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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

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 provided a donation service to support people affected by flooding in Kerala

Google Kerala Donate

Tales of humanity and hope

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Kerala Floods Quiz

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  • Published: 19 November 2022

Wave induced coastal flooding along the southwest coast of India during tropical cyclone Tauktae

  • Ratheesh Ramakrishnan 1 ,
  • P. G. Remya 2 ,
  • Anup Mandal 1 ,
  • Prakash Mohanty 2 ,
  • Prince Arayakandy 3 ,
  • R. S. Mahendra 2 &
  • T. M. Balakrishnan Nair 2  

Scientific Reports volume  12 , Article number:  19966 ( 2022 ) Cite this article

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  • Ocean sciences
  • Physical oceanography

The coastal flood during the tropical cyclone Tauktae, 2021, at Chellanam coast, Kerala, India, has invited wide attention as the wave overtopping severely affected coastal properties and livelihood. We used a combination of WAVEWATCHIII and XBeach to study the coastal inundation during high waves. The effect of low-frequency waves and the rise in the coastal water level due to wave setup caused the inundation at Chellanam, even during low tide with negligible surge height. Wave setup raised the water level at the coast with steep slopes to more than 0.6 m and peaked during low tide, facilitating wave breaking at the nearshore region. The coastal regions adjacent to these steep slopes were subjected to severe inundation. The combined effect of long and short waves over wave setup formed extreme wave runups that flooded inland areas. At gently sloping beaches, the longwave component dominated and overtopped the seawalls and damaged households along the shoreline. The study emphasizes the importance of longwave and wave setup and its interaction with nearshore bathymetry during the high wave. The present study shall lead to the development of a coastal inundation prediction system for the low-lying hot spots using the combination of WAVEWATCHIII and XBeach models.

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Climate change imposes diverse adverse impacts on coastal areas worldwide. Presently, intense cyclones, sea-level rise, storm surges, and extreme waves in the changing climate are the leading causes of coastal vulnerability problems in most coastal regions across the globe 1 . The unprecedented urbanization rate in coastal areas, especially in developing countries, makes coastal vulnerability a serious concern 2 , 3 . India has a vast coastline covering nine states, and most of these coastal states are densely populated 4 . One of the severe threats to these coastal areas is the intense tropical cyclones and associated coastal flooding and damage 4 .

The Indian Ocean is one of the world’s six cyclone-prone areas 5 . The occurrence of an average of 5–6 intense cyclones per year is expected in the North Indian Ocean (NIO). In the NIO region, cyclone occurrence has been high in the Bay of Bengal (BoB) compared to the Arabian Sea (AS), with an occurrence ratio of 4:1 until the recent past 6 . Recently this ratio has changed mainly because of the rapid warming of the AS, which supports cyclone formation, another visible impact of climate change. The AS started witnessing more intense tropical cyclones (a 150% increase during the last two decades), making India’s west coast vulnerable to cyclones imposing threats like storm surges and high waves 7 . Until recently, the west coast was least prepared for severe cyclones. From Very Severe Cyclone Storm (VSCS) Okhi onwards, the coast experienced the worst damage along the western coastal regions. This was not different in the case of VSCS Tauktae (hereafter referred to as TC Tauktae) in May 2021. The cyclone caused severe damage to many coastal regions as the cyclone traversed parallel to the west coast. High waves were lashing on the coastal areas, which posed a severe threat to the life and property of the coastal population along the west coast until it made landfall in Gujarat on May 17, 2021. In both cases, one of the most affected states was Kerala.

TC Tauktae caused widespread damage in Kerala, especially in the coastal regions, through coastal flooding, erosion, and destruction of houses in vulnerable areas along the coast. The high wave attacks, erosion and flooding, forced the evacuation of hundreds of families in each affected District. The ocean state, weather, and storm surge forecasts were well in place 8 . The storm surge predicted with the operational forecast system is about 0.15 m at the Chellanam coast and showed no coastal inundation in the present operational storm surge inundation forecast system. Moreover, the impact period at the Chellanam coast corresponded with the low tide. Despite these conditions, the coast was severely flooded with wave overwash that highlighted the complex coastal wave dynamics and its interaction with the underlying bathymetry.

The flooding at Chellanam is purportedly due to the infragravity waves and the wave setup that cause a resultant increase in the mean water level at the coast, facilitating wave overwash and inland inundation. The infragravity or long frequency waves associated with the incoming short wave bands 9 elevate the total wave runup. As the infragravity waves increase the coastal surface water elevation, they might significantly contribute towards extending the coastal inundation during the wave overwash under cyclone conditions. The coastal water elevations are also increased due to the wave setup formed under breaking waves, where the cross-shore gradient in the radiation stress results in the rise of the mean water level at the coast 10 . The infragravity waves are not resolved by the operational forecast system for coastal inundation during the cyclone. Even though the forecast system includes the effect of wave radiation stress, a coarser grid resolution of ~ 100 m at the shoreline has failed to simulate the coastal inundation at Chellanam during the TC Tauktae. A forecast system for coastal inundation that incorporates the complex coastal wave hydrodynamics is very much needed in places like Chellanam, Kerala, where the high waves create frequent coastal inundations and destruction to livelihood. Hence, the present study attempts to predict wave-induced coastal inundation during the TC Tauktae to explore the possibility of an inundation forecast system for Chellanam. We used a combination of WAVEWATCHIII and XBeach models for the study.

Tropical cyclone Tauktae

TC Tauktae was the first very severe cyclonic storm over the north Indian Ocean in 2021 and the most intense cyclone of the AS during the satellite era (1961–2021) after the Kandla cyclone in 1998. A well-marked low-pressure area formed over the southeast AS and adjoining Lakshadweep area on May 13, 2021. Under favourable environmental conditions, it concentrated into a depression over the Lakshadweep area in the morning of May 14, 2021, and intensified into a deep depression in the afternoon. The deep depression further intensified into cyclonic storm “Tauktae” at the same midnight of May 14 over the same region, which then intensified into a severe cyclonic storm and moved northward on May 15 (Fig.  1 ). Continuing to move nearly northwards, it intensified into VSCS in the early hours of May 16. It gradually started moving north-northwestwards from noon (1130 hours IST/0600 UTC) of May 16 and intensified rapidly into an extremely severe cyclonic storm in the early hours of May 17. After that, it entered a marginally unfavourable environment, weakened gradually and crossed the Saurashtra coast near latitude 20.8° N and longitude 71.1° E, close to the northeast of Diu during 2000–2300 hours IST of May 17, 2021 with a maximum sustained wind speed of 160–170 kmph gusting to 185 kmph. TC Tauktae caused adverse weather and damage over entire west coast states, Union Territories and Lakshadweep as it moved parallel to the west coast and crossed Gujarat.

figure 1

Study region showing ( a ) Arabian sea overlaid with the track of TC Tauktae, location of wave rider buoy AD07 and Ratnagiri used to validate WW3 is marked, ( b ) LISS-IV image of Chellanam region, location of time series Wave Watch III data used to force the XBeach model is shown as white circle; ( c ) Bathymetry of the domain used to simulate the nearshore wave dynamics using XBeach model, BW is the break water, the inset demarcates regions as A and B and the point locations 1 to 10 are used to estimate H ln [We have used licensed version of ArcGIS desktop version 10.5 available at Space Applications Centre to prepare this figure, http://www.esri.com/ ].

Chellanam is a coastal village located on the southwest border of the Ernakulam district. The coastal stretch of Chellanam village extends to about 15 km (Fig.  1 ). A total population of almost 16,000, mostly belonging to the working class and farming community, fishing, agriculture, aquaculture etc., with relatively modest or poor living conditions, are staying in the village. The major issue faced is coastal erosion and inundation, which has been creating serious havoc among the people due to the destruction and loss of houses constructed near the shore, especially during high swell events and monsoon. Recently the passage of TC Taukate badly affected the entire coastal belt of Chellanam. Huge waves overtopped the sea wall resulting in floods in the low-lying areas. Severe damages occurred to the houses, household items, vehicles and other infrastructure facilities. The adopted protection measures (Seawall and geotubes or a combination of these measures) all along the coast are inadequate to manage the erosion and inundation along the Chellanam coastal stretch. These protection structures were critically damaged in several places on the Chellanam coast, causing overtopping during high waves 11 .

Data and methodology

The present study has used a coastal high-resolution blended bathymetry merged with a topographic database. The bathymetry data is a blend of in-situ data (hydrographic charts, surveyed data from ships) for coastal regions and the General Bathymetric Chart of Ocean (GEBCO) data of 30 m spatial resolution towards the offshore. The outlier filtering was performed using a 2-sigma of semi-variance value within a 9 × 9 kernel spatial running window to avoid the abnormal spatial spike on the blended bathymetry. The blended coastal bathymetry is accurate, with an RMSE of 0.66 m in shallow waters (up to 60 m depth), which is essential to enhance the accuracy of the coastal modelling and inundation simulations. A high-resolution (5 m) Airborne Lidar Terrain Mapping (ALTM) topography data with 30 cm vertical accuracy up to 2 km from the coast and Cartosat-1 DEM (CartoDEM) data beyond 2 km were used as sources of the land elevation along the coastal zones of the study area. All these datasets were corrected to a common MSL datum.

Models used

Wavewatch iii.

