Recent estimates of the world’s swan Cygnus sp. populations indicate that there are currently between 1.5–1.6 million birds in 8 species, including the Coscoroba Swan Coscoroba coscoroba as an honorary swan. Monitoring programmes in Europe and North America indicate that most populations increased following the introduction of national and international legislation to protect the species during the early- to mid-20th century. A switch from feeding primarily on aquatic vegetation to foraging on farmland (especially high-energy arable crops) in winter during the second half of the 20th century, is also considered a contributing factor. Trumpeter Swans Cygnus buccinator famously increased from just 69 individuals known to exist in 1935 (although small numbers were missed) to c. 76,000 at the present time, and most of the northern hemisphere swan populations have continued to show increasing/stable trends over the last 20 years. The exception to this pattern is a decline since 1995 in the Northwest European Bewick’s Swan population, following an increase in its population size during the 1970s–1980s, which is now being addressed through implementation of an International Single Species Action Plan. A proposal to change enforcement regulations of the Migratory Bird Treaty Act in the United States is also of concern, as potentially undermining protection for Trumpeter Swans in North America, illustrating the importance of politics and legislation as well as on-the-ground measures for species conservation. Elsewhere, less is known about the trends and conservation status for swans in central and eastern Asia, though count and research programmes introduced in China, added to those underway in Japan and Korea, have recently greatly enhanced our knowledge of swan populations on the East Asian flyway. Trends for the Black Swan Cygnus atratus in Australia and for the Black-necked Swan Cygnus melancoryphus in South America are also poorly known, because of the large numbers involved for the former and a lack of coordinated counts across difficult terrain for the latter. These southern hemisphere species are considered vulnerable to water resource developments (i.e. where diversion of water is shrinking wetlands), and to droughts associated with El Nino events and climate change. More extensive monitoring is therefore required to determine whether swan populations and species are stable, fluctuating or in decline.
Soil chronosequences on alpine moraine complexes have been used to help unravel the glacial histories of the eastern Sierra Nevada. The moraine sequence along Bishop Creek includes well-preserved moraines that have been previously dated using cosmogenic 36Cl surface exposure ages. The goal of this study was to interpret pedogenesis within a soil geomorphic context on these quantitatively dated moraines. Soil development, surface clast cover, and moraine morphology were studied on seven of the moraines, ranging in age from 15 to 170 ka. Older moraines had gentler slopes, broader crests, and decreased surface rock cover. Soils showed weak development across the chronosequence of moraines. Pedogenesis involved slight increases in clay, the formation of clay lamellae, development of a vesicular horizon in a surface layer of aeolian dust, and weathering of surface and subsurface granitic clasts. Soil reddening and structure development were minimal. Soil formation was likely inhibited by the arid to semi-arid climate and intermittent wind and water erosion during the time span of the chronosequence.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.catena.2019.104222","usgsCitation":"Rossi, A., Graham, R., and Kendrick, K.J., 2019, Pedogenic evolution on the arid Bishop Creek moraines, eastern Sierra Nevada, California: Catena, v. 183, 104222, 14 p., https://doi.org/10.1016/j.catena.2019.104222.","productDescription":"104222, 14 p.","ipdsId":"IP-102151","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":378160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Eastern Sierra Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.87207031250001,\n 37.69251435532741\n ],\n [\n -118.49304199218749,\n 37.69251435532741\n ],\n [\n -118.49304199218749,\n 37.84015683604136\n ],\n [\n -118.87207031250001,\n 37.84015683604136\n ],\n [\n -118.87207031250001,\n 37.69251435532741\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"183","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rossi, Annie 0000-0001-8955-0065","orcid":"https://orcid.org/0000-0001-8955-0065","contributorId":239863,"corporation":false,"usgs":false,"family":"Rossi","given":"Annie","email":"","affiliations":[{"id":48012,"text":"U.S.D.A. - NRCS","active":true,"usgs":false}],"preferred":false,"id":797908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graham, Robert","contributorId":239864,"corporation":false,"usgs":false,"family":"Graham","given":"Robert","affiliations":[{"id":12655,"text":"University of California, Riverside","active":true,"usgs":false}],"preferred":false,"id":797909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kendrick, Katherine J. 0000-0002-9839-6861","orcid":"https://orcid.org/0000-0002-9839-6861","contributorId":207907,"corporation":false,"usgs":true,"family":"Kendrick","given":"Katherine","email":"","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797910,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208836,"text":"70208836 - 2019 - Gopherus agassizii (Cooper 1861) – Agassiz’s Desert Tortoise, Mojave Desert Tortoise","interactions":[],"lastModifiedDate":"2021-12-10T15:22:19.1894","indexId":"70208836","displayToPublicDate":"2019-12-31T06:51:39","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5938,"text":"Chelonian Research Monographs","printIssn":"1088-7105","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Gopherus agassizii (Cooper 1861) – Agassiz’s Desert Tortoise, Mojave Desert Tortoise","title":"Gopherus agassizii (Cooper 1861) – Agassiz’s Desert Tortoise, Mojave Desert Tortoise","docAbstract":"Throughout the western United States, efforts are underway to better understand and preserve migration and movement corridors for mule deer and other big game and to minimize the impacts of development and other land-use change on populations. San Diego County is home to a unique non-migratory subspecies of mule deer, the Southern mule deer (Odocoileus hemionus fuliginatus; herein referred to as “mule deer”). Because it is the only large herbivorous mammal in San Diego, connectivity among mule deer groups is an important indicator of functional connectivity throughout San Diego County urban preserves and has therefore been monitored within central and eastern San Diego County using DNA fingerprinting since 2005. To continue this effort and to assess genetic connectivity in north San Diego County (herein “North County”), we genotyped scat samples from preserves in the area and tissue samples from Marine Corps Base Camp Pendleton (MCBCP). We used non-invasive capture/recapture analyses and pedigree analyses for assessing short-term movement and population clustering analyses to assess gene flow in North County. Additionally, we performed similar analyses on the combined San Diego County dataset, which was composed of the North County dataset collected for this study and a previously collected dataset from central and eastern San Diego County. Using recapture data, we found multiple instances of mule deer crossing roads in urban North County preserves, with several of these events occurring in areas where there are underpasses and culverts known to be used by mule deer. Corroborating previous studies in the region and statewide, pedigree and population structure analyses support the presence of two genetic clusters for mule deer in San Diego County—the “Coastal” and “Inland/Mountain” clusters. Low estimates of effective population size, especially in the Coastal cluster, suggest that to further understand potential vulnerabilities of mule deer in this region, it is important to continue to monitor connectivity, in particular, at the boundary between these two clusters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191138","usgsCitation":"Mitelberg, A., Smith, J.G., and Vandergast, A.G., 2019, DNA Fingerprinting of Southern mule deer (Odocoileus hemionus fuliginatus) in north San Diego County, California (2018–19): U.S. Geological Survey Open-File Report 2019–1138, 25 p., https://doi.org/10.3133/ofr20191138.","productDescription":"vi, 25 p.","numberOfPages":"25","onlineOnly":"Y","ipdsId":"IP-112707","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":437245,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YXWXA9","text":"USGS data release","linkHelpText":"Microsatellite Genetic Marker Genotypes from Southern Mule Deer (Odocoileus hemionus fuliginatus) Sampled in San Diego County, California"},{"id":370869,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1138/ofr20191138.pdf","text":"Report","size":"31 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":370868,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1138/coverthb.jpg"}],"country":"United States","state":"California","county":"San Diego County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.31201171875001,\n 32.713355353177555\n ],\n [\n -116.05957031249999,\n 32.713355353177555\n ],\n [\n -116.05957031249999,\n 33.25706340236547\n ],\n [\n -117.31201171875001,\n 33.25706340236547\n ],\n [\n -117.31201171875001,\n 32.713355353177555\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
Reliable estimates of life history parameters and their functional role in animal population trajectories are critical, yet often missing, components in conservation and management. We developed seasonal matrix population models of the Red-tailed Hawk Buteo jamaicensis jamaicensis in the upper and lower forests of the Luquillo Mountains, Puerto Rico, to describe the influence of early life stages (nestling and clutch survival) on population growth. Modelled populations exhibited positive discrete rates of growth in forests above 400 m (λ highlands = 1.05) and in forests below 400 m (λ lowlands = 1.27) of the Luquillo Mountains. Further, adult survival was the parameter with the highest proportional effect and direct contribution to growth of the population. Besides survival of adults, our results identified that nestling survival had the second greatest influence on λ, stressing the importance of this life stage for the population growth rate of Red-tailed Hawks in our study area. Seasonal matrices are not commonly used to describe population dynamics of birds. However, these may be a useful tool to analyse the influence of life stages in the annual cycle to better address conservation and management needs, especially for species inhabiting oceanic islands.
