In 2024, there were ** hurricanes registered worldwide, up from ** hurricanes a year earlier. This was nevertheless below the average of ** 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 ** 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 ******* deaths due to tropical cyclones have been reported worldwide. In the past decade, the number of such casualties stood at some ******, 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 *** billion U.S. dollars from 2010 to 2019.

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 the 2024 season, the Northwest Pacific was the ocean basin with the highest number of hurricanes recorded, with ** occurrences registered. The North Atlantic basin came in second, with a total of ** hurricanes recorded that year. In the period from 1990 to 2024, there were an average of ** hurricanes registered worldwide per year.

In 2024, there were four hurricanes tracked in the Atlantic basin, up from ***** recorded a year earlier. 2020 had recorded the second most active hurricane season in the displayed period. It only ranked behind 2005, when ** hurricanes were recorded in the region. Between 1990 and 2021, there were on average ***** hurricanes tracked per year in the Atlantic. In the same period, ** hurricanes made landfall in the U.S.

In 2023, there were ** named storms registered worldwide, down from ** storms in the previous year. Overall, there was an average of ** named tropical cyclones registered per year from 1980 to 2023. Japan was the country most exposed to this type of event worldwide. What is a tropical cyclone? Tropical cyclones are intense rotating storms that form over warm tropical waters, characterized by heavy rain and strong winds. Once a cyclone sustains wind speeds exceeding ** kilometers per hour, they are considered a tropical storm and receive a name. Named tropical storms can also receive further classification depending on their intensity and location (also known as basin). High-speed cyclones in the Northern Atlantic and Eastern Pacific basins are called hurricanes, while in the Western Pacific they are called typhoons. When the event takes place within the South Pacific and Indian Ocean, it is known as a cyclone. Frequency of tropical cyclones worldwide The Northwest Pacific basin is one of the most active for tropical cyclones worldwide. In 2023, there were ** named storms reported in the region, of which more than half were classified as hurricanes. Meanwhile, the North Indian Ocean represented one of the least active basins for tropical cyclones, with an annual average of * named storms recorded from 1990 to 2023.

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 36"x24" National Hurricane Center poster depicts the complete tracks of all major hurricanes in the north Atlantic and eastern north Pacific basins since as early as 1851. A major hurricane is defined as a category-3 hurricane or greater with sustained one-minute average winds of 111 mph (96kts) or greater.

Atlantic Tropical Cyclone Rainfall Climatology in the USA
Data sources (see references): NEXRAD level III data, hourly precipitation; IBtracs best track data; University of Colorado extended best track data
Available as NetCDF files and Matlab structure

Classification as TC precipitation criteria: within radius of outermost closed isobar of a TC at a given time

Scope: 100km radius around corresponding radar station

Dealing with radar outages: up to 2h gap - interpolation of precipitation, larger gaps - rescaling of frequency with fraction of available data (see formulas)

Available variables per radar station:

• Location: name [ ], coordinates [°N, °W]
• Grid: lat [°N], lon [°E]
• Frequency rescaling: re_freq [ ]

Available variables per event:

• Storm identifiers: name [ ], year [a]
• Storm total precipitation
  • Area distribution: Ptot [kg/m²], gridded (0.1x0.1°)
  • Area average: Ptot_av [kg/m²]
  • Area maximum within 0.5x0.5°: Ptot_max [kg/m²]
• Annual exceedance frequency: f(Ptot_max) [a^-1]

Relevant formulas:

re_freq = total duration of storm exposure / duration of viable measurements
f (Ptot_max) = (number of events exceeding Ptot_max / length of observation) * re_freq

Matlab structure:

• Level 1: TCP_climatology
• Level 2: station variables -> station_event_data leads to event variables
• Level 3: event variables -> Ptot leads to spatially gridded precipitation
• Level 4: Ptot-grid

Named Storms in the Atlantic Since 1950

Maximum Wind Speed, Storm Dates, and More

About this dataset

This dataset contains information on all of the named storms that have occurred in the Atlantic basin since 1950. It includes the storm's name, dates, minimum pressure, maximum wind speed, and storm type. This dataset is a great resource for anyone interested in studying hurricanes and other tropical storms

How to use the dataset

This dataset can be used to investigate the characteristics of named storms in the Atlantic basin since 1950. The variables in the dataset include the storm name, start date, end date, maximum wind speed, minimum pressure, and storm type. This dataset can be used to answer questions such as: - What has been the most intense storm in the Atlantic basin since 1950? - What is the average lifespan of a named storm in the Atlantic basin? - What is the most common type of storm in the Atlantic basin?