WAVEWATCH III (WW3) version 6.07, with ST4 parameterization scheme 12 and with 4 grid mosaic a global grid of 1° spatial resolution, two regional grids (Indian Ocean (0.5) and northern Indian Ocean (0.25°)) and a coastal grid (0.04°)) for the Indian Ocean region was forced with ECMWF wind fields and generated the wave fields 13 . The model uses a spectral grid that consists of 29 frequencies and 36 directions. The wave spectrum extracted along the location shown in Fig.  1 is used as the open boundary condition for the 2D XBeach model.

XBeach model

The XBeach surf beat mode resolves the short wave variations on the wave group scale and allows the representation of long waves 14 . A dependent wave-action balance equation is solved using the dissipation model to derive the wave group forcing 15 , 16 . The momentum after breaking is represented by a roller model 17 . The associated radiation stress gradients exert force on the water column, thus representing the setup, wave-driven currents and longwave swash. The nonlinear shallow water equations solve the long-period waves and unsteady currents 14 . The mathematical description of the model and the numerical schemes involved are detailed in 15 , 16 .

A report of the under-prediction of longwave runup 18 prompted subsequent improvements in the XBeach with a single direction scheme to better predict the short wave groupiness. The performance of the XBeach in predicting long-period waves was evaluated for the Hambantota Port in Sri Lanka and observed accurate prediction of long waves in the open domain 19 . Although using stationary wave conditions, the performance of the XBeach in simulating coastal erosion has been evaluated for the Indian coastal region by 20 , 21 .

The XBeach model is configured in 2D, where we have used varying grid resolution in the across-shore direction with 20 m resolution set to the coastal region, and the longshore grid resolution is kept constant at 20 m. The high-resolution blended bathymetry and topography (“ Bathymetry ” section) are used to create the domain shown in Fig.  1 . As the present study focuses on coastal inundation, we have excluded sediment transport and morphological updating. The directional wave spectrum from 13 to 17 March 2021 extracted for the location shown in Fig.  1 b from WW3 is used to force the XBeach model along with the predicted tidal elevation using the Global Tide Model of MIKE21 toolbox developed by DTU Space 22 .

The significant wave height of the longwave (H ln ) and the short wave (H sh ) is computed from the time series information of model output written for point locations marked from 1 to 10 in Fig.  1 . The energy spectrum is obtained from the variance of the time series surface elevation filtered within the frequency range of infragravity waves (0.005–0.04 Hz) at the locations and the zero-order moment of the energy spectrum ( m 0 ) is used to estimate H ln 14 , 19 , 23 as

Results and discussions

The inundation of the coastal area along the Chellanam hamlet on the southern coast of India during the TC Tauktae was in the limelight as several households, roads and public facilities were severely affected. The XBeach model was applied in surfbeat mode to simulate the wave conditions from May 13 to May 17, 2021. Figure  2 shows the significant wave height (Hs) validation at an offshore and coastal buoy location (Fig.  1 a) during TC Tauktae. It indicates the ability of the operational WW3 wave model to accurately simulate the cyclone-induced high waves in the area of interest, thereby ensuring the correctness of the wave boundary conditions given to the XBeach model.

figure 2

Validation of WW3 significant wave height forecast with buoy observations ( a ) offshore ( b ) coastal.

The significant wave height of the longwave component (H ln ) is estimated as described in section “ XBeach model ” for the point locations shown in Fig.  1 c. Five locations are taken for each region corresponding to offshore bathymetry contours of − 15, − 10 and − 5 m near the shoreline. Figure  3 b,c show the time series H ln estimated for the point locations at regions A and B, and the offshore wave condition is plotted in Fig.  3 a. A notable increase in the H ln can be observed from 14:00 h on May 14 until 10:00 h on May 15, 2021, specifically at point locations near the coast. Hln peaks at point locations in both regions correspond to a − 5 m bathymetry contour. The relative increase in H ln corresponds to the time when high waves (H sh , Fig.  3 a) generated by TC Tauktae reached the coast of Chellanam. The amplitude of the long wave is approximately proportional to the height of the incident short wave and independent of the period 24 .

figure 3

( a ) Shot wave parameters at the offshore boundary; ( b ) Significant wave height at points 1 to 5 (Fig.  1 ); ( c ) Significant wave height at points 6 to 10 (Fig.  1 ).

Figure  4 shows the change in the significant wave height of H sh and H ln for regions A and B, from the offshore boundary to the coast during the highest wave event of the cyclone impact. While approaching the coast, the energy of the short wave gets dissipated, and the wave height is reduced. In contrast, the wave height of the longwave component increases from negligible height at the boundary toward the coast. In both regions, the peak of H ln at − 5 m is observed to reduce as the wave approaches the shoreline. The significant wave height of H ln at the shoreline of region A is about 0.7 m, while at the shoreline of region B, the H ln is about 0.8 m. Ruju et al. 25 observed the energy of the infragravity waves to increase at the outer surf zone, where the gradient in the radiation stress balance the nonlinear energy transfer from swell to infragravity waves. The increase in the infragravity waves is limited at the outer surf zone, where the dissipation starts towards the shoreline. Infragravity wave growths in the inner surf zone can be higher along gently sloping bathymetry due to long propagation time 26 . The coastal slope at region B is gentle compared to region A (Fig.  6 ) and shows an increased infragravity wave height near the coast.

figure 4

The change in the significant wave height of H sh and H ln at region A and B from offshore boundary to the shoreline.

The momentum of the waves is transferred to the water column in the surf zone, which leads to an increase in the water level called the wave setup. The water level from May 14, 14:00 h to May 15, 10:00 h, corresponding to the peak storm, is analyzed to obtain the maximum water level at each grid and is shown in Fig.  5 a, and the significant wave height obtained with the same procedure is shown in Fig.  5 b. Along the coastal zone, the maximum significant wave height shows spatial variability, where the coastline in region A is impacted with higher waves compared to the region marked as B. Spatial variation in the maximum water level due to wave setup (Fig.  5 a) is prominent along the coast. The water levels are high on the northern coast (region marked as A), and in the region marked as B, the highest water level falls far from the coast. Figure  6 shows the average maximum water level (Fig.  5 ) estimated along 10 cross-shore profiles at regions A and B, and the corresponding cross-shore bathymetry profiles are plotted. In region A, the cross-shore bathymetry from − 6 m to the shoreline has a sudden decrease in depth, forming a steep slope of 0.22. At the same time, the bathymetric slope at region B is relatively steep, between − 10 and − 6 m, which is located away from the coast. From − 6 m to the shoreline, the bathymetry shows a gentle slope of 0.08 in region B. The water level at region A is steep towards the coastal region; the elevation reaching a maximum of over 0.6 m near the shoreline 27 established an empirical relationship for wave setup that is proportional to the slope. It can be observed that the wave setup is steep at region A, where the bathymetry profile forms a steep slope. Whereas the water elevation at region B reaches a maximum of about 0.6 m at a distance of about 1 km from the shoreline, and then it gradually drops to around 0.4 m at the shoreline. As observed from the bathymetry profile of region B, the slope is steep away from the coast between − 10 to − 6 m, which possibly has increased the wave setup. Moreover, towards the coast, the slope reduced with a gradual decrease in the surface water elevation.

figure 5

Simulated maximum ( a ) wave setup and ( b ) significant wave height (short wave) during the period of TC Tauktae at Chellanam.

figure 6

Maximum water level due to wave setup at region A, B, along with the corresponding bathymetry profiles.

The impact of the TC Tauktae at the Chellanam coastal region occurred during low tide, which may have increased the wave setup. From the time series water elevation at point locations 5 and 10, the average is estimated for 15-min intervals and is plotted in Fig.  7 a along with the tidal condition. During the storm wave conditions, the peak in wave setup is concomitant to the low tide. A small peak in wave setup is also observed during the non-storm condition coinciding with the low tide condition. We carried out two experimental simulations to understand the effect of tidal conditions on wave setup. In the first simulation, the model is forced with the out-of-phase tide, and in the second simulation, a constant tide of 0.4 m is given while retaining the same wave boundary parameters.

figure 7

( a ) Predicted tide at Chellanam and wave set up averaged over 15 min at locations 5 and 10 of regions A and B, respectively. ( b ) Experimental simulation with the out-of-phase tide and constant tide of 0.4 m at region A.

The out-of-phase tide and corresponding averaged surface water elevation at station 5 are plotted in Fig.  7 b, where it can be observed that the peak in wave setup during the storm wave shifted in time to be concurrent with the low tide. The surface water elevation simulated with the constant tide is also shown in Fig.  7 b. The peak wave setup with the constant tide has decreased to 0.17 m. In comparison, the wave setup simulated with tidal variation has peak values of more than 0.25 m which corresponds to the low tidal condition 28 give a plausible reason that with increased water depth during high tide, large waves reach the shore without breaking, resulting in a reduced height of wave setup. During low to mid-tide, the wave setup gets pronounced due to nearshore wave breaking. The shoreline of Chellanam is protected with a seawall, and due to the presence of steep coastal bathymetry, during high tide, the waves may reach the seawall without breaking, while the low tide favours nearshore wave breaking that induces wave setup and elevated water level at the coast.

