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Cyclone Idai

The cause, primary and secondary effects and immediate and long term responses to Cyclone Idai

Cyclones are tropical storms that occur in the Indian Ocean. Cyclone Idai is the strongest tropical cyclone on record to affect Africa and the Southern Hemisphere.

Cyclone Idai satellite image

Cyclone Idai satellite image

What caused Cyclone Idai?

In early March 2019, a storm cell brought heavy rains to Malawi before heading out to sea off the coast of Mozambique. The storm intensified into Cyclone Idai and returned to land on the evening of 14th March 2019. Often, storms that develop there don’t strengthen as much as those that form north and east of Madagascar, but Cyclone Idai was fed by warm water temperatures. The storm, with winds of up to 115 mph/185 kph and more than 150mm of rain in 24 hours, wreaked havoc in the Mozambique port city of Beira, home to 500,000 people, along with surrounding districts. It then swept inland and on to Zimbabwe. The storm caused widespread devastation and the loss of life and livelihoods of hundreds of thousands more people.

Location of Cyclone Idai

The location of Cyclone Idai

March 3 2019

Tropical disturbance forms.

The tropical disturbance that would become Cyclone Idai develops and begins to strengthen near the coast of Africa.

March 5th 2019

Heavy rains cause severe flooding across Mozambique and Malawi.

March 11 2019

Tropical depression.

Now a tropical depression, the storm becomes more intense between coastal  Africa and Madagascar. 

March 14-15 2019

Tropical cyclone idai makes landfall.

Tropical Cyclone Idai makes landfall near Beira, Mozambique, as a Category 2 storm with sustained winds exceeding 105 mph.

March 20 2019

Heavy rain continues.

Heavy rains continue along with search and rescue operations and damage assessments.

March 21 to 27

Aid response.

Governments and humanitarian aid agencies begin responding with life-saving relief supplies to the affected areas.

Search called off

The Mozambique government calls off the search for survivors of Cyclone Idai.

Cholera Cases

Cholera cases in Mozambique top 1,400, according to health officials.

What were the effects?

Flooding in Southern Africa has affected nearly 3 million people in Mozambique, Malawi, and Zimbabwe since the rain began in early March and Cyclone Idai struck March 14 and 15. The death toll has exceeded 843 people, and many more remain missing. Over 1 million people were displaced by the storm.

It was not just heavy rainfall that led to flooding, storm surges between 3.5m to 4m hit the coastal city of Beira. The ocean floor along the coast by Mozambique is conducive to give storm surges.

The image below shows the area around Beira before and after the cyclone.

According to the Red Cross, up to 90% of Beira, Mozambique’s fourth largest city, has been damaged or destroyed. The devastated city became an island amid the flooded area with communications, power and clean water severely disrupted or non-existent. Houses, roads and crops disappeared beneath the water that was six metres (19ft) deep in places. Rescuers struggling to reach survivors who may have spent up to a week sheltering on roofs and in trees. A woman gave birth in a mango tree while escaping floods in central Mozambique.

The coastal lowlands, located between the higher plateau and the mountainous areas to the west near the Zimbabwean border were the hardest hit by the floods.

At least 180 people in Zimbabwe known to have been killed by landslides triggered by Idai. Nasa satellite images depict the extensive landslide activity associated with Cyclone Idai . The landslides were partly caused by deforestation.

People were still being rescued a week and a half after the storm.

As flood waters receded, survivors struggled to obtain food, clean water, and shelter.

According to the World Bank the cyclone affected about 3 million people, damaging infrastructure and livelihoods. Unicef reported that over half of the 3 million people in urgent need of humanitarian help were children.

The UN World Food Programme (WFP) says that Cyclone Idai wiped out a whole year’s worth of crops across swathes of Mozambique, Malawi and Zimbabwe. At least 1 million acres of crops were destroyed.

The cyclone is expected to cost Malawi, Mozambique and Zimbabwe more than $2bn, the World Bank has said.

Cholera infected at least 1,052 people in Mozambique’s cyclone-hit region.

What was the immediate response?

As part of the forward planning for severe weather, safe zones had been created in rural areas of Mozambique for evacuation above the flood plain . However, the flooding was far worse than had been expected.

The meteorological office of Mozambique, Inam, issued weather alerts as the storm developed. The highest possible alert was raised by the government three days before the cyclone struck, telling people to evacuate threatened areas.

Some people were evacuated by boat before the cyclone struck, however many people in rural areas didn’t respond to the warnings or were not aware of them.

According to the mayor of the Mozambican city of Beira, the government failed to warn people in the areas worst hit by Cyclone Idai despite a “red alert” being issued two days before it struck.

The South African air force and the Indian army, which happened to have a ship in the area, drove the initial rescue effort. Opposition groups in Mozambique blamed the limited government preparation and response on corruption.

Last year, the government of Mozambique received support from international donors for a disaster fund of $18.3m (£13.9m) for 2018 and 2019. This is the main source of funding for any disaster response and is intended specifically for search and rescue within the first 72 hours.

More than 130,000 newly homeless people were taken into reception centres.

Two weeks after the disaster 900,000 doses of oral cholera vaccines arrived in the cyclone-battered Beira city, from the global stockpile for an emergency, according to the World Health Organisation (WHO).

As flood waters receded the International Committee of the Red Cross supported flood-affected communities to recover bodies, identify them and bury them in clearly marked graves.

The Mozambique government announced the search and rescue operation to find survivors from Cyclone Idai was over two weeks after the storm.

With the help of OpenStreetMap – an open-source mapping resource – thousands of volunteers worldwide digitised satellite imagery and created maps of the affected area to support ground workers. Through the Missing Maps Project , an army of arm-chair mappers has already mapped more than 200,000 buildings and nearly 17,000 km of roads in the affected areas.

A large number of international charities launched appeals to fund aid to support those affected by Cyclone Idai including The Red Cross, Unicef, DEC, CAFOD and MSF (Doctors Without Borders).

What was the long term response?

Two weeks after the storm the government of Mozambique announced a new phase in the recovery operation was beginning to help those affected and rebuild the education, health, energy, transport, industry and trade sectors, which were all devastated by the cyclone.

The UN has appealed for donations of $282m to fund emergency assistance for the next three months.

Useful Resources

NASA Products for Cyclone Idai 2019

Virtual OSOCC Tropical Cyclone Idai in Mozambique

Virtual OSOCC Tropical Cyclone Idai in Zimbabwe

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Weather news, curious case of asani: birth, evolution, impact, and eventual waning of 2022's first cyclonic storm, by twc india edit team, 14 may, 2022.

Cyclone Asani (N Kanaka/BCCL Vishakhapatnam)

This week began on a wet note for the states of Andhra Pradesh, Odisha and West Bengal, owing to the formation of Cyclone Asani over the Bay of Bengal. And while most cyclones are wont to cause a lot of devastation, Asani was relatively polite. Despite all the fanfare that accompanied its arrival, Asani left quietly, only bringing heavy rainfall and some respite from summer heat to the states along the East coast and across the southern peninsula.

Still, its journey from an innocuous cyclonic circulation to a full-blown Severe Cyclonic Storm is definitely worth talking about. Below, we dive into the rise and fall of 2022's first cyclone, as reported by the India Meteorological Department (IMD)!

Bend it like Asani

Despite its minimal impact, Asani's journey was far from straightforward. The cyclonic storm saw itself recurving quite a bit, making it rather unpredictable till the last minute. The majority of models predicted a shift in the system's path from northwest to northeast as it approached the coast. However, on May 11, the deep depression (remains of cyclone Asani) drifted slowly northward/northwestwards until dark, then west-southwestwards.

The cyclonic storm was expected to proceed northeastwards near the coast under the influence of a short-amplitude westerly trough in the middle and upper troposphere approaching from the west. Instead, as the storm weakened as it approached the coast, the storm's height reduced, and it was confined to middle tropospheric levels. As a result, the storm's steering wind shifted from southeasterly to northwesterly, causing it to proceed northwestward.

But an anticyclone over peninsular India, northwestward progress was restricted. Therefore, the system moved slowly and stayed almost stationary along the shore, followed by a gradual west-southwestward movement until fading into a well-defined low-pressure area over the region on the morning of May, explains IMD.

Asani's impact

Man crosses a road in heavy rains (SUBHRAJIT CHANDRA/ BCCL - KOLKATA)

Around the storm's centre, the maximum sustained wind speed was estimated to be around 30 knots (50-60 kmph) along and off the coast of Andhra Pradesh. On May 11, the high wind speed recorder at IMD, Machilipatnam, recorded a peak wind speed of 30 knots (55 kmph).

As far as rains are concerned, several Andhra, Yanam, Rayalaseema and Odisha districts received more than or equal to 7 cm of rainfall on May 11 and 12.

But the impact of the cyclone was far and wide in terms of rain and temperature. The cyclone-induced clouds hovered over the entire southern peninsula and blocked harsh summer sunshine. Its cooling effects were so strong that the faraway Bengaluru’s daytime mercury levels down to 24.3°C, making May 11 the coldest May day the Karnataka capital has experienced since the year 2000.

The genesis and evolution

Asani's birth began as that of any other cyclone, in the form of a low-pressure area. The LPA formed over the South Andaman Sea and the adjoining southeast Bay of Bengal on May 6. There began its process of intensification.

At first, it moved northwestwards, strengthening into a depression on the afternoon of May 7 and then a deep depression by the evening of the same day. During the early hours of May 8, continuing to move in the same direction, it intensified into a cyclonic storm and a severe cyclonic storm in the same evening.

