Partially eroded stratovolcanoes worldwide, notably Mounts Rainier and Adams in the Cascades and several volcanoes in Japan, record episodic periods of eruption and geothermal activity that produce zones of hydrothermal alteration. The partly eroded core of late Pleistocene Brokeoff volcano on the south side of Lassen Peak exposes the upper 1 km of multiple ancient (ca. 410–300 ka) magmatic-hydrothermal alteration zones in a 3.5 by 5 km area that allows characterization of the three-dimensional hydrothermal evolution of the volcano. Both acid- and neutral-pH hydrothermal solutions produced distinctive alteration mineral assemblages in close proximity. Early hydrothermal activity is characterized by alunite-rich alteration that is temporally and spatially related to shallow intrusions in the center of the volcano. Younger acid alteration and a large area of neutral-pH alteration formed along the volcano’s flanks. The neutral-pH alteration is vertically zoned over 1000 m from shallow zeolite ± adularia through intermediate argillic (smectite-pyrite ± illite) to deep propylitic (chlorite-calcite-albite-illite) alteration. Pleistocene alteration is partly overprinted by surficial, steam-heated alteration related to Lassen’s modern hydrothermal activity. A large (∼3.5 km2), shallow (≤300 m), ca. 1.5 Ma alunite-rich magmatic-hydrothermal alteration zone is exposed on the northeast flank of the nearby Maidu volcanic center.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02049.1","usgsCitation":"John, D.A., Lee, R.G., Breit, G.N., Dilles, J.H., Calvert, A.T., Muffler, L.P., and Clynne, M.A., 2019, Pleistocene hydrothermal activity on Brokeoff volcano and in the Maidu volcanic center, Lassen Peak area, northeast California: Evolution of magmatic-hydrothermal systems on stratovolcanoes: Geopshere, v. 15, no. 3, p. 946-982, https://doi.org/10.1130/GES02049.1.","productDescription":"37 p.","startPage":"946","endPage":"982","ipdsId":"IP-100523","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":467668,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02049.1","text":"Publisher Index Page"},{"id":437481,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PZQGJG","text":"USGS data release","linkHelpText":"Geochemical and Geochronological Data for Hydrothermal Systems on Brokeoff Volcano and in the Maidu Volcanic Center, Lassen Peak area, northeastern California"},{"id":379596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Lassen Peak area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.01965332031249,\n 39.52522954427751\n ],\n [\n -120.2947998046875,\n 39.52522954427751\n ],\n [\n -120.2947998046875,\n 41.10832999732831\n ],\n [\n -122.01965332031249,\n 41.10832999732831\n ],\n [\n -122.01965332031249,\n 39.52522954427751\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-04-26","publicationStatus":"PW","contributors":{"authors":[{"text":"John, David A. 0000-0001-7977-9106 djohn@usgs.gov","orcid":"https://orcid.org/0000-0001-7977-9106","contributorId":1748,"corporation":false,"usgs":true,"family":"John","given":"David","email":"djohn@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":802450,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Robert G.","contributorId":243516,"corporation":false,"usgs":false,"family":"Lee","given":"Robert","email":"","middleInitial":"G.","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":802451,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Breit, George N. 0000-0003-2188-6798 gbreit@usgs.gov","orcid":"https://orcid.org/0000-0003-2188-6798","contributorId":1480,"corporation":false,"usgs":true,"family":"Breit","given":"George","email":"gbreit@usgs.gov","middleInitial":"N.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":802452,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dilles, John H.","contributorId":243517,"corporation":false,"usgs":false,"family":"Dilles","given":"John","email":"","middleInitial":"H.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":802453,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Calvert, Andrew T. 0000-0001-5237-2218 acalvert@usgs.gov","orcid":"https://orcid.org/0000-0001-5237-2218","contributorId":2694,"corporation":false,"usgs":true,"family":"Calvert","given":"Andrew","email":"acalvert@usgs.gov","middleInitial":"T.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":802454,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Muffler, L.J. Patrick 0000-0001-6638-7218 pmuffler@usgs.gov","orcid":"https://orcid.org/0000-0001-6638-7218","contributorId":3322,"corporation":false,"usgs":true,"family":"Muffler","given":"L.J.","email":"pmuffler@usgs.gov","middleInitial":"Patrick","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":802455,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Clynne, Michael A. 0000-0002-4220-2968 mclynne@usgs.gov","orcid":"https://orcid.org/0000-0002-4220-2968","contributorId":2032,"corporation":false,"usgs":true,"family":"Clynne","given":"Michael","email":"mclynne@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":802456,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70204619,"text":"70204619 - 2019 - Changing climates and challenges to Charadrius plover success throughout the annual