The Taupo Volcanic Zone (TVZ), New Zealand, is the most productive area of explosive silicic volcanism in the world. Faulted early and middle Pleistocene volcanic products are generally concealed beneath voluminous, generally unfaulted, younger volcanic products. An exception is the southeast margin of the TVZ where the two parallel, northeast-trending Paeroa and Te Weta Fault blocks expose Quaternary volcanic products consisting predominantly of caldera-related, rhyolitic ignimbrites and lacustrine sediments. The Taupo-Reporoa Basin is situated along the eastern part of the map area, and its northernmost part underwent collapse to form Reporoa Caldera.
The Paeroa Fault block is the largest exposed fault block within the TVZ, and it encompasses early and middle Pleistocene ignimbrites and sedimentary deposits that are buried throughout the Taupo-Reporoa Basin to the east. This map displays the volcanic and sedimentary geology of ~430 km2 of the Paeroa Fault block and the adjacent Te Weta Fault block at a scale of 1:50,000. Volcanic and sedimentary rocks are divided into the Reporoa Group, Whakamaru Group, and Huka Group (from oldest to youngest), which are overlain by relatively unfaulted late Pleistocene and Holocene surficial volcanic and sedimentary deposits.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201021","usgsCitation":"Downs, D.T., Leonard, G.S., Wilson, C.J.N., and Rowland, J.V., 2020, Geologic map of the Paeroa Fault block and surrounding area, Taupo Volcanic Zone, New Zealand: U.S. Geological Survey Open-File Report 2020–1021, scale 1:50,000, https://doi.org/10.3133/ofr20201021.","productDescription":"1 Map: 50.22 x 34.79 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-107861","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":373326,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DYBBGX","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"Database for the geologic map of the Paeroa fault block and surrounding area, Taupo Volcanic Zone, New Zealand"},{"id":373324,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1021/coverthb.jpg"},{"id":373325,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2020/1021/ofr20201021.pdf","text":"Sheet","size":"5.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1021"}],"country":"New Zealand ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 165.948486328125,\n -46.72103466129568\n ],\n [\n 170.7550048828125,\n -46.72103466129568\n ],\n [\n 170.7550048828125,\n -44.15462243076732\n ],\n [\n 165.948486328125,\n -44.15462243076732\n ],\n [\n 165.948486328125,\n -46.72103466129568\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Volcano Observatory staff
Alaska Volcano Observatory
4210 University Drive
Anchorage, AK 99508
Landslides in Puerto Rico range from nuisances to deadly events. Centuries of agricultural and urban modification of the landscape have perturbed many already unstable hillsides on the tropical island. One of the main triggers of mass wasting on the island is the high-intensity rainfall that is associated with tropical atmospheric systems. Puerto Rico’s geographic position and rugged topography render millions of residents vulnerable to widespread landslide events. In this study, a high-resolution (5 meters), high-intensity rainfall-induced landslide susceptibility model was produced using the frequency-ratio method. Datasets utilized in the model included a complete-island landslide inventory created from imagery obtained after Hurricanes Irma and María impacted the island during September 2017, slope inclination, land-surface curvature, soil type, geologic terrane, mean annual precipitation, land use, soil moisture, and distance to roadways and streams. The final data product (plate 1) is a statistically viable representation of where landslides are likely to initiate during or soon after intense rainfall, with a robust receiver operating characteristic area-under-curve value of 0.87. The model output raster pixel values were binned into 100 equal-area quantiles and then classified into Low, Moderate, High, Very High, and Extremely High classes of susceptibility. The Extremely High susceptibility classification represents the most vulnerable 1 percent of the island, whereas Very High, High, Moderate, and Low classifications cover 9, 20, 30, and 40 percent of the island, respectively. The susceptibility map is intended to assist in planning future development, mitigation measures, and post-event emergency response; however, it is not a substitute for site-specific, slope-stability assessments performed by licensed geologists and engineers. Additionally, the map does not portray locations where landslide material may travel after mobilization, and which may be at extreme risk; nor does it necessarily portray where landslides may occur during earthquakes or mass wasting triggered by prolonged, relatively low-intensity rainfall.
","language":"English, Spanish","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20201022","collaboration":"Prepared in cooperation with the University of Puerto Rico at Mayagüez","usgsCitation":"Hughes, K.S., and Schulz, W.H., 2020, Map depicting susceptibility to landslides triggered by intense rainfall, Puerto Rico: U.S. Geological Survey Open-File Report 2020–1022, 91 p., 1 plate, scale 1:150,000, https://doi.org/10.3133/ofr20201022.","productDescription":"Report: viii, 91 pages; 2 Sheets: 49.11 x 33.86 inches; Application Sites; Data Release; Read Me","onlineOnly":"Y","ipdsId":"IP-116377","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":373245,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_pamphlet.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1022 pamphlet","linkHelpText":"English language"},{"id":373195,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1022/coverthb.jpg"},{"id":383760,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_pamphlet_esp.pdf","text":"Reporte","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1022 pamphlet Spanish language","linkHelpText":"En Español"},{"id":373196,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VK2FAL","text":"USGS data release","linkHelpText":"Results from frequency-ratio analyses of soil classification and land use related to landslide locations in Puerto Rico following Hurricane Maria"},{"id":388108,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P990ZP4C","text":"USGS data release","linkHelpText":"Geographic Information System Layer of a Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico"},{"id":373241,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://hazards.colorado.edu/uploads/documents/PuertoRico_LandslideGuide_2020.pdf","text":"Landslide Guide for Residents of Puerto Rico"},{"id":373242,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://hazards.colorado.edu/uploads/documents/PuertoRico_GuiaDerrumbe_2020.pdf","text":"Guía sobre deslizamientos de tierra para residentes de Puerto Rico"},{"id":373256,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_kmz.zip","text":"Landslide susceptibility map as a Google Earth file","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2020-1022 kmz"},{"id":373246,"rank":9,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1022/SI_raster_for_SMAP.zip","text":"SI raster for SMAP","linkFileType":{"id":6,"text":"zip"},"description":"SI raster for SMAP"},{"id":373247,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet.pdf","text":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico","linkFileType":{"id":1,"text":"pdf"},"description":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico"},{"id":376481,"rank":16,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet_esp.pdf","text":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico","linkFileType":{"id":1,"text":"pdf"},"description":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico"},{"id":376485,"rank":18,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet_esp_georeferenced.tif","text":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico, georreferenciado (Geotiff)","description":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico, georreferenciado (Geotiff)"},{"id":399457,"rank":19,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109788.htm"},{"id":373261,"rank":14,"type":{"id":4,"text":"Application Site"},"url":"https://usgs.maps.arcgis.com/apps/webappviewer/index.html?id=10506ecc7f15491daee17647f19248ee","text":"Landslide susceptibility map interactive web viewer"},{"id":373262,"rank":15,"type":{"id":4,"text":"Application Site"},"url":"https://usgs.maps.arcgis.com/apps/webappviewer/index.html?id=9378c6a890164d378a2e66da4b7970c0","text":"Mapa de susceptibilidad a deslizamientos de tierra visor web interactivo"},{"id":373248,"rank":11,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet_geospatial.pdf","text":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico, Geo-referenced","linkFileType":{"id":1,"text":"pdf"},"description":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico, Geo-referenced"},{"id":373249,"rank":12,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet_georeferenced.tif","text":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico, Geo-referenced (Geotiff)","description":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico (Geotiff)"},{"id":373260,"rank":13,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_readme.txt","text":"Read Me","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2020-1022 readme file"},{"id":376484,"rank":17,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet_esp_georeferenced.pdf","text":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico, georreferenciado","linkFileType":{"id":1,"text":"pdf"},"description":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico, georreferenciado"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.3736572265625,\n 17.832374329567518\n ],\n [\n -65.577392578125,\n 17.832374329567518\n ],\n [\n -65.577392578125,\n 18.58377568837094\n ],\n [\n -67.3736572265625,\n 18.58377568837094\n ],\n [\n -67.3736572265625,\n 17.832374329567518\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046
Barrier islands, such as Dauphin Island, Alabama, provide numerous invaluable ecosystem services including storm damage reduction and erosion control to the mainland, habitat for fish and wildlife, carbon sequestration in marshes, water catchment and purification, recreation, and tourism. These islands are dynamic environments that are gradually shaped by currents, waves, and tides under quiescent conditions yet can evolve in the time scale of hours to days during hurricanes and other extreme storms. The ecosystems associated with these islands also face numerous other hazards, including accelerated sea-level rise, oil spills, and anthropogenic stressors.