This rapid intensification helped Asani reach its peak intensity of 55 knots (100-110 kmph gusting to 120 kmph) early the following day (May 9). It maintained its peak intensity till 10th noon.

Asani's consequent downfall

Asani, which first de-intensified into a cyclone from a severe cyclone, moved slowly northwards before weakening into a deep depression over the west-central Bay of Bengal near the Andhra Pradesh coast on the evening of May 11.

It then crossed the Andhra Pradesh coast between Machilipatnam and Narsapur during the evening hours of May 11 as a severe depression with maximum sustained wind speeds of 55-65 kmph gusting to 75 kmph. It subsequently proceeded slowly west-southwestwards, weakening into a depression early the next morning and a well-defined low-pressure area over coastal Andhra Pradesh the next morning.

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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.

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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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What made Cyclone Biparjoy unique, why its path was difficult to predict

The case of biparjoy is a reminder that despite the enormous progress made in developing warning systems and acting on them, cyclones remain a huge threat..

case study of recent cyclone in india

Cyclone Biparjoy, which struck India last week, was not unusual. Cyclones of this nature and ferocity routinely hit the Indian coastline about three to four times a year. May and June are months when cyclones are expected. On the western coast, Gujarat happens to be the most likely place for the east-moving cyclones in Arabian Sea to make landfall . And yet, Biparjoy had some characteristics that not only made it difficult to predict its path, but also made the cyclone potentially more dangerous.

The case of Biparjoy is a reminder that despite the enormous progress made in developing warning systems and acting on them, cyclones remain a huge threat. The fact that the reported death toll from Biparjoy has been in lower single digits, almost all of them accidental, is a marker of the success of the work done in the past 15 years. But much more needs to be done to minimise the damage to infrastructure, loss of cattle and other animals, and livelihoods of local populations.

case study of recent cyclone in india

Uncertain path

Unlike many other natural hazards, cyclones give adequate warning of their arrival. In the Indian context, it takes them between four and five days to reach the landmass from the north Indian Ocean, both on the Arabian Sea and the Bay of Bengal sides. If a sufficient number of weather instruments are monitoring them, from the oceans as well as from satellites, everything about the cyclones — speed, intensity, trajectory, associated wind speeds — can be predicted accurately.

Biparjoy developed into a cyclonic storm on June 6 and made its landfall on June 15. The 10-day life period, during which it developed into a very severe cyclonic storm and then an extremely severe cyclonic storm, was longer than the average but not the longest. One of the reasons for its longer stay on the sea was its relatively slow speed. Cyclones in the Arabian Sea typically progress with a speed of about 12-14 km per hour. Biparjoy, through most of its life, moved at a speed of 5-7 km an hour while covering a distance of nearly 1200 km to Gujarat.

“Biparjoy was sandwiched between two anticyclonic systems. One of them had the effect of aiding its northwards movement, while the other was sort of pulling it back. The combined effect was that it moved relatively slowly,” explained Mrutyunjay Mohapatra, director general of India Meteorological Department and expert on cyclones.

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The influence of these anticyclonic systems also made its trajectory wobble. “We call it recurving tracks cyclone. The trajectory of such cyclones tends to change directions frequently. Predicting the trajectory of recurving cyclones is extremely challenging, with an extra element of uncertainty,” Mohapatra said.

Towards Gujarat

Cyclone Biparjoy was earlier predicted to proceed towards Karachi in Pakistan. The Indian coastline would have felt the impact, but the landfall was not expected over Indian land. It was only on June 11 that the IMD declared that the cyclone was headed towards the northwestern Gujarat coast.

“At that time, most other international agencies were still saying the cyclone was headed to Karachi. That was because a few weather models were indeed predicting that. But we have a strong observational network in this area, and good experience with forecasting cyclones. By Sunday (June 11), we were reasonably sure the cyclone was coming to the Gujarat coast,” Mohapatra, credited with improving India’s cyclone forecast system, said.

Taking an early call was crucial, because that set in motion the response mechanism. A meeting of the National Crisis Management Committee on June 12 studied the forecast and sent out directives to the state government and the local administration to prepare for a landfall three days later. This was sufficient time to evacuate nearly one lakh people from the danger zones to safer locations.

The intensity of the cyclone was showing unusual variations. At times, it appeared that it was weakening, only to regain its strength later. That produced additional complexities in predicting its likely damage potential.

Persistent cyclone

The relatively slow speed of Biparjoy had extended till the landfall, making the process slightly longer than average, though not extraordinary. Most cyclones of this intensity complete the landfall in about three to four hours. Biparjoy took about five hours. The slow speed meant that even after reaching land, the cyclone remained close enough to the sea to draw moisture and sustain itself.

Longer landfalls have a greater potential to cause destruction. The most dramatic landfall was in the case of the Odisha supercyclone of 1998, the most devastating cyclone to have hit India in recent decades. That process had continued for nearly 30 hours.

Usually, cyclones lose their energy very quickly once they cross over to land. But because it could sustain itself for longer, Biparjoy kept moving on land as well, though with significantly reduced intensity. Its remnants had reached as far inside as Ajmer in Rajasthan on Monday, four days after landfall. Many parts in western and central India received widespread rains because of this system travelling over land.

“In a way, every cyclone is unique. No two cyclones have the same characteristics. Biparjoy had some additional complexities, which made forecasting extremely challenging. But our cyclone forecasting is now among the world’s best. That said, we need to keep improving it because future cyclones, under the influence of climate change, are going to throw bigger challenges,” Mohapatra said.


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Death Toll in India From Cyclone Biparjoy Climbs to 5

A father and son were killed after being swept away by floodwaters, the authorities said. The storm weakened to the equivalent of a tropical storm after making landfall as a cyclone.

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By Sameer Yasir ,  Judson Jones ,  Zia ur-Rehman and Claire Moses

Judson Jones is a meteorologist and a reporter for The Times. Sameer Yasir contributed reporting from New Delhi, and Zia ur-Rehman from Karachi, Pakistan.

At least two people in India were killed and dozens were injured after Cyclone Biparjoy made landfall in the western part of the country near the border with Pakistan, knocking out power in more than 4,000 villages, damaging roads and uprooting trees, the authorities said on Friday.

Officials viewed the relatively low death toll as a result of mass evacuations ahead of the storm: Over 100,000 people in India and over 70,000 in Pakistan were moved from vulnerable areas. In India, the authorities said they had evacuated 100,000 people.

The storm, which had lingered for days over the Arabian Sea, was weakening after it made landfall on Thursday night in the western Indian state of Gujarat, near the town of Naliya. By Friday night, Biparjoy was forecast to become the equivalent of a tropical depression, the India Meteorological Department said in a bulletin .

This graphic is no longer being updated, as the Global Disaster Alert and Coordination System has stopped providing data for Biparjoy.

Source: Global Disaster Alert and Coordination System

Note: All times are India Standard Time, which is 9.5 hours ahead of Eastern time.

By Madison Dong

A father and son, both shepherds, were swept away after they entered a flooded area to save their livestock in Bhavnagar district, according to state officials in Gujarat. So far, Pakistan has not reported any casualties.

Their deaths raised the number of causalities related to Biparjoy, which means “disaster” in Bengali, to five. Early this week, three boys were killed after they drowned off the coast of Mumbai, officials said, and another was missing.

“Early identification of areas that were likely to be impacted by the cyclone and timely evacuation of people living within 10 km of the coasts are the biggest reasons” for the relatively low death toll, Kamal Dayani, a senior Gujarat government official, told the Reuters news agency. “Our focus from the beginning was on preventing loss of lives, not just human lives but even animals.”

The state government in Gujarat provided more than 1,500 shelters for those living in the path of the cyclone.

The storm had caused power outages in more than 4,600 villages in Gujarat, officials said in a news release , but electricity had been restored on Friday in about 3,580 villages. Strong winds had caused about 5,000 power poles and 1,100 trees to be knocked down, state officials said.

“This is one of the largest evacuations in Gujarat’s history,” Alok Pandey, the commissioner of relief in Gujarat, said in a statement. “Today we have been able to save thousands of lives after facing a natural calamity for almost five days.”

Bhavesh Patel, a farmer from the Mandvi district of Gujarat who evacuated along with his family and neighbors, said he was waiting for the rain to stop in order to return home. He said some residents in his area were stuck on upper floors of their homes after they were flooded.

“Today or tomorrow we will go back to our homes to see what is left there,” he said. “These kind of floods are happening often now and we are some how getting to used to this.”

Two men use a red flag to mark a danger zone along the coast in the outskirts of Karachi, Pakistan.

Pakistan is still reeling from devastating floods last year that submerged large parts of the country, killed almost 1,700 people and displaced a large population.

Compared to Biparjoy, cyclones have killed far more in India in recent years. In 2021, more than 100 people, most in Gujarat, were killed when Cyclone Tauktae struck, although tens of thousands evacuated ahead of time. In 2019, Cyclone Fani tore through the eastern state of Odisha, killing at least 89 people, according to the U.N.

A powerful cyclone that struck eastern India in 1999 killed more than 10,000 people . Since then, Indian authorities have significantly improved disaster preparation and response capabilities, and subsequent major storms have resulted in far fewer deaths.

Tropical cyclones in the Arabian Sea have become more frequent in recent decades because of warming sea-surface temperatures in the region that are enhanced by a warming climate, according to researchers .

Eduardo Medina , Christine Hauser and Mike Ives contributed reporting.

