Between 2011 and 2020, 19 hurricanes made landfall in the United States, the same figure reported in the previous decade. This is the highest number recorded for a 10-year timespan since the 1940s, which holds the current record for most landfalls, with 24 hurricanes. In 2023, only hurricane Ian made landfall in the U.S.

In 2024, there were 42 hurricanes registered worldwide, up from 45 hurricanes a year earlier. This was nevertheless below the average of 47 hurricanes per year registered from 1990 to 2022. The years of 1992 and 2018 tied as the most active in the indicated period, each with 59 hurricanes recorded. The Pacific Northwest basin recorded the largest number of hurricanes in 2024. Most exposed countries to hurricanes With the Pacific Northwest basin being one of the most active for hurricanes in the world, there is perhaps no surprise that Japan and the Philippines were two of the countries most exposed to tropical cyclones in 2024, both West Pacific nations. Meanwhile, the Dominican Republic was the most exposed country in the Atlantic Ocean and ranked first as the most exposed country worldwide during the same year. Effects of tropical cyclones From 1970 to 2019, almost 800,000 deaths due to tropical cyclones have been reported worldwide. In the past decade, the number of such casualties stood at some 19,600, the lowest decadal figure in the last half-century. In contrast to the lower number of deaths, economic losses caused by tropical cyclones have continuously grown since 1970, reaching a record high of more than 700 billion U.S. dollars from 2010 to 2019.

This is an annual edition poster showing all of the hurricanes having impacted the continental U.S. from 1950 to 2022. This 36x28 inch glossy poster gives a quick look of the location and strength of each hurricane which impacted the continental United States. The poster is also available to download as a PDF file. The map includes the name, category strength, year, and approximate strike location of each hurricane. For the 2022 edition two new hurricanes were added: Hurricane Ian, a Category-4 Hurricane hitting the western Florida Peninsula with a secondary landfall in South Carolina, and Hurricane Nicole, a Category-1 hurricane hitting the east coast of Florida.

Note: This is a real-time dataset. If you do not see any data on the map, there may not be an event taking place. The Atlantic hurricane season begins on June 1 and ends on November 30, and the eastern Pacific hurricane season begins on May 15 and ends on November 30.Hurricanes, also known as typhoons and cyclones, fall under the scientific term tropical cyclone. Tropical cyclones that develop over the Atlantic and eastern Pacific Ocean are considered hurricanes.Meteorologists have classified the development of a tropical cyclone into four stages: tropical disturbance, tropical depression, tropical storm, and tropical cyclone. Tropical cyclones begin as small tropical disturbances where rain clouds build over warm ocean waters. Eventually, the clouds grow large enough to develop a pattern, where the wind begins to circulate around a center point. As winds are drawn higher, increasing air pressure causes the rising thunderstorms to disperse from the center of the storm. This creates an area of rotating thunderstorms called a tropical depression with winds 62 kmph (38 mph) or less. Systems with wind speeds between 63 kmph (39 mph) and 118 kmph (73 mph) are considered tropical storms. If the winds of the tropical storm hit 119 kmph (74 mph), the storm is classified as a hurricane. Tropical cyclones need two primary ingredients to form: warm water and constant wind directions. Warm ocean waters of at least 26 degrees Celsius (74 degrees Fahrenheit) provide the energy needed for the storm to become a hurricane. Hurricanes can maintain winds in a constant direction at increasing speeds as air rotates about and gathers into the hurricane’s center. This inward and upward spiral prevents the storm from ripping itself apart. Hurricanes have distinctive parts: the eye, eyewall, and rain bands. The eye is the calm center of the hurricane where the cooler drier air sinks back down to the surface of the water. Here, winds are tranquil, and skies are partly cloudy, sometimes even clear. The eyewall is composed of the strongest ring of thunderstorms and surrounds the eye. This is where rain and winds are the strongest and heaviest. Rain bands are stretches of rain clouds that go far beyond the hurricane’s eyewall, usually hundreds of kilometers. Scientists typically use the Saffir-Simpson Hurricane Wind Scale to measure the strength of a hurricane’s winds and intensity. This scale gives a 1 to 5 rating based on the hurricane’s maximum sustained winds. Hurricanes rated category 3 or higher are recognized as major hurricanes. Category 1: Wind speeds are between 119 and 153 kmph (74 and 95 mph). Although this is the lowest category of hurricane, category 1 hurricanes still produce dangerous winds and could result in damaged roofs, power lines, or fallen tree branches. Category 2: Wind speeds are between 154 and 177 kmph (96 and 110 mph). These dangerous winds are likely to cause moderate damage; enough to snap or uproot small trees, destroy roofs, and cause power outages. Category 3: Wind speeds are between 178 and 208 kmph (111 and 129 mph). At this strength, extensive damage may occur. Well-built homes could incur damage to their exterior and many trees will likely be snapped or uprooted. Water and electricity could be unavailable for at least several days after the hurricane passes. Category 4: Wind speeds are between 209 and 251 kmph (130 and 156 mph). Extreme damage will occur. Most of the area will be uninhabitable for weeks or months after the hurricane. Well-built homes could sustain major damage to their exterior, most trees may be snapped or uprooted, and power outages could last weeks to months. Category 5: Wind speeds are 252 kmph (157 mph) or higher. Catastrophic damage will occur. Most of the area will be uninhabitable for weeks or months after the hurricane. A significant amount of well-built, framed homes will likely be destroyed, uprooted trees may isolate residential areas, and power outages could last weeks to months. This map is built with data from the NOAA National Hurricane Center (NHC) and the Joint Typhoon Warning Center (JTWC). The map shows recent, observed, and forecasted hurricane tracks and positions, uncertainties, wind speeds, and associated storm watches and warnings. This is a real-time dataset that is programed to check for updates from the NHC and JTWC every 15 minutes. If you are in an area experiencing a tropical cyclone, tune into local sources for more up-to-date information and important safety instructions. This map includes the following information: Forecast position points: These points mark the locations where the NHC predict the tropical cyclone will be at 12, 24, 36, 48, 72, 96, and 120 hours in the future.Observed position points: These points mark the locations where the tropical cyclone has been.Forecast track: This is the line that connects the forecast points and marks the expected path of the hurricane.Observed track: This line marks the path the tropical cyclone has already taken.Cone of uncertainty: Due to the complexity of ocean atmospheric interactions, there are many different factors that can influence the path of a hurricane. This uncertainty is represented on the map by a cone. The further into the future the forecast is, the wider the cone due to the greater uncertainty in the precise path of the storm. Remember rain, wind, and storm surge from the hurricane will likely impact areas outside the cone of uncertainty. This broader impact of wind can be seen if you turn on or off Tropical Storm Force (34 Knots) 5-Day Wind Probability, Strong Tropical Storm Force (50 Knots) 5-Day Wind Probability, or Hurricane Force (64 Knots) 5-Day Wind Probability map layers.Watches and warnings: Storm watches or warnings depend on the strength and distance from the location of the forecasted event. Watches indicate an increased risk for severe weather, while a warning means you should immediately move to a safe space.Tropical storm watch: The NHC issues this for areas that might be impacted by tropical cyclones with wind speeds of 34 to 63 knots (63 to 119 kilometers per hour or 39 to 74 miles per hour) in the next 48 hours. In addition to high winds, the region may experience storm surge or flooding.Tropical storm warning: The NHC issues this for places that will be impacted by hurricanes with wind speeds of 34 to 63 knots (63 to 119 kilometers per hour or 39 to 74 miles per hour) in the next 36 hours. As with the watch, the area may also experience storm surge or flooding.Hurricane watch: The NHC issues this watch for areas where a tropical cyclone with sustained wind speeds of 64 knots (119 kilometers per hour or 74 miles per hour) or greater in the next 48 hours may be possible. In addition to high winds, the region may experience storm surge or flooding.Hurricane warning: The NHC issues this warning for areas where hurricanes with sustained wind speeds of 64 knots (119 kilometers per hour or 74 miles per hour) or greater in the next 36 hours are expected. As with the watch, the region may experience storm surge or flooding. This warning is also posted when dangerously high water and waves continue even after wind speeds have fallen below 64 knots.Recent hurricanes: These points and tracks mark tropical cyclones that have occurred this year but are no longer active.