Hurricane Katrina in 2005 and the Deepwater Horizon oil spill in 2010 are two major events that have affected habitats and natural resources on Dauphin Island, Ala. The latter event prompted a cooperative effort between the U.S. Geological Survey and the U.S. Army Corps of Engineers to investigate viable, sustainable restoration measures that reduce degradation and enhance the natural resources of Dauphin Island, Ala. In collaboration with the State of Alabama and the National Fish and Wildlife Foundation, the overarching goal of the Alabama Barrier Island Restoration Feasibility Assessment project was to document baseline conditions and forecast potential conditions under varying sea-level change and storm scenarios for a no-action alternative along with a variety of restoration measures including beach and dune restoration, marsh and back-barrier restoration, and placement of sand in the littoral zone. The modeling component of this project used decadal hydrodynamic geomorphic, water quality, and habitat modeling to better understand how the various restoration measures may influence the habitat composition, sustainability, and resiliency of Dauphin Island under potential future conditions, benchmarked against the no-action case.
The report covers the habitat modeling efforts associated with the Alabama Barrier Island Restoration Feasibility Assessment project. For various potential future island configurations for Dauphin Island, we predicted coverage of habitat types (for example, beach, dune, intertidal marsh, and woody vegetation) using a spatially explicit habitat model based on landscape-position information (for example, elevation and distance from shore) extracted from the hydrodynamic geomorphic outputs. Similarly, we forecasted habitat suitability for oysters and seagrass using habitat suitability index models. Another component of the Alabama Barrier Island Restoration Feasibility Assessment project, presented separately, integrates these habitat model results into a structured decision-making framework that accounts for competing objectives. Collectively, this information provides insights to natural resource managers and planners on how a restoration measure may maintain or impede natural coastal processes and provide information critical for making future-focused decisions regarding barrier island restoration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201003","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers and in collaboration with the State of Alabama and the National Fish and Wildlife Foundation","usgsCitation":"Enwright, N.M., Wang, H., Dalyander, P.S., and Godsey, E., eds., 2020, Predicting barrier island habitats and oyster and seagrass habitat suitability for various restoration measures and future conditions for Dauphin Island, Alabama: U.S. Geological Survey Open-File Report 2020–1003, 99 p., https://doi.org/10.3133/ofr20201003.","productDescription":"Report: x, 99 p.; 3 Data Releases","numberOfPages":"114","onlineOnly":"Y","ipdsId":"IP-113342","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":372971,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1003/ofr20201003.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1003"},{"id":372973,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O30XMZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Oyster habitat suitability modeling for the Alabama Barrier Island restoration assessment at Dauphin Island"},{"id":399449,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109756.htm"},{"id":372974,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B32VTE","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Seagrass habitat suitability modeling for the Alabama Barrier Island restoration assessment at Dauphin Island"},{"id":372972,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PK0EH0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Landscape position-based habitat modeling for the Alabama Barrier Island feasibility assessment at Dauphin Island"},{"id":372970,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1003/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.341064453125,\n 30.166500980766052\n ],\n [\n -88.0389404296875,\n 30.166500980766052\n ],\n [\n -88.0389404296875,\n 30.311245603935003\n ],\n [\n -88.341064453125,\n 30.311245603935003\n ],\n [\n -88.341064453125,\n 30.166500980766052\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506
Predicting the decadal evolution of barrier island systems is important for coastal managers who propose restoration or preservation alternatives aimed at increasing the resiliency of the island and its associated habitats or communities. Existing numerical models for simulating morphologic changes typically include either long-term (for example, longshore transport under quiescent conditions) or short-term (for example, storm-driven waves) processes, with limited capacity to predict the decadal time-scale that is often most relevant in coastal planning. As part of the Alabama Barrier Island Restoration Assessment, a methodology was developed to predict barrier island evolution on decadal time scales. The developed modeling scheme uses multiple models including (1) Delft3D; (2) the empirical dune growth model (EDGR); and (3) XBeach that run sequentially to simulate evolution of barrier island geomorphology. The model framework was developed and applied to hindcast the evolution of Dauphin Island, Alabama, between 2004 and 2015, and was assessed using lidar data over the same period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191139","usgsCitation":"Mickey, R.C., Long, J.W., Dalyander, P.S., Jenkins, R.L., III, Thompson, D.M., Passeri, D.L., and Plant, N.G., 2019, Development of a modeling framework for predicting decadal barrier island evolution: U.S. Geological Survey Open-File Report 2019–1139, 46 p., https://doi.org/10.3133/ofr20191139.","productDescription":"Report: vi, 46 p.; Data Release","ipdsId":"IP-111247","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":399428,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109735.htm"},{"id":372441,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91ALL6C","text":"USGS data release","linkHelpText":"Dauphin Island decadal hindcast model inputs and results"},{"id":372678,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ofr/2019/1139/ofr20191139.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1139"},{"id":372308,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201001","text":"Open-File Report 2020-1001","linkHelpText":"- Application of Decadal Modeling Approach to Forecast Barrier Island Evolution, Dauphin Island, Alabama"},{"id":372306,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ofr/2019/1139/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.37677001953125,\n 30.18905718468536\n ],\n [\n -87.99156188964844,\n 30.18905718468536\n ],\n [\n -87.99156188964844,\n 30.34088005484784\n ],\n [\n -88.37677001953125,\n 30.34088005484784\n ],\n [\n -88.37677001953125,\n 30.18905718468536\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Forecasting barrier island evolution provides coastal managers and stakeholders the ability to assess the resiliency of these important coastal environments that are home to both established communities and existing natural habitats. This study uses an established coupled model framework to assess how Dauphin Island, Alabama, responds to various storm and sea-level change scenarios, along with a suite of restoration measures, over the course of a decade. The coupled model framework uses validated models for long-term alongshore sediment transport (Delft 3D), short-term storm induced impacts (XBeach), as well as dune building and recovery (empirical dune growth model). This model framework was simulated with the various storm and sea-level change scenarios on a non-restored Dauphin Island, then a subset of the storm and sea-level change scenarios were applied to a suite of seven different restoration measures to determine how they would influence the morphologic evolution over a decadal period. Topographic and bathymetric changes captured in post-simulation digital elevation models were then passed on to partners for various simulations to determine the effects on habitat evolution and water quality as it relates to oyster reef and submerged aquatic vegetation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201001","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, The Water Institute of the Gulf, and the University of North Carolina at Wilmington","usgsCitation":"Mickey, R.C., Godsey, E., Dalyander, P.S., Gonzalez, V., Jenkins, R.L., III, Long, J.W., Thompson, D.M., and Plant, N.G., 2020, Application of decadal modeling approach to forecast barrier island evolution, Dauphin Island, Alabama: U.S. Geological Survey Open-File Report 2020–1001, 45 p., https://doi.org/10.3133/ofr20201001.","productDescription":"Report: viii, 45 p.; Data Release","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113301","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":399440,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109734.htm"},{"id":372467,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ofr/2020/1001/ofr20201001.