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Cyclone Idai

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

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

Cyclone Idai satellite image

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

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Climate intelligence at work: the case of Cyclone Freddy

• Marina Menga
• April 19, 2023

Cyclone Freddy has recently made the headlines worldwide because of its destructive impacts on land and its record-breaking intensity. Indeed, Freddy had quite a peculiar behaviour. It developed near the western coast of Australia at the beginning of February 2023, and it crossed the Southern Indian Ocean westward, reaching the eastern coast of Africa over a few weeks. It made a first landfall on Madagascar, crossed the Channel and made landfall over the coast of Mozambique.

Typically, cyclones are fed by heat and energy from the ocean, so they lose intensity when they touch land and tend to dissipate. Unusually, instead of dissipating after landfall, Freddy travelled back to the ocean, where it gained more energy and inverted its direction, hitting land again over the coast of Mozambique and then over Malawi. This uncommon behavior made it the longest tropical Cyclone ever recorded, with a duration of 38 days , beating the previous record of 30 days by more than a week and travelling a total distance of more than 8,000 kilometres.

Freddy is now also recognized as the most intense tropical cyclone ever recorded in terms of accumulated cyclone energy (ACE), a metric which expresses the energy released by a tropical cyclone during its lifetime.

This metric is particularly convenient because it gives an estimate of both the cyclone’s intensity, which is typically its maximum velocity, and its duration, giving a measure of the dissipated energy, which is more representative of the cyclone’s overall activity. According to NASA , Freddy is the highest-ACE-producing tropical cyclone ever recorded worldwide.

Unfortunately, Cyclone Freddy stood out also for its violence and destructive force, being the third-deadliest tropical cyclone ever recorded in the Southern Hemisphere, after Cyclone Idai in 2019 and Flores cyclone in 1973. It produced extraordinarily heavy rains, strong winds, and excessive flooding, destroying houses, crops, and infrastructures, primarily in Mozambique and Malawi.

The death toll in South-East Africa is estimated to be hundreds, with Malawi being the most affected country. The cyclone further compromised communities already struggling with the spread of cholera and often lacking an adequate healthcare system to respond to emergencies.

As reported by Reuters , Malawi President Lazarus Chakwera said that “the death toll from Cyclone Freddy has risen sharply to more than 1,000 people”. Hundreds of people are still missing in the region, and more than half a million are displaced.

The role of climate change

But do these extremes follow an overall tendency in worsening extreme climate events? And is this caused by climate change?

Attribution science, or extreme event attribution, is a relatively recent field in climate science that tries to quantitatively determine if an extreme weather event was caused or worsened by climate change or was simply due to natural variations.

The Intergovernmental Panel on Climate Change (IPCC) had already warned in its reports about an expected rise in intense precipitations, floods, mean wind speed and tropical cyclones, with stronger associated precipitations, in the areas of South-East Africa and Madagascar.

However, it is not always easy to clearly determine if climate change plays a crucial role in the occurrence of an extreme event, and rigorous analyses are not yet available in the case of Cyclone Freddy.

There are mainly two ways to do an attribution analysis for an event such as a tropical cyclone.

The first is of a statistical kind: analyzing the number, frequency, intensity, and impacts of extreme events like Freddy that happen in the present, and making a comparison with the same parameters from pre-industrial times, can give an estimate of the possible influence of climate change on these phenomena.

Another way is to analyse a particular storm through model simulations under different conditions typical of the past, the present, and the future. This will point out under which climate scenario a storm would be more likely.

Climate Intelligence

“Many studies have been carried out at CMCC to try and understand the effects of climate change on tropical cyclones,” says Enrico Scoccimarro of the Climate Simulations and Prediction (CSP) division at the Euro-Mediterranean Center on Climate Change (CMCC) . “On one hand, it is true that with a warmer climate we have a more stable atmosphere, and thus we expect less tropical cyclones. On the other hand, however, it is also true that a higher availability of energy in the ocean leads to more intense storms. Moreover, if a storm happens to go back to the ocean, it has a higher probability to re-strengthen and hit land again, and this is just what happened with Freddy recently.”

A crucial factor in the creation of the perfect storm is the stratification of the ocean. Tropical cyclones are characterized by a mechanism of negative feedback that tends to slow-down and sometimes dissipate the storms. Cyclones absorb heat from water while crossing the ocean, and gain energy from it, making it cooler. In addition to the induced upwelling, in which cooler layers from the bottom of the ocean travel upwards, a process of turbulent mixing of water layers also takes place. With less heat available on the surface, slower cyclones tend to lose their strength and slowly fade, while faster and more violent storms tend to proceed almost undisturbed.

“The point is that this negative feedback is more or less efficient depending on the storm speed and on the level of ocean stratification,” says Scoccimarro. “The stratification is not the same in past or future climate scenarios, and it also varies in the different areas of the world. On average, in a warmer climate, more intense storms are more likely to happen. In a changed climate, we will likely have fewer storms, but they will likely be much more intense .”  

CLINT (CLimate INTelligence) project  has the goal to improve detection methods of extreme events and their causation and attribution through Machine Learning techniques. Extreme events include not only tropical cyclones but also heat waves or floods, and the project has a focus on South-East Africa for analysing extreme precipitation associated with cyclones. “We are trying to develop improved tools to better quantify the amount of precipitation in the area, in this case, associated with cyclone Freddy,” says Scoccimarro. “This could also be useful to characterize floods and drought events on the dominion of the Zambesi River, which is very close to the impacted areas.”  

Predicting the birth of a cyclone like Freddy is not straightforward, but there are parameters and models that can be used to determine if the physical conditions of an area over a certain period of time are favourable. Genesis potential indexes (or GPI) are generally empirical formulations that give an estimate of the occurrence of a tropical cyclone in a cell of 5 degrees longitude by 5 degrees latitude. “Within the CLINT project, we are using Machine Learning to improve this empirical index,” said Scoccimarro. “That is, we are working to improve the correlation of this index and the actual occurrence of a cyclone in a certain area, which is crucial for allowing an optimal response and preparedness to disasters.”

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• Open access
• Published: 09 March 2021

Tropical cyclone exposure is associated with increased hospitalization rates in older adults

• Robbie M. Parks   ORCID: orcid.org/0000-0002-7916-1717 1 , 2 ,
• G. Brooke Anderson 3 ,
• Rachel C. Nethery 4 ,
• Ana Navas-Acien 2 ,
• Francesca Dominici 4 &
• Marianthi-Anna Kioumourtzoglou 2  

Nature Communications volume  12 , Article number:  1545 ( 2021 ) Cite this article

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• Environmental impact
• Interdisciplinary studies
• Public health
• Risk factors

Hurricanes and other tropical cyclones have devastating effects on society. Previous case studies have quantified their impact on some health outcomes for particular tropical cyclones, but a comprehensive assessment over longer periods is currently missing. Here, we used data on 70 million Medicare hospitalizations and tropical cyclone exposures over 16 years (1999–2014). We formulated a conditional quasi-Poisson model to examine how tropical cyclone exposure (days greater than Beaufort scale gale-force wind speed; ≥34 knots) affect hospitalizations for 13 mutually-exclusive, clinically-meaningful causes. We found that tropical cyclone exposure was associated with average increases in hospitalizations from several causes over the week following exposure, including respiratory diseases (14.2%; 95% confidence interval [CI]: 10.9–17.9%); infectious and parasitic diseases (4.3%; 95%CI: 1.2–8.1%); and injuries (8.7%; 95%CI: 6.0–11.8%). Average decadal tropical cyclone exposure in all impacted counties would be associated with an estimated 16,772 (95%CI: 8,265–25,278) additional hospitalizations. Our findings demonstrate the need for targeted preparedness strategies for hospital personnel before, during, and after tropical cyclones.

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Introduction

Tropical cyclones, such as hurricanes and tropical storms, have a devastating impact on the economy 1 , 2 , 3 , environment 4 , 5 , and human health 6 , 7 , 8 . Exposure to such events is an important public health concern and one of the key drivers for seeking disaster risk reduction measures 9 . The intensity of tropical cyclones is predicted to change due to anthropogenic climate change 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 . Land subsidence 20 and increases in the proportion of impervious surfaces 21 may further exacerbate cyclone impacts. A recent report by the National Centers for Environmental Information estimated that storm-related costs in the United States between 1980 and 2017 exceeded $1.3 trillion 22 . Previous assessments of health impacts have largely focused on single extreme events, including well-known examples, such as Hurricane Katrina in New Orleans (2005) 23 , 24 , 25 and Hurricane Sandy in New York City (2012) 4 , 26 , 27 .

Some studies have reviewed general evidence of health impacts of storms and hurricanes, primarily using case studies, for cardiovascular diseases, respiratory diseases, dialysis-, and injury-related hospitalizations, showing harmful impacts overall 6 , 28 , 29 , 30 , 31 , 32 , 33 . Other studies have previously used claims and Medicare data to measure impacts of disasters, including how mortality and morbidity was affected in the Medicare population after Hurricane Katrina 34 , and changes in Medicare cost and utilization 35 . Findings from these studies motivate exploring whether other causes of hospitalization are impacted by tropical cyclones. There are plausible behavioral and physiological pathways for a relationship between tropical cyclone exposure and many adverse health outcomes—such as respiratory complications, due to electricity cuts affecting breathing apparatus 6 , 36 , injuries from trying to evacuate or repair a damaged property 6 , 26 , or dietary problems due to disrupted food supply lines 6 , 37 . Despite these prior findings and biological plausibility, there is an overall knowledge gap in consistently and comprehensively quantifying how tropical cyclone exposure drives hospitalizations across time and space.