Sameer Yasir is a reporter based in New Delhi. He joined The Times in 2020. More about Sameer Yasir

Judson Jones is a meteorologist and reporter for The Times, covering the most extreme storms across the globe. More about Judson Jones

Claire Moses is a reporter for the Express desk in London. More about Claire Moses

Explore Our Weather Coverage

Extreme Weather Maps: Track the possibility of extreme weather in the places that are important to you .

Tornado Alerts: A tornado warning demands instant action. Here’s what to do if one comes your wa y.

Flash Flooding: Fast rising water can be deadly. Here’s what to do if you’re caught off guard , and how to prepare for a future flooding event.

Evacuating Pets: When disaster strikes, household pets’ lives are among the most vulnerable. You can avoid the worst by planning ahead .

Climate Change: What’s causing global warming? How can we fix it? Our F.A.Q. tackles your climate questions big and small .

Cyclone Nisarga: Rare storm in decades pounds India’s west coast

Over 100,000 people, including coronavirus patients, moved to safety as rare cyclonic storm lashes Mumbai and suburbs.

Commuters drive along Marine Drive as rain falls in Mumbai on June 3, 2020 as cyclone Nisarga barrels towards India''s western coast. - Mumbai authorities shut offices, banned small gatherings and told

Cyclone Nisarga, which intensified into a severe cyclonic storm in the Arabian Sea, is making landfall along India’s western coast, forcing a high alert in the financial hub of Mumbai and evacuation of tens of thousands of people.

“[The] landfall process started and it will be completed during the next three hours. The northeast sector of the eye of the severe cyclonic storm Nisarga is entering into the land,” India’s meteorological department said on Wednesday.

Keep reading

Mumbai coronavirus cases spike as india begins to ease lockdown, cyclone amphan death toll hits 88 in india, bangladesh, landslides kill at least 20 in india’s assam state.

Nisarga dropped heavy rains and winds gusting up to 120km (75 miles) per hour as a category 4 cyclone near the coastal city of Alibagh, about 98km (60 miles) south of Mumbai, the capital of Maharashtra state and  ho me to more than 18 million people.

At least 100,000 people, including coronavirus patients, were moved to safer locations, according to officials. The storm surge threatened to flood beaches and low-lying slums as city authorities struggle to contain the pandemic.

Cyclone Nisarga map

Live TV coverage showed inky black clouds framing the sea on India’s western coastline. Trees swayed wildly, as the rain pounded the coastal towns and villages of Maharashtra.

In Mumbai, the home of Bollywood and India’s largest stock exchange, high winds whipped skyscrapers and ripped apart shanty houses near the beach.

Reporting from New Delhi, Al Jazeera’s Elizabeth Puranam said there are many dwellings along the coastal areas which are not structurally sound to withstand the storm.

“We have seen many trees uprooted in parts of Maharashtra as India’s west coast, including the states of Goa and Gujrat, are hit by really strong winds and heavy rain and high tides even before Cyclone Nisarga made landfall,” she said.

Media reports said Nisarga is the worst cyclone to hit the region in more than 70 years, raising concerns about readiness in Mumbai and neighbouring areas.

The cyclone threatens to worsen prospects for an economic turnaround as a nine-week-long government-imposed coronavirus lockdown began to ease this week.

India’s largest container port, Jawaharlal Nehru Port Trust, on the outskirts of Mumbai, was ordered to shut for 24 hours, the port said in a statement.

cyclone Nisarga

Area grappling with pandemic

The storm came as the Indian region grapples with the ongoing coronavirus pandemic.

Maharashtra and Gujarat states have reported about 44 percent of India’s more than 200,000 COVID-19 cases nationwide, and 61 percent of all virus deaths.

The metropolis of Mumbai is already struggling with the highest number of coronavirus cases with more than 41,000 infections.

Local news reports have shown an overwhelmed hospital system in Mumbai, with patients resting on hospital floors until beds become available and bodies left in wards.

Maharashtra Chief Minister, Uddhav Balasheb Thackeray, said some 150 coronavirus patients had been moved out of a hospital near the city’s beachfront.

Some 100,000 people were evacuated from low-lying areas in Maharashtra and neighboring Gujarat, according to the Press Trust of India news agency.

Both states, already among the hardest hit by the coronavirus pandemic, activated disaster response teams, fearing extensive flooding could further impair overwhelmed health systems.

“If hospitals and clinics are damaged by the cyclone, the city won’t be able to cope with the large number of COVID-19 cases, and social distancing measures will become virtually impossible to follow,” Bidisha Pillai, chief executive of Save the Children in India, said in a statement.

The weather #CycloneNisarg pic.twitter.com/uDZlf72463 — Rana Ayyub (@RanaAyyub) June 3, 2020

Rare cyclone

Cyclones often skirt densely populated Mumbai, though every year during torrential rains of the June-September monsoon season roads are submerged, and the suburban railway service that serves millions of people comes to a halt.

But the city has rarely faced the brunt of cyclones – the last severe storm to hit the city struck in 1948, killing 12 people and injuring more than 100.

The NDRF has mobilised 32 teams, and a total of 1,500 men are ready in the two states to help with evacuations and relief.

Nisarga is the second cyclone to strike India in a little less than two weeks. On May 21, Cyclone Amphan battered the country’s eastern coast including Kolkata, and neighbouring Bangladesh, killing more than 100 people and leaving a trail of destruction.

Although post-monsoon flooding is common in Mumbai in the fall, some experts fear the city is not prepared for the strong winds and storm surges that come with the cyclone.

“There’s been no test of how the city does in a cyclone,” said Adam Sobel, a climate scientist at Columbia University who has studied the risk to Mumbai. “It just makes me nervous.”

cyclone Nisarga



Appraisal of climate change and cyclone trends in Indian coastal states: a systematic approach towards climate action

  • Original Paper
  • Open access
  • Published: 20 April 2022
  • Volume 15 , article number  814 , ( 2022 )

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case study of recent cyclone in india

  • Komali Kantamaneni 1 ,
  • Sigamani Panneer 2 ,
  • Annaidasan Krishnan 3 ,
  • Sulochana Shekhar 3 ,
  • Lekha Bhat 4 ,
  • Aswathi K. R 2 &
  • Louis Rice 5  

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Indian coastal regions have often been affected by frequent climate-induced natural disasters such as cyclones, floods, droughts and other related hazards in recent decades. Existing literature was not sufficient to fully understand these event trends from diverse perspectives in a systematised manner at current scenarios. Therefore, a systematic approach has been employed to assess the climate change and cyclone trends of nine Indian coastal states by using various geographical information system (GIS) tools for 2006–2020. The results showed that 61 cyclones occurred in nine coastal states from 2006 to 2020; the highest numbers were recorded in Odisha (20), West Bengal (14) and Andhra Pradesh (11). Accordingly, these three coastal states emerged as the most vulnerable for high-intensity cyclones. The results also identified that the highest average temperature (29.3 °C) was recorded at Tamil Nadu and Gujarat, and the lowest temperature (26.7 °C) was recorded in West Bengal and Odisha. Most of the coastal states showed fluctuations in temperatures during the study period. At the same time, Kerala and Karnataka states recorded the highest average rainfall (2341 mm and 2261 mm) and highest relative humidity (78.11% and 76.57%). Conversely, the Gujarat and West Bengal states recorded the lowest relative humidity at 59.65% and 70.78%. Based on these results, the current study generated GIS vulnerability maps for climate change and cyclone activity, allowing one to rank each state’s vulnerability. Cumulatively, these results and maps assist in understanding the driving mechanisms of climate change, cyclones and will contribute towards more effective and efficient sustainable disaster management in the future.

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Coastal states of India are often affected by frequent climate induced natural disasters such as cyclones, floods, droughts and other related hazards in recent years (Mirza 2003 ; Patel et al. 2020 ; Thomalla and Schmuck 2004 ; Yadav and Barve 2017 ). Climate change is having a significant impact in tropical and subtropical countries, especially coastal regions. Coastal areas in some countries particularly in the global south are highly susceptible to the various impacts of climate change due to anthropogenic and natural climatic factors (Bouwer 2011 ; DasGupta and Shaw 2013 ; Nath and Behera 2011 ; Sivakumar and Stefanski 2010 ). Severe changes in climatic and weather conditions, rapid sea-level rise (SLR), storm surge, temperature fluctuations and irregular rainfall trends have increased coastal vulnerability problems in the majority of coastal regions across the globe, resulting in huge losses of coastlines, properties and damage to coastal communities (Burkett 2012 ; Gupta et al. 2019 ; Lal 2003 ; Mimura 2013 ; Sánchez-Arcilla et al. 2011 ). Likewise, many coastal states of India suffer severe cyclonic storms leading to flooding. Furthermore, some of these coastal states are particularly highly populated: Maharashtra, West Bengal, Tamil Nadu, Karnataka, Andhra Pradesh, and Gujarat states’ coastal communities have been highly impacted by climate change and cyclones (Baig et al. 2020 ; Kantamaneni et al. 2019 ; Mazumdar and Paul 2016 ; Rao et al. 2020a ; Rehman et al. 2020 ). The rapid urbanisation increases the risk of pluvial floods in the coastal areas (Zhang et al. 2018 ; Zhu et al. 2021 , 2015 ), and the impact of climate change on coastal states is a serious concern.