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1001"},{"id":372442,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PDM1OJ","text":"USGS data release","linkHelpText":"Dauphin Island decadal forecast evolution model inputs and results"},{"id":372303,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20191139","text":"Open-File Report 2019-1139","linkHelpText":"- Development of a Modeling Framework for Predicting Decadal Barrier Island Evolution"},{"id":372301,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ofr/2020/1001/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.3355712890625,\n 30.180747605060766\n ],\n [\n -88.05541992187499,\n 30.180747605060766\n ],\n [\n -88.05541992187499,\n 30.288717426233095\n ],\n [\n -88.3355712890625,\n 30.288717426233095\n ],\n [\n -88.3355712890625,\n 30.180747605060766\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Dauphin Island, Alabama, located in the Northern Gulf of Mexico just outside of Mobile Bay, is Alabama’s only barrier island and provides an array of historical, natural, and economic resources. The dynamic island shoreline of Dauphin Island evolved across time scales while constantly acted upon by waves and currents during both storms and calm periods. Reductions in the vulnerability and enhancements to the resiliency of Dauphin Island—through offshore sand placement, breach closure, berm construction, and other means—have been used to protect the island and its vital resources. Planning for a resilient Dauphin Island requires predicting the long-term evolution of the barrier island system and the dominant, temporally varying processes that influence it, including littoral alongshore sediment transport under typical wave conditions, beach and dune erosion, the island overwash and breaching that occur rapidly during storm events, and the recovery of primary sand dunes through Aeolian transport over decadal time scales. Littoral sediment transport within the Dauphin Island decadal-scale framework was simulated using the Delft-3D modeling software suite. The influences of wind, waves, water levels, and sediment transport are incorporated into the model. Model skill in the prediction of waves, water levels, currents, volumetric flow rates through inlets, and shoreline position was assessed by using a set of deterministic and statistical hindcast simulations. The Delft-3D modeling application described here can be coupled with validated models of storm-response and dune recovery to predict the evolution of Dauphin Island on decadal time scales.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201011","usgsCitation":"Jenkins, R.L., III, Long, J.W., Dalyander, P.S., Thompson, D.M., and Mickey, R.C., 2020, Development of a process-based littoral sediment transport model for Dauphin Island, Alabama: U.S. Geological Survey Open-File Report 2020–1011, 43 p., https://doi.org/10.3133/ofr20201011.","productDescription":"vii, 43 p.","numberOfPages":"51","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-109477","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":399455,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109731.htm"},{"id":372743,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1011/ofr20201011.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1011"},{"id":372742,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1011/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.36715698242186,\n 30.210421455819937\n ],\n [\n -88.06640625,\n 30.210421455819937\n ],\n [\n -88.06640625,\n 30.26974231529823\n ],\n [\n -88.36715698242186,\n 30.26974231529823\n ],\n [\n -88.36715698242186,\n 30.210421455819937\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Since the 2018 eruption of Kīlauea Volcano, the topic of whether commercial developments not only caused the eruption to occur in the lower East Rift Zone (LERZ), but also caused its high eruption rate has been a subject of public discussion. We review Kīlauea Volcano publications from the past several decades and show that the eruptive behavior of the volcano has varied and that the 2018 eruption was similar to past eruptions in many ways. We find no evidence to support any human influence on Kīlauea Volcano.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201017","usgsCitation":"Kauahikaua, J. and Trusdell, F., 2020, Have humans influenced volcanic activity on the lower East Rift Zone of Kīlauea Volcano? A publication review: U.S. Geological Survey Open-File Report 2020–1017, 17 p., https://doi.org/10.3133/ofr20201017.","productDescription":"iv, 17","numberOfPages":"17","onlineOnly":"Y","ipdsId":"IP-111187","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":372511,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1017/ofr20201017.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1017"},{"id":372510,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1017/coverthb.jpg"},{"id":399456,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109729.htm"}],"country":"United States","state":"Hawaii","otherGeospatial":"Lower East Rift Zone of Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.3741455078125,\n 19.19835036116298\n ],\n [\n -155.26016235351562,\n 19.281332062593734\n ],\n [\n -155.20523071289062,\n 19.26059057084779\n ],\n [\n -155.14892578125,\n 19.264479800497103\n ],\n [\n -155.0665283203125,\n 19.30466310133747\n ],\n [\n -154.9456787109375,\n 19.37334071336406\n ],\n [\n -154.82070922851562,\n 19.474360774988355\n ],\n [\n -154.80422973632812,\n 19.530024424775405\n ],\n [\n -154.92233276367188,\n 19.596019240312494\n ],\n [\n -154.9456787109375,\n 19.621892180319374\n ],\n [\n -154.97177124023438,\n 19.641294152538578\n ],\n [\n -154.97177124023438,\n 19.676211792974332\n ],\n [\n -155.050048828125,\n 19.590844152960923\n ],\n [\n -155.12832641601562,\n 19.520964205879825\n ],\n [\n -155.18875122070312,\n 19.449759112405612\n ],\n [\n -155.26565551757812,\n 19.42256346067618\n ],\n [\n -155.35629272460938,\n 19.359089245934307\n ],\n [\n -155.38787841796872,\n 19.299478713495898\n ],\n [\n -155.4071044921875,\n 19.23206673568465\n ],\n [\n -155.4071044921875,\n 19.19186565046399\n ],\n [\n -155.3741455078125,\n 19.19835036116298\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contacts, Hawaiian Volcano Observatory
U.S. Geological Survey
1266 Kamehameha Avenue, Suite A-8
Hilo, HI 96720
From 2017 to 2018, the U.S. Geological Survey (USGS) Hawaiian Volcano Observatory (HVO) responded to ongoing and changing eruptions at Kīlauea Volcano as part of its mission to monitor volcanic processes, issue warnings of dangerous activity, and assess volcanic hazards. To formalize short-term hazards assessments—and, in some cases, issue prognoses for future activity—and make results discoverable to both the public and the authorities, HVO released reports online. These reports were published rapidly, received peer review under the USGS’s Fundamental Science Practice guidelines, and were intended to address a focused question posed by one or more cooperating agencies—for this reason, they were called “cooperator reports.” This Open-File Report concatenates four such products issued in 2017 and 2018 into a single publication. These reports have been reformatted and lightly edited for clarity, but the content has not otherwise been changed from the versions first publicly released.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201002","usgsCitation":"Neal, C.A., and Anderson, K.R., 2020, Preliminary analyses of volcanic hazards at Kīlauea Volcano, Hawai‘i, 2017–2018: U.S. Geological Survey Open-File Report 2020–1002, 34 p., https://doi.org/10.3133/ofr20201002.","productDescription":"iv, 34 p.","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-114660","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":399444,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109722.htm"},{"id":372588,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1002/coverthb.jpg"},{"id":372589,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1002/ofr20201002.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.3114,\n 19.1644\n ],\n [\n -154.8036,\n 19.1644\n ],\n [\n -154.8036,\n 19.4433\n ],\n [\n -155.3114,\n 19.4433\n ],\n [\n -155.3114,\n 19.1644\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Hawaiian Volcano Observatory
U.S. Geological Survey
1266 Kamehameha Avenue, Suite A-8
Hilo, HI 96720
The U.S. Geological Survey (USGS), in cooperation with the California State Water Resources Control Board, is evaluating several questions about oil and gas development and groundwater resources in California, including (1) the location of groundwater resources; (2) the proximity of oil and gas operations and groundwater and the geologic materials between them; (3) the location of evidence (or no evidence) of fluids from oil and gas sources in groundwater; and (4) the pathways or processes responsible when fluids from oil and gas sources are present in groundwater (U.S. Geological Survey, 2019). As part of this evaluation, the USGS installed a multiple-well monitoring site in the southern San Joaquin Valley near Lost Hills, California, adjacent to the Lost Hills oil field. Data collected at the Lost Hills multiple-well monitoring site (LHSP) provide information about the geology, hydrology, geophysics, and geochemistry of the aquifer system, thus enhancing understanding of relations between adjacent groundwater and the Lost Hills oil field in an area where there is little groundwater data. This report presents construction information for the LHSP and initial geohydrologic data collected from the site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191114","collaboration":"Prepared in cooperation with California State Water Resources Control Board","usgsCitation":"Everett, R.R., Kjos, A., Brown, A.A., Gillespie, J.M., and McMahon, P.B., 2020, Multiple-well monitoring site adjacent to the Lost Hills oil field, Kern County, California: U.S. Geological Survey Open-File Report 2019–1114, 8 p., https://doi.org/10.3133/ofr20191114.","productDescription":"8 p.","numberOfPages":"8","onlineOnly":"Y","ipdsId":"IP-104714","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":437100,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LGXIN8","text":"USGS data release","linkHelpText":"Aquifer test data for multiple-well monitoring site LHSP, Kern County, California"},{"id":399418,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109721.htm"},{"id":372427,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1114/coverthb.jpg"},{"id":372428,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1114/ofr20191114.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","county":"Kern County","otherGeospatial":"Lost Hills Oil Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.0167,\n 35.5294\n ],\n [\n -119.4833,\n 35.5294\n ],\n [\n -119.4833,\n 35.7667\n ],\n [\n -120.0167,\n 35.7667\n ],\n [\n -120.0167,\n 35.5294\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The surface trace tool comprises a Python script written for ArcGIS that will determine the line of intersection between a planar feature and a surface. Specifically, this tool was designed for geologic applications where geologic planar-feature orientations are reported as strike and dip, and the intersecting surface is the ground. The tool output will show how planar geologic layers intersect with topography.
