Weather Disaster Costs and Deaths

Costs and Deaths of Billion Dollar Weather Disasters in the US

By Throwback Thursday [source]

About this dataset

The Billion Dollar Weather Disasters in the US dataset is a valuable resource containing comprehensive historical data on weather events in the United States that have caused billions of dollars in damages and resulted in loss of lives. It provides insights into various types and categories of weather disasters, such as hurricanes, tornadoes, floods, wildfires, and more.

The dataset includes essential information about each weather disaster event, starting with its name or title referred to as Disaster. A brief summary or description of each event is provided under the column Description, giving readers an understanding of its impact and extent. Furthermore, the dataset categorizes each disaster based on its type under the column Disaster Type. This classification helps researchers and analysts to identify patterns or common characteristics among similar types of weather disasters.

One crucial aspect covered by this dataset is the economic impact of these severe weather events. The total cost incurred due to each catastrophic occurrence has been meticulously recorded in millions of dollars. To ensure accuracy across different time periods, these costs are adjusted for inflation using the Consumer Price Index (CPI), providing a standardized measure that enables meaningful comparisons between different events.

A significant measure reflecting the severity of these weather disasters is the number of deaths they have caused. This dataset presents this valuable statistic under the column Deaths, allowing researchers to assess not only economic implications but also human impacts associated with each disaster event.

Obtained from NOAA National Centers for Environmental Information (NCEI) U.S., this data serves as a reliable source for understanding past weather calamities within US borders. Its wide range includes devastating storms, destructive wildfires, deadly heatwaves, crippling droughts; all contributing to one overarching objective – better preparedness for future climate-related challenges.

By analyzing this comprehensive dataset, researchers can gain insights into trends over time while identifying regions most vulnerable to specific types of extreme weather events. These findings allow policymakers and emergency response planners to make informed decisions regarding resource allocation, risk mitigation strategies, and community resilience-building initiatives

How to use the dataset

1. Understanding the Columns

The dataset contains several columns that provide important information about each weather disaster event. Let's understand what each column represents:

• Disaster: The name or title of the weather disaster event.
• Disaster Type: The type or category of the weather disaster event.
• Total CPI-Adjusted Cost (Millions of Dollars): The total cost of the weather disaster event in millions of dollars, adjusted for inflation using the Consumer Price Index (CPI).
• Deaths: The number of deaths caused by the weather disaster event.
• Description: A brief description or summary of the weather disaster event.

2. Exploring Total Cost and Deaths

One key aspect to explore is how much damage was caused by each weather disaster event, as well as its human impact in terms of fatalities. By analyzing these factors, you can gain insights into which types of disasters are more costly and have a higher mortality rate.

You can start by visualizing the Total CPI-Adjusted Cost (Millions of Dollars) column to identify which disasters have been more financially devastating over time. Additionally, you can analyze the Deaths column to gauge which types of disasters have had a greater impact on human lives.

3. Comparing Disasters

Another interesting analysis would involve comparing different disasters based on their characteristics such as type, cost, and fatalities. You can group similar types together and compare their costs or death tolls across different time periods.

For example, you could examine whether hurricanes tend to cause higher financial losses compared to floods or wildfires. Or, you could analyze if certain types of disasters have been more deadly than others.

4. Analyzing Descriptions

The Description column provides a brief summary of each weather disaster event. Analyzing the descriptions can give you valuable insights into the specific circumstances surrounding each event. By understanding the context and conditions, you can get a better understanding of why some events resulted i...

From 1980 to 1989, the number of deaths due to climate disasters was about 667 thousand, the highest in recent years. In the following decade, the number of deaths dropped to 329 thousand. After 2010, the number of deaths due to climate disasters dropped to 185 thousand, down by 140 thousand from the previous decade.

Billion Dollar Weather Disasters

Financial and human impact of significant weather disasters in the United States

By Throwback Thursday [source]

About this dataset

Introduction:

Dataset Details: This dataset presents comprehensive information related to billion-dollar weather disasters that occurred in the United States. Each entry includes specific details about a particular disaster event:

1. Disaster: This column contains the name or title associated with each weather disaster.

2. Disaster Type: This column categorizes each disaster into specific types or categories such as hurricanes, floods, heatwaves, tornadoes, wildfires.

3. Beginning Date: The starting date when a particular weather disaster occurred.

4. Ending Date: The end date marking the conclusion of a given weather disaster.

5. Total CPI-Adjusted Cost (Millions of Dollars): This column provides an accurate representation of the total cost incurred by each disaster in millions of dollars while being adjusted for inflation using the Consumer Price Index (CPI).

6. Deaths: This numeric column records the number of deaths caused by each specific weather event.

7. Description: A brief yet informative summary describing key characteristics or impacts associated with a particular weather disaster.

By utilizing this rich dataset combined with advanced analytical tools and visualizations techniques; researchers can derive meaningful insights to support effective decision-making processes aimed at mitigating future damage caused by such destructive phenomena

How to use the dataset

Understanding the Columns

Before we delve into analyzing and visualizing the data, it's important to understand the meaning of each column:

• Disaster: The name or title of the weather disaster.
• Disaster Type: The type or category of the weather disaster.
• Total CPI-Adjusted Cost (Millions of Dollars): The total cost of each weather disaster in millions of dollars adjusted for inflation using the Consumer Price Index (CPI).
• Deaths: The number of deaths caused by each weather disaster.
• Description: A brief description or summary detailing each weather disaster.

Exploring Data Analysis Opportunities

Now that we have a clear understanding of what each column represents let's explore how you can use this dataset for analyzing billion-dollar weather disasters in more depth:

• Analyzing Financial Impact

  Utilize the Total CPI-Adjusted Cost column to analyze and compare the financial impact caused by different types or categoriesof billion-dollar disasters. You can plot graphs, compute averages, identify outliers or trends over time.

• Assessing HumanImpact

  Use data from Deaths column todeterminehow different typesorcategoriesofweatherdisastersvaryin theirhumanimpact.Visualizeandcomparethedeath tolls associated with various catastrophic events.

• Identifying Frequent Disaster Types

  Observe which types or categoriesofweatherdisastersoccurmore frequently than othersbyanalyzingtheDisaster Typecolumn.PlotagraptoshowthedistributionandfrequencyofthedisastertypesintheUnitedStates.

• Exploring Disaster Descriptions

  Dive deeper into the unique aspects of each weather disaster by studying the Description column. This will provide additional context and insight into the specific events.

Making Data Visualizations

Data visualizations can help you represent, summarize, and communicate patterns or insights hidden within the dataset. Here are a few ideas for creating impactful visualizations:

• Create a bar chart depicting the financial cost (Total CPI-Adjusted Cost) of different disaster types.

• Develop a line graph showing how deaths have varied over time for various weather disasters.

• Design a pie chart

Research Ideas

• Analyzing the financial impact of different types of weather disasters: This dataset provides information on the total cost of billion-dollar weather disasters, adjusted for inflation. By analyzing this data, one can gain insights into which types of weather events have the highest financial impact, helping to prioritize preparedness and mitigation efforts.
• Examining trends in weather disasters over time: With information on the beginning and ending dates of each event, this dataset can be used to analyze trends in the frequency and duration of billion-dollar weather disasters in the United States. This analysis could help identify if certain types of ...

Flash flooding is the top weather-related killer, responsible for an average of 140 deaths per year across the United States. Although precipitation forecasting and understanding of flash flood causes have improved in recent years, there are still many unknown factors that play into flash flooding. Despite having accurate and timely rainfall reports, some river basins simply do not respond to rainfall as meteorologists might expect. The Flash Flood Potential Index (FFPI) was developed in order to gain insight into these “problem basins”, giving National Weather Service (NWS) meteorologists insight into the intrinsic properties of a river basin and the potential for swift and copious rainfall runoff.