Tropical cyclones are one of the greatest threats to human life and property even during the initial stages of cyclonic development. In the last 50 years, 1, 942 disasters have been identified as tropical cyclones, killing 77,9324 people and causing $1,407.6 billion fiscal destruction across the world (World Meteorological Organisation 2021 ). Nearly, 630 million people will live below estimated annual flood levels for 2100; 1 hundred million people live below high tide areas globally (Kulp and Strauss 2019 ), and the number of high intensity global tropical cyclones will likely rise due to anthropogenic global warming in the twenty-first century (GFDL - Geophysical Fluid Dynamics Laboratory 2021 ). The different downscaling and Multiple Earth System Models (Emanuel 2017 , 2021 ; Irvine et al. 2019 ; Knutson et al. 2020 ; Michaelis and Lackmann 2019 ; Patricola and Wehner 2018 ; Wehner et al. 2014 ) forecast that anthropogenic climate change will increase the frequency and intensity of the most intense tropical cyclones and amount of rainfall (Irvine et al. 2019 ). The warmer sea surface temperature has resulted in large stocks of moisture and the intensification of cyclones which hit land. Over the past 50 years, the number of landfalling hurricanes in the North Atlantic has increased by 94% (Michaelis and Lackmann 2019 ), and East Asia and North West Pacific have experienced an increasing trend in rapid intensification of tropical cyclones with an escalated cost of destructions (Basconcillo and Moon 2022 ; Chan et al. 2021 ; Liu and Chan 2022 ).

The changes in global sectoral interactions in several countries due to tropical cyclones over the period of 1990–2015 show that tropical cyclones have a substantial adverse effect on the yearly growth rate of almost all sectors. Damage to productive capital, infrastructure, or buildings can have direct negative consequences and lead to a negative income shock for the entire economy (Kousky 2012 ). The intensification of tropical cyclones due to global warming exposes more people to it and also intensifies the future cost of climate change (Kunze 2021 ). The extreme weather events in 2020 caused significant damage to the economies in several countries across the globe particularly in Asian Countries such as China (USS238), India (USS87), and Japan (USS83) (WMO-World Meterological Organisation 2021 ).

The Indian Ocean area is one of the 6 most prone cyclone areas in the world with five to six cyclones on average per year (Sahoo and Bhaskaran 2018 ). Indian coastal regions with low-lying terrain, high population density, frequent cyclones and storms, and a high rate of coastal environmental degradation lead to many disasters and extreme vulnerability for the coastal states. In the Indian region, more cyclones occur in the Bay of Bengal than in the Arabian Sea at a ratio of 4:1(Rao et al. 2020b ). Many different types of coastal ecosystems can be found along the Indian coastline, i.e. coastal wetlands, major estuaries, lagoons, and mangroves. Total coastal wetland covers 43, 230 km 2 of the coastal states; 97 major estuaries and 34 major lagoons are found throughout the study area; 31 mangrove areas are located on the coastline and total mangrove areas covered 6740 km 2 , where 57% of mangrove areas are situated the East coast, and 23% of the area covered along the West coast and 20% of the mangroves area is located at the Andaman and Nicobar Islands (Central Marine Fisheries Research Institute 2021 ). The coastal states have tropical climates and monsoons with a dry and rainy season. The rains are more or less intense and long-lasting depending on the area (Nandargi and Mulye 2012 ).

Frequent occurrence of cyclones is very common in the Indian coast and causes heavy damage resulting from the effects of storm surges and high tides (Rao et al. 2020b ; Shaji et al. 2014 ; Unnikrishnan et al. 2006 ). Estimated sea-level projections for future years and centuries indicate the potential exposure of the coastal population to the various hazards; coastal planning is vital for further improvement of adaption strategies. Additionally, three megacities are located on the Indian coastline, i.e. Mumbai, Kolkata, and Chennai, and some growing cities with millions of inhabitants are at high risk. Furthermore, the impact of climate change is reflected in sea surface temperatures and tropical storm characteristics which are increasing every year. India (coastal cities) has been chosen as a research area as one of the tropical countries in South Asia; however, the existing literature was not sufficient to fully understand these events’ trends in coastal areas from diverse perspectives. Therefore, the current study examines climate change and cyclone trends in 9 coastal states. Also, this research address the UN-SDG (United Nations Sustainability Goal) 13 (climate change) by doing the above mentioned assessments in diverse ways. Consequently, this research explains how climate change will be a barrier to achieve SDG 13 in coastal states of India and offer some guidelines to overcome the problem. The changing environment and climatic conditions and the increasing number of extreme events (such as frequent floods, droughts, cyclones and other related disasters) push millions of people into chronic poverty worldwide. This increases the imbalances in physical, social and economic systems and affects sustainable development. It disturbs various economic activities, including agriculture, food safety and tourism. It threatens the very existence of island counties and coastal cities. Different research findings proved the effects of climate vulnerability on vector-borne diseases. Increasing sea surface temperature raises coastal vulnerability. Overall, it has a significant impact on the Sustainable Development Goals (SDGs), which we plan to achieve by 2030. Though SDG 13 focuses on combating climate change and its impacts, and national governments are taking various steps to build climate-change-resilient communities, climate change is still a great challenge and an impediment to achieving all the other SDGs. These results will help for future planning and policy making and efficient sustainable coastal management.

India is the seventh-largest country in the world by area (3.28 million sq. km) and the second-largest by population (1.3 billion), with 28 states and 8 union territories (Government of India 2022 ). It occupies a significant portion of the South Asian subcontinent, which has nine coastal states and two coastal union territories. These states’ boundaries occupy the Bay of Bengal, the Arabian Sea and the Indian Ocean as shown in Fig.  1 . The total length of the coastline is 7, 516.6 km, comprising the mainland with 5, 422.6 km and island territories of 2094 km (Table 1 ). The exclusive economic zone (the areas identified as economically beneficial) is an identified 2.02 × 106 million km2, and three states include megacities with the largest population pressure, i.e. Maharashtra, West Bengal and Tamil Nadu. The total population of coastal states and union territories is 560 million, and it comprises 46.2% of the total population (Kumar et al. 2006 ; Singh 2003 ). The inland consists of four areas called the plains of the Ganga and the Indus (1), the great mountain zone (2), the desert region (3) and the southern peninsula (4). India is a tropical country with hot to extremely hot weather in summers and dry winters with four main periods per year called winter (December–February), summer (March–June), pre-monsoon season (June–September) and post-monsoon season (October–November) (Government of India 2022 ). However, significant changes have been recorded in these four seasons — extended hot summers and shortened winters.

figure 1

Map of the case study area: Indian coastal states

According to the NDMA (National Disaster Management Authority), in 2022, 75% of the Indian coastline is susceptible to cyclones and related hazards. More than 60 districts and > 14% of the coastal states and the population of the union territories are frequently exposed to different levels of cyclones. Though 7% of global cyclones originate in the Arabian Sea and the Bay of Bengal, the impact of cyclones on Indian coastal states is enormous. The geography of India and the fluctuations in climatic conditions (temperature, rainfall trends, humidity, etc.) are vital factors that lead to an increase in the intensity and frequency of cyclones and in the damage inflicted on the coastal areas of India. Among all natural disasters, cyclones contributed to 15% of the total number of natural disasters that occurred in India between 1999 and 2020, and they rank third after floods and earthquakes (Government of India 2022 ). Based on these reasons, nine Indian coastal states (excluding two union territories) have been selected for the assessment to offer updated knowledge on climate changes and cyclone trends.


The current study adopted the Donnadieu et al. ( 2017 )’s systematic approach to assess climate change and the cyclone trends in Indian coastal states. This systematic approach accumulates all the empirical evidence that is related to corresponding research, and it has been considered the answer to an explicit research question. Besides, the systematic approach method reduces bias and leads to more trustworthy findings for decision-making. Accordingly, a systematic approach has been used to assess the trends of climate change and cyclones in a logical order to offer updated knowledge. The present research is an appraisal of historical climatic data (temperature, rainfall and relative humidity) in relation to cyclone intensity. The data was acquired from NASA Power Data Access ( https://power.larc.nasa.gov/data-access-viewer/ ) and NASA Langley Research Center. Temperature and humidity were measured 2 m above the surface. Correspondingly, the data about cyclones (Table 1 ) was gathered from IMD E-atlas, the Indian Meteorological Department, and was divided into three categories based on the intensity (i.e. depressions [D], cyclonic storms [CS] and severe cyclonic storms [SCS]). The current study analysed the spatial exhibitions of the cyclone-prone areas and the spatial exhibitions of the cyclonic paths using the ‘Display XY Data’ GIS tools – ArcGIS 10.6. Temperature, rainfall, and humidity data were constructed annually to reveal the in-depth climatic changes of Indian coastal states (Table 2 ).

The study categorised the cyclones into three types based upon the intensity derived from the criteria-based classification, Indian Meteorological Department, such as cyclonic storms and severe cyclonic storms (Fig.  2 ). Moreover, the climatic data was calculated year-wise from 2006 to 2020, which was considered an average for each year. In addition, spatial and temporal analysis was accomplished using the choropleth method using ArcGIS 10.6 software. Spatial representations into four categories based on numerical values on the chronological order using the natural breaking tool in ArcGIS 10.6 were created; the units of measurement of the climatic data includes rainfall (mm), temperature (°C) and humidity (%). The study reveals the impacts of climate change per state. This study considered 15 years of climatic data for an appraisal of climate changes.

figure 2

Number of crossed cyclones thorough the coastal states for 2006–2020

Results and discussion

State-wise paths of cyclones.

There were 61 cyclonic disturbances across the Indian coastline during 2006–2020. The maximum number of cyclonic disturbances occurred in Odisha (20), and the second highest cyclonic turbulences traversed West Bengal (14) followed by Andhra Pradesh (11) and Tamil Nadu (11) during 2006–2020. The maximum cyclonic disturbances were crossed at Gujarat (2) and Goa (3). However, Karnataka, Kerala and Maharashtra coastlines had no cyclones during 2006–2020 (Fig.  2 ). The identification and tracking of cyclonic turbulences assist in facilitation of the necessary precautions and warnings for vulnerable coastal communities. The study considered the starting location of each cyclone which are the Bay of Bengal, Arabian Sea, Indian Ocean and Inland. The results show the state-wise cyclonic paths for the selected study period.

