case study on cyclone in india

• 0 Shopping Cart

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

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.

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

Premium Resources

Please support internet geography.

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

Related Topics

Use the images below to explore related GeoTopics.

Cyclone Eline

Weather and climate home, hurricane andrew, share this:.

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

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

Search Internet Geography

Top posts and pages.

Latest Blog Entries

Pin It on Pinterest

• Click to share
• Print Friendly

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

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

2978 Accesses

6 Citations

1 Altmetric

Metrics details

• 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.

Similar content being viewed by others

Evaluation of future estuarine floods in a sea level rise context

Coastal flooding and mean sea-level rise allowances in atoll island

An assessment of measured and computed depth of closure around Japan

Introduction.

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.

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.

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 .

( 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.

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.

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

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.

( 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 ).

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.

Conclusions

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

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

Data availability

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

IPCC Summary for policymakers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al. ) (Cambridge University Press, 2021).

Google Scholar  

Zhang, W., Villarini, G., Vecchi, G. A. & Smith, J. A. Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Huston. Nat. Lett. 563 , 384–398 (2018).

Article   ADS   CAS   Google Scholar  

Zhu, L., Emanuel, K. & Quiring, S. M. Elevated risk of tropical cyclone precipitation and pluvial flood in Houston under global warming. Environ. Res. Lett. 16 , 094030. https://doi.org/10.1088/1748-9326/ac1e3d (2021).

Article   ADS   Google Scholar  

Rehman, S., Sahana, M., Kumar, P., Ahmed, R. & Sajjad, H. Assessing hazards induced vulnerability in coastal districts of India using site-specific indicators: An integrated approach. GeoJournal 86 , 2245–2266 (2020).

Article   Google Scholar  

Sahoo, B. & Bhaskaran, P. K. A comprehensive data set for tropical cyclone storm surge-induced inundation for the east coast of India. Int. J. Climatol. 38 , 403–419 (2018).

Singh, O. P. et al. Has the frequency of intense tropical cyclones increased in the north Indian Ocean?. Curr. Sci. 80 (4), 575–580 (2001).

Unnikrishnan, A. S., Kumar, K. R., Fernandes, S. E., Michael, G. S. & Patwardhan, S. K. Sea level changes along the Indian coast: Observations and projections. Curr. Sci. 90 (3), 362–368 (2006).

Mandal, A. K., Ratheesh, R., Pandey, S., Rao, A. D. & Kumar, P. An early warning system for inundation forecast due to a tropical cyclone along the east coast of India. Nat. Hazards 103 , 2277–2293 (2020).

Bertin, X. et al. Infragravity waver: From driving mechanisms to impacts. Earth Sci. Rev. 177 , 774–799 (2018).

Wu, G., Shi, F., Kirby, J. T., Liang, B. & Shi, J. Modeling wave effects on storm surge and coastal inundation. Coast. Eng. 140 , 371–382 (2018).

Restoration of Chellanam panchayath, Interim Report submitted to the Hon’ble Minister for Fisheries, Culture and Youth Affairs Government of Kerala, Kerala University of Fisheries and Ocean Studies (KUFOS), July (2021).

Ardhuin, F. et al. Semi empirical dissipation source functions for ocean waves. Part I: Definition, calibration, and validation. J. Phys. Oceanogr. 40 (9), 1917–1941 (2010).

Remya, P. G., Ranjan, T. R., Sirisha, P., Harikumar, R. & Nair, B. Indian Ocean wave forecasting system for wind waves: Development and its validation. J. Oper. Oceanogr. 15 (1), 1–16 (2020).

Roelvink, D., McCall, R., Mehvar, S., Nederhoff, K. & Dastgheib, A. Improving predictions of swash dynamics in XBeach: The role of groupiness and incident-band runup. Coast. Eng. 134 , 103–123 (2018).

Roelvink, J. A. et al. Modelling storm impacts on beaches, dunes and barrier islands. Coast. Eng. 56 , 1133–1152 (2009).

Roelvink, J. A. et al. XBeach Model Description and Manual (Delft University of Technology, 2010).

Roelvink, D. & Reniers, A. A Guide to Modeling Coastal Morphology Advances in Ocean Engineering (World Scientific, ISBN: 978-981-4304-25-2, 2011).

Stockdon, H. F., Holman, R. A., Howd, P. A. & Sallenger, A. H. Empirical parameterization of setup, swash, and runup. Coast. Eng. 53 , 573–588 (2006).

Guo, L., Ma, X. & Dong, G. Performance accuracy of surfbeat in modelling infragravity waves near and inside a harbour. J. Mar. Sci. Eng. 9 , 918 (2021).

Article   CAS   Google Scholar  

Ratheesh, R. et al. Modelling coastal erosion: A case study of Yarada beach near Visakhapatnam, east coast of India. Ocean Coast. Manag. 156 , 239–248 (2018).

Ratheesh, R. et al . A numerical modelling approach for beach erosion forecast during the southwest monsoon season. J. Earth Syst. Sci . (2022) (accepted) .

Cheng, Y. & Andersen, O. B. Improvement in global ocean tide model in shallow water regions. Poster, SV.1-68 45 (OSTST, Lisbon, Oct. 18–22, 2010).

Lashley, C. H., Bertin, X., Roelvink, D. & Arnaud, G. Contribution of infragravity waves to runup and overwash in the Pertuis Breton embayment (France). J. Mar. Sci. Eng. 7 , 205 (2019).

Munk, W. Surf beat. Eos transactions. AGU 30 , 849–854 (1949).

ADS   Google Scholar  

Ruju, A., Lara, J. L. & Losada, I. J. Radiation stress and low-frequency energy balance within the surf zone: A numerical approach. Coast. Eng. 68 , 44–55 (2012).

De Bakker, A. T. M., Tissier, M. F. S. & Ruessink, B. G. Beach steepness effects on nonlinear infragravity-wave interactions: A numerical study. J. Geophys. Res. Oceans 124 , 554–570 (2016).

Stockdon, H. F., Thompson, D. M., Plant, N. G. & Long, J. W. Evaluation of wave runup predictions from numerical and parametric models. Coast. Eng. 92 , 1–11 (2014).

Xie, D., Zou, Q. P., Mignone, A. & McRae, J. D. Coastal flooding from wave overtopping sea level rise adaptation in the northeastern USA. Coast. Eng. 150 , 39–58 (2019).

Download references

Acknowledgements

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).

Author information

Authors and affiliations.

Space Applications Centre (SAC), ISRO, Ahmedabad, India

Ratheesh Ramakrishnan & Anup Mandal

Indian National Centre for Ocean Information Services (INCOIS), Ministry of Earth Sciences, Govt. of India, Hyderabad, India

P. G. Remya, Prakash Mohanty, R. S. Mahendra & T. M. Balakrishnan Nair

Fisheries Engineering, Kerala University of Fisheries and Ocean Studies (KUFOS), Kochi, Kerala, India

Prince Arayakandy

You can also search for this author in PubMed   Google Scholar

Contributions

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.

Corresponding author

Correspondence to P. G. Remya .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

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

Rights and permissions

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

Reprints and permissions

About this article

Cite this article.

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

Download citation

Received : 09 August 2022

Accepted : 16 November 2022

Published : 19 November 2022

DOI : https://doi.org/10.1038/s41598-022-24557-z

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

A multi-risk approach for projecting climate change-associated coastal flood, applied to india.

• Aysha Jennath
• Saikat Paul

Natural Hazards (2024)

Comparative assessment of WRF’s parameterization scheme combinations in assessing land-surface feedback flux and its drivers: a case study of Phailin tropical cyclone

• Subhadeep Mandal
• Bhabagrahi Sahoo
• Ashok Mishra

Theoretical and Applied Climatology (2024)

Dynamics of land, ocean, and atmospheric parameters associated with Tauktae cyclone

• Rajesh Kumar
• Prity Singh Pippal
• Jagvir Singh

Environmental Science and Pollution Research (2024)

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

Quick links

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

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

• International
• Today’s Paper
• 🗳️ History of Elections
• Premium Stories
• 🇮🇳 Elections 2024
• Brand Solutions

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..

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.

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.

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.

After a wedding kiss in Hapur, a full-blown brawl and Subscriber Only

In 2nd UPA term, how pro-public policies gave way to Subscriber Only

How Vajpayee lost a no-confidence motion by one vote in Subscriber Only

UPSC Key | Nuclear debate, cyber crime, FTAs and more Subscriber Only

Akhilesh & Rahul Gandhi's strategy — promise of caste census Subscriber Only

How women party workers are reshaping Indian politics Subscriber Only

Days after Bangladesh MP’s ‘murder’, police in two countries grapple Subscriber Only

How joblessness in India hurt all communities over the last Subscriber Only

Ashmita Chaliha’s rise from sheltered life to ambitious competitor Subscriber Only

• Cyclone Biparjoy
• Explained Climate

Hassan MP Prajwal Revanna, facing sexual assault charges, was arrested by Karnataka Police at Bengaluru's airport after Interpol alerted them of his arrival. He had fled the country after voting in his constituency and was taken into custody by the SIT. The MP had said he would surrender on Friday and had filed an anticipatory bail plea.