Groundwater withdrawals since the 1940s have lowered water levels, altered groundwater-flow directions, and caused saltwater to intrude within some freshwater-containing sands of the fluvial-deltaic Southern Hills regional aquifer system beneath Baton Rouge, Louisiana. New interpretations of stratigraphic correlations amongst geophysical well logs were utilized to revise a hydrogeologic framework that delineates the depth and thickness variations of aquifers and confining units in the Southern Hills regional aquifer system. A groundwater-flow and chloride-transport model incorporating the revised framework was constructed to assess the effects of groundwater withdrawals on the rate and pathways of saltwater migration in the “1,500-foot” sand, “2,400-foot” sand, and the “2,800-foot” sand. Groundwater withdrawals reported since 1940 were compiled to specify annual average withdrawal rates through 2016 for 722 wells. Regional groundwater flow throughout the Southern Hills regional aquifer system was first simulated with MODFLOW, and flow-model parameters were calibrated to 8,810 water levels observed through 2016 by using the parameter-estimation code PEST++. Saltwater transport was subsequently simulated for the “1,500-foot” sand, “2,400-foot” sand, and the “2,800-foot” sand by using the variable-density code, SEAWAT. Chloride-concentration measurements were used as a proxy for saltwater to formulate the concentration initial conditions and calibrate the transport-model parameters.
Three groundwater-management scenarios were simulated to evaluate the effects of different groundwater withdrawals on future groundwater levels and saltwater concentrations in the “1,500-foot” sand, “2,400-foot” sand, and “2,800-foot” sand. All three scenarios simulated the period from 2017 through 2112 (96 years), and the water levels and concentrations simulated for 2047 and 2112 were compared among the scenarios. The first scenario simulated a continuation of groundwater withdrawals at 2016 rates and represents the “status quo” of groundwater withdrawals. The second scenario simulated the effects of discontinuing 10,620 gallons per minute (gal/min) of withdrawals from the “2,800-foot” sand, and the third scenario simulated reallocating 2,000 gal/min of withdrawals from the “1,500-foot” sand to the “2,800-foot” sand. Continuation of the “status quo” withdrawals results in lower water levels by 2047 around groundwater-withdrawal locations in the “1,500-foot” sand, “2,400-foot” sand, and “2,800-foot” sand. By 2112, water levels recover to higher levels as flow in the aquifer approaches equilibrium. Saltwater within the “1,500-foot” sand would continue migrating toward public-supply wells located 2.4 miles (mi) north of the Baton Rouge Fault, but a “scavenger well” that removes relatively concentrated water from the base of the “1,500-foot” sand attenuates chloride concentrations at the public-supply wells. Saltwater within the “2,400-foot” sand would continue to encroach on a well with large withdrawals and farther east within an area about 1 mi north of the Baton Rouge Fault. Saltwater within the “2,800-foot” sand would migrate northward toward withdrawal wells located about 3 mi north of the industrial district. Cessation of 10,620 gal/min of industrial withdrawals from the “2,800-foot” sand about 12 mi northwest of the industrial district (scenario 2) would cause a substantial water-level recovery in the “2,800-foot” sand in the area of discontinued withdrawals. Groundwater levels 3 mi north of the industrial district would be 25–30 feet higher in 2047 than predicted for the “status quo” withdrawals. Saltwater encroachment toward wells north of the industrial district would be slowed because of the decreased hydraulic gradient. Reallocating 2,000 gal/min of withdrawals from the “1,500-foot” sand to the “2,800-foot” sand 12 mi northwest of the industrial district (scenario 3) would have a negligible effect on water levels and chloride concentrations in the “1,500-foot” sand 15 mi to the south-southeast where saltwater is encroaching toward wells in the “1,500-foot” sand. Within the “2,800-foot” sand, the area of saltwater encroachment is only 3 mi from increased withdrawals in the “2,800-foot” sand, and water levels would be about 5 feet lower in 2047 than for the “status quo” scenario. A larger hydraulic gradient would cause slightly faster saltwater transport and higher chloride concentrations within this area of the “2,800-foot” sand.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195102","collaboration":"Prepared in cooperation with the Capital Area Groundwater Conservation Commission; the Louisiana Department of Transportation and Development, Public Works and Water Resources Division; and the City of Baton Rouge and Parish of East Baton Rouge","usgsCitation":"Heywood, C.E., Lindaman, M., and Lovelace, J.K., 2019, Simulation of groundwater flow and chloride transport in the “1,500-foot” sand, “2,400-foot” sand, and “2,800-foot” sand of the Baton Rouge area, Louisiana: U.S. Geological Survey Scientific Investigations Report 2019–5102, 49 p., https://doi.org/10.3133/sir20195102.","productDescription":"Report: ix, 49 p.; Data Release","numberOfPages":"63","onlineOnly":"N","ipdsId":"IP-099059","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":399545,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109561.htm"},{"id":370615,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5102/sir20195102.pdf","text":"Report","size":"22.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5102"},{"id":370616,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9URJ38Q","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SEAWAT model archive of chloride transport in the “1,500-foot”, “2,400-foot”, and “2,800-foot” sands of the Baton Rouge Area, Louisiana"},{"id":370614,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5102/coverthb.jpg"}],"country":"United States","state":"Louisiana","city":"Baton Rouge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.75,\n 31.25\n ],\n [\n -90.5,\n 31.25\n ],\n [\n -90.5,\n 30.25\n ],\n [\n -91.75,\n 30.25\n ],\n [\n -91.75,\n 31.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park Drive, Suite 100
Nashville, TN 37211
The assessment of the Little Snake River coal field and Red Desert area covers approximately 2,300 square miles in the eastern portion of the Greater Green River Basin in south-central Wyoming. Coal-bearing formations are present throughout the Eocene, Paleocene, and Cretaceous strata in the assessment area. Paleogene-age coal beds are present in the Eocene Wasatch Formation and Paleocene Fort Union Formation. Cretaceous-age coal beds are present in the Lance, Almond, and Allen Ridge Formations. Utilizing over 4,000 data points, 55 individual coal beds were identified in the assessment area. Coal resources were calculated using geologic models generated from these data points, using criteria for minimum thickness and areal extent. The geologic modeling criteria indicated that 33 of the 55 individual coal beds had sufficient thickness and areal extent to be economically significant. Calculated original coal resources within the assessment area were approximately 73.2 billion short tons (BST). After excluding coal resources lost due to land use and technical restrictions, recoverable coal resources were approximately 19.37 BST, including 2.14 BST of coal resources that could be extracted using surface mining methods and 17.23 BST of coal resources that could be extracted using underground mining methods. Due to mining costs and projected low potential sales value of the coal resources, only approximately 167 million short tons (MST) can be classified as reserves, which is less than 1 percent of the recoverable coal resources
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193053","usgsCitation":"Shaffer, B.N., Pierce, P.E., Kinney, S.A., Olea, R., and Luppens, J.A., 2019, Assessment of coal resources and reserves in the Little Snake River coal field and Red Desert assessment area, Greater Green River Basin, Wyoming: U.S. Geological Survey Fact Sheet 2019–3053, 6 p., https://doi.org/10.3133/fs20193053.","productDescription":"6 p.","onlineOnly":"N","ipdsId":"IP-109360","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":370380,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3053/coverthb.jpg"},{"id":370382,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1836","text":"Coal Geology and Assessment of Resources and Reserves in the Little Snake River Coal Field and Red Desert Assessment Area, Greater Green River Basin, Wyoming"},{"id":370381,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3053/fs20193053.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019-3053"},{"id":399136,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109583.htm"}],"country":"United States","state":"Wyoming","otherGeospatial":"Little Snake River coal field, Red Desert assessment area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.35,\n 41\n ],\n [\n -107.3333,\n 41\n ],\n [\n -107.3333,\n 42.1078\n ],\n [\n -108.35,\n 42.1078\n ],\n [\n -108.35,\n 41\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
The U.S. Geological Survey is studying regional-scale assessments of resources and reserves of primary coal beds in the major coal bed basins in the United States to help formulate policy for Federal, State, and local energy and land use. This report summarizes the geology and coal resources and reserves in the Little Snake River coal field and Red Desert assessment area in the Greater Green River Basin, southwestern Wyoming. These areas are contiguous and referred to as the “assessment area” in this report. The assessment area covers about 2,300 square miles of the eastern section of the 15,400-square-mile Greater Green River Basin. This area was prioritized for assessment because no comprehensive resource assessment had previously been completed in the area; abundant, previously unavailable drill hole data from cooperators and stakeholders became available; and there are active coal mines in the Greater Green River Basin area producing from some of the same formations as those found in the assessment area.