cycle","interactions":[],"lastModifiedDate":"2019-08-07T10:12:13","indexId":"70204619","displayToPublicDate":"2019-04-26T10:07:42","publicationYear":"2019","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"chapter":"3","title":"Changing climates and challenges to Charadrius plover success throughout the annual cycle","docAbstract":"The Arctic tundra, as well as coastal and inland mudflats and beaches occupied by the 63 Charadrius plover species and subspecies around the world encompass some of the habitats most threatened by current climatic challenges. The migratory habits of most plover species further intensifies these effects as the birds occupy more than one major biome during the annual cycle. And yet there have only been two plover species where specific issues related to climate change have been addressed. Therefore in this chapter, I summarize climate-related issues in areas occupied by the world’s Charadrius plovers to highlight further research and management to at least slow the negative effects of our changing world on their success. To be most strategic and effective, management and research approaches carried out with full knowledge or investigation of the species’ annual cycle and migratory connectivity will be most informative. Given the dearth of climate-related information for this group of birds, future work will likely help not only plovers but other species occupying similar habitats around the world.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The population ecology and conservation of Charadrius Plovers","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CRC Press","doi":"10.1201/9781315152882-3","usgsCitation":"Haig, S.M., 2019, Changing climates and challenges to Charadrius plover success throughout the annual cycle, chap. 3 of The population ecology and conservation of Charadrius Plovers, p. 45-62, https://doi.org/10.1201/9781315152882-3.","productDescription":"18 p.","startPage":"45","endPage":"62","ipdsId":"IP-088577","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":366332,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":767795,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70204620,"text":"70204620 - 2019 - Future challenges for Charadruis Plovers","interactions":[],"lastModifiedDate":"2019-08-07T10:06:37","indexId":"70204620","displayToPublicDate":"2019-04-26T10:05:44","publicationYear":"2019","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"chapter":"12","title":"Future challenges for Charadruis Plovers","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The population ecology and conservation of Charadrius Plovers","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CRC Press","doi":"10.1201/9781315152882-12","usgsCitation":"Haig, S.M., and Colwell, M., 2019, Future challenges for Charadruis Plovers, chap. 12 of The population ecology and conservation of Charadrius Plovers, p. 311-318, https://doi.org/10.1201/9781315152882-12.","productDescription":"8 p.","startPage":"311","endPage":"318","ipdsId":"IP-093511","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":366331,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":767796,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Colwell, Mark A","contributorId":217912,"corporation":false,"usgs":false,"family":"Colwell","given":"Mark A","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":767797,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203366,"text":"70203366 - 2019 - Formation of pedestalled, relict lakes on the McMurdo Ice Shelf, Antarctica","interactions":[],"lastModifiedDate":"2019-05-09T08:56:24","indexId":"70203366","displayToPublicDate":"2019-04-26T09:52:35","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2328,"text":"Journal of Glaciology","active":true,"publicationSubtype":{"id":10}},"title":"Formation of pedestalled, relict lakes on the McMurdo Ice Shelf, Antarctica","docAbstract":"Surface debris covers much of the western portion of the McMurdo Ice Shelf and has a strong influence on the local surface albedo and energy balance. Differential ablation between debris-covered and debris-free areas creates an unusual heterogeneous surface of topographically low, high-ablation, and topographically raised (‘pedestalled’), low-ablation areas. Analysis of Landsat and MODIS satellite imagery from 1999 to 2018, alongside field observations from the 2016/2017 austral summer, shows that pedestalled relict lakes (‘pedestals’) form when an active surface meltwater lake that develops in the summer, freezes-over in winter, resulting in the lake-bottom debris being masked by a high-albedo, superimposed, ice surface. If this ice surface fails to melt during a subsequent melt season, it experiences reduced surface ablation relative to the surrounding debris-covered areas of the ice shelf. We propose that this differential ablation, and resultant hydrostatic and flexural readjustments of the ice shelf, causes the former supraglacial lake surface to become increasingly pedestalled above the lower topography of the surrounding ice shelf. Consequently, meltwater streams cannot flow onto these pedestalled features, and instead divert around them. We suggest that the development of pedestals has a significant influence on the surface-energy balance, hydrology and flexure of the ice shelf.