More Explained

Best of Express

EXPRESS OPINION

May 31: Latest News

• 01 Delhi fire dept got over 200 calls on May 29, highest so far this year
• 02 Pune: Road closures and traffic diversions in place due to ongoing construction on Ganeshkhind road
• 03 Bharti Airtel names Sharat Sinha Airtel Business CEO
• 04 NATO chief Stoltenberg seeks €40 billion per year for Ukraine military aid: Sources
• 05 Two Delhi artists lead a community art exhibit to highlight air pollution
• Elections 2024
• Political Pulse
• Entertainment
• Movie Review
• Newsletters
• Web Stories

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Loss and Damages from Cyclone: A Case Study from Odisha, a Coastal State

2020, Development in Coastal Zones and Disaster Management

Natural disasters such as cyclones result in tremendous loss and damages to life and property of coastal communities. However, studies assessing loss and damages are limited in the literature. This study attempts to document the loss and damages incurred by the marine fishing community affected by Cyclone Phailin in 2013, on the coast of Gopalpur in Odisha (India). A survey composed of 300 responses was conducted and it was found that a high percentage (72.67%) of the community experienced decline in income after the cyclone. This may be a result of damage to fishing gear from the cyclone. Although most fishermen were able to start fishing one to three weeks after the cyclone, their income returned to previous levels (before the cyclone) at a much later time. Fortunately, there were no deaths in the surveyed households as a result of the cyclone. Lastly, it was seen that the time and average cost to rebuild houses was greater than that to repair gear. Given the importance of assessi...

Related Papers

Vineetha Nalla

Asian Development Bank

Hippu Salk Kristle Nathan

This is the a chapter in the report Cyclone Fani: Damage, Loss, and Needs Assessment which was published by Asian Development Bank jointly with United Nations India and The World Bank in collaboration with Govt. of Odisha. Cyclone Fani has had comparatively higher and differential impact on the socially vulnerable and marginalised population groups, especially women and adolescent girls, children, members of the SC and ST communities, PwDs, fisher-folk, daily wage earners such as brick kiln workers, small traders, artisans, and urban slum dwellers. Poverty, location of residence, inequality, social and gender discrimination were some factors that further compounded the pre-cyclone vulnerabilities of these groups and resulted in a differential impact (Figure 0.5). An analysis considering the five dimensions—Health, Education, Agri-livelihood, Living standards, and Safe housing (HEALS)—across the 14 affected districts shows that Fani has further increased the incidence of income poverty in Odisha which could be transient but needs special attention. A build back better (BBB) approach with community-specific, occupation-specific and location-specific interventions involving different stakeholders will prevent the increase in incidence of income poverty in the state.

Garima Jain

Greeshma Hegde

India has a coastline of about 7,516 km of which 5, 400 km is along the main land. Thirteen coastal states and Union Territories (UTs) in the country are being affected by climatic vulnerability. Four states (Tamil Nadu, Andhra Pradesh, Odisha and West Bengal) are rather highly vulnerable to cyclone hazards. The Bay of Bengal is world's most cyclone prone region. Odisha is one of the most vulnerable states of India towards climate change. Natural calamities from time-to-time seriously affect livelihoods in this state and the income level of people. Poor societies have low adaptive capacities to withstand these adverse impacts of climate change, due to the high dependence of a majority of the population on climate-sensitive sectors like agriculture, forestry and fishery. The direct impacts of adverse climate cause loss of life, livelihood, assets, infrastructure etc. The present paper is an attempt to know the real sufferings of the villagers living in the coastal regions of the Ganjam District of Odisha who are frequently being affected by the rudeness of climatic vulnerability. They regularly loss a lot in their general livelihood, starting from extreme scarcity of food, drinking water and fuel to the extreme effect on health, education and infrastructure. The traditional marine fishermen living in the coastal regions of Ganjam district are the worst sufferers.

Current Journal of Applied Science and Technology

Kiran Bains

India has been facing the wrath of natural calamities pertaining to its unique geography and varied climatic patterns from time immemorial. The purpose of this paper is to gather data pertaining to food assistance provided to stranded evacuees in the aftermath of Natural Calamities. Food assistance forms crucial part of humanitarian assistance to provide immediate relief to victims and help in their speedy recovery from injuries, illness and psychological distress. We aimed to collect information on the type of food, quantities of food and cultural competence of food because India has a wide diversity in food eating patterns across its regions. We also took into account the rescue operations involving role of different stakeholders like government organizations, Armed forces, paramilitary forces, NGOs, international donors and volunteers who usually work independently but gather together aftermath of any calamity or disaster, to address the problems that arise with a common shared g...

Kamal Barik

Bay of Bengal is prone to maximum rate of cyclogenesis of cyclonic disturbances and intensified cyclonic storms. The cyclones in the Bay of Bengal basin are most devastating, causing a large number of fatalities and huge infrastructural and pecuniary losses. The Coastline of Odisha had witnessed major land falls of cyclonic storms in comparison to other coastal states in east coast of Indian peninsula. Pre-monsoon cyclonic storms are rare compared to post monsoon in strength and frequency. The extreme severe cyclonic storm “Fani” has ransacked the Odisha coast causing 43 fatalities from 159 blocks in 14 districts, and huge pecuniary losses amounting to 2417.6 billion INR in spite of war footing precautionary measures. In this paper, the tracks of various intensified pre-monsoon and post-monsoon storms and their impacts are studied. The climatological impact of various dominating systems in Indian Ocean like El-Nino, La-Nina, El Niño-Southern Oscillation, Madden–Julian Oscillation, I...

AN ETHNOGRAPHIC ACCOUNT ON THE LIVED EXPERIENCES OF PHAILIN AND ITS IMPACT AMONG THE FISHERMEN OF PENTAKOTA, PURI, ODISHA, INDIA

Pinaki Dey Mullick

The state of Odisha having severely exposed to the natural hazard, faces a great difficulty multiple times in past few years. The impact of natural disasters threatens the life and living of the local people of Odisha; repetitively raising their social and psychological resilience under a great challenge. In this context, the present study tries to explore the experiences of vulnerability and resilience among the fishermen of Pentakota - a coastline settlement of Puri, Odisha, after the cyclone - Phailin and its disastrous impacts on 11th October, 2013. Five Participants were selected through nested sampling design and interviewed using a semistructured interview schedule. The detailed and extensive rich data have been transcribed verbatim to include the insider’s perspectives of the concerned issues that leaded to the themes of concerned, like- (a) Sensing the Sea and Risks in Economic Living (“Perception of the Sea and Economic Living”, “Perception of Risks and Vulnerabilities in Daily Life” and “Spirituality and Resilience”), (b) Warning, Preparation, Disruption and the Terror of Phailin (“The Warning and Communication Prior to the Event”, “Facing the Unexpected Threats of Phailin” and “The Perception of Loss” ) and (c) The Issues of Resilience and Post-Disaster Recovery (“The Issues of Relief, Politics, Mistrusts and Annoyance”, “Feeling of Helplessness and Anxiety”, and “Bouncing Back the Troubles and Getting in to the Altered Life”). The nature of the content of the current effort is descriptive, specific and subjective that may claim to contribute knowledge for better policies and actions.

RELATED PAPERS

UNU-EHS Report

Kees van der Geest

IOSR Journals

Indian Social Institute

Joseph Xavier

Geography and you

Prabhakar Jayaprakash

Sustainability

shahab saqib

Tuhin Ghosh

Sudhakar Kulkarni

Ettore Lancellotti

Review of Development and Change

Dr.Nilakantha Panigrahi

Bayes Ahmed

Sadeque Ahmed , Habib Torikul

IP Innovative Publication Pvt. Ltd.

Environment and Urbanization

Sneha Krishnan

Climate Change and Community Resilience

Saudamini Das

United Nations Development Programme (UNDP) India and the School of International Development, UEA, Norwich

Joe K W Hill

Architecture Papers of the Faculty of Architecture and Design STU

Anika Sohaana , Md Arifur Rahman

Ramdas Bhattacharya , Karunakaran Akhil Dev

Habibullah Ansari

Joy Jacqueline Pereira

Jeebanjyoti Mohanty

Md. Golam M Sarwar

UNU-EHS Gibika reports

Kees van der Geest , Dr Sonja Ayeb-Karlsson

Disasters and Development, Vol.7, No.1&2

Mausumi Chetia

Indian Journal of Social work

s.mohammed irshad

Habib Torikul , Naimul Islam

Bhaba Datta Sapkota , Indra Mali Malakar

Samudra Report

International Collective in Support of Fishworkers (ICSF)

Water Policy

niranjan padhan

Paul Shitangsu , Shitangsu Paul

American Journal of Rural Development

Bishawjit Mallick

G. Bhaskara Rao

Subhashree Banerjee

Snigdha Chatterjee

Natural Hazards

Forruq Rumi

asfiandi zulfiar

lalatendu kesari das , Ranit Chatterjee , Ambika Dabral

chinmayee sahoo

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Content Search

Study report on gaja cyclone 2018.