State-wise cyclonic data consists of the data/m/year (which is shown in Table 3 ) contains the data of the depression, cyclonic storm and severe cyclonic storm. Furthermore, the Southwest monsoon played a major role in producing 29 cyclonic disturbances near the coastal region and 27 cyclonic turbulences associated with the Northeast monsoon, whereas 5 cyclones occurred during the non-monsoon season. Consequently, the study has evaluated three cyclones seasons, i.e. Southwest monsoon (June to September), Northeast monsoon (October to December) and non-monsoon season (January to May), and these were assessed for climate change appraisal.

In-depth analysis: state wise of cyclones

Eastern coastal india, andhra pradesh.

Andhra Pradesh is one of the Indian coastal states located in the south-eastern India, where cyclones frequently pass during the monsoon season every year. Consequently, the study has identified that most of the cyclones cross the state during the Northeast monsoon. Andhra Pradesh is a vulnerable coastal zone, experiencing 11 cyclonic turbulences (Fig.  3 ), 5 depressions (D) were documented during the years 2007, 2008, 2010, 2013 and 2018; although 2 cyclonic storms occurred during years 2006 and 2018, and 4 severe cyclonic storms (SCS) were measured in 2010, 2013, 2014 and 2020. These severe Cyclonic Storms resulted in 95 fatalities and resulted in the displacement of 4,93,732 people in Andhra Pradesh (Chapman et al. 2020 ; Dhara 2019 ; NASA Power Data Access 2020 ). Depressions would typically initiate heavy rainfall in the coastal districts: namely Nellore, East Godavari and Krishna, which are very highly prone cyclonic areas, whereas Srikakulam, Guntur, Visakhapatnam, West Godavari, Prakasam, and Vizianagaram districts are the highly prone areas as classified by the Indian Meteorological Department, Government of India.

figure 3

The number of cyclones occurring in nine Indian coastal states for 2006–2020

West Bengal

The coastline of West Bengal consists of two coastal districts, i.e. Midnapur and South Parganas district, and it encompasses the Sundarbans mangrove ecosystem (Zhang et al. 2021 ). Fourteen cyclones traversed through West Bengal (Fig.  3 ); 9 cyclonic disturbances provided the lowest impacts on coastal districts, and 3 cyclonic turbulences created the largest impacts. In addition, 2 cyclonic storms traversed through West Bengal, and 3 severe cyclonic storms crossed, which created a high impact on humans and the environment as these cyclones lead to torrential rain. The resulted in a total of 138 fatalities and displacement of 8,38,000 people in the state (Ehrnsten et al. 2019 ; Serpetti et al. 2017 ). These cyclonic turbulences had major impacts on Kolkata and several districts, Sabang, North and South 24 Parganas, East and West Midnapore, Bankura, Howrah, Burdwan, Hoogly, Purulia, Ghatal, Darjeeling, Sabang, Pingla, Cooch Behar, Datan and Jhargra and Cooch Behar districts, were severely affected.

Based on the appraisal of historical cyclonic data, Odisha state was defined as a highly cyclone-prone area. In total, 20 cyclones crossed throughout the period 2006–2020: 17 cyclonic depressions (Fig.  3 ) formed on the Bay of Bengal, which resulted in heavy rainfall to the entire coastal district; 1 cyclonic storm came across the Odisha coastline; and 2 severe cyclonic storms (SCS) occurred during the Northeast monsoon. These cyclonic turbulences directly impacted the Balasore, Kendrapara, Jagatsinghpur, Bhadrak, Ganjam, Puri and Khordha districts leading to water-based disasters (floods), with 2007 and 2014 chronicling extreme floods in Odisha. The districts Balasore, Bhadrak, Jagatsinghpur, Jaipur, Keonjhar, and Mayurbhanj, Balikuda and Naugaon experienced 94 deaths, and 90,000 people were displaced during the study period. It is noteworthy that tropical monsoons impacted on one-third of the entire state.

Tamil Nadu comprises 14 coastal districts, i.e. Chennai, Kancheepuram, Thiruvallur, Villupuram, Cuddalore, Mayiladuthurai, Nagapattinam, Thiruvarur, Thanjavur, Pudukkottai, Ramanathapuram, Thoothukudi, Thirunelveli and Kanyakumari (organised from north to south), and is one of the most prone to cyclones. According to the Indian Meteorological Department (IMD), 11 cyclones were caused by the northeast monsoon and 5 depressions traversed Tamil Nadu (Fig.  3 ). The state was predominantly affected by severe cyclonic storms during the northeast monsoon; these were accompanied by various losses such as human life, environment and habitats. Furthermore, two major floods occurred in 2014 and 2015, caused by a tropical cyclone crossing the east coast of India; and low-pressure systems caused flooding in Tamil Nadu. These resulted in the loss of 360 human lives in the Chennai and Cuddalore districts.

Western coastal India

Kerala, karnataka and maharashtra.

The Kerala, Karnataka and Maharashtra coastal states are located in the west India coastal area, lying vertically from north to south sharing boundaries with the Arabian Sea. No cyclones hit during 2006–2020; the Southwest monsoon generated continuous rainfall for the period of June to September causing heavy rainfall and floods near the coastal districts. In 2010 monsoon rain caused floods and landslides in both southern parts (Kerala and Assam) where 50 people were killed and approximately 500,000 people were relocated (ReliefWeb 2021 ). In 2018, Kerala was severely affected by high rainfall for all of its 14 districts, namely Alappuzha, Kasaragod, Wayanad, Kannur, Kozhikode, Malappuram, Palakkad, Ernakulam, Thrissur, Idukki, Kottayam, Pathanamthitta, Kollam and Thiruvananthapuram. Moreover, water was released from multiple dam reservoirs, and deaths were documented at 359 with 7 people missing. In 2009, heavy monsoon rain caused flash floods, the states Andhra Pradesh, Karnataka and Kerala were severely affected by the floods, and many places saw exceptional monsoon rainfall, severe river flooding and landslides affecting west coast areas and southern provinces.

Goa and Gujarat

The state of Goa is divided into north and south administrative districts. Goa suffered 2 cyclonic disturbances (Fig.  3 ), crossing the Goa and Konkan regions during 2006–2020. Bicholim, Sattari, Ponda, Canacona and Sanguem areas were affected by floods, and Pernem, Bardez, Tiswadi, Salcete, Mormugao and the Canacona regions were affected by coastal erosion. During this period, 3 depressions crossed Gujarat caused by the southwest monsoon (June), and most of the coastal districts were affected by heavy monsoon rainfall. In 2007, monsoon flooding harshly affected and/or damaged most of the regions, i.e. Saurashtra region, Surendranagar, Rajkot, Bhavnagar, Jamnagar, Junagadh and Amreli; in South Gujarat, Bharuch, Narmada, Surat, Junagarh, Patan, Mehsana, Gandhinagar and Sabarkantha; and in the Kutch region, Vadodara, Ahmedabad, Anand, Panchmahal, Daskroi, Dholka, Valsad and Kheda.

Climate change and data analysis

The research appraised the impacts of climate change on the coastal states from the east to west coast, i.e. West Bengal, Odisha, Andhra Pradesh, Tamil Nadu, Kerala, Karnataka, Goa, Maharashtra and Gujarat using the empirical climate data. These states measured climate change aspects from 2006 to 2020. The study revealed average temperature, average rainfall and average humidity for the selected study period. The result identified that Tamil Nadu and Gujarat had the highest temperatures 29.3 and 29.0 °C, and the lowest temperatures were in West Bengal and Odisha 26.7 and 27.5 °C. Kerala and Karnataka states recorded the highest average rainfall of 2341 and 2261 mm. Conversely, Odisha and Tamil Nadu states had the lowest rainfall levels of 994 and 1075 mm from 2006 to 2020. Kerala and Karnataka states had the highest humidity of 78.11 and 76.57 percent, whilst Gujarat and West Bengal states recorded the lowest humidity of approximately 59.65 and 70.78°. The overall appraisal of climatic data identifies the Kerala and Karnataka states as being highly affected by climate change (Table 4 ).