Research Ideas

• Creating a dashboard to track the progress of hurricane seasons
• comparing different hurricane seasons
• determining which areas are most vulnerable to hurricanes

Acknowledgements

This dataset was compiled by the National Hurricane Center (NHC) and the National Centers for Environmental Information (NCEI)

License

Unknown License - Please check the dataset description for more information.

Columns

File: Named Storm Data - since 1950.csv | Column name | Description | |:-------------------------|:-----------------------------------------------| | Year | The year the storm occurred. (Integer) | | Storm Name | The name of the storm. (String) | | Start Date | The date the storm began. (Date) | | End Date | The date the storm ended. (Date) | | Dates | The dates the storm occurred. (Date) | | Max Wind Speed (mph) | The maximum wind speed of the storm. (Integer) | | Min pressure (mb) | The minimum pressure of the storm. (Integer) | | Storm Type | The type of storm. (String) |

Acknowledgements

If you use this dataset in your research, please credit Aaron Simmons.

This project used a combination of historical research and computer modeling to study the impacts of hurricanes in New England since 1620. For details on methods and results, please see the published paper (Boose, E. R., K. E. Chamberlin and D. R. Foster. 2001. Landscape and regional impacts of hurricanes in New England. Ecological Monographs 71: 27-48). The Abstract from the paper is reproduced below. "Hurricanes are a major factor controlling ecosystem structure, function and dynamics in many coastal forests and yet their ecological role can be understood only by assessing impacts in space and time over a period of centuries. We present a new method for reconstructing hurricane disturbance regimes using a combination of historical research and computer modeling. Historical data on wind damage for each hurricane in the selected region are quantified using the Fujita scale to produce regional maps of actual damage. A simple meteorological model (HURRECON), parameterized and tested for selected recent hurricanes, provides regional estimates of wind speed, direction, and damage for each storm. Individual reconstructions are compiled to analyze spatial and temporal patterns of hurricane impacts. Long-term effects of topography on a landscape scale are then examined with a simple topographic exposure model (EXPOS). "We applied this method to New England, USA, examining hurricanes since European settlement in 1620. Results showed strong regional gradients in hurricane frequency and intensity from southeast to northwest: average return intervals for F0 damage on the Fujita scale (loss of leaves and branches) ranged from 5 to 85 years, average return intervals for F1 damage (scattered blowdowns, small gaps) ranged from 10 to more than 200 years, and average return intervals for F2 damage (extensive blowdowns, large gaps) ranged from 85 to more than 380 years. On a landscape scale, average return intervals for F2 damage in the town of Petersham MA ranged from 125 years across most sites to more than 380 years on scattered lee slopes. Actual forest damage was strongly dependent on land-use and natural disturbance history. Annual and decadal timing of hurricanes varied widely. There was no clear century-scale trend in the number of major hurricanes. "The historical-modeling approach is applicable to any region with good historical records and will enable ecologists and land managers to incorporate insights on hurricane disturbance regimes into the interpretation and conservation of forests at landscape to regional scales."

Nearly ** percent of all hurricanes that made landfall in the United States between 1851 and 2022 hit Florida. The state was hit by *** hurricanes in the period, of which ** were major hurricanes (category * or higher). Texas and Louisiana were the second and third most hit states in the country, with ** and ** hurricanes, respectively.

This dataset contains a coastal erosion hazards analysis for Hurricane Michael. The analysis is based on a storm-impact scaling model that combines observations of beach morphology with hydrodynamic models to predict how sandy beaches, the first line of defense for many coasts exposed to tropical storms and hurricanes, will respond during a direct landfall. Storm-induced total water levels, due to both surge and waves, are compared to beach and dune elevations to determine the probabilities of three types of coastal change - collision (dune erosion), overwash, and inundation. The storm surge elevations along the open coast were obtained from the National Oceanic and Atmospheric Administration's (NOAA) probabilistic surge forecast (psurge), which is based on conditions specific to the landfalling storm. Errors in hurricane forecasts are included in order to identify probable surge levels. The 10% exceedance surge level was used to represent the worst-case scenario. Maximum wave heights in 20-m water depth, obtained from the NOAA WaveWatch3 model 7-day forecast, were used to compute wave runup elevations at the shoreline. Dune elevations were extracted from lidar topographic surveys.