• Govt. India

Attachments

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.

Related Content

Local sitrep: heavy rain and hailstorm in manipur (06 may 2024), the story of a village and its transformation, action plan to prevent floods, leaving no one behind: lessons from the kerala disasters.

• Arts & Culture

Get Involved

Autumn 2023

Exhibition Opening: Louder than Hearts, Women Photographers from the Arab World and Iran

Annual Gala Dinner

Internships

Learning from Deaths in Disasters: The Case of Odisha, India

Nibedita S Ray-Bennett

Over the last 25 years, the world has seen a rise in the frequency of natural disasters in rich and poor countries alike. Today, there are more people at risk from natural hazards than ever before, with those in developing countries particularly at risk. T his essay series is intended to explore measures that have been taken, and could be taken, in order to improve responses to the threat or occurrence of natural disasters in the MENA and Indo-Pacific regions. Read  more ...  

Odisha (renamed from Orissa in 2011) is one of the eastern states in the Indian union . According to the 2011 census the population of Odisha was at about 41 million, which makes it the 11th most populated state in India. [1] Odisha has 30 districts, [2] of which 13 are coastal. The coastal districts are highly prone to cyclones, floods, droughts, and heat waves due to geographic location. Its coastline adjoins the Bay of Bengal for 300 miles, which makes it four to five times more likely to experience storms than it would if it were located in the Arabian Sea. Tropical cyclones from the Bay of Bengal bring severe and widespread destruction, especially when accompanied by storm surges, high winds, and extreme rainfall that results in riverine flooding. [3]

On October 29-30, 1999, Odisha was hit by a cyclone affecting all coastal districts. The Indian Meteorological Department called it a ‘super cyclone’ due to its high wind velocity of 170-185 miles per hour; its unprecedented storm surge, which was 16-23 feet high; and the torrential rainfall over 48 hours, which caused devastating floods in the major river basins. The intensity of the cyclone killed more than 10,000 people, [4] caused severe economic devastation, and activated the Orissa Relief Code (the then sole disaster policy document for the state). It also put Odisha in the spotlight internationally because the super cyclone coincided with the tail end of the United Nations International Decade for Natural Disaster Reduction (I.D.N.D.R.). [5]

Fourteen years after the super cyclone, on October 12, 2013, Odisha was hit by Cyclone Phailin, which was accompanied by a storm surge of 5 feet and heavy rainfall that caused extensive floods in the major river basins. According to the National Institute of Disaster Management, 45 people were reported killed (44 in Odisha and 1 in Andhra Pradesh). [6] This begs the question as to what the Government of Odisha did that contributed to the relatively low death toll. We have provided some answers in this article based on three months of fieldwork and seven interviews with senior officials.

Compared to the super cyclone of 1999, Phailin was less intense in three aspects. The wind velocity of the super cyclone reached 185 miles per hour, compared to 160 miles per hour in Phailin. [7] Second, the storm surge reached 11 feet in the coastal regions, according to United Nations Environment Programme, compared to 20 feet during the super cyclone. [8] Third, a 24-hour precipitation total of 6.5 inches was recorded on October 13, 2013, whereas a 24-hour precipitation total of about 20.5 inches was recorded at the weather station of Paradip on October 30, 1999. [9] Although the anatomy of these two tropical cyclones differed, they are comparable on two grounds: first, they tested the disaster management systems of Odisha to their limits. Second, they presented a window of opportunity to assess the strengths and limitations of the disaster management system built by the government and nongovernment organizations, at the interface with technology between 1999 and 2013.

Why Were There More Than 10,000 Deaths in the Super Cyclone?

We argue that the high death toll in 1999 was due to lack of coordination, communication, and complacent worldviews that existed in the disaster management system. Coordination problems arise when ‘core information’ is unavailable for Category 1 and 2 responders to develop an effective response system. Core information is the most valuable information both to avoid unnecessary deaths and to increase the efficacy of a disaster response system. This information is generated by meteorologists and meteorological offices using early warning systems. The unavailability of this core information will 'blind' a response system. [10]

 According to the director of the Indian Meteorological Department in Odisha, coordination of core information failed because:

Prior to 1999 there was no coordination between the government departments. The technology was underdeveloped. We had to rely on New Delhi and Kolkata for weather forecasts over telephone. There was delay in receiving weather warnings. [11]

According to Harriman, [12] the Indian Meteorological Department was able to generate early warnings for the super cyclone only two days prior, compared to four days prior in the case of Phailin. The delay in generating core information affected the decision making processes of local responders. Decision making is a crucial component of coordination in uncertain situations. Leadership is also a critical component of decision-making. [13] Critics blamed the then chief minister of the state, Mr. Giridhar Gamang, for his weak leadership. He was unable to rise to the situation as a leader of the state, to generate an objective of saving lives for his government and his bureaucrats. The consequence of this was unnecessary human deaths.

In addition, the communication systems—both in terms of generating and disseminating an effective early warning—were underdeveloped. The failure of the coordination system was described as “lack of [a] plan and planning” by the district emergency officer of Ganjam, and “no coordination” whatsoever by the director of the Indian Meteorological Department. [14] This lack of coordination was hindered further because “there was no authority to monitor relief and rescue” operations from Bhubaneswar [15] according to the district emergency officer of Ganjam. Lack of coordination was also acknowledged as a major failure during the super cyclone, by the deputy relief commissioner of the Special Relief Organisation. [16]

The coordination suffered further, due to a culture of complacency, which was rife in 1999—both at home and abroad. It was only in the midterm evaluation of the I.D.N.D.R. in 1994 in Yokohama, Japan that the international community began to grasp the deleterious effect of disasters on the developing world. [17]  Proactive disaster management, even at the international level, was in its early stages.

During the super cyclone, this unpreparedness manifested through a reactive response system, inadequate measures for evacuation, and a lack of imagination among the district-level responders. A culture of complacency was also rife among the at-risk population, which did not heed the early warnings due to a fatalistic mind-set, which hindered evacuation. [18] The evacuation process was further hindered by a lack of shelters. In 1999 there were only 75 cyclone shelters on the entire coastline. [19] These shelters, which were built by the Red Cross Society, saved thousands of lives. The culture of complacency was fueled further by a “lack of experiencing” a devastating cyclone prior to 1999. [20] So, neither the responding actors nor the at-risk population imagined that a hazard of low-probability but of such great impact could affect Odisha coastal areas. Together, these factors contributed to a disaster management system that was disjointed, ill-prepared, and as a consequence, was unable to save lives during the super cyclone.

How Were Deaths Prevented in Cyclone Phailin?

Jagatsinghpur's district emergency officer described the period between 1999 and 2013 as an “inter-disaster period . ” During this period, the Government of Odisha developed a new disaster management system which had two notable features. [21] First, there was increased interaction between the national and state governments, Indian Meteorological Department, nongovernmental organizations, and the at-risk communities. Second, this new disaster management system interfaced with technology. In doing so, the government was able to rectify the issues of coordination failure, communication failure, and the conservative world views evident in 1999 super cyclone.

In the aftermath of the super cyclone, the capacity of the Indian Meteorological Department was enhanced by space technology, the Meteo France International synergy system and a high-power computing system in order to help with predictions. [22] Furthermore, in 2007 the Government of India passed the first Disaster Management Act, which among other things, created a knowledge network that included the Indian Meteorological Department, Earth System Science Observation, the Indianan Space Research Organisation, Central Water Commission, Geological Survey of India, and National Remote Sensing Centre. [23] This network was crucial in generating core information during Phailin, which was effectively communicated to the at-risk population. [24] Information and communication tools such as media, mobile text messaging, hotlines and VSat—to name just a few—were fully exploited to disseminate the core information to the at-risk population.

The generation of accurate core information prior to Phailin’s landfall was instrumental in developing an effective response system. It helped guide primary responders’ actions. As a result, responders were able to evacuate 1.2 million people from 18 districts. [25] This evacuation is considered as one of the largest emergency operations ever undertaken in India. [26] An operation of this scale was only possible because of the coordination between actors, the availability of core information, effective evacuation planning, flexibility in the standard operating procedures, and responders' dedication and commitment to save lives.