The study considered climatic parameters such as temperature, rainfall and humidity which was calculated as an average for each year from 2006 to 2020 (Fig.  4 ). West Bengal experienced the highest average temperature documented at 25.96 °C in 2015, and the lowest average temperature identified at 25.32 °C in 2008. Odisha’s average temperature was recorded at 27.17 °C in 2009, whereas the lowest temperature noted at 26.10 °C in the year 2020. Andhra Pradesh’s highest average temperature was recorded at 27.83 °C in 2009, and the lowest average temperature documented as 27.05 °C in 2007. Tamil Nadu highest average temperature was 35.62 °C in 2019, and their lowest average temperature was documented at 27.27 °C in 2020. Kerala’s highest average temperature was 27.41 °C in 2019, while the lowest average temperature measured 26.50 °C in 2008. Karnataka’s maximum average temperature of 27.06 °C was in 2019, and their minimum average temperature measured 25.35 °C in 2007. The smallest state of Goa recorded their maximum average temperature of 27.58 °C in 2020 and lowest average temperature of 27.10 °C in 2011. Maharashtra’s highest average temperature hit 27.66 °C in 2011, and their lowest average temperature measured 27.26 °C in 2007. The maximum temperature of Gujarat was 28.30 °C in 2015, and their lowest average temperature was 27.4 °C in 2013. However, whilst the distribution of average temperatures varies throughout the study period, the maximum average temperature overall was found in Tamil Nadu. Tropical cyclones form based upon intensifying temperatures in oceanic regions. This is also related to greenhouse gas (GHG)-induced surface temperature increase. Furthermore, the world meteorological organization (WMO) global level appraisal (Knutson et al., 2020 ) concluded that tropical cyclones are extremely unusual compared with other natural causes.

figure 4

Average temperatures 2006–2020

The appraisal of average rainfall in Indian coastal states dates from 2006 to 2020 (Fig.  5 ), and the study describes the maximum and minimum rainfall for each state. West Bengal’s maximum average rainfall observed 2080 mm in the year 2017 and minimum average rainfall measured at 1197 mm in 2012. Odisha’s maximum average rainfall was 1913 mm in 2018, with a minimum average rainfall of 1317 mm in 2009. Andhra Pradesh recorded their highest average rainfall of 1682 mm in 2010 and lowest rainfall of 865 mm in 2009. Tamil Nadu observed their highest average rainfall of 1515 mm in 2015 and lowest average rainfall of 796 mm in 2012. Kerala measured their highest rainfall of 2960 mm in 2006 and lowest rainfall of 1691 mm in 2012. Karnataka observed their highest average rainfall of 3044 mm in 2020 and in 2008 their lowest rainfall of 1848 mm. Goa measured their maximum average rainfall of 1915 mm in 2019 and minimum average rainfall of 1131 mm in 2008. A highest average rainfall for Maharashtra was 2713 mm in 2006 and lowest average rainfall of 1464 mm in 2015. Gujarat received their highest average rainfall of 1349 mm in 2007 and their lowest average rainfall of 693 mm in 2018. The highest distribution of average rainfall was dispersed across the states. Most of the states, i.e. Odisha, Tamil Nadu and Kerala states, received the lowest average rainfalls, particularly in the year 2012, when the monsoon failed to arrive.

figure 5

Average rainfall 2006–2020

The humidity is recorded during the study period as a state-wide quantity representing the amount of water vapour in the atmosphere as an average distribution. The highest average humidity was measured in West Bengal at 75.04% in 2020 (Fig.  6 ), and their lowest humidity was measured at 68.06% in 2012. Odisha’s highest average humidity was recorded at 75.4% in 2020 and their lowest humidity measured 69.0% in 2009. Andhra Pradesh’s extreme humidity was 74.3% in 2020, while their lowest humidity was 69.3 in 2009. The highest average humidity measured at Tamil Nadu was 76.9% in 2006, and their lowest was 72.0% in 2016. Kerala recorded their highest value of humidity at 79.2% in 2006 and lowest value of 76.8 in 2019. The highest humidity measured in Karnataka was 79.7% in 2006, and the lowest was 74.6 in 2007. Goa’s maximum humidity was 76.9% in 2010, and the lowest was 74.1% in 2015. Maharashtra’s highest humidity at 72.7% in 2010 and lowest humidity was 67.5% in 2018. Gujarat’s highest humidity was 64.0% in 2020, and the lowest was 54.7% in 2018. The study revealed the West Bengal, Odisha, Andhra Pradesh and Gujarat states experienced the highest humidity, particularly the year 2020.

figure 6

Average relative humidity 2006–2020

In the overall assessment of temperatures, rainfall and humidity, the study identified that the temperature increased in Kerala (0.78 °C) and Tamil Nadu (0.71 °C), whereas in Karnataka (− 0.64 °C) and West Bengal (− 0.60 °C) the temperature decreased. Karnataka (1072 mm) and Goa (605 mm) states received the highest rainfall. Kerala (− 556 mm) and Tamil Nadu (− 285 mm) states rainfall decreased from 2006 to 2020. West Bengal’s (4.7%) and Gujarat’s (2.8%) humidity increased; Tamil Nadu (− 2.0%) and Karnataka (− 1.6%) observed decreased humidity from 2006 to 2020. Therefore, the overall appraisal noted that the highest changes were documented in Karnataka and Kerala, and the results indicate these states are acquiring rapid climatic changes, with the rest of the coastal states experiencing no change, based on the appraisal of climatic data.

Overall trends of climate change and cyclones in Indian coastal states

Climate change and fluctuations in the climate are the major challenges for sustainable coastal zone management across India and also barriers to achieving the UN SDG 13. This study evaluated the climatic data for the past 15 years, identifying the impacts of climate change on coastal states and revealing that the climate is changing for most of the coastal states with Karnataka, Goa, Kerala, Tamil Nadu, West Bengal and Andhra Pradesh experiencing increasing or decreasing temperatures, abnormal rainfall and humid conditions. As a result of these climate changes, increasing sea level rises (1.7 mm/year) is contributing to other challenges including habitat loss, degradation of coastlines and coastal ecosystems and shoreline changes. Furthermore, climate change is exacerbating coastal erosion within this short time period, leading to coastal area flooding and seawater intrusion. West Bengal suffered the largest proportion of erosion between 1989 and 2001, with alteration along 70% of its coast, followed by Kerala (65%), Gujarat (60%) and Odisha (50%). Sea-level rise and floods may cause greater evacuation in major coastal cities, in addition to the displacement of people along the eastern coast (Dhara 2019 ). These aforementioned issues are impacting on the Indian coastal states. Cyclones are a natural disaster that strike India almost every year, claiming many lives and wreaking havoc on property. Based upon historic cyclonic data from 2006 to 2020, the study reveals that Tamil Nadu, Andhra Pradesh, Odisha and West Bengal states are severely affected by cyclones during the northeast monsoon. The state of Gujarat is highly vulnerable to tropical cyclones every year by the southwest monsoon. In recent years, cyclonic intensity has been very high and damaged the human and coastal environment.

The direct impact of climate change to the coastal area is also from heavy rains, unbearably high temperatures, humidity, etc. (Chapman et al. 2020 ; Zhang et al. 2021 ). These studies have demonstrated the vulnerability of the coastal area and reveal the impacts of climate change on coastal states (Serpetti et al. 2017 ). A large number of drivers, such as cyclones and floods, are closely associated with climate change (Ehrnsten et al. 2019 ). This study has been undertaken in coastal states affected by climatic factors such as cyclone, temperature, rainfall and humidity. The study shows the influence of climate change to the coastal states of India. In addition, this should identify the natural hazards determined by climate change and associated human stress in these coastal states. Adaptation to climate change must be implemented for resilience to future disasters as well as to achieve UN-SDG 13.

The study limitations

This study focused on the appraisal of climate change in Indian coastal states where the climatic data was available from 2006 to 2020. Consequently, the analysis is limited to the study duration, and it is not focused on the wider trends of climate change and water-based disasters such as flood and landslide. Furthermore, due to time constraints, the study did not evaluate the impacts of climate change on human health. While the researchers took this into consideration during the conception of the present study, it was not possible to achieve that goal on time.


Climate change (temperature, rainfall and humidity) and cyclones are some of the world’s most destructive issues and often lead to destruction in South Asia, particularly India. The regular occurrence of cyclones is widespread in the Indian coastal states and causes significant damage resulting from the effect of storm surges and high tides. To improve information regarding these issues, the current study has assessed the climate change and cyclone trends in nine coastal states of India from 2006 to 2020 by using GIS tools, NASA and the IMD E-atlas and Indian Meteorological Data. The study identified that there has been a rise in high-intensity cyclones during the study period. Odisha and West Bengal are the most cyclone-prone states, and Maharashtra, Kerala and Karnataka are the least prone states, though Kerala and Karnataka states recorded the highest average rainfall and highest humidity. The highest average temperature was recorded in Tamil Nadu and Gujarat, and the lowest temperature was recorded in West Bengal and Odisha, although these two states have the highest cyclone vulnerability. These trends once again raise the urgency of further investigations on the relationship between climate change and cyclone activities. The GIS maps, generated from the study results, help to identify the intensity of climate change and cyclones state-wise. The study results also help policy and decision-makers to progress and improve effective strategies for sustainable coastal management. Also, these results help towards achieving one of the goals of the UN-SDG 13 (climate change), which further helps to improve the sustainability of coastal management in Indian coastal regions and also people’s lives.

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The authors are grateful to the Indian Meteorological Department (IMD) and NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program for providing cyclones and climatic data used in this study.

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Komali Kantamaneni

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Sigamani Panneer & Aswathi K. R

Department of Geography, School of Earth Sciences, Central University of Tamil Nadu, Thiruvarur, Tamil Nadu, 610005, India

Annaidasan Krishnan & Sulochana Shekhar

Department of Epidemiology & Public Health, School of Life Sciences, Central University of Tamil Nadu, Thiruvarur, Tamil Nadu, 610005, India

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Kantamaneni, K., Panneer, S., Krishnan, A. et al. Appraisal of climate change and cyclone trends in Indian coastal states: a systematic approach towards climate action. Arab J Geosci 15 , 814 (2022). https://doi.org/10.1007/s12517-022-10076-8

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DOI : https://doi.org/10.1007/s12517-022-10076-8

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Study report on gaja cyclone 2018.

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Executive Summary

Tamil Nadu is historically one of the most vulnerable States to tropical cyclone. The total geographical area of Tamil Nadu is 13 Million hectares and it has a coastline of 1,076 km which is about 15% of the coastline of India. The State is multi-hazard prone, the major natural hazards being Cyclonic storms, Urban and Rural floods, and periodic Droughts. Some of the tropical cyclones that hit Tamil Nadu are Gaja (2018), Ockhi (2017), Vardha (2016), Nilam (2012), Thane (2011), Jal (2010) and Nisha (2008).

