In 2024, a total of 1,374 earthquakes with magnitude of five or more were recorded worldwide as of December that year. The Ring of Fire Large earthquakes generally result in higher death tolls in developing countries or countries where building codes are less stringent. China has suffered from a number of strong earthquakes that have resulted in extremely high death tolls. While earthquakes occur around the globe along the various tectonic plate boundaries, a significant proportion occur around the basin of the Pacific Ocean, in what is referred to as the Ring of Fire due to the high degree of tectonic activity. Many of the countries in the Ring of Fire, including Japan, Chile, the United States and New Zealand, led the way in earthquake policy and science as a result. The impacts of earthquakes The tragic loss of life is not the only major negative effect of earthquakes, a number of earthquakes have caused billions of dollars worth of damage to infrastructure and private property. The high cost of damage in the 2011 Fukushima and Christchurch earthquakes in Japan and New Zealand respectively demonstrates that even wealthy, developed countries who are experienced in dealing with earthquakes are ill-equipped when the large earthquakes hit.

Since 1900, the earthquake in Tangshan in China in 1976 caused the highest number of deaths, reaching over 240,000. However, some estimate the number to be over 650,000 fatalities. The earthquake in Haiti in 2010 has the second-highest death toll, but also here numbers vary from just above 100,000 to over 300,000 fatalities. Four of the 10 deadliest earthquakes during the period were registered in China.

The 1960 Great Chilean earthquake was the largest measured on the Richter scale, with a magnitude of 9.5. The second most powerful earthquake since 1900 took place in Alaska in 1964. An earthquake with a magnitude of 9.0 and higher is defined as causing near or total destruction, including severe damage or collapse to all buildings. Earthquakes and the Richter scale – additional information An earthquake occurs when two tectonic plates under the Earth’s surface slip past one another, resulting in the sudden release of energy in the Earth's crust. The sudden violent tremors can cause destruction to infrastructure, human injury, and even death. There are a number of ways to measure the magnitude of an earthquake. One of the first and most widely-used methods is the Richter scale. The Richter magnitude scale was developed by the seismologist, Charles F. Richter, in 1935. On the Richter Scale, magnitude is expressed in whole numbers and decimal fractions, but is based on a logarithmic scale. For example, a magnitude 5.3 might be computed for a moderate earthquake, and a strong earthquake might be rated as magnitude 6.3. Deadliest earthquakes Despite its strong magnitude, the earthquake in Chile in 1960 does not appear on the list of the 10 deadliest earthquakes in the world since 1900. The 1976 earthquake in Tangshan, China, caused the highest death toll, while the earthquake that hit Haiti in 2010 caused the second highest death toll.