Figure  8 shows the maximum inundation extent during the period overlaid on Google Earth. Even though the waves overtopped and inundated the entire coastline, the landward inundation is maximum to the northern part of the domain. The XBeach model in surfbeat mode has simulated the long period infragravity waves that increased its height as the wave propagated to the shoreline and had a peak value of more than 0.7 m near the coast. The maximum surface water elevation at the shoreline for region A due to the wave setup was 0.7 m. The combined effect of infragravity waves and wave setup increased the coastal water elevation to about 1.5 m, over which the storm waves acted along the coast, overtopped the coastal structures and inundated the low-lying regions. The inland inundation reached about 300 m in the northern part. Reports during the TC Tauktae have confirmed the inundation of the coastal road around 300 m away from the shoreline at places ( https://www.thehindu.com/news/national/kerala/cyclone-tauktae-chellanam-continues-to-reel-under-flooding-people-shifted-to-relief-camps/article34565220.ece ).

figure 8

Simulated coastal inundation at Chellanam over Google Earth images. The point locations shown are ( a ) Cheriyakadavu, ( b ) Kannamali, ( c ) Velankanni, ( d ) Kandakkadavu and the corresponding photographs of inundation are shown in the right panel.


The simulation carried out to study the inundation of Chellanam emphasizes the contribution of infragravity waves and wave setup on the overtopping of the waves inundating the coastal regions that are often ignored in the operational framework of coastal inundation during cyclone conditions. The coastal inundation at Chellanam is important, as the storm surge during the cyclone was negligible, as observed from the tide station data at the adjacent Cochin Port and the time of high wave impact corresponds to the low tidal conditions. Despite the above conditions, the inundation at Chellanam has severely affected the settlements. The waves severely damaged many houses, and overtopped water flushed past beach road and caused waterlogging even at those on the eastern side of the road.

The bathymetry slope has crucially controlled the wave setup elevation, which peaked at about 0.7 m at the shoreline with steep bathymetry profiles. The temporal variability is influenced by the incoming short and long waves and tidal conditions. The simulation results show that the wave setup has peak elevation during the low tide time. Experimental simulation with constant high tide conditions significantly reduced the wave setup elevation, showing the effect of low to mid-tide conditions in enhancing the wave setup elevation. The combined impact of short wave, longwave component and wave setup on the maximum runup extent is modulated by the steepness of the bathymetry and the tidal conditions. The peak in the longwave and wave setup corresponded to the high waves from the TC Tauktae, resulting in wave overwash that caused severe flooding, and the coastal residences at Chellanam were severely affected. The study also envisages the modelling framework to include the longwave component and the wave setup for operational inundation forecast during the cyclone and the coastal flooding during the high swell waves or the Kallakadal phenomenon. The development of wave-induced inundation and erosion forecast systems for selected hot spots is the need of the hour as extreme waves may cause extreme damage to the coast, and in the anticipated climate change scenario, with increased storm surges; heavy rains and rising sea level, the impact on the coastal region will be extremely adverse.

Data availability

The mooring observations used in this article can be accessed upon request from INCOIS ( https://incois.gov.in/portal/datainfo/drform.jsp ).

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Director, INCOIS is acknowledged for facilitating this research work. This research falls under OCCAS-Deep Ocean Mission, Ministry of Earth Sciences (MoES), Govt. India. Authors thank MoES for the support. Authors are also thankful to Shri Nilesh Desai, Director, SAC-ISRO, Ahmedabad and Dr. I. M. Bahuguna, Deputy Director, EPSA, for opportunity to carry out this work and overall guidance. Authors are grateful to Dr. R. P Singh, Director IIRS, ISRO and Dr. D. Ram Rajak, Group Head, MISA, PPEG for their support and encouragement. This work is carried out in collaboration between Space Applications Centre (SAC, ISRO), Ahmedabad and INCOIS, Hyderabad (INCOIS Contribution No. 478).

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R.R. and R.P.G. conceived the idea. R.R. did the model runs with help from R.P.G. P.M. and M.R.S. created the bathymetry data. A.M. helped in the data preparation and plotting. P.A. provided the study area details and helped in the plotting of Fig.  8 . T.M.B. provided critical revisions on the first draft, and all authors contributed equally to finalize this version of the article.

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Ramakrishnan, R., Remya, P.G., Mandal, A. et al. Wave induced coastal flooding along the southwest coast of India during tropical cyclone Tauktae. Sci Rep 12 , 19966 (2022). https://doi.org/10.1038/s41598-022-24557-z

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case study of flood in india 2021

A woman up to her waist in water in a flooded street in Chennai during a downpour.

Chennai’s floods: the city has learned nothing from the past – here’s what it can do

case study of flood in india 2021

PhD Candidate in Disaster Risk Reduction, Edinburgh Napier University

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November 2021 was a devastating month for flooding in the city of Chennai, the capital of Tamil Nadu in southern India. With 1,000mm of rainfall in just four weeks, these were the worst rains since the devastating floods of 2015 when it poured for 22 out of 30 days in December, setting a record of 1,049mm.

Then , as now , the flooding in Chennai was described as a man-made disaster, despite occurring during a storm called Cyclone Nivar . On both occasions, death, disruption and destruction was the result. India has faced more than 300 weather events in the last two decades, resulting in over 79,000 deaths.

Tamil Nadu has long been known for its susceptibility to a long list of natural disasters: cyclones, storm surges, coastal flooding, torrential rainfall, earthquakes and tsunamis. India accounts for 24% of all disasters in Asia and Chennai ranks seventh in the list of India’s most vulnerable districts to extreme flooding and cyclones.

The southern state’s 1,076km of coastline makes up more than 13% of the total Indian coastline, bordered by the Bay of Bengal in the east, the Indian Ocean in the south and the Arabian Sea in the west.

Cyclones need a lot of heat (at least 26°C) to form, and warm tropical sea beds provide this energy. The Bay of Bengal has a deep layer of warm water, which fuels the rapid formation of cyclones that gather force within a short period of time.

Since the catastrophic 2004 Boxing Day tsunami which killed 10,000 people in Tamil Nadu, there have been 14 cyclones and regular flooding in this area. Yet the preparation and response to these extreme events have been woefully inadequate over the last 15 years.

Why has nothing changed?

The Indian Meteorological Department issued a red alert warning on November 12 2021. But 48 hours later Chennai had once again ground to a standstill due to heavy rainfall triggered by the north-east monsoon season which occurs between October-December annually.

Seventeen deaths were recorded, and power outages, submerged dams and underpasses hampered movement around the city, including rescue operations by the National Disaster Risk Force (NDRF).

As Tamil Nadu’s tech-hub “ smart city ”, Chennai has attracted newcomers with the prospect of good jobs, housing and amenities, swelling its population to just over 11m people . This has put even more pressure on existing infrastructure and services.

Despite the continuous development and increasing population, nothing in the city’s disaster response has changed over the last 16 years, even after the devastating 2015 floods. Officials failed to take preventive measures and released dam water without announcement, over-filling the Chembarambakkam reservoir flooding the nearby areas. The city’s densely packed housing has only made preparation for any future extreme weather events more difficult.

Gridlocked systems

Most of Chennai’s problems revolve around inadequate and poorly managed infrastructure, resulting in leaks, blocked drains and over-burdened sewage systems – problems that have existed in Chennai for decades. Bad planning is also an issue in the city, where the state government has allowed building and development on marshlands and wetlands which would normally have soaked up floodwater.

The Cooum, which flows through the city, has slowly become a highly polluted, toxic river full of sewage. During the 2015 Chennai floods, it quickly became an open sewer as the city’s drains overflowed and the water submerged sewage systems.

One of the greatest problems the city faces is plastic pollution. Around 3.4 million tonnes of plastic waste are generated by India each year. Chennai ranks second in the country for plastic waste, producing 429 tonnes a day. The plastic clogs up drains and sewers so that during heavy rainfall floodwater has nowhere to go, resulting in waterlogged streets.

Failure of policies and inadequate infrastructure coupled with excessive bureaucracy and inaction mean that every time there are heavy rains or storms, Chennai’s citizens face disruption, displacement and tragedy as they did in November. It is essential that the city significantly reduces its plastic pollution, invests in proper floodwater drainage and continually re-assesses the changing risks and vulnerabilities to future flood scenarios.

Making the right decisions and implementing them may not be a straightforward process in a country like India because of widespread poverty, lack of planning, coordination and proper channelling of funding. Perhaps cities like Chennai need to adopt alternative approaches such as community-based flood management programmes .

This would encourage communities to take responsibility and action, empowering the very people whom the flooding affects most. It would also help to dispel the dangerous sense of inevitability that pervades the city about flooding.

There are specific reasons Chennai floods so easily, but they can be addressed. Awareness of how plastic pollution contributes to the problem and what can be done about it at a local level would be a good place to start.

  • Bay of Bengal
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  • Plastic pollution

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Icy flood that killed at least 41 in India’s northeast had been feared for years

Hundreds of rescuers dug through slushy debris and fast-flowing, icy water Friday in a search for survivors after a glacial lake overflowed and burst through a dam in India’s Himalayan north , a disaster that many had warned was possible for years.

The flood began in the early hours of Wednesday, when water overflowed a mountain lake with enough force to break through the concrete of a major hydroelectric dam downstream. It then poured into the valley below, where it killed at least 41 people and forced thousands to flee their homes.

Police said that 22 of the dead were found miles downriver in West Bengal state, while 100 people are still missing.