Disclaimer: This product is based on published research results of the USGS National Assessment of Coastal Change Hazards Project and is intended to indicate the potential for coastal change caused by storm surge and wave runup. This product is based on an analysis that simplifies complex coastal change processes to two important aspects - measured dune elevations and predicted total water levels. As such, the actual changes that occur during extreme storms may be different than what is described here. Results apply to open coast environments and do not consider potential coastal change along inland waters. The public should not base evacuation decisions on this product. Citizens should follow the evacuation advice of local emergency management authorities.

This dataset contains a coastal erosion hazards analysis for Hurricane Idalia. The analysis is based on a storm-impact scaling model that combines observations of beach morphology with hydrodynamic models to predict how sandy beaches, the first line of defense for many coasts exposed to tropical storms and hurricanes, will respond during a direct landfall. Storm-induced total water levels, due to both surge and waves, are compared to beach and dune elevations to determine the probabilities of three types of coastal change - collision (dune erosion), overwash, and inundation. The storm surge elevations along the open coast were obtained from the National Oceanic and Atmospheric Administration's (NOAA) probabilistic surge forecast (psurge), which is based on conditions specific to the landfalling storm. Errors in hurricane forecasts are included in order to identify probable surge levels. The 10% exceedance surge level was used to represent the worst-case scenario. Maximum wave heights in 20-m water depth, obtained from the NOAA WaveWatch3 model 7-day forecast, were used to compute wave runup elevations at the shoreline. Dune elevations were extracted from lidar topographic surveys.

Disclaimer: This product is based on published research results of the USGS National Assessment of Coastal Change Hazards Project and is intended to indicate the potential for coastal change caused by storm surge and wave runup. This product is based on an analysis that simplifies complex coastal change processes to two important aspects - measured dune elevations and predicted total water levels. As such, the actual changes that occur during extreme storms may be different than what is described here. Results apply to open coast environments and do not consider potential coastal change along inland waters. The public should not base evacuation decisions on this product. Citizens should follow the evacuation advice of local emergency management authorities.

This dataset contains a coastal erosion hazards analysis for Hurricane Milton. The analysis is based on a storm-impact scaling model that combines observations of beach morphology with hydrodynamic models to predict how sandy beaches, the first line of defense for many coasts exposed to tropical storms and hurricanes, will respond during a direct landfall. Storm-induced total water levels, due to both surge and waves, are compared to beach and dune elevations to determine the probabilities of three types of coastal change - collision (dune erosion), overwash, and inundation. The storm surge elevations along the open coast were obtained from the National Oceanic and Atmospheric Administration's (NOAA) probabilistic surge forecast (psurge), which is based on conditions specific to the landfalling storm. Errors in hurricane forecasts are included in order to identify probable surge levels. The 10% exceedance surge level was used to represent the worst-case scenario. Maximum wave heights in 20-m water depth, obtained from the NOAA WaveWatch3 model 7-day forecast, were used to compute wave runup elevations at the shoreline. Dune elevations were extracted from lidar topographic surveys.

Disclaimer: This product is based on published research results of the USGS National Assessment of Coastal Change Hazards Project and is intended to indicate the potential for coastal change caused by storm surge and wave runup. This product is based on an analysis that simplifies complex coastal change processes to two important aspects - measured dune elevations and predicted total water levels. As such, the actual changes that occur during extreme storms may be different than what is described here. Results apply to open coast environments and do not consider potential coastal change along inland waters. The public should not base evacuation decisions on this product. Citizens should follow the evacuation advice of local emergency management authorities.

EcoDRR global classification scheme based on spatial combination of ecosystem coverage and natural hazard physical exposure. The physical exposure data-set shows the product of hazard frequency and people exposed to this hazard in the same 100 square kilometer cell. For a specific natural hazard, a 0.01 degree resolution raster is generated, showing hazard annual frequency weighted with portion of pixel potentially affected. In the case of tropical cyclones, annual frequency is calculated using the category one of the Saffir-Simpson scale. It corresponds to the largest wind buffer of each past event footprint.