This layer shows the potential ground shaking intensity from earthquakes.When an earthquake happens, the more the ground shakes, the more damage occurs. The shaking hazard which occurs during an earthquake is measured by horizontal acceleration. Peak ground acceleration is the maximum amount of lateral shaking from an earthquake, as measured by ground instruments.Unlike the Richter scale, ground acceleration is not a measure of the total energy (magnitude, or size) of an earthquake event, but rather the geographically specific effect of that event. Ground acceleration is the intensity of how hard the earth shakes sideways in a given area.Geologists use earthquake risk maps to estimate stability and landslide potential of hillsides. The level of earthquake risk is important to engineers, particularly those involved in landfill and highway bridge construction. Insurance companies set rates by earthquake risk maps, and emergency planners use them to allocate assistance funds for education and preparedness. Businesses analyze earthquake risk maps to site distribution centers and critical infrastructure away from zones of potential damage. In many cases, building codes require that construction in zones of high earthquake risk be more resistant to damaging earthquakes.Dataset SummaryThe peak acceleration value that is shown by this layer is an estimate of the worst amount of shaking due to earthquakes experienced in the place indicated on a map in about a 500 year time frame.Predicted horizontal acceleration (shaking) values in this dataset are expressed as a percentage of the acceleration of gravity (g). The values in this dataset do not exceed 100, so keep in mind a 100 on the map means the model is predicting a value greater than or equal to 100% g, violent or extreme shaking. (100% g is an acceleration of 9.80665 m/s²)Horizontal ground acceleration correlates well with the Modified Mercalli Intensity (MMI) Scale in measurement of earthquake intensity. Instrument intensity is USGS's term for equivalent Modified Mercalli Scale (MMI) values as measured by instruments: Example ground acceleration values from past earthquakes: 0.1% g (0.01 m/s²) perceptible by people 2% g (0.2 m/s²) people lose their balance 8% g The Mall, Washington DC, 2011 Louisa County Virginia Earthquake 16% g Treasure Island, San Francisco Bay, 1989 Loma Prieta Earthquake 16% g Tokyo (373 km from epicenter), 2011 Tohoku Earthquake 31% g Seward Park, Seattle, 2001 Nisqually Earthquake 42% g UC Santa Cruz Seismic Lab, 1989 Loma Prieta Earthquake 50% g Well-designed buildings can survive if the duration is short 90% g Sylmar, California (16km from epicenter), 1994 Northridge Earthquake 270% g Miyagi Prefecture, Japan (75km from epicenter), 2011 Tohoku Earthquake The data cover the continental U.S., Alaska, Hawaii and Puerto Rico.What can you do with this layer?The vector features for this layer can be used for visualization and analysis in ArcGIS. The features in this layer draws at all scales. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.

From 1900 to 2016, China was the country with highest amount of earthquakes. Between that time period, they reported 157 earthquakes. Indonesia, Iran, Turkey, and Japan rounded out the top five countries with the most earthquakes.

What causes earthquakes?

When two tectonic plates, which make up the Earth’s crust, shift, it forces shock waves to shake the Earth’s surface, resulting in an earthquake. Earthquakes are measured are on the Richter scale, assessed on a scale of one to nine and higher. The earthquake in Chile in 1960 was the strongest earthquake worldwide, according to the Richter scale, with a magnitude of 9.5. Because earthquakes are not able to be predicted, they can cause more damage than other natural disasters which can be predicted.

Earthquake effects

Earthquakes have caused a lot of physical damage and casualties. 2004 saw the highest global death toll due to earthquakes, with 298,101 casualties. As a result of this scale of damage, a lot of money goes into repair. For example, the January 17, 1994 California earthquake was the most expensive earthquake to the insurance industry in the United States. Not only did China have the highest number of earthquakes, it was also the country with the most natural disasters in 2018.

An earthquake hazard map provides, at any location, the value of a ground motion intensity measure (for example, horizontal peak ground acceleration, PGA) that is expected to be exceeded at least once in 100 year mean return period. The earthquake hazard maps are developed by determining the simulated ground motion intensities at every gridded location for 10,000 realizations of next-year activity of earthquake events. At each grid location, the intensities are ranked and the ground motion intensity of the mean return period of interest is recorded. Spectral = 1 second spectral acceleration. The size of the finest grid is 9 arc seconds and was resampled to coarser resolutions (up to approximately 7 arc minutes) for some locations. Compiled by AIR Worldwide.

Epicenters of known M≥5 earthquakes from 1769 to 2016 are shown for California and a 100 km area bordering the state. Earthquakes are grouped by: M = 5-6; M = 6-7; M = 7+.

100 earthquakes from the South Australian earthquake catalogue, between 2007 to mid-2015, were selected in an area of the Fleurieu Peninsula with the densest seismograph coverage. The model consists of an earthquake magnitude map, ranging from... 100 earthquakes from the South Australian earthquake catalogue, between 2007 to mid-2015, were selected in an area of the Fleurieu Peninsula with the densest seismograph coverage. The model consists of an earthquake magnitude map, ranging from near 0.0 to 3.8 near Mount Barker and a 'quake reliability' map showing the error ellipses (2σ) of the hypocentres. The model includes surface elevation exaggerated 5 times, a super-imposed solid geology map and fault network surfaces. Very little is known of the location of the major faults. The surfaces have been constructed using the mapped surface expression at an angle of 45 degrees in the generally expected dip direction. All the faults have been extended to a depth of 10 km, except the Bremer Fault, which has been extended to 5 km. The model shows that, contrary to accepted wisdom, the earthquakes do not fall on particular fault planes, but are scattered quite widely. It also shows that the majority occur deeper than 10 km. There is no clear pattern; some areas seem to have many small events, but the largest event seems to be in a volume that otherwise has very few events.