Tornadoes in North America

Magnitude, Fatalities, Injuries, and Crop Loss Data

By Homeland Infrastructure Foundation [source]

About this dataset

This dataset compiles historical data on tornadoes in the United States, Puerto Rico, and the U.S. Virgin Islands – providing a critical resource to researchers and policy-makers alike. Obtained from the National Weather Service's Storm Prediction Center (SPC), it contains an intricate wealth of information that sheds light onto patterns of tornado outbreaks across time & geographical space yielding insights into factors like magnitude, fatalities/injuries caused and losses incurred by these devastating weather disasters. With attributes such as Start Longitude/Latitude, End Longitude/Latitude, Day of Origin & Time Zone – this dataset will enable a comprehensive analysis of changes over time in regards to both intensity & frequency for those interested in studying climate change and its impact on extreme weather events such as tornadoes. For disaster management personnel dealing with natural hazards like floods or hurricanes - a familiarity with this dataset can help identify areas prone to frequent storms - thereby empowering proactive measures towards their mitigation.*

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How to use the dataset

This dataset contains historical tornado tracks in the United States, Puerto Rico, and the U.S. Virgin Islands. The data was obtained from the National Weather Service's Storm Prediction Center (SPC). It includes thirty-seven columns of statistics which you can use to analyze when, where, and how frequently tornadoes occur in North America over time.

Research Ideas

• Creating a tornado watch and warning system using Geographic Information Systems (GIS) technology to track and predict the path of dangerous storms.
• Developing an insurance system that gives detailed information on historical data related to natural disasters including tornadoes, hurricanes, floods, etc., in order to better assess risk levels for insuring homes and businesses in vulnerable areas.
• Developing an app that provides real-time notifications for potential tornadoes by utilizing the dataset's coordinates and forecasting data from the National Weather Service (NWS). The app could even provide shelter locations near users based on their current location ensuring that people are aware of potential active threats nearby them quickly increasing safety levels as much as possible when these hazardous events occur

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Historical_Tornado_Tracks.csv | Column name | Description | |:--------------|:-------------------------------------| | OM | Origin Mode (Point or Line) (String) | | YR | Year (Integer) | | MO | Month (Integer) | | DY | Day (Integer) | | DATE | Date (String) | | TIME | Time (String) | | TZ | Time Zone (String) | | ST | State (String) | | STF | FIPS State Code (String) | | STN | State Name (String) | | MAG | Magnitude (Integer) | | INJ | Injuries (Integer) | | FAT | Fatalities (Integer) | | LOSS | Loss (Integer) | | CLOSS | Crop Loss (Integer) | | SLAT | Starting Latitude (Float) | | SLON | Starting Longitude (Float) | | ELAT | Ending Latitude (Float) | | ELON | Ending Longitude (Float) | | LEN | Length of Track (Float) ...

The notebook that generates this dataset is here: https://www.kaggle.com/johnjdavisiv/us-counties-weather-sociohealth-location-data

For an introduction to the data, check out this notebook: https://www.kaggle.com/johnjdavisiv/intro-to-the-us-counties-covid19-data

The 3,142 counties of the United States span a diverse range of social, economic, health, and weather conditions. Because of the COVID19 pandemic, over 2,400 of these counties have already experienced some COVID19 cases.

Combining county-level data on health, socioeconomics, and weather can help us address identify which populations are at risk for COVID19 and help prepare high-risk communities.

Temperature and humidity may affect the transmissibility of COVID19, but in the United States, warmer regions also tend to have markedly different socioeconomic and health demographics. As such, it's important to be able to control for factors like obesity, diabetes, access to healthcare, and poverty rates, since these factors themselves likely play a role in COVID19 transmission and fatality rates.

This dataset provides all of this information, formatted, cleaned, and ready for analysis. Most columns have little or no missing data. A small number have larger amounts of missing data; see the kernel that generated this dataset for details.

As global warming intensifies, hot extremes and heavy precipitation frequently happen in East of China. Meanwhile, severe surface ozone (O3) pollution resulting from the interactions of anthropogenic emissions and meteorological conditions also occur more frequently. In this study, we quantified the impact of weather extremes on ground-level O3 concentration during the summers of 2015–2021 and associated premature deaths in East of China. The O3 pollution influenced by hot extremes [maximum 8-h average O3 concentration (MDA8 O3) = 152.7 μg m−3] was 64.2% more severe than that associated with heavy rain (MDA8 O3 = 93 μg m−3) on the daily time scale. The compound hot and dry air extremes had a larger impact, and the associated MDA8 O3 could be up to 165.5 μg m−3. Thus, weather extremes could drastically perturb the O3 level in the air to exhibit large variability. Based on GEOS-Chem simulations with fixed anthropogenic emissions, forcing of weather extremes could successfully reproduce the large daily variability of O3 concentration because the weather extremes significantly influenced the physicochemical processes in the atmosphere. Furthermore, hot extremes magnified the single-day O3-related premature death to 153% of that under other-condition events, while heavy rain events decreased it to 70% in East of China. The findings of the present study have the potential to promote daily to weekly O3 forecasts and further improve our comprehensive understanding of the health effects of weather extremes and air pollution.

This dataset contains data on damages, deaths, and injuries associated with eight aggregate extreme weather event types by county and year-month, covering the lower 48 states from 1995 to 2022. This dataset is created by processing the NOAA Storm Events database and used in an analysis of the impacts of extreme weather events across across the contiguous United States in the Resources blog post Series “Storm Watch Series: Weather Volatility in the United States”.

The Measurement template document is available at the archived version of this page on the UK Government Web Archive.

DfT Business plan

Geographical coverage: Great Britain

Information broken down by: Accident site. Data are available by geographical area, age, gender and type of road user.

Latest data

In 2013:

• 1,713 people were killed in reported road traffic accidents in Great Britain, 2% (41) fewer than in 2012. This is the lowest number of fatalities since national records began in 1926. The total number of people killed in 2013 was 39% lower than the 2005-09 baseline average
• the number of fatalities decreased for pedestrians, pedal cyclists and car occupants, by 5%, 8% and 2% respectively, but increased for motorcycle users by 1%. Over the same period motor vehicle traffic remained broadly stable, with a small increase of 0.4% between 2012 and 2013
• with the exception of 2011, road deaths have fallen every year since 2004. Adverse weather (heavy snow falls) experienced in the first and last quarters of 2010 but not in 2011, is likely to be the main factor behind the increase in fatalities recorded in 2011

The complete set of data is available for download.

Background information

The indicator can be broken down by any geographical area (eg country, region, local authority) since a grid reference is collected for each accident. Information is also available by age, gender, type of road user and road type. Numbers will be relatively small for more detailed breakdowns of the total and may therefore fluctuate from year to year. This needs to be taken into account when assessing trends.

• publishing schedule: annual
• last updated: September 2014
• next update: July 2015

Other related data and information

More detailed analysis and time series can be found in Reported road casualties Great Britain: annual report.

Record level data on accidents and casualties can be found in http://data.gov.uk/dataset/road-accidents-safety-data/">Record level data

Further information

• Business Plan 2012 to 2015
• Input and impact indicators
• Analyst contact: Anil Bhagat, roadacc.stats@dft.gsi.gov.uk

Data extracted from the NOAA database processed and transformed to CSV, in addition to information on damages and direct deaths caused by hurricanes from 2012-2021.

HURDAT description header: The original HURDAT format substantially limited the type of best track information that could be conveyed. The format of this new version - HURDAT2 (HURricane DATa 2nd generation) - is based upon the “best tracks” available from the b-decks in the Automated Tropical Cyclone Forecast (ATCF – Sampson and Schrader 2000) system database and is described below. Reasons for the revised version include: 1) inclusion of non-synoptic (other than 00, 06, 12, and 18Z) best track times (mainly to indicate landfalls and intensity maxima); 2) inclusion of nondeveloping tropical depressions; and 3) inclusion of best track wind radii.

In 2023, storms caused nearly ****** deaths across the globe. the third-largest figure recorded since 1990. In the past three decades, the highest annual deathtoll due to storms was registered in 1991, when storm events were responsible for the death of more than *** thousand people worldwide. That year, a massive cyclone hit Bangladesh, becoming one of the deadliest storms of the century. The death count due to storms was also remarkably high in 2008, mainly associated with a cyclone which hit Myanmar in May.

Context

There is a beginning and an end to every life. But when do people die in The Netherlands? Do seasonal factors cause a difference? I might add weather and birth data to this graph to get a more full picture.

Content

Data is per week, per age group (0-65, 65-80, 80+) and sex. Notice that a full year is never exactly 52 weeks, so there are always two halve weeks in de data set. What do you do with this difference? Data is downloaded from the CBS site (see Source).

More explanation (in Dutch) can be found here: https://www.cbs.nl/nl-nl/onze-diensten/methoden/onderzoeksomschrijvingen/korte-onderzoeksbeschrijvingen/bevolkingsstatistiek.

Remarks

Since the start of 2010, more late death registrations have been counted.

Inspiration

Is it possible to see the small change in measurement from 2010? How many deaths have gone unnoticed before that year without using this method? In which season do more people die? And which group causes this difference? And a more gruesome question: how many people will die next week?

Source

Downloaded from the Dutch Bureau for Statistics (CBS) at 21-12-2019 with their tool Statline. Source: https://opendata.cbs.nl/statline/#/CBS/nl/dataset/70895ned/table?fromstatweb

Copyright (c) Centraal Bureau voor de Statistiek, Den Haag / Heerlen Verveelvoudiging is toegestaan, mits het CBS als bron wordt vermeld. Translation: Can be shared when source is mentioned.

Cover photo by Aron Visuals on Unsplash (https://unsplash.com/@aronvisuals?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)

Data for this app is from the National Weather Service. Details about the attributes can be found here.

This metadata considers the 2023 data including the year 2010 Euro equivalent values as a baseline on total and insured economic losses and the number of fatalities from weather- and climate-related events in EU Member States and EEA member countries since 1980. Weather- and climate-related hazards considered are those types classified as meteorological hazards (e.g. storms), hydrological hazards (e.g. floods) and climatological hazards (e.g. heatwaves) based on the classification by the International Council for Science (ICSU). The geophysical hazards (e.g. earthquakes and volcanoes) are included for comparison purposes. An event can occur in several countries, but the information is split per country.

The data is based on the RiskLayer CATDAT datasets (received under institutional agreement), and on the Eurostat collection of economic indicators, whereas data from earlier years not covered by Eurostat have been completed using data from the Annual Macro-Economic Database of the European Commission (AMECO), the International Monetary Fund’s (IMF) World Economic Outlook (WEO), the Total Economy Database (TED) and the World Bank database. The average population of a country over the period of the time series is used.

The data contains details related to EEA’s indicator “Economic losses from climate-related events in Europe” (https://www.eea.europa.eu/ims/economic-losses-from-climate-related), updated annually. Additional detail on the data and the indicator can be found in the EEA briefing "Economic losses and fatalities from weather- and climate-related events in Europe", 2024 (https://www.eea.europa.eu/publications/economic-losses-and-fatalities-from/economic-losses-and-fatalities-from).

This second version of the 1980-2023 dataset contains data from six additional countries: Albania, Bosnia and Herzegovina, Montenegro, North Macedonia, Serbia, and Kosovo*.

*) This designation is without prejudice to positions on status, and is in line with UNSC 1244 and the ICJ Opinion on the Kosovo Declaration of Independence.

Cary, NC Crash Data

Injuries, Fatalities, and Contributing Factors

By Town of Cary [source]

About this dataset

The Town of Cary Crash Database contains five years worth of detailed crash data up to the current date. Each incident is mapped based on National Incident-Based Reporting System (NIBRS) criteria, providing greater accuracy and access to all available crashes in the County.

This valuable resource is constantly being updated – every day fresh data is added and older records are subject to change. The locations featured in this dataset reflect approximate points of intersection or impact. In cases when essential detail elements are missing or rendered unmapable, certain crash incidents may not appear on maps within this source.

We invite you to explore how crashes have influenced the Town of Cary over the past five years – from changes in weather conditions and traffic controls to vehicular types, contributing factors, travel zones and more! Whether it's analyzing road design elements or assessing fatality rates – come take a deeper look at what has shaped modern day policies for safe driving today!

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Featured Notebooks

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How to use the dataset

• Understanding Data Elements – The first step in using this dataset is understanding what information is included in it. The data elements include location descriptions, road features, character traits of roads and more that are associated with each crash. Additionally, the data provides details about contributing factors, light conditions, weather conditions and more that can be used to understand why certain crashes happen in certain locations or under certain circumstances.

Research Ideas

• Analyzing trends in crash locations to better understand where crashes are more likely to occur. For example, using machine learning techniques and geographical mapping tools to identify patterns in the data that could enable prediction of future hotspots of crashes.
• Investigating the correlations between roadway characteristics (e.g., surface, configuration and class) and accident severities in order to recommend improvements or additional preventative measures at certain intersections or road segments which may help reduce crash-related fatalities/injuries.
• Using data from various contributing factors (e.g., traffic control, weather conditions, work area) as an input for a predictive model for analyzing the risk factors associated with different types of crashes such as head-on collisions, rear-end collisions or side swipe accidents so that safety alerts can be issued for public awareness campaigns during specific times/days/conditions where such incidents have been known to occur more often or have increased severity repercussions than usual (i.e., near schools during school days)

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: crash-data-3.csv | Column name | Description | |:--------------|:-----------------------------------------------------------------------------------------------------| | type | The type of crash, such as single-vehicle, multi-vehicle, or pedestrian. (String) | | features | The features of the crash, such as location, contributing factors, vehicle types, and more. (String) |

File: crash-data-1.csv | Column name | Description | |:-------------------------|:----------...

We provide a dataset of tornadoes and waterspouts in Chile from 1554 to present based in chronicles, newspaper articles, social media, scientific literature and books. The database includes only those events that have been qualified as more than likely a tornado or waterspout based on a subjective qualification by the researchers. For each tornado we provide at least one geographical location point, the local and UTC hour (if known) and for most cases an estimation of the intensity based on the Enhanced Fujita damage scale.The following are the parameters contained in the database:N°: This is the entry number or identifier for each record in the file.Location: The name of the place where the weather event occurred.Latitude: The geographical latitude coordinate of the event's location.Longitude: The geographical longitude coordinate of the event's location.Date (Gregorian Calendar): The date when the event occurred, according to the Gregorian calendar.Hour (local): The local time when the event occurred.Hour (UTC): The time of the event in Coordinated Universal Time (UTC).Sound: A binary indicator (usually 1 for 'Yes' and 0 for 'No') showing whether there was a notable sound associated with the event.Hail: A binary indicator showing whether hail was a feature of the weather event.Electric Storm: A binary indicator showing whether the event involved an electric storm.Damage: A binary indicator showing whether there was any damage resulting from the event.Tornado: A binary indicator showing whether a tornado was a part of the event.Waterspout: A binary indicator showing whether a waterspout was observed during the event.Register: This column refers to the existence of some witness account or visual material of a rotating column.Max. EF Rating: The maximum Enhanced Fujita Scale rating assigned to the tornado, indicating its intensity.Analyst: The name or initials of the person who analyzed or reported the event.Fatalities: The number of fatalities (deaths) caused by the event.Injured: The number of injuries reported due to the event.Link to Documents: References or links to documents where the event is described or recorded.Sources: The sources or references from where the information about the event is derived.Comments: Additional remarks or notes about the event, providing context or extra details.

Extreme weather events are becoming more frequent and severe, leading to an increase in direct, adverse thermoregulatory impacts to wildlife. Here we document an unprecedented, single day, heat-related mortality event of Magellanic penguins (Spheniscus magellanicus) at Punta Tombo, Chubut Province, Argentina, one of the largest breeding colonies for this species. We found 264 dead adults and 90 dead chicks in the breeding colony and along the beaches after recording the highest temperature in the shade (44°C on 19 January 2019) since the study started in December 1982. We found dead adults and chicks in postures used to release heat, i.e., lying prone with flippers and feet extended away from the body and/or bills open. We found no evidence for other causes of mortality other than heat (e.g., disease, toxic algae, starvation). Adults likely died of dehydration, because dead adults were in significantly worse body condition than adults that survived. Dead adults had either empty stomachs or < 50g of food and 27% of the dead adults died traveling between the nesting area and the water. More males died than females (83% male, 17% female, n=94). In one section of the colony, ~5% of 1153 adults died in the heat. Mortality rates of adults were unevenly distributed across the colony, suggesting that the presence of microclimates or easier beach access were important factors to penguin survival. The body condition indices of dead and live chicks were similar. Chicks that died from heat had food in their stomachs (mean = 405 ± 128g, n=14), suggesting digestion inhibited their ability to thermoregulate. Documenting the effects of extreme weather events on populations is crucial to predicting how they will respond to climate change because these events, although rare, are expected to become more frequent and could have severe impacts on populations.

This dataset contains information on hurricanes that affect the continent of North America. The columns are as follows:

Year - The year

Name - The name of the hurricane

Category - The category of the hurricane. They are: - TS - Tropical Storm - 1 - Category 1 - 2 - Category 2 - 3 - Category 3 - 4 - Category 4 - 5 - Category 5

Rain Inch. - The amount of rain that fell in inches

Highest Wind Speed - The highest wind speed achieved by the hurricane

Damage(USD) - The cost of damage in US dollars

Fatalities - The amount of deaths

Areas Affected - The area affected by the hurricane

I hope you will like this dataset. God bless you.

In 2024, there were roughly 18,100 reported fatalities caused by natural disaster events worldwide. This was well below the 21st-century average and significantly lower than the fatalities recorded in 2023, which were driven by the earthquakes that hit Turkey and Syria on February and became the deadliest catastrophes in 2023, with nearly ****** reported deaths. Economic losses due to natural disasters The economic losses due to natural disaster events worldwide amounted to about *** billion U.S. dollars in 2024. Although figures in recent years have remained mostly stable, 2011 remains the costliest year to date. Among the different types of natural disaster events, tropical cyclones caused the largest economic losses across the globe in 2024. What does a natural disaster cost? Hurricane Katrina has been one of the costliest disasters in the world, costing the insurance industry some *** billion U.S. dollars. The resilience of societies against catastrophes have been boosted by insurance industry payouts. Nevertheless, insurance payouts are primarily garnered by industrialized countries. In emerging and developing regions, disaster insurance coverage is still limited, despite the need for improved risk management and resilience as a method to mitigate the impact of disasters and to promote sustainable growth.

Flooding is the leading cause of weather-related deaths in Texas. According to Texas Tribune a digital-first, nonpartisan media organization, almost 6 million Texans, or about 20% of the population, live in an area susceptible to flooding (Tribune Texas). The Harris county on average experience a major flood somewhere every two years; this is according to MAAPnext a project funded by FEMA to deliver a transformative step in the management and regulation of Harris County’s floodplains, further contributing to it's resilience .