It wasn’t clear what triggered the flood. Experts pointed to intense rain, and a 6.2 magnitude earthquake that struck nearby Nepal on Tuesday afternoon, as possible contributors.

The deadly flood was the latest to hit northeast India in a year of unusually heavy monsoon rains. Nearly 50 people died in flash floods and landslides in August in nearby Himachal Pradesh state, and record rains in northern India killed more than 100 people over two weeks in July.

The design and placement of the 6-year-old Teesta 3 dam, the largest in Sikkim state, were controversial from the time it was built, part of an Indian push to expand hydropower energy.

Local activists argued that extreme weather caused by climate changes makes dam-building in the Himalayas too dangerous, and warned that the dam’s design didn’t include enough safety measures.

A report compiled by the Sikkim State Disaster Management Authority in 2019 had identified the lake the Teesta 3 dam was built to contain as “highly vulnerable” to flooding that could cause extensive damage to life and property in downstream areas, warning of the risk of flash floods that could break through dams.

The dam’s operator, and local agencies responsible for dam safety, did not respond to requests for comment Friday.

A flood that burst through a major hydroelectric dam in India's Himalayan northeast killed at least 31 people, officials said Friday, as ice-cold water swept through mountain towns, washing away houses and bridges and forcing thousands of people to leave their homes.

A vehicle is seen partially submerged in water after flash floods triggered by a sudden heavy rainfall swamped the Rangpo town in Sikkim, India, Thursday, Oct.5. 2023. The flooding took place along the Teesta River in the Lachen Valley of the north-eastern state, and was worsened when parts of a dam were washed away. (AP Photo/Prakash Adhikari)

A 2021 study by researchers in India, the United States and Switzerland warned that a catastrophic flood was becoming more likely as melting glaciers caused water levels in the lake to rise.

The Teesta 3 hydropower project, built on the Teesta River, took nine years and cost $1.5 billion to construct. The project was capable of producing 1,200 megawatts of electricity — enough to power 1.5 million Indian homes — and began operation in 2017.

Buildings are inundated after flash floods triggered by a sudden heavy rainfall swamped the Rangpo town in Sikkim, India, Thursday, Oct.5. 2023. The flooding took place along the Teesta River in the Lachen Valley of the north-eastern state, and was worsened when parts of a dam were washed away. (AP Photo/Prakash Adhikari)

“Despite being the biggest project in the state, there were no early warning systems installed even though the glacier overflowing was a known risk,” said Himanshu Thakkar of the non-governmental organization South Asian Network for Rivers, Dams and People.

According to a release from India’s National Disaster Management Agency Friday, they plan to set up early warning systems for real-time alerts at most of India’s 56 known at-risk glacial lakes.

Thakkar said authorities failed to apply the lessons from a 2021 dam breach in Himalayan state of Uttarakhand that killed 81 people, allowing an “eerily similar” disaster to occur.

“We knew that this was coming,” said Gyatso Lepcha, general secretary of Affected Citizens of Teesta, an environmental organization based in Sikkim. “The same can happen with other dams also,” he wrote, in a statement that called for a safety review of all dams in the state.

India Sikkim Flood Damage

Disasters caused by landslides and floods are common in India’s Himalayan region during the June-September monsoon season. Scientists say they are becoming more frequent as global warming contributes to the melting of glaciers there.

Despite the risks, the Indian government has approved hundreds of new hydroelectric dams to be built across the Himalayan region. To meet ambitious clean energy goals, it aims to increase India’s hydroelectric dam output by half, to 70,000 megawatts, by 2030.

But the growing frequency and intensity of extreme weather, driven in part by climate change, puts many of these dams and the people living downstream from them at risk. A 2016 study found that over a fifth of the 177 dams built close Himalayan glaciers could fail if glacial lakes burst, including the dam in Sikkim.

In 2021, the Indian federal government passed a dam safety law that requires operators and local governments to plan for emergencies, but the Teesta-3 dam is not listed as being monitored for safety by India’s chief dam regulator, the Central Water Commission.

Last month, dam breaches caused by Storm Daniel caused devastating damage to the city of Derna in Libya.

More than 2,000 people were rescued after Wednesday’s floods, the Sikkim State Disaster Management Authority said in a statement, adding that state authorities set up 26 relief camps for more than 22,000 people impacted by the floods.

Rescue work continues after flash floods triggered by a sudden heavy rainfall swamped the Rangpo town in Sikkim, India, Thursday, Oct.5. 2023. The flooding took place along the Teesta River in the Lachen Valley of the north-eastern state, and was worsened when parts of a dam were washed away. (AP Photo/Prakash Adhikari)

This photo provided by the Indian Army shows army vehicles that got washed away in flash floods triggered by a sudden heavy rainfall in Sikkim, India, Thursday, Oct.5. 2023. (Indian Army via AP)

One soldier was previously reported missing was rescued, and the bodies of seven have been found, state police said.

Eleven bridges in the Lachan Valley were washed away by the floodwaters, which also hit pipelines and damaged or destroyed more than 270 houses in four districts, officials said.

The army said it was providing medical aid and phone connectivity to civilians in the areas of Chungthang, Lachung and Lachen, and local media reported that said the army was erecting temporary bridges to bring food to affected areas.

case study of flood in india 2021

The Associated Press

’10 times deadlier’: Floods devastate town in India’s Maharashtra

A week after floods destroyed their town and killed many, residents in Konkan region’s Chiplun remember the tragedy.

case study of flood in india 2021

Chiplun, Maharashtra, India – At least 209 people are confirmed dead due to floods caused by the heavy monsoon rainfall since July 22 in the western Indian state of Maharashtra.

Ratnagiri and Raigad, the two coastal districts in the state’s Konkan region, were the worst hit, reporting 130 of the total deaths in floods and landslides.

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Ratnagiri’s Chiplun and Raigad’s Mahad bore the maximum brunt of the disaster, forcing Maharashtra Chief Minister Uddhav Thackeray to visit the two towns on Sunday.

“We are standing with you to ensure you get back up on your feet,” assured CM Uddhav Balasaheb Thackeray while comforting the traders and shopkeepers in the Chiplun market. pic.twitter.com/rr6Mnn08Aq — CMO Maharashtra (@CMOMaharashtra) July 25, 2021

The floods are reminiscent of a similar catastrophe that hit the region in 2005, killing more than 1,000 people, including nearly 500 in Mumbai, India’s financial capital, alone.

But residents in Chiplun, home to about 150,000 people, say the tragedy this year was worse.

case study of flood in india 2021

“The 2021 floods were 10 times deadlier than the 2005 floods,” 58-year-old Vrunda Gandhi from Chiplun’s Peth-Maap area told Al Jazeera.

Chiplun is located at the foothills of the Sahyadri mountain range in the Konkan region, with two sides of the town surrounded by Vashishthi and Shiv rivers.

The Arabian Sea is barely 25km (15 miles) away, making the area prone to water coming in from the rivers’ tributaries as well.

Moreover, excess water from the Koyna Dam, one of Maharashtra’s largest – some 90km (56 miles) away, arrives in a reservoir close to Chiplun and gets mixed with the Vashishthi River.

So, the town gets choked from all sides if there is excessive rainfall during the monsoons.

Pravin Pawar, a senior government official in Chiplun, told Al Jazeera the area received 450mm (17.7 inches) of rainfall in a single day on July 22. For comparison, the highest single-day rainfall recorded in the capital, New Delhi, in the last 15 years was 144mm (5.7 inches) in 2016.

How the floods started

The people in Chiplun went to sleep on the night of July 21 amid the relentless downpour. At about 3am the next day, WhatsApp messages started alerting people of water levels rising in the coastal town.

case study of flood in india 2021

Vaibhav Chavan, 42, says he began warning the local municipal office at about 1.30am. He alleges no steps were taken to alert the sleeping residents.

“By 5am, water started seeping into people’s houses. It was only then that some people started waking up to find 5-6 feet [1.5-1.8 metres] of water outside their homes,” he told Al Jazeera.

“The situation turned chaotic … By the morning, there was almost 13-15 feet [4-4.6 metres] of floodwater, and the people were stuck inside their homes.”

In such a scenario, India’s National Disaster Response Force (NDRF) is usually assigned to rescue people from flooded areas.

But the residents say the NDRF did not arrive in Chiplun for more than 24 hours of flooding, while the local police could not start any rescue operation as they lacked the tools.

“We kept waiting for any help to arrive from the government, but that didn’t happen. The local boys helped my family reach a safe place,” said Gandhi, whose house is on the ground floor.

Pawar told Al Jazeera the water current was so strong that no rescue operation could have been done as the “boats were being turned because of the water flow”.

For a full day, there was no clarity on the situation as rising waters did not allow anyone to enter the town.

case study of flood in india 2021

Reports say at least eight COVID-19 patients admitted to a private hospital in Chiplun died as a power outage shut the oxygen machines and there was no diesel available to switch on the generator.

The medical staff at the hospital allegedly ran away from the scene fearing attacks by the relatives of the patients who had died.

Residents spent the night in fear as water continued to drown the town, with some local volunteers helping the community.

Raju Vikhare, 53, said saving his and his family’s life became his only priority after a point.