description: This part of DS 781 presents data for faults for the geologic and geomorphic map of the Offshore of Fort Ross map area, California. The vector data file is included in "Faults_OffshoreFortRoss.zip," which is accessible from http://pubs.usgs.gov/ds/781/OffshoreFortRoss/data_catalog_OffshoreFortRoss.html. The Offshore of Fort Ross map area is cut by the northwest-trending San Andreas Fault, the right-lateral transform boundary between the North American and Pacific tectonic plates. The San Andreas extends across the inner shelf in the southern part of the map, then crosses the shoreline at Fort Ross and continues onland for about 75 km to the east flank of Point Arena (fig. 8-1). Seismic-reflection data are used to map the offshore fault trace, and reveal a relatively simple, 200- to 500-m wide zone typically characterized by one or two primary strands. About 1500 m west of the San Andreas Fault, the mid shelf (between water depths of 40 m and 70 m) in the southernmost part of the map area includes an about 5-km-wide field of elongate, shore-normal sediment lobes (unit Qmsl). Individual lobes within the field are as much as 650-m long and 200-m wide, have as much as 1.5 m (check with Steve) of relief above the surrounding smooth seafloor, and are commonly connected with upslope chutes. Given their morphology and proxmity to the San Andreas fault, we infer that these lobes result from slope failures associated with strong ground motions triggered by large San Andreas earthquakes. Movement on the San Andreas has juxtaposed different coastal bedrock blocks (Blake and others, 2002). Rocks east of the fault that occur along the coast and in the nearshore belong to the late Tertiary, Cretaceous, and Jurassic Franciscan Complex, either sandstone of the Coastal Belt or Central Belt (unit TKfs) or melange of the central terrane (unit fsr). Bedrock west of the fault are considered part of the Gualala Block (Elder, 1998) and include the Eocene and Paleocene German Rancho Formation (unit Tgr) and the Miocene sandstone and mudstone of the Fort Ross area (unit Tsm). This section of the San Andreas Fault onland has an estimated slip rate of about 17 to 25 mm/yr (Bryant and Lundberg, 2002). The devastating Great 1906 California earthquake (M 7.8) is thought to have nucleated on the San Andreas Fault about 100 kilometers south of this map area offshore of San Francisco (e.g., Bolt, 1968; Lomax, 2005), with the rupture extending northward through the Offshore of Fort Ross map area to the south flank of Cape Mendocino. Emergent marine terraces along the coast in the Offshore of Fort Ross map area record recent contractional deformation associated with the San Andreas Fault system. Prentice and Kelson (2006) report uplift rates of 0.3 to 0.6 mm/yr for a late Pleistocene terrace exposed at Fort Ross, and this recent uplift must also have affect the nearshore and inner shelf. Previously, McCulloch (1987) mapped a nearshore (within 3 to 5 km of the coast) fault zone from Point Arena to Fort Ross (Fig. 8-1) using primarily deeper industry seismic-reflection data. Subsequently, Dickinson and others (2005) named this structure the "Gualala Fault." Our mapping, also based on seismic-reflection data, reveals this structure as a steep, northeast trending fault and similarly shows the fault ending to the south in the northern part of the Offshore of Fort Ross map area. We have designated the zone of faulting and folding above this structure the "Gualala Fault deformation zone." Faults were primarily mapped by interpretation of seismic reflection profile data (see field activity S-8-09-NC). The seismic reflection profiles were collected between 2007 and 2010. References Cited Blake, M.C., Jr., Graymer, R.W., and Stamski, R.E., 2002, Geologic map and map database of western Sonoma, northernmost Marin, and southernmost Mendocino counties, California: U.S. Geological Survey Miscellaneous Field Studies Map 2402, scale 1:100,000. Bolt, B.A., 1968, The focus of the 1906 California earthquake: Bulletin of the Seismological Society of America, v. 58, p. 457-471. Bryant, W.A., and Lundberg, M.M., compilers, 2002, Fault number 1b, San Andreas fault zone, North Coast section, in Quaternary fault and fold database of the United States: U.S. Geological Survey website, accessed April 4, 2013, at http://earthquakes.usgs.gov/hazards/qfaults. Dickinson, W.R., Ducea, M., Rosenberg, L.I., Greene, H.G., Graham, S.A., Clark, J.C., Weber, G.E., Kidder, S., Ernst, W.G., and Brabb, E.E., 2005, Net dextral slip, Neogene San Gregorio-Hosgri Fault Zone, coastal California: Geologic evidence and tectonic implications: Geological Society of America Special Paper 391, 43 p. Elder, W.P., ed., 1998, Geology and tectonics of the Gualala Block, northern California: Pacific Section, Society of Economic Paleontologists and Mineralogists, Book 84, 222 p. Lomax, A., 2005, A reanalysis of the hypocentral location and related observations for the Great 1906 California