A case study of groundwater contamination is a detailed study of a single site contaminated with a chemical or mixture that is known to be a problem at many sites. The goal of case studies is to provide insights into the physical, chemical, and biological processes controlling migration, natural attenuation, or remediation of common groundwater contaminants. Ideally, processes occurring at a case study site are representative of other sites so that knowledge gained from these intensive studies can be applied at thousands of sites where fewer data are available. Several characteristics of case studies contribute to their value. First, they may have tens to hundreds of monitoring wells, compared to fewer than ten wells at some contaminated sites. Second, some case studies continue for many years or even decades, providing insights into temporal progression of slow processes. Third, analytical methods prohibitively expensive for routine use or under development may be tested at case study sites. Finally, the ongoing characterization typical of case study sites builds a foundation of knowledge that facilitates sophisticated experimental design and testing of new methods. This article is divided into sections based on the contaminant type because the chemical and biological processes required for remediation vary for each contaminant. Most importantly, some contaminants can be biodegraded whereas metals and radionuclides cannot be destroyed but can be immobilized or rendered less toxic. The emphasis is on case studies of natural processes that control the fate and transport of contaminants in groundwater rather than on active remediation methods. The principles learned from these studies may form the basis for design of remedial strategies. The organic contaminants are divided into: petroleum hydrocarbons, fuel oxygenates, coal tar and wastes from manufactured gas plants, and chlorinated solvents. The inorganic contaminants covered are metals and radionuclides, arsenic, and nitrate. Case studies of mixed waste plumes from landfills are also described. Experimental sites where contaminants have been introduced into an aquifer as an emplaced source or a controlled release may not meet the above definition of case studies, but some are included because the overall goal is to impart lessons learned from detailed field studies. It is impossible to cover all case studies in this short format. Conversely, focusing on one or two does not convey the breadth of research results in entire range of case studies. Instead, the strategy is to describe the evolution of knowledge for each contaminant class while providing citations of relevant case studies. Much of the progress in understanding of the fate of contaminants in groundwater is based on laboratory studies; thus whenever possible, papers that included both field and laboratory results have been included among the citations. Two topics of growing importance have not been covered. These are the fate of pharmaceuticals in groundwater and discharge of contaminant plumes to surface water. These topics merit coverage in the future as knowledge grows and case studies increase in number.