Determining where geologic features crop out on the surface can be used to guide new geologic mapping as well as reviewing existing geologic mapping. This tool was developed to aid in more efficient mapping of an unknown area. These unknown areas may be missing data, either owing to a lack of suitable outcrops or being difficult to traverse, and data about the areas may be extrapolated using this tool and surrounding data to determine where planar features might appear on the ground.
Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
In 2014, groundwater samples from residential and public supply wells in the vicinity of two former U.S. Navy bases at Willow Grove and Warminster, and an active Air National Guard Station at Horsham, Bucks and Montgomery Counties, Pennsylvania, were found to have concentrations of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), which are per- and polyfluoroalkyl substances (PFAS), above U.S. Environmental Protection Agency (EPA) provisional health advisory (HA) levels for drinking water. Five supply wells near the bases were shut down because of PFAS contamination. In 2016, after EPA established a Lifetime HA for PFAS in drinking water that is lower than the provisional HA in place in 2014, at least 13 additional supply wells near the bases were shut down because of PFAS contamination. At the request of the U.S. Navy, and in consultation with other Federal and State agencies and local stakeholders, the U.S. Geological Survey used historical and recent data on well withdrawals, recharge rates, aquifer properties, groundwater levels, and stream base flow to evaluate regional groundwater-flow paths from identified areas of PFAS groundwater contamination or potential PFAS sources at the bases. Groundwater withdrawals near the bases from public supply and other large wells decreased substantially from the 1990s to 2017, increasing the proportion of groundwater recharge that discharged to local streams. A preliminary groundwater-flow model, calibrated using 1,009 groundwater levels and 17 stream base flow estimates, simulated regional flow paths from the bases and showed that recharge at the bases discharged to withdrawal wells and local streams, generally within a mile or two of the bases. Supply and remediation wells at the bases captured some of the recharge on base areas of possible PFAS contamination, whereas other base recharge was simulated to flow to nearby public supply wells and streams, depending on water use and aquifer recharge conditions between 1999 and 2017. The locations of many residential wells near the bases that were identified by the Navy and Air National Guard as having elevated PFAS concentrations were generally consistent with the simulated flow paths from possible sources at the bases. However, there are some areas of observed PFAS contamination where no flow paths from base sources were simulated. Additionally, no data were available on PFAS concentrations in groundwater in some areas of simulated flow paths from base sources. Data and models used for this study are provided in this report and in digital data releases to support further investigations and model revisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191137","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Goode, D.J., and Senior, L.A., 2020, Groundwater withdrawals and regional flow paths at and near Willow Grove and Warminster, Pennsylvania—Data compilation and preliminary simulations for conditions in 1999, 2010, 2013, 2016, and 2017: U.S. Geological Survey Open-File Report 2019–1137, 127 p., https://doi.org/10.3133/ofr20191137.","productDescription":"Report: x, 127 p.; 2 Data Releases","numberOfPages":"138","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113639","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":399427,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109664.htm"},{"id":371906,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZGEI67","text":"USGS data release","linkHelpText":"Groundwater levels, groundwater withdrawals, and point-source discharges to streams in the vicinity of Willow Grove and Warminster, Bucks and Montgomery Counties, Pennsylvania, for selected years during 1999–2017"},{"id":371905,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K36P5S","text":"USGS data release","linkHelpText":"MODFLOW 6 and MODPATH 7 model data sets used to evaluate groundwater flow in the vicinity of Horsham and Warminster, Bucks and Montgomery Counties, Pennsylvania—Preliminary simulations for conditions in 1999, 2010, 2013, 2016, and 2017"},{"id":372113,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1137/ofr20191137.pdf","text":"Report","size":"21.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1137"},{"id":371903,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1137/coverthb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Bucks County, Montgomery County","city":"Warminster, Willow Grove","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.3536,\n 40.0678\n ],\n [\n -74.9167,\n 40.0678\n ],\n [\n -74.9167,\n 40.2967\n ],\n [\n -75.3536,\n 40.2967\n ],\n [\n -75.3536,\n 40.0678\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
Estimating the number of breeding pairs in a mixed-species waterbird colony is difficult because colonial waterbirds are vulnerable to human intrusion and their colonies are often in remote areas with limited access. We investigated methods to estimate the number of nests of waterbirds at a large, mixed-species colony at Chase Lake National Wildlife Refuge in south-central North Dakota. The primary goals of this study were to evaluate survey methods for shrub- and ground-nesting colonial waterbirds at Chase Lake National Wildlife Refuge and to develop protocols for estimating abundance of the different species. The specific objectives were (1) to assess visible-nest counts for ciconiiform species from the perimeter of nesting areas (hereafter, perimeter counts) and observational surveys from fixed points outside the colony to count flights of adult ciconiiforms in and out of the colony (hereafter, flightline surveys) as alternatives to within-colony counts of ciconiiform nests, and (2) to assess semiautomated, pixel-based image-analysis techniques to estimate abundance of American White Pelicans (Pelecanus erythrorhynchos) as an alternative to traditional manual counts from aerial photographs.
For shrub-nesting ciconiiform species, observers counted 2,259 and 1,759 active ciconiiform nests in 2012 and 2013, respectively, during within-colony counts of ciconiiform nests. Results from within-colony counts of ciconiiform nests indicated a positive relation between the number of nests and the area of the shrub subcolony for the three most common ciconiiform species and all ciconiiform species combined. The perimeter nest counts of ciconiiform nests at Chase Lake represented only 18.8 percent of the total active ciconiiform nests counted in 11 subcolonies in 2012, which was well below the recommended target of 50 percent. Although we found a positive relationship between the number of nests counted during perimeter counts and the number of nests counted during within-colony counts for the three most common ciconiiform species and all ciconiiform species combined, perimeter counts at Chase Lake were hampered by disturbance to nesting birds. Thus, we discontinued the perimeter counts before they were completed. We did not develop predictive models from these perimeter counts in 2012 because these models could be misleading due to inconsistent application of the survey methods, which likely would have provided inaccurate perimeter counts. The extent of this issue is unknown. Flightline surveys at Chase Lake documented patterns of ciconiiform activity that were unknown for this region. For the common ciconiiform species, the number of flights to and from the South Island at Chase Lake were greatest in the morning (7:00−12:00 central daylight time [CDT]) and least in the afternoon (12:00−17:00), and least early in the breeding season (May 29–June 20, 2013) and greatest later in the breeding season (June 24–August 1, 2013). Flightline surveys are an index but lacked comparability with within-colony nest counts because the two methods provide measures of different things (that is, adult activity away from the colony as compared to the number of nests within the colony). The overall proportions of flights generally reflected the proportions of the within-colony nest counts for the four most common species: Black-crowned Night-Heron (Nycticorax nycticorax), Cattle Egret (Bubulcus ibis), Great Egret (Ardea alba), and Snowy Egret (Egretta thula). Flightline surveys at Chase Lake indicated apparent variation related to the time of day and season, as well as a variation in detection of inbound and outbound adult ciconiiforms. For ciconiiforms at Chase Lake, the most appropriate combination of survey approaches will depend on the need for annual estimates of nest abundance of ciconiiform species, balanced with the financial, personnel, and logistical constraints associated with the survey methods.