earthquake: Bulletin of the Seismological Society of America, v. 95, p. 861-877. McCulloch, D.S., 1987, Regional geology and hydrocarbon potential of offshore central California, in Scholl, D.W., Grantz, A., and Vedder, J.G., eds., Geology and Resource Potential of the Continental Margin of Western North America and Adjacent Oceans -- Beaufort Sea to Baja California: Houston, Texas, Circum-Pacific Council for Energy and Mineral Resources, Earth Science Series, v. 6., p. 353-401. Prentice, C.S., and Kelson, K.I., 2006, The San Andreas fault in Sonoma and Mendocino counties, in Prentice, C.S., Scotchmoor, J.G., Moores, E.M., and Kiland, J.P., eds., 1906 San Francisco Earthquake Centennial Field Guides: Field trips associated with the 100th Anniversary Conference, 18-23 April 2006, San Francisco, California: Geological Society of America Field Guide 7, p. 127-156.; abstract: This part of DS 781 presents data for faults for the geologic and geomorphic map of the Offshore of Fort Ross map area, California. The vector data file is included in "Faults_OffshoreFortRoss.zip," which is accessible from http://pubs.usgs.gov/ds/781/OffshoreFortRoss/data_catalog_OffshoreFortRoss.html. The Offshore of Fort Ross map area is cut by the northwest-trending San Andreas Fault, the right-lateral transform boundary between the North American and Pacific tectonic plates. The San Andreas extends across the inner shelf in the southern part of the map, then crosses the shoreline at Fort Ross and continues onland for about 75 km to the east flank of Point Arena (fig. 8-1). Seismic-reflection data are used to map the offshore fault trace, and reveal a relatively simple, 200- to 500-m wide zone typically characterized by one or two primary strands. About 1500 m west of the San Andreas Fault, the mid shelf (between water depths of 40 m and 70 m) in the southernmost part of the map area includes an about 5-km-wide field of elongate, shore-normal sediment lobes (unit Qmsl). Individual lobes within the field are as much as 650-m long and 200-m wide, have as much as 1.5 m (check with Steve) of relief above the surrounding smooth seafloor, and are commonly connected with upslope chutes. Given their morphology and proxmity to the San Andreas fault, we infer that these lobes result from slope failures associated with strong ground motions triggered by large San Andreas earthquakes. Movement on the San Andreas has juxtaposed different coastal bedrock blocks (Blake and others, 2002). Rocks east of the fault that occur along the coast and in the nearshore belong to the late Tertiary, Cretaceous, and Jurassic Franciscan Complex, either sandstone of the Coastal Belt or Central Belt (unit TKfs) or melange of the central terrane (unit fsr). Bedrock west of the fault are considered part of the Gualala Block (Elder, 1998) and include the Eocene and Paleocene German Rancho Formation (unit Tgr) and the Miocene sandstone and mudstone of the Fort Ross area (unit Tsm). This section of the San Andreas Fault onland has an estimated slip rate of about 17 to 25 mm/yr (Bryant and Lundberg, 2002). The devastating Great 1906 California earthquake (M 7.8) is thought to have nucleated on the San Andreas Fault about 100 kilometers south of this map area offshore of San Francisco (e.g., Bolt, 1968; Lomax, 2005), with the rupture extending northward through the Offshore of Fort Ross map area to the south flank of Cape Mendocino. Emergent marine terraces along the coast in the Offshore of Fort Ross map area record recent contractional deformation associated with the San Andreas Fault system. Prentice and Kelson (2006) report uplift rates of 0.3 to 0.6 mm/yr for a late Pleistocene terrace exposed at Fort Ross, and this recent uplift must also have affect the nearshore and inner shelf. Previously, McCulloch (1987) mapped a nearshore (within 3 to 5 km of the coast) fault zone from Point Arena to Fort Ross (Fig. 8-1) using primarily deeper industry seismic-reflection data. Subsequently, Dickinson and others (2005) named this structure the "Gualala Fault." Our mapping, also based on seismic-reflection data, reveals this structure as a steep, northeast trending fault and similarly shows the fault ending to the south in the northern part of the Offshore of Fort Ross map area. We have designated the zone of faulting and folding above this structure the "Gualala Fault deformation zone." Faults were primarily mapped by interpretation of seismic reflection profile data (see field activity S-8-09-NC). The seismic reflection profiles were collected between 2007 and 2010. References Cited Blake, M.C., Jr., Graymer, R.W., and Stamski, R.E., 2002, Geologic map and map database of western Sonoma, northernmost Marin, and southernmost Mendocino counties, California: U.S. Geological Survey Miscellaneous Field Studies Map 2402, scale 1:100,000. Bolt, B.A., 1968, The focus of the 1906 California earthquake: Bulletin of the Seismological Society of America, v. 58, p. 457-471. Bryant, W.A.,

