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 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
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
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
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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 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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":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","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.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Mercury and the Everglades. A Synthesis and Model for Complex Ecosystem Restoration","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-32057-7_2","usgsCitation":"Orem, W.H., Krabbenhoft, D.P., Poulin, B., and George Aiken, 2019, Sulfur contamination in the Everglades, a major control on mercury methylation, chap. 2 of Mercury and the Everglades. 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":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest 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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PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Frazier, Abby G.","contributorId":221112,"corporation":false,"usgs":false,"family":"Frazier","given":"Abby","email":"","middleInitial":"G.","affiliations":[{"id":40321,"text":"USDA Forest Service, Pacific Southwest Research Station","active":true,"usgs":false}],"preferred":false,"id":777050,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Deenik, Jonathan","contributorId":221113,"corporation":false,"usgs":false,"family":"Deenik","given":"Jonathan","email":"","affiliations":[{"id":40322,"text":"East-West Center, Honolulu, HI","active":true,"usgs":false}],"preferred":false,"id":777051,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fujii, Neal","contributorId":221114,"corporation":false,"usgs":false,"family":"Fujii","given":"Neal","email":"","affiliations":[{"id":40323,"text":"University of Hawai‘i at Mānoa, Department of Tropical Plant and Soil 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Center","active":true,"usgs":true}],"preferred":true,"id":777057,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Marra, John ","contributorId":221119,"corporation":false,"usgs":false,"family":"Marra","given":"John ","affiliations":[{"id":40326,"text":"NOAA, National Environmental Satellite, Data, and Information Service","active":true,"usgs":false}],"preferred":false,"id":777058,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McDaniel, Sierra","contributorId":221120,"corporation":false,"usgs":false,"family":"McDaniel","given":"Sierra","email":"","affiliations":[{"id":40324,"text":"Hawai‘i Volcanoes National Park, Hawai‘i, USA","active":true,"usgs":false}],"preferred":false,"id":777059,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ohye, Lenore","contributorId":221121,"corporation":false,"usgs":false,"family":"Ohye","given":"Lenore","email":"","affiliations":[{"id":40327,"text":"State of Hawai‘i, Department of Land and Natural Resources, Commission on Water Resource Management","active":true,"usgs":false}],"preferred":false,"id":777060,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Oki, Delwyn S. 0000-0002-6913-8804","orcid":"https://orcid.org/0000-0002-6913-8804","contributorId":221122,"corporation":false,"usgs":true,"family":"Oki","given":"Delwyn","email":"","middleInitial":"S.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":777061,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Parsons, Elliott","contributorId":221123,"corporation":false,"usgs":false,"family":"Parsons","given":"Elliott","affiliations":[{"id":40328,"text":"State of Hawai‘i Division of Forestry and Wildlife, Pu‘u Wa‘awa‘a Forest Reserve","active":true,"usgs":false}],"preferred":false,"id":777062,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Strauch, Ayron","contributorId":221124,"corporation":false,"usgs":false,"family":"Strauch","given":"Ayron","email":"","affiliations":[{"id":40327,"text":"State of Hawai‘i, Department of Land and Natural Resources, Commission on Water Resource Management","active":true,"usgs":false}],"preferred":false,"id":777063,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Trauernicht, Clay","contributorId":221125,"corporation":false,"usgs":false,"family":"Trauernicht","given":"Clay","email":"","affiliations":[{"id":40329,"text":"University of Hawai‘i at Mānoa, Department of Natural Resources and Environmental Management","active":true,"usgs":false}],"preferred":false,"id":777064,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70216464,"text":"70216464 - 2019 - A shallow rift basin segmented in space and time: The southern San Luis Basin, Rio Grande rift, northern New Mexico, U.S.A.","interactions":[],"lastModifiedDate":"2020-11-20T14:11:15.783736","indexId":"70216464","displayToPublicDate":"2019-12-01T07:59:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3310,"text":"Rocky Mountain Geology","active":true,"publicationSubtype":{"id":10}},"title":"A shallow rift basin segmented in space and time: The southern San Luis Basin, Rio Grande rift, northern New Mexico, U.S.A.","docAbstract":"Interpretation of gravity, magnetotelluric, and aeromagnetic data in conjunction with geologic constraints reveals details of basin geometry, thickness, and spatiotemporal evolution of the southern San Luis Basin, one of the major basins of the northern Rio Grande rift. Spatial variations of low-density basin-fill thickness are estimated primarily using a 3D gravity inversion method that improves on previous modeling efforts by separating the effects of the low-density basin fill from the effects of pre-rift rocks. The basin is found to be significantly narrower—and more complex in the subsurface—than indicated or implied by previous modeling efforts. The basin is also estimated to be significantly shallower than previously estimated. Five distinct subbasins are recognized within the broader southern San Luis Basin. The oldest and shallowest subbasin is the Las Mesitas graben along the northwestern basin margin, formed during the Oligocene transition from Southern Rocky Mountain volcanic field magmatism to rifting. In this subbasin, sediments are estimated to reach a maximum thickness of ~400 m within a north–south elongated structural depression. Other subbasins that likely initially developed during the Miocene are the dominant tectonic features in the southern San Luis Basin. This includes the Tres Orejas subbasin, which formed in the southwestern portion of the basin by the Embudo fault zone and a hypothesized fault zone along its western margin. This subbasin reaches a maximum thickness of ~2 km, as indicated by magnetotelluric and gravity modeling. The Sunshine Valley, Questa, and Taos subbasins occupy the eastern part of the southern San Luis Basin. The southern Sangre de Cristo fault zone is the dominant tectonic feature that controlled their development after ~20 Ma. The east-down Gorge fault zone controlled the western margins of significant parts of these eastern subbasins, although much of the Taos subbasin may be superimposed on the Tres Orejas subbasin. Maximum low-density basin-fill thicknesses are estimated to be 1.2 km for the Sunshine Valley subbasin, 800 m for the Questa subbasin, and 1.8 km for the Taos subbasin. Subbasin-forming tectonic activity along the Gorge fault zone and within the Tres Orejas subbasin ceased by the end of the development of the largely Pliocene Taos Plateau volcanic field. After that, rift-related subsidence became more narrowly centered on the eastern margin of the basin, controlled mainly by the linked Embudo and southern Sangre de Cristo fault zones.
","language":"English","publisher":"Rocky Mountain Geology","doi":"10.24872/rmgjournal.54.2.97","usgsCitation":"Drenth, B.J., Grauch, V.J., Turner, K.J., Rodriguez, B.D., Thompson, R., and Bauer, P.W., 2019, A shallow rift basin segmented in space and time: The southern San Luis Basin, Rio Grande rift, northern New Mexico, U.S.A.: Rocky Mountain Geology, v. 54, no. 2, p. 97-131, https://doi.org/10.24872/rmgjournal.54.2.97.","productDescription":"35 p.","startPage":"97","endPage":"131","ipdsId":"IP-104797","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":459077,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.24872/rmgjournal.54.2.97","text":"Publisher Index Page"},{"id":380645,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"San Luis Basin, Rio Grande rift","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.45751953125,\n 35.817813158696616\n ],\n [\n -104.78759765625,\n 35.817813158696616\n ],\n [\n -104.78759765625,\n 37.01132594307015\n ],\n [\n -106.45751953125,\n 37.01132594307015\n ],\n [\n -106.45751953125,\n 35.817813158696616\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Drenth, Benjamin J. 0000-0002-3954-8124 bdrenth@usgs.gov","orcid":"https://orcid.org/0000-0002-3954-8124","contributorId":1315,"corporation":false,"usgs":true,"family":"Drenth","given":"Benjamin","email":"bdrenth@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":805195,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grauch, V. J. 0000-0002-0761-3489 