“We thought we would die. When I took my family to a neighbour’s two-story house, the water was reaching my neck. As I started going back, I realised I cannot go on top of the house since I could slip easily from the roof and fall right into the floodwater,” he told Al Jazeera.

An NDRF team finally arrived at about 8am on July 23, say the residents, nearly 26 hours after the floods hit.

Since Chiplun is on the foot of the Sahyadri, the water flows downhill. So the floodwaters had gone down by the time the NDRF team arrived.

But the devastating floods have left heaps of mud that has not been cleared a week after the disaster, which destroyed dozens of houses and businesses, and made nearly 1,000 people homeless in Chiplun.

case study of flood in india 2021

Sahil Takale, who owns an electronics store in the town, estimated his losses at $235,000.

“I lost six warehouses and one big showroom full of electronic goods. We tried walking in the floodwater to try and save some [sections] of our shop, but it was not possible at all,” he said.

Takale said the insurance of his businesses can salvage a percentage of the loss. “We are OK, but there are many small businessmen in the city who don’t buy insurance. Only God knows how they will emerge from this tragedy.”

The entire market in Chiplun looked as if it had no roads, with the entire surface turning brown due to mud.

‘Man-made disaster’

Residents say the flood in Chiplun was worsened by Koyna Dam releasing millions of gallons of water.

“We understand that the dam needs to release water, but at least make us aware and don’t surprise us like this in the middle of the night,” local politician Faisal Kaskar told Al Jazeera.

But experts find it hard to believe that only water released from the dam could cause destruction of this level.

Pankaj Dalwi, environmentalist and founder of Konkan Alert NGO, says an “unparalleled deforestation” and absence of urban planning measures were the prime reasons for the “man-made disaster”.

“There has been immense amount of earth-moving and tree-cutting for the purpose of the new Mumbai-Goa National Highway and Pune-Bijapur state highway. This deforestation led to the water bodies changing their routes and seeping into the cities,” Dalwi told Al Jazeera.

Dalwi said Chiplun used to be a wetland area, but in the past 10-15 years, there has been the construction of residential societies on those lands.

“Chiplun did not follow any urban policy guidelines and ignored the environmental impact of the constructions on these wetlands,” he said.

Pawar agreed with the claims. “We have to accept it. This is due to climate change.”

case study of flood in india 2021

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Modeling, mapping and analysis of urban floods in India—a review on geospatial methodologies

  • Review Article
  • Published: 09 October 2021
  • Volume 28 , pages 67940–67956, ( 2021 )

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case study of flood in india 2021

  • Sreechanth Sundaram 1 ,
  • Suresh Devaraj 2 &
  • Kiran Yarrakula 3  

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An increasing trend of urban floods in India from past several years causes major damages on Indian cities. By 2050, more than half of the population in the developing countries like India are expected to migrate to urban regions. Urbanization is triggered in developing countries as people migrate to cities in search of employment opportunities resulting in formation of new slums. With high density of population concentration in cities, urban floods are triggered leading to a significant impact of human life and economy of the country. The review focuses on addressing the urban flood occurrence in India and its relationship with population growth climate change. The study also describes the impact of urban floods to the environment and integrated methodologies adopted over decades for the prediction and effective mitigation and management during a disaster event.

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Sundaram, S., Devaraj, S. & Yarrakula, K. Modeling, mapping and analysis of urban floods in India—a review on geospatial methodologies. Environ Sci Pollut Res 28 , 67940–67956 (2021). https://doi.org/10.1007/s11356-021-16747-5

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Protecting people from a changing climate: The case for resilience

About the authors.

This article is a collaborative effort by Harry Bowcott , Lori Fomenko, Alastair Hamilton , Mekala Krishnan , Mihir Mysore , Alexis Trittipo, and Oliver Walker.

The United Nations’ 2021 Intergovernmental Panel on Climate Change (IPCC) report stated —with higher confidence than ever before—that, without meaningful decarbonization, global temperatures will rise to at least 1.5°C above preindustrial levels within the next two decades. 1 Climate change 2021: The physical science basis , Intergovernmental Panel on Climate Change (IPCC), August 2021, ipcc.ch. This could have potentially dangerous and irreversible effects. A better understanding of how a changing climate could affect people around the world is a necessary first step toward defining solutions for protecting communities and building resilience. 2 For further details on how a changing climate will impact a range of socioeconomic systems, see “ Climate risk and response: Physical hazards and socioeconomic impacts ,” McKinsey Global Institute, January 16, 2020.

As part of our knowledge partnership with Race to Resilience at the UN Climate Change Conference of the Parties (COP26) in Glasgow, we have built a detailed, global assessment of the number of people exposed to four key physical climate hazards, primarily under two different warming scenarios. This paper lays out our methodology and our conclusions from this independent assessment.

A climate risk analysis focused on people: Our methodology in brief

Our research consists of a global analysis of the exposure of people’s lives and livelihoods to multiple hazards related to a changing climate. This analysis identifies people who are potentially vulnerable to four core climate hazards—heat stress, urban water stress, agricultural drought, and riverine and coastal flooding—even if warming is kept within 2.0°C above preindustrial levels.

Our methodology

The study integrates climate and socioeconomic data sources at a granular level to evaluate exposure to climate hazards. We used an ensemble mean of a selection of Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models under Representative Concentration Pathway (RCP) 8.5 —using a Shared Socioeconomic Pathway (SSP2) for urban water stress—with analysis conducted under two potential warming scenarios: global mean temperature increases above preindustrial levels of 1.5°C and 2.0°C. We sometimes use the shorthand of “1.5°C warming scenario” and “2.0°C warming scenario” to describe these scenarios. Our modeling of temperatures in 2030 refers to a multidecadal average between 2021 and 2040. When we say 2050, we refer to a multidecadal average between 2041 and 2060. These are considered relative to a reference period, which is dependent on hazard basis data availability (which we sometimes refer to as “today”).

We built our analysis by applying 2030 and 2050 population-growth projections to our 1.5°C and 2.0°C warming scenarios, respectively. This amount of warming by those time periods is consistent with an RCP 8.5 scenario, relative to the preindustrial average. Climate science makes extensive use of scenarios. We chose a higher emissions scenario of RCP 8.5 to measure the full inherent risk from a changing climate. Research also suggests that cumulative historical emissions, which indicate the actual degree of warming, have been in line with RCP 8.5. 1 For further details, see “ Climate risk and response ,” January 16, 2020, appendix; see also Philip B. Duffy, Spencer Glendon, and Christopher R. Schwalm, “RCP8.5 tracks cumulative CO2 emissions,” Proceedings of the National Academy of Sciences of the United States of America (PNAS) , August 2020, Volume 117, Number 33, pp. 19656–7, pnas.org. In some instances, we have also considered a scenario in which decarbonization actions limit warming and 1.5°C of warming relative to the preindustrial levels is only achieved in 2050, rather than in 2030. For our analysis we used models which differ to some extent on their exact amount of warming and timing, even across the same emissions scenario (RCP 8.5). Naturally, all forward-looking climate models are subject to uncertainty, and taking such an ensemble approach to our model allows us to account for some of that model uncertainty and error. 2 For a more detailed discussion of these uncertainties, see chapter 1 of “ Climate risk and response: Physical hazards and socioeconomic impacts ,” McKinsey Global Institute, January 16, 2020. However, the mean amount of warming typically seen across our ensemble of models is approximately 1.5°C by 2030 and 2.0°C by 2050.

Our analysis consisted of three major steps (see technical appendix for details on our methodology):

First, we divided the surface of the planet into a grid composed of five-kilometer cells, with climate hazards and socioeconomic data mapped for each cell.

Second, in each of those cells, we combined climate and socioeconomic data to estimate the number and vulnerability of people likely to be exposed to climate hazards. These data were categorized on the basis of severity and classified on the basis of exposure to one or more hazards at the grid-cell level.

Third, taking into account people’s vulnerability, we examined the potential impact of our four core hazards on the current and future global population. To do this, we assessed, globally, the number and vulnerability of people affected by different types and severities of hazards. We then aggregated the data from each cell up to the subnational, national, subcontinental, continental, and global levels to allow for comparison across countries.

It’s important to note that we carefully selected these four hazards because they capture the bulk of hazards likely to affect populations on a global scale. We did not account for a range of other hazards such as wildfires, extreme cold, and snow events. Further, our analysis accounts only for first-order effects of climate hazards and does not take into account secondary or indirect effects, which can have meaningful impact. Drought, for example, can lead to higher food prices and even migration—none of which are included in our analysis. Thus, the number of people affected by climate hazards is potentially underestimated in this work.

A focus on four main climate hazards

For our study, we used global data sets covering four key hazards: heat stress, urban water stress, agricultural drought, and riverine and coastal flooding. We relied on data from a selection of CMIP5 climate models, unless otherwise specified. For further details, see the technical appendix.

Heat stress

Heat stress can have meaningful impacts on lives and livelihoods as the climate changes. Heat stress is measured using wet-bulb temperature, which combines heat and humidity. We assess heat stress in the form of acute exposure to humid heat-wave occurrence as well as potential chronic loss in effective working hours, both of which depend on daily wet-bulb temperatures. Above a wet-bulb temperature of 35°C, heat stress can be fatal.