Analysis of ‘CGS Map Sheet 48: Historic Earthquakes, 1769 to 2015 - California (Magnitude 5.0-plus)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ba702498-6e4f-488a-a42d-e49504f952f2 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Epicenters of known M≥5 earthquakes from 1769 to 2016 are shown for California and a 100 km area bordering the state. Earthquakes are grouped by: M = 5-6; M = 6-7; M = 7+.

--- Original source retains full ownership of the source dataset ---

In 2010, the Deepwater Horizon (DWH) oil spill had unprecedented impacts on the Gulf of America ecosystem, including the twenty cetacean species inhabiting the oceanic waters of this semi-enclosed large marine ecosystem. Due to the impacts from DWH oil, restoration projects focused on oceanic cetaceans are being enacted in the Gulf. These projects require basic information on species’ spatiotemporal density patterns, Gulf-wide movement patterns, Gulf-wide population sizes, long-term abundance trends, and species’ responses to oceanographic and anthropogenic processes, along with information on Gulf-wide ambient noise levels and the contributions from anthropogenic noise sources. To address these needs, NOAA’s Southeast Fisheries Science Center (SEFSC), UCSD’s Scripps Institution of Oceanography (SIO), and partners initiated a comprehensive, long-term, multi-scale passive acoustic monitoring program throughout US and Mexican Gulf waters over the 2020 – 2025 period. This program collects data needed to develop predictive habitat models to assess the processes driving seasonal, interannual, and decadal trends in spatial distribution, density, and abundance of oceanic cetaceans and to assess contributions of ambient noise sources to the Gulf soundscape. This collaborative study annually deploys moored HARP instruments, continuously recording over the 10 Hz to 100 kHz band, over the five-year period at a total of:• 8 five-year long-term sites to identify temporal trends and variability at reference sites over the study period,• 20 one-year short-term sites over a broad area of the Gulf to capture spatial trends and variability in cetacean density and environmental processes,• 3 six-month sites with targeted sampling using tracking arrays to obtain acoustic behavior data for density estimation, and• 2 three-to-five-year sites focused on areas of importance to the DWH Restoration noise reduction project.This feature layer contains data that was focused on anthropogenic noise, converted from a csv file to a feature layer.