Want to learn more about how hurricanes form? Check out Forces of Nature or explore The Ten Most Damaging Hurricanes in U.S. History story.

This web map includes the track points, track lines, and hazard layers for historical Hurricane Georges, AL071998.DATA OVERVIEWKinetic Analysis's Tropical Cyclone datasets use best-track data for the requested storm as is available from IBTRaCS (or, for recent storms where there is no best-track, we use ATCF a-deck data provided by the U.S. National Hurricane Center, Joint Typhoon Warning Center, or Central Pacific Hurricane Center) to drive in-house, advanced numerical modeling that computes the spatial distribution of maximum wind speedwinds by Saffir-Simpson categorieswave heightsstorm surge inundationcumulative rainfallUSE CASESWhile this data may be used in a variety of ways, the most common ways we see it in action is by insurance, emergency management, disaster relief, supply chain, and governmental agencies/organization in making decisions about actions to take before, during, and after a tropical cyclone. A collection of historical tropical cyclone data can provide information on the probability and trends that can be expected for a given location affected by tropical cyclones in the future. Claims officers, for example, can use this information to determine the vulnerability and exposure level of a given area or property. Government agencies can use impact data to determine where to focus on building climate resilience safeguards and resources next.DATA SOURCEHazard footprints are based on observed storm track, intensity and wind radii provided by the designated expert-reviewed sources U.S. NHC (National Hurricane Center), JTWC (Joint Typhoon Warning Center), CPHC (Central Pacific Hurricane Center) - collectively termed OFCL (Official). UPDATE FREQUENCYSince these are historical/past storms, as long as the storm's path was recorded and publicly available, the resulting hazards and impacts can be modeled by Kinetic Analysis at any time upon request.SCALE/RESOLUTIONThis post-event data is provided at a 30 arcsecond (~1 km) resolution. AREA COVEREDWorldINTERESTED IN MORE?Our full ArcGIS Marketplace listing grants you access to the Kinetic Analysis Corporation's proprietary tropical storm hazard data for a past/historical tropical cyclone of your choice per purchase, to be custom-generated for you upon purchase request. Different price options are available for those who wish to purchase to purchase footprints for multiple historical storms, bundle with our real-time data, or make other custom requests.Customized resolutions, best track data source, and data units (default is SI) are available upon request to sales@kinanco.com. Learn more on the Kinetic Analysis website.GLOSSARY/DATA FIELDSTrack Points - These points indicate the locations of a storm over time. They are generated by forecast agencies and numerical model guidance.Track Line - This is the line formed by connecting all the track points. It depicts a continuous path for the storm by interpolating between any two track points.ATCF ID - Unique ID associated with a tropical cyclone, defined using the Automated Tropical Cyclone Forecasting (ATCF) system. The format is usually a two-letter abbreviation of the ocean basin (see "Storm Basin" below for list) in which the storm can be found, the annual cyclone number starting from 1 for the first storm in each basin per year, and the 4-digit year. For example, AL112017 (Hurricane Irma) refers to AL (Atlantic basin), 11th storm of the year in that basin, in the year 2017.Storm Name - The World Meteorological Organization (WMO) tropical cyclone name, such as Irma, Katrina, and Rai.Storm Basin - Ocean basin in which the storm is taking place. These include AL (North Atlantic), WP (Western North Pacific), CP (Central North Pacific), EP (Eastern North Pacific), IO (North Indian Ocean), SH (South-West Indian Ocean, Australian region, and South Pacific Ocean), and LS (Southern Atlantic).Storm Age - Number of days the storm has been active at time of forecastCategory Description - How the selected layer would be categorized against similar data. For example, data in a wind layer may be categorized into groups of 5 mph each, such as 100-105 mph for one group and 105-110 mph for another group. In such a case, the category description field displays which grouping the selected location belongs to. This is a variable/field separate from the name of each map layer.Latitude & Longitude - Geographic indicators of a storm's past, current, or forecast location derived from dividing the Earth into grids measured in degrees.Wind Speed - Maximum wind speed of the storm at that location. The units are knots for track points and track line layers and miles per hour (mph) for the wind speed hazard layer. These represent terrain-adjusted, 2-minute sustained winds at 10-meter elevation and are consistent with wind speeds reported by Automated Surface Observing Stations (ASOS weather stations). They can differ from wind speed forecast by different agencies because, in contrast with winds forecast by agencies such as the NHC, Kinetic Analysis-generated winds account for the effects of surface roughness and topography. In addition, different agencies can report winds based on different averaging times. For example, the NHC and JTWC report 1-minute sustained winds while the World Meteorological Organization (WMO) standard is 10-minute sustained winds.Minimum Sea Level Pressure - The lowest sea level pressure at that storm location. Measured in millibars.Radius of Max Winds - The distance between the storm's center, where the central pressure is lowest, and the maximum winds of a storm. Measured in nautical miles. Forward Speed - How fast a storm is moving at the selected location. Measured in meters per second (m/s).Storm Direction - The direction toward which a storm is moving at the selected location. Measured with a 360-degree system where North is represented by 0 degrees and East by 90 degrees.Forecast Time - Time at which an agency (such as OFCL) released its newest update of storm track data. This is the set of data used to simulate the model results displayed. Simulation Time - Time at which Kinetic Analysis's models processed the current data.Model in Simulation - The forecast agency, or model that generated the inputs for the Kinetic Analysis-simulated storm hazard data.NOTE: This map and its data are provided for informational purposes only. Due to limitations in modern modeling technology, this data may not reflect the ultimate path, hazards, and/or impacts of a storm with 100% accuracy. Usage of this map and its data voids Kinetic Analysis of any responsibilities for consequences that may arise from using it to make personal or business decisions.

Hurricane tracks and positions provide information on where the storm has been, where it is currently located, and where it is predicted to go. Each storm location is depicted by the sustained wind speed, according to the Saffir-Simpson Scale. It should be noted that the Saffir-Simpson Scale only applies to hurricanes in the Atlantic and Eastern Pacific basins, however all storms are still symbolized using that classification for consistency.Data SourceThis data is provided by NOAA National Hurricane Center (NHC) for the Central+East Pacific and Atlantic, and the Joint Typhoon Warning Center for the West+Central Pacific and Indian basins. For more disaster-related live feeds visit the Disaster Web Maps & Feeds ArcGIS Online Group.Sample DataSee Sample Layer Item for sample data during inactive Hurricane Season!Update FrequencyThe Aggregated Live Feeds methodology checks the Source for updates every 15 minutes. Tropical cyclones are normally issued every six hours at 5:00 AM EDT, 11:00 AM EDT, 5:00 PM EDT, and 11:00 PM EDT (or 4:00 AM EST, 10:00 AM EST, 4:00 PM EST, and 10:00 PM EST).Public advisories for Eastern Pacific tropical cyclones are normally issued every six hours at 2:00 AM PDT, 8:00 AM PDT, 2:00 PM PDT, and 8:00 PM PDT (or 1:00 AM PST, 7:00 AM PST, 1:00 PM PST, and 7:00 PM PST).Intermediate public advisories may be issued every 3 hours when coastal watches or warnings are in effect, and every 2 hours when coastal watches or warnings are in effect and land-based radars have identified a reliable storm center. Additionally, special public advisories may be issued at any time due to significant changes in warnings or in a cyclone. For the NHC data source you can subscribe to RSS Feeds.North Pacific and North Indian Ocean tropical cyclone warnings are updated every 6 hours, and South Indian and South Pacific Ocean tropical cyclone warnings are routinely updated every 12 hours. Times are set to Zulu/UTC.Scale/ResolutionThe horizontal accuracy of these datasets is not stated but it is important to remember that tropical cyclone track forecasts are subject to error, and that the effects of a tropical cyclone can span many hundreds of miles from the center.Area CoveredWorldGlossaryForecast location: Represents the official NHC forecast locations for the center of a tropical cyclone. Forecast center positions are given for projections valid 12, 24, 36, 48, 72, 96, and 120 hours after the forecast's nominal initial time. Click here for more information.

Forecast points from the JTWC are valid 12, 24, 36, 48 and 72 hours after the forecast’s initial time.Forecast track: This product aids in the visualization of an NHC official track forecast, the forecast points are connected by a red line. The track lines are not a forecast product, as such, the lines should not be interpreted as representing a specific forecast for the location of a tropical cyclone in between official forecast points. It is also important to remember that tropical cyclone track forecasts are subject to error, and that the effects of a tropical cyclone can span many hundreds of miles from the center. Click here for more information.The Cone of Uncertainty: Cyclone paths are hard to predict with absolute certainty, especially days in advance.

The cone represents the probable track of the center of a tropical cyclone and is formed by enclosing the area swept out by a set of circles along the forecast track (at 12, 24, 36 hours, etc). The size of each circle is scaled so that two-thirds of the historical official forecast errors over a 5-year sample fall within the circle. Based on forecasts over the previous 5 years, the entire track of a tropical cyclone can be expected to remain within the cone roughly 60-70% of the time. It is important to note that the area affected by a tropical cyclone can extend well beyond the confines of the cone enclosing the most likely track area of the center. Click here for more information. Now includes 'Danger Area' Polygons from JTWC, detailing US Navy Ship Avoidance Area when Wind speeds exceed 34 Knots!Coastal Watch/Warning: Coastal areas are placed under watches and warnings depending on the proximity and intensity of the approaching storm.Tropical Storm Watch is issued when a tropical cyclone containing winds of 34 to 63 knots (39 to 73 mph) or higher poses a possible threat, generally within 48 hours. These winds may be accompanied by storm surge, coastal flooding, and/or river flooding. The watch does not mean that tropical storm conditions will occur. It only means that these conditions are possible.Tropical Storm Warning is issued when sustained winds of 34 to 63 knots (39 to 73 mph) or higher associated with a tropical cyclone are expected in 36 hours or less. These winds may be accompanied by storm surge, coastal flooding, and/or river flooding.Hurricane Watch is issued when a tropical cyclone containing winds of 64 knots (74 mph) or higher poses a possible threat, generally within 48 hours. These winds may be accompanied by storm surge, coastal flooding, and/or river flooding. The watch does not mean that hurricane conditions will occur. It only means that these conditions are possible.Hurricane Warning is issued when sustained winds of 64 knots (74 mph) or higher associated with a tropical cyclone are expected in 36 hours or less. These winds may be accompanied by storm surge, coastal flooding, and/or river flooding. A hurricane warning can remain in effect when dangerously high water or a combination of dangerously high water and exceptionally high waves continue, even though winds may be less than hurricane force.RevisionsMar 13, 2025: Altered 'Forecast Error Cone' layer to include 'Danger Area' with updated symbology.Nov 20, 2023: Added Event Label to 'Forecast Position' layer, showing arrival time and wind speed localized to user's location.Mar 27, 2022: Added UID, Max_SS, Max_Wind, Max_Gust, and Max_Label fields to ForecastErrorCone layer.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

This layer features tropical storm (hurricanes, typhoons, cyclones) tracks, positions, and observed wind swaths from the past hurricane season for the Atlantic, Pacific, and Indian Basins. These are products from the National Hurricane Center (NHC) and Joint Typhoon Warning Center (JTWC). They are part of an archive of tropical storm data maintained in the International Best Track Archive for Climate Stewardship (IBTrACS) database by the NOAA National Centers for Environmental Information.Data SourceNOAA National Hurricane Center tropical cyclone best track archive.Update FrequencyWe automatically check these products for updates every 15 minutes from the NHC GIS Data page.The NHC shapefiles are parsed using the Aggregated Live Feeds methodology to take the returned information and serve the data through ArcGIS Server as a map service.Area CoveredWorldWhat can you do with this layer?Customize the display of each attribute by using the ‘Change Style’ option for any layer.Run a filter to query the layer and display only specific types of storms or areas.Add to your map with other weather data layers to provide insight on hazardous weather events.Use ArcGIS Online analysis tools like ‘Enrich Data’ on the Observed Wind Swath layer to determine the impact of cyclone events on populations.Visualize data in ArcGIS Insights or Operations Dashboards.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

In 2021, there were 68 fatalities due to hurricanes reported in the United States. Since the beginning of the century, the highest number of fatalities was recorded in 2005, when four major hurricanes – including Hurricane Katrina – resulted in 1,518 deaths.

The worst hurricanes in U.S. history
Hurricane Katrina, which made landfall in August 2005, ranked as the third deadliest hurricane in the U.S. since records began. Affecting mainly the city of New Orleans and its surroundings, the category 3 hurricane caused an estimated 1,500 fatalities. Katrina was also the costliest tropical cyclone to hit the U.S. in the past seven decades, with damages amounting to roughly 186 billion U.S. dollars. Hurricanes Harvey and Maria, both of which made landfall in 2017, ranked second and third, resulting in damage costs of 149 and 107 billion dollars, respectively.

How are hurricanes classified?
According to the Saffir-Simpson scale, hurricanes can be classified into five categories, depending on their maximum sustained wind speed. Most of the hurricanes that have made landfall in the U.S. since 1851 are category 1, the mildest of the five. Hurricanes rated category 3 or above are considered major hurricanes and can cause devastating damage. In 2021, there were 38 hurricanes recorded across the globe, of which 17 were major hurricanes.

The 2004 U.S. Landfalling Hurricanes poster is a special edition poster which contains two sets of images of Hurricanes Charley, Frances, Ivan, and Jeanne, created from NOAA's operational satellites. In addtion to the images, the poster has a map depicting the general track of each storm; information on each storm's landfall location, date of landfall, and category level at time of landfall; as well as, a Saffir-Simpson Hurricane Scale chart. Poster size is 34"x27".

Predictions of tropical cyclone (TC) frequencies are hampered by insufficient knowledge of their natural variability in the past. A 30-m-long sediment core from the Great Blue Hole, a marine sinkhole offshore Belize, provides the longest available, continuous and annually-resolved TC-frequency record. This record expands our understanding, derived from instrumental monitoring (73-years), historical documentations (173-years) and paleotempestological records (2000-years), to the past 5700 years. A total of 694 event-layers were identified. They display a distinct regional trend of increasing storminess in the south-western Caribbean, which follows an orbitally-driven shift in the Intertropical Convergence Zone. Superimposed short-term variations match Holocene climate intervals and originate from solar irradiance-controlled sea-surface temperature anomalies and climate phenomena modes. A 21st century extrapolation suggests an unprecedented increase in TC-frequency, attributable to the In..., , , # An annually resolved 5700-years storm archive reveals drivers of Caribbean cyclone frequency

https://doi.org/10.5061/dryad.fn2z34v57

Description of the data and file structure

This supplementary dataset, integral to our research paper, contains all the raw data for understanding the key findings of our study. It includes: a historical record calibration (Table S1.), a stratigraphic and chronological correlation of event-layers in different Great Blue Hole cores (Table S2.), radiocarbon ages and δ13C values (Table S3.), quantitative textural analyses of 694 event-layer and 125 fair-weather samples (Table S4.), fine material analyses (Table S5.), correlation tests (Table S.6), XRF data (Table S7.), gray scale measurements (Table S9.) and event-layer counts in binned 50- and 100-years counting intervals (Table S9.). All the diffrent raw data promote transparency and allow varied data processing and re-analysis methods.

Files and ...

National Hurricane Center - National Storm Surge Hazard Maps - https://www.nhc.noaa.gov/nationalsurge/The SLOSH (Sea, Lake, and Overland Surges from Hurricanes) model is a numerical model used by NWS to compute storm surge. Storm surge is defined as the abnormal rise of water generated by a storm, over and above the predicted astronomical tides. Flooding from storm surge depends on many factors, such as the track, intensity, size, and forward speed of the hurricane and the characteristics of the coastline where it comes ashore or passes nearby. For planning purposes, the NHC uses a representative sample of hypothetical storms to estimate the near worst-case scenario of flooding for each hurricane category.This is version 3 of the NHC National Storm Surge Risk Maps. The updates in this version include data mapped to 10m DEMs for the US Gulf and East Coast. The following new regions have been added: Southern California (hurricane wind category 1 and 2 storms), Guam, American Samoa, and the Yucatan Peninsula for parts of Mexico, Belize, and Honduras. For simplicity, the tiled map services are published by hurricane wind category and all available mapped regions for that category are provided in that web map.The following areas are mapped in the hurricane wind Category 4 Maps:US Gulf and East CoastPuerto Rico and US Virgin IslandsHawaiiGuamAmerican SamoaHispaniolaYucatan Peninsula- parts of Mexico, Belize, Guatemala, and western HondurasSLOSH employs curvilinear polar, elliptical, and hyperbolic telescoping mesh grids to simulate the storm surge hazard. The spatial coverage for each SLOSH grid ranges from an area the size of a few counties to a few states. The resolution of individual grid cells within each basin ranges from tens to hundreds of meters to a kilometer or more. Sub-grid scale water features and topographic obstructions such as channels, rivers, and cuts and levees, barriers, and roads, respectively, are parameterized to improve the modeled water levels.The NHC provides two products based on hypothetical hurricanes: MEOWs and MOMs. MEOWs are created by computing the maximum storm surge resulting from up to 100,000 hypothetical storms simulated through each SLOSH grid of varying forward speed, radius of maximum wind, intensity (Categories 1-5), landfall location, tide level, and storm direction. A MEOW product is created for each combination of category, forward speed, storm direction, and tide level. SLOSH products exclude Category 5 storms north of the NC/VA border. SLOSH products only include hurricane wind Category 1-4 scenarios for Hawaii and hurricane wind category 1-2 scenarios for Southern California. For each storm combination, parallel storms make landfall in 5 to 10 mile increments along the coast within the SLOSH grid, and the maximum storm surge footprint from each simulation is composited, retaining the maximum height of storm surge in a given basin grid cell. These are called MEOWs and no single hurricane will produce the regional flooding depicted in the MEOWs. SLOSH model MOMs are an ensemble product of maximum storm surge heights. SLOSH MOMs are created for each storm category by retaining the maximum storm surge value in each grid cell for all the MEOWs, regardless of the forward speed, storm trajectory, or landfall location. SLOSH MOMs are available for mean tide and high tide scenarios and represent the near worst-case scenario of flooding under ideal storm conditions. A high tide initial water level was used for the storm surge hazard maps.This product uses the expertise of the NHC Storm Surge Unit to merge the operational SLOSH grids to build a seamless map of storm surge hazard scenarios using the MOM product. Each individual SLOSH grid for the Category 1-5 MOMs are merged into a single, seamless grid. The seamless grid is then resampled, interpolated, and processed with a DEM (Digital Elevation Model, i.e. topography) to compute the storm surge hazard above ground for each hurricane wind category. The SLOSH MOM storm surge hazard data used to create these maps are constrained by the extent of the SLOSH grids and users should be aware that risk due to storm surge flooding could extend beyond the areas depicted in these maps.Users of this hazard map should be aware that potential storm surge flooding is not depicted within some levee areas, such as the Hurricane & Storm Damage Risk Reduction System in Louisiana. These areas are highly complex and water levels resulting from overtopping are difficult to predict. Users are urged to consult local officials for flood risk inside these leveed areas. If applicable to the region displayed by the map, these leveed areas will be depicted with a black and white diagonal hatch pattern. Not all levee areas are included in this analysis - in particular, local features such as construction walls, levees, berms, pumping systems, or other mitigation systems found at the local level may not be included in this analysis. Additionally, some marshy or low lying areas are not mapped in this analysis.In locations that have a steep and narrow continental shelf, wave setup can be a substantial contributor to the total water level rise observed during a tropical cyclone. Wave setup is defined as the increase in mean water level due to momentum transfer to the water column by waves that are breaking or otherwise dissipating their energy. The following locations use SLOSH+Wave Setup simulations to create MEOW and MOM products that account for the increase in the mean water level due to wave setup: Puerto Rico, US Virgin Islands, Hawaii, Hispaniola, Guam, American Samoa, and Southern California. Through the USAID/WMO Coastal Inundation and Flooding Demonstration Project, these SLOSH storm surge risk products were created for the Island of Hispaniola.

On September 20, 2017, Hurricane Maria hit the U.S. territory of Puerto Rico as a category 4 storm. Heavy rainfall caused landslides in mountainous regions throughout the territory. This data release presents geospatial data describing the concentration of landslides generated by Hurricane Maria in Puerto Rico. We used post-hurricane satellite and aerial imagery collected between September 26, 2017 and October 8, 2017 to visually estimate the number of landslides over nearly the whole territory. This was done by dividing the territory into a grid with 4 km2 cells (2 km x 2 km). Each 4 km2 grid cell was classified as either containing no landslides, fewer than 25 landslides/km2 or more than 25 landslides/km2. We used 12 WorldView satellite images (~0.5 m-resolution) available from Digital Globe, Inc. and ~0.15 m-resolution aerial imagery collected by Sanborn and QuantumSpatial (http://www.arcgis.com/home/item.html?id=b1949283c1084b0daf2987d896392ac2). Because leaves were stripped from much of the vegetation, landslide scars were readily visible in both sources of imagery. We assume that the majority of landslides were triggered by rainfall from Hurricane Maria, but rainfall from Hurricane Irma during the first week of September and rainfall from thunderstorms after Hurricane Maria may have also initiated landslides. During this investigation, we visually examined a total area of 8103 km2, which encompassed most of the territory and nearly all the mountainous areas. Approximately 846 km2 of the land area of the territory was not examined because either 1) imagery was unavailable or 2) the area was obscured by cloud cover. Approximately 61% of the examined area was unaffected by landslides. Landslides were observed in the remaining 39% of the examined area, but, for the most part , the landslide density was less than 25 landslides/km2 (37% of the examined area). Landslide density was greater than 25 landslides/km2 in about 2% of the examined area (156 km2), which includes parts of the Añasco, Mayagüez, Las Marías, Maricao, Lares, Utuado, Adjuntas, and Jayuya municipalities. Based on visual examination of imagery, the municipality of Utuado appears to have been the most severely impacted, with about 40% of the municipality having a density of landslides greater than 25 landslides/km2. This preliminary assessment serves to inform response and recovery efforts and as a basis for more detailed studies on the impacts of landslides in Puerto Rico caused by Hurricane Maria.

The United States experienced a significant surge in tornado activity in 2024, with 1,910 reported across the country. This marked a substantial increase from previous years, highlighting the unpredictable nature of these violent atmospheric phenomena. Fatalities and economic impact While tornado frequency increased, the death toll from such events remained relatively low compared to historical peaks. In 2023, 86 fatalities were reported due to tornadoes, a notable increase from the 23 deaths in 2022 but far below the 553 lives lost in 2011. Moreover, the economic impact of these storms was substantial, with tornado damage in 2023 amounting to approximately 1.38 billion U.S. dollars, nearly doubling from the previous year. However, this pales in comparison to the record-setting damage of 9.5 billion U.S. dollars in 2011. Comparison to other extreme weather events While tornadoes pose significant risks, hurricanes have historically caused more extensive damage and loss of life in the United States. Hurricane Katrina in 2005 remains the costliest tropical cyclone in recent decades, with damages totaling 200 billion U.S. dollars when adjusted to 2024 values. The impact of such extreme weather events extends beyond immediate destruction, as evidenced by the 1,518 hurricane-related fatalities recorded in 2005. As climate change continues to influence weather patterns, both tornado and hurricane activity may see further shifts in frequency and intensity in the years to come.

In late September 2017, intense precipitation associated with Hurricane Maria caused extensive landsliding across Puerto Rico. Much of the Lares municipality in central-western Puerto Rico was severely impacted by landslides., Landslide density in this region was mapped as greater than 25 landslides/km2 (Bessette-Kirton et al., 2019). In order to better understand the controlling variables of landslide occurrence and runout in this region, three 2.5-km2 study areas were selected and all landslides within were mapped in detail using remote-sensing data. Included in the data release are five separate shapefiles: geographic areas representing the mapping extent of the four distinct areas (map areas, filename: map_areas), initiation location polygons (source areas, filename: SourceArea), polygons of the entire impacted area consisting of source, transport, and deposition (affected areas, filename: AffectArea), points on the furthest upslope extent of the landslide source areas (headscarp point, filename: HSPoint), and lines reflecting the approximate travel paths from the furthest upslope extent to the furthest downslope extent of the landslides (runout lines, filename: RunoutLine). These shapefiles contain a number of attributes, some subjective (including general geomorphic setting and impact of human activity), some geometric (including length, width, and depth), and others on the underlying geology and soil of the landslides. A table detailing each attribute, attribute abbreviations, the possible choices for each attribute, and a short description of each attribute is provided as a table in the file labeled AttributeDescription.docx. The headscarp point shapefile attribute tables contain closest distance between headscarp and paved road (road_d_m; road data from U.S. Census Bureau, 2015). The runout line shapefile attribute table reflects if the landslide was considered independently unmappable past a road or river (term_drain), the horizontal length of the runout (length_m), the fall height from the headscarp to termination (h_m), the ratio of fall height to runout length (hlratio), distance to nearest paved road (road_d_m), and the watershed area upslope from the upper end of the runout line (wtrshd_m2). All quantitative metrics were calculated using tools available in ESRI ArcMap v. 10.6. The source area shapefile attribute table reflects general source area vegetation (vegetat) and land use (land_use), whether the slide significantly disaggregated during movement (flow), the failure mode (failmode), if the slide was a reactivation of a previous one (reactivate), if the landslide directly impacted the occurrence of another slide (ls_complex), the proportion of source material that left the source area (sourc_evac), the state of the remaining material (remaining), the curvature of the source area (sourc_curv), potential human impact on landslide occurrence (human_caus), potential landslide impact on human society (human_effc), if a building exists within 10 meters of the source area (buildng10m), if a road exists within 50 meters of the source area (road50m), the planimetric area of the source area (area_m2), the dimension of the source area perpendicular to the direction of motion (width_m), the dimension of the source area parallel to the direction of motion (length_m), the geologic formation of the source area (FMATN; from Bawiec, W.J., 1998), the soil type of the source area (MUNAME; from Acevido, G., 2020), the root-zone (0-100 cm deep) soil moisture estimated by the NASA SMAP mission for 9:30 am Atlantic Standard Time on 21 September 2017 (the day after Hurricane María) (smap; NASA, 2017), the average precipitation amount in the source area for the duration of the hurricane (pptn_mm; from Ramos-Scharrón, C.E., and Arima, E., 2019), the source area mean slope (mn_slp_d), the source area median slope (mdn_slp_d), the average depth change of material from the source area after the landslide (mn_dpth_m), the median depth change of material from the source area after the landslide (mdn_dpt_m), the sum of the volumetric change of material in the source area after the landslide (ldr_sm_m3), the major geomorphic landform of the source (maj_ldfrm), and the landcover of the source area (PRGAP_CL; from Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan, 2004). The affected area shapefile attribute table reflects the general affected area vegetation type (vegetat), the major geomorphic landform on which the landslide occurred (maj_ldfrm), whether the slide disaggregated during movement (flow), the general land use (land_use), the planimetric area of the affected area (area_m2), the dominant geologic formation of the affected area (FMATN; from Bawiec, W.J., 1998), the dominant soil type of the affected area (MUNAME; from Acevido, G., 2020), the sum of the volumetric change of material in all the contributing source areas for the affected area (Sum_ldr_sm), the average volumetric change of material in all the contributing source areas for the affected area (Avg_ldr_sm), if the landslide was considered independently unmappable past a road or river (term_drain), the number of contributing source areas to the affected area (num_srce), and the dominant landcover of the affected area (PRGAP_CL; from Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan, 2004). Mapping was conducted using aerial imagery collected between 9-15 October 2017 at 25-cm resolution (Quantum Spatial, Inc., 2017), a 1-m-resolution pre-event lidar digital elevation model (DEM) (U.S. Geological Survey, 2018), and a 1-m-resolution post-event lidar DEM (U.S. Geological Survey, 2020). In order to accurately determine the extent of the mapped landslides and to verify the georeferencing of the aerial imagery, aerial photographs were overlain with each DEM as well as a pre- and post-event lidar difference (2016-2018), and corrections were made as needed. Additional data sources described in the AttributeDescription document and metadata were used to extract spatial data once mapping was complete and results were appended to the shapefile attribute tables. Data in this release are provided as ArcGIS point (HSPoint), line (RunoutLine), and polygon (AffectArea and SourceArea) feature class files. Bessette-Kirton, E.K., Cerovski-Darriau, C., Schulz, W.H., Coe, J.A., Kean, J.W., Godt, J.W, Thomas, M.A., and Hughes, K. Stephen, 2019, Landslides Triggered by Hurricane Maria: Assessment of an Extreme Event in Puerto Rico: GSA Today, v. 29, doi:10.1130/GSATG383A.1 U.S. Census Bureau, 2015, 2015 TIGER/Line Shapefiles, State, Puerto Rico, primary and secondary roads State-based Shapefile: United States Census Bureau, accessed September 12, 2019, at http://www2.census.gov/geo/tiger/TIGER2015/ PRISECROADS/tl_2015_72_prisecroads.zip. Bawiec, W.J., 1998, Geology, geochemistry, geophysics, mineral occurrences and mineral resource assessment for the Commonwealth of Puerto Rico: U.S. Geological Survey Open-File Report 98-38, https://pubs.usgs.gov/of/1998/of98-038/ (accessed May 2020). Acevido, G., 2020, Soil Survey of Arecibo Area of Norther Puerto Rico: United States Department of Agriculture, Soil Conservation Service. National Aeronautics and Space Administration [NASA], 2017, SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update, Version 4: National Snow & Ice Data Center web page, accessed September 12, 2019, at https://nsidc.org/data/SPL4SMAU/versions/4. Ramos-Scharrón, C.E., and Arima, E., 2019, Hurricane María’s precipitation signature in Puerto Rico—A conceivable presage of rains to come: Scientific Reports, v. 9, no. 1, article no. 15612, accessed February 28, 2020, at https://doi.org/10.1038/ s41598-019-52198-2. Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan, 2004, Development of a 2001 National Landcover Database for the United States: Photogrammetric Engineering and Remote Sensing, Vol. 70, No. 7, July 2004, pp. 829-840. Quantum Spatial, Inc., 2017, FEMA PR Imagery: https://s3.amazonaws.com/fema-cap-imagery/Others/Maria (accessed October 2017). U.S. Geological Survey, 2018, USGS NED Original Product Resolution PR Puerto Rico 2015: http://nationalmap.gov/elevation.html (accessed October 2018). U.S. Geological Survey, 2020, USGS NED Original Product Resolution PR Puerto Rico 2018: http://nationalmap.gov/elevation.html (accessed June 2020). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

This nowCOAST™ map service provides maps depicting the latest official NWS Potential Storm Surge Flooding Map for any significant landfalling tropical cyclone expected to impact the Atlantic or Gulf of Mexico Coasts of the Contiguous United States. The map layers depict the risk associated with coastal flooding from storm surge associated with tropical cyclones.

The Potential Storm Surge Flooding Map depicts the geographical areas where inundation from storm surge could occur along with the heights, above ground, that water could reach in those areas. These potential heights are represented with different colors based on water level: 1) Greater than 1 foot above ground (blue), 2) Greater than 3 feet above ground (yellow), 3) Greater than 6 feet above ground (orange), and 4) Greater than 9 feet above ground (red). Two versions of this graphic are provided in this map--one with a mask (depicted in gray) identifying Intertidal Zone/Estuarine Wetland areas, and another version without the mask where Intertidal Zone/Estuarine Wetland areas are symbolized with the same colors as other areas.

Two additional layers are provided to depict 1) the full geographic extent for which the Potential Storm Surge Flooding Map is presently valid (the "map boundary"), and 2) Levee Areas, if any, within the affected area (symbolized with a black-and-white diagonal hatch pattern).

If the Potential Storm Surge Flooding Map is not presently active, all layers will be blank except for the Map Boundary layer, which will display a shaded region indicating the coverage area for any potential future graphics along with a text label indicating that the map is not presently active.

This map service is updated approximately every 10 minutes on nowCOAST™ to ensure the latest information is provided to the user as soon as it becomes available. Once issued, the Potential Storm Surge Flooding Map will be updated by NHC every six hours alongside each new Forecast Advisory for the associated tropical cyclone. However, due to processing requirements during the creation of this product, the flooding map becomes available approximately 60 to 90 minutes following the release of the associated NHC Forecast Advisory, at which point nowCOAST™ will acquire it and update this map service within the next 10 to 20 minutes (i.e., this product will be updated on nowCOAST™ within approximately 70 to 110 minutes after the associated Forecast Advisory is released). For more detailed information about layer update frequency and timing, please reference the
nowCOAST™ Dataset Update Schedule.

Background Information

Developed by National Hurricane Center (NHC) over the course of several years in consultation with social scientists, emergency managers, broadcast meteorologists, and others, the Potential Storm Surge Flooding Map is intended to depict the risk associated with coastal flooding from storm surge associated with tropical cyclones. On June 1, 2016 it became an operational product, issued on demand for certain tropical cyclones that are expected to affect the Atlantic and Gulf Coasts of the United States. The product is not available for tropical cyclones that may affect coastal areas in the Eastern or Central Pacific regions.

From the NHC Website:

"What the Map Takes into Account

The Potential Storm Surge Flooding Map is based on the NWS Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model and takes into account forecast uncertainty in the tropical cyclone track, intensity, and wind field. The map is based on probabilistic storm surge guidance developed by the NWS Meteorological Development Laboratory (MDL), in cooperation with NHC, called Probabilistic Hurricane Storm Surge (P-Surge 2.5).

P-Surge 2.5 derives storm surge probabilities by statistically evaluating a large set of SLOSH model simulations based on the current NHC official forecast, and takes into account historical errors in the official NHC track and intensity forecasts. P-Surge 2.5 combines the results of hundreds of individual SLOSH simulations to calculate the statistical distribution, or probabilities of possible storm surge heights at locations along the coast. All major factors that influence the amount of storm surge generated by a storm at a given location are accounted for, including the hurricane's landfall location, forward speed, and angle of approach to the coast; the storm intensity and wind field; the shape of the coastline; the slope of the ocean bottom; and local features such as barrier islands, bays, and rivers. The Potential Storm Surge Flooding Map is created by processing the resulting 10 percent exceedance levels from P-Surge 2.5, or storm surge values that have a 1-in-10 chance of being exceeded at each location.

The Potential Storm Surge Flooding Map takes into account:

Flooding due to storm surge from the ocean, including adjoining tidal rivers, sounds, and bays Normal astronomical tides Land elevation Uncertainties in the landfall location, forward speed, angle of approach to the coast, intensity, and wind field of the cyclone

The Potential Storm Surge Flooding Map does not take into account:

Wave action Freshwater flooding from rainfall Flooding resulting from levee failures For mapped leveed areas - flooding inside levees, overtopping of levees

Potential storm surge flooding is not depicted within certain levee areas, such as the Hurricane & Storm Damage Risk Reduction System in Louisiana. These areas are highly complex and water levels resulting from overtopping are difficult to predict. Users are urged to consult local officials for flood risk inside these leveed areas. If applicable to the region displayed by the map, these leveed areas will be depicted with a black and white diagonal hatch pattern.

The intertidal zone, or generally speaking, the area that is above water at low tide and under water at high tide, will be displayed with a user selectable mask layer on the Potential Storm Surge Flooding Map. Locations of estuarine wetlands, or lands that are saturated with water, either permanently or seasonally, are also used to help define this mask layer. This mask layer will allow users to differentiate between areas that could experience consequential flooding of normally dry ground and areas that routinely flood during typical high tides. The intertidal mask will be depicted as gray on the Potential Storm Surge Flooding Map.

What the Map Represents

The Potential Storm Surge Flooding Map represents the storm surge heights that a person should prepare for before a storm, given the uncertainties in the meteorological forecast. The map shows a reasonable worst-case scenario (i.e., a reasonable upper bound) of the flooding of normally dry land at particular locations due to storm surge. There is approximately a 1-in-10 chance that storm surge flooding at any particular location could be higher than the values shown on the map. Roadways are included in the basemap layer for aiding in geographical referencing only. The map will not indicate which roadways may flood from fresh or salt water in a hurricane situation."

For more information about the NHC Potential Storm Surge Flooding Map, please consult the NHC Website or the associated NWS Product Description Document (PDD).

Time Information

This nowCOAST™ map service is not time-enabled.

References

NHC, 2016: Potential Storm Surge Flooding Map, NWS/NCEP National Hurricane Center, Miami, FL. (Available at https://www.nhc.noaa.gov/surge/inundation/).

NWS, 2016: Potential Storm Surge Flooding Map Product Description Document, NWS, Silver Spring, MD (Available at https://www.nhc.noaa.gov/pdf/PDD-PotentialStormSurgeFloodingMap.pdf).

In September 2017, Hurricane Maria caused widespread landsliding throughout mountainous regions of Puerto Rico. Nearly all landslides mobilized as debris flows (Bessette-Kirton et al., 2019), but herein, we simply use the term “landslides” when describing all types of slope failures that occurred during Hurricane Maria. To examine the extent and physical characteristics of landslides in severely impacted areas (defined as having high landslide density (>25 landslides/km2) by Bessette-Kirton et al., 2017, 2019), we mapped individual landslides at scales between 1:600 and 1:1,000 in four 2.5 km2 study areas in the Mayagüez/Añasco/Las Marías (LAM1), Las Marías/Lares (LAM2), Naranjito (NAR), and Utuado (UTU) municipalities. We used aerial imagery collected between 9-15 October 2017 (Quantum Spatial, Inc., 2017) to map landslide source and runout areas. In addition to imagery, we used 1 m-resolution pre-event LiDAR (U.S. Geological Survey, 2018) as a digital base map for our mapping. The map data consist of landslide polygons, headscarp points, and travel distance lines. Each landslide polygon includes the total affected area (LSArea) and the number of mapped headscarp points (HeadscarpPoints). Each headscarp point contains attributes for the elevation measured at the distal end of the associated travel distance line (MinElev), the elevation at the headscarp point (MaxElev), slope at the headscarp point (Slope), aspect at the headscarp point (Aspect), the length of the associated maximum travel distance line (MaxTravelDist), and the ratio of total fall height (H: MaxElev-MinElev) to maximum travel distance (L: MaxTravelDist). Each travel distance line includes the length of the line (MaxTravelDist). Areas containing a combination of landslide and flood deposits were mapped as landslide/flood deposits (LSFlood). The data also include study area boundaries (AOIs). The data are provided as ArcGIS point, line, and polygon feature class files, all of which are contained within a file geodatabase (PRMariaLandslides.gdb). References: Bessette-Kirton, E.K., Cerovski-Darriau, C., Schulz, W.H., Coe, J.A., Kean, J.W., Godt, J.W, Thomas, M.A., and Hughes, K. Stephen, 2019, Landslides Triggered by Hurricane Maria: Assessment of an Extreme Event in Puerto Rico: GSA Today, v. 29, doi:10.1130/GSATG383A.1 Bessette-Kirton, E.K., Coe, J.A., Godt, J.W., Kean, J.W., Rengers, F.K., Schulz, W.H., Baum, R.L., Jones, E.S., and Staley, D.M., 2017, Map data showing concentration of landslides caused by Hurricane Maria in Puerto Rico: U.S. Geological Survey data release, https://doi.org/10.5066/F7JD4VRF. Quantum Spatial, Inc., 2017, FEMA PR Imagery: https://s3.amazonaws.com/fema-cap-imagery/Others/Maria (accessed October 2017). U.S. Geological Survey, 2018, USGS NED Original Product Resolution PR Puerto Rico 2015: http://nationalmap.gov/elevation.html (accessed October 2018).

In a time of global change, having an understanding of the nature of biotic and abiotic factors that drive a species’ range may be the sharpest tool in the arsenal of conservation and management of threatened species. However, such information is lacking for most tropical and epiphytic species due to the complexity of life history, the roles of stochastic events, and the diversity of habitat across the span of a distribution. In this study, we conducted repeated censuses across the core and peripheral range of Trichocentrum undulatum, a threatened orchid that is found throughout the island of Cuba (species core range) and southern Florida (the northern peripheral range). We used demographic matrix modeling as well as stochastic simulations to investigate the impacts of herbivory, hurricanes, and logging (in Cuba) on projected population growth rates (? and ?s) among sites. Methods Field methods Censuses took place between 2013 and 2021. The longest census period was that of the Peripheral population with a total of nine years (2013–2021). All four populations in Cuba used in demographic modeling that were censused more than once: Core 1 site (2016–2019, four years), Core 2 site (2018–2019, two years), Core 3 (2016 and 2018 two years), and Core 4 (2018–2019, two years) (Appendix S1: Table S1). In November 2017, Hurricane Irma hit parts of Cuba and southern Florida, impacting the Peripheral population. The Core 5 population (censused on 2016 and 2018) was small (N=17) with low survival on the second census due to logging. Three additional populations in Cuba were visited only once, Core 6, Core 7, and Core 8 (Table 1). Sites with one census or with a small sample size (Core 5) were not included in the life history and matrix model analyses of this paper due to the lack of population transition information, but they were included in the analysis on the correlation between herbivory and fruit rate, as well as the use of mortality observations from logging for modeling. All Cuban sites were located between Western and Central Cuba, spanning four provinces: Mayabeque (Core 1), Pinar del Rio (Core 2 and Core 6), Matanzas (Core 3 and Core 5), and Sancti Spiritus (Core 4, Core 7, Core 8). At each population of T. undulatum presented in this study, individuals were studied within ~1-km strips where T. undulatum occurrence was deemed representative of the site, mostly occurring along informal forest trails. Once an individual of T. undulatum was located, all trees within a 5-m radius were searched for additional individuals. Since tagging was not permitted, we used a combination of information to track individual plants for the repeated censuses. These include the host species, height of the orchid, DBH of the host tree, and hand-drawn maps. Individual plants were also marked by GPS at the Everglades Peripheral site. If a host tree was found bearing more than one T. undulatum, then we systematically recorded the orchids in order from the lowest to highest as well as used the previous years’ observations in future censuses for individualized notes and size records. We recorded plant size and reproductive variables during each census including: the number of leaves, length of the longest leaf (cm), number of inflorescence stalks, number of flowers, and the number of mature fruits. We also noted any presence of herbivory, such as signs of being bored by M. miamensis, and whether an inflorescence was partially or completely affected by the fly, and whether there was other herbivory, such as D. boisduvalii on leaves. We used logistic regression analysis to examine the effects of year (at the Peripheral site) and sites (all sites) on the presence or absence of inflorescence herbivory at all the sites. Cross tabulation and chi-square analysis were done to examine the associations between whether a plant was able to fruit and the presence of floral herbivory by M. miamensis. The herbivory was scored as either complete or partial. During the orchid population scouting expeditions, we came across a small population in the Matanzas province (Core 5, within 10 km of the Core 3 site) and recorded the demographic information. Although the sampled population was small (N = 17), we were able to observe logging impacts at the site and recorded logging-associated mortality on the subsequent return to the site. Matrix modeling Definition of size-stage classes To assess the life stage transitions and population structures for each plant for each population’s census period we first defined the stage classes for the species. The categorization for each plant’s stage class depended on both its size and reproductive capabilities, a method deemed appropriate for plants (Lefkovitch 1965, Cochran and Ellner 1992). A size index score was calculated for each plant by taking the total number of observed leaves and adding the length of the longest leaf, an indication of accumulated biomass (Borrero et al. 2016). The smallest plant size that attempted to produce an inflorescence is considered the minimum size for an adult plant. Plants were classified by stage based on their size index and flowering capacity as the following: (1) seedlings (or new recruits), i.e., new and small plants with a size index score of less than 6, (2) juveniles, i.e., plants with a size index score of less than 15 with no observed history of flowering, (3) adults, plants with size index scores of 15 or greater. Adult plants of this size or larger are capable of flowering but may not produce an inflorescence in a given year. The orchid’s population matrix models were constructed based on these stages. In general, orchid seedlings are notoriously difficult to observe and easily overlooked in the field due to the small size of protocorms. A newly found juvenile on a subsequent site visit (not the first year) may therefore be considered having previously been a seedling in the preceding year. In this study, we use the discovered “seedlings” as indicatory of recruitment for the populations. Adult plants are able to shrink or transition into the smaller juvenile stage class, but a juvenile cannot shrink to the seedling stage. Matrix elements and population vital rates calculations Annual transition probabilities for every stage class were calculated. A total of 16 site- and year-specific matrices were constructed. When seedling or juvenile sample sizes were < 9, the transitions were estimated using the nearest year or site matrix elements as a proxy. Due to the length of the study and variety of vegetation types with a generally large population size at each site, transition substitutions were made with the average stage transition from all years at the site as priors. If the sample size of the averaged stage was still too small, the averaged transition from a different population located at the same vegetation type was used. We avoided using transition values from populations found in different vegetation types to conserve potential environmental differences. A total of 20% (27/135) of the matrix elements were estimated in this fashion, the majority being seedling stage transitions (19/27) and noted in the Appendices alongside population size (Appendix S1: Table S1). The fertility element transitions from reproductive adults to seedlings were calculated as the number of seedlings produced (and that survived to the census) per adult plant. Deterministic modeling analysis We used integral projection models (IPM) to project the long-term population growth rates for each time period and population. The finite population growth rate (?), stochastic long-term growth rate (?s), and the elasticity were projected for each matrices using R Popbio Package 2.4.4 (Stubben and Milligan 2007, Caswell 2001). The elasticity matrices were summarized by placing each element into one of three categories: fecundity (transition from reproductive adults to seedling stage), growth (all transitions to new and more advanced stage, excluding the fecundity), and stasis (plants that transitioned into the same or a less advanced stage on subsequent census) (Liu et al. 2005). Life table response experiments (LTREs) were conducted to identify the stage transitions that had the greatest effects on observed differences in population growth between select sites and years (i.e., pre-post hurricane impact and site comparisons of same vegetation type). Due to the frequent disturbances that epiphytes in general experience as well as our species’ distribution in hurricane-prone areas, we ran transient dynamic models that assume that the populations censused were not at stable stage distributions (Stott et al. 2011). We calculated three indices for short-term transient dynamics to capture the variation during a 15-year transition period: reactivity, maximum amplification, and amplified inertia. Reactivity measures a population’s growth in a single measured timestep relative to the stable-stage growth, during the simulated transition period. Maximum amplification and amplified inertia are the maximum of future population density and the maximum long-term population density, respectively, relative to a stable-stage population that began at the same initial density (Stott et al. 2011). For these analyses, we used a mean matrix for Core 1, Core 2 Core 3, and Core 4 sites and the population structure of their last census. For the Peripheral site, we averaged the last three matrices post-hurricane disturbance and used the most-recent population structure. We standardized the indices across sites with the assumption of initial population density equal to 1 (Stott et al. 2011). Analysis was done using R Popdemo version 1.3-0 (Stott et al. 2012b). Stochastic simulation We created matrices to simulate the effects of episodic recruitment, hurricane impacts, herbivory, and logging (Appendix S1: Table S2). The Peripheral population is the longest-running site with nine years of censuses (eight

Between 2000 and 2020, 25 tropical cyclones have hit the Bahamas. The years with the highest number of storms were 2005 and 2011, each with three cyclones. In 2020, two storms made contact with the group of islands, Isaias as a hurricane category 1 in July and Eta as a tropical storm at the end of October.