Leadership is central to promoting an effective response system as well as counteracting complacent world views. Mr. Naveen Patnaik, the chief minister of Odisha, provided much-needed leadership in the aftermath of the super cyclone. From 2000 onwards, he commemorated October 29 as Disaster Preparedness Day for Odisha. This created a culture of disaster preparedness. He also concentrated much of his effort in building the state's infrastructure—one that is essential to supporting a disaster response system. Thanks to funds made available by the World Bank and the central government, Patnaik was able to build roads, bridges, concrete houses, and multipurpose cyclone shelters. [27] Good road conditions as well as their connectivity with cyclone shelters facilitated the evacuation process during Phailin. [28]

During Cyclone Phailin, Patnaik also exhibited the traits of a strategic leader by declared "saving precious lives" to be “a goal” [29]  for all actors involved in mitigating the effect of the storm. This goal was communicated to the district and village level responders. This led to a dramatic reduction in deaths.

What Can We Learn From the Case of Odisha?

Several lessons can be generalized from the case of Odisha. Three in particular are mentioned here. First, deaths in disasters can be reduced even by poor nation-states when the disaster management system is aligned skillfully. Here, the generation of accurate core information as well as effective coordination and communication of this information with the relevant actors to develop an effective response system is crucial. In this light, the modern disaster management system is conceived as a system that works in interface with humans and technology. As such, policy makers and U.N. bodies should invest both in technology and capacity development in order to promote effective coordination and communication. This system should also work closely with early warning systems rather than in isolation.

Second, the case of Odisha illustrates the increasing role and involvement of political leadership before, during, and after a disaster. When there is proactive political leadership, a disaster response system can be aligned with the goal of saving lives. Political leadership can promote a culture of disaster preparedness, too. In the case of Phailin, the chief minister set as a goal “saving lives at any cost.” [30]  Accordingly, all actors and responders organized themselves to achieve this target. In this light, the United Nations and other international funding organizations could do a great deal by encouraging political leadership to implement ‘priorities for action’ for effective disaster management.

Third, reducing deaths in disasters is of paramount importance, and indicates how robust the system is. This ethos is now reflected in the first global target of the United Nations’s Sendai Framework for Action (2015-2030), [31] which urges reducing “global disaster mortality by 2030.” The case of Odisha suggests that setting an objective of reducing deaths and promoting a socio-technical disaster management system—and a culture of disaster preparedness—are vital ingredients for achieving the first global target of the Sendai Framework.

[1] Population Census 2011, Census Organization of India, “Orissa Population Census Data 2011,” accessed January 5, 2016, http://www.census2011.co.in/census/state/orissa.html .

[2] “Indian states comprise a three-tier administrative structure. Several gram sansad (villages) or wards (hamlets) constitute a gram panchayat (GP), several GPs constitute a panchayat samiti (PS) or block, and several PSs constitute a zilla parishad or a district.” See Nibedita S. Ray-Bennett, Caste, Class and Gender in Multiple Disasters: The Experiences of Women-Headed Households in an Oriya Village (Saarbrucken: VDM Verlag, 2009), 12.

[3] Government of Odisha, Managing Disasters in Orissa: Background, Challenges and Perspectives (Bhubaneswar: Orissa State Disaster Mitigation Authority, 2002).

[4] The World Bank, “Cyclone Devastation Averted: India Weathers Phailin,” October 17, 2013, accessed April 27, 2016, http://www.worldbank.org/en/news/feature/2013/10/17/india-cyclone-phail… .

[5] The U.N. General Assembly, in December 1987, declared the 1990s as the International Decade for Natural Disaster Reduction.

[6] National Institute of Disaster Management, Ministry of Home Affairs, Government of India, India Disaster Report 2013, accessed April 27, 2016, http://nidm.gov.in/PDF/pubs/India%20Disaster%20Report%202013.pdf , 41.

[7] S. Haeseler, “Super cyclone Phailin across India in October 2013,” Deutscher Wetterdienst (DWD) (2013), accessed April 5, 2016, https://www.dwd.de/EN/ourservices/specialevents/storms/20131018_phailin_indien_en.pdf?__blob=publicationFile&v=3 .

[8] L. Harriman, “Cyclone Phailin in India: Early warning and timely actions saves lives,” UNEP Global Environmental Alert Services (GEAS) (2013), accessed May 20, 2015, http://na.unep.net/geas/archive/pdfs/GEAS_Feb2013_DustStorm.pdf .

[9] Haeseler, “Super-Cyclone Phailin.”  

[10] Louise K. Comfort, Kilkon Ko, and Adam Zagorecki, “Coordination in rapidly evolving disaster response systems: The role of information,” American Behavioural Scientist , 48 (2004): 295-313.

[11] Summarized from author’s field diary, meeting held in Bhubaneswar on July 21, 2014, Indian Meteorology Office.

[12] Harriman, “Cyclone Phailin.”

[13] Peter Senge, “The leader’s new work: Building learning organizations,” Sloan Management Review 32 (1990): 7-23.

[14] Harriman, “Cyclone Phailin.”

[15] Bhubaneswar is the capital of Odisha.

[16] Harriman, “Cyclone Phailin.”

[17] Elaine Enarson, “Through women’s eyes: A gender research agenda for disaster social science,” Disasters 22 (1998): 157-73.

[18] Kishor C. Samal, Shibalal Meher, and Nilkantha Panigrahi, Beyond Relief Disaster Mitigation, Livelihood Rehabilitation and the Post-Cyclone Recovery in Orissa: Village Level Studies in Three Most Cyclone Affected Districts in Orissa (Bhubaneswar: Nabo Krishna Centre for Development Studies Publication, 2003).

[19] Harriman, “Cyclone Phailin.”

[20] Samal et al., Beyond Relief.

[21] Government of Odisha, Procedures/guidelines for maintenance of records relating to the relief operations on account of natural calamities (No. 768/SR), (Bhubaneswar: Office of the Special Relief Commissioner, 2012), accessed June 2, 2015, http://www.odisha.gov.in/disaster/src/Procedure_Guidelines/Maintenance_NC.pdf .

[22] Bibhuti Barik, “Met Office goes digital,” The Telegraph , February 18, 2014; and interview with the Director of Indian Meteorology Department in Bhubaneswar, July 22, 2014.

[23] Sanjay K. Srivastava, “Making a technological choice for disaster management and poverty alleviation,” Disasters 33 (2009): 58-81.

[24] Interview with the Director of Indian Meteorology Department in Bhubaneswar, July 21-22, 2014.

[25] B.N. Mishra, “Tryst with Phailin: The deadliest cyclone in 2013,” The Response 13 (2013): 5-7.

[26] “Disaster Update: Cyclone Phailin,” Disaster Recovery Journal , October 16, 2013, accessed April 27, 2016, http://www.drj.com/industry/industry-hot-news/disaster-update-cyclone-p… .

[27] State Programme Officer of U.N.D.P., interview by author, Bhubaneswar, July 23, 2014.

[28] Deputy Relief Commissioner interview by author, Bhubaneswar, July 23, 2014..

[29] Gwilym Meirion Jenkins, “The systems approach,” Journal of Systems Engineering 1 (1969): 3-49.

[30] District Emergency Officer of Puri, interview with author, Puri, July 31, 2014.

[31] The Sendai Framework is the successor of the Hyogo Framework. It is a 15-year, voluntary, non-binding agreement approved by the 185 U.N. Member States in the Third U.N. World Conference on Disaster Risk Reduction, held from March 14 to 18, 2015 in Sendai, Japan. World Conference on Disaster Risk Reduction (WCDRR) resolution, “Sendai Framework for Disaster Risk Reduction 2015-2030,” March 18, 2015, accessed June 25, 2015, http://www.preventionweb.net/files/43291_sendaiframeworkfordrren.pdf .

The Middle East Institute (MEI) is an independent, non-partisan, non-for-profit, educational organization. It does not engage in advocacy and its scholars’ opinions are their own. MEI welcomes financial donations, but retains sole editorial control over its work and its publications reflect only the authors’ views. For a listing of MEI donors, please click here .

• My View My View
• Following Following
• Saved Saved

Cyclone kills 16 in India, Bangladesh and cuts power to millions

• Medium Text

MANGROVE FORESTS FLOODED

Sign up here.

Reporting by Mohammad Ponir Hossain in Satkhira, Bangladesh, Ruma Paul in Dhaka and Subrata Nag Choudhury in Kolkata; Writing by Sudipto Ganguly; Editing by Clarence Fernandez and Nick Macfie

Our Standards: The Thomson Reuters Trust Principles. New Tab , opens new tab

World Chevron

At least 15 dead in eastern india over 24 hours as temperatures soar.

At least 15 people have died of suspected heatstroke in India's eastern states of Bihar and Odisha on Thursday, authorities said, with the region gripped in a debilitating heatwave expected to continue until Saturday.

Traffic was suspended in Turkey's Bosphorus on Friday after a cargo ship had an engine failure during northbound transit through the strait that runs through Istanbul, shipping agent Tribeca said.

A Russian missile attack on Kyiv sparked a fire in a non-residential building early on Friday, the head of the military administration of the Ukrainian capital said.

Two people were injured when an oil depot in the Krasnodar region caught fire after a Ukraine missile and drone strike early on Friday targeting oil facilities, Russian officials said.