For ground-nesting American White Pelicans, the results from this study indicated that digital-image processing using remote-sensing software provides an accurate estimate of the number of American White Pelican nests. Estimates of the number of pelican nests from digital-image processing, using two commercially available remote-sensing software packages, produced nest estimates that were comparable to those of traditional manual counts from aerial photographs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201008","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Igl, L.D., Bartos, A.J., Woodward, R.O., Scherr, P., and Sovada, M.A, 2020, Evaluation of survey methods for colonial waterbirds at Chase Lake National Wildlife Refuge, North Dakota: U.S. Geological Survey Open-File Report 2020–1008, 44 p., https://doi.org/10.3133/ofr20201008. ","productDescription":"Report: viii, 44 p.; Data Release","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-112516","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":371775,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1008/ofr20201008.pdf","text":"Report","size":"7.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1008"},{"id":371774,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1008/coverthb.jpg"},{"id":371776,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90NK31K","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Evaluation of Survey Methods for Colonial Waterbirds at Chase Lake National Wildlife Refuge, North Dakota, data release"}],"country":"United States","state":"North Dakota ","otherGeospatial":"Chase Lake National Wildlife Refuge","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -99.481105,46.983794 ], [ -99.481105,47.030693 ], [ -99.417191,47.030693 ], [ -99.417191,46.983794 ], [ -99.481105,46.983794 ] ] ] } } ] }","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, North Dakota 58401
Aftershocks (earthquakes clustered spatially and chronologically near the occurrence of a causative earthquake) are ongoing in southwestern Puerto Rico after a series of earthquakes, which include a magnitude 6.4 earthquake that occurred near Barrio Indios, Guayanilla, on January 7, 2020, and affected the surrounding area. This report estimates the expected duration of these aftershocks by incorporating observations of aftershocks as of January 17, 2020, into a well-established statistical model of how earthquake sequences behave. Aftershocks will persist for years to decades, although with decreasing frequency, and earthquakes will likely be felt on a daily basis for up to several months. These estimates have significant uncertainty owing to different scenarios of how the earthquake sequence may evolve over time and could also change if a new large aftershock occurs. This report also estimates the amount of time remaining until the annual probability of magnitude 5, 6, and 7 or greater aftershocks—which could cause additional damage—decreases to 50, 25, 10, 5, and 1 percent. As of this writing, the chance of having a magnitude 6 or greater earthquake within a given year, going forward, will not fall below 25 percent for another 3 months to 3 years. The chance of having a magnitude 5 or greater earthquake will not fall below 25 percent for a decade or more. The aftershocks discussed in this report would be located in the same general area as the aftershocks that have already occurred. Our results do not imply a change in the risk of earthquakes in other parts of Puerto Rico.
Las réplicas (terremotos agrupados espacial y cronológicamente cerca de la ocurrencia de un terremoto causante) están en curso en el suroeste de Puerto Rico después de una serie de terremotos, que incluyen un terremoto de magnitud 6.4, ocurrido cerca del Barrio Indios, Guayanilla, el 7 de enero de 2020 y que afectaron las áreas circundantes. Este informe estima la duración esperada de las réplicas incorporando observaciones de réplicas a partir del 17 de enero de 2020 en un modelo estadístico bien establecido de cómo se comportan las secuencias de terremotos. Las réplicas persistirán durante años o décadas, aunque con una frecuencia decreciente, y los terremotos probablemente se sentirán a diario durante varios meses. Estos estimados tienen una incertidumbre significativa debido a diferentes escenarios de cómo la secuencia del terremoto puede evolucionar con el tiempo y también podrían cambiar si ocurre una nueva réplica grande. Este informe también estima la cantidad de tiempo restante hasta que la probabilidad anual de réplicas de magnitud 5, 6 y 7 o más - que podría causar daños adicionales- disminuya a un 50, 25, 10, 5 y 1 por ciento. Al momento de escribir este artículo, la posibilidad de tener un terremoto de magnitud 6 en un año determinado, en el futuro, no caerá por debajo del 25 por ciento durante otros 3 meses a 3 años. La probabilidad de tener un terremoto de magnitud 5 o mayor no será inferior al 25 por ciento durante una década o más. Las réplicas discutidas en este informe se ubicarían en la misma área general que las réplicas que ya han ocurrido. Nuestros resultados no implican un cambio en el riesgo de terremotos en otras partes de Puerto Rico.
","language":"English, Spanish","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201009","usgsCitation":"van der Elst, N.J., Hardebeck, J.L., and Michael, A.J., 2020, Potential duration of aftershocks of the 2020 southwestern Puerto Rico earthquake: U.S. Geological Survey Open-File Report 2020–1009, 5 p., https://doi.org/10.3133/ofr20201009.","productDescription":"v, 5 p.","onlineOnly":"Y","ipdsId":"IP-115560","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":399452,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109627.htm"},{"id":371666,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1009/coverthb.jpg"},{"id":371665,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1009/ofr20201009_spanish.pdf","text":"Report (Español)","linkFileType":{"id":1,"text":"pdf"}},{"id":371664,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1009/ofr20201009.pdf","text":"Report (English)","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.21,\n 17.7683\n ],\n [\n -66.5942,\n 17.7683\n ],\n [\n -66.5942,\n 18.0558\n ],\n [\n -67.21,\n 18.0558\n ],\n [\n -67.21,\n 17.7683\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, California 94025
Historical cross-shore positions of the shoreline and dune base were used as inputs for a Kalman filter algorithm to forecast the positions of these features in the year 2028. The beach width was also computed as the cross-shore distance between the forecasted 2028 shoreline and dune-base positions. While it does not evaluate the suitability of a nesting beach or identify optimal nesting habitat, the beach width can be used as a proxy for habitat availability. An analysis was conducted along the Florida Atlantic coast with an initial goal of demonstrating a method that combines available data for shoreline and dune positions with a Kalman Filter algorithm developed to predict decadal-scale shoreline evolution and then uses these features to define future beach width. This section of the southeastern United States hosts the largest assemblage of nesting loggerhead sea turtles (Caretta caretta) in the world, in addition to other species, and critical habitat is designated as part of the species’ listing package under the Endangered Species Act of 1973 (16 U.S.C. ch. 35 § 1531 et seq) for most of the nesting beaches within the study area. This work introduces an approach to inform ecosystem services assessments using data typically derived for shoreline change and storm vulnerability models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191150","usgsCitation":"Long, J.W., Henderson, R.E., and Thompson, D.M., 2020, Forecasting future beach width—A case study along the Florida Atlantic coast: U.S. Geological Survey Open-File Report 2019–1150, 13 p., https://doi.org/10.3133/ofr20191150.","productDescription":"vi, 13 p.","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112427","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":371572,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1150/coverthb.jpg"},{"id":371577,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1150/ofr20191150.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1150"},{"id":399439,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109625.htm"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.771484375,\n 25.16517336866393\n ],\n [\n -80.09033203125,\n 25.264568475331583\n ],\n [\n -79.5849609375,\n 26.62781822639305\n ],\n [\n -80.244140625,\n 28.013801376380712\n ],\n [\n -81.23291015625,\n 30.751277776257812\n ],\n [\n -81.76025390625,\n 30.770159115784214\n ],\n [\n -81.73828125,\n 30.334953881988564\n ],\n [\n -80.947265625,\n 28.401064827220896\n ],\n [\n -80.419921875,\n 27.11781284232125\n ],\n [\n -80.35400390625,\n 26.43122806450644\n ],\n [\n -80.771484375,\n 25.16517336866393\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
The Mississippi Barrier Islands in the northern Gulf of Mexico experienced high rates of spatial change over recorded history. Wave-induced sediment transport induced island migration, landward retreat, and inlet evolution. These processes can be measured using repeat bathymetric surveys to analyze elevation change over time. This study analyzes digital elevation models created from three time periods where bathymetric data have been collected: the 1920s, 2009, and 2016. The models are compared to assess volumetric change between surveys and characterize morphologic responses to natural and human-influenced processes. Although all the islands within the study area experienced a loss of area over the period of study, the nearshore and tidal inlets experience both accretion and erosion that vary spatially and temporally. Major morphologic changes include westward island migration, expanding ebb-tidal deltas, and changes in inlet dimensions. This study is a collaboration between the U.S. Geological Survey, the U.S. Army Corps of Engineers, and the National Park Service to establish baseline physical and pre-restoration morphologic conditions preceding a major restoration of the islands as part of the Mississippi Coastal Improvement Project.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191140","usgsCitation":"Flocks, J.G., Buster, N.A., and Brenner, O.T., 2020, Seafloor change around the Mississippi barrier islands, 1920 to 2016—The influence of storm effects on inlet and island morphodynamics: U.S. Geological Survey Open-File Report 2019–1140, 23 p., https://doi.org/10.3133/ofr20191140.","productDescription":"vi, 23 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-106950","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":371197,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1140/coverthb.jpg"},{"id":371199,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1140/ofr20191140.pdf","text":"Report","size":"7.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1140"}],"country":"United States","otherGeospatial":"Mississippi barrier islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.91113281249999,\n 28.76765910569123\n ],\n [\n -87.4951171875,\n 28.76765910569123\n ],\n [\n -87.4951171875,\n 30.977609093348686\n ],\n [\n -93.91113281249999,\n 30.977609093348686\n ],\n [\n -93.91113281249999,\n 28.76765910569123\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
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To assess and track changes to the rocky subtidal communities surrounding San Nicolas Island, the U.S. Navy entered into an agreement with the U.S. Geological Survey (USGS) in 2014 to conduct an ecological monitoring program at several sites around the island. Four permanent sites—Nav Fac 100, West End, Dutch Harbor, and Daytona 100—were established. The sites were based on ones that had been monitored since 1980 by USGS and were combined or expanded for better comparability with monitoring programs conducted at the other California Channel Islands. At the sites, scientists from USGS and our cooperator, the University of California, Santa Cruz, measured bottom cover of algae and sessile invertebrate species in quadrats, counted and sized fish on swimming transects, and counted a suite of kelps and invertebrates on benthic band transects. Holdfast diameter and number of stipes of giant kelp (Macrocystis pyrifera) were recorded on these transects, and size data were collected for urchins, sea stars, and shelled mollusks. Bottom temperatures were recorded at hourly intervals by archival data loggers that were deployed at the sites. This report focuses primarily on data collected in fall 2017 and spring 2018 and makes comparisons with data collected in previous years, beginning in fall 2014.