OverviewThe multiple hazard index for the United States Counties was designed to map natural hazard relating to exposure to multiple natural disasters. The index was created to provide communities and public health officials with an overview of the risks that are prominent in their county, and to facilitate the comparison of hazard level between counties. Most existing hazard maps focus on a single disaster type. By creating a measure that aggregates the hazard from individual disasters, the increased hazard that results from exposure to multiple natural disasters can be better understood. The multiple hazard index represents the aggregate of hazard from eleven individual disasters. Layers displaying the hazard from each individual disaster are also included.

The hazard index is displayed visually as a choropleth map, with the color blue representing areas with less hazard and red representing areas with higher hazard. Users can click on each county to view its hazard index value, and the level of hazard for each individual disaster. Layers describing the relative level of hazard from each individual disaster are also available as choropleth maps with red areas representing high, orange representing medium, and yellow representing low levels of hazard.Methodology and Data CitationsMultiple Hazard Index

The multiple hazard index was created by coding the individual hazard classifications and summing the coded values for each United States County. Each individual hazard is weighted equally in the multiple hazard index. Alaska and Hawaii were excluded from analysis because one third of individual hazard datasets only describe the coterminous United States.

Avalanche Hazard

University of South Carolina Hazards and Vulnerability Research Institute. “Spatial Hazard Events and Losses Database”. United States Counties. “Avalanches United States 2001-2009”. < http://hvri.geog.sc.edu/SHELDUS/

Downloaded 06/2016.

Classification

Avalanche hazard was classified by dividing counties based upon the number of avalanches they experienced over the nine year period in the dataset. Avalanche hazard was not normalized by total county area because it caused an over-emphasis on small counties, and because avalanches are a highly local hazard.

None = 0 AvalanchesLow = 1 AvalancheMedium = 2-5 AvalanchesHigh = 6-10 Avalanches

Earthquake Hazard

United States Geological Survey. “Earthquake Hazard Maps”. 1:2,000,000. “Peak Ground Acceleration 2% in 50 Years”. < http://earthquake.usgs.gov/hazards/products/conterminous/

. Downloaded 07/2016.

Classification

Peak ground acceleration (% gravity) with a 2% likelihood in 50 years was averaged by United States County, and the earthquake hazard of counties was classified based upon this average.

Low = 0 - 14.25 % gravity peak ground accelerationMedium = 14.26 - 47.5 % gravity peak ground accelerationHigh = 47.5+ % gravity peak ground acceleration

Flood Hazard

United States Federal Emergency Management Administration. “National Flood Hazard Layer”. 1:10,000. “0.2 Percent Annual Flood Area”. < https://data.femadata.com/FIMA/Risk_MAP/NFHL/

. Downloaded 07/2016.

Classification

The National Flood Hazard Layer 0.2 Percent Annual Flood Area was spatially intersected with the United States Counties layer, splitting flood areas by county and adding county information to flood areas. Flood area was aggregated by county, expressed as a fraction of the total county land area, and flood hazard was classified based upon percentage of land that is susceptible to flooding. National Flood Hazard Layer does not cover the entire United States; coverage is focused on populated areas. Areas not included in National Flood Hazard Layer were assigned flood risk of Low in order to include these areas in further analysis.