• Election 2024
• Entertainment
• Newsletters
• Photography
• Personal Finance
• AP Investigations
• AP Buyline Personal Finance
• AP Buyline Shopping
• Press Releases
• Israel-Hamas War
• Russia-Ukraine War
• Global elections
• Asia Pacific
• Latin America
• Middle East
• Election Results
• Delegate Tracker
• AP & Elections
• Auto Racing
• 2024 Paris Olympic Games
• Movie reviews
• Book reviews
• Personal finance
• Financial Markets
• Business Highlights
• Financial wellness
• Artificial Intelligence
• Social Media

Bangladesh evacuates hundreds of thousands as a severe cyclone approaches from the Bay of Bengal

Bangladesh evacuated nearly 800,000 people from vulnerable areas on Sunday as the country and neighbouring India awaited the arrival of a severe cyclone that has formed over the Bay of Bengal.

In this screen-grab taken from the website of India Meteorological Department, Government of India, cyclonic activity is visible over the Bay of Bengal on the eastern coast of India, Sunday, May 26, 2024. The India Meteorological Department says the cyclonic storm will cross Bangladesh and India’s West Bengal coasts around midnight Sunday. (India Meteorological Department via AP)

• Copy Link copied

A man salvages a cart and other material as water flows on to the Kuakata beach on the coast of Bay of Bengal caused by the advancing Cyclone Remal in Barisal, Bangladesh, Sunday, May 26, 2024. (AP Photo/Abdul Goni)

A boy stands on a broken signboard as water flows on to the Kuakata beach on the coast of Bay of Bengal caused by the advancing Cyclone Remal in Barisal, Bangladesh, Sunday, May 26, 2024. (AP Photo/Abdul Goni)

Strong winds hit Kuakata beach on the coast of Bay of Bengal as Cyclone Remal advances in Barisal, Bangladesh, Sunday, May 26, 2024. (AP Photo/Abdul Goni)

Men salvage a reclining chair and other material as water flows on to the Kuakata beach on the coast of Bay of Bengal caused by the advancing Cyclone Remal in Barisal, Bangladesh, Sunday, May 26, 2024. (AP Photo/Abdul Goni)

Men move fishing nets as water flows on to the Kuakata beach on the coast of Bay of Bengal caused by the advancing Cyclone Remal in Barisal, Bangladesh, Sunday, May 26, 2024. (AP Photo/Abdul Goni)

Vendors run to salvage their belongings as strong winds caused by the advancing Cyclone Remal hit the Kuakata beach on the coast of Bay of Bengal in Barisal, Bangladesh, Sunday, May 26, 2024. (AP Photo/Abdul Goni)

Men salvage a refrigerator as water flows on to the Kuakata beach on the coast of Bay of Bengal caused by the advancing Cyclone Remal in Barisal, Bangladesh, Sunday, May 26, 2024. (AP Photo/Abdul Goni)

Members of the Bangladesh Red Crescent Society ask people to vacate as strong winds caused by the advancing Cyclone Remal hit the Kuakata beach on the coast of Bay of Bengal in Barisal, Bangladesh, Sunday, May 26, 2024. (AP Photo/Abdul Goni)

A man salvages a recliner as strong winds caused by the advancing Cyclone Remal hit the Kuakata beach on the coast of Bay of Bengal in Barisal, Bangladesh, Sunday, May 26, 2024. (AP Photo/Abdul Goni)

NEW DELHI (AP) — Bangladesh evacuated nearly 800,000 people from vulnerable areas on Sunday as the country and neighboring India awaited the arrival of a severe cyclone that has formed over the Bay of Bengal.

The storm is expected to cross Bangladesh and India’s West Bengal coasts around midnight Sunday. The India Meteorological Department said it is expected to reach maximum wind speeds of up to 120 kilometers per hour (75 mph), with gusts up to 135 kph (85 mph) hitting West Bengal’s Sagar Island and Bangladesh’s Khepupara region on Sunday night.

Bangladesh’s junior minister for disaster management and relief, Mohibur Rahman, said volunteers have been deployed to evacuate people to 4,000 cyclone shelters across the country’s coastal region. The government also closed all schools in the region until further notice.

India’s Kolkata airport will be closed for 21 hours from midnight Sunday. Bangladesh shut down the airport in the southeastern city of Chattogram and canceled all domestic flights to and from Cox’s Bazar.

Bangladeshi authorities also suspended loading and unloading in the country’s largest main seaport in Chittagong and started moving more than a dozen ships from the jetties to the deep sea as a precaution.

This is the first cyclone in the Bay of Bengal ahead of this year’s monsoon season, which runs from June to September.

Moderate to heavy rainfall is expected in most places over coastal districts in India’s West Bengal state. A storm surge about 1 meter (3.1 feet) high is expected to flood low-lying areas of coastal West Bengal and Bangladesh.

Such storms can uproot trees and cause major damage to thatched homes and power and communication lines, the statement said.

India’s coasts are often hit by cyclones, but changing climate patterns have caused them to become more intense, making preparations for natural disasters more urgent.

• Ground Reports
• 50-Word Edit
• National Interest
• Campus Voice
• Security Code
• Off The Cuff
• Democracy Wall
• Around Town
• PastForward
• In Pictures
• Last Laughs
• ThePrint Essential

Aizawl/Guwahati: A t least 23 people were killed in rain-related disasters, including a quarry collapse, in Mizoram as heavy rains hindered rescue efforts in the region, authorities said.

Mudslides were reported from several localities of the state capital Aizawl Tuesday morning leading to loss of lives and property. Aizawl remains cut off from the rest of India with landslides at several places bringing traffic movement to a halt on National Highway-6. 

Additionally, several intrastate highways have also been disrupted by rockfalls. The extent of the damage caused by heavy rains is still being assessed. Mizoram Chief Minister Lalduhoma has announced Rs 15 crore for relief work and Rs 4 lakh each to the kin of those who died in these disasters.

State officials told ThePrint that a mudslide was reported at a stone quarry around 6 am in the southern part of Aizawl, between the localities of Melthum and Hlimen. While 13 bodies have been recovered so far in Melthum, some are feared to be trapped under the debris, officials said.

Another landslide took place in Falkawn locality, resulting in the death of one person. Three of a family perished when their house was swept away in the landslide at Salem Veng locality. Six bodies were recovered from Hlimen  locality in Aizawl.

An extensive search has been carried out by the authorities aided by the Young Mizo Association, the largest civil society organisation in the state. 

Cyclone Remal has triggered several landslides in the northeast, including Mizoram, informed officials in the state Disaster Management and Rehabilitation Department (DM&R). 

“We have seen pictures and videos showing the devastation caused by wind and rain in different parts of Mizoram. Relief work is ongoing in these areas,” Mizoram DM&R minister K. Sapdanga told ThePrint,

The police, however, fear that rescue efforts might be hindered due to the inclement weather.

All schools in the state remained closed Tuesday because of the strong winds and rain.

A notification issued by the state school education department late Monday evening said the decision to close government offices and schools was taken in consultation with the state disaster department, following warnings issued by the meteorological department.

Also Read: Cyclone Remal kills 2 in Bengal before weakening, Met says moderate rain to continue

Situation in Assam, Manipur

Three people were killed in Morigaon, Kamrup and Kamrup Metro districts in cyclonic storms in the past 24 hours, according to Assam State Disaster Management Authority (ASDMA).

Of the three casualties, one was 17-year-old student Kaushik Amphi, who was killed in Morigaon district when a large tree crashed on the vehicle he was travelling in. Four others were injured.

Putul Gogoi, a worker of the NHPC project in Gerukamukh along Assam-Arunachal border, was killed in a landsite at the site, a press statement from the CM’s Public Relations Cell stated. However, the ASDMA authorities have not confirmed the death as yet.

Assam Chief Minister Himanta Biswa Sarma has directed the chief secretary to ensure all necessary assistance to the affected families and timely treatment for the injured.

Devashish Sharma, Deputy Commissioner of Morigaon district, told ThePrint that a circular was issued around 8 am Tuesday for schools to remain closed because of the cyclonic storm.

In Sonitpur district, 12 students of the Ursula English School in Dhekiajuli were injured when a heavy branch fell on their school bus. The injured were attended to and rushed to the nearby medical centre.

Taking to X, Assam Chief Minister Himanta Biswa Sarma assured people that the state government was monitoring the situation and requested everyone to stay indoors until the situation stabilised. “Power supply is disrupted in lower Assam, including Guwahati,” he added.

Cyclone Remal has affected Assam, causing storms in many areas. Tragically, a student, Kausik Bordoloi Amphi (17), died in Marigaon, and 12 students were injured in Dekhiajuli due to falling trees. In Guwahati,Uprooted trees in places like Cotton University and Jyoti Chitraban… — Himanta Biswa Sarma (Modi Ka Parivar) (@himantabiswa) May 28, 2024

While roads are being cleared in several places, there were reports of flash floods in Karimganj district. Schools have also been shut in some areas.

According to the Regional Meteorological Department, the depression or residue of the cyclonic storm Remal is likely to move east-northeastwards, and weaken into a well-marked low-pressure area over Assam and neighbouring areas by Tuesday evening.

In Manipur, the cyclonic storm caused landslides and flash floods in the valley districts and hill areas. There was damage to property but no loss of life has been reported.

Chief Minister N. Biren Singh mentioned in a social media post that one gate of the Ethai Barrage in Bishnupur district had been opened.

“We are closely monitoring the situation, and any further openings will be done in consultation with the Water Resources Department (WRD) and the National Hydroelectric Power Corporation (NHPC),” he wrote. “Our official teams are working tirelessly around the clock to manage this situation effectively and ensure the safety of our communities.”

(This is an updated version of the report)

(Edited by Tikli Basu)

Also Read: More than 1,300 Myanmar nationals pour into Mizoram to escape clashes across border

Subscribe to our channels on YouTube , Telegram & WhatsApp

Support Our Journalism

India needs fair, non-hyphenated and questioning journalism, packed with on-ground reporting. ThePrint – with exceptional reporters, columnists and editors – is doing just that.

Sustaining this needs support from wonderful readers like you.

Whether you live in India or overseas, you can take a paid subscription by clicking here .

LEAVE A REPLY Cancel

Save my name, email, and website in this browser for the next time I comment.

Most Popular

Mullaperiyar row flares up again as kerala seeks to proceed with dam study, tn warns of legal action, patanjali case has changed ad regulations. sc mandate only causes more trouble, pune porsche case: can the 17-yr-old accused be treated as an adult here’s what the law says.

Required fields are marked *

Copyright © 2024 Printline Media Pvt. Ltd. All rights reserved.

• Terms of Use
• Privacy Policy

Comparative assessment of WRF’s parameterization scheme combinations in assessing land-surface feedback flux and its drivers: a case study of Phailin tropical cyclone

• Published: 27 May 2024

Cite this article

• Subhadeep Mandal   ORCID: orcid.org/0009-0007-1460-9211 1 ,
• Bhabagrahi Sahoo   ORCID: orcid.org/0000-0003-4226-0815 2 &
• Ashok Mishra   ORCID: orcid.org/0000-0003-4309-7626 2  

11 Accesses

Explore all metrics

During the life cycle of a tropical cyclone from the homogeneous ocean-phase to the heterogeneous land-phase, estimating the catchment-scale evapotranspiration (ET) for agricultural water management is a challenging task. For assessing the catchment-scale ET and the causative weather variables associated with the land–atmosphere interactions during the Tropical Cyclone Phailin (TC Phailin), some popular parameterization scheme combinations in the Weather Research and Forecasting (WRF) model were selected. The suitability of different WRF parameterization scheme combinations (PSCs) were evaluated in the Brahmani River basin in eastern India to reproduce the observed weather variables of surface (2-m) air temperature, precipitation and atmospheric pressure at hourly and daily temporal resolutions during the pre-, at-, and post events of the TC Phailin. This study found that the ‘Rapid Update Cycle’ (RUC) LSM with ‘Purdue Lin’ microphysics and ‘Arakawa convective’ cumulus scheme performed the best. Overall, the PSCs could simulate the surface air temperature better than the precipitation during the short timeframe of the extreme event, whereas the overall regional pressure simulation showed a constant bias. The results reveal that WRF-LSM model that accounts for both local (clothesline) and global (oasis) advection effects could better simulate the ET flux compared to the corresponding MOD16A2 remote sensing product and the Food and Agricultural Organization (FAO)-56 Penmen-Monteith (PM) equation, especially during cloudy days. The local feedback of the TC Phailin over the land-surface ET flux and its climatic and land-surface drivers (soil moisture) during the pre-, at-, post-cyclone events reveal that: (a) the negative Bowen Ratio estimates during the heavy rainfall resulted in a reduced ET flux, (b) the negative sensible heat flux during this period facilitates for flow of heat from surface to atmosphere, cooling the soil of the river basin. Overall, this study aids in a better understanding of the moisture flux and energy transfer dynamics between the land–atmosphere system during the onset of a cyclone.

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

Access this article

Price excludes VAT (USA) Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Sensitivity of the weather research and forecasting model to parameterization schemes for regional climate of nile river basin.

Assessment of physical parameterization schemes in WRF over national capital region of India

Assessment of land surface models in a high-resolution atmospheric model during indian summer monsoon, data availability.

Data will be made available on request.

Abbreviations

Betts-Millar-Janjic

Community Land Model

Evapotranspiration

Food and Agriculture Organization

India Meteorological Department

Land-Surface Temperature

Land Surface Model

Maximum Entropy Production

MODerate resolution Imaging Spectroradiometer

Moisture transport

National Centers for Environmental Prediction

Oregon State University

Planetary Boundary Layer

Penman-Monteith

Parameterization scheme combinations

Priestley-Taylor

Root Mean Square Deviation

Correlation Coefficient

Remote Sensing

Rapid Update Cycle

Surface Air Temperature

Surface Energy Balance

Simplified Simple Biosphere

Tropical Cyclone

Surface temperature-Vegetation Index

Weather Research and Forecasting

Younsei University

Allen RG, Tasumi M, Morse A, Trezza R (2005) A Landsat-based energy balance and evapotranspiration model in Western US water rights regulation and planning. Irrig Drain Syst 19(3):251–268

Article   Google Scholar  

Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56, vol 300, no 9. Fao, Rome, p D05109.  http://www.climasouth.eu/sites/default/files/FAO%2056.pdf

Athar H, Ammar K (2016) Seasonal characteristics of the large-scale moisture flux transport over the Arabian Peninsula. Theoret Appl Climatol 124(3–4):565–578. https://doi.org/10.1007/s00704-015-1437-7

Attada R, Kumar P, Dasari HP (2018) Assessment of land surface models in a high-resolution atmospheric model during indian summer monsoon. Pure Appl Geophys 175(10):3671–3696. https://doi.org/10.1007/s00024-018-1868-z

Baki H, Chinta S, Balaji C, Srinivasan B (2022) Determining the sensitive parameters of the Weather Research and Forecasting (WRF) model for the simulation of tropical cyclones in the Bay of Bengal using global sensitivity analysis and machine learning. Geosci Model Dev 15(5):2133–2155. https://doi.org/10.5194/gmd-15-2133-2022

Balaguru K, Taraphdar S, Leung LR, Foltz GR (2014) Increase in the intensity of postmonsoon Bay of Bengal tropical cyclones. Geophys Res Lett 41(10):3594–3601. https://doi.org/10.1002/2014GL060197

Bastiaanssen WGM, Menenti M, Feddes RA, Holtslag AAM (1998) A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation J Hydrol 212–213:198–212. https://doi.org/10.1016/S0022-1694(98)00253-4

Bhalachandran S, Nadimpalli R, Osuri KK, Marks FD Jr, Gopalakrishnan S, Subramanian S, Mohanty UC, Niyogi D (2019) On the processes influencing rapid intensity changes of tropical cyclones over the Bay of Bengal. Scientific Reports 9(1):1. https://doi.org/10.1038/s41598-019-40332-z

Article   CAS   Google Scholar  

Brun J, Barros AP (2014) Mapping the role of tropical cyclones on the hydroclimate of the southeast United States: 2002–2011. Int J Climatol 34(2):494–517. https://doi.org/10.1002/joc.3703

Brutsaert W, Stricker H (1979) An advection-aridity approach to estimate actual regional evapotranspiration. Water Resour Res 15(2):443–450. https://doi.org/10.1029/WR015i002p00443

Chaturvedi MC (2011) India’s Waters: Environment, Economy, and Development. CRC Press

Book   Google Scholar  

Chen S-H, Sun W-Y (2002) A one-dimensional time dependent cloud model. J Meteorol Soc Japan. Ser. II 80(1):99–118. https://doi.org/10.2151/jmsj.80.99

Cho H, Kug J-S, Jun S-Y (2022) Influence of the recent winter Arctic sea ice loss in short-term simulations of a regional atmospheric model. Sci Reports 12(1):1. https://doi.org/10.1038/s41598-022-12783-4

Choudhury D, Das S (2017) The sensitivity to the microphysical schemes on the skill of forecasting the track and intensity of tropical cyclones using WRF-ARW model. J Earth Syst Sci 126:1. https://doi.org/10.1007/s12040-017-0830-2

Copernicus Climate Change Service (2018) ERA5 hourly data on pressure levels from 1940 to present. ECMWF. https://doi.org/10.24381/CDS.BD0915C6

Copernicus Climate Change Service (2019) ERA5-Land hourly data from 2001 to present. ECMWF. https://doi.org/10.24381/CDS.E2161BAC

Dash SS, Sahoo B, Raghuwanshi NS (2021) How reliable are the evapotranspiration estimates by Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC) models for catchment-scale drought assessment and irrigation planning? J Hydrol 592:125838. https://doi.org/10.1016/j.jhydrol.2020.125838

Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46(20):3077–3107. https://doi.org/10.1175/1520-0469(1989)046%3c3077:NSOCOD%3e2.0.CO;2

Falk M, Pyles RD, Ustin SL, Paw KTU, Xu L, Whiting ML, Sanden BL, Brown PH (2014) Evaluated Crop Evapotranspiration over a Region of Irrigated Orchards with the Improved ACASA–WRF Model. J ogy 15(2):744–7588. https://doi.org/10.1175/JHM-D-12-0183.1

Fisher JB, Melton F, Middleton E, Hain C, Anderson M, Allen R, McCabe MF, Hook S, Baldocchi D, Townsend PA, Kilic A, Tu K, Miralles DD, Perret J, Lagouarde J-P, Waliser D, Purdy AJ, French A, Schimel D, Wood EF (2017) The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resour Res 53(4):2618–2626. https://doi.org/10.1002/2016WR020175

Hirano A (2021) Effects of climate change on spatiotemporal patterns of tropical cyclone tracks and their implications for coastal agriculture in Myanmar. Paddy Water Environ, 19(2):261–269. https://doi.org/10.1007/s10333-021-00842-x

Hong S-Y, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134(9):2318–2341. https://doi.org/10.1175/MWR3199.1

Iacono M, Delamere J, Mlawer E, Shephard M, Clough S, Collins W (2008) Radiative Forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models. J Geophys Res 113. https://doi.org/10.1029/2008JD009944

Johari P, Kumar S, Pattanayak S, Sahu DK, Routray A (2023) Performance of land surface schemes on simulation of land falling tropical cyclones over Bay of Bengal using ARW model. MAUSAM 74(4):1155–1172. https://doi.org/10.54302/mausam.v74i4.5861

Jung M, Reichstein M, Ciais P, Seneviratne SI, Sheffield J, Goulden ML, Bonan G, Cescatti A, Chen J, de Jeu R, Dolman AJ, Eugster W, Gerten D, Gianelle D, Gobron N, Heinke J, Kimball J, Law BE, Montagnani L, Zhang K (2010) Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 467(7318):7318. https://doi.org/10.1038/nature09396

Kain JS, Fritsch JM (1990) A one-dimensional entraining/detraining plume model and its application in convective parameterization. J Atmos Sci 47(23):2784–2802. https://doi.org/10.1175/1520-0469(1990)047%3c2784:AODEPM%3e2.0.CO;2

Kumar S, Routray A, Tiwari G, Chauhan R, Jain I (2016) Simulation of tropical cyclone ‘Phailin’ using WRF modeling system. In tropical cyclone activity over the North Indian Ocean, pp 291–300. https://doi.org/10.1007/978-3-319-40576-6_21

Lakshmi DD, Murty PLN, Bhaskaran PK, Sahoo B, Kumar TS, Shenoi SSC, Srikanth AS (2017) Performance of WRF-ARW winds on computed storm surge using hydodynamic model for Phailin and Hudhud cyclones. Ocean Eng 131:135–148. https://doi.org/10.1016/j.oceaneng.2017.01.005

Lin Y-L, Farley RD, Orville HD (1983) Bulk parameterization of the snow field in a cloud model. J Appl Meteorol Climatol 22(6):1065–1092. https://doi.org/10.1175/1520-0450(1983)022%3c1065:BPOTSF%3e2.0.CO;2

Lodh A (2020) Reassessment of land–atmosphere interactions over India during summer monsoon using state-of-the-art regional climate models. Theoret Appl Climatol 142(3–4):1649–1673. https://doi.org/10.1007/s00704-020-03395-x

Lodh A, Routray A, Dutta D, George JP, Mitra AK (2022) Improving the prediction of monsoon depressions by assimilating ASCAT soil moisture in NCUM-R modeling system. Atmos Res 272:106130. https://doi.org/10.1016/j.atmosres.2022.106130

Long D, Longuevergne L, Scanlon BR (2014) Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites. Water Resour Res 50(2):1131–1151. https://doi.org/10.1002/2013WR014581

Lorente-Plazas R, Jiménez PA, Dudhia J, Montávez JP (2016) Evaluating and improving the impact of the atmospheric stability and orography on surface winds in the WRF model. Mon Weather Rev 144(7):2685–2693. https://doi.org/10.1175/MWR-D-15-0449.1

Ma N, Szilagyi J (2019) The CR of evaporation: A calibration-free diagnostic and benchmarking tool for large-scale terrestrial evapotranspiration modeling. Water Resour Res 55(8):7246–7274

Ma Y, Zhu Z, Zhong L, Wang B, Han C, Wang Z, Wang Y, Lu L, Amatya PM, Ma W (2014) Combining MODIS, AVHRR and in situ data for evapotranspiration estimation over heterogeneous landscape of the Tibetan Plateau. Atmos Chem Phys 14(3):1507–1515

Mahala BK, Mohanty PK, Nayak BK (2015) Impact of Microphysics Schemes in the Simulation of Cyclone Phailinusing WRF Model. Procedia Engineering 116:655–662. https://doi.org/10.1016/j.proeng.2015.08.342

Mandal M, Singh KS, Balaji M, Mohapatra M (2016) Performance of WRF-ARW model in real-time prediction of Bay of Bengal cyclone ‘Phailin.’ Pure Appl Geophys 173(5):1783–1801. https://doi.org/10.1007/s00024-015-1206-7

McCabe MF, Wood EF (2006) Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors. Remote Sens Environ 105(4):271–285. https://doi.org/10.1016/j.rse.2006.07.006

Mittal R, Tewari M, Radhakrishnan C, Ray P, Singh T, Nickerson AK (2019) Response of tropical cyclone Phailin (2013) in the Bay of Bengal to climate perturbations. Clim Dyn 53(3):2013–2030. https://doi.org/10.1007/s00382-019-04761-w

Mohan PR, Srinivas CV, Venkatraman B (2022) Convection-permitting WRF simulations of tropical cyclones over the north indian ocean. Pure Appl Geophys 179(4):1333–1363. https://doi.org/10.1007/s00024-022-02985-2

Mohanty MR, Mohanty UC (2023) Inter-comparison of two regional climate models (RegCM and WRF) in downscaling CFSv2 for the seasonal prediction of Indian summer monsoon. Theoret Appl Climatol 151(1–2):99–114. https://doi.org/10.1007/s00704-022-04278-z

Mooney PA, Mulligan FJ, Broderick C (2016) Diurnal cycle of precipitation over the British Isles in a 0.44° WRF multiphysics regional climate ensemble over the period 1990–1995. Clim Dyn 47(9–10):3281–3300. https://doi.org/10.1007/s00382-016-3026-6

Mu Q, Heinsch FA, Zhao M, Running SW (2007) Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens Environ 111(4):519–536. https://doi.org/10.1016/j.rse.2007.04.015

Mu Q, Zhao M, Running SW (2011) Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens Environ 115(8):1781–1800. https://doi.org/10.1016/j.rse.2011.02.019

Muñoz-Sabater J, Dutra E, Agustí-Panareda A, Albergel C, Arduini G, Balsamo G, Boussetta S, Choulga M, Harrigan S, Hersbach H, Martens B, Miralles DG, Piles M, Rodríguez-Fernández NJ, Zsoter E, Buontempo C, Thépaut J-N (2021) ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data 13(9):4349–4383. https://doi.org/10.5194/essd-13-4349-2021

National Centers for Environmental Prediction, National Weather Service, NOAA, U.S. Department of Commerce (2000) NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. https://doi.org/10.5065/D6M043C6

Osuri KK, Nadimpalli R, Mohanty UC, Niyogi D (2017) Prediction of rapid intensification of tropical cyclone Phailin over the Bay of Bengal using the HWRF modelling system. Q J R Meteorol Soc 143(703):678–690. https://doi.org/10.1002/qj.2956

Pai DS, Rajeevan M, Sreejith OP, Mukhopadhyay B, Satbha NS (2014) Development of a new high spatial resolution (0.25° × 0.25°) long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. MAUSAM 65(1):1. https://doi.org/10.54302/mausam.v65i1.851

Pant V, Prakash KR (2020) Response of air-sea fluxes and oceanic features to the coupling of ocean–atmosphere–wave during the passage of a tropical cyclone. Pure Appl Geophys 177(8):3999–4023. https://doi.org/10.1007/s00024-020-02441-z

Penman HL, Keen BA (1948) Natural evaporation from open water, bare soil and grass. Proc R Soc Lond A 193(1032):120–145. https://doi.org/10.1098/rspa.1948.0037

Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100(2):81–92. https://doi.org/10.1175/1520-0493(1972)100%3c0081:OTAOSH%3e2.3.CO;2

Ramakrishnan R, Remya PG, Mandal A, Mohanty P, Arayakandy P, Mahendra RS, Nair TMB (2022) Wave induced coastal flooding along the southwest coast of India during tropical cyclone Tauktae. Scientific Reports 12(1):1. https://doi.org/10.1038/s41598-022-24557-z