To reduce future earthquake injuries and casualties, it is important that people understand how their behavior, during and immediately following earthquake shaking, exposes them to increased risk of injury or death. Research confirms that protective actions can reduce injuries and that prior training can help prepare people to take appropriate actions. In this paper, we examine barriers to participation in the ShakeOut drills in New Zealand. Through citizen science research, volunteers observed people performing the drills in 2012 and 2015. Observers reported how long it took to perform the drill and why they thought some people may not have completed it. Our findings illustrate that children, elderly, and those with both mental and physical disabilities struggled with the drill. Furthermore, embarrassment was a reported leading cause for non-participation; we recommend more inclusive messaging to address potential causes of embarrassment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijdrr.2019.101150","usgsCitation":"McBride, S., Becker, J., and Johnston, D.M., 2019, Exploring the barriers for people taking protective actions during the 2012 and 2015 New Zealand shakeout drills: International Journal of Disaster Risk Reduction, v. 37, 101150, 11 p., https://doi.org/10.1016/j.ijdrr.2019.101150.","productDescription":"101150, 11 p.","ipdsId":"IP-102011","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":467671,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijdrr.2019.101150","text":"Publisher Index Page"},{"id":377872,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[173.02037,-40.91905],[173.24723,-41.332],[173.95841,-40.9267],[174.24759,-41.34916],[174.24852,-41.77001],[173.87645,-42.23318],[173.22274,-42.97004],[172.71125,-43.37229],[173.08011,-43.85334],[172.30858,-43.86569],[171.45293,-44.24252],[171.18514,-44.8971],[170.6167,-45.90893],[169.83142,-46.35577],[169.33233,-46.64124],[168.41135,-46.61994],[167.76374,-46.2902],[166.67689,-46.21992],[166.50914,-45.8527],[167.04642,-45.11094],[168.30376,-44.12397],[168.94941,-43.93582],[169.66781,-43.55533],[170.52492,-43.03169],[171.12509,-42.51275],[171.56971,-41.76742],[171.94871,-41.51442],[172.09723,-40.9561],[172.79858,-40.49396],[173.02037,-40.91905]]],[[[174.61201,-36.1564],[175.33662,-37.2091],[175.3576,-36.52619],[175.80889,-36.79894],[175.95849,-37.55538],[176.7632,-37.88125],[177.43881,-37.96125],[178.01035,-37.57982],[178.51709,-37.69537],[178.27473,-38.58281],[177.97046,-39.16634],[177.20699,-39.14578],[176.93998,-39.44974],[177.03295,-39.87994],[176.88582,-40.06598],[176.50802,-40.60481],[176.01244,-41.28962],[175.23957,-41.68831],[175.0679,-41.42589],[174.65097,-41.28182],[175.22763,-40.45924],[174.90016,-39.90893],[173.82405,-39.50885],[173.85226,-39.1466],[174.5748,-38.79768],[174.74347,-38.02781],[174.69702,-37.38113],[174.29203,-36.71109],[174.319,-36.53482],[173.841,-36.12198],[173.05417,-35.23713],[172.63601,-34.52911],[173.00704,-34.45066],[173.5513,-35.00618],[174.32939,-35.2655],[174.61201,-36.1564]]]]},\"properties\":{\"name\":\"New Zealand\"}}]}","volume":"37","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McBride, Sara K. 0000-0002-8062-6542","orcid":"https://orcid.org/0000-0002-8062-6542","contributorId":206933,"corporation":false,"usgs":true,"family":"McBride","given":"Sara K.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797312,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Becker, Julia S.","contributorId":217541,"corporation":false,"usgs":false,"family":"Becker","given":"Julia S.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":797313,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnston, David M. 0000-0001-5114-5355","orcid":"https://orcid.org/0000-0001-5114-5355","contributorId":239591,"corporation":false,"usgs":false,"family":"Johnston","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":797314,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202346,"text":"sir20195006 - 2019 - Streamflow Gain and Loss, Hydrograph Separation, and Water Quality of Abandoned Mine Lands in the Daniel Boone National Forest, Eastern Kentucky, 2015–17","interactions":[],"lastModifiedDate":"2019-04-26T16:11:09","indexId":"sir20195006","displayToPublicDate":"2019-04-25T16:50:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5006","displayTitle":"Streamflow Gain and Loss, Hydrograph Separation, and Water Quality of Abandoned Mine Lands in the Daniel Boone National Forest, Eastern Kentucky, 2015–17","title":"Streamflow Gain and Loss, Hydrograph Separation, and Water Quality of Abandoned Mine Lands in the Daniel Boone National Forest, Eastern Kentucky, 2015–17","docAbstract":"During 2015–17, the U.S. Geological Survey, in cooperation with the U.S. Department of Agriculture Forest Service (Forest Service), carried out a study to characterize the hydrology and water chemistry in two study areas within the Daniel Boone National Forest. One study area was within the Rock Creek drainage and the other study area included the Wildcat and Addison Branch drainages. Both study areas historically were mined for coal prior to the Surface Mining Control and Reclamation Act of 1977 and contain abandoned coal mine sites that have since been the focus of remediation efforts. Synoptic surveys of streamflow and water-quality properties (water temperature, pH, specific conductance, and dissolved oxygen) of Rock Creek were done during November 2015 and May 2016, and surveys of Wildcat and Addison Branches were done during June 2016 and May 2017. Streamflow measurements were used to quantify contributions from tributaries and to compute streamflow gain and loss in designated reaches. Discrete measurements of water temperature, pH, specific conductance, and dissolved oxygen were used to evaluate conditions during a short timeframe and for comparison between study areas. Study designs for the two study areas differed because there was an operating streamgage on Rock Creek near Yamacraw, Kentucky (station number 03410590) where streamflow and water-quality properties (water temperature, specific conductance, pH, dissolved oxygen, and turbidity) were monitored continuously, while Addison and Wildcat Branches were ungaged. Several hydrograph separation methods were used to estimate base flow and runoff at the Rock Creek gage. These data will be used by the Forest Service to evaluate the current (2018) conditions and plan remediation efforts.