Nav Fac 100 is a site with a relatively low benthic profile, situated on the north side of San Nicolas Island. It was previously urchin dominated but underwent a dramatic decline in purple sea urchins in 2015 and 2016. Since then, macroalgae has become more prevalent as both annual brown algae, such as Dictyota, and perennials (for example, Cystoseira) have become established. The invasive brown alga Sargassum horneri has also become established. West End, on the southwest side of the island, also lacks much bottom relief but has more crevice habitat associated with boulders. It remains dominated by kelps and red algae, but red algae have decreased recently. Dutch Harbor, on the south side, has many high relief rocky reefs and had the greatest fish and non-motile invertebrate densities. It remains the most stable of the sites. Daytona 100, on the southeast side, has moderate relief and has remained a patchwork of kelp and urchin dominated areas with moderate fish density.
The main change at the sites during the last 4 years was the decline in urchin numbers at Nav Fac 100. There was storm-related mortality and subsequent recruitment in the M. pyrifera population at several of the sites in both 2016 and 2017. The winter of 2018, however, was relatively mild, with less destructive storm-related disturbance. The invasive brown alga S. horneri, first seen at San Nicolas Island at Nav Fac 100 in fall 2015, has become firmly established there during the last 2 sampling years. Finally, moderate increases were observed in purple urchin densities at all sites this spring. Long-term data are presented to illustrate trends and changes over the past three decades. Results indicate continued monitoring to evaluate ecosystem effects from perturbations owing to natural processes and anthropomorphic factors, including recovery of the sea otter population, changes in fisheries, invasive species and changing environmental conditions, could be valuable to inform managers’ decision-making.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191147","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Kenner, M.C., and Tomoleoni, J., 2020, Kelp forest monitoring at Naval Base Ventura County, San Nicolas Island, California: Fall 2017 and Spring 2018, Fourth Annual Report: U.S. Geological Survey Open-File Report 2019–1147, 76 p., https://doi.org/10.3133/ofr20191147.","productDescription":"vi, 76 p.","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-111796","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":399434,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109596.htm"},{"id":371253,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1147/coverthb.jpg"},{"id":371254,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1147/ofr20191147.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1147"}],"country":"United States","state":"California","otherGeospatial":"San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.64111328125,\n 33.18353672893615\n ],\n [\n -119.37744140625,\n 33.18353672893615\n ],\n [\n -119.37744140625,\n 33.32134852669881\n ],\n [\n -119.64111328125,\n 33.32134852669881\n ],\n [\n -119.64111328125,\n 33.18353672893615\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The primary objective of the project was to locate previously unknown stands of mast-producing hardwood forest trees in the Upper Mississippi River floodplain using existing information. We located and mapped 399 previously unknown hardwood forest stands within the Mississippi River floodplain area of navigation pools 9, 10, and 11. Using color infrared images in combination with true-color imagery was useful for identifying hardwood forest stands. We recommend our result be refined by visiting the forest stands we identified to evaluate our classification rate and determine which stands are regenerating. In combination with regeneration information, our results can help better inform flood inundation modeling, which will help improve the efficacy of restoration design. Although we had some success using the best available information, to obtain more relevant observations, we recommend acquiring color infrared aerial imagery during the late fall season if providing detailed mapping of forest stands is a management priority. Imagery of this type collected in the fall, when trees may be distinguished by their differing senescence, has the potential to uniquely identify individual species or perhaps even individual trees. Gaining a better understanding of forest diversity and developing conservation strategies to preserve that diversity is timely because remaining aging trees, established before lock-and-dam installation on the Mississippi River, are nearing the end of their life expectancy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191089","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Hanson, J.L., King, R., Hoy, E.E., 2019, Remnant hardwood forest mapping within the Upper Mississippi River floodplain: U.S. Geological Survey Open-File Report 2019–1089, 10 p., https://doi.org/10.3133/ofr20191089.","productDescription":"Report: vi, 10 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-102264","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":399413,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109582.htm"},{"id":370698,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1089/ofr20191089.pdf","text":"Report","size":"4.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1089"},{"id":370697,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1089/coverthb.jpg"},{"id":370699,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TD9WNW","text":"USGS data release","description":"USGS Data Release","linkHelpText":"FWS McGregor District Mast Hardwood Floodplain Forest Community"}],"country":"United States","state":"Iowa, Minnesota, Wisconsin","otherGeospatial":"Upper Mississippi floodplain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.42822265625,\n 43.67581809328341\n ],\n [\n -91.5380859375,\n 43.6599240747891\n ],\n [\n -91.49414062499999,\n 43.48481212891603\n ],\n [\n -91.60400390625,\n 43.100982876188546\n ],\n [\n -91.2744140625,\n 42.65012181368022\n ],\n [\n -90.81298828125,\n 42.52069952914966\n ],\n [\n -90.37353515625,\n 42.52069952914966\n ],\n [\n -90.68115234375,\n 42.97250158602597\n ],\n [\n -90.72509765625,\n 43.30919109985686\n ],\n [\n -91.01074218749999,\n 43.61221676817573\n ],\n [\n -91.42822265625,\n 43.67581809328341\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54602
Federal agencies need credible scientific information to determine the production and value of ecosystem services in an efficient and timely manner. The U.S. Geological Survey addresses this scientific information need through the Sustaining Environmental Capital Initiative project. The project has relied on U.S. Geological Survey expertise related to water, fisheries, advanced modeling, and economics and other social sciences to conduct eight case studies across a range of environment types, including water-based environments, deserts, sagebrush ecosystems, floodplains, and forests. The Sustaining Environmental Capital Initiative also supported the development and expansion of four tools with the intent of adding content and usability for partners’ decision-making needs. The tools are the Natural Value Resource Center, Benefit Transfer Toolkit, Riverine Environmental Flow Decision Support System, and Artificial Intelligence for Ecosystem Services modeling platform.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191117","usgsCitation":"Huber, C., Meldrum, J.R., Schuster, R.M., Ancona, Z.H., Bagstad, K.J., Beck, S.M., Carlisle, D.M., Claggett, P.R., Franco, F., Galbraith, H.S., Haefele, M., Hoelting, K.R., Hogan, D.M., Hopkins, K.G., Kern, T., Lawrence, C.B., Lischka, S., Loomis, J.B., Mueller, J.M., Noe, G.B., Pindilli, E.J., Quay, B., Semmens, D.J., Sinclair, W., Spooner, D.E., Voigt, B., and St. John White, B., 2020, Sustaining Environmental Capital Initiative summary report: U.S. Geological Survey Open-File Report 2019–1117, 23 p., https://doi.org/10.3133/ofr20191117.","productDescription":"v, 23 p.","onlineOnly":"Y","ipdsId":"IP-099772","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":370958,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1117/ofr20191117.pdf","text":"Report","size":"348 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1117"},{"id":370957,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1117/coverthb.jpg"}],"contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
This report estimates the economic impacts on the Wyoming economy from investments made by the Wyoming Landscape Conservation Initiative (WLCI) on conservation and restoration projects. The WLCI has been working in southwestern Wyoming since 2007 to coordinate science and management decisions among government and private entities that invest in conservation projects aimed at restoring and enhancing wildlife habitat. These investments support jobs and generate business activities within the Wyoming economy. WLCI conservation and restoration projects occur on both publicly managed and privately owned lands and are supported by leveraging funds from Federal bureaus, Wyoming State and local government agencies, and private contributions. During 2007–2018, the WLCI invested a total of more than $69,100,000 (in 2018 dollars) on conservation projects within the State of Wyoming. These pooled funds have been used to purchase conservation easements and hire business contractors to complete restoration projects, with 98 percent of project funds awarded to Wyoming-based businesses. Including both direct and secondary effects, the U.S. Geological Survey estimates that local spending on these conservation and restoration projects during 2007–2018 supported an estimated 1,055 job-years (the number of annualized full- and part-time jobs generated or supported), more than $30,500,000 in labor income, almost $40,900,000 in value added, and almost $68,200,000 in economic output within the Wyoming economy. These results demonstrate how investments in WLCI conservation projects support jobs, livelihoods, small businesses, and rural economies in Wyoming.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191135","isbn":"978-1-4113-4342-9","usgsCitation":"Huber, C., Flyr, M., and Cullinane Thomas, C., 2020, Economic impacts of Wyoming Landscape Conservation Initiative conservation projects in Wyoming: U.S. Geological Survey Open-File Report 2019–1135, 11 p., https://doi.org/10.3133/ofr20191135","productDescription":"iv, 11 p.","onlineOnly":"N","ipdsId":"IP-111077","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":370967,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1135/coverthb.jpg"},{"id":370968,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1135/ofr20191135.pdf","text":"Report","size":"384 kB ","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1135"}],"country":"United 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2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
Rare earth elements (REEs) are critical mineral commodities for the United States. In response to a need for information on potential domestic sources of REEs in mineral deposits, the U.S. Geological Survey (USGS) identified broad focus areas throughout the conterminous United States and Alaska as a guide for selecting new geoscience research areas. This study was done to support the USGS Earth Mapping Resources Initiative (Earth MRI).