Acute humid heat waves are defined by the average wet-bulb temperature of the hottest six-hour period during a rolling three-day period in which the daily maximum wet-bulb temperature exceeds 34°C for three consecutive days. 3 Analysis of lethal heat waves in our previous McKinsey Global Institute report (see “ Climate risk and response ,” January 16, 2020) was limited to urban populations, and the temperature threshold was set to 34°C wet-bulb temperature under the assumption that the true wet-bulb temperature would actually be 35°C due to an additional 1°C from the urban heat-island effect. Heat-wave occurrence was calculated for each year for both a reference time period 4 The reference period for heat stress refers to the average between 1998 and 2017. and our two future time periods and translated into annual probabilities. Exposure was defined as anyone living in either an urban or rural location with at least a 2 percent annual probability of experiencing such a humid heat wave in any given year. Acute humid heat waves of 34°C or higher can be detrimental to health, even for a healthy and well-hydrated human resting in the shade, because the body begins to struggle with core body-temperature regulation and the likelihood of experiencing a heat stroke increases.

Chronic heat stress was assessed for select livelihoods and defined by processing daily mean air temperature and relative humidity data into a heat index and translating that into the fraction of average annual effective working hours lost due to heat exposure. This calculation was conducted following the methods of John P. Dunne et al., 5 John P. Dunne, Ronald J. Stouffer, and Jasmin G. John, “Reductions in labour capacity from heat stress under climate warming,” Nature Climate Change , 2013, Volume 3, Number 6, pp. 563–6, nature.com. using empirically corrected International Organization for Standardization (ISO) heat-exposure standards from Josh Foster et al. 6 Josh Foster et al., “A new paradigm to quantify the reduction of physical work capacity in the heat,” Medicine and Science in Sports and Exercise , 2019, Volume 51, Number 6S, p. 15, journals.lww.com.

We combined groups of people who were exposed to both chronic and acute heat stress to assess the aggregate number of people exposed. Heat stress can affect livelihoods, particularly for those employed in outdoor occupations, most prominently because an increased need for rest and a reduction in the body’s efficiency reduce effective working hours. Therefore, our analysis of potential exposure to chronic heat stress was limited to people estimated to be working in agriculture, crafts and trades, elementary, factory-based, and manufacturing occupations likely to experience at least a 5 percent loss of effective working hours on average annually. We excluded managers, professional staff, and others who are more likely to work indoors, in offices, or in other cooled environments from this analysis.

Urban water stress

Urban water stress 7 The reference period for water stress refers to the average between 1950 and 2010. often occurs in areas in which demand for water from residents, local industries, municipalities, and others exceeds the available supply. This issue can become progressively worse over time as demand for water continues to increase and supply either remains constant, decreases due to a changing climate, or even increases but not quickly enough to match demand. This can reduce urban residents’ access to drinking water or slow production in urban industry and agriculture.

Our analysis of water stress is limited to urban areas partially because water stress is primarily a demand-driven issue that is more influenced by socioeconomic factors than by changes in climate. We also wanted to avoid methodological overlap with our agricultural drought analysis, which mostly focused on rural areas.

We define urban water stress as the ratio of water demand to supply for urban areas globally. We used World Resources Institute (WRI) data for baseline water stress today and the SSP2 scenario for future water stress outlooks, where 2030 represents the 1.5°C warming scenario and 2040 represents the 2.0°C warming scenario. We only considered severe water stress, defined as withdrawals of 80 percent or more of the total supply, which WRI classifies as “extremely high” water stress.

We make a distinction for “most severe” urban water stress, defined as withdrawals of more than 100 percent of the total supply, to show how many people could be affected by water running out—a situation that will require meaningful interventions to avoid. However, for the sake of the overall exposure analysis, people exposed to the most severe category are considered to be exposed to “severe” water stress unless otherwise noted (exhibit).

Agricultural drought

Agricultural drought 8 The reference period for agricultural drought refers to the average between 1986 and 2005. is a slow-onset hazard defined by a period of months or years that is dry relative to a region’s normal precipitation and soil-moisture conditions, specifically, anomalously dry soils in areas where crops are grown. Drought can inhibit plant growth and reduce plant production, potentially leading to poor yields and crop failures. For more details, see the technical appendix.

Riverine and coastal flooding

We define flooding as the presence of water at least one centimeter deep on normally dry land. We analyze two types of flooding here: riverine flooding from rivers bursting their banks and coastal flooding from storm surges and rising sea levels pushing water onto coastal land. Both coastal and riverine flooding can damage property and infrastructure. In severe cases, they could lead to loss of life. 9 The reference period for riverine flooding refers to the average between 1960 and 1999; the reference period for coastal flooding refers to the average between 1979 and 2014. For more details, see the technical appendix.

Based on a combination of frequency and intensity metrics, we estimated three severity levels of each climate hazard: mild, moderate, and severe (exhibit).

Even when we only look at first-order effects, it is clear that building resilience and protecting people from climate hazards are critical. Our analysis provides data that may be used to identify the areas of highest potential exposure and vulnerability and to help build a case for investing in climate resilience on a global scale.

Our findings suggest the following conclusions:

  • Under a scenario with 1.5°C of warming above preindustrial levels by 2030, almost half of the world’s population could be exposed to a climate hazard related to heat stress, drought, flood, or water stress in the next decade, up from 43 percent today 3 Climate science makes extensive use of scenarios; we have chosen Representative Concentration Pathway (RCP) 8.5 and a multimodel ensemble to best model the full inherent risk absent mitigation and adaption. Scenario 1 consists of a mean global temperature rise of 1.5°C above preindustrial levels, which is reached by about 2030 under this RCP; Scenario 2 consists of a mean global temperature rise of 2.0°C above preindustrial levels, reached around 2050 under this RCP. Following standard practice, future estimates for 2030 and 2050 represent average climatic behavior over multidecadal periods: 2030 represents the average of the 2021–2040 period, and 2050 represents the average of the 2041–2060 period. We also compare results with today, also based on multidecadal averages, which differ by hazard. For further details, see technical appendix. —and almost a quarter of the world’s population would be exposed to severe hazards. (For detailed explanations of these hazards and how we define “severe,” see sidebar “A climate risk analysis focused on people: Our methodology in brief.”)
  • Indeed, as severe climate events become more common, even in a scenario where the world reaches 1.5°C of warming above preindustrial levels by 2050 rather than 2030, nearly one in four people could be exposed to a severe climate hazard that could affect their lives or livelihoods.
  • Climate hazards are unevenly distributed. On average, lower-income countries are more likely to be exposed to certain climate hazards compared with many upper-income countries, primarily due to their geographical location but also to the nature of their economies. (That said, both warming scenarios outlined here are likely to expose a larger share of people in nearly all nations to one of the four modeled climate hazards compared with today.) Those who fall within the most vulnerable categories are also more likely to be exposed to a physical climate hazard.

These human-centric data can help leaders identify the best areas of focus and the scale of response needed to help people—particularly the most vulnerable—build their climate resilience.

A larger proportion of the global population could be exposed to a severe climate hazard compared with today

Under a scenario with 1.5°C of warming above preindustrial levels by 2030, almost half of the world’s population—approximately 5.0 billion people—could be exposed to a climate hazard related to heat stress, drought, flood, or water stress in the next decade, up from 43 percent (3.3 billion people) today.

In much of the discussion below, we focus on severe climate hazards to highlight the most significant effects from a changing climate. We find that regardless of whether warming is limited to 1.5°C or reaches 2.0°C above preindustrial levels by 2050, severe hazard occurrence is likely to increase, and a much larger proportion of the global population could be exposed compared with today (Exhibit 1).

This proportion could more than double, with approximately one in three people likely to be exposed to a severe hazard under a 2.0°C warming scenario by 2050, compared with an estimated one in six exposed today. This amounts to about 2.0 billion additional people likely to be exposed by 2050. Even in a scenario where aggressive decarbonization results in just 1.5°C of warming above preindustrial levels by 2050, the number of people exposed to severe climate hazards could still increase to nearly one in four of the total projected global population, compared with one in six today.

One-sixth of the total projected global population, or about 1.4 billion people, could be exposed to severe heat stress, either acute (humid heat waves) or chronic (lost effective working hours), under a 2.0°C warming scenario above preindustrial levels by 2050, compared with less than 1 percent, or about 0.1 billion people, likely to be exposed today (Exhibit 2).

Our results suggest that both the severity and the geographic reach of severe heat stress may increase to affect more people globally, despite modeled projections of population growth, population shifts from rural to urban areas, and economic migration. Our analysis does not attempt to account for climate-change-related migration or resilience interventions, which could decrease exposure by either forcing people to move away from hot spots or mitigating impacts from severe heat stress.

For those with livelihoods affected by severe chronic heat stress, it could become too hot to work outside during at least 25 percent of effective working hours in any given year. This would likely affect incomes and might even require certain industries to rethink their operations and the nature of workers’ roles. For outdoor workers, extreme heat exposure could also result in chronic exhaustion and other long-term health issues. Heat stress can cause reductions in worker productivity and hours worked due to physiological limits on the human body, as well as an increased need for rest.