Low = 0-.001% area susceptibleMedium = .00101 % - .005 % area susceptibleHigh = .00501+ % area susceptible

Heat Wave Hazard

United States Center for Disease Control and Prevention. “National Climate Assessment”. Contiguous United States Counties. “Extreme Heat Events: Heat Wave Days in May - September for years 1981-2010”. Downloaded 06/2016.

Classification

Heat wave was classified by dividing counties based upon the number of heat wave days they experienced over the 30 year time period described in the dataset.

Low = 126 - 171 Heat wave DaysMedium = 172 – 187 Heat wave DaysHigh = 188 – 255 Heat wave Days

Hurricane Hazard

National Oceanic and Atmospheric Administration. Coastal Services Center. “Historical North Atlantic Tropical Cyclone Tracks, 1851-2004”. 1: 2,000,000. < https://catalog.data.gov/dataset/historical-north-atlantic-tropical-cyclone-tracks-1851-2004-direct-download

. Downloaded 06/2016.

National Oceanic and Atmospheric Administration. Coastal Services Center. “Historical North Pacific Tropical Cyclone Tracks, 1851-2004”. 1: 2,000,000. < https://catalog.data.gov/dataset/historical-north-atlantic-tropical-cyclone-tracks-1851-2004-direct-download

. Downloaded 06/2016.

Classification

Atlantic and Pacific datasets were merged. Tropical storm and disturbance tracks were filtered out leaving hurricane tracks. Each hurricane track was assigned the value of the category number that describes that event. Weighting each event by intensity ensures that areas with higher intensity events are characterized as being more hazardous. Values describing each hurricane event were aggregated by United States County, normalized by total county area, and the hurricane hazard of counties was classified based upon the normalized value.

Landslide Hazard

United States Geological Survey. “Landslide Overview Map of the United States”. 1:4,000,000. “Landslide Incidence and Susceptibility in the Conterminous United States”. < https://catalog.data.gov/dataset/landslide-incidence-and-susceptibility-in-the-conterminous-united-states-direct-download

. Downloaded 07/2016.

Classification

The classifications of High, Moderate, and Low landslide susceptibility and incidence from the study were numerically coded, the average value was computed for each county, and the landslide hazard was classified based upon the average value.

Long-Term Drought Hazard

United States Drought Monitor, Drought Mitigation Center, United States Department of Agriculture, National Oceanic and Atmospheric Administration. “Drought Monitor Summary Map”. “Long-Term Drought Impact”. < http://droughtmonitor.unl.edu/MapsAndData/GISData.aspx >. Downloaded 06/2016.

Classification

Short-term drought areas were filtered from the data; leaving only long-term drought areas. United States Counties were assigned the average U.S. Drought Monitor Classification Scheme Drought Severity Classification value that characterizes the county area. County long-term drought hazard was classified based upon average Drought Severity Classification value.

Low = 1 – 1.75 average Drought Severity Classification valueMedium = 1.76 -3.0 average Drought Severity Classification valueHigh = 3.0+ average Drought Severity Classification value

Snowfall Hazard

United States National Oceanic and Atmospheric Administration. “1981-2010 U.S. Climate Normals”. 1: 2,000,000. “Annual Snow Normal”. < http://www1.ncdc.noaa.gov/pub/data/normals/1981-2010/products/precipitation/

. Downloaded 08/2016.

Classification

Average yearly snowfall was joined with point location of weather measurement stations, and stations without valid snowfall measurements were filtered out (leaving 6233 stations). Snowfall was interpolated using least squared distance interpolation to create a .05 degree raster describing an estimate of yearly snowfall for the United States. The average yearly snowfall raster was aggregated by county to yield the average yearly snowfall per United States County. The snowfall risk of counties was classified by average snowfall.

None = 0 inchesLow = .01- 10 inchesMedium = 10.01- 50 inchesHigh = 50.01+ inches

Tornado Hazard

United States National Oceanic and Atmospheric Administration Storm Prediction Center. “Severe Thunderstorm Database and Storm Data Publication”. 1: 2,000,000. “United States Tornado Touchdown Points 1950-2004”. < https://catalog.data.gov/dataset/united-states-tornado-touchdown-points-1950-2004-direct-download

. Downloaded 07/2016.