tien@usgs.gov","orcid":"https://orcid.org/0000-0002-0761-3489","contributorId":152256,"corporation":false,"usgs":true,"family":"Grauch","given":"V.","email":"tien@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":805196,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Turner, Kenzie J. 0000-0002-4940-3981 kturner@usgs.gov","orcid":"https://orcid.org/0000-0002-4940-3981","contributorId":496,"corporation":false,"usgs":true,"family":"Turner","given":"Kenzie","email":"kturner@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":805197,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rodriguez, Brian D. 0000-0002-2263-611X brod@usgs.gov","orcid":"https://orcid.org/0000-0002-2263-611X","contributorId":836,"corporation":false,"usgs":true,"family":"Rodriguez","given":"Brian","email":"brod@usgs.gov","middleInitial":"D.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":805198,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, Ren A. 0000-0002-3044-3043","orcid":"https://orcid.org/0000-0002-3044-3043","contributorId":207982,"corporation":false,"usgs":true,"family":"Thompson","given":"Ren A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":805199,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bauer, Paul W.","contributorId":145562,"corporation":false,"usgs":false,"family":"Bauer","given":"Paul","email":"","middleInitial":"W.","affiliations":[{"id":16150,"text":"New Mexico Bureau of Geology and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":805200,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70217620,"text":"70217620 - 2019 - A shrubbier future: Forest transformation in the eastern Jemez Mountains","interactions":[],"lastModifiedDate":"2021-01-25T15:25:03.465327","indexId":"70217620","displayToPublicDate":"2019-11-30T09:24:16","publicationYear":"2019","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"A shrubbier future: Forest transformation in the eastern Jemez Mountains","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Fire ghosts","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","usgsCitation":"Allen, C.D., 2019, A shrubbier future: Forest transformation in the eastern Jemez Mountains, chap. of Fire ghosts, p. 85-88.","productDescription":"4 p.","startPage":"85","endPage":"88","ipdsId":"IP-109025","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":382546,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.875,\n 35.715298012125295\n ],\n [\n -106.31469726562499,\n 35.715298012125295\n ],\n [\n -106.31469726562499,\n 36.37485644939407\n ],\n [\n -106.875,\n 36.37485644939407\n ],\n [\n -106.875,\n 35.715298012125295\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Allen, Craig D. 0000-0002-8777-5989 craig_allen@usgs.gov","orcid":"https://orcid.org/0000-0002-8777-5989","contributorId":2597,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"craig_allen@usgs.gov","middleInitial":"D.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":808921,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70214574,"text":"70214574 - 2019 - Wave-current interaction between Hurricane Matthew wave fields and the Gulf Stream","interactions":[],"lastModifiedDate":"2020-09-30T14:08:22.311228","indexId":"70214574","displayToPublicDate":"2019-11-29T09:03:53","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2426,"text":"Journal of Physical Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Wave-current interaction between Hurricane Matthew wave fields and the Gulf Stream","docAbstract":"Hurricanes interact with the Gulf Stream in the South Atlantic Bight (SAB) through a wide variety of processes, which are crucial to understand for prediction of open-ocean and coastal hazards during storms. However, it remains unclear how waves are modified by large-scale ocean currents under storm conditions, when waves are aligned with the storm-driven circulation and tightly coupled to the overlying wind field. Hurricane Matthew (2016) impacted the US southeast coast, causing extensive coastal change due to large waves and elevated water levels. The hurricane traveled on the continental shelf parallel to the SAB coastline, with the right side of the hurricane directly over the Gulf Stream. Using the Coupled Ocean Atmosphere Wave Sediment Transport Modeling System, we investigate wave-current interaction between Hurricane Matthew and the Gulf Stream. The model simulates ocean currents and waves over a grid encompassing the US east coast, with varied coupling of the hydrodynamic and wave components to isolate the effect of the currents on the waves, and the effect of the Gulf Stream relative