Coal-bearing Eocene, Paleocene, and Upper Cretaceous formations have a composite thickness of more than 11,000 feet in the assessment area. Stratigraphic sequences that contain multiple coal beds within a formation or member are referred to as coal zones in this report. Paleogene coal beds are found within coal zones in the Eocene Wasatch Formation and the Paleocene Fort Union Formation. Cretaceous coal beds are within coal zones in the Lance, Almond, and Allen Ridge Formations.
A total of 4,214 drill holes and measured sections were used to construct a geologic database for this assessment. From these data, 7 coal zones containing 55 individual coal beds were identified. Not all 55 coal beds were assessed; only those beds that were at least 3 feet thick and had at least a 2-square-mile areal extent were considered. Using a geology-based assessment methodology, the U.S. Geological Survey estimated original, available, and recoverable coal resources for 33 coal beds that met those criteria.
An original resource of 73.2 billion short tons of coal was calculated for the 33 coal beds that met the criteria in the assessment area. To be considered extractable by surface mining methods, coal beds had to be equal to or greater than 3 feet thick and less than 300 feet deep. Of the 73.2 billion short tons (BST), 19.3 BST were determined to be recoverable resources, of which approximately 2.1 BST were considered as recoverable resources by surface mining methods at a stripping ratio of 10:1 or less. (defined as recoverable resources for this assessment, based on economic modeling and regional mining analogs). Recoverable resources for underground mining methods (coal 8 to 15 feet thick and between 300 and 3,000 feet deep) totaled 17.2 BST. Out of the 19.3 BST assessed as recoverable coal resources, approximately 167 million short tons (MST) were considered to be reserves.
Within the 7 coal zones, the Wasatch coal zone contains an original resource of about 6.8 BST billion short tons of coal, of which 2.6 BST are considered a recoverable resource and approximately 26.7 MST are considered reserves. The Overland coal zone, in the Fort Union Formation, contains an original resource of approximately 23 BST of coal, of which 8.4 BST are considered a recoverable resource and approximately 74 MST are considered reserves. The China Butte coal zone, in the Fort Union Formation, contains an original resource of 36.2 BST of coal, of which 6.3 BST are considered a recoverable resource and approximately 5.5 MST are considered reserves. The Almond coal zone, in the Almond Formation, contains an original resource of 7.0 BST, of which approximately 2.0 BST are considered a recoverable resource and 61 MST are considered reserves. Resources were not calculated for the coal zones within the Niland Tongue of the Wasatch Formation or the Cretaceous Lance and Allen Ridge Formations because the coal beds in those zones are relatively thin, discontinuous, and have a limited areal extent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1836","usgsCitation":"Scott, D.C., Shaffer, B.N., Haacke, J.E., Pierce, P.E., and Kinney, S.A., 2019, Coal geology and assessment of resources and reserves in the Little Snake River coal field and Red Desert assessment area, Greater Green River Basin, Wyoming: U.S. Geological Survey Professional Paper 1836, 169 p., https://doi.org/10.3133/pp1836.","productDescription":"xiii, 169 p.","onlineOnly":"Y","ipdsId":"IP-077210","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":370383,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20193053","text":"Assessment of Coal Resources and Reserves in the Little Snake River Coal Field and Red Desert Assessment Area, Greater Green River Basin, Wyoming"},{"id":356626,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1836/pp1836.pdf","text":"Report","size":"32.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1836"},{"id":356625,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1836/coverthb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.02783203125,\n 43.14909399920127\n ],\n [\n -111.07177734375,\n 41.02964338716638\n ],\n [\n -108.3251953125,\n 40.9964840143779\n ],\n [\n -106.8310546875,\n 41.02964338716638\n ],\n [\n -105.380859375,\n 41.04621681452063\n ],\n [\n -104.9853515625,\n 41.47566020027821\n ],\n [\n -107.24853515625,\n 42.90816007196054\n ],\n [\n -109.09423828125,\n 43.8028187190472\n ],\n [\n -110.9619140625,\n 43.8503744993026\n ],\n [\n -111.02783203125,\n 43.14909399920127\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
Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
White-tailed deer (Odocoileus virginianus) are among the most impactful herbivores in the eastern United States. Legacy forest effects, those accrued from intense herbivory over time, manifest as low seedling regeneration, high cover of plant species that are infrequently browsed by deer, presence or expansion of nonnative or invasive plant species, few herbaceous species, and diminished capacity for recovery. Interfering vegetation (that is, species that increase in cover and density due to avoidance by deer, such as American beech sprouts, Pennsylvania sedge, and hay-scented fern) increase competition for light and hinder recruitment of trees into the forest canopy.
The lower Hudson Valley in New York has been heavily browsed by white-tailed deer since the early 20th century. The region has some of the lowest tree regeneration rates in New York State as a result of deer browsing and subsequent increases in interfering vegetation. The U.S. Geological Survey and the State University of New York College of Environmental Science and Forestry studied sites where deer hunting is permitted (case sites) and nearby sites where hunting is currently prohibited (control sites) to assess and identify forest structure and composition differences.
Instead of using deer exclosures, which are time-consuming and expensive to install and maintain, we used a case-control study because such studies are well-suited to effects with long latency and rare outcomes. Case-control studies seek to describe the relation between an outcome of interest (in this study, forest understory recovery from chronic herbivory) and forest condition. We inferred recovery by comparing these characteristics on adjacent sites in the lower Hudson Valley with similar forest communities and land uses but different deer population management histories. Case plots were on lands where deer management has taken place annually for several decades. Control plots were on lands where deer populations have not been consistently managed to lowered abundance. We accounted for differences in forest recovery not attributable to deer by first matching case and control plots along several important environmental gradients (slope, aspect, elevation, moisture, canopy openness). By controlling for these gradients, we looked for associations between measured forest conditions and deer herbivory reduction through population management.
We surveyed more than 200 plots in upland forest types across case and control sites where we assessed forest condition by estimating density (number per unit area) and composition and cover (percent) of important vegetation constituents in ground, shrub, subcanopy, and canopy layers of the forest. We recorded 37 tree species, 22 shrub species, 57 herbaceous species, and 19 species of grasses and sedges in our plot surveys, including a number of nonnative and invasive plants. We also estimated the ages of a number of common canopy trees by counting rings from cores extracted from individual stems.
Effects of more than 100 years of chronic deer browsing manifested in low herbaceous ground cover and little to no tree recruitment (saplings) on lands without deer management. In contrast, sustained deer management resulted in forests with conditions that indicated substantial recovery from chronic herbivory in the ground, shrub, and subcanopy layers. Sites with ongoing deer management exhibited greater ground cover of tree seedlings and herbs and less ground cover of interfering vegetation and nonnative species. The well-developed sub-canopy layer of small trees, saplings, and tall shrubs on sites with deer management indicates a high potential for sapling recruitment to the canopy of the future forest.
Of the 25 subcanopy trees sampled on control sites, most were more than 100 years old, indicating little to no regeneration in areas sampled for more than 100 years. The forest canopy, a relic of land uses of bygone days, requires a source of young trees to replace itself as older trees die. Without an abundant layer of young trees in the subcanopy, a forest cannot be sustained over time. Reduction in deer herbivory promotes forest recovery and could benefit Harriman and Bear Mountain State Parks (the control sites for the study), but removal of interfering vegetation may be necessary to mitigate legacy effects where they currently hinder ground layer recovery. To successfully promote a more desirable forest condition that includes elimination of nonnative plant species, promotion of tree recruitment into the forest canopy, and development of diverse and abundant herbaceous cover in ground layer vegetation, future management decisions could include information on herbivory reduction and management of interfering vegetation where necessary.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191116","collaboration":"Prepared in cooperation with the New York State Parks, Recreation, and Historic Preservation","usgsCitation":"Kilheffer, C.R., Underwood, H.B., Leopold, D.J., and Guerrieri, R., 2019, Evaluating legacy effects of hyperabundant white-tailed deer (Odocoileus virginianus) in forested stands of Harriman and Bear Mountain State Parks, New York: U.S. Geological Survey Open-File Report 2019–1116, 36 p., https://doi.org/10.3133/ofr20191116.","productDescription":"Report: viii, 35 p.; Dataset","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-111113","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":369987,"rank":2,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.7910/DVN/3ZFKFS","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- Data for evaluation of effects of white-tailed deer at Harriman and Bear Mountain State Parks, New York"},{"id":370098,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1116/ofr20191116.pdf","text":"Report","size":"16.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1116"},{"id":369982,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1116/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Harriman, Bear Mountain State Parks","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-73.9525,41.59],[-73.9526,41.5841],[-73.9578,41.5751],[-73.9685,41.562],[-73.9866,41.5472],[-73.9948,41.5374],[-73.9975,41.526],[-73.9985,41.4788],[-74.0002,41.4543],[-73.9997,41.4498],[-73.9955,41.4475],[-73.9869,41.4451],[-73.9828,41.4401],[-73.9703,41.4222],[-73.9643,41.4135],[-73.9573,41.4016],[-73.9495,41.3947],[-73.9507,41.3916],[-73.9546,41.3834],[-73.9586,41.3699],[-73.9621,41.3472],[-73.9653,41.3432],[-73.9696,41.3391],[-73.9765,41.3338],[-73.9821,41.3279],[-73.9834,41.3248],[-73.9829,41.3212],[-73.971,41.306],[-73.9601,41.2982],[-73.9474,41.2921],[-73.9444,41.2907],[-73.9439,41.288],[-73.9476,41.2853],[-73.9638,41.271],[-73.9694,41.2652],[-73.9726,41.2616],[-73.9732,41.2584],[-73.9739,41.2552],[-73.9722,41.2511],[-73.9668,41.247],[-73.959,41.2378],[-73.9334,41.2057],[-73.9222,41.1888],[-73.9104,41.1705],[-73.8922,41.1417],[-73.8905,41.1321],[-73.892,41.0968],[-73.892,41.0677],[-73.8899,41.0523],[-73.8936,40.9965],[-73.9015,40.9976],[-73.9057,40.9994],[-73.9841,41.0339],[-74.0005,41.0409],[-74.0246,41.0521],[-74.1533,41.1098],[-74.2129,41.1344],[-74.2253,41.1395],[-74.2338,41.1431],[-74.3179,41.1791],[-74.3259,41.1823],[-74.3343,41.1864],[-74.3677,41.2033],[-74.3831,41.2111],[-74.4101,41.2248],[-74.5363,41.284],[-74.605,41.3152],[-74.6492,41.3359],[-74.6955,41.3576],[-74.6913,41.3598],[-74.6901,41.3621],[-74.69,41.3639],[-74.6912,41.3662],[-74.6934,41.3683],[-74.6938,41.3688],[-74.6962,41.3713],[-74.6985,41.373],[-74.7011,41.3753],[-74.7064,41.3803],[-74.7105,41.3842],[-74.7126,41.3866],[-74.7137,41.389],[-74.7154,41.3917],[-74.7175,41.3929],[-74.7205,41.3947],[-74.7247,41.3958],[-74.7278,41.3963],[-74.732,41.3973],[-74.7349,41.3987],[-74.7376,41.4003],[-74.7392,41.4025],[-74.7409,41.4066],[-74.7421,41.4094],[-74.7419,41.4103],[-74.7415,41.4123],[-74.7412,41.4145],[-74.7405,41.4166],[-74.7391,41.4197],[-74.7384,41.4229],[-74.7376,41.4261],[-74.7389,41.4286],[-74.7408,41.4298],[-74.7438,41.4305],[-74.7461,41.4303],[-74.7487,41.4287],[-74.7506,41.4274],[-74.7523,41.4328],[-74.7535,41.4373],[-74.7559,41.4401],[-74.7589,41.4451],[-74.7601,41.4501],[-74.7588,41.4573],[-74.7557,41.4614],[-74.7514,41.4659],[-74.7513,41.4686],[-74.7537,41.4741],[-74.7579,41.4814],[-74.7597,41.4868],[-74.7591,41.4896],[-74.756,41.4923],[-74.7541,41.4945],[-74.5928,41.4989],[-74.4781,41.5031],[-74.4743,41.5085],[-74.4688,41.5139],[-74.4668,41.522],[-74.4599,41.5302],[-74.4488,41.5364],[-74.4469,41.5423],[-74.4338,41.5545],[-74.4276,41.5589],[-74.4201,41.5666],[-74.4084,41.5724],[-74.3985,41.5778],[-74.3941,41.5809],[-74.3867,41.5854],[-74.3749,41.5889],[-74.3675,41.5916],[-74.3583,41.5938],[-74.3521,41.5982],[-74.3404,41.5954],[-74.3187,41.6084],[-74.3156,41.6115],[-74.2989,41.6182],[-74.281,41.6257],[-74.2754,41.6284],[-74.2667,41.6324],[-74.2606,41.6337],[-74.2502,41.6291],[-74.25,41.6059],[-74.2458,41.6036],[-74.1907,41.5913],[-74.187,41.5908],[-74.1858,41.5944],[-74.1282,41.5833],[-74.1325,41.6152],[-74.1246,41.6133],[-74.0983,41.6089],[-74.0886,41.5988],[-74.0677,41.604],[-74.0575,41.5926],[-74.0521,41.5816],[-73.9999,41.5855],[-73.9525,41.59]]]},\"properties\":{\"name\":\"Orange\",\"state\":\"NY\"}}]}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708-4039
The Arctic Alaska Province encompasses all lands and adjacent continental shelf areas north of the Brooks Range-Herald Arch tectonic belts and south of the northern (outboard) margin of the Alaska rift shoulder. Even though only a small part is thoroughly explored, it is one of the most prolific petroleum provinces in North America, with total known resources (cumulative production plus proved reserves) of about 28 billion barrels of oil equivalent.
For assessment purposes, the province is divided into a platform assessment unit, comprising the Alaska rift shoulder and its relatively undeformed flanks, and a fold-and-thrust belt assessment unit, comprising the deformed area north of the Brooks Range and Herald Arch tectonic belts. Mean estimates of undiscovered, technically recoverable resources include nearly 28 billion barrels of oil and 122 trillion cubic feet of nonassociated gas in the platform assessment unit and 2 billion barrels of oil and 59 trillion cubic feet of nonassociated gas in the fold-and-thrust belt assessment unit.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1824E","usgsCitation":"Houseknecht, D.W., Bird, K.J., and Garrity, C.P., 2019, Geology and assessment of undiscovered oil and gas resources of the Arctic Alaska Province, 2008, chap. E of Moore, T.E., and Gautier, D.L., eds., The 2008 Circum-Arctic Resource Appraisal: U.S. Geological Survey Professional Paper 1824, 25 p., https://doi.org/10.3133/pp1824E. [Supersedes USGS Scientific Investigations Report 2012–5147.]","productDescription":"Report: viii, 25 p.; 2 Appendixes","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-114210","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":370053,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/e/pp1824e_appendix1.xlsx","text":"Appendix 1","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1824 Chapter E Appendix 1"},{"id":370052,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1824/e/pp1824e.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1824 Chapter E"},{"id":370051,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1824/e/coverthb.jpg"},{"id":399506,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109507.htm"},{"id":370054,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/e/pp1824e_appendix2.xlsx","text":"Appendix 2","linkFileType":{"id":3,"text":"xlsx"},"description":"PP 1824 Chapter E Appendix 2"}],"country":"Canada, United States","state":"Alaska, Yukon","otherGeospatial":"Arctic Alaska Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -136.0986328125,\n 68.89518688943544\n ],\n [\n -137.3291015625,\n 69.54987728327795\n ],\n [\n -156.7529296875,\n 71.37110941823617\n ],\n [\n -159.5654296875,\n 70.85908719717143\n ],\n [\n -161.015625,\n 70.27428967614655\n ],\n [\n -162.1142578125,\n 70.28911664330674\n ],\n [\n -164.3994140625,\n 68.98992503056704\n ],\n [\n -166.11328125,\n 68.942606818121\n ],\n [\n -166.5087890625,\n 68.38299634059615\n ],\n [\n -162.7294921875,\n 66.80922097449334\n ],\n [\n -161.19140625,\n 66.73990169639414\n ],\n [\n -154.599609375,\n 66.75724984139227\n ],\n [\n -149.4140625,\n 66.7745857647255\n ],\n [\n -143.61328125,\n 68.15520923883976\n ],\n [\n -141.1083984375,\n 68.13885164925573\n ],\n [\n -136.6259765625,\n 67.69277095059344\n ],\n [\n -135.3076171875,\n 68.15520923883976\n ],\n [\n -136.0986328125,\n 68.89518688943544\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
FAX 650-329-4936
The Yorktown-Eastover aquifer system of the Virginia Eastern Shore consists of upper, middle, and lower confined aquifers overlain by correspondingly named confining units and underlain by the Saint Marys confining unit. Miocene- to Pliocene-age marine-shelf sediments observed in 205 boreholes include medium- to coarse-grained sand and shells that compose the aquifers and fine-grained sand, silt, and clay that compose the confining units. The upper confining unit also includes fine-grained and organic-rich back-barrier and estuarine sediments of Pleistocene age. An overlying surficial aquifer is composed mostly of Pleistocene-age nearshore sand and gravel with smaller amounts of cobbles and boulders.
In addition, Pleistocene-age sediments that fill three buried paleochannels are for the first time explicitly delineated here as distinct hydrogeologic units. Two aquifers are composed of medium- to coarse-grained fluvial sand and gravel, and an intervening confining unit is composed of fine-grained estuarine sand, silt, clay, and organic material. Aquifer and confining-unit sediments are also mixed with reworked marine-shelf sediments eroded from the sides of the paleochannels.
Hydrogeologic units of the Yorktown-Eastover aquifer system generally dip eastward, are as much as several tens of feet thick, and have an undulating configuration possibly resulting from the underlying Chesapeake Bay impact crater. Aquifers and confining units are incised by the three paleochannels along an upward-widening and eastward-lengthening series of structural “windows.” Hydrogeologic units within mainstems and branching tributaries of the paleochannels dip southeastward parallel to slopes of the paleochannels, are as much as several tens of feet thick, and laterally abut the Yorktown-Eastover aquifer system along paleochannel sidewalls. The Yorktown-Eastover aquifer system is thereby hydraulically breached by the paleochannels to alternately create barriers to or conduits for groundwater flow.
Results of previously documented aquifer tests at 58 wells indicate that transmissivity is generally greatest in young, shallow, and coarse-grained nearshore and fluvial sediments of the surficial aquifer and paleochannels. Transmissivity progressively decreases with depth in older, deeper, and finer grained marine-shelf sediments of the Yorktown-Eastover aquifer system, probably because they have undergone compaction as a result of greater overburden pressure over longer periods of time.
Compiled chloride concentrations in samples from 330 wells generally increase downward, with most of the samples collected at altitudes above −300 feet and with most concentrations less than 250 milligrams per liter. The saltwater-transition zone has a broad trough-like shape aligned with the peninsula, being relatively shallow along the coastline and deeper along the central “spine.” Because movement of the saltwater is slow, the configuration largely reflects groundwater flow prior to widespread groundwater withdrawals. Fresh groundwater has leaked downward along deep parts of the saltwater-transition zone and leaked upward along shallower parts to discharge at the coast.
The saltwater-transition zone also exhibits an anomalous ridge across the center of the peninsula. Groundwater levels indicate that the saltwater ridge formed primarily by the Exmore paleochannel acting as a large lateral collector drain. Groundwater levels were lowered, and the position of saltwater-transition zone was elevated, by a flow conduit that intercepted groundwater that otherwise would have flowed toward and discharged along the coastline.
Nearly all freshwater on the Virginia Eastern Shore is supplied by groundwater withdrawals, which have lowered water levels, altered hydraulic gradients, and created a concern for saltwater intrusion. Previous characterizations of groundwater conditions that are relied on to manage groundwater development have been limited by a lack of hydrogeologic information, particularly data on buried paleochannels that are critical to safeguarding the groundwater supply. Using recently available expanded information, the U.S. Geological Survey undertook a study in cooperation with the Virginia Department of Environmental Quality during 2016–19 to develop an improved description of the groundwater system called a “hydrogeologic framework.”
The hydrogeologic framework can aid water-supply planning and development by providing information on broad trends in aquifer configurations, hydraulic properties, and proximity to saltwater to avoid chloride contamination. Digital models to evaluate effects of groundwater withdrawals can also be improved with expanded data and capabilities to evaluate paleochannel hydraulic connections and the potential for saltwater movement.
The hydrogeologic framework is limited by the nonuniform distribution of boreholes and the subjective delineation of aquifers and confining units, including those within paleochannels that are regarded as preliminary. The configuration of the saltwater-transition zone is also regarded as preliminary because of the nonuniform distribution of groundwater samples. Low well-sampling frequency precludes characterizing movement of the saltwater-transition zone. A monitoring strategy of sampling and possibly electromagnetic-induction well logging could be used to detect saltwater movement.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195093","collaboration":"Prepared in cooperation with the Virginia Department of Environmental Quality","usgsCitation":"McFarland, E.R., and Beach, T.A., 2019, Hydrogeologic framework of the Virginia Eastern Shore: U.S. Geological Survey Scientific Investigations Report 2019–5093, 26 p., 13 pl., https://doi.org/10.3133/sir20195093.","productDescription":"Report: viii, 26 p.; 13 Plates: 11.00 x 17.00 inches or smaller; Data Release","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-108409","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":369803,"rank":16,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093.pdf","text":"Report","size":"3.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5093"},{"id":369731,"rank":15,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate13.pdf","text":"Plate 13","size":"352 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Locations and Numbers of Sampled Wells and Altitude of the 250-Milligram-Per-Liter Chloride-Concentration Surface on the Virginia Eastern Shore"},{"id":369730,"rank":14,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate12.pdf","text":"Plate 12","size":"332 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Upper Confining Unit on the Virginia Eastern Shore"},{"id":369725,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate07.pdf","text":"Plate 7","size":"346 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Middle Confining Unit on the Virginia Eastern 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Shore"},{"id":369720,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate02.pdf","text":"Plate 2","size":"336 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Hydrogeologic Section through the Virginia Eastern Shore"},{"id":369719,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate01.pdf","text":"Plate 1","size":"339 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Locations and Numbers of Boreholes on the Virginia Eastern Shore"},{"id":369714,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MPE5SD","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Borehole hydrogeologic-unit top-surface altitudes, aquifer hydraulic properties, and groundwater-sample chloride-concentration data from 1906 through 2016 for the Virginia Eastern Shore"},{"id":369728,"rank":12,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate10.pdf","text":"Plate 10","size":"319 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Paleochannel Confining Unit on the Virginia Eastern Shore"},{"id":369727,"rank":11,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate09.pdf","text":"Plate 9","size":"316 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Paleochannel Lower Aquifer on the Virginia Eastern Shore"},{"id":369726,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sir/2019/5093/sir20195093_plate08.pdf","text":"Plate 8","size":"350 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Top-Surface Altitude of the Upper Aquifer on the Virginia Eastern 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U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable mean resources of 144 million barrels of shale oil and 559 billion cubic feet of shale gas in the Mississippian Delle Phosphatic Member of the Woodman Formation in the Eastern Great Basin Province of Nevada, Utah, and Idaho.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193062","usgsCitation":"Schenk, C.J., Mercier, T.J., Finn, T.M., Marra, K.R., Le, P.A., Brownfield, M.E., and Leathers-Miller, H.M., 2019, Assessment of continuous oil and gas resources in the Mississippian Delle Phosphatic Member of the Woodman Formation in the Eastern Great Basin Province of Nevada, Utah, and Idaho, 2019: U.S. Geological Survey Fact Sheet 2019–3062, 2 p., https://doi.org/10.3133/fs20193062.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-109728","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":399141,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109485.htm"},{"id":374287,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YWP926","text":"USGS data release","linkHelpText":"USGS National and Global 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U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
The American Woodcock (Scolopax minor; hereafter, woodcock) Singing-ground Survey (SGS) is conducted annually during the woodcock breeding season, and survey points along survey routes are set 0.4 mile (0.65 km) apart to avoid counting individual birds from >1 listening location. The effective area surveyed (EAS) at a listening point is not known, and may vary as a function of land-cover type or other factors. To define the relationship describing distance between vocalizing woodcock and an observer and how cover types influence that relationship, we broadcast a recording of woodcock vocalizations in 2 land-cover types (forest and field) at varying distance. We evaluated the proportion of call broadcasts detected as a function of distance and fit regression curves to detection data to estimate a distance (r*) where the area above the curve at distances <r* was equal to the area under the curve at distances >r*, which allowed determination of the radius of an area where detection probability was effectively 1.0. This EAS had a radius (r*) of 198 m for forest, 384 m for field, and 309 m for both of these land-cover types combined, and an estimated size of 12.3 ha for forest, 46.3 ha for field, and 30.0 ha for both land-cover types combined. We used this information to estimate density of displaying male woodcock based on counts from the SGS in east-central Minnesota that incorporated variation in EAS, probability of detection, survey date, and survey route. Our density estimates (5.0 birds/100 ha in 2009 and 7.1 birds/100 ha in 2010) represent the highest density of singing male American woodcock yet reported, and indicated a substantive increase in density between years.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the eleventh American woodcock symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Eleventh American Woodcock Symposium","conferenceDate":"Oct 24-27, 2017","conferenceLocation":"Roscommon, MI","language":"English","publisher":"University of Minnesota Libraries Publishing","doi":"10.24926/AWS.0125","usgsCitation":"Bergh, S.M., and Andersen, D.E., 2019, Estimating density and effective area surveyed for American woodcock, in Proceedings of the eleventh American woodcock symposium, v. 11, Roscommon, MI, Oct 24-27, 2017, p. 193-199, https://doi.org/10.24926/AWS.0125.","productDescription":"7 p.","startPage":"193","endPage":"199","ipdsId":"IP-057491","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":459030,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Stefanie M.","contributorId":272056,"corporation":false,"usgs":false,"family":"Bergh","given":"Stefanie","email":"","middleInitial":"M.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":831784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":831678,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227757,"text":"70227757 - 2019 - Using pointing dogs and hierarchical models to evaluate American woodcock winter occupancy and densities","interactions":[],"lastModifiedDate":"2022-01-28T14:59:18.950521","indexId":"70227757","displayToPublicDate":"2019-12-02T08:35:12","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Using pointing dogs and hierarchical models to evaluate American woodcock winter occupancy and densities","docAbstract":"Use of dogs has increased for multiple wildlife research purposes ranging from carnivore scat detection to estimation of reptile abundance. Use of dogs is not particularly novel for upland gamebird biologists, and pointing dogs have been long considered an important research tool. However, recent advances in Global Positioning System (GPS) technology and the development of hierarchical modeling approaches that account for imperfect detection may improve estimates of occupancy and density of cryptic species such as the American woodcock (Scolopax minor; hereafter, woodcock). We conducted surveys for woodcock using a trained pointing dog wearing a GPS collar during the winters of 2010–2011 and 2011–2012 in East Texas, USA. We surveyed 0.5-km-radius circular plots (n = 24; survey sites) randomly placed along secondary roads in Davy Crockett National Forest and on private timber property. Surveys lasted 1.5 hrs and were repeated 3–5 times each winter. We estimated woodcock occupancy and density using multiple modeling approaches at the survey site and forest stand scales within survey sites. Woodcock occupied 88% (21/24) of survey sites and 48% (39/82) of forest stands (i.e., unique cover types) within sites. Using a modified distance sampling technique, we estimated an average density of 0.16 birds/ha (SE = 0.13) throughout both study areas. We describe the first attempt to blend use of pointing dogs with hierarchical modeling approaches to derive estimates of regional diurnal woodcock occupancy and density, and describe relationships between these estimates of abundance and habitat covariates. Although forest stand occupancy estimates had the lowest coefficients of variation, our estimates of density provided the most useful inference of habitat use. Surveys using pointing dogs paired with hierarchical models of occupancy and density may provide a cost-efficient and effective approach to estimate habitat abundance at broad spatial scales.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the eleventh American Woodcock Symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"American Woodcock Symposium","conferenceDate":"2017","conferenceLocation":"Michigan, United States","language":"English","publisher":"University of Minnesota Press","doi":"10.24926/AWS.0122","usgsCitation":"Sullins, D.S., Conway, W.C., Haukos, D.A., and Comer, C.E., 2019, Using pointing dogs and hierarchical models to evaluate American woodcock winter occupancy and densities, in Proceedings of the eleventh American Woodcock Symposium, Michigan, United States, 2017, p. 154-167, https://doi.org/10.24926/AWS.0122.","productDescription":"14 p.","startPage":"154","endPage":"167","ipdsId":"IP-090750","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":459048,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.24926/aws.0122","text":"Publisher Index Page"},{"id":395048,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","county":"Houston County, San Augustine County, Trinity County","otherGeospatial":"Davy Crockett National Forest, West Gulf Coastal Plain Bird Conservation Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.24793243408203,\n 31.45473238771609\n ],\n [\n -94.14974212646484,\n 31.45473238771609\n ],\n [\n -94.14974212646484,\n 31.511532395628638\n ],\n [\n -94.24793243408203,\n 31.511532395628638\n ],\n [\n -94.24793243408203,\n 31.45473238771609\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.44509887695312,\n 30.935212690426727\n ],\n [\n -94.75296020507811,\n 30.935212690426727\n ],\n [\n -94.75296020507811,\n 31.67675841879551\n ],\n [\n -95.44509887695312,\n 31.67675841879551\n ],\n [\n -95.44509887695312,\n 30.935212690426727\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sullins, Daniel S.","contributorId":166689,"corporation":false,"usgs":false,"family":"Sullins","given":"Daniel","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":832103,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Warren C.","contributorId":51550,"corporation":false,"usgs":true,"family":"Conway","given":"Warren","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":832104,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832054,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Comer, Christopher E.","contributorId":166690,"corporation":false,"usgs":false,"family":"Comer","given":"Christopher","email":"","middleInitial":"E.","affiliations":[{"id":32360,"text":"Stephen F. Austin State University, Nacogdoches, TX","active":true,"usgs":false}],"preferred":false,"id":832105,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70206712,"text":"70206712 - 2019 - Geologic map of the Blythe 7.5' quadrangle, La Paz County, Arizona and Riverside County, California","interactions":[],"lastModifiedDate":"2020-01-08T17:12:32","indexId":"70206712","displayToPublicDate":"2019-12-01T17:12:08","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5907,"text":" Arizona Geological Survey Digital Geologic Map","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"DGM-124","title":"Geologic map of the Blythe 7.5' quadrangle, La Paz County, Arizona and Riverside County, California","docAbstract":"The geologic map of the Blythe 7.5' quadrangle spans about 60 percent of the width of the Holocene floodplain and valley floor of the lower Colorado River and the adjacent lower piedmont on the east side of the Colorado River Valley. This map depicts a composite geologic record of the river’s response to the transition from a natural flow regime to a strictly regulated one created by a series of upstream dams and channelization of much of its length. The floodplain map was developed using archival data sources including notes and maps from early river expeditions, early cadastral and topographical surveys, and a series of historical aerial photographs. The floodplain surface and its underlying young alluvial fill is herein referred to as the Blythe Alluvium, and this report provides the basis for defining it as a formal stratigraphic unit. Along the eastern edge of the map are piedmont deposits intercalated with Pliocene and Pleistocene Colorado River sediments underlying the Blythe Alluvium. The piedmont units include an array of washes and alluvial fans sourced in the Trigo and Dome Rock Mountains. These deposits were divided and mapped based on stratigraphic and geomorphic criteria including relative topographic relationships, and cross-cutting and inset stratigraphic relations among individual piedmont units and with ancestral Colorado River deposits. Varying thicknesses of those units likely exist below the Holocene floodplain, and this report presents those in the form of a lithologic-section of the valley based on available well data and accompanying descriptions.","language":"English","publisher":"Arizona Geological Survey","usgsCitation":"Block, D., Gootee, B.F., House, K., and Pearthree, P.A., 2019, Geologic map of the Blythe 7.5' quadrangle, La Paz County, Arizona and Riverside County, California: Arizona Geological Survey Digital Geologic Map DGM-124, Report: 45 p.; 2 Sheets: 36 x 29.30 inches and 25.23 x 22.07 inches.","productDescription":"Report: 45 p.; 2 Sheets: 36 x 29.30 inches and 25.23 x 22.07 inches","ipdsId":"IP-089999","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":371092,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":371091,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://repository.azgs.az.gov/uri_gin/azgs/dlio/1932"}],"country":"United States","state":"Arizona, California","county":"La Paz County, Riverside County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.08453369140625,\n 32.82421110161336\n ],\n [\n -114.20013427734375,\n 32.82421110161336\n ],\n [\n -114.20013427734375,\n 33.813384329112786\n ],\n [\n -115.08453369140625,\n 33.813384329112786\n ],\n [\n -115.08453369140625,\n 32.82421110161336\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Block, Debra 0000-0001-7348-3064 dblock@usgs.gov","orcid":"https://orcid.org/0000-0001-7348-3064","contributorId":198448,"corporation":false,"usgs":true,"family":"Block","given":"Debra","email":"dblock@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":775516,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gootee, Brian F. 0000-0001-5251-9080 bgootee@email.arizona.edu","orcid":"https://orcid.org/0000-0001-5251-9080","contributorId":201637,"corporation":false,"usgs":false,"family":"Gootee","given":"Brian","email":"bgootee@email.arizona.edu","middleInitial":"F.","affiliations":[{"id":34160,"text":"Arizona Geological Survey","active":true,"usgs":false}],"preferred":false,"id":775513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"House, Kyle 0000-0002-0019-8075 khouse@usgs.gov","orcid":"https://orcid.org/0000-0002-0019-8075","contributorId":2293,"corporation":false,"usgs":true,"family":"House","given":"Kyle","email":"khouse@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":775514,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pearthree, Philip A 0000-0001-7676-8145","orcid":"https://orcid.org/0000-0001-7676-8145","contributorId":220713,"corporation":false,"usgs":false,"family":"Pearthree","given":"Philip","email":"","middleInitial":"A","affiliations":[{"id":34160,"text":"Arizona Geological Survey","active":true,"usgs":false}],"preferred":false,"id":775515,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208117,"text":"70208117 - 2019 - Geochemistry and geophysics of iron oxide-apatite deposits and associated waste piles with implications for potential rare earth element resources from ore and historic mine waste in the eastern Adirondack Highlands, New York, USA","interactions":[],"lastModifiedDate":"2020-01-28T15:40:48","indexId":"70208117","displayToPublicDate":"2019-12-01T15:29:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Geochemistry and geophysics of iron oxide-apatite deposits and associated waste piles with implications for potential rare earth element resources from ore and historic mine waste in the eastern Adirondack Highlands, New York, USA","docAbstract":"The iron oxide-apatite (IOA) deposits of the eastern Adirondack Highlands, New York, are historical high-grade magnetite mines that contain variable concentrations of rare earth element (REE)-bearing apatite crystals. The majority of the deposits are hosted within sodically altered Lyon Mountain granite gneiss, although some deposits occur within paragneiss, gabbro, anorthosite, or potassically altered Lyon Mountain granite gneiss. The IOA deposits and the waste and/or tailings piles associated with them have potential as an unconventional resource for REEs. Reprocessing of these piles would have the advantage of partial recycling of the waste material to produce a set of critical elements.
Thirty-four ore, nine rock, 25 waste-pile, and four tailings-pile samples were collected and analyzed for major, minor, and trace elements. At the tailings- and waste-pile sites, composite samples were collected by combining 30 to >50 subsamples randomly distributed over each pile. The total REE content of the waste and tailings piles varied from approximately 10 to 22,000 ppm, whereas the ore sample concentrations ranged from approximately 15 to 48,000 ppm total REEs. A positive correlation exists between the total REE content of ore and its associated waste pile. Median light REE/heavy REE values were 2.14 for waste/tailings piles and 2.25 for ore, which is a substantial relative enrichment in the heavy REEs in comparison to many developed REE mines, such as the mined carbonatites of Bayan Obo, China, and Mountain Pass, California. Importantly, the ore and waste samples are significantly enriched in both Y and Nd compared to other REEs in the samples. Other minor components such as Th are also elevated. Airborne radiometric surveys show large positive eTh and eU anomalies corresponding to tailings piles.
Although it is a limited data set, geochemical data of unaltered and altered host rocks suggest a speculative new model for IOA ore formation in the Adirondack Highlands that is consistent with the geology and previously published data. The ferroan ore-hosting Lyon Mountain granite gneiss underwent localized potassic alteration that enriched the altered rock in Fe, REEs, Th, and other metals. A later sodic alteration event affected the previously potassically altered Lyon Mountain granite gneiss, which increased rock porosity and remobilized Fe, REEs, and other elements from the host rock into the iron ore seams. The sodic fluids responsible for ore formation were enriched in F and Cl.
In this chapter sulfur contamination of the Everglades and its role as a major control on methylmercury (MeHg) production is examined. Sulfate concentrations over large portions of the Everglades (60% of the ecosystem) are elevated or greatly elevated compared to background conditions of <1 mg/L. Land and water management practices in south Florida are the primary reason for the high levels of sulfate loading to the Everglades. Marshes in the northern Everglades that are highly enriched in sulfate have average concentrations of 60 mg/L, but water in canals in the Everglades Agricultural Area (EAA) contain the highest concentrations of sulfate averaging 60–70 mg/L. Studies that examined the mass balance of sulfur to the Everglades have determined that the primary sources of sulfate include: sulfur currently used in agriculture, and natural and legacy agricultural sulfur released by oxidation of organic soil within the EAA. The extensive loading of sulfate to the ecosystem increases microbial sulfate reduction, the dominant microbial process driving mercury methylation and MeHg production. The biogeochemical processes linking sulfate loading and MeHg production, however, are complex. MeHg production increases as sulfate levels rise from levels <1 mg/L up to about 20 mg/L. However, production of sulfide (a byproduct of microbial sulfate reduction) starts to inhibit MeHg production above 20 mg/L. Sulfate loading to canals in the EAA has impacted the northern Everglades the most, but the Everglades canal system can transport sulfate as far as Everglades National Park (ENP), 80 km further south. Plans to deliver more water to ENP as part of restoration may increase overall sulfate loads to the southern Everglades.
Reduction of sulfate loading should be a major goal of Everglades restoration because of the many negative effects of sulfate on the ecosystem. The ecosystem has been shown to respond quickly to reductions in sulfate loading, and strategies for reducing sulfate loading may produce positive outcomes for the Everglades in the near-term. Strategies for reducing sulfate loading will need to include: best management practices for agricultural use of sulfate, approaches to minimize soil oxidation in the EAA, and modifications to stormwater treatment areas to improve sulfate retention.
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A Synthesis and Model for Complex Ecosystem Restoration, p. 13-48, https://doi.org/10.1007/978-3-030-32057-7_2.","productDescription":"36 p.","startPage":"13","endPage":"48","ipdsId":"IP-106372","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":369873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.3153076171875,\n 25.075648445630527\n ],\n [\n -80.33752441406249,\n 25.075648445630527\n ],\n [\n -80.33752441406249,\n 25.854280326572407\n ],\n [\n -81.3153076171875,\n 25.854280326572407\n ],\n [\n -81.3153076171875,\n 25.075648445630527\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Orem, William H. 0000-0003-4990-0539 borem@usgs.gov","orcid":"https://orcid.org/0000-0003-4990-0539","contributorId":577,"corporation":false,"usgs":true,"family":"Orem","given":"William","email":"borem@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":762941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":762942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poulin, Brett 0000-0002-5555-7733 bpoulin@usgs.gov","orcid":"https://orcid.org/0000-0002-5555-7733","contributorId":194253,"corporation":false,"usgs":true,"family":"Poulin","given":"Brett","email":"bpoulin@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":762943,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"George Aiken","contributorId":215670,"corporation":false,"usgs":false,"family":"George Aiken","affiliations":[{"id":39302,"text":"USGS WMA Boulder (Deceased)","active":true,"usgs":false}],"preferred":false,"id":762944,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70207160,"text":"70207160 - 2019 - Managing effects of drought in Hawai’i and U.S.-affiliated Pacific Islands","interactions":[],"lastModifiedDate":"2020-12-08T16:49:59.298008","indexId":"70207160","displayToPublicDate":"2019-12-01T08:14:15","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":32,"text":"General Technical Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"WO-98","chapter":"5","title":"Managing effects of drought in Hawai’i and U.S.-affiliated Pacific Islands","docAbstract":"How is drought expressed in Hawai‘i & USAPI? Drought is a significant climate feature in Hawai‘i and the U.S.-Affiliated Pacific Islands (USAPI), at times causing severe impacts across multiple sectors. Below average precipitation anomalies are often accompanied by higher than average temperatures and reduced cloud cover. The resulting higher insolation and evapotranspiration can magnify the effects of rainfall deficits. These altered meteorological conditions lead to decreased soil moisture, which, depending on the persistence and severity of the conditions, can cause plant stress, affecting both agricultural and natural systems. The hydrological effects of drought include reductions in streamflow, groundwater recharge, and groundwater discharge to springs, streams, and the ocean. Drought also has socioeconomic impacts, where reduced water supply and other effects of drought have negative financial consequences. For these reasons, drought has been defined from at least five different perspectives: meteorological, ecological, agricultural, hydrological, and socioeconomic drought. In this chapter, we explore how these five faces of drought are expressed in Hawai‘i and the USAPI, and how managers operating within one or more these five perspectives address drought-related stressors to their systems. Not all droughts are the same, varying with respect to duration, frequency, extent, and severity. For example, the region receives severe episodic droughts during which an area will have little or no rainfall for months, even in areas that normally have no dry season. El Niño events fall into this category, and these moderate frequency events are typically responsible for shorter-lived but intense drought events that affect large areas. Drought can also be expressed as infrequent but long duration events of moderate severity, or long-term rainfall decline where the baseline condition appears to be changing when examined on longer time scales. From the perspective of the manager, understanding drought duration, frequency, extent, and severity is critical to understanding the duration, frequency, extent and severity of the response. For example, how an agency responds to El Niño events, with a focus on large-scale but short-lived emergency response campaigns, may differ from how an agency responds to baseline change or an increase in the frequency of extended dry periods, with a focus on longer-lived institutional, infrastructure, and personnel responses. The legislative and policy environment will also respond differently to different types of drought. Understanding and characterizing meteorological drought relies on a long-term network of climate stations. Rainfall has been extensively monitored in Hawai‘i since the early 1900s owing to the expansion of plantation agriculture (Giambelluca and others 1986), while rainfall monitoring for most of the USAPI began in earnest after World War II (Polhemus 2017). Due to prevailing winds, most of Hawai‘i’s land area is characterized by a wet season from November to April and a dry season from May to October. However, important dynamic features affect climate systems of the Pacific. For example, due to their tropical location, rainfall patterns in both Hawai‘i and the USAPI are strongly controlled by large-scale modes of climate variability, including the El Niño-Southern Oscillation (ENSO). El Niño events are typically associated with drier than average winter wet seasons and wetter dry seasons, while La Niña events often result in a wetter than average wet season and a drier dry season. Many historical drought events have been attributed to El Niño events, which produce atmospheric conditions that are unfavorable for rainfall (Chu 1995). However, not all El Niño events result in drought, and effects differ depending on whether the El Niño is classified as Central Pacific (CP) or Eastern Pacific (EP) (Bai 2017; Polhemus 2017).
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Effects of drought on forests and rangelands in the United States: Translating science into management responses","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"USDA","doi":"10.2737/WO-GTR-98","usgsCitation":"Frazier, A.G., Deenik, J., Fujii, N., Funderburk, G., Giambelluca, T., Giardina, C., Helweg, D., Keener, V., Mair, D., Marra, J., McDaniel, S., Ohye, L., Oki, D.S., Parsons, E., Strauch, A., and Trauernicht, C., 2019, Managing effects of drought in Hawai’i and U.S.-affiliated Pacific Islands: General Technical Report WO-98, 27 p., https://doi.org/10.2737/WO-GTR-98.","productDescription":"27 p.","startPage":"95","endPage":"121","ipdsId":"IP-105580","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science 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