We have already seen some of the impacts of acute heat stress in recent years. In the summer of 2010 in Russia, tens of thousands of people died of respiratory illness or heat stress during a large heat-wave event in which temperatures rose to more than 10°C (50°F) higher than average temperatures for those dates. One academic study claims “an approximate 80 percent probability” that the new record high temperature “would not have occurred without climate warming.” 4 Dim Coumou and Stefan Rahmstorf, “Increase of extreme events in a warming world,” Proceedings of the National Academy of Sciences of the United States of America (PNAS) , November 2011, Volume 108, Number 44, pp. 17905–9, pnas.org. To date these impacts have been isolated events, but the potential impact of heat stress on a much broader scale is possible in a 1.5°C or 2.0°C warming scenario in the coming decades.

While we did not assess second-order impacts, they could also be meaningful. Secondary impacts from heat stress may include loss of power, and therefore air conditioning, due to greater stress on electrical grids during acute heat waves, 5 Sofia Aivalioti, Electricity sector adaptation to heat waves , Sabin Center for Climate Change Law, Columbia University, 2015, academiccommons.columbia.edu. increased stress on hospitals due to increased emergency room visits and admission rates primarily during acute heat-stress events, 6 Climate change and extreme heat events , Centers for Disease Control and Prevention, 2015, cdc.gov. and migration driven primarily by impacts from chronic heat stress. 7 Mariam Traore Chazalnoël, Dina Ionesco, and Eva Mach, Extreme heat and migration , International Organization for Migration, United Nations, 2017, environmentalmigration.iom.int.

The rate of growth in global urban water demand is highly likely to outpace that of urban water supply under future warming and socioeconomic pathway scenarios, compared with the overall historical baseline period (1950–2010). In most geographies, this problem is primarily caused not by climate change but by population growth and a corresponding growth in demand for water. However, in some geographies, urban water stress can be exacerbated by the impact of climate change on water supply. In a 2.0°C warming scenario above preindustrial levels by 2050, about 800 million additional people could be living in urban areas under severe water stress compared with today (Exhibit 3). This could result in lack of access to water supplies for drinking, washing and cleaning, and maintaining industrial operations. In some areas, this could make a case for investment in infrastructure such as pipes and desalination plants to make up for the deficit.

Agricultural drought is most likely to directly affect people employed in the agricultural sector: in conditions of anomalously dry soils, plants do not have an adequate water supply, which inhibits plant growth and reduces production. This in turn could have adverse impacts on agricultural livelihoods.

In a scenario with warming 2.0°C above preindustrial levels by 2050, nearly 100 million people—or approximately one in seven of the total global rural population projected to be employed in the agricultural sector by 2050—could be exposed to a severe level of drought, defined as an average of seven to eight drought years per decade. This could severely diminish people’s ability to maintain a livelihood in rainfed agriculture. Additional irrigation would be required, placing further strain on water demand, and yields could still be reduced if exposed to other heat-related hazards.

While our analysis focused on the first-order effects of agricultural drought, the real-world impact could be much larger. Meaningful second-order effects of agricultural drought include reduced access to drinking water and widespread malnutrition. In addition, drought in regions with insufficient aid can cause infectious disease to spread.

Further, although our analysis did not cover food security, many other studies have posited that if people are unable to appropriately adapt, this level of warming would raise the risk of breadbasket failures and could lead to higher food prices. 8 For more on how a changing climate might affect global breadbaskets, see “ Will the world’s breadbaskets become less reliable? ,” McKinsey Global Institute, May 18, 2020.

Primarily as a result of surging demand exacerbated by climate change, 9 Salvatore Pascale et al., “Increasing risk of another Cape Town ‘Day Zero’ drought in the 21st century, Proceedings of the National Academy of Sciences of the United States of America (PNAS) , November 2020, Volume 117, Number 47, pp. 29495–503, pnas.org. Cape Town, South Africa, a semi-arid country, recently experienced a water shortage. From 2015 to 2018, unusually high temperatures contributed to higher rates of evaporation with less refresh due to low rainfall, contributing to decline in water reserves which fell to the point of emergency 10 “Cape Town’s Water is Running Out,” NASA Earth Observatory, January 14, 2018, earthobservatory.nasa.gov. —by January 2018, about 4.3 million residents of South Africa had endured years of constant restrictions on water use in both urban and agricultural settings. Area farmers recorded losses, and many agricultural workers lost their jobs. In the city, businesses were hit with steep water tariffs, jobs were lost, and residents had to ration water.

Under a scenario with warming 2.0°C above preindustrial levels by 2050, about 400 million people could be exposed to severe riverine or coastal flooding, which may breach existing defenses in place today. As the planet warms, patterns of flooding are likely to shift. This could lead to decreased flood depth in some regions and increases likely beyond the capacity of existing defenses in others.

Riverine floods can disrupt travel and supply chains, damage homes and infrastructure, and even lead to loss of life in extreme cases. The most vulnerable are likely to be disproportionately affected—fragile homes in informal coastal settlements are highly vulnerable to flood-related damages.

This analysis does not account for the secondary impacts of floods that may affect people. In rural areas, floods could cause the salinity of soil to increase, which in turn could damage agricultural productivity. Flooding could also make rural roads impassable, limiting residents’ ability to evacuate and their access to emergency response. Major floods sometimes lead to widespread impacts caused by population displacement, healthcare disruptions, food supply disruptions, drinking-water contamination, psychological trauma, and the spread of respiratory and insect-borne disease. 11 Christopher Ohl and Sue Tapsell, “Flooding and human health: The dangers posed are not always obvious,” British Medical Journal (BMJ) , 2000, Volume 321, Number 7270, pp. 1167–8, bmj.com; Shuili Du, C.B. Bhattacharya, and Sankar Sen, “Maximizing business returns to corporate social responsibility (CSR): The role of CSR communication,” International Journal of Management Reviews (IJMR) , 2010, Volume 12, Number 1, pp. 8–19, onlinelibrary.wiley.com. The severity of these impacts varies meaningfully across geographic and socioeconomic factors. 12 Roger Few et al., Floods, health and climate change: A strategic review , Tyndall Centre working paper, number 63, November 2004, unisdr.org.

People in lower-income countries tend to have higher levels of exposure to hazards

Our analysis suggests that exposure to climate hazards is unevenly distributed. Overall, a greater proportion of people living in lower-income countries are likely to be exposed to one or more climate hazards (Exhibit 4). Under a scenario with warming 2.0°C above preindustrial levels by 2050, more than half the total projected global population could be affected by a climate hazard. On the other hand, only 10 percent of the total population in high-income countries is likely to be exposed. That said, there could also be meaningful increases in overall exposure in developed nations. For example, based on 2050 population projections, about 160 million people in the United States—almost forty percent of the US population—could be exposed to at least one of the four climate hazards in a 2.0°C warming scenario by 2050.

In all, our analysis suggests that nearly twice as many highly vulnerable people (those estimated to have lower income and who may also have inadequate shelter, transportation, skills, or funds to protect themselves from climate risks) could be exposed to a climate hazard (Exhibit 5).

One of the implications of these findings is that certain countries are likely to be disproportionately affected. Two-thirds of the people who could be exposed to a climate hazard in a 2.0°C warming scenario by 2050 are concentrated in just ten countries. In two of these, Bangladesh and Pakistan, more than 90 percent of the population could be exposed to at least one climate hazard.

India’s vulnerability to climate hazards

Today, India accounts for more than 17 percent of the world’s population. In a scenario with 2.0°C warming above preindustrial levels by 2050, nearly 70 percent of India’s projected population, or 1.2 billion people, is likely to be exposed to one of the four climate hazards analyzed in this report, compared with the current exposure of nearly half of India’s population (0.7 billion). India could account for about 25 percent of the total global population likely to be exposed to a climate hazard under a 2.0°C warming scenario by 2050, relative to today.

Just as the absolute number of people likely to be exposed to hazards is increasing, so too is the proportion of people likely to be exposed to a severe climate hazard. Today, approximately one in six people in India are likely to be exposed to a severe climate hazard that puts lives and livelihoods at risk. Using 2050 population estimates and a scenario with 2.0°C warming above preindustrial levels by 2050, we estimate that this proportion could increase to nearly one in two people.

Severe heat stress is the primary culprit of severe climate hazard exposure, potentially affecting approximately 650 million residents of India by 2050 in the 2.0°C warming scenario, compared with just under ten million today (exhibit).

A vast number of people in India could also be exposed. Under a scenario with warming 2.0°C above preindustrial levels by 2050, nearly half of India’s projected population—approximately 850 million—could be exposed to a severe climate hazard. This equates to nearly one-quarter of the estimated 3.1 billion people likely to be exposed to a severe climate hazard globally by 2050 under a 2.0°C warming scenario (see sidebar “India’s vulnerability to climate hazards”).

Between now and 2050, population models 13 “Spatial Population Scenarios,” City University of New York and NCAR, updated August 2018, cgd.ucar.edu. project that the world could gain an additional 1.6 billion people, a proportion of whom are likely to be more exposed, more vulnerable, and less resilient to climate impacts.

For example, much of this population growth is likely to come from urban areas. Urbanization is likely to exacerbate the urban heat-island effect—in which human activities cause cities to be warmer than outlying areas—and humid heat waves could take an even greater toll. Urbanization is likely a driver in increased exposure of populations in coastal and riverine cities.

In India and other less developed economies, water stress is less of a climate problem and more of a socioeconomic problem. Our work and previous work on the topic has shown that increased water stress is mostly due to increases in demand—which is primarily driven by population growth in urban areas.