to storm-driven circulation. The Gulf Stream modifies the direction of the storm-driven currents beneath the right side of the hurricane. Waves transitioned from following currents that result in wave lengthening, through negative current gradients that result in wave steepening and dissipation. Wave-current interaction over the Gulf Stream modified maximum coastal total water levels, and changed incident wave directions at the coast by up to 20°, with strong implications for the morphodynamic response and stability of the coast to the hurricane.","language":"English","publisher":"American Meteorology Society","doi":"10.1175/JPO-D-19-0124.1","usgsCitation":"Hegermiller, C., Warner, J., Olabarrieta, M., and Sherwood, C.R., 2019, Wave-current interaction between Hurricane Matthew wave fields and the Gulf Stream: Journal of Physical Oceanography, v. 49, no. 11, p. 2883-2900, https://doi.org/10.1175/JPO-D-19-0124.1.","productDescription":"18 p.","startPage":"2883","endPage":"2900","ipdsId":"IP-109198","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":459087,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/jpo-d-19-0124.1","text":"Publisher Index Page"},{"id":378900,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"East Coast, Gulf Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.12109375,\n 16.97274101999902\n ],\n [\n -78.046875,\n 24.5271348225978\n ],\n [\n -73.47656249999999,\n 36.1733569352216\n ],\n [\n -65.390625,\n 44.213709909702054\n ],\n [\n -68.73046875,\n 46.07323062540835\n ],\n [\n -80.85937499999999,\n 34.016241889667015\n ],\n [\n -92.63671875,\n 32.24997445586331\n ],\n [\n -101.77734374999999,\n 25.799891182088334\n ],\n [\n -97.3828125,\n 19.145168196205297\n ],\n [\n -90.17578124999999,\n 16.29905101458183\n ],\n [\n -89.12109375,\n 16.97274101999902\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hegermiller, Christie 0000-0002-6383-7508","orcid":"https://orcid.org/0000-0002-6383-7508","contributorId":241895,"corporation":false,"usgs":true,"family":"Hegermiller","given":"Christie","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":true,"id":800130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":2681,"corporation":false,"usgs":true,"family":"Warner","given":"John C.","email":"jcwarner@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800131,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olabarrieta, Maitane 0000-0002-7619-7992 molabarrieta@usgs.gov","orcid":"https://orcid.org/0000-0002-7619-7992","contributorId":211373,"corporation":false,"usgs":false,"family":"Olabarrieta","given":"Maitane","email":"molabarrieta@usgs.gov","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":800132,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800133,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211034,"text":"70211034 - 2019 - The 2018 update of the US National Seismic Hazard Model: Overview of model and implications","interactions":[],"lastModifiedDate":"2020-07-13T12:34:13.1599","indexId":"70211034","displayToPublicDate":"2019-11-28T15:52:54","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"The 2018 update of the US National Seismic Hazard Model: Overview of model and implications","docAbstract":"During 2017–2018, the National Seismic Hazard Model for the conterminous United States was updated as follows: (1) an updated seismicity catalog was incorporated, which includes new earthquakes that occurred from 2013 to 2017; (2) in the central and eastern United States (CEUS), new ground motion models were updated that incorporate updated median estimates, modified assessments of the associated epistemic uncertainties and aleatory variabilities, and new soil amplification factors; (3) in the western United States (WUS), amplified shaking estimates of long-period ground motions at sites overlying deep sedimentary basins in the Los Angeles, San Francisco, Seattle, and Salt Lake City areas were incorporated; and (4) in the conterminous United States, seismic hazard is calculated for 22 periods (from 0.01 to 10 s) and 8 uniform VS30 maps (ranging from 1500 to 150 m/s). We also include a description of updated computer codes and modeling details. Results show increased ground shaking in many (but not all) locations across the CEUS (up to ~30%), as well as near the four urban areas overlying deep sedimentary basins in the WUS (up to ~50%). Due to population growth and these increased hazard estimates, more people live or work in areas of high or moderate seismic hazard than ever before, leading to higher risk of undesirable consequences from forecasted future ground shaking.