In this work, our aim was to evaluate how hospitalizations from various causes in the United States are associated with tropical cyclone exposure that occurs today, and could become increasingly intense, on average, as a result of global climate change 13 , 14 , 15 . We found that (i) tropical cyclone exposure was associated with overall increases in hospitalization rates for many causes and sub-causes in the week after exposure, with decreases for some chronic conditions; (ii) hurricane-force tropical cyclone exposure amplified the impact of weaker winds on hospitalization rates; and (iii) after tropical cyclone exposure, increases in hospitalization rates were driven by increases in emergency hospitalizations, with decreases in rates driven by decreases in nonemergency hospitalizations.

Tropical cyclone exposure

We assigned tropical cyclone exposure to a particular day and county if the peak sustained wind that day in that county reached or exceeded gale force (≥34 knots) on the Beaufort scale, when the tropical cyclone was at the point of closest approach to that county, described in “Methods”. In total, there were 2547 tropical cyclone exposure days in 898 counties during our study period (1999–2014). By county, the number of tropical cyclone exposure days across our study period ranged from 1 to 15, with median of 2 and mean of 2.8. Tropical cyclone exposure occurred from May to October, with the greatest occurrence in September ( n  = 1337 tropical cyclone exposure days; 52% of all tropical cyclone exposure days). Tropical cyclone exposures were most frequent in the eastern and south-eastern coastal counties (Fig.  1 ). North Carolina was the single state with the most days of tropical cyclone exposure during the period ( n  = 413), with Jones and Pamlico Counties, both in North Carolina, each experiencing the most county-level exposure days ( n  = 15).

Number of days with tropical cyclone exposure by county for 1999–2014.

Medicare hospitalizations

We used data on enrollees from the dynamic Medicare cohort in the 898 counties from 30 states and districts in the United States, which experienced at least one tropical cyclone during our 16-year study period, with information on underlying primary cause of hospitalization and county of residence. From 1999 to 2014, there were 69,682,674 Medicare hospitalizations in the 898 counties impacted by tropical cyclones (Supplementary Table  1 ). The hospitalizations from these counties included 47.2% of all hospitalizations nationwide during our study period. We grouped the 15,072 possible International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes into 13 main causes (Fig.  2 ), using the Clinical Classifications Software (CCS) algorithm (Supplementary Table  2 ) 38 . Hospitalizations from these 13 causes accounted for 94.8% of the total hospitalizations in our study. Hospitalizations not included in these causes were other hospitalizations (Fig.  2 ), mainly including rare compilations during pregnancy and ill-defined causes. Cardiovascular (30%), respiratory (12.5%), and digestive system diseases (10%) were the three leading hospitalization causes in our study (Supplementary Table  1 ).

Number of Medicare hospitalizations by year and cause of hospitalization for counties with at least one tropical cyclone exposure for 1999–2014.

Association of tropical cyclones exposure with hospitalization rates

We analyzed the association between tropical cyclone exposure and daily hospitalization rates up to 7 days after the day of exposure, using a conditional quasi-Poisson model, described in detail in “Methods”. We present these results in Fig.  3 , which displays results as relative (percentage) changes in hospitalization rates after tropical cyclone exposure. We observed the highest increases in hospitalization rates from respiratory diseases; increases occurred across all studied days after the day of exposure, peaking 1 day after exposure (23.8%; 95% confidence interval [CI]: 18.6, 29.3%). Injury hospitalization rates increased across all studied days after the day of exposure, with a peak at 2 days after the day of exposure (13.5%; 95% CI: 8.5, 18.9%). We observed decreased hospitalization rates on the day of exposure for all other causes. For several causes (cardiovascular diseases, endocrine disorders, genitourinary diseases, infectious and parasitic diseases, nervous system diseases, and skin and subcutaneous tissue diseases) hospitalization risk followed a similar pattern, decreasing on the day of exposure, peaking 1–3 days later, and gradually returning to the rate expected during unexposed days within about a week.

Lag time is measured in days after tropical cyclone exposure. Dots show the point estimates and error bars represent Bonferroni-corrected 95% confidence intervals.

In Fig.  4 , we present average relative (percentage) changes in hospitalization rates across the eight examined lag days across the 13 causes in the main analysis, as well as for sub-causes with at least 50,000 hospitalizations during our study period. The sub-causes are linked to the 13 main causes in Supplementary Table  2 . Respiratory diseases exhibited the largest average increase in hospitalizations (14.2%; 95% CI: 10.9, 17.9%). We observed the largest decreases in hospitalization rates for cancers (−4.4%; 95% CI: −2.9, −5.8%). Cardiovascular diseases did not change overall (−0.3%; 95% CI: −1.3, 0.7%). There was variation in changes to hospitalization rates for hospitalization sub-causes within each larger cause. For example, within respiratory diseases, we observed the largest increase in hospitalization rates for chronic obstructive pulmonary disease (COPD; 44.7%; 95% CI: 36.6, 54.2%) and the largest decrease in other upper respiratory infections (−8.8%; 95% CI: −6.8, −10.1%).

Average percentage change in hospitalization rates is across studied lag period (0–7 days after tropical cyclone exposure). Dots show the point estimates and error bars represent 95% confidence intervals.

In addition, we examined the distinct impact of tropical cyclone exposures, in which the county’s peak sustained wind was hurricane force (Beaufort scale hurricane-force winds, ≥64 knots) compared to tropical cyclone exposures with lower local winds (Beaufort scale gale- to violent storm-force winds, ≥34 to <64 knots; Fig.  5 ). Of the 2547 county-day exposures (Fig.  1 ), 116 (5%) were hurricane force and came from 20 hurricanes. Across causes, the relative (percentage) changes in hospitalization rates during hurricane-force exposures broadly amplified the overall tropical cyclone effects presented in Fig.  3 . We observed the highest estimates for respiratory disease hospitalizations, with an 100.3% (95% CI: 77.2, 126.4%) increase 1 day after hurricane exposure compared with a 17.1% (95% CI: 11.8, 22.7%) increase 1 day after exposure to tropical cyclones with lower local winds.

We also examined the association between tropical cyclone exposure and daily hospitalization rates by type of hospital admission (emergency vs. nonemergency; Fig.  6 ). Generally, nonemergency hospitalization rates decreased in the first few days after tropical cyclone exposure before returning to no change in subsequent days, with the exception of infectious and parasitic diseases, for which we estimated increases in nonemergency hospitalizations for lags 1, 2, and 4. Emergency hospitalization rates for cardiovascular diseases, respiratory diseases, and injuries increased in the days after tropical cyclone exposure, with other causes generally showing lower or no decreases across days, in comparison to nonemergency hospitalizations.

Additional hospitalizations for tropical cyclone exposure

Finally, we estimated the total number of additional hospitalizations for tropical cyclone exposure per decade in the week following the day of exposure across all counties included in our analysis. We used the relative (percentage) changes in hospitalization rate estimates of each cause on day of and each day after exposure, as shown in Figs.  3 and 4 , along with the average hospitalization rates during May to October in 1999–2014 and average decadal tropical cyclone exposure, described in detail in “Methods”. Based on this analysis, there would be an estimated 16,772 (95% CI: 8265, 25,278) excess hospitalizations per decade in the eight days (i.e., day of and up to 7 days after exposure), following tropical cyclone exposures in the 30 states and districts included in our analysis (Fig.  7 ). Respiratory diseases would make up the largest number of additional hospitalizations with 16,413 (95% CI: 13,054, 19,925), followed by injuries with 10,645 (95% CI: 7767, 13,660) and infectious and parasitic diseases with 2185 (95% CI: 458, 4,041). Musculoskeletal and connective tissue disease-related hospitalizations would decrease the most, with 5899 (95% CI: 3970, 7723) fewer overall hospitalizations.

The top row shows the break down by cause covering the day of tropical cyclone exposure to 7 days afterward, with black bars representing Bonferroni-corrected 95% confidence interval. The bottom row shows the break down by lag days after the exposure.

Sensitivity analyses

Our results were robust to sensitivity analyses of several methods for temperature adjustment. We also fit models (1) including the temperature on the day of tropical cyclone exposure, as well as temperature terms of up to 7 days after exposure and (2) without a temperature term. Our results were robust to these sensitivity analyses.

We used data over 16 years on 70 million Medicare hospitalizations and a comprehensive database of county-level local winds associated with tropical cyclones to examine how tropical cyclone wind exposures affect hospitalizations from 13 mutually-exclusive, clinically-meaningful causes, along with common sub-causes. We found the highest increases in respiratory disease hospitalization rates the day of and up to 7 days after the day of tropical cyclone exposure. Hospitalization rates from several causes decreased on the day of exposure, then increased 1–3 days after the day of exposure, returning back to no association by 7 days after. There was variation by sub-cause of hospitalization within each of the 13 causes. Hurricane-force tropical cyclone exposure amplified the impacts compared to cyclones with weaker winds. Changes in emergency hospitalization rates drove increases in hospitalization rates, with decreases driven by reductions in nonemergency hospitalizations. By our analysis, on average there would be increases in overall number of hospitalizations following tropical cyclone wind exposure, with the largest increases in respiratory diseases and injuries.

Acute vs. chronic causes of hospitalization

The observed decreases in hospitalization rates on the day of exposure for most causes are plausible, as the immediate risk to life by making a journey during a tropical cyclone may deter many from seeking treatment at hospital. In general, the decision to make an urgent visit to a hospital for the treatment would be largely based on patients’ feeling of pain, shortness of breath, and which symptoms of a potential adverse health condition they may have noticed. Unless a negative health outcome is so acute that it requires immediate treatment, patients may delay care due to risk of additional harm from tropical cyclone exposure. Consistent with this, we observed general increases in average hospitalization rates in a county exposed to tropical cyclone winds for more acute adverse health outcomes, such as COPD and leukemia, while hospitalizations for chronic conditions, such as hemorrhoids or osteoarthritis, decreased. Canceled inpatient appointments might also play a key factor here, with nonemergency procedures being delayed or rescheduled 39 . The subsequent peak 1–3 days after exposure may in part be driven by patients visiting the hospital for care missed at locations other than the hospital (e.g., at home or at the family physician’s offices) due to disruption from a tropical cyclone. There is also evidence that proximity to a tropical cyclone’s path may result in the area’s ambulatory (outpatient) care being disrupted 39 .

Direct vs. indirect tropical cyclone hospitalizations

Tropical cyclone wind exposure can impact hospitalizations via direct (e.g., from physical trauma during exposure) or indirect (e.g., disrupting normal care management at local health care providers, causing damage to critical infrastructure which subsequently impacts health, or via longer-term impacts from stress) pathways 6 . Direct impacts would have more immediate impacts on hospitalizations. Longer-term, indirect impacts, such as from stress to do with the loss of property from a disaster, may not be fully measured by our analysis, as they could manifest themselves further in time than a week after a disaster 40 . There may be cases where tropical cyclone exposure could prevent normal medical care or management, compelling people go to the hospital to access resources that they would otherwise get outside the hospital without the storm.

Respiratory diseases

One likely explanation for the elevated rates in respiratory hospitalizations following tropical cyclone exposure is that those with respiratory issues may need power for medical equipment to breathe 6 . Power outages commonly result from tropical cyclone winds 36 . During or following a tropical cyclone, the potential loss of power can trigger a faster hospital visit for some in this group, as existing chronic conditions may become unmanageable without ventilators, nebulizers, or oxygen concentrators 41 . Because of the immediate risk to life, even the danger of leaving home in a storm or hurricane may not deter those seeking medical care for respiratory diseases.

Injury hospitalizations are impacted by tropical cyclone exposure both directly and indirectly. During and immediately after tropical cyclones exposure, common injuries originate from transport accidents, structural collapse of buildings, wind-borne debris, falling trees, and downed power lines 32 . Days after exposure, other injuries such as puncture wounds, lacerations, falls from roof structures, chainsaw mishaps, and burns take more prominence in hospitalizations 32 . Though tropical cyclone wind exposure may bring about injuries in the Medicare population, those injured may not choose to seek treatment on a dangerous day to leave a property, which would explain the decrease in hospitalizations on the day of tropical cyclone exposure. This may be because the injury may not be life-threatening and the risk of getting caught in a tropical cyclone may be viewed as greater than not seeking treatment immediately 42 . There is also possibility of the indirect injuries occurring in the days following a tropical cyclone, e.g., during a clean-up process 6 , 43 . Houses and properties caught in a tropical cyclone may be severely damaged or nearly destroyed 2 ; the subsequent clean up may present risks for hurting oneself accidentally, e.g., from electrocution 6 .

Cardiovascular diseases

In our analyses, we did not observe overall changes in average cardiovascular disease hospitalization rates after tropical cyclone exposure. Specifically, we observed a decrease in hospitalization rates on the day of and day after tropical cyclone, followed by a small increase over 2–6 days afterward. This contrasts previous studies reporting overall average increases, though those studies focused on case studies of single hurricane events 28 , 31 . For average changes in cause-specific hospitalizations within the broader cardiovascular disease cause group, we observed both positive and negative associations; acute myocardial infarctions (heart attacks) increased after tropical cyclone wind exposures, but non-acute cardiovascular hospitalizations, such as heart valve disorders, decreased. In a study associating snowstorms in Boston with cardiovascular disease hospitalizations, a similar lag pattern was observed 44 . Tropical cyclone exposure could also indirectly lead to increases in acute cardiovascular disease hospitalizations, due to increased stress and physical challenges brought about during and following exposure 6 . Disruption of access to essential medicines from closure of local supply sources, such as pharmacies, may also contribute to negative cardiovascular health outcomes 45 , 46 . Although we did not observe short-term changes (i.e., in first week after tropical cyclone exposure) in cardiovascular disease hospitalizations, longer-term negative impacts of tropical cyclone exposure on cardiovascular diseases have been observed, several years after exposure itself 47 .

Neuropsychiatric disorders

Hospitalizations from neuropsychiatric disorders showed no overall average association with tropical cyclone exposure, though we observed an initial decrease of hospitalizations on the day of exposure. We observed an increase in delirium and dementia hospitalizations in the week after tropical cyclone exposure. There is evidence from studies of earthquakes in Japan that disasters can aggravate dementia, both over short- and long-term periods; short-term increases in dementia hospitalizations may be due to moving vulnerable dementia patients out of care and nursing homes, which can cause stress from moving, while also providing worse care in evacuation sites 48 . Longer-term impacts after disasters may be due to the stress of a domestic property being damaged or destroyed. Tropical cyclone exposure can cause stress and anxiety following potential financial concerns, intimate partner violence, loss of property, loss of family and friends, and other sources of insecurity 28 , 40 , 49 .

We observed an overall reduction in cancer-related hospitalizations during the day of and week after tropical cyclone exposure. This association was largely driven by the reduction in cancer-related admissions on the day of and the day after exposure. Hospitalizations and treatment for cancers decrease in general in the aftermath of natural disasters, due to the damage to infrastructure, communication systems and medication, and medical record losses 50 . We observed different and distinct associations between tropical cyclone exposure and cause-specific hospitalizations within the broader cancer cause group. This may be due to more acute admissions needing immediate attention, such as leukemia or brain and nervous system cancer, while some prearranged admissions for patients with known cancers may be delayed 39 . For some cancers, lack of access to essential cancer medicine due to supply line disruption may compel a patient to travel to hospital 6 .

Endocrine disorders and genitourinary diseases

An overall increase in endocrine disorder and genitourinary hospitalizations is plausible, as tropical cyclones can result in decreased availability of adequate food, water, and medicine, as well as electricity to store medicines properly 6 . Tropical cyclones can severely disrupt food and water supply lines, as well as close off medicine sources, either by locations closing temporarily or by discouraging those who need it from venturing into danger 6 . Electricity supplies which maintain medicines are often cut off during a storm or hurricane, at least temporarily 36 . Access to dialysis due to renal failure in the aftermath of a tropical cyclone would also rely on constant supply of electricity, which — when cut at home or unavailable at a local care provider — may result in additional hospitalizations for fluid and electrolyte disorders 51 .

Other diseases

When a storm or hurricane passes through an area, stagnant and unclean water is often left behind 52 , 53 , which can be optimal breeding grounds for many diseases, including infectious and parasitic, skin and subcutaneous, and blood and digestive system diseases 53 , 54 , though absolute numbers are small compared with other hospitalization causes. Following the tornado in Joplin, Missouri, in 2011, unusual fungal skin infections were recorded 55 . Hospitalizations were caused by debris from the tornado infected with some fungus that is more common in uninhabited areas, but rarely makes it into humans’ bodies. The increase in skin and subcutaneous disease may follow this pathway too, as of a result of cleaning up after the storm. Infectious diseases may also take time to be noticeable, as symptoms would only show up a few days after acquisition of an infection.

Planning for tropical cyclone hospitalizations

Other disasters, such as earthquakes and tsunamis, can also overwhelm hospital capacity in a very short time 56 , 57 . Similar lessons of how to best assign hospital personnel from our work will be valuable to minimize patient suffering or, ultimately, death. While some cause-specific hospitalization rates may not change on average in the week following a tropical cyclone, the  changing distribution of hospitalization rates during the post-cyclone week requires careful planning. Although many health care systems already have provisions in place, findings from our study may further inform planning.

Strengths and limitations

For our study, we leveraged hospitalization data from 70 million Medicare hospitalizations and linked those to a curated tropical cyclone exposure dataset in 30 states and districts over a 16-year period to comprehensively characterize the impact of tropical cyclones on cause-specific hospitalization rates in a vulnerable population. Nonetheless, our study also has some limitations. First, exposure misclassification is likely. Our results are based on patient county of residence; this may not necessarily be the location of the patient during a tropical cyclone. Furthermore, although we conducted analyses at the county level, tropical cyclone wind fields tend to be larger than the size of a county 58 . Any misclassification, nonetheless, is likely non-differential as it is not expected to be correlated with the outcomes assessed. Any resulting bias, therefore, would be toward the null 59 . Second, we also cannot exclude the possibility of some residual confounding. By design (matching), our study controls for all factors varying across counties, as well as month and season. We further adjusted for day of week, long-term trends, and temperature. Any variable inducing residual confounding bias, thus, would have to covary with hospitalization rates and tropical cyclone exposure within county and be independent of the variables we have already incorporated in analyses. It is unlikely that our results are attributable to confounding bias.

Future research

We chose to focus on seniors, an already vulnerable population. Our results may not generalize to younger populations; further studies to investigate associations in different age groups are warranted. It will also be important to understand the differential impacts of tropical cyclones on health outcomes by geography, as well as socioeconomic and demographic factors. Further work is needed to specifically understand which hospitals would need to be prepared with the forecast of a tropical cyclone, along with which sources of health care are disrupted. Some negative health outcomes may also be acute and severe enough that those afflicted never get to hospital and die; in these cases, as with other disasters, such as earthquakes 60 , rapid access to treatment is essential. There is some limited evidence to suggest that there are measurable long-term impacts on health in the years after a disaster 47 , 48 . There are plausible causal links between health outcomes and tropical cyclone exposure for many of the associations here 6 , 28 , 29 , 30 , 31 , 32 , 33 , but more work needs to be done to identify and formalize these pathways. Characterizing longer-term health impacts of tropical cyclones is critical. We also focused on defining a tropical cyclone by wind speed, as it has direct relevance for identifying a tropical cyclone and therefore emergency planning 33 , 61 . Understanding in more detail whether including more information about specific tropical cyclone-related hazards, such as rainfall and flooding, in combination with wind, modify the impact of tropical cyclones on health outcomes will be an important direction of future research. Our study included millions of hospitalizations over a decade across all counties impacted by tropical cyclones in the United States during this period. In more recent years, however, many catastrophic tropical cyclones have made landfall and future studies should include these data.

Our work provides valuable information on how cause-specific hospitalizations can be impacted by tropical cyclones, which can be used for preparedness planning, including hospital and physician preparedness. Adequate forecasting of tropical cyclones might help, for example, in the planning of setting up shelters to provide electricity and common medications and creating easy ways for vulnerable people with certain chronic conditions to find and use those resources outside of the hospital. While our study is the first step in identifying areas of improvement in hospital preparation, enabling and improving planning in this way would be a major innovation, which could save many lives of those who are hospitalized during tropical cyclones, and should be a public health priority.

Study population

We obtained Medicare inpatient claims data from the Center for Medicare and Medicaid Service (CMS) and assembled data from Medicare beneficiaries, aged 65 years or older, enrolled in the fee-for-service program for at least 1 month from January 1, 1999, to December 31, 2014, and residing in the United States. For each enrollee, we extracted county and state of residence from the Medicare enrollee record file, and the date and principal ICD-9-CM code for each hospitalization from the Medicare Provider Analysis and Review (MEDPAR) file. We restricted analyses to counties that experienced at least one tropical cyclone exposure during the study period. Hospitalizations were aggregated to the county level based on the patient’s county of residence, and included both emergency and nonemergency hospitalizations.

This study was approved by the Institutional Review Board at the Harvard T.H. Chan School of Public Health.

Outcome assessment

We grouped the 15,072 possible ICD-9-CM codes by the CCS hierarchy algorithm 38 , developed by the Agency for Healthcare Research and Quality, into 18 mutually-exclusive and clinically-meaningful CCS level 1 disease causes. We excluded five causes that occur rarely in older adults, such as pregnancy or fertility issues, and those that were ill-defined. This left 13 level 1 causes, accounting for 94.8% of recorded hospitalizations. These 13 level 1 causes were: cardiovascular diseases, respiratory diseases, cancers, injuries, neuropsychiatric disorders, blood diseases, digestive system diseases, endocrine disorders, genitourinary diseases, infectious and parasitic diseases, musculoskeletal and connective tissue diseases, nervous system diseases, and skin and subcutaneous tissue diseases. We additionally investigated associations with CCS level 3 causes; to avoid unstable model outputs, we restricted this secondary analysis to sub-causes with >50,000 total hospitalizations across all counties during the study period.

Exposure assessment

We obtained data on tropical cyclone wind exposure in the United States from the hurricaneexposure (version 0.1.1) and hurricaneexposuredata (version 0.1.0) packages in R, with full space and time coverage over our study period, and described in detail elsewhere 58 , 62 , 63 , 64 . In brief, an exhaustive assessment of tropical cyclones was generated from those recorded in the HURDAT2 dataset based on wind field modeling and validation against observations from weather stations 65 . First, all tropical cyclones were measured for how close they came to at least one US county. Cyclones that came within 250 km were retained for further wind modeling 58 . For these, the wind field at each county’s population mean center was modeled every 15 min, while the storm was tracked, providing an estimate of peak local winds that the storm brought to that county 66 . This modeling used a double exponential wind model to estimate 1-min surface wind at each county center, based on the storm’s forward speed, direction, and maximum wind speed 66 , 67 . We used daily estimates of maximum wind sustained speed by county to generate classifications of these exposures. This tropical cyclone wind exposure dataset covered the 898 counties in 30 state or district units (29 states and Washington DC) with at least one tropical cyclone wind exposure during 1999–2014.

From continuous estimates of local storm-associated winds, we classified a county as exposed to tropical cyclone winds based using a cut point from the Beaufort wind scale 68 . The Beaufort scale is an empirical measure that relates locally measured wind speed to observed conditions on sea or land from 0 (calm) to 12 (Hurricane force). In contrast, the Saffir–Simpson wind scale provides a classification for storm-wide, rather than local, wind intensity 69 . Our primary analysis focused on tropical cyclone winds, i.e., ≥34 knots, which include both hurricanes and tropical storms. We defined tropical storm exposure when the peak sustained wind that day in the population center of the county associated with the tropical cyclone reached or exceeded 34 knots (63 km/h, 39 mph; gale-force wind on the Beaufort scale) up to 64 knots (119 km/h, 74 mph; violent storm-force wind on the Beaufort scale), when the tropical cyclone was at the point of closest approach to that county. We defined hurricane exposure when the peak sustained wind that day in the population center of the county associated with the tropical cyclone reached or exceeded 64 knots (119 km/h, 74 mph; hurricane-force wind on the Beaufort scale), when the tropical cyclone was at the point of closest approach to that county. In secondary analysis, we used a three-category exposure variable (unexposed, gale to violent storm wind exposure, and hurricane wind exposure).

Covariate data

We obtained data on temperature from the Parameter-elevation Regressions on Independent Slopes Model (PRISM), which gathers climate observations from a wide range of monitoring networks and applies sophisticated quality control measures to generate a nationwide temperature dataset, with full space and time coverage over our study period 70 . We used gridded daily estimates at a resolution of 4 km 2 to generate area-weighted daily temperatures by county.

Statistical analysis

We analyzed the association between daily hospitalization rates and tropical cyclone exposure by applying a conditional quasi-Poisson model 71 . The quasi-Poisson formulation accounts for potentially overdispersed outcomes. This approach examines contrasts within matched strata, similar to a case-crossover study design, thus eliminating any confounding bias that could arise by factors varying across strata in a computationally efficient way 71 . Specifically, we matched on county and Julian day of the year, controlling for non-time-varying factors varying across counties in our analyses, as well as seasonality. We flexibly adjusted for longer-term time trends in factors that varied over our study period, and could covary with both tropical cyclone exposure and hospitalization rates. We also adjusted for the mean temperature at the day of the tropical cyclone exposure and day of week. Finally, we included in this model unconstrained distributed lag terms for the exposure 72 , 73 , to quantify the association between tropical cyclone exposure and hospitalization rates up to 7 days after exposure. Specifically, we fit the following model:

where \(Y_{ct}\) denotes the number of CCS level 1-defined cause-specific hospitalizations in county c and day t ; \(a_{ct}\) the stratum-specific intercepts (not estimated in the conditional Poisson model); \(\beta _l\) lag-specific coefficients (log rate ratios) for tropical cyclone exposure, with \(l \in [0,7]\) lags between the day of tropical cyclone exposure and day of hospitalization and \({{\Sigma }}\beta _l\) the cumulative effect of tropical cyclone exposure on cause-specific hospitalizations over eight days (lags 0–7); \({{DOW}_{t}}\) the day of week; \(ns(year_t)\) a natural spline with two degrees of freedom to flexibly model time trends (seasonal trends are captured through matching); and \({\mathrm{log}}\left( {{\mathrm{Population}}_{{{ct}}}} \right)\) the offset with population the number of Medicare enrollees in each county and year. We applied the Bonferroni–Holm method to adjust CIs for multiple comparisons 74 . The 95% CIs were corrected by using α  = 0.05/ D , where D  = 13, the number of causes in our main analysis 75 .

We assessed whether estimated effects varied for emergency vs. nonemergency CCS level 1 hospitalization causes by fitting stratified analyses by admission type, using the same model as described above. In secondary analyses, we evaluated associations between tropical cyclone exposure and CCS level 3 cause-specific hospitalizations. We present the CCS level 3 associations as the average change in cause-specific hospitalization rate over lags 0–7, i.e., \({{\Sigma }}\beta _l/8\) . We used a three-category exposure term to estimate independent effects of tropical cyclone wind exposures separated into gale-force to violent storm-force and hurricane-force intensities for CCS level 1 causes.

Finally, we used the cumulative rate ratio estimates and average cause-specific hospitalization rates during May to October for each cause across 1999–2014, to calculate the expected number of excess (or fewer) hospitalizations during the week following the expected number of tropical cyclone exposures by county over a decade. Specifically, we multiplied the observed average weekly hospitalization rates for each county in May to October across 1999–2014 by the corresponding population of Medicare enrollees, ( \(\left. {{\mathrm{exp}}({{\Sigma }}\beta _l)} \right)^n - 1\) ) (ref. 73 ), where n is the average number of tropical cyclone exposures per year in each country times ten (number of years in a decade).

We present all results as percentage changes in hospitalization rates, unless otherwise noted. We conducted all statistical analyses using the R Statistical Software, version 3.6.3 (Foundation for Statistical Computing, Vienna, Austria) 76 . To specify the models, we used the gnm function from the gnm package, version 1.1-1 (refs. 71 , 77 ). We also used the ns function from the splines package, version 3.6.3 (ref. 78 ).

We assessed the sensitivity of our results to temperature adjustment. We fit models (1) including the described temperature term, as well as temperature terms of up to 7 days after exposure and (2) with no temperature term. Our results were robust to these sensitivity analyses.

Data availability

Tropical cyclone exposure data are publicly available via the R packages hurricaneexposure (version 0.1.1) and hurricaneexposuredata (version 0.1.0) [ https://github.com/geanders/hurricaneexposuredata/blob/master/data/storm_winds.rda ], based on tropical cyclones recorded in the HURDAT2 dataset [ https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html ]. Medicare enrollees dynamic cohort data are publicly available, upon purchase and after an application process, from the Centers for Medicare & Medicaid Services (CMS) [ https://www.cms.gov/Research-Statistics-Data-and-Systems/CMS-Information-Technology/AccesstoDataApplication/index ].

Code availability

All code for analysis and visualization presented in this manuscript is available at www.github.com/rmp15/tropical_cyclones_hospitalizations_nat_comms .

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Acknowledgements

R.M.P. was supported by the Earth Institute postdoctoral research fellowship at Columbia University. F.D. was funded by the Climate Change Solutions Fund. Funding was also provided by the National Institute of Environmental Health Sciences (NIEHS) grants R01 ES030616, R01 ES028805, R01 ES028033, R01 MD012769, R01 AG066793, R01 ES029950, R21 ES028472, P30 ES009089, and P42 ES010349. The computations in this paper were run on the Research Computing Environment (RCE) supported by the Institute for Quantitative Social Science in the Faculty of Arts and Sciences at Harvard University. We thank Ben Sabath and Danielle Braun for assistance with computational challenges, Jane W. Baldwin for discussions on tropical cyclones, and James E. Bennett for discussions on the statistical model.

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All authors contributed to study concept and interpretation of results. R.M.P. collated and organized hospitalization files. R.M.P. collated and organized the storm and hurricane data from the dataset provided by G.B.A. R.M.P., G.B.A., and M.-A.K. developed the statistical model, which was implemented by R.M.P. R.M.P. performed the analysis, with input from M.-A.K., G.B.A., R.C.N., and F.D. A.N.-A. assisted with interpretation of results. R.M.P. and M.-A.K. wrote the first draft of the paper; all authors contributed to revising and finalizing the paper.

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Parks, R.M., Anderson, G.B., Nethery, R.C. et al. Tropical cyclone exposure is associated with increased hospitalization rates in older adults. Nat Commun 12 , 1545 (2021). https://doi.org/10.1038/s41467-021-21777-1

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

• First Online: 14 October 2020

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• Trupti Mishra 5 &
• Krishna Malakar 6  

Part of the book series: Disaster Research and Management Series on the Global South ((DRMSGS))

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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 assessing loss and damages in vulnerable communities, this study contributes to the literature by providing a basic overview of the experiences of coastal fishing communities in the aftermath of Cyclone Phailin.

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Senapati, A. (2013). UN Felicitates Odisha for Its Disaster Management Model During Phailin. Down to Earth . Retrieved from https://www.downtoearth.org.in/news/un-felicitates-odisha-for-its-disaster-management-model-during-phailin-43087

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Acknowledgment

The authors are grateful to the Indian Council of Social Science Research (ICSSR) for funding the study.

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Mishra, T., Malakar, K. (2020). Loss and Damages from Cyclone: A Case Study from Odisha, a Coastal State. In: Singh, A., Fernando, R.L.S., Haran, N.P. (eds) Development in Coastal Zones and Disaster Management. Disaster Research and Management Series on the Global South. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-15-4294-7_19

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13.2: Midlatitude Cyclone Evolution - a Case Study

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13.2.1. Summary of 3 to 4 April 2014 Cyclone

An upper-level trough (Fig. 13.10a) near the USA Rocky Mountains at 00 UTC on 3 April 2014 propagates eastward, reaching the Midwest and Mississippi Valley a day and a half later, at 12 UTC on 4 April 2014. A surface low-pressure center forms east of the trough axis (Fig. 13.10b), and strengthens as the low moves first eastward, then north-eastward.

Extending south of this low is a dry line that evolves into a cold front (Figs. 13.10b & 13.11), which sweeps into the Mississippi Valley. Ahead of the front is a squall line of severe thunderstorms (Figs. 13.11 & 13.12). Local time there is Central Daylight Time (CDT), which is 6 hours earlier than UTC.

During the 24 hours starting at 6 AM CDT (12 UTC) on 3 April 2014 there were a total of 392 storm reports recorded by the US Storm Prediction Center (Fig. 13.9). This included 17 tornado reports, 186 hail reports (of which 23 reported large hailstones greater than 5 cm diameter), 189 wind reports (of which 2 had speeds greater than 33 m/s). Next, we focus on the 12 UTC 4 April 2014 weather (Figs. 13.13 - 13.19).

INFO • Isosurfaces & Their Utility

Lows and other synoptic features have five-dimensions (3-D spatial structure + 1-D time evolution + 1-D multiple variables). To accurately analyze and forecast the weather, you should try to form in your mind a multi-dimensional picture of the weather. Although some computer-graphics packages can display 5-dimensional data, most of the time you are stuck with flat 2-D weather maps or graphs.

By viewing multiple 2-D slices of the atmosphere as drawn on weather maps (Fig. c), you can picture the 5-D structure. Examples of such 2-D maps include:

• uniform height maps
• isobaric (uniform pressure) maps
• isentropic (uniform potential temperature) maps
• thickness maps
• vertical cross-section maps
• time-height maps
• time-variable maps (meteograms

Computer animations of maps can show time evolutions. In Chapter 1 is a table of other iso-surfaces.

Mean-sea-level ( MSL ) maps represent a uniform height of z = 0 relative to the ocean surface. For most land areas that are above sea level, these maps are created by extrapolating atmospheric conditions below ground. (A few land-surface locations are below sea level, such as Death Valley and the Salton Sea USA, or the Dead Sea in Israel and Jordan).

Meteorologists commonly plot air pressure (reduced to sea level) and fronts on this uniform-height surface. These are called “MSL pressure” maps.

Recall that pressure decreases monotonically with increasing altitude. Thus, lower pressures correspond to higher heights.

For any one pressure, such as 70 kPa (which is about 3 km above sea level on average), that pressure is closer to the ground (i.e., less than 3 km) in some locations and is further from the ground in other locations, as was discussed in the Forces and Winds chapter. If you conceptually draw a surface that passes through all the points that have pressure 70 kPa, then that isobaric surface looks like rolling terrain with peaks, valleys (troughs), and ridges.

Like a topographic map, you could draw contour lines connecting points of the same height. This is called a “70 kPa height” chart. Low heights on an isobaric surface correspond to low pressure on a uniform-height surface.

Back to the analogy of hilly terrain, suppose you went hiking with a thermometer and measured the air temperature at eye level at many locations within a hilly region. You could write those temperatures on a map and then draw isotherms connecting points of the same temperature. But you would realize that these temperatures on your map correspond to the hilly terrain that had ridges and valleys.

You can do the same with isobaric charts; namely, you can plot the temperatures that are found at different locations on the undulating isobaric surface. If you did this for the 70 kPa isobaric surface, you would have a “70 kPa isotherm” chart. You can plot any variable on any isobaric surface, such as “90 kPa isohumes”, “50 kPa vorticity”, “30 kPa isotachs”, etc.

You can even plot multiple weather variables on any single isobaric map, such as “30 kPa heights and isotachs” or “50 kPa heights and vorticity” (Fig. c). The first chart tells you information about jet-stream speed and direction, and the second chart can be used to estimate cyclogenesis processes.

Now picture two different isobaric surfaces over the same region, such as sketched a the top of Fig. c. An example is 100 kPa heights and 50 kPa heights. At each location on the map, you could measure the height difference between these two pressure surfaces, which tells you the thickness of air in that layer. After drawing isopleths connecting points of equal thickness, the resulting contour map is known as a “100 to 50 kPa thickness” map.

You learned in the General Circulation chapter that the thermal-wind vectors are parallel to thickness contours, and that these vectors indicate shear in the geostrophic wind. That chapter also showed that the 100-50 kPa thickness is proportional to average temperature in the bottom half of the troposphere.

Potential Temperature

On average, potential temperature θ increases toward the equator and with increasing height. Day-today variability is superimposed on that average. Over any region for any valid time you can create a surface called an isentropic surface that follows any desired potential temperature, such as the θ = 310 K isentropic surface shaded in blue in Fig. d. below.

As was done for isobaric surfaces, you can also plot contours of the height of that surface above mean sea level (e.g., “310 K heights”). Or you can plot other weather variables on that surface, such as “310 K isohumes”.

If you were to look straight down from above the top diagram in Fig. d, you would see a view such as shown in the bottom of Fig. d. This bottom diagram is the isentropic chart that would be presented as a weather map.

For adiabatic processes, unsaturated air parcels that are blown by the wind tend to follow the isentropic surface that corresponds to the parcel’s potential temperature. The reason is that if the parcel were to stray off of that surface, then buoyant forces would move it back to that surface.

For example, if an air parcel has θ = 310 K and is located near the tail of the arrow in the bottom of Fig. d, then as the parcel moves with the wind it will the 310 K isentropic surface. In this illustration, the parcel descends (and its temperature would warm adiabatically while its θ is constant). Because of adiabatic warming and cooling, winds descending along an isentrope will warm and be cloud free, while winds rising along isentropes will cool and become cloudy.

Turbulence, condensation and radiation are not adiabatic (i.e., are diabatic ), and cause the parcel’s θ to change. This would cause the parcel to shift to a different isentropic surface (one that matches the parcel’s new θ).

Potential Vorticity (PVU)

Recall the definition of isentropic potential vorticity from the General Circulation chapter. That chapter also defined potential vorticity units (PVU) for this variable. PVUs are very large in the stratosphere, and small in the troposphere, with the tropopause often at about 1.5 PVU.

Thus, contour plots of the height of the 1.5 PVU surface approximate the altitude of the tropopause at different locations. The tropopause could be relatively low (at z ≈ 6 km MSL, at P ≈ 35 kPa) near the poles, and relatively high near the equator (z ≈ 15 - 18 km MSL, and P ≈ 10 kPa). This contour plot can indicate features such as tropopause folds where stratospheric air can be injected into the troposphere.

The “ surface weather map ” shows weather at the elevation of the Earth’s surface. Namely, it follows the terrain up and down, and is not necessarily at mean sea level.

It is impossible to have two different pressures, or two different potential temperatures, at the same point in space at any instant. For this reason, isobaric surfaces cannot cross other isobaric surfaces (e.g., the 70 kPa and 60 kPa isobaric surfaces cannot intersect). Similar rules apply for isentropic surfaces. But isobaric surfaces can cross isentropes, and they both can intersect the ground surface.

(b) Isopleths are of isentropic potential vorticity in potential vorticity units (PVU). These are plotted on the 25 kPa isobaric surface. Values greater than 1.5 PVU usually are associated with stratospheric air. The diagonal straight dotted line shows the cross section location plotted in (a). The “bulls eye” just west of the surface low “X” indicates a tropopause fold, where stratospheric air descends closer to the ground. Surface fronts are also drawn on this map.

Special thanks to Drs. Greg West and David Siuta for creating many of the case-study maps in Figs. 13.10 - 13.19 and elsewhere in this chapter.

13.2.2. Weather-map Discussion for this Case

As recommended for most weather discussions, we will start with the big picture, and will progress toward the details. Also, we will work downward from the top of the troposphere.

13.2.2.1. • Hemispheric Map — Top of Troposphere

Starting with the planetary scale, Fig. 13.18 shows Hemispheric 20 kPa Geopotential Height Contours . It shows five long Rossby-wave troughs around the globe. The broad trough over N. America also has two short-wave troughs superimposed — one along the west coast and the other in the middle of N. America. The jet stream flows from west to east along the height contours plotted in this diagram, with faster winds where the contours are packed closer together.

Next, zoom to the synoptic scale over N. America. This is discussed in the next several subsections using Figs. 13.13 - 13.15.

13.2.2.2. • 20 kPa Charts — Top of Troposphere (z ≈ 11.5 km MSL)

Focus on the top row of charts in Figs. 13.13 - 13.15. The thick dashed lines on the 20 kPa Height contour map show the two short-wave trough axes. The trough over the central USA is the one associated with the case-study cyclone. This trough is west of the location of the surface low-pressure center (“X”). The 20 kPa Wind Vector map shows generally westerly winds aloft, switching to southwesterly over most of the eastern third of the USA. Wind speeds in the 20 kPa Isotach chart show two jet streaks (shaded in yellow) — one with max winds greater than 70 m s–1 in Texas and northern Mexico, and a weaker jet streak over the Great Lakes.

The 20 kPa Temperature chart shows a “bullseye” of relatively warm air (–50°C) aloft just west of the “X”. This is associated with an intrusion of stratospheric air down into the troposphere (Fig. 13.19). The 20 kPa Divergence map shows strong horizontal divergence (plotted with the blue contour lines) along and just east of the surface cold front and low center.

13.2.2.3. • 50 kPa Charts — Middle of Troposphere (z ≈5.5 km MSL)

Focus on the second row of charts in Figs. 13.13 - 13.14. The 50 kPa Height chart shows a trough axis closer to the surface-low center (“X”). This low-pressure region has almost become a “closed low”, where the height contours form closed ovals. 50 kPa Wind Vectors show the predominantly westerly winds turning in such a way as to bring colder air equatorward on the west side of the low, and bringing warmer air poleward on the east side of the low (shown in the 50 kPa Temperature chart). The 50 kPa Absolute Vorticity chart shows a bulls-eye of positive vorticity just west of the surface low.

13.2.2.4. • 70 kPa Charts — (z ≈ 3 km MSL)

The second row of charts in Fig. 13.15 shows a closed low on the 70 kPa Height chart, just west of the surface low. At this altitude the warm air advection poleward and cold-air advection equatorward are even more obvious east and west of the low, respectively, as shown on the 70 kPa Temperature chart.

13.2.2.5. • 85 kPa Charts — (z ≈ 1.4 km MSL)

Focus on the third row of charts in Figs. 13.13 - 13.14. The 85 kPa Height chart shows a deep closed low immediately to the west of the surface low. Associated with this system is a complete counterclockwise circulation of winds around the low, as shown in the 85 kPa Wind Vector chart.

The strong temperature advection east and west of the low center are creating denser packing of isotherms along the frontal zones, as shown in the 85 kPa Temperature chart. The cyclonically rotating flow causes a large magnitude of vorticity in the 85 kPa Absolute Vorticity chart.

13.2.2.6. • 100 kPa & other Near-Surface Charts

Focus on the last row of charts in Figs. 13.13-13.14. The approximate surface-frontal locations have also been drawn on most of these charts. The 100 kPa Height chart shows the surface low that is deep relative to the higher pressures surrounding it. The 10 m Wind Vectors chart shows sharp wind shifts across the frontal zones.

Isentropes of 2 m Equivalent Potential Temperature clearly demarcate the cold and warm frontal zones with tightly packed (closely spaced) isentropes. Recall that fronts on weather maps are drawn on the warm sides of the frontal zones. Southeast of the low center is a humid “warm sector” with strong moisture gradients across the warm and cold fronts as is apparent by the tight isohume packing in the 2 m Water Vapor Mixing Ratio chart.

Next, focus on row 3 of Fig. 13.15. The high humidities also cause large values of Precipitable water (moisture summed over the whole depth of the atmosphere), particularly along the frontal zones. So it is no surprise to see the rain showers in the MSL Pressure, 85 kPa Temperature and 1-h Precipitation chart.

13.2.2.7. • 100 to 50 kPa Thickness

Fig. 13.16 shows the vertical distance between the 100 and 50 kPa isobaric surfaces. Namely, it shows the thickness of the 100 to 50 kPa layer of air. This thickness is proportional to the average temperature in the bottom half of the troposphere, as described by the hypsometric eq. The warm-air sector (red isopleths) southeast of the surface low, and the cold air north and west (blue isopleths) are apparent. Recall that the thermal wind vector (i.e., the vertical shear of the geostrophic wind) is parallel to the thickness lines, with a direction such that cold air (thin thicknesses) is on the left side of the vector.

13.2.2.8. • Tropopause & Vertical Cross Section

Fig. 13.17 shows the pressure altitude of the tropopause. A higher tropopause would have lower pressure. Above the surface low (”X”) the tropopause is at about the 20 kPa (= 200 hPa) level. Further to the south over Florida, the tropopause is at even higher altitude (where P ≈ 10 kPa = 100 hPa). North and west of the “X” the tropopause is at lower altitude, where P = 30 kPa (=300 hPa) or greater. Globally, the tropopause is higher over the subtropics and lower over the sub-polar regions.

Fig. 13.19 shows a vertical slice through the atmosphere. Lines of uniform potential temperature (isentropes), rather than absolute temperature, are plotted so as to exclude the adiabatic temperature change associated with the pressure decrease with height. Tight packing of isentropes indicates strong static stability, such as in the stratosphere, upper-tropospheric (U.T.) fronts (also called tropopause folds ), and surface fronts.

In the next sections, we see how dynamics can be used to explain cyclone formation and evolution.

INFO • Multi-field Charts

Most of the weather maps presented in the previous case study contained plots of only one field, such as the wind field or height field. Because many fields are related to each other or work together, meteorologists often plot multiple fields on the same chart.

To help you discriminate between the different fields, they are usually plotted differently. One might use solid lines and the other dashed. Or one might be contoured and the other shaded (see Fig. e, Fig. 13.7a, or Fig. 13.15-right map). Look for a legend or caption that describes which lines go with which fields, and gives units.

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Physics > Atmospheric and Oceanic Physics

Title: super cyclone amphan: a dynamical case study.

Abstract: Cyclone Amphan, a super cyclone in the Bay of Bengal after 21 years, intensified from a cyclonic storm (CAT 1) to a super cyclone (CAT 5) in less than 36 hours. It went on to make landfall over West Bengal as a Very Severe Cyclonic Storm (VSCS) with winds close to 155 kmph. Here, we analyze the dynamics that led to its rapid intensification, given that the system struggled to develop initially despite the presence of ripe conditions like high Sea Surface Temperature (SST) in the Bay. Our analysis clearly reveals that a Convectively Coupled Kelvin Wave (CCKW) from upper troposphere might have initiated strong instabilities in the tropopause, which then propagated vertically downward and interacted with surface disturbances to promote convective coupling with the Madden Julian Oscillations (MJO). Such convective coupling resulted in a burst of westerly winds along with enhanced vertical mixing and moisture convergence, which eventually led to the formation and intensification of super cyclone, Amphan.

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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 her e .

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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...

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

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

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.

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Farmers in India are hit hard by extreme weather. Some say expanding natural farming is the answer

GUNTUR, India — There’s a pungent odor on Ratna Raju’s farm that he says is protecting his crops from the unpredictable and extreme weather that’s become more frequent with human-caused climate change .

The smell comes from a concoction of cow urine, an unrefined sugar known as jaggery, and other organic materials that act as fertilizers, pesticides and bad weather barriers for his corn, rice, leafy greens and other vegetables on his farm in Guntur in India’s southern Andhra Pradesh state. The region is frequently hit by cyclones and extreme heat, and farmers say that so-called natural farming protects their crops because the soil can hold more water, and their more robust roots help the plants withstand strong winds.

Andhra Pradesh has become a positive example of the benefits of natural farming, and advocates say active government support is the primary driver for the state’s success. Experts say these methods should be expanded across India’s vast agricultural lands as climate change and decreasing profits have led to multiple farmers' protests this year. But fledgling government support across the country for these methods means most farmers still use chemical pesticides and fertilizers, making them more vulnerable when extreme weather hits. Many farmers are calling for greater federal and state investment to help farms switch to more climate change-proof practices.

For many, the benefits of greater investment in natural farming are already obvious: In December, Cyclone Michaung , a storm moving up to 110 kilometers per hour (62 miles per hour) brought heavy rainfall across India’s southeastern coast, flooding towns and fields. A preliminary assessment conducted a few weeks later found that 600,000 acres of crops were destroyed in Andhra Pradesh state.

On Raju’s natural farm, however, where he was growing paddy at the time, “the rainwater on our farms seeped into the ground in one day,” he said. The soil can absorb more water because it’s more porous than pesticide-laden soil which is crusty and dry. Planting different kinds of crops throughout the year — as opposed to the more standard single crop farms — also helps keep the soil healthy, he said.

But neighboring farmer Srikanth Kanapala’s fields, that rely on chemical pesticides and fertilizers, were flooded for four days after the cyclone. He said seeing Raju’s crops hold firm while his failed has made him curious about alternative farming methods.

“I incurred huge losses,” said Kanapala, who estimates he lost up to $600 because of the cyclone, a substantial sum for a small farmer in India. “For the next planting season, I plan to use natural farming methods too.”

Local and federal government initiatives have resulted in an estimated 700,000 farmers shifting to natural farming in the state according to Rythu Sadhikara Samstha, a government-backed not-for-profit launched in 2016 to promote natural farming. The state of Andhra Pradesh hopes to inspire all of its six million farmers to take up natural farming by the end of the decade.

The Indian federal government’s agriculture ministry has spent upwards of $8 million to promote natural farming and says farmers tilling nearly a million acres across the country have shifted to the practice. In March last year, India’s junior minister for agriculture said he hoped at least 25% of farms across India would use organic and natural farming techniques.

But farmers like Meerabi Chunduru, one of the first in the region to switch to natural farming, said more government and political support is needed. Chunduru said she switched to the practice after her husband’s health deteriorated, which she believes is because of prolonged exposure to some harmful pesticides.

While the health effects of various pesticides have not yet been studied in detail, farm workers around the world have long claimed extended exposure has caused health problems. In February, a Philadelphia jury awarded $2.25 billion in damages in a case where a weed killer with Glyphosate — restricted in India since just 2022 — was linked to a resident’s blood cancer. In India, 63 farmers died in the western state of Maharashtra in 2017, believed to be linked to a pesticide containing the chemical Diafenthiuron, which is currently banned in the European Union, but not in India.

“Right now, not many politicians are talking about natural farming. There is some support but we need more,” said Chunduru. She called for more subsidies for seeds such as groundnuts, black gram, sorghum, vegetable crops and maize that can help farmers make the switch.

Farmers’ rights activists said skepticism about natural farming among political leaders, government bureaucrats and scientists is still pervasive because they still trust the existing farming models that use fertilizers, insecticides and pesticides to achieve maximum productivity. In the short-term, chemical alternatives can be cheaper and more effective, but in the long term they take a toll on the soil’s health, meaning larger quantities of chemicals are needed to maintain crops, causing a cycle of greater costs and poorer soil, natural farming advocates say.

“Agroecological initiatives are not getting adequate attention or budgetary outlays,” said Kavitha Kuruganti, an activist who has advocated for sustainable farming practices for nearly three decades. The Indian government spends less than three percent of its total budget on agriculture. It has earmarked nearly $20 billion in fertilizer subsidies this year, but only $55 million has been allocated by the federal government to encourage natural farming. Kuruganti said there are a handful of politicians who support the practice but scaling it up remains a challenge in India.

A lack of national standards and guidelines or a viable supply chain that farmers can sell their produce through is also keeping natural farming relatively niche, said NS Suresh, a research scientist at the Center for Study of Science, Technology and Policy, a Bengaluru-based think-tank.

But because the practice helps keep the plants and the soil healthy across various soil types and all kinds of unpredictable weather conditions, it’s beneficial for farmers all around India, from its mountains to its coasts, experts say. And the practice of planting different crops year-round means farmers have produce to harvest at any given time, giving an extra boost to their soil and their wallets.

Chunduru, who’s been practicing natural farming for four years now, hopes that prioritizing natural farming in the country can have benefits for producers and consumers of crops alike, and other farmers avoid the kind of harms her husband has faced.

“We can provide nutrient-rich food, soil and physical health” to future generations, she said.

Arasu reported from Bengaluru, India.

The Associated Press’ climate and environmental coverage receives financial support from multiple private foundations. AP is solely responsible for all content. Find AP’s standards for working with philanthropies, a list of supporters and funded coverage areas at AP.org .

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Farmers in India are hit hard by extreme weather. Some say expanding natural farming is the answer

India’s southern Andhra Pradesh state has become a positive example of the benefits of natural farming, a process of using organic matter as fertilizers and pesticides that makes crops more resilient to bad weather. (AP Video by Shawn Sebastian and Altaf Qadri. Produced by Teresa de Miguel)

Bhaskar Rao, a farm worker, sprays natural pesticide at a multi-crop farm belonging to Meerabi Chunduru, an avid practitioner and advocate of natural farming techniques, in Aremanda village in Guntur district of southern India’s Andhra Pradesh state, Sunday, Feb. 11, 2024. The area has become a positive example of the benefits of natural farming, a process of using organic matter as fertilizers and pesticides that makes crops more resilient to bad weather. (AP Photo/Altaf Qadri)

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Ratna Raju a farmer who is part of a collective who practice natural farming, harvests spinach at his farm in Pedavuppudu village, Guntur district of southern India’s Andhra Pradesh state, Monday, Feb. 12, 2024. The soil can absorb more water because it’s more porous than pesticide-laden soil which is crusty and dry. (AP Photo/Altaf Qadri)

Meerabi Chunduru, right, an avid practitioner and advocate of natural farming techniques, is assisted by Bhaskar Rao as they prepare ‘Ghana Jeevamrtutham’, a natural dry pesticide inoculant at her farm in Aremanda village in Guntur district of southern India’s Andhra Pradesh state, Sunday, Feb. 11, 2024. Chunduru said she switched to the practice after her husband’s health deteriorated, which she believes is because of prolonged exposure to some harmful pesticides. (AP Photo/Altaf Qadri)

Meerabi Chunduru, an avid practitioner and advocate of natural farming techniques, pours natural pesticide into a sprayer carried by a worker at her farm in Aremanda village in Guntur district of southern India’s Andhra Pradesh state, Sunday, Feb. 11, 2024. Chunduru said she switched to the practice after her husband’s health deteriorated, which she believes is because of prolonged exposure to some harmful pesticides. (AP Photo/Altaf Qadri)

Meerabi Chunduru an avid practitioner and advocate of natural farming techniques, works at her farm in Aremanda village in Guntur district of southern India’s Andhra Pradesh state, Sunday, Feb. 11, 2024. Chunduru said she switched to the practice after her husband’s health deteriorated, which she believes is because of prolonged exposure to some harmful pesticides. (AP Photo/Altaf Qadri)

Bhaskar Rao, right, a farm worker, sprays natural pesticide as Meerabi Chunduru, left, an avid practitioner and advocate of natural farming techniques, works at her farm in Aremanda village in Guntur district of southern India’s Andhra Pradesh state, Sunday, Feb. 11, 2024. The area has become a positive example of the benefits of natural farming, a process of using organic matter as fertilizers and pesticides that makes crops more resilient to bad weather. (AP Photo/Altaf Qadri)

Farm workers pack freshly harvested cauliflowers at a farm in Pedavuppudu village, Guntur district of southern India’s Andhra Pradesh state, Monday, Feb. 12, 2024. (AP Photo/Altaf Qadri)

M Jojiamma, a natural farmer, feeds buffaloes at her house in Pedavuppudu village, Guntur district of southern India’s Andhra Pradesh state, Monday, Feb. 12, 2024. The area has become a positive example of the benefits of natural farming, a process of using organic matter as fertilizers and pesticides that makes crops more resilient to bad weather. (AP Photo/Altaf Qadri)

Meerabi Chunduru, an avid practitioner and advocate of natural farming techniques, works at her farm in Aremanda village in Guntur district of southern India’s Andhra Pradesh state, Sunday, Feb. 11, 2024. (AP Photo/Altaf Qadri)

V Vanisaree, center, district project manager RySS, a regional government backed not-for-profit that promotes natural farming, demonstrates techniques of how to shield seeds with natural inoculants to the members of a group in Pamidipadu village, Bapatla district of southern India’s Andhra Pradesh state, Sunday, Feb. 11, 2024. (AP Photo/Altaf Qadri)

V Vanisaree, center, district project manager RySS, a regional government backed not-for-profit that promotes natural farming, explains the techniques involving natural fertilizer to members of a group in Pamidipadu village, Bapatla district of southern India’s Andhra Pradesh state, Sunday, Feb. 11, 2024. (AP Photo/Altaf Qadri)

D Rani, a natural farmer, harvests peas as she works at her farm in Pamidipadu village, Bapatla district of southern India’s Andhra Pradesh state, Sunday, Feb. 11, 2024. The area has become a positive example of the benefits of natural farming, a process of using organic matter as fertilizers and pesticides that makes crops more resilient to bad weather. (AP Photo/Altaf Qadri)

A farmer separates chaff from wheat grains in Aremanda village in Guntur district of southern India’s Andhra Pradesh state, Sunday, Feb. 11, 2024. (AP Photo/Altaf Qadri)

Ratna Raju, a farmer who is part of a collective who practice natural farming, sprays natural pesticide on his farm in Pedavuppudu village, Guntur district of southern India’s Andhra Pradesh state, Monday, Feb. 12, 2024. The soil can absorb more water because it’s more porous than pesticide-laden soil which is crusty and dry. (AP Photo/Altaf Qadri)

Ratna Raju, a farmer who is part of a collective that practices natural farming, works at his farm in Pedavuppudu village, Guntur district of southern India’s Andhra Pradesh state, Monday, Feb. 12, 2024. The soil can absorb more water because it’s more porous than pesticide-laden soil which is crusty and dry. (AP Photo/Altaf Qadri)

Workers carry cattle dung, used to make natural fertilizer, in Pedavuppudu village, Guntur district of southern India’s Andhra Pradesh state, Monday, Feb. 12, 2024. The area has become a positive example of the benefits of natural farming, a process of using organic matter as fertilizers and pesticides that makes crops more resilient to bad weather. (AP Photo/Altaf Qadri)

GUNTUR, India (AP) — There’s a pungent odor on Ratna Raju’s farm that he says is protecting his crops from the unpredictable and extreme weather that’s become more frequent with human-caused climate change .

The smell comes from a concoction of cow urine, an unrefined sugar known as jaggery, and other organic materials that act as fertilizers, pesticides and bad weather barriers for his corn, rice, leafy greens and other vegetables on his farm in Guntur in India’s southern Andhra Pradesh state. The region is frequently hit by cyclones and extreme heat, and farmers say that so-called natural farming protects their crops because the soil can hold more water, and their more robust roots help the plants withstand strong winds.

Andhra Pradesh has become a positive example of the benefits of natural farming, and advocates say active government support is the primary driver for the state’s success. Experts say these methods should be expanded across India’s vast agricultural lands as climate change and decreasing profits have led to multiple farmers’ protests this year. But fledgling government support across the country for these methods means most farmers still use chemical pesticides and fertilizers, making them more vulnerable when extreme weather hits. Many farmers are calling for greater federal and state investment to help farms switch to more climate change-proof practices.

Meerabi Chunduru an avid practitioner and advocate of natural farming techniques, works at her farm in Aremanda village in Guntur district of southern India’s Andhra Pradesh state, Sunday, Feb. 11, 2024. (AP Photo/Altaf Qadri)

For many, the benefits of greater investment in natural farming are already obvious: In December, Cyclone Michaung , a storm moving up to 110 kilometers per hour (62 miles per hour) brought heavy rainfall across India’s southeastern coast, flooding towns and fields. A preliminary assessment conducted a few weeks later found that 600,000 acres of crops were destroyed in Andhra Pradesh state.

On Raju’s natural farm, however, where he was growing paddy at the time, “the rainwater on our farms seeped into the ground in one day,” he said. The soil can absorb more water because it’s more porous than pesticide-laden soil which is crusty and dry. Planting different kinds of crops throughout the year — as opposed to the more standard single crop farms — also helps keep the soil healthy, he said.

But neighboring farmer Srikanth Kanapala’s fields, that rely on chemical pesticides and fertilizers, were flooded for four days after the cyclone. He said seeing Raju’s crops hold firm while his failed has made him curious about alternative farming methods.

“I incurred huge losses,” said Kanapala, who estimates he lost up to $600 because of the cyclone, a substantial sum for a small farmer in India. “For the next planting season, I plan to use natural farming methods too.”

Bhaskar Rao, right, a farm worker, sprays natural pesticide as Meerabi Chunduru, left, an avid practitioner and advocate of natural farming techniques, works at her farm in Aremanda village in Guntur district of southern India’s Andhra Pradesh state, Sunday, Feb. 11, 2024. (AP Photo/Altaf Qadri)

Local and federal government initiatives have resulted in an estimated 700,000 farmers shifting to natural farming in the state according to Rythu Sadhikara Samstha, a government-backed not-for-profit launched in 2016 to promote natural farming. The state of Andhra Pradesh hopes to inspire all of its six million farmers to take up natural farming by the end of the decade.

The Indian federal government’s agriculture ministry has spent upwards of $8 million to promote natural farming and says farmers tilling nearly a million acres across the country have shifted to the practice. In March last year, India’s junior minister for agriculture said he hoped at least 25% of farms across India would use organic and natural farming techniques.

But farmers like Meerabi Chunduru, one of the first in the region to switch to natural farming, said more government and political support is needed. Chunduru said she switched to the practice after her husband’s health deteriorated, which she believes is because of prolonged exposure to some harmful pesticides.

While the health effects of various pesticides have not yet been studied in detail, farm workers around the world have long claimed extended exposure has caused health problems. In February, a Philadelphia jury awarded $2.25 billion in damages in a case where a weed killer with Glyphosate — restricted in India since just 2022 — was linked to a resident’s blood cancer. In India, 63 farmers died in the western state of Maharashtra in 2017, believed to be linked to a pesticide containing the chemical Diafenthiuron, which is currently banned in the European Union, but not in India.

“Right now, not many politicians are talking about natural farming. There is some support but we need more,” said Chunduru. She called for more subsidies for seeds such as groundnuts, black gram, sorghum, vegetable crops and maize that can help farmers make the switch.

Ratna Raju, a farmer who is part of a collective who practice natural farming, sprays natural pesticide on his farm in Pedavuppudu village, Guntur district of southern India’s Andhra Pradesh state, Monday, Feb. 12, 2024. (AP Photo/Altaf Qadri)

Ratna Raju, a farmer who is part of a collective that practices natural farming, works at his farm in Pedavuppudu village, Guntur district of southern India’s Andhra Pradesh state, Monday, Feb. 12, 2024. (AP Photo/Altaf Qadri)

Farmers’ rights activists said skepticism about natural farming among political leaders, government bureaucrats and scientists is still pervasive because they still trust the existing farming models that use fertilizers, insecticides and pesticides to achieve maximum productivity. In the short-term, chemical alternatives can be cheaper and more effective, but in the long term they take a toll on the soil’s health, meaning larger quantities of chemicals are needed to maintain crops, causing a cycle of greater costs and poorer soil, natural farming advocates say.

“Agroecological initiatives are not getting adequate attention or budgetary outlays,” said Kavitha Kuruganti, an activist who has advocated for sustainable farming practices for nearly three decades. The Indian government spends less than three percent of its total budget on agriculture. It has earmarked nearly $20 billion in fertilizer subsidies this year, but only $55 million has been allocated by the federal government to encourage natural farming. Kuruganti said there are a handful of politicians who support the practice but scaling it up remains a challenge in India.

A lack of national standards and guidelines or a viable supply chain that farmers can sell their produce through is also keeping natural farming relatively niche, said NS Suresh, a research scientist at the Center for Study of Science, Technology and Policy, a Bengaluru-based think-tank.

But because the practice helps keep the plants and the soil healthy across various soil types and all kinds of unpredictable weather conditions, it’s beneficial for farmers all around India, from its mountains to its coasts, experts say. And the practice of planting different crops year-round means farmers have produce to harvest at any given time, giving an extra boost to their soil and their wallets.

Chunduru, who’s been practicing natural farming for four years now, hopes that prioritizing natural farming in the country can have benefits for producers and consumers of crops alike, and other farmers avoid the kind of harms her husband has faced.

“We can provide nutrient-rich food, soil and physical health” to future generations, she said.

Workers carry cattle dung, used to make natural fertilizer, in Pedavuppudu village, Guntur district of southern India’s Andhra Pradesh state, Monday, Feb. 12, 2024. (AP Photo/Altaf Qadri)

Arasu reported from Bengaluru, India.

The Associated Press’ climate and environmental coverage receives financial support from multiple private foundations. AP is solely responsible for all content. Find AP’s standards for working with philanthropies, a list of supporters and funded coverage areas at AP.org .