200–217.

Falae, P. O., Agarwal, E., Pain, A., Dash, R. K., & Kanungo, D. P. (2021a). A data driven efficient framework for the probabilistic slope stability analysis of Pakhi landslide, Garhwal Himalaya. Journal of Earth System Science, 130 (3), 1–15.

Falae, P. O., Dash, R. K., Kanungo, D. P., & Chauhan, P. K. S. (2021b). Interpretation on water seepage and degree of weathering in a landslide based on pre-and post-monsoon electrical resistivity tomography. Near Surface Geophysics, 19 (3), 315–333.

Falae, P. O., Dash, R. K., Samanta, M., & Kanungo, D. P. (2021c). Geo-integrated assessment of the landslide zone around Gadora along NH 58 of the Garhwal Himalayas, India. Near Surface Geophysics, 19 , 183–198.

Falae, P. O., Kanungo, D. P., Chauhan, P. K. S., & Dash, R. K. (2019). Electrical resistivity tomography (ERT) based subsurface characterisation of Pakhi Landslide, Garhwal Himalayas, India. Environmental Earth Sciences, 78 (14), 1–18.

Gu, F., Chen, J., Sun, X., Li, Y., Zhang, Y., & Wang, Q. (2023). Comparison of machine learning and traditional statistical methods in debris flow susceptibility assessment: A case study of Changping district. Beijing. Water, 15 (4), 705.

Gupta, N., Pal, S. K., & Das, J. (2022). GIS-based evolution and comparisons of landslide susceptibility mapping of the East Sikkim Himalaya. Annals of GIS, 28 (3), 359–384.

Gupta, V., Ram, P., Tandon, R. S., & Vishwakarma, N. (2023). Efficacy of landslide susceptibility maps prepared using different bivariate methods: Case study from Mussoorie Township, Garhwal Himalaya. Journal of the Geological Society of India, 99 (3), 370–376.

Hodasová, K., & Bednarik, M. (2021). Effect of using various weighting methods in a process of landslide susceptibility assessment. Natural Hazards, 105 , 481–499.

Hong, H., Pourghasemi, H. R., & Pourtaghi, Z. S. (2016). Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology, 259 , 105–118.

Huang, H., Wang, Y., Li, Y., Zhou, Y., & Zeng, Z. (2022). Debris-flow susceptibility assessment in China: A comparison between traditional statistical and machine learning methods. Remote Sensing, 14 (18), 4475.

Ilinca, V., Şandric, I., Jurchescu, M., & Chiţu, Z. (2022). Identifying the role of structural and lithological control of landslides using TOBIA and Weight of Evidence: Case studies from Romania. Landslides, 19 (9), 2117–2134.

Jaafari, A., & Pourghasemi, H. R. (2019). Factors influencing regional-scale wildfire probability in Iran: An application of random forest and support vector machine. In H. R. Pourghasemi & C. Gokceoglu (Eds.), Spatial modeling in GIS and R for earth and environmental sciences (pp. 607–619). Elsevier.

Jain, N., Roy, P., Martha, T.P., Jalan, P., & Nanda, A. (2023). Landslide Atlas of India (Mapping, monitoring and advance techniques using space-based inputs). NRSC special publication. NRSC/ISRO. Document number: NRSC-RSA-GSG-GMED-FEB 2023-TR-0002167-V1.0 P

Kanungo, D. P., Singh, R., & Dash, R. K. (2020). Field observations and lessons learnt from the 2018 landslide disasters in Idukki District, Kerala, India. Current Science, 119 (11), 1797.

Kavzoglu, T., & Teke, A. (2022a). Predictive Performances of ensemble machine learning algorithms in landslide susceptibility mapping using random forest, extreme gradient boosting (XGBoost) and natural gradient boosting (NGBoost). Arabian Journal for Science and Engineering, 47 (6), 7367–7385.

Kavzoglu, T., & Teke, A. (2022b). Advanced hyperparameter optimization for improved spatial prediction of shallow landslides using extreme gradient boosting (XGBoost). Bulletin of Engineering Geology and the Environment, 81 (5), 201.

Kumar, S., & Gupta, V. (2021). Evaluation of spatial probability of landslides using bivariate and multivariate approaches in the Goriganga valley, Kumaun Himalaya, India. Natural Hazards, 109 , 2461–2488.