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Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5117","displayTitle":"Groundwater-Flow Model and Analysis of Groundwater and Surface-Water Interactions for the Big Sioux Aquifer, Sioux Falls, South Dakota","title":"Groundwater-flow model and analysis of groundwater and surface-water interactions for the Big Sioux aquifer, Sioux Falls, South Dakota","docAbstract":"The city of Sioux Falls, in southeastern South Dakota, is the largest city in South Dakota. The U.S. Geological Survey (USGS), in cooperation with the city of Sioux Falls, completed a groundwater-flow model to use for improving the understanding of groundwater-flow processes, estimating hydrogeologic properties, and analyzing groundwater and surface-water interactions for the Big Sioux aquifer in the model area.
The model area includes the Big Sioux aquifer and the underlying hydrogeologic units from Dell Rapids, South Dakota, to the confluence of the Big Sioux River and the outlet of the Sioux Falls Diversion Channel in eastern Sioux Falls, S. Dak. The Big Sioux aquifer is the primary aquifer in the model area and the focus of the groundwater-flow model. The Big Sioux River is the largest stream in the model area and is in hydraulic connection with the Big Sioux aquifer.
A conceptual model for the area was constructed and includes a characterization of the hydrogeologic framework, analysis and construction of potentiometric surfaces, and summary of estimated water budget components in the model area. The primary hydrogeologic units in the model area consist of (1) the Big Sioux aquifer, (2) a glacial till confining unit, and (3) bedrock aquifers (Split Rock Creek and Sioux Quartzite aquifers). Sources of groundwater recharge included infiltration of precipitation, stream seepage, and groundwater exchanges among the hydraulically connected Big Sioux aquifer, glacial till confining unit, and bedrock aquifers. Groundwater losses included evapotranspiration, groundwater discharge to streams, and groundwater withdrawal to supply water-use needs.
A numerical groundwater-flow model (numerical model) was constructed and was used to simulate all aspects of the conceptual model for predevelopment (steady-state) and time-varying (transient) monthly conditions for 1950–2017. The numerical model was constructed using the USGS modular hydrologic simulation program, MODFLOW–6, and was calibrated using the Parameter ESTimation software, PEST++.
The transient numerical model was calibrated for steady-state and transient monthly conditions for 1950–2017. Calibration targets were observations of hydraulic head, changes in hydraulic head, monthly mean streamflow (as a rate), and cumulative monthly stream discharge (as a volume). Parameters adjusted during model calibration were horizontal and vertical hydraulic conductivity, specific storage, specific yield, recharge and evapotranspiration multipliers, and streambed hydraulic conductivity. Horizontal and vertical hydraulic conductivity were estimated at pilot points distributed within the model area; specific storage and specific yield were assigned to uniform values in each layer in the model area; recharge and evapotranspiration multipliers were assigned uniformly for every stress period in the numerical model; and streambed hydraulic conductivity values were assigned uniformly between stream confluences.
The final calibrated parameter values of horizontal and vertical hydraulic conductivity, specific yield, specific storage, streambed hydraulic conductivity, recharge, and evapotranspiration were considered reasonable for the hydrogeologic materials and conditions in the model area for 1950–2017.
Overall, simulated hydraulic head altitudes had a linear regression coefficient of determination (R2) of 0.48. Hydraulic head altitude residuals for the glacial till confining unit and bedrock aquifers were typically greater in magnitude when compared to residuals in the Big Sioux aquifer, but simulated hydraulic head altitudes in the Big Sioux aquifer compared favorably with mean observed hydraulic head altitudes and had a linear regression R2 of 0.93.