Focus areas are identified in four regions of the United States (Alaska, West, Central, and East) by mineral deposit type. The areas are described in a companion USGS data release that consists of a map in a geographic information system and accompanying tables that document the rationale for each focus area (C.L. Dicken and others, 2019, https://doi.org/10.5066/P95CHIL0). This open-file report describes the methodology that was used to identify focus areas and determine new data acquisition needs. Deposit types that are likely to be of interest for future exploration and development of domestic nonfuel REE resources include deposits associated with carbonatites and peralkaline rocks, iron oxide-apatite deposits, monazite-bearing placers, and REE-enriched phosphorites.
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A of U.S. Geological Survey, Focus areas for data acquisition for potential domestic sources of critical minerals: U.S. Geological Survey Open-File Report 2019–1023, 11 p, https://doi.org/10.3133/ofr20191023A.","productDescription":"Report: vi, 11 p.; Data Release","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-104700","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":501521,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_108444.htm","linkFileType":{"id":5,"text":"html"}},{"id":403730,"rank":9,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/ofr20191023E","text":"Open-File Report 2019-1023-E","linkHelpText":"- Alaska Focus Area Definition for Data Acquisition for Potential Domestic Sources of Critical Minerals in Alaska for Antimony, Barite, Beryllium, Chromium, Fluorspar, Hafnium, Magnesium, Manganese, Uranium, Vanadium, and Zirconium"},{"id":403727,"rank":6,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/ofr20191023B","text":"Open-File Report 2019-1023-B","linkHelpText":"- Focus Areas for Data Acquisition for Potential Domestic Resources of 11 Critical Minerals in the Conterminous United States, Hawaii, and Puerto Rico—Aluminum, Cobalt, Graphite, Lithium, Niobium, Platinum-Group Elements, Rare Earth Elements, Tantalum, Tin, Titanium, and Tungsten"},{"id":403468,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2019/1023/a/versionHist.txt","size":"2.97 KB","linkFileType":{"id":2,"text":"txt"}},{"id":361988,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95CHIL0","text":"USGS data release","description":"USGS data release"},{"id":361987,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20193007","text":"Fact Sheet 2019–3007","linkHelpText":"- The Earth Mapping Resources Initiative (Earth MRI): Mapping the Nation’s Critical Mineral Resources"},{"id":361986,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1023/a/ofr20191023a.pdf","text":"Report","size":"1.65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1023"},{"id":420419,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1023/a/coverthb2.jpg"},{"id":403729,"rank":8,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/ofr20191023D","text":"Open-File Report 2019-1023-D","linkHelpText":"- Focus Areas for Data Acquisition for Potential Domestic Resources of 13 Critical Minerals in the Conterminous United States and Puerto Rico—Antimony, Barite, Beryllium, Chromium, Fluorspar, Hafnium, Helium, Magnesium, Manganese, Potash, Uranium, Vanadium, and Zirconium"},{"id":403728,"rank":7,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/ofr20191023C","text":"Open-File Report 2019-1023-C","linkHelpText":"- Focus Areas for Data Acquisition for Potential Domestic Resources of 11 Critical Minerals in Alaska—Aluminum, Cobalt, Graphite, Lithium, Niobium, Platinum Group Elements, Rare Earth Elements, Tantalum, Tin, Titanium, and Tungsten"}],"country":"United 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U.S. Geological Survey
913 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Email: Minerals@usgs.gov
Wildland fire characteristics, such as area burned, number of large fires, burn intensity, and fire season duration, have increased steadily over the past 30 years, resulting in substantial increases in the costs of suppressing fires and managing damages from wildland fire events (National Academies of Sciences, Engineering, and Medicine, 2017). Wildland fire management could benefit from sound decision making based on reliable scientific information. Fire scientists produce data, tools, and information to support fire and land management decision making. With ever-changing land use scenarios, environmental conditions, and emerging technological capabilities, new assessments and studies are continually needed. Established by Congress in 1879, the U.S. Geological Survey (USGS) is the primary science branch of the Department of the Interior (DOI), which manages more than 400 million acres of public lands in the United States. The USGS has more than 100 scientists across seven Mission Areas that help address the wildland fire science needs of DOI bureaus and their stakeholders. The diverse expertise of these scientists allows them to address complex interdisciplinary challenges. In this report, we identify and characterize scientific literature produced by USGS scientists during 2006–17 that addresses topics associated with wildland fire science. Our goals were to (1) make the most complete list possible of product citations readily available in an organized format, and (2) use bibliometric analysis approaches to highlight the productivity of USGS scientists and the impact of contributions that the Bureau has provided to the scientific, land management, and fire management communities.