Li, Y., Jiang, W., Feng, X., Lv, S., Yu, W., & Ma, E. (2024). Debris flow susceptibility mapping in alpine canyon region: A case study of Nujiang Prefecture. Bulletin of Engineering Geology and the Environment, 83 (5), 169.

Liang, Z., Wang, C. M., Zhang, Z. M., & Khan, K. U. J. (2020). A comparison of statistical and machine learning methods for debris flow susceptibility mapping. Stochastic Environmental Research and Risk Assessment, 34 , 1887–1907.

Liu, J., & Duan, Z. (2018). Quantitative assessment of landslide susceptibility comparing statistical index, index of entropy, and weights of evidence in the Shangnan area, China. Entropy, 20 (11), 868.

Ma, T. M., Yamamori, K., & Thida, A. (2020). A comparative approach to Naïve Bayes classifier and support vector machine for email spam classification. In 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE) . pp 324–326.

Malviya, D. K., Samanta, M., Dash, R. K., & Kanungo, D. P. (2023). Anthropogenically induced instability in road cut slopes along NH-39, Manipur, North-East Indian Himalaya: Assessment and Mitigation Measures. Environment, Development and Sustainability, 26 , 1–30.

Martha, T. R., Roy, P., Jain, N., Khanna, K., Mrinalni, K., Kumar, K. V., & Rao, P. V. N. (2021). Geospatial landslide inventory of India—An insight into occurrence and exposure on a national scale. Landslides, 18 (6), 2125–2141.

Merghadi, A., Yunus, A. P., Dou, J., Whiteley, J., ThaiPham, B., Bui, D. T., Avtar, R., & Abderrahmane, B. (2020). Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science Reviews, 207 , 103225.

Mittal, V., Samanta, M., Dash, R. K., Falae, P. O., & Kanungo, D. P. (2023). Subsurface explorations and investigation of foundation performance for distress assessment of a building. Journal of Performance of Constructed Facilities, 37 (2), 04023011.

Mondal, S., & Mandal, S. (2019). Landslide susceptibility mapping of Darjeeling Himalaya, India using index of entropy (IOE) model. Applied Geomatics, 11 , 129–146.

Panda, S. D., Kumar, S., Pradhan, S. P., Singh, J., Kralia, A., & Thakur, M. (2023). Effect of groundwater table fluctuation on slope instability: A comprehensive 3D simulation approach for Kotropi landslide, India. Landslides, 20 (3), 663–682.

Pati, R., Dash, R. K., & Kanungo, D.P. (2021). Application of UAV for landslide mapping, modelling and monitoring. In Proc. Int. Conf. EGCON-2021 .

Peethambaran, B., Anbalagan, R., Kanungo, D. P., Goswami, A., & Shihabudheen, K. V. (2020). A comparative evaluation of supervised machine learning algorithms for township level landslide susceptibility zonation in parts of Indian Himalayas. CATENA, 195 , 104751.

Pham, B. T., Nguyen-Thoi, T., Qi, C., Phong, T. V., Dou, J., Ho, L. S., Le, H. V., & Prakash, I. (2020). Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. CATENA, 195 , 104805.

Pham, B. T., Pradhan, B., Bui, D. T., Prakash, I., & Dholakia, M. B. (2016). A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India). Environmental Modelling & Software, 84 , 240–250.

Pourghasemi, H. R., Mohammady, M., & Pradhan, B. (2012). Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena, 97 , 71–84.

Pourghasemi, H. R., & Rahmati, O. (2018). Prediction of the landslide susceptibility: Which algorithm, which precision? CATENA, 162 , 177–192.

Pradhan, S. P., Vishal, V., & Singh, T. N. (2018). Finite element modelling of landslide prone slopes around Rudraprayag and Agastyamuni in Uttarakhand Himalayan terrain. Natural Hazards, 94 , 181–200.

Ram, P., & Gupta, V. (2021). Landslide hazard, vulnerability, and risk assessment (HVRA), Mussoorie township, lesser himalaya, India. Environment, Development and Sustainability, 24 , 473.

Ram, P., Gupta, V., Devi, M., & Vishwakarma, N. (2020). Landslide susceptibility mapping using bivariate statistical method for the hilly township of Mussoorie and its surrounding areas, Uttarakhand Himalaya. Journal of Earth System Science, 129 , 1–18.

Regmi, N. R., Giardino, J. R., & Vitek, J. D. (2010). Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology, 115 (1–2), 172–187.

Roul, A. R., Pradhan, S. P., & Mohanty, D. P. (2021). Investigation to slope instability along railway cut slopes in Eastern Ghats mountain range, India: A comparative study based on slope mass rating, finite element modelling and probabilistic methods. Journal of Earth System Science, 130 , 1–25.

Roul, A. R., Pradhan, S. P., & Sahoo, K. C. (2022). Mass movement and initiation of landslide dam burst in the Eastern Ghats, India during the Titli cyclone. Journal of the Geological Society of India, 98 (4), 538–544.

Sahana, M., Pham, B. T., Shukla, M., Costache, R., Thu, D. X., Chakrabortty, R., Satyam, N., Nguyen, H. D., Phong, T. V., Le, H. V., & Pal, S. C. (2022). Rainfall induced landslide susceptibility mapping using novel hybrid soft computing methods based on multi-layer perceptron neural network classifier. Geocarto International, 37 (10), 2747–2771.

Sahin, E. K., Colkesen, I., Acmali, S. S., Akgun, A., & Aydinoglu, A. C. (2020). Developing comprehensive geocomputation tools for landslide susceptibility mapping: LSM tool pack. Computers & Geosciences, 144 , 104592.

Sangeeta, Maheshwari, B. K., & Kanungo, D. P. (2020). GIS-based pre-and post-earthquake landslide susceptibility zonation with reference to 1999 Chamoli earthquake. Journal of Earth System Science, 129 , 1–20.

Sarkar, S., Kanungo, D. P., & Patra, A. K. (2006). Landslides in the Alaknanda valley of Garhwal Himalaya, India. Quarterly Journal of Engineering Geology and Hydrogeology, 39 (1), 79–82.

Sarkar, S., Kanungo, D. P., Patra, A. K., & Kumar, P. (2008). GIS based spatial data analysis for landslide susceptibility mapping. Journal of Mountain Science, 5 , 52–62.

Sarkar, S., Kanungo, D. P., & Sharma, S. (2015). Landslide hazard assessment in the upper Alaknanda valley of Indian Himalayas. Geomatics, Natural Hazards and Risk, 6 (4), 308–325.

Sarkar, S., Pandit, K., Dahiya, N., & Chandna, P. (2021). Quantified landslide hazard assessment based on finite element slope stability analysis for Uttarkashi-Gangnani Highway in Indian Himalayas. Natural Hazards, 106 , 1895–1914.

Sharma, C. P., Kumar, A., Chahal, P., Shukla, U. K., Srivastava, P., & Jaiswal, M. K. (2023). Debris flow susceptibility assessment of Leh Valley, Ladakh, based on concepts of connectivity, propagation and evidence-based probability. Natural Hazards, 115 (2), 1833–1859.

Singh, A., Pal, S., & Kanungo, D. P. (2021a). An integrated approach for landslide susceptibility–vulnerability–risk assessment of building infrastructures in hilly regions of India. Environment, Development and Sustainability, 23 (4), 5058–5095.

Singh, P., Sharma, A., Sur, U., & Rai, P. K. (2021b). Comparative landslide susceptibility assessment using statistical information value and index of entropy model in Bhanupali-Beri region, Himachal Pradesh, India. Environment, Development and Sustainability, 23 , 5233–5250.

Sweta, K., Goswami, A., Nath, R. R., & Bahuguna, I. M. (2022a). Performance assessment for three statistical models of landslide susceptibility zonation mapping: A case study for Dharamshala Region, Himachal Pradesh, India. Journal of Earth System Science, 131 (3), 143.

Sweta, K., Goswami, A., Peethambaran, B., Bahuguna, I. M., & Rajawat, A. S. (2022b). Landslide susceptibility zonation around Dharamshala, Himachal Pradesh, India: An artificial intelligence model–based assessment. Bulletin of Engineering Geology and the Environment, 81 (8), 310.

Tien Bui, D., Pradhan, B., Lofman, O., & Revhaug, I. (2012). Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and Naïve Bayes models. Mathematical problems in Engineering, 2012 , 974638.

Tien Bui, D., Tuan, T. A., Klempe, H., Pradhan, B., & Revhaug, I. (2016). Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13 , 361–378.

Vishal, V., Siddique, T., Purohit, R., Phophliya, M. K., & Pradhan, S. P. (2017). Hazard assessment in rockfall-prone Himalayan slopes along National Highway-58, India: Rating and simulation. Natural Hazards, 85 , 487–503.

Xiong, K., Adhikari, B. R., Stamatopoulos, C. A., Zhan, Y., Wu, S., Dong, Z., & Di, B. (2020). Comparison of different machine learning methods for debris flow susceptibility mapping: A case study in the Sichuan Province, China. Remote Sensing, 12 (2), 295.

Xu, W., Yu, W., Jing, S., Zhang, G., & Huang, J. (2013). Debris flow susceptibility assessment by GIS and information value model in a large-scale region, Sichuan Province (China). Natural Hazards, 65 , 1379–1392.

Yan, H., & Chen, W. (2022). Landslide susceptibility modeling based on GIS and ensemble techniques. Arabian Journal of Geosciences, 15 (8), 762.

Yavuz Ozalp, A., Akinci, H., & Zeybek, M. (2023). Comparative analysis of tree-based ensemble learning algorithms for landslide susceptibility mapping: A case study in Rize, Turkey. Water, 15 (14), 2661.

Ye, P., Yu, B., Chen, W., Liu, K., & Ye, L. (2022). Rainfall-induced landslide susceptibility mapping using machine learning algorithms and comparison of their performance in Hilly area of Fujian Province, China. Natural Hazards, 113 (2), 965–995.

Yilmaz, I. (2010). The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks. Environmental Earth Sciences, 60 , 505–519.

Zhang, Y., Ge, T., Tian, W., & Liou, Y. A. (2019). Debris flow susceptibility mapping using machine-learning techniques in Shigatse area, China. Remote Sensing, 11 (23), 2801.

Zhao, H., Wei, A., Ma, F., Dai, F., Jiang, Y., & Li, H. (2024). Comparison of debris flow susceptibility assessment methods: Support vector machine, particle swarm optimization, and feature selection techniques. Journal of Mountain Science, 21 (2), 397–412.

Download references

Acknowledgements

The authors are thankful to the Director, CSIR-Central Building Research Institute, Roorkee, India for granting permission to publish this work.

Author information

Authors and affiliations.

Geotechnical Engineering and Geo Hazards Group, CSIR-Central Building Research Institute, Roorkee, Uttarakhand, 247 667, India

Rajesh Kumar Dash, Rajashree Pati & Debi Prasanna Kanungo

Indian Institute of Technology (IIT), Roorkee, Uttarakhand, 247 667, India

Department of Geology, Afe Babalola University, Ado Ekiti, Nigeria

Philips Omowumi Falae

Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201 002, India

Rajashree Pati & Debi Prasanna Kanungo

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Debi Prasanna Kanungo .

Ethics declarations

Conflict of interest.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's note.

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

Rights and permissions

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

Reprints and permissions

About this article

Dash, R.K., Gupta, N., Falae, P.O. et al. A comparative evaluation of statistical and machine learning approaches for debris flow susceptibility zonation mapping in the Indian Himalayas. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-05398-4

Download citation

Received : 20 June 2023

Accepted : 02 September 2024

Published : 14 September 2024

DOI : https://doi.org/10.1007/s10668-024-05398-4

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Debris flow susceptibility zonation (DFSZ)
• Random Forest (RF)
• Naïve Bayes (NB)
• Weight of evidence (WoE)
• Index of entropy (IoE)
• Extreme gradient boosting (XGBoost)
• Find a journal
• Publish with us
• Track your research

"Rs 5 Crore To Red Cross": Bail For 4 In Coaching Centre Deaths Case

The unprecedented bail condition seeks to deter people from committing the same or similar offence. the bail will be in effect till january 30, 2025.

Three IAS aspirants died on July 27 after a surge in rainwater flooded coaching centre basement

Four co-owners of a basement in a central Delhi building used as a coaching centre where three civil service aspirants drowned in rainwater have been granted bail by the Delhi High Court. However, the four must deposit Rs 5 crore with the Red Cross for the bail to be applicable to them, the court said.

The unprecedented bail condition seeks to deter people from committing the same or similar offence. The bail will be in effect till January 30, 2025.

The court asked the Delhi Lieutenant Governor to form a committee under the supervision of a retired high court judge to ensure that no coaching centres are run from basements in the national capital.

Justice Dinesh Kumar Sharma of the Delhi High Court said the conduct of the co-owners is "unpardonable" and an "act of greed". Justice Sharma said the authorities must designate a place in Delhi where coaching centres can operate.

Three Indian Administrative Service (IAS) aspirants - Shreya Yadav, 25; Tanya Soni, 25, and Nevin Delvin, 24 - died on July 27 after a sudden surge in rainwater flooded the basement of a building in central Delhi's Old Rajinder Nagar, where Rau's IAS Study Circle ran a coaching centre.

The co-owners of the basement -- Parvinder Singh, Tajinder Singh, Harvinder Singh, and Sarbjit Singh -- sought bail on the grounds that they were simply the landlords of the basement, which was rented out to the coaching centre, and so they have no link with the incident.

Promoted Listen to the latest songs, only on JioSaavn.com

The Central Bureau of Investigation (CBI) opposed the bail application, saying the probe was in an initial stage and the accused should not be given relief until independent witnesses are examined.

The case is being investigated under the new criminal law Bharatiya Nyaya Sanhita (BNS), including Section 105 (culpable homicide not amounting to murder), after it was transferred from Delhi Police to the CBI on the high court's order.

Track Budget 2023 and get Latest News Live on NDTV.com.

Track Latest News Live on NDTV.com and get news updates from India and around the world .

India Elections | Read Latest News on Lok Sabha Elections 2024 Live on NDTV.com . Get Election Schedule , information on candidates, in-depth ground reports and more - #ElectionsWithNDTV

Watch Live News:

• India – Size and Location Class 9 Case Study Social Science Geography Chapter 1

Last Updated on September 9, 2024 by XAM CONTENT

Hello students, we are providing case study questions for class 9 social science. Case study questions are the new question format that is introduced in CBSE board. The resources for case study questions are very less. So, to help students we have created chapterwise case study questions for class 9 social science. In this article, you will find case study for CBSE Class 9 Social Science Geography Chapter 1 India – Size and Location. It is a part of Case Study Questions for CBSE Class 9 Social Science Series.

Table of Contents

Case Study Questions on India – Size and Location Class 9

Read the following passage and answer the questions:

The Indian landmass has a central location between the East and the West Asia. India is a Southward extension of the Asian continent. The trans Indian Ocean routes, which connect the countries of Europe in the West and the countries of East Asia, provide a strategic central location to India. Note that the Deccan Peninsula protrudes into the Indian Ocean, thus helping India to establish close contact with West Asia, Africa and Europe from the Western coast and with South-East and East Asia from the Eastern coast. No other country has a long coastline on the Indian Ocean as India has and indeed, it is India’s eminent position in the Indian Ocean, which justifies the naming of an Ocean after it.

Q. 1. Of which continent is India a Southward extension? a. Asia b. Europe c. Antarctica d. Sri Lanka

Q. 2. Which routes connect the countries of Europe in the West and the countries of East Asia? a. Palk Strait route b. Trans Indian Ocean route c. Suez Canal route d. Bay of Bengal route

Q. 3. Name the only country in the world after which an ocean is named. a. Bangladesh b. Myanmar c. India d. Sri Lanka

Q. 4. What is the total length of the coastline of the Indian mainland? a. 7646 km b. 7243.6 km c. 7516.6 km d. 7526.8 km

Q. 5. Which of the following helps India to establish close contact with West Asia, Africa and Europe from the Western coast? a. Gulf of Mannar b. Palk Strait c. Deccan Peninsula d. Indian Peninsula

Q. 6. Where is the Indian landmass located in Asia? a. East located b. West located c. Southwards d. Centrally located

• Electoral Politics Class 9 Case Study Social Science Political Science Chapter 3
• Constitutional Design Class 9 Case Study Social Science Political Science Chapter 2
• Food Security in India Class 9 Case Study Social Science Economics Chapter 4
• What is Democracy? Why Democracy? Class 9 Case Study Social Science Political Science Chapter 1
• Poverty as a Challenge Class 9 Case Study Social Science Economics Chapter 3
• People as Resources Class 9 Case Study Social Science Economics Chapter 2
• Drainage Class 9 Case Study Social Science Geography Chapter 3
• Physical Features of India Class 9 Case Study Social Science Geography Chapter 2
• Forest Society and Colonialism Class 9 Case Study Social Science History Chapter 4
• Nazism and the Rise of Hitler Class 9 Case Study Social Science History Chapter 3

Socialism in Europe and the Russian Revolution Class 9 Case Study Social Science History Chapter 2

The french revolution class 9 case study social science history chapter 1, topics from which case study questions may be asked.

Here is a list of topics from which case study questions may be asked.

• Why Deforestation?
• Rise of Commercial Forestry
• How were Lives of People Affected
• Rebellion in the Forests
• The Fear of the People
• Forest Transformation in Java
• New Development in Forestry

India shares its international border with Pakistan and Afghanistan in the North-west.

Frequently Asked Questions (FAQs) on India – Size and Location Class 9 Case Study

Q1: what are case study questions.

A1: Case study questions are a type of question that presents a detailed scenario or a real-life situation related to a specific topic. Students are required to analyze the situation, apply their knowledge, and provide answers or solutions based on the information given in the case study. These questions help students develop critical thinking and problem-solving skills.

Q2: How should I approach case study questions in exams?

A2: To approach case study questions effectively, follow these steps: Read the case study carefully: Understand the scenario and identify the key points. Analyze the information: Look for clues and relevant details that will help you answer the questions. Apply your knowledge: Use what you have learned in your course to interpret the case study and answer the questions. Structure your answers: Write clear and concise responses, making sure to address all parts of the question.

Q3: What are the benefits of practicing case study questions from your website?

A3: Practicing case study questions from our website offers several benefits: Enhanced understanding: Our case studies are designed to deepen your understanding of historical events and concepts. Exam preparation: Regular practice helps you become familiar with the format and types of questions you might encounter in exams. Critical thinking: Analyzing case studies improves your ability to think critically and make connections between different historical events and ideas. Confidence: Practicing with our materials can boost your confidence and improve your performance in exams.

Q4: What are the important keywords in this chapter “India – Size and Location”?

A4: Important keywords for CBSE Class 9 India – Size and Location are given below: Peninsula:  An area of land surrounded by water bodies on three sides. Subcontinent:  Landmass having distinct physical and cultural identity within the continent. Strategic Central Location:  Nuclear-like existence from where the entire periphery is controlled, manipulated and governed or linked. Strait:  A relatively narrow water way linking two large bodies of water. Island:  A land surrounded on all sides by water bodies. Indian Union:  Refers to the union country of India comprising 29 states and 7 union territories. Continent:  A large area of land that is surrounded or almost surrounded by oceans and that usually consists of several countries. Coastline:  It refers to the line forming the boundary between land and water. Gulf:  A large area of sea partly surrounded by land. Maritime:  Activities of trade and commerce relating to the sea.

Q5: What is a subcontinent? What two features that make India a subcontinent?

A5: A landmass with distinct physical and cultural diversity within a continent is called a subcontinent. Example: India is a subcontinent. Following qualities (features) make India a subcontinent: (i) India’s self-contained landmass forms a sub-division of the Asian continent. (ii) It is separated from the Asian continent by the Himalayas in the North, Karakoram mountains in the north-east and Arakan Hills in the east.

Q6: Which countries comprise the Indian subcontinent?

A6: The countries which comprise the Indian subcontinent are: (i) Pakistan (ii) Nepal (iii) Bhutan (iv) Myanmar (v) Bangladesh (vi) India

Q7: What is the difference between a continent and a subcontinent?

A7: A Subcontinent: A subcontinent is a part of a continent. It is an independent geographical unit and separated from the main continent. Example: India. Continent: A continent is a vast landmass. It stands as a separate physical unit. There are seven continents in the world. Example: Asia, Australia, North America, South America, Antarctica, Africa and Europe.

Q8: Where does India rank in terms of landmass in the world?

A8: India is ranked seventh in terms of landmass in the world.

Q9: Are there any online resources or tools available for practicing “ India – Size and Location” case study questions?

A10: We provide case study questions for CBSE Class 9 Social Science on our website. Students can visit the website and practice sufficient case study questions and prepare for their exams.

Related Posts