As labor shifts away from agriculture and other outdoor occupations toward indoor work, fewer people may be exposed to the effects of agricultural drought and heat stress. But on balance, many more people will likely be exposed to climate hazards by 2050 than today under either a 1.5°C or a 2.0°C warming scenario above preindustrial levels.

Many regions of the world are already experiencing elevated warming on a regional scale. It is estimated that 20 to 40 percent of today’s global population (depending on the temperature data set used) has experienced mean temperatures of at least 1.5°C higher than the preindustrial average in at least one season. 14 “Chapter 1: Framing and context,” Special report: Global warming of 1.5°C , International Panel on Climate Change (IPCC), 2018, ipcc.ch.

Mitigation will be critical to minimizing risk. However, much of the warming likely to occur in the next decade has already been “locked in” based on past emissions and physical inertia in the climate system. 15 H. Damon Matthews et al., “Focus on cumulative emissions, global carbon budgets, and the implications for climate mitigation targets,” Environmental Research Letters, January 2018, Volume 13, Number 1. Therefore, in addition to accelerating a path to lower emissions, leaders need to build resilience against climate events into their plans.

Around the world, there are examples of innovative ways to build resilience against climate hazards. For example, the regional government of Quintana Roo on Mexico’s Yucatán Peninsula insured its coral reefs in an arrangement with an insurance firm, providing incentives for the insurer to manage any degradation, 16 “World’s first coral reef insurance policy triggered by Hurricane Delta,” Nature Conservancy, December 7, 2020, nature.org. and a redesigned levee system put in place after Hurricane Katrina may have mitigated the worst effects of Hurricane Ida for the citizens of New Orleans. 17 Sarah McQuate, “UW engineer explains how the redesigned levee system in New Orleans helped mitigate the impact of Hurricane Ida,” University of Washington, September 2, 2021, washington.edu.

Nonstate actors may have particular opportunities to help build resilience. For instance, insurance companies may be in a position to encourage institutions to build resilience by offering insurance products for those that make the right investments. This can lower reliance on public money as the first source of funding for recovery from climate events. Civil-engineering companies can participate in innovative public–private partnerships to accelerate infrastructure projects. Companies in the agricultural and food sectors can help farmers around the world mitigate the effects that climate hazards can have on food production—for example, offers of financing can encourage farmers to make investments in resilience. The financial-services sector can get involved by offering better financing rates to borrowers who agree to disclose and reduce emissions and make progress on sustainability goals. And, among other actions, all companies can work to make their own operations and supply chains more resilient.

Accelerating this innovation, and scaling solutions that work quickly, could help us build resilience ahead of the most severe climate hazards.

Harry Bowcott is a senior partner in McKinsey’s London office, Lori Fomenko is a consultant in the Denver office, Alastair Hamilton is a partner in the London office, Mekala Krishnan is a partner at the McKinsey Global Institute (MGI) and a partner in the Boston office, Mihir Mysore is a partner in the Houston office, Alexis Trittipo is an associate partner in the New York office, and Oliver Walker is a director at Vivid Economics, part of McKinsey’s Sustainability Practice.

The authors wish to thank Shruti Badri, Riley Brady, Zach Bruick, Hauke Engel, Meredith Fish, Fabian Franzini, Kelly Kochanski, Romain Paniagua, Hamid Samandari, Humayun Tai, and Kasia Torkarska for their contributions to this article. They also wish to thank external adviser Guiling Wang and the Woodwell Climate Research Center.

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Two Deadly Fires in Rapid Succession Expose India’s Gaps in Safety

Disasters over the weekend that claimed at least 34 lives prompted condolences, arrests and finger-pointing. But systemic change remains elusive, analysts say.

People stand watching as bulldozers and power shovels work amid large piles of burned, twisted metal.

By Mujib Mashal ,  Pragati K.B. and Hari Kumar

Reporting from New Delhi

Seven newborn babies lost their lives after their New Delhi neonatal clinic was engulfed in flames. What remained of the two-story building on Sunday morning was its burned facade, a charred spiral staircase and oxygen cylinders covered in soot.

Hours earlier, in the western Indian city of Rajkot, an amusement park of trampolines and bowling lanes had turned to an inferno. The families of people who had come to enjoy a discounted offer of all-you-can-play to celebrate the start of summer vacation were left trying to identify bodies among the at least 27 dead, many of them children too charred to be recognizable.

As after every such deadly episode, political leaders were quick with messages of condolence, announcements of arrests, creations of inquiries — and finger-pointing. But to analysts and experts who had warned for years about India’s abysmal fire preparedness, the back-to-back disasters on Saturday were the latest reminder that systemic change to make the country safer was still missing.

Building safety compliance remains abysmal across India, the world’s most populous nation. The fire services have long faced huge gaps in the numbers of stations, personnel and equipment. Government audits after mass-casualty disasters unearth glaring shortcomings, with little follow-up.

Though the number has gone down over the past decade, more than 20 fire-related deaths occur every day in India, according to government statistics. Many of the fires — particularly in crowded urban centers — are caused by short circuits, an alarming prospect as India faces an intense period of heat waves that strains electrical wires.

R.C. Sharma, a former fire service chief in Delhi, said that one major problem is that fire regulations go unenforced. Another is that fire-response resources have failed to keep up with urbanization that is happening rapidly and often without regard to safety.

“We are not in a good condition,” Mr. Sharma said. “In other countries, you have fire hydrants and everything at all the places. But in India, we don’t even have drinking water around the clock, so we do not think of having firefighting water around the clock.”

Data provided to the Indian Parliament in 2019 by the country’s Home Ministry painted a dire state of preparedness, with major deficiencies. India had only 3,377 fire stations when regulations called for 8,559. The shortfall in personnel and equipment was even worse. The fire service had about 55,000 people, when a half-million were called for, and 7,300 vehicles, when it should have had 33,000.

It is unclear how much of those gaps have been filled in the five years since. A new $600 million program for expansion and modernization of the fire service announced by India’s central government last year, with additional resources to be pooled from the states, suggests a lot of it remains undone.

Government audits have repeatedly flagged the vulnerability of public buildings, particularly hospitals.

A study last year of hospitals across India where there had been a fire in the past decade showed that half were not legally compliant on safety measures. Private and public hospitals were about equally bad. Short circuits were the cause of the fires in nearly 90 percent of the episodes.

In one state, after a fire killed 10 babies in a neonatal care unit, assessments found that more than 80 percent of the state’s hospitals had never carried out fire safety audits; half had never conducted fire drills; and only a few had fire safety certificates.

“The tendency is to comply in letter, not spirit,” said S.A. Abbasi, an emeritus professor at Pondicherry University, who was the lead author of the report. “Lapses and laxity continue to be the norms rather than exceptions.”

What caused the fire at the amusement park in Rajkot, in the state of Gujarat, was not known. But the initial police complaint, a copy of which was seen by The New York Times, made clear that the facility lacked both a clearance certificate from the fire department and effective equipment and protocols in case of fire.

Ilesh Kher, Rajkot’s chief fire officer, said the fire at the facility had started just before 6 p.m., and the flames were contained in a little over an hour. He did not know how many people were present when the blaze broke out, but witness accounts in local news suggested over 100.

The building appeared to be a temporary structure made of iron poles and metal sheets.

Daksh Kujadia, a teenager who had gone bowling with a cousin, said the fire had started under an emergency exit. About 30 people became trapped in the bowling lanes.

“We didn’t have an option but to tear the metal sheet in a corner,” he told local news media . “Fifteen of us got out by jumping from there.”

The two-story Delhi neonatal hospital that caught fire just before midnight was operating out of a residential building. Neighbors described frequent disputes, as trucks often blocked the road outside the hospital to unload large cylinders of oxygen.

“A few of us climbed on top of each other and climbed into the building from the back side,” said Ravi Gupta, who lives in the area and helped evacuate a dozen babies from the back of the building as the front caught fire and multiple explosions were heard as oxygen cylinders burst. “We brought ladders and bedsheets from our houses. I carried infants in my hands from the fire and brought them down.”

Health care in Delhi, India’s capital, has in recent years has been caught in a messy political fight between the central government of Prime Minister Narendra Modi and Delhi’s elected local government, run by a smaller opposition party, the Aam Aadmi Party, or A.A.P. The local administration has accused Mr. Modi of using his control over government officials to handicap its efforts.

Accusations continued to fly after Saturday’s deadly hospital fire.

Pankaj Luthra, a local official affiliated with Modi’s party in the neighborhood where the hospital is, blamed the A.A.P. for giving the hospital its license. There had been, he said, complaints of illegal oxygen cylinder refilling at the hospital.

Saurabh Bhardwaj, A.A.P.’s health minister for Delhi, released a statement complaining that the most senior official in Delhi’s health department — a civil servant technically supervised by Mr. Bhardwaj, but in fact answering to the central government — was ignoring his calls and messages.

“I got to know about this incident through a media flash,” Mr. Bhardwaj said.

Mujib Mashal is the South Asia bureau chief for The Times, helping to lead coverage of India and the diverse region around it, including Bangladesh, Sri Lanka, Nepal and Bhutan. More about Mujib Mashal

Pragati K.B. is a reporter based in the New Delhi bureau, covering news from across India. More about Pragati K.B.

Hari Kumar covers India, based out of New Delhi. He has been a journalist for more than two decades. More about Hari Kumar


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