The water quality at Rock Creek was less affected by acid mine drainage (AMD) than the Wildcat or Addison Branches. Appreciable losing reaches, where water flowed underground, were identified in both study areas. All losing reaches coincided with karst topography. Streamflow increased in areas with openings to underground mine tunnels, known as portals.
Six hydrograph separation methods (Base-flow index [BFI; standard and modified], HYSEP [fixed interval, sliding interval, and local minimum], and PART) were applied to daily mean streamflow collected from August 2015 to August 2017 at station number 03410590. The hydrograph separation methods partition total streamflow into base flow and streamflow that originated from surface runoff. Base flow typically reacts slowly to precipitation infiltration and is largely sustained by groundwater discharge. The estimated daily base flow and runoff made with the different separation methods are not highly different. On average, base flow accounted for more total streamflow than surface runoff during the study period, irrespective of method.
Water temperature, pH, dissolved oxygen, specific conductance, and turbidity values were measured from July 2016 through July 2017 with a continuous monitor installed at station number 03410590. Nearly neutral pH values that ranged from 6.8 to 7.9 standard units likely limited metal solubility in the surface water. The continuous specific conductance values ranged between 30 and 259 microsiemens per centimeter at 25 degrees Celsius. The previous remediation efforts are likely continuing to improve the effect of AMD in the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195006","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture Forest Service ","usgsCitation":"Cherry, M.A., 2019, Streamflow gain and loss, hydrograph separation, and water quality of abandoned mine lands in the Daniel Boone National Forest, eastern Kentucky, 2015–17: U.S. Geological Survey Scientific Investigations Report 2019–5006, 36 p., https://doi.org/10.3133/sir20195006.","productDescription":"Report: viii, 36 p.;Data Release","numberOfPages":"49","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-091989","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":363195,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5006/sir20195006.pdf","text":"Report","size":"11.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5006"},{"id":363196,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FX78D9","text":"USGS data release","description":"USGS data release","linkHelpText":"Streamflow and water-quality data for selected streams in the Daniel Boone National Forest, eastern Kentucky, 2015–17"},{"id":363194,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5006/coverthb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"Daniel Boone National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.67437744140625,\n 36.63536611993544\n ],\n [\n -84.20333862304688,\n 36.63536611993544\n ],\n [\n -84.20333862304688,\n 37.004746084814784\n ],\n [\n -84.67437744140625,\n 37.004746084814784\n ],\n [\n -84.67437744140625,\n 36.63536611993544\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299
Weather has been recognized as a density independent factor influencing the abundance, distribution, and behavior of vertebrates. Male wild turkeys’ (Meleagris gallopavo) breeding behavior includes vocalizations and courtship displays to attract females, the phenology of which can vary with latitude. State biologists design spring turkey-hunting season frameworks centered on annual vocalization patterns to maximize hunter engagement. The Mississippi Department of Wildlife, Fisheries, and Parks has traditionally instituted a statewide, 7-week, spring harvest season. However, hunters routinely argue that different peaks in gobbling activity across the state exist. The objective of this study was to determine whether differences in peak gobbling activity existed across a latitudinal gradient of Mississippi and assess the effect of weather on gobbling. During 2008 and 2009, we conducted a statewide gobbling survey. We used generalized additive mixed models to describe the probability and frequency of gobbling activity within northern and southern regions of the state. We also investigated the effect of daily weather conditions on gobbling activity. Our results revealed an approximate 10–14-day difference in peak gobbling activity between southern and northern Mississippi. The majority of all gobbling activity occurred within the current spring harvest framework. Perhaps more importantly, gobbling activity was more prevalent on days of regionally dry conditions (i.e., less humid) according to the Spatial Synoptic Classification. Our results provide information on gobbling activity phenology relative to hunting-season dates and weather-response information. Our approach may be particularly applicable in states with relatively shorter seasons or highly variable daily weather conditions that moderate gobbling frequency.