Severe Cyclonic Storm Gaja originated as a low-pressure system over the Gulf of Thailand. The weak system intensified into a depression over the Bay of Bengal on November 10 and further intensified to a cyclonic storm on November 11, being classified 'Gaja'. Cyclone Gaja made landfall in South India, at Vedaranyam, Tamil Nadu. At the time of landfall of the cyclone, 100-120 kmph speed was experienced. The highest sustained speed was recorded in Adhirampattinam at 165 kmph and 160 kmph at Muthupet. The cyclone Gaja affected 08 districts of Tamil Nadu, namely, Nagapattinam, Thanjavur, Thiruvarur, Pudukottai, Karaikal, Cuddalore, Trichy and Ramanathapuram.

To build upon the learning of Cyclone “Gaja” and to document the lessons learnt and best practices, the present study was undertaken with the following objectives:

The objectives of this study were as follows:

• To critically analyze the role of disaster managers in the management of Cyclone Gaja with special reference to early warning, preparedness, impact, response, and community preparedness.

• To assess the impact of Cyclone Gaja on the infrastructure, services, and communities.

• To study the measures undertaken by the Central Government, State Governments and District Administrations to reduce the mortality and impact of cyclones in the State of Tamil Nadu.

• To document the best practices undertaken during the management of Cyclone Gaja.

• Suggest evidence-based recommendations for better management of Cyclones in the future.

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[Updated] List of Cyclones that hit India in 2019 - 2023

List of cyclones that hit india in 2019-2023: check here the list of intense cyclones that have hit the indian states in the year 2019-2023.     .

Arfa Javaid

What is a Cyclone?

List of cyclones in india 2019-2023, cyclone biparjoy.

Cyclone Warning for Saurashtra and Kutch Coasts: RED MESSAGE. VSCS BIPARJOY at 0830IST today near lat 22.6N & long 67.1E, about 170km WSW of Jakhau Port (Gujarat) and 210km West of Devbhumi Dwarka. To cross near Jakhau Port (Gujarat) by evening of today as VSCS. pic.twitter.com/iESz82jRRW — India Meteorological Department (@Indiametdept) June 15, 2023

Cyclone Mandous

The cyclonic storm “MANDOUS weakened into a Deep Depression over north Tamilnadu coast. To move nearly west-northwestwards and gradually weaken into a depression by noon of 10th december.For details visit : https://t.co/KLRdEFHiFR pic.twitter.com/Zt41j7960h — India Meteorological Department (@Indiametdept) December 10, 2022

Cyclone Sitrang

Cyclonic storm “SITRANG” over EC and adjoining areas of WC & NW BoB near lat 17.80N and long 88.60E, 430 km south of Sagar Island and 580 km S-SW of Barisal . To move N-NE and intensify further into a SCS in next 12 hours. To cross Bangladesh coast 25th October early mrng pic.twitter.com/GB6LChqpVZ — India Meteorological Department (@Indiametdept) October 24, 2022

Cyclone Asani

Severe Cyclonic Storm Asani is about 450 km southeast of Visakhapatnam (Andhra Pradesh) at 1130 IST of 9th May. It is very likely to move northwestwards till 10th May. Thereafter recurve N-NEwards. It is likely to weaken gradually into a Cyclonic Storm during next 24 hours. pic.twitter.com/6Jamqf5VI2 — India Meteorological Department (@Indiametdept) May 9, 2022

Cyclone Jawad

After three cyclonic storms in 2021, another cyclone is expected to hit the Indian states of Odisha and Andhra Pradesh. The Met Department has issued a warning that the deep depression over the Bay of Bengal is likely to intensify into a cyclonic storm during the next 12 hours. Ahead of Cyclone Jawad, Prime Minister Modi reviewed the preparedness of the situation arising out of the cyclonic storm and various authorities are on standby.  The state governments have also chalked out the disaster management strategy and as many as 95 trains have been cancelled that were either passing over or originating from East Coast Railway. 

Cyclone Gulaab

Cyclone Alert for north Andhra Pradesh and adjoining south Odisha coasts : DD is centered near 18.4°N/88.7°E .To cross north Andhra Pradesh - south Odisha coasts b/w Kalingapatnam & Gopalpur by evening of 26. pic.twitter.com/QNwlJHbwBR — India Meteorological Department (@Indiametdept) September 25, 2021
The Deep Depression over south Odisha and adjoining north Andhra Pradesh, lay centered at 0530 hrs IST of 27th September, likely to move nearly westwards and weaken further into a Depression during next 12 hours. pic.twitter.com/Pxht5L6wuj — India Meteorological Department (@Indiametdept) September 27, 2021

Cyclone Tauktae

It was the first cyclonic storm of 2021 that emerged from the Arabian Sea. It hit southern Gujarat on 17 May 2021 and was classified as a Very Severe Cyclonic Storm (VSCS). As many as 24 people were killed across three Indian states. Twelve people died in Maharashtra, eight in Karnataka, and four people in Gujarat.

Very Severe Cyclonic Storm “Tauktae” over Eastcentral Arabian Sea intensified into an Extremely Severe Cyclonic Storm: Cyclone Warning & post landfall outlook for Gujarat & Diu coasts (Red message). https://t.co/nIG8rzj9Vh pic.twitter.com/DAJCsnuRVw — India Meteorological Department (@Indiametdept) May 17, 2021
CM Uddhav Balasaheb Thackeray is closely monitoring the #CycloneTauktae situation in the State. Mumbai, Thane & Palghar districts are on orange alert while Raigad district is on red alert. — CMO Maharashtra (@CMOMaharashtra) May 17, 2021
In the wake of #CycloneTauktae , CM Shri @vijayrupanibjp announces the suspension of Corona vaccination drive for the next two days i.e. May 17 & 18 - Monday & Tuesday, and urges citizens to remain indoors considering the possibility of heavy rains along with cyclone in the state. pic.twitter.com/zUyW9tJzha — CMO Gujarat (@CMOGuj) May 16, 2021
Significant Weather Features dated 19-05-2021 are: ♦ A Depression (remnant of the Extremely Severe Cyclonic Storm “Tauktae”) lay centred at 0830 hours IST of today, the 19th May, 2021 near latitude 24.9°N and — India Meteorological Department (@Indiametdept) May 19, 2021

Cyclone Yaas

Deep Depression over Eastcentral Bay of Bengal intensified into Cyclonic Storm ‘Yaas’ and about 600 km of Port Blair. To intensify into a Severe Cyclonic Storm during next 24 hours and into a Very Severe Cyclonic Storm during subsequent 24 hours. pic.twitter.com/HfREdsMtOL — India Meteorological Department (@Indiametdept) May 24, 2021

Ahead of the impending Cyclone Yaas, Prime Minister Modi reviewed the preparedness of the state as well as central agencies to deal with the situation. He further called for a timely evacuation of those involved in offshore activities.

New Low Pressure area likely to form in Bay of Bengal around 22 May To intensify into a Cyclonic Storm and reach West Bengal and Odisha Coast by 26 May. Sea conditions to remain rough in Bay of Bengal from 21 May onwards. Fishermen requested to return to shores. #CycloneAlert pic.twitter.com/eHnInU33y2 — NDMA India | राष्ट्रीय आपदा प्रबंधन प्राधिकरण 🇮🇳 (@ndmaindia) May 19, 2021
Depression (Remnant of Very Severe Cyclonic Storm “YAAS”) over Bihar & adjoining Jharkhand has weakened into a well-marked low pressure area lay over Bihar adjoining East UP at 0530 hrs IST of today the 28th May 2021. pic.twitter.com/wD9pawJxEn — India Meteorological Department (@Indiametdept) May 28, 2021

Cyclone Nisarga

Cyclone Nisarga was the second pre-monsoon cyclone that emerged from the Arabian Sea. It hit Alibag in Mumbai and weakened in 6 hours. It was the first cyclone to impact Mumbai since Phyan of 2009. The cyclone caused 6 deaths and 16 injuries in Maharashtra.

Cyclone Amphan

Cyclone Amphan was a  powerful tropical cyclone that led to the destruction of lives and property in the Indian states of Odisha and West Bengal. Cyclone Amphan was the first pre-monsoon super cyclone of this century that emerged from the Bay of Bengal. 

Cyclone Kyarr 

Cyclone maha, cyclone vayu, cyclon hikka, cyclone fani.

Cyclone Fani was the strongest tropical storm that hit Odisha since the 1998 Odisha Cyclone. Cyclone Fani emerged from the Indian Ocean and caused huge destruction of lives and property in Odisha, West Bengal, Andhra Pradesh and East India. Outside India, it hit Bangladesh, Bhutan and Sri Lanka. 

Cyclone Bulbul

Cyclone Bulbul was a very severe cyclonic storm that hit West Bengal in India. It caused huge rainfall, floods, etc. that resulted in the destruction of lives and property. Outside India, it hit Bangladesh. 

What is an Ex-Tropical Cyclone?

How are cyclones named in the world?

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Financial Resurgence: A Comprehensive Exploration of the Recent Boom in Mutual Fund Investments in India

Recent case studies.