Classification

Each tornado touchdown point was assigned the value of the Fujita Scale that describes that event. Weighting each event by intensity ensures that areas with higher intensity events are characterized as more hazardous. Values describing each tornado event were aggregated by United States County, normalized by total county area, and the tornado hazard of counties was classified based upon the normalized value.

Volcano Hazard

Smithsonian Institution National Volcanism Program. “Volcanoes of the World”. “Holocene Volcanoes”. < http://volcano.si.edu/search_volcano.cfm

. Downloaded 07/2016.

Classification

Volcano coordinate locations from spreadsheet were mapped and aggregated by United States County. Volcano count was normalized by county area, and the volcano hazard of counties was classified based upon the number of volcanoes present per unit area.

None = 0 volcanoes/100 kilometersLow = 0.000915 - 0.007611 volcanoes / 100 kilometersMedium = 0.007612 - 0.018376 volcanoes / 100 kilometersHigh = 0.018377- 0.150538 volcanoes / 100 kilometers

Wildfire Hazard

United States Department of Agriculture, Forest Service, Fire, Fuel, and Smoke Science Program. “Classified 2014 Wildfire Hazard Potential”. 270 meters. < http://www.firelab.org/document/classified-2014-whp-gis-data-and-maps

. Downloaded 06/2016.

Classification

The classifications of Very High, High, Moderate, Low, Very Low, and Non-Burnable/Water wildfire hazard from the study were numerically coded, the average value was computed for each county, and the wildfire hazard was classified based upon the average value.

The polygon data layer in this feature service consists of an updated National Earthquake Hazards Reduction Program (NEHRP) soil classification map of Massachusetts at 100-meter resolution. This is a statewide coverage that classifies soils according to the NEHRP soil categories A, B, C, D, and E. The category into which a soil is classified is determined by the average shear wave velocity in the top 30 meters (100 feet) of the earth’s surface. The classification is also dependent on the thickness of the soil cover that lies over the bedrock. This updated NEHRP soil classification map incorporates overburden thickness into the soil category determination that was previously unavailable.Map service also available.See the full metadata page.

In 2010, the Deepwater Horizon (DWH) oil spill had unprecedented impacts on the Gulf of America ecosystem, including the twenty cetacean species inhabiting the oceanic waters of this semi-enclosed large marine ecosystem. Due to the impacts from DWH oil, restoration projects focused on oceanic cetaceans are being enacted in the Gulf. These projects require basic information on species’ spatiotemporal density patterns, Gulf-wide movement patterns, Gulf-wide population sizes, long-term abundance trends, and species’ responses to oceanographic and anthropogenic processes, along with information on Gulf-wide ambient noise levels and the contributions from anthropogenic noise sources. To address these needs, NOAA’s Southeast Fisheries Science Center (SEFSC), UCSD’s Scripps Institution of Oceanography (SIO), and partners initiated a comprehensive, long-term, multi-scale passive acoustic monitoring program throughout US and Mexican Gulf waters over the 2020 – 2025 period. This program collects data needed to develop predictive habitat models to assess the processes driving seasonal, interannual, and decadal trends in spatial distribution, density, and abundance of oceanic cetaceans and to assess contributions of ambient noise sources to the Gulf soundscape. This collaborative study annually deploys moored HARP instruments, continuously recording over the 10 Hz to 100 kHz band, over the five-year period at a total of:• 8 five-year long-term sites to identify temporal trends and variability at reference sites over the study period,• 20 one-year short-term sites over a broad area of the Gulf to capture spatial trends and variability in cetacean density and environmental processes,• 3 six-month sites with targeted sampling using tracking arrays to obtain acoustic behavior data for density estimation, and• 2 three-to-five-year sites focused on areas of importance to the DWH Restoration noise reduction project.This feature layer contains data that was focused on anthropogenic noise, converted from a csv file to a feature layer.