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Ecosystems Mission Area
U.S. Geological Survey
Mail Stop 300
12201 Sunrise Valley Drive
Reston, VA 20192
Bottomhole temperature (BHT) measurements offer a useful way to characterize the subsurface thermal regime as long as they are corrected to represent in situ reservoir temperatures. BHT correction methods calibrated for the domestic onshore Gulf of Mexico basin were established in this study. These corrections are empirically derived and based on newly compiled databases of BHT wireline measurements and, to a lesser extent, drill stem test data. A unified BHT correction for the onshore Gulf Coast region, as well as 12 distinct BHT correction equations for each of the 12 physiographic provinces within the onshore Gulf Coast region, are provided. This study also characterizes the geothermal gradient across the onshore Gulf of Mexico basin, which ranges from 1.89 degrees Fahrenheit per 100 feet in the Sabine Uplift area to 1.39 degrees Fahrenheit per 100 feet in the Southern Louisiana Salt Basin. This report disseminates the slides presented at the 68th annual convention of the Gulf Coast Association of Geological Societies and the Gulf Coast Section of the Society of Economic Paleontologists and Mineralogists that was held September 30–October 2, 2018, in Shreveport, Louisiana.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20191143","usgsCitation":"Burke, L.A., Pearson, O.N., and Kinney, S.A., 2020, New method for correcting bottomhole temperatures acquired from wireline logging measurements and calibrated for the onshore Gulf of Mexico basin, U.S.A.: U.S. Geological Survey Open-File Report 2019–1143, 12 p., https://doi.org/10.3133/ofr20191143.","productDescription":"iii, 12 p.","onlineOnly":"Y","ipdsId":"IP-102769","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":399430,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109663.htm"},{"id":371939,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1143/ofr20191143.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1143"},{"id":371937,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1143/coverthb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf of Mexico basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.06640625,\n 30.675715404167743\n ],\n [\n -87.978515625,\n 31.50362930577303\n ],\n [\n -87.8466796875,\n 32.194208672875384\n ],\n [\n -91.23046875,\n 33.394759218577995\n ],\n [\n -95.64697265625,\n 33.52307880890422\n ],\n [\n -97.470703125,\n 33.30298618122413\n ],\n [\n -98.3056640625,\n 30.694611546632277\n ],\n [\n -98.10791015625,\n 29.611670115197377\n ],\n [\n -100.4150390625,\n 28.786918085420226\n ],\n [\n -99.6240234375,\n 27.566721430409707\n ],\n [\n -99.404296875,\n 27.15692045688088\n ],\n [\n -99.11865234374999,\n 26.43122806450644\n ],\n [\n -98.63525390624999,\n 26.27371402440643\n ],\n [\n -98.15185546874999,\n 26.05678288577881\n ],\n [\n -97.36083984375,\n 25.918526162075153\n ],\n [\n -97.0751953125,\n 25.918526162075153\n ],\n [\n -96.87744140625,\n 27.449790329784214\n ],\n [\n -95.03173828125,\n 28.76765910569123\n ],\n [\n -93.3837890625,\n 29.477861195816843\n ],\n [\n -91.99951171875,\n 29.19053283229458\n ],\n [\n -90.703125,\n 28.9600886880068\n ],\n [\n -89.62646484375,\n 28.459033019728043\n ],\n [\n -87.7587890625,\n 29.132970130878636\n ],\n [\n -88.06640625,\n 30.675715404167743\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Bedrock geologic mapping of the Lahore, Va., 7.5-minute quadrangle was completed as part of a broader project, undertaken jointly between the U.S. Geological Survey, the Virginia Division of Geology and Mineral Resources, and other Federal and State agencies to better understand the causative mechanisms of the magnitude-5.8 (M5.8) earthquake that occurred near Mineral, Va., on August 23, 2011. This project involved detailed mapping of eight quadrangles in the epicentral region of the Mineral, Va., earthquake in order to improve our understanding of the geologic framework of the central Virginia seismic zone, which has a long record of historical and prehistoric seismicity.
The Lahore 7.5-minute quadrangle contains the contact between Ordovician to Silurian, dioritic and granodioritic rocks of the Lahore and Ellisville plutons and older metasedimentary and metavolcanic rocks. The Lahore quadrangle is northeast of the Ferncliff and Louisa, Va., quadrangles, where the Shores complex is intruded by the Ellisville pluton along the pluton’s southwestern margin. The new mapping in the Lahore quadrangle shows that the Shores complex continues northeast of the Ellisville pluton. A northeast-trending mafic- and ultramafic-bearing belt within the Shores complex is a fault-bounded accretionary zone (accretionary wedge) between rocks of the Chopawamsic Formation and Laurentian slope-and-rise deposits. In the Lahore quadrangle, this belt contains several mappable, northeast- to southwest-trending mafic bodies and also includes small exposures of gabbro and talc schist.
The Lahore quadrangle contains structures of both early- and late-Paleozoic age that correspond to the Taconic and Alleghanian orogenies. Taconic (Late Ordovician) S1 schistosity in layered rocks is typically fine-grained and parallel to compositional layering, when present. Alleghanian (Pennsylvanian) S2 schistosity is coarser and more micaceous than S1 and is locally accompanied by a lineation that is represented by mineral lineations, micro-crenulations, or mullion fabric, and represents the hinges of F2 folds. A foliation in the plutonic rocks is represented by an equilibrium assemblage of aligned mafic minerals and is early Paleozoic in age.
Metamorphic grade in the non-plutonic rocks of the Lahore quadrangle ranges from lower-greenschist to the northwest to upper-greenschist to the southeast, as represented by mineral assemblages in non-plutonic rocks. The biotite isograd may be, in part, lithologically controlled by the contact between the informally-named Hardware and Byrd Mill formations, and locally affected by contact metamorphism by the Lahore pluton (western part of map). The Taconic garnet isograd is defined by the sparse presence of small (less than 1 millimeter), euhedral garnet crystals. Both the biotite and garnet isograds continue along strike to the southwest into the Ferncliff and Louisa quadrangles, where the isograds have been identified as Ordovician age (Taconic orogeny) based on muscovite, biotite, and amphibole 40Ar/39Ar cooling ages.
Regionally, the most common trend and plunge of joints is northwest and subvertical, respectively, and orthogonal to the regional strike of foliation. Early Mesozoic extension may have reactivated the Harris Creek fault (southeast corner of map), a late Paleozoic (Alleghanian orogeny) transpressional fault that marks the contact between granodiorite of the Ellisville pluton and the Chopawamsic Formation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191110","usgsCitation":"Burton, W.C., 2019, Preliminary bedrock geologic map of the Lahore 7.5-minute quadrangle, Orange, Spotsylvania, and Louisa Counties, Virginia: U.S. Geological Survey Open-File Report 2019–1110, 1 sheet, scale 1:24,000, https://doi.org/10.3133/ofr20191110.","productDescription":"1 Sheet: 37.00 x 34.00 inches; ReadMe; Database; Metadata","numberOfPages":"1","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098254","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":399417,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109624.htm"},{"id":371645,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/of/2019/1110/LahoreVAOFR20191110_database.zip","text":"Geodatabase","linkFileType":{"id":6,"text":"zip"}},{"id":371644,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2019/1110/ofr20191110_openaccess.zip","text":"Open Access","linkFileType":{"id":6,"text":"zip"}},{"id":371643,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2019/1110/LahoreVAOFR20191110Metadata.zip","text":"Metadata","linkFileType":{"id":6,"text":"zip"}},{"id":371642,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2019/1110/ofr20191110_readme.txt","text":"Read Me","linkFileType":{"id":2,"text":"txt"}},{"id":371347,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2019/1110/ofr20191110.pdf","text":"Geologic Map","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1110"},{"id":370151,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1110/coverthb.jpg"}],"scale":"24000","country":"United States","state":"Virginia","county":"Louisa County, Orange County, Spotsylvania County","otherGeospatial":"Lahore 7.5-minute quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78,\n 38.125\n ],\n [\n -77.875,\n 38.125\n ],\n [\n -77.875,\n 38.25\n ],\n [\n -78,\n 38.25\n ],\n [\n -78,\n 38.125\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Florence Bascom Geoscience Center
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The U.S. Geological Survey National Crustal Model (NCM) is being developed to assist with earthquake hazard and risk assessment by supporting estimates of ground shaking in response to an earthquake. The period-dependent intensity and duration of shaking depend upon the three-dimensional seismic velocity, seismic attenuation, and density distribution of a region, which in turn is governed to a large degree by geology and how that geology behaves under varying temperatures and pressures.
A three-dimensional temperature model is presented here to support the estimation of physical parameters within the U.S. Geological Survey NCM. The crustal model is defined by a geological framework consisting of various lithologies with distinct mineral compositions. A temperature model is needed to calculate mineral density and bulk and shear modulus as a function of position within the crust. These properties control seismic velocity and impedance, which are needed to accurately estimate earthquake travel times and seismic amplitudes in earthquake hazard analyses. The temperature model is constrained by observations of surface temperature, temperature gradient, and conductivity, inferred Moho temperature and depth, and assumed conductivity at the base of the crust. The continental plate is assumed to have heat production that decreases exponentially with depth and thermal conductivity that exponentially changes from a surface value to 3.6 watts per meter-Kelvin at the Moho. The oceanic plate cools as a half-space with a geotherm dependent on plate age. Under these conditions, and the application of observed surface heat production, predicted Moho temperatures match Moho temperatures inferred from seismic P-wave velocities, on average. As has been noted in previous studies, high crustal temperatures are found in the western United States, particularly beneath areas of recent volcanism. In the central and eastern United States, elevated temperatures are found from southeast Texas, into the Mississippi Embayment, and up through Wisconsin. A USGS ScienceBase data release that supports this report is available and consists of grids covering the NCM across the conterminous United States, for example, surface temperature and temperature gradient, that are needed to produce temperature profiles.
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U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046