Rao NN, Rao VB, Ramakrishna SSVS, Rao BRS (2019) Moisture Budget of the Tropical Cyclones Formed over the Bay of Bengal: Role of Soil Moisture After Landfall. Pure Appl Geophys 176(1):441–461. https://doi.org/10.1007/s00024-018-1964-0

Roland S (2006) The atmospheric boundary layer. Atmospheric Science, 2nd edn, pp 375–417. https://doi.org/10.1016/B978-0-12-732951-2.50014-4

Routray A, Lodh A, Dutta D, George JP, Mitra AK (2023) Influence of ASCAT soil moisture on prediction of track and intensity of landfall tropical cyclones. Int J Remote Sens 44(1):341–380. https://doi.org/10.1080/01431161.2022.2164232

Ryu Y, Baldocchi DD, Kobayashi H, van Ingen C, Li J, Black TA., Beringer J, van Gorsel E, Knohl A, Law BE, Roupsard O (2011) Integration of MODIS land and atmosphere products with a coupled-process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales. Glob Biogeochem Cycles 25(4). https://doi.org/10.1029/2011GB004053

Sahoo B, Walling I, Deka BC, Bhatt BP (2012) Standardization of reference evapotranspiration models for a subhumid valley rangeland in the eastern himalayas. J Irrig Drain Eng 138(10):880–895. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000476

Sahoo B, Bhaskaran PK, Pradhan AK (2019) Application of weather forecasting model WRF for operational electric power network management—A case study for Phailin cyclone. Theoret Appl Climatol 137(1):871–891

Senay GB, Verdin JP, Lietzow R, Melesse AM (2008) Global daily reference evapotranspiration modeling and evaluation1. JAWRA J Am Water Resour Assoc 44(4):969–979. https://doi.org/10.1111/j.1752-1688.2008.00195.x

Singh KS, Bhaskaran PK (2017) Impact of PBL and convection parameterization schemes for prediction of severe land-falling Bay of Bengal cyclones using WRF-ARW model. J Atmos Solar Terr Phys 165–166:10–24. https://doi.org/10.1016/j.jastp.2017.11.004

Singh KS, Tyagi B (2019) Impact of data assimilation and air−sea flux parameterization schemes on the prediction of cyclone Phailin over the Bay of Bengal using the WRF-ARW model. Meteorol Appl 26(1):36–48. https://doi.org/10.1002/met.1734

Huang X (2021) A description of the advanced research WRF model version 4.3. https://doi.org/10.5065/1dfh-6p97

Smirnova TG, Brown JM, Benjamin SG (1997) Performance of different soil model configurations in simulating ground surface temperature and surface fluxes. Mon Weather Rev 125(8):1870–1884. https://doi.org/10.1175/1520-0493(1997)125%3c1870:PODSMC%3e2.0.CO;2

Sridhar V, Elliott RL, Chen F, Brotzge JA (2002) Validation of the NOAH-OSU land surface model using surface flux measurements in Oklahoma. J Geophys Res Atmos 107(D20):ACL 3-1-ACL3-18. https://doi.org/10.1029/2001JD001306

Srivastava A, Sahoo B, Raghuwanshi NS, Singh R (2017) Evaluation of variable-infiltration capacity model and modis-terra satellite-derived grid-scale evapotranspiration estimates in a river basin with tropical monsoon-type climatology. J Irrig Drain Eng 143(8):04017028. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001199

Steve R, Mu Q, Zhao M (2017) MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD16A2.006

Su Z (2002) The surface energy balance system (SEBS) for estimation of turbulent heat fluxes. Hydrol Earth Syst Sci 6(1):85–100. https://doi.org/10.5194/hess-6-85-2002

Tang Q, Peterson S, Cuenca RH, Hagimoto Y, Lettenmaier DP (2009) Satellite-based near-real-time estimation of irrigated crop water consumption. J Geophys Res Atmos 114(D5). https://doi.org/10.1029/2008JD010854

Ukkola AM, Pitman AJ, Decker M, De Kauwe MG, Abramowitz G, Kala J, Wang Y-P (2016) Modelling evapotranspiration during precipitation deficits: Identifying critical processes in a land surface model. Hydrol Earth Syst Sci 20(6):2403–2419. https://doi.org/10.5194/hess-20-2403-2016

Walter I, Allen R, Elliott R, Itenfisu D, Brown P, Jensen M, Mecham B, Howell T, Snyder R, Eching S (2002) The ASCE standardized reference evapotranspiration equation, Report Task Com. On Standardized Reference Evapotranspiration July 9, 2002, EWRI-Am. Soc Civil Engr, Reston. https://www.mesonet.org/images/site/ASCE_Evapotranspiration_Formula.pdf

Wan Z, Zhang K, Xue X, Hong Z, Hong Y, Gourley JJ (2015) Water balance-based actual evapotranspiration reconstruction from ground and satellite observations over the conterminous United States. Water Resour Res 51(8):6485–6499. https://doi.org/10.1002/2015WR017311

Wang J, Bras RL (2011) A model of evapotranspiration based on the theory of maximum entropy production. Water Resour Res 47(3). https://doi.org/10.1029/2010WR009392

Wang K, Wang P, Li Z, Cribb M, Sparrow M (2007) A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature. J Geophys Res Atmos 112(D15). https://doi.org/10.1029/2006JD008351

Warrach-Sagi K, Schwitalla T, Wulfmeyer V, Bauer H-S (2013) Evaluation of a climate simulation in Europe based on the WRF–NOAH model system: Precipitation in Germany. Clim Dyn 41(3–4):755–774. https://doi.org/10.1007/s00382-013-1727-7

Wilson TH, Fovell RG (2018) Modeling the Evolution and Life Cycle of Radiative Cold Pools and Fog. Weather Forecast 33(1):203–220. https://doi.org/10.1175/WAF-D-17-0109.1

WRF Community (2000) Weather Research and Forecasting (WRF) Model. https://doi.org/10.5065/D6MK6B4K

Wright IR, Gash JHC, Da Rocha HR, Shuttleworth WJ, Nobre CA, Maitelli GT, Zamparoni CAGP, Carvalho PRA (1992) Dry season micrometeorology of central amazonian ranchland. Q J R Meteorol Soc 118(508):1083–1099. https://doi.org/10.1002/qj.49711850804

Xu T, Guo Z, Xia Y, Ferreira VG, Liu S, Wang K, Yao Y, Zhang X, Zhao C (2019) Evaluation of twelve evapotranspiration products from machine learning, remote sensing and land surface models over conterminous United States. J Hydrol 578:124105. https://doi.org/10.1016/j.jhydrol.2019.124105

Xu X, Li X, Wang X, He C, Tian W, Tian J, Yang L (2020) Estimating daily evapotranspiration in the agricultural-pastoral ecotone in Northwest China: A comparative analysis of the Complementary Relationship, WRF-CLM4. 0, and WRF-Noah methods. Sci Total Environ 729:138635

Zhang K, Kimball JS, Running SW (2016) A review of remote sensing based actual evapotranspiration estimation. Wires Water 3(6):834–853. https://doi.org/10.1002/wat2.1168

Download references

Acknowledgements

The authors are thankful to the IMD, Pune for providing the necessary meteorological data sets to carry out this research. This data can be accessed from these agencies after fulfilling the data sharing policy. The research fellowship received by the first author under the Project: DST/CCP/CoE/79/2014(G) from the Department of Science and Technology (DST), Government of India, is duly acknowledged.

Author information

Authors and affiliations.

School of Water Resources, Indian Institute of Technology Kharagpur, Kharagpur, India

Subhadeep Mandal

Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, India

Bhabagrahi Sahoo & Ashok Mishra

You can also search for this author in PubMed   Google Scholar

Contributions

Subhadeep Mandal:  Conceptualization, Data curation, Software, Formal analysis, Writing—original draft.  Bhabagrahi Sahoo:  Conceptualization, Supervision, Investigation, Writing—review & editing. Ashok Mishra:  Conceptualization, Supervision, Resources, Writing—review & editing.

Corresponding author

Correspondence to Bhabagrahi Sahoo .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 16685 KB)

Rights and permissions.

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

Reprints and permissions

About this article

Mandal, S., Sahoo, B. & Mishra, A. Comparative assessment of WRF’s parameterization scheme combinations in assessing land-surface feedback flux and its drivers: a case study of Phailin tropical cyclone. Theor Appl Climatol (2024). https://doi.org/10.1007/s00704-024-05032-3

Download citation

Received : 12 October 2023

Accepted : 16 May 2024

Published : 27 May 2024

DOI : https://doi.org/10.1007/s00704-024-05032-3

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Find a journal
• Publish with us
• Track your research

• Education News

INI CET Result 2024 July session released at aiimsexams.ac.in, direct link to download

Visual Stories