Financial Resurgence: A Comprehensive Exploration of the Recent Boom in Mutual Fund Investments in India

Mutual funds play an indispensable role in capital markets, serving as a gateway for individuals and institutions to access a diverse range of securities. The Indian mutual fund industry has undergone a profound transformation over the past decade, showcasing remarkable resilience and expansion. The industry's assets under management (AUM) surged from US$ 108.8 billion (Rs. 9 Lakh Crore) in January 2014 to US$ 635.3 billion (Rs. 52.7 lakh crore), growing six-fold over ten years. This exponential rise can be attributed to a number of factors beyond market fluctuations. In this case study, we aim to present a thorough analysis of the mutual fund industry in India, focusing on the overarching landscape and exploring the key factors that underpin the recent boom in mutual fund investments in the country. This discussion also delves into the profound significance of mutual funds within capital markets, emphasizing their pivotal functions in fostering investment, managing risk, and enhancing market efficiency and liquidity.

Introduction to mutual funds

A mutual fund is a pool of money managed by a professional fund manager. It is a trust that collects money from several investors who share a common investment objective and invests the same in equities, bonds, money market instruments and/or other securities. The returns or profits from this collaborative investment are distributed to investors in accordance with their contributions following the deductibility of applicable fees and charges. This is done by computing the ‘Net Asset Value’ (NAV) of the plan. A mutual fund's worth is essentially derived from the total amount of money given by all its investors. Simply, a mutual fund is the sum of the money contributed by numerous individuals.

At the heart of their operation, mutual funds serve as conduits for pooling funds from investors and deploying them across a spectrum of financial instruments, including stocks, bonds, and money market securities. By integrating resources from a broad spectrum of investors with varied investment objectives, mutual funds democratise investing. This enables even small investors to benefit from professional fund management and diversification, which may be challenging to achieve individually.

Mutual funds as financial intermediaries

Financial intermediaries play a vital role in connecting borrowers and lenders within an economy. They accept funds from savers and allocate them to borrowers, facilitating the flow of capital. Mutual funds function as one such intermediary, pooling money from various investors to capitalise in a diversified portfolio of assets such as stocks, bonds, and money market instruments. A financial intermediary serves as a go-between for parties involved in financial transactions, such as a commercial bank, investment bank, mutual fund, or pension fund. These entities offer several advantages to regular consumers, including security, accessibility to funds and cost efficiencies in banking and asset management. By facilitating transactions, financial intermediaries contribute to creating efficient markets and reducing business costs. They can also offer services like leasing and factoring, but they do not take deposits from the general public. Financial intermediaries help distribute risk, lower expenses, and provide scale efficiencies, among other benefits.

Mutual funds offer active management of pooled capital contributed by investors. The fund manager invests the capital in stocks of companies that are expected to perform better than the market. This process allows the manager to provide investors with assets, companies with capital and the market with liquidity. Savers can combine their funds through a financial intermediary, allowing them to make significant investments that benefit the target entity. Additionally, financial intermediaries help distribute risk by diversifying funds across various investments and loans.

FinTech fusion

The digital revolution also played a pivotal role in shaping investor behaviour, particularly among younger generations. Millennials and Gen Z investors, known for their tech-savvy nature, are increasingly turning to online platforms and mobile apps to invest in mutual funds. The convenience, accessibility and user-friendly interfaces of digital investment platforms have democratised investing, making it accessible to a wide segment of the population. Moreover, COVID-19 accelerated certain trends in investor behaviour, such as the adoption of digital channels for investment transactions and focus on financial planning and risk management. The uncertainties brought about by the pandemic have underscored the importance of maintaining a well-diversified investment portfolio and adopting a disciplined approach to long-term wealth creation, driving investors towards mutual funds.

The integration of technology within the mutual fund industry in India significantly accelerated innovation and growth. Fintech fusion revolutionised the sector by streamlining operations, enhancing customer experience, and expanding access to investment opportunities. With seamless online transactions, real-time monitoring, and personalized financial advice, fintech has made investing convenient and inclusive for a broader audience. The transformative impact of fintech in the Indian mutual fund industry not only modernised traditional processes but also catalysed the industry's evolution towards a more efficient, customer-focused, and technologically advanced landscape.

The digital integration in mutual fund operations has brought about a paradigm shift in how investments are made and managed. By leveraging technology, mutual fund companies can offer investors a user-friendly platform to invest, redeem and track their investments seamlessly. This digital transformation eliminated the need for physical paperwork, reduced transaction times and enhanced overall efficiency in managing investment portfolios. Investors now have easy access to real-time information, enabling them to make informed decisions and manage their investments effectively. The significant increase in digital transactions from 1% to 21% of the total transaction value over the decade underscores the growing preference for digital channels in mutual fund investments among investors, especially the younger demographic. This shift towards digital investing signifies a broader trend towards technological advancement and accessibility in the mutual fund industry, shaping an inclusive and tech-savvy investment landscape for all types of investors.

Fintech companies have played a pivotal role in reshaping the investment landscape in India by introducing innovative investment products and services, personalised financial advice, and intuitive interfaces that cater to the diverse needs of investors. The democratisation of investment opportunities through fintech solutions has made investing more accessible to a wider audience, empowering individuals from various backgrounds to participate in the financial markets and build wealth over time. FinTech’s have emerged as the primary distributors of mutual funds in India, mirroring the success observed in the online stock-broking sector, similar to companies like Zerodha. Notably, Groww, AngelOne and PhonePe are key players in the industry, contributing significantly to the addition of new SIPs every month.

In November 2023 alone, Groww issued over seven lakh new SIPs, while AngelOne and PhonePe generated more than two lakh and over a lakh new SIPs, respectively. Data from asset management companies, shared with ET and AMFI, indicated that in November 2023, the entire industry generated 30 lakh new SIPs, with approximately 42% of these being issued through fintech platforms like Groww, Paytm Money, AngelOne, among others. The AMFI noted that over 3 million new SIPs were created in November 2023, of which around 1.3 million were opened on fintech platforms, according to industry estimates. Traditional entities in this field, such as NJWealth and State Bank of India, are also actively generating new SIPs, both issuing more than a lakh per month. In contrast, banks like HDFC, ICICI and Axis created between 30,000 and 50,000 new SIPs. Fintech firms leverage their distribution capabilities to capture market share in wealth management. For instance, PhonePe uses its extensive user base from its payments service to provide mutual funds’ investments as an additional service to retain users. Alternatively, Groww uses mutual funds as a means to attract investors to eventually participate directly in the equities market. The digital transformation in mutual fund operations supported this trend, with fintech platforms offering smooth online transactions and real-time monitoring to enhance the investing experience for a broad audience.

The emergence of new-age startups in the mutual fund space fuelled innovation within the industry by leveraging cutting-edge technologies such as data analytics and artificial intelligence to develop unique investment solutions like robo-advisory platforms and thematic investing strategies. By focusing on customer-centricity and enhancing user experience, these startups redefined the mutual fund landscape in India, offering investors innovative ways to diversify their portfolios and achieve their financial goals.

Ease of investing driving higher penetration

Mutual funds have indeed emerged as significant economic catalysts in India, driving growth within the capital markets. They not only provide investors with diversified investment options but also play a crucial role in channelling savings into productive investments. By pooling funds from individual and institutional investors, mutual funds create a substantial corpus that can be invested across various asset classes, fuelling economic expansion. With a diversified portfolio of securities, mutual funds offer investors the opportunity to earn potentially high returns and minimise risk. The ease of investing and professional management provided by mutual fund companies contributed to the growth of AUM in the mutual fund industry. Moreover, mutual funds offer different types of schemes catering to various investment objectives and risk profiles. Investing in mutual funds provides individuals with access to a wide range of financial instruments such as stocks, bonds, and money market instruments. This diversification helps mitigate risks associated with investing solely in individual securities. Additionally, mutual funds allow for flexibility in terms of investment amount and frequency.

A noteworthy trend emerged as investors substantially increased in states like Bihar (+42% YoY) and Uttar Pradesh (+41% YoY), underscoring the growing participation of smaller towns in the investment landscape. This trend highlights the expanding reach of mutual funds into previously untapped regions, bringing investment opportunities to a broader segment of the population, including those in rural areas. The increasing participation of retail investors, including those from smaller towns in states like Bihar and Uttar Pradesh, showcases the democratization of investing facilitated by mutual funds. This broader access to professionally managed portfolios across different sectors and securities fosters financial inclusion and deepens the investor base, contributing to the growth and dynamism of India's capital markets. One of the most positive changes observed in the mutual fund industry is the increase in retail investor participation. As per the data reported by AMFI for December 2023, individual investors held 60.1% of assets, compared to 57.8% in December 2022. Institutional investors account for 39.9% of assets, with corporates holding 96%, while the remaining portion is held by Indian and foreign institutions and banks. Equity-oriented schemes exhibit an interesting trend, with 89% of their assets sourced from individual investors (retail + HNIs). Conversely, certain schemes are predominantly influenced by institutional investors, with institutions controlling 90% of assets in fund of funds and 86% in money market schemes.

Additionally, mutual funds enhance market liquidity and efficiency by actively trading in financial markets, contributing to price discovery and fair value trading of securities. This liquidity attracts more investors to the market, boosting market activity and fostering healthy competition.

Furthermore, mutual funds' role in investing in startups and emerging companies through venture capital funds promotes innovation, entrepreneurship, and job creation, driving economic progress and technological advancements. In conclusion, mutual funds are vital drivers of economic development in India's capital markets, mobilising savings, promoting financial inclusion, enhancing market efficiency, fostering innovation, and supporting sustainable development. As mutual funds continue to evolve and expand their reach into previously underserved regions like Bihar and Uttar Pradesh, they are poised to be increasingly crucial in shaping India's financial landscape and driving long-term economic growth.

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