","language":"English","publisher":"Springer","doi":"10.1007/s00484-019-01720-2","usgsCitation":"Palumbo, M.D., Vilella, F., Wang, G., Strickland, B.K., Godwin, D., Dixon, P.G., Rubin, B.D., and Lashley, M., 2019, Latitude and daily-weather effects on gobbling activity of wild turkeys in Mississippi: International Journal of Biometeorology, v. 63, no. 8, p. 1059-1067, https://doi.org/10.1007/s00484-019-01720-2.","productDescription":"9 p.","startPage":"1059","endPage":"1067","ipdsId":"IP-068111","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":370060,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"63","issue":"8","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Palumbo, Matthew D.","contributorId":146265,"corporation":false,"usgs":false,"family":"Palumbo","given":"Matthew","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":776889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vilella, Francisco 0000-0003-1552-9989 fvilella@usgs.gov","orcid":"https://orcid.org/0000-0003-1552-9989","contributorId":171363,"corporation":false,"usgs":true,"family":"Vilella","given":"Francisco","email":"fvilella@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":718110,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Guiming","contributorId":146267,"corporation":false,"usgs":false,"family":"Wang","given":"Guiming","affiliations":[],"preferred":false,"id":776890,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Strickland, Bronson K.","contributorId":146266,"corporation":false,"usgs":false,"family":"Strickland","given":"Bronson","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":776891,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Godwin, Dave","contributorId":146268,"corporation":false,"usgs":false,"family":"Godwin","given":"Dave","email":"","affiliations":[],"preferred":false,"id":776892,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dixon, P. Grady","contributorId":221067,"corporation":false,"usgs":false,"family":"Dixon","given":"P.","email":"","middleInitial":"Grady","affiliations":[],"preferred":false,"id":776893,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rubin, Benjamin D.","contributorId":221068,"corporation":false,"usgs":false,"family":"Rubin","given":"Benjamin","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":776894,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lashley, Marcus A.","contributorId":187467,"corporation":false,"usgs":false,"family":"Lashley","given":"Marcus A.","affiliations":[],"preferred":false,"id":776895,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70203193,"text":"70203193 - 2019 - The dependence of hydroclimate projections in snow‐dominated regions of the western United States on the choice of statistically downscaled climate data","interactions":[],"lastModifiedDate":"2019-04-26T09:55:28","indexId":"70203193","displayToPublicDate":"2019-04-25T15:26:05","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"The dependence of hydroclimate projections in snow‐dominated regions of the western United States on the choice of statistically downscaled climate data","docAbstract":"We assess monthly temperature and precipitation data produced by four statistically based techniques that were used to downscale general circulation models (GCMs) in the Climate Model Intercomparison Program Phase 5 (CMIP5) (Taylor et al., 2012). We drive a simple water-balance model with the downscaled data to demonstrate the effect of the methods on the cold season hydrology of three, snow dominated regions in the western U.S. Independent of substantial variation among the GCM simulations over the regions (maximum range of ~3.5 °C and 50% change in precipitation), the four methods produce disparate high resolution representations of the magnitude and spatial patterns of future temperature and precipitation simulated by the models that range for up to ~3 °C and 30% change in precipitation that propagate into the hydrologic simulations. Temperature-dependent snowfall, accumulation, and melt in the model are sensitive to how atmospheric lapse rates are applied in the gridded observations that are used to remove the bias in raw GCM temperatures. By the end of the century the same downscaling method (Bias Corrected Spatial Disaggregation) yields a loss of cold-season snowpack of 34% over the Greater Yellowstone Area under a constant lapse rate ( 6.5°C km-1), whereas spatially variable lapse rates nearly double the loss to 66%, highlighting the roll of both lapse rates and high elevation stations in the bias correction dataset. The two newest downscaling methods (Multivariate Adaptive Constructed Analogs and Localized Constructed Analogs) preserve the magnitude of change simulated GCMs better than the other methods and the produce comparable hydrologic projections. Because the downscaled data from the methods vary spatially and by GCM, the downscaled data should be evaluated carefully as part of the process of using downscaled climate products to drive hydrological models over the area of interest.