Simulated streamflow hydrographs matched the general trends of observed increases and decreases in streamflow for USGS streamgages 06482000 (Big Sioux River at Sioux Falls, S. Dak.) and 06482020 (Big Sioux River at North Cliff Avenue at Sioux Falls, S. Dak.), but larger streamflows were overestimated at the first streamgage and underestimated at the second streamgage. The numerical model reasonably estimated cumulative monthly stream discharge for the first 10–15 years of available streamflow records at both USGS streamgages. After the first 10–15 years of available streamflow record, cumulative monthly stream discharge was closely estimated for USGS streamgage 06482000 and underestimated at USGS streamgage 06482020.
Composite sensitivities without regularization were calculated by PEST++ for the calibrated numerical model parameters and were averaged by parameter group. The parameter group with the highest mean composite sensitivity was the recharge multiplier parameter group.
Model simplifications, assumptions, and limitations were necessary for construction of the conceptual and numerical models and for calibration efficiency. Spatial simplification of hydraulic properties could cause the numerical model to misrepresent reactions to changes in localized stresses, such as additional demands for groundwater withdrawal. The numerical model was temporally discretized into monthly periods and required scaling daily rates into representative monthly rates for model input and calibration targets. Based on the comparison between the observed and simulated groundwater levels, monthly mean streamflow and cumulative monthly stream discharge, and general groundwater distribution and flow, the numerical model favorably simulated the flow in the Big Sioux aquifer.
Eventual capture was calculated in the model area using a steady-state numerical groundwater-flow model. The eventual capture map shows areas of higher streamflow capture adjacent to the Big Sioux River north of the city of Sioux Falls and along the lower part of the Sioux Falls Diversion Channel, and areas of lower streamflow capture along aquifer boundaries and near the southern Sioux Quartzite barrier.
The timing of capture was determined using a transient numerical groundwater-flow model to determine the likely captured water sources for 30 years of groundwater withdrawal at three hypothetical wells using three continuous withdrawal rates (112.5, 450.0, and 900.0 gallons per minute). Supply for all three hypothetical wells became capture-dominated after only a short period of continuous withdrawal. Capture stabilized after about 10–15 years for well A, and after 20–25 years for well B, and after about 10–15 years for well C.
The groundwater-flow model is a suitable tool to use for improving the understanding of groundwater-flow processes, estimating hydrogeologic properties, and analyzing groundwater and surface-water interactions for the Big Sioux aquifer near Sioux Falls, S. Dak. The numerical model can be used to simulate hydrologic scenarios, advance understanding of groundwater budgets, compute system response to stress, and determine likely sources of water supplied to wells.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195117","collaboration":"Prepared in cooperation with the city of Sioux Falls","usgsCitation":"Davis, K.W., Eldridge, W.G., Valder, J.F., and Valseth, K.J., 2019, Groundwater-flow model and analysis of groundwater and surface-water interactions for the Big Sioux aquifer, Sioux Falls, South Dakota: U.S. Geological Survey Scientific Investigations Report 2019–5117, 86 p., https://doi.org/10.3133/sir20195117.","productDescription":"Report: xi, 86 p.; Data Release","numberOfPages":"102","onlineOnly":"Y","ipdsId":"IP-105956","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":369602,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20195013","text":"SIR 2019–5013","linkHelpText":"– Hydraulic conductivity estimates from slug tests in the Big Sioux aquifer near Sioux Falls, South Dakota"},{"id":369600,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3393","text":"SIM 3393","linkHelpText":"– Delineation of the hydrogeologic framework of the Big Sioux aquifer near Sioux Falls, South Dakota, using airborne electromagnetic data"},{"id":369601,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/F79885XC","text":"USGS data release for SIM 3393","linkHelpText":"– Airborne electromagnetic and magnetic survey data, Big Sioux aquifer, October 2015, Sioux Falls, South Dakota"},{"id":369603,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P9LUB44J","text":"USGS data release for SIR 2019–5013","linkHelpText":"– Water-level data and AQTESOLV Pro analysis results for slug tests in the Big Sioux Aquifer, Sioux Falls, South Dakota, 2017"},{"id":369535,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5117/coverthb.jpg"},{"id":369536,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5117/sir20195117.pdf","text":"Report","size":"13.