Sources: The dataset includes an estimate of tropical cyclone frequency of Saffir-Simpson category 1. It is based on two sources: 1) IBTrACS v02r01 (1969 - 2008, http://www.ncdc.noaa.gov/oa/ibtracs/), year 2009 completed by online data from JMA, JTWC, UNISYS, Meteo France and data sent by Alan Sharp from the Australian Bureau of Meteorology. 2) A GIS modeling based on an initial equation from Greg Holland, which was further modified to take into consideration the movement of the cyclones through time. Unit is expected average number of event per 100 years multiplied by 100. This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: Raw data: IBTrACS, compilation and GIS processing UNEP/GRID-Europe.

Between 1851 and 2023, there were *** hurricane direct hits in the United States, of which ** percent were category * hurricanes. In the same period, ** major hurricanes (with a category * or higher) made landfall in the country. Hurricane Michael, in 2018, was the latest category * hurricane to hit the North American country. Florida was the state most commonly hit by hurricanes.

Increasing hurricane frequency and intensity with climate change is likely to affect soil organic carbon (C) stocks in tropical forests. We examined the cycling of C between soil pools and with depth at the Luquillo Experimental Forest in Puerto Rico in soils over a 30-year period that spanned repeated hurricanes. We used a non-linear matrix model of soil C pools and fluxes (“soilR”) and constrained the parameters with soil and litter survey data. Soil chemistry and stable and radiocarbon isotopes were measured from three soil depths across a topographic gradient in 1988 and 2018. Our results suggest that pulses and subsequent reduction of inputs caused by severe hurricanes in 1989, 1998, and two in 2017 led to faster mean transit times and younger mean ages of soil C in the particulate, occluded, and mineral-associated soil organic matter pools at 0–10 cm and 35–60 cm depths relative to a modeled control soil with constant inputs over the thirty years. Between 1988 and 2018, the occluded C stock increased, and d13C in all pools decreased, while changes in particulate and mineral-associated C were undetectable. The differences between 1988 and 2018 suggest that hurricane disturbance results in a dilution of the occluded light C pool with an influx of young, debris-deposited C, and possible microbial scavenging of old and young C in the particulate and mineral-associated pools. These effects led to a younger total soil C pool with faster mean transit times. Our results suggest that increasing frequency of intense hurricanes will speed up rates of C cycling in tropical forests, and eventually lead to net losses of C from tropical forest soils. Methods Site description Soils were sampled from the Bisley Experimental Watershed of the LEF, Puerto Rico (18.3157 deg. N, 65.7487 deg W), a Long-Term Ecological Research and Critical Zone Observatory and Network site (https://luq.lter.network). The mean maximum daily temperature at Bisley was 27 ºC between 1993 and 2010 (Gonzales, 2020), with little seasonality. The mean annual precipitation at Bisley was 3883 (± 864 s.d.) mm y-1 from 1988 through 2014 (González, 2017; Murphy et al., 2017). Rainfall occurs all year, though January through April experience slightly less precipitation than other months (Heartsill-Scalley et al., 2007). The site is a humid tropical forest with a diverse tree community of approximately 170 species > 4 cm diameter at breast height (Weaver & Murphy, 1990), and dominated by tabonuco (Dacryodes excelsa Vahl). Elevation of Bisley spans from 261 m a.s.l. at the base to 450 m a.s.l. on the ridges (Scatena, 1989). Soils in Bisley are derived from volcaniclastic sediments of andesitic parent material (Scatena, 1989). Ridge soils are classified as Ultisols (Typic Haplohumults), while slope soils are classified as Oxisols (inceptic and Aquic Hapludox), and valley soils are classified as Inceptisols (Typic Epiaquaepts) (Hall et al., 2015; McDowell et al., 2012; Scatena, 1989). Detailed site descriptions can be found in Scatena (1989), Heartsill-Scalley et al (2010), and McDowell et al (2012). Here we refer to soil organic C (SOC) and soil C interchangeably because there is no detectable inorganic C in these soils. Hurricane occurrence Figure 1: Timeline of major hurricanes that have affected Luquillo Experimental Forest between sampling dates. Nine major hurricanes (category 3 or higher) have impacted Puerto Rico between 1851 and 2019 (López-Marrero et al., 2019), and five of these hurricanes have impacted the LEF. Until 1998, hurricanes had historically directly impacted the LEF approximately every 60 years (Scatena & Larsen, 1991). Before the initial sampling campaign of this study, Hurricane San Ciprián in 1932 was the most recent storm to cause major disturbance to the LEF (Scatena & Larsen, 1991). However, since sampling in 1988, four major hurricanes have impacted the forest (Figure 1). Hurricane Hugo (Category 3-4) in 1989, Hurricane Georges (Category 3) in 1998, and Hurricanes Irma and Maria (Categories 5 and 4, respectively) within two weeks in 2017. The trajectory and windspeeds of all these hurricanes caused widespread defoliation. Litterfall historically takes over five years to return to pre-hurricane levels (Scatena et al., 1996). Sampling Sample collection occurred in 1988 and again in 2018. In both years, samples were collected from three depths: 0–10 cm (the A horizon), 10–35 cm (all of the B1 horizon and part of B2), and 35–60 cm (B2 to C) using an 8 cm diameter soil auger. Soils in this study were sampled at three separate sites at least 40 m from one another for each of three topographic locations, ridge, slope, and upland valley. Two separate cores were taken from a fourth topographic location in the riparian valley, that characterized a smaller proportion of the area of these watersheds (Scatena & Lugo, 1995). Riparian valley sites were ephemeral streambeds with a high boulder presence that limited sampling to less than 25 cm depth in one case. Sampling sites from 1988 were marked with flags, and samples from 2018 were collected from within 15 m of the same locations as the replicates from 1988, for consistency. Samples collected in 1988 were analyzed for bulk density, pH, soil moisture, and a suite of soil chemical properties (see Silver et al. 1994). Samples were then air-dried and stored in closed Ziploc bags within paper bags in a storage facility in Richmond, CA, USA before density fractionation in 2018. Fresh samples collected in 2018 were also characterized for pH, soil moisture, and soil chemistry. Approximately 3 g subsamples from each fresh sample in 2018 were immediately extracted with 45 mL of 0.2 M sodium citrate/0.5 M ascorbate solution, shaken for 16 hours, then centrifuged and the supernatant decanted to measure concentrations of poorly crystalline iron (Fe) oxides. Within two days of being double-bagged in Ziploc bags, fresh samples were further subsampled and analyzed for pH in a 1:1 soil-to-water slurry (Thomas, 1996) and for gravimetric soil moisture by oven-drying ~10 g subsamples at 105 ºC until a constant weight. Soil samples were air-dried before further processing and analysis. Air-dried soils from both sampling years were sieved to 2 mm and large roots were sorted out. Soil Density fractionation Soil was fractionated by density following the method of Swanston et al. (2005), as modified by Marin-Spiotta et al., (2009). Approximately 20 g of air-dried soil was added to centrifuge tubes. Sodium polytungstate (SPT, Na6 [H2W12O40] TC-Tungsten Compounds, Bavaria, Germany) in solution of density 1.85 g cm-3 was added to centrifuge tubes and agitated before centrifuging. The density of the SPT followed previous studies from this and nearby sites to allow direct comparison (Gutiérrez del Arroyo & Silver, 2018; Hall et al., 2015). Particulate organic matter floating at the surface after centrifugation, the free light fraction (FLF), was aspirated and then rinsed with 100 ml of deionized water 5 times on a 0.8 µm pore polycarbonate filter (Whatman Nuclepore Track Etch Membrane, Darmstadt, Germany). Rinsed FLF was oven-dried at 65 ºC until weight had stabilized. The remainder of the sample was combined with 70 ml of additional SPT and mixed using an electric benchtop mixer (G3U05R, Lightning, New York, NY, USA) at 1700 rpm for 1 min and sonicated in an ice bath for 3 min at 70% pulse (Branson 450 Sonifier, Danbury, CT, USA). Sonication is intended to disrupt soil structure and liberate organic matter that has been occluded in aggregates. The sonicated slurry was centrifuged again, and the light fraction at the surface, the occluded light fraction (OLF), was aspirated, rinsed, and dried using the same method as for the FLF. The remaining soil pellet was considered the heavy fraction (HF), or mineral-associated organic matter fraction. The HF was rinsed by thoroughly mixing with 150 ml of deionized water in the centrifuge tube, centrifuging, and removing the supernatant repeatedly until the fraction had been rinsed 5 times. The rinsed HF was oven-dried at 105 ºC until weight stabilized. The average mass recovery was 98%. Soil C and N and δ13C Dried bulk and HF soils were homogenized separately using a Spex Ball mill (SPEX Sample Prep Mixer Mill 8000D, Metuchen, NJ). The FLF and OLF were homogenized separately by hand using a mortar and pestle. All homogenized samples were then analyzed at U. C. Berkeley for C and N concentrations on the CE Elantech elemental analyzer (Lakewood, NJ) and for δ13C in the Stable Isotope Laboratory at UC Berkeley, using a CHNOS Elemental Analyzer interfaced to an IsoPrime 100 mass spectrometer (Cheadle Hulme, UK), with a long-term external precision of 0.10 %. Soil C stocks were calculated by multiplying the C concentrations (%) by the oven-dry mass of bulk soil (< 2 mm) and dividing by depth and the bulk density as measured in 1988 (Silver et al., 1994; Throop et al., 2012). Radiocarbon Homogenized soil samples were combusted to CO2 in sealed glass tubes along with silver (Ag) and copper oxide (CuO) at the Center for Accelerator Mass Spectrometry at Lawrence Livermore National Lab. The CO2 was then graphitized on Fe powder under pressurized hydrogen gas (Vogel et al., 1984). Graphite was pressed into aluminum targets and run on the Compact Accelerator Mass Spectrometer for radiocarbon analysis (Broek et al., 2021). Radiocarbon is reported in Δ14C, following Stuiver & Polach (1977), and calculated based on the fraction of modern isotope composition, corrected for the year of sampling, and corrected for mass-dependent fractionation with observed δ13C values of the sample. The compact AMS had an average Δ14C precision of 3.2 %. We report the corrected Δ14C value and ΔΔ14C, which is calculated as Δ14C of the sample minus Δ14C of the atmosphere, to account for rapidly changing atmospheric Δ14C during the study period. Atmospheric radiocarbon has been

This layer presents an estimation of surges triggered by tropical cyclone frequency of Saffir-Simpson category (Don't forget to zoom on the coasts you are interested in!) Here, unit is expected average number of event per year. However, source unit is expected average number of event per 1000 year. For more information, visit the Global Risk Data Platform: http://preview.grid.unep.ch/index.php?preview=data&events=surges&evcat=2&lang=eng

This dataset contains information related to the probabilities of erosion on sandy beaches along the U.S. coast during extreme storm conditions. The analysis is based on a storm-impact scaling model that uses observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast will respond to storms. Storm-induced water levels, due to both surge and waves, are compared to beach and dune elevations every 1-km along the coast to determine the likelihood of collision (dune erosion), overwash, and inundation during a direct impact. Related morphologic (dune crest and toe elevation) and hydrodynamic (storm surge, wave setup and runup) data are also included. For additional information, please see publications listed on the USGS National Assessment of Storm-Induced Coastal Change Hazards website (http://coastal.er.usgs.gov/hurricanes/).

This Dataset contains year, month and season-wise frequency of Cyclonic Disturbances, Cyclones and above and Severe Cyclones and above over Bay of Bengal, Arabian Sea and Land

Note:

1) Cyclones and above: A cyclonic disturbance in which the maximum average surface wind speed is in the range of 34 to 47 knots (62 to 88 kmph).

2) Cyclonic Disturbances: A non frontal synoptic scale low pressure system originating over tropical waters with organized convection and definite cyclonic wind circulation. It is called as a depression if the associated sustained maximum wind speed at surface level is 17-27 knots. It is called as a deep depression if the sustained maximum wind speed is 28-33 knots.

3) Severe Cyclone and above: A cyclonic disturbance in which the maximum average surface wind speed is in the range of 48 to 63 knots (89 to 117 kmph).