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR023458","usgsCitation":"Alder, J.R., and Hostetler, S.W., 2019, The dependence of hydroclimate projections in snow‐dominated regions of the western United States on the choice of statistically downscaled climate data: Water Resources Research, v. 55, no. 3, p. 2279-2300, https://doi.org/10.1029/2018WR023458.","productDescription":"22 p.","startPage":"2279","endPage":"2300","ipdsId":"IP-097120","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":437482,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O9EB1C","text":"USGS data release","linkHelpText":"Data Release for The dependence of hydroclimate projections in snow-dominated regions of the western U.S. on the choice of statistically downscaled climate data"},{"id":363240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana,Nevada, New Mexico, Oregon, Washington, Wyoming","otherGeospatial":"Columbia River Basin, Greater Yellowstone Area, Sierra Nevada, Upper Colorado Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.33300781249999,\n 34.66935854524543\n ],\n [\n -104.80957031249999,\n 34.66935854524543\n ],\n [\n -104.80957031249999,\n 48.69096039092549\n ],\n [\n -121.33300781249999,\n 48.69096039092549\n ],\n [\n -121.33300781249999,\n 34.66935854524543\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Alder, Jay R. 0000-0003-2378-2853 jalder@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-2853","contributorId":5118,"corporation":false,"usgs":true,"family":"Alder","given":"Jay","email":"jalder@usgs.gov","middleInitial":"R.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":761576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hostetler, Steven W. 0000-0003-2272-8302 swhostet@usgs.gov","orcid":"https://orcid.org/0000-0003-2272-8302","contributorId":3249,"corporation":false,"usgs":true,"family":"Hostetler","given":"Steven","email":"swhostet@usgs.gov","middleInitial":"W.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":761577,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203422,"text":"70203422 - 2019 - Improving estimates of coral reef construction and erosion with in-situ measurements","interactions":[],"lastModifiedDate":"2019-09-16T12:15:11","indexId":"70203422","displayToPublicDate":"2019-04-25T12:37:50","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Improving estimates of coral reef construction and erosion with in-situ measurements","docAbstract":"The decline in living coral since the 1970s has conspicuously slowed reef construction on a global scale, but the related process of reef erosion is less visible and not often quantified. Here we present new data on the constructional and deconstructional side of the carbonate-budget equation in the Florida Keys, U.S.A. We documented Orbicella spp. calcification rates at four offshore reefs and quantified decadal-scale rates of Orbicella-reef erosion at a mid-shore patch reef. Using Orbicella coral heads fitted with permanent markers in 1998, we measured reef-elevation loss at 28 stations over 17.3 years to estimate a mean erosion rate of -5.5 (± 3.2, SD) mm yr-1. This loss equates to an erosion rate of -8.2 (± 4.8, SD) kg m-2 yr-1 on dead Orbicella colonies, or -6.6 kg m-2 yr-1 when adjusted reef-wide. Calculating net carbonate production using a census-based approach on the same patch reef in 2017, we estimated a reef-wide bioerosion rate of -1.9 (± 2.0, SD) kg m-2 yr-1, and a net carbonate production rate of 0.5 (± 0.3, SD) kg m-2 yr-1. Substituting the erosion rate we estimated with the markers would suggest that net carbonate production at this patch reef was lower and negative, -4.2 kg m-2 yr-1. This divergence could be a function of high erosion rates measured on the tops of Orbicella colonies, which may be preferentially targeted by parrotfish. Nonetheless, our study suggests the need for new field