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5117"},{"id":369537,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O59RO0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-6 model of the Big Sioux aquifer, Sioux Falls, South Dakota"}],"country":"United States","state":"South Dakota","city":"Sioux Falls","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.06146240234375,\n 43.29919735147067\n ],\n [\n -96.42425537109375,\n 43.29919735147067\n ],\n [\n -96.42425537109375,\n 43.757208878849376\n ],\n [\n -97.06146240234375,\n 43.757208878849376\n ],\n [\n -97.06146240234375,\n 43.29919735147067\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
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As much as 22 inches of rain fell in Oklahoma in May 2019, resulting in historic flooding along the Arkansas River in Oklahoma and Arkansas. The flooding along the Arkansas River and its tributaries that began in May continued into June 2019. Peaks of record were measured at 12 U.S. Geological Survey (USGS) streamgages on various streams in eastern and northeastern Oklahoma. This report documents the peak streamflows and stages for seven selected streamgages along the Arkansas River in Oklahoma and Arkansas. Most of the flood peaks occurred from May 26 to June 4, 2019. The historic flooding caused homes to fall into the river as a result of bank erosion, forced some towns to be evacuated, and resulted in the highest flood depths in Tulsa, Oklahoma, since 1986. Along the Arkansas River, peak streamflows were recorded at six of the seven selected USGS streamgages, with the seventh streamgage on the Arkansas River having the second highest peak of record at that site since regulation began.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191129","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency and the U.S. Army Corps of Engineers","usgsCitation":"Lewis, J.M., and Trevisan, A.R., 2019, Peak streamflow and stages at selected streamgages on the Arkansas River in Oklahoma and Arkansas, May to June 2019: U.S. Geological Survey Open-File Report 2019–1129, 10 p., https://doi.org/10.3133/ofr20191129.","productDescription":"iv, 10 p.","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-112483","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":369335,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1129/ofr20191129.pdf","text":"Report","size":"4.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 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\"}}]}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Red tree corals (Primnoa pacifica) are abundant in the eastern Gulf of Alaska, from the glacial fjords of Southeast Alaska where they emerge to as shallow as 6 m, to the continental shelf edge and seamounts where they are more commonly found at depths greater than 150 – 500 m. This keystone species forms large thickets, creating habitat for many associated species, including economically valuable fishes and crabs, and so are important benthic suspension feeders in this region. Though the reproductive periodicity of this species was reported in 2014 from a shallow fjord (Tracy Arm), this study examined reproductive ecologies from 8 sites – two within Glacier Bay National Park and Preserve, three on the continental shelf edge, one within Endicott Arm (Holkham Bay) and two time points from the Tracy Arm (Holkham Bay) study. Male reproductive traits were similar at all sites but there were distinct differences in oogenesis. Though per polyp fecundity mostly showed no significant difference between sites, there was a non-significant trend of increasing number of oocytes with depth. In addition, the average oocyte size from Tracy Arm (the shallowest site) was 105 μm, whereas from Shutter Ridge (one of the deepest sites) the average size was 309 μm. Moreover, the maximum oocyte size at Endicott Arm was 221 μm and at Tracy Arm was 802 μm (both shallow sites), whereas at Dixon Entrance (a deep site) it was 2120 μm, a difference not usually observed within a single species. We propose two theories to explain the observed differences, (a) this species shows great phenotypic plasticity in reproductive ecology, adjusting to different environmental variables based on energetic need and potentially demonstrating micro-evolution; or (b) the fjord sites are at a reproductive dead end, with the stress of shallow-water conditions effectively preventing gametogenesis reaching full potential and likely limiting successful reproductive events from occurring, at least on a regular basis.