data to improve estimates of reef-structure persistence as coral reefs continue to degrade.","language":"English","publisher":"ASLO","doi":"10.1002/lno.11184","usgsCitation":"Kuffner, I.B., Toth, L., Hudson, J.H., Goodwin, W.B., Stathakopoulos, A., Bartlett, L., and Whitcher, E.M., 2019, Improving estimates of coral reef construction and erosion with in-situ measurements: Limnology and Oceanography, v. 64, no. 5, p. 2283-2294, https://doi.org/10.1002/lno.11184.","productDescription":"12 p.","startPage":"2283","endPage":"2294","ipdsId":"IP-101455","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467672,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.11184","text":"Publisher Index Page"},{"id":437483,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92NVINW","text":"USGS data release","linkHelpText":"Experimental Data on Construction and Erosion of Orbicella Coral Reefs in the Florida Keys, U.S.A."},{"id":363776,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.72430419921875,\n 24.084081797317943\n ],\n [\n -80.01068115234375,\n 24.084081797317943\n ],\n [\n -80.01068115234375,\n 26.165298896316042\n ],\n [\n -82.72430419921875,\n 26.165298896316042\n ],\n [\n -82.72430419921875,\n 24.084081797317943\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"64","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Kuffner, Ilsa B. 0000-0001-8804-7847 ikuffner@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7847","contributorId":3105,"corporation":false,"usgs":true,"family":"Kuffner","given":"Ilsa","email":"ikuffner@usgs.gov","middleInitial":"B.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":762632,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":762633,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hudson, J. Harold","contributorId":214860,"corporation":false,"usgs":false,"family":"Hudson","given":"J.","email":"","middleInitial":"Harold","affiliations":[{"id":39127,"text":"Reef Tech, Inc.","active":true,"usgs":false}],"preferred":false,"id":762634,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goodwin, William B.","contributorId":214861,"corporation":false,"usgs":false,"family":"Goodwin","given":"William","email":"","middleInitial":"B.","affiliations":[{"id":39128,"text":"NOAA Florida Keys National Marine Sanctuary,","active":true,"usgs":false}],"preferred":false,"id":762635,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stathakopoulos, Anastasios 0000-0002-4404-035X astathakopoulos@usgs.gov","orcid":"https://orcid.org/0000-0002-4404-035X","contributorId":147744,"corporation":false,"usgs":true,"family":"Stathakopoulos","given":"Anastasios","email":"astathakopoulos@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":762636,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bartlett, Lucy 0000-0001-6603-7090","orcid":"https://orcid.org/0000-0001-6603-7090","contributorId":214863,"corporation":false,"usgs":true,"family":"Bartlett","given":"Lucy","email":"","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":762637,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Whitcher, Elizabeth M.","contributorId":214862,"corporation":false,"usgs":false,"family":"Whitcher","given":"Elizabeth","email":"","middleInitial":"M.","affiliations":[{"id":17748,"text":"Florida Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":762638,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70194290,"text":"sir20175118 - 2019 - Geochemical and mineralogical maps, with interpretation, for soils of the conterminous United States","interactions":[],"lastModifiedDate":"2025-05-15T13:21:20.301081","indexId":"sir20175118","displayToPublicDate":"2019-04-25T11:25:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5118","displayTitle":"Geochemical and Mineralogical Maps, with Interpretation, for Soils of the Conterminous United States","title":"Geochemical and mineralogical maps, with interpretation, for soils of the conterminous United States","docAbstract":"Between 2007 and 2013, the U.S. Geological Survey conducted a low-density (1 site per 1,600 square kilometers, 4,857 sites) geochemical and mineralogical survey of soils in the conterminous United States. The sampling protocol for the national-scale survey included, at each site, a sample from a depth of 0 to 5 centimeters, a composite of the soil A horizon, and a deeper sample from the soil C horizon or, if the top of the C horizon was at a depth greater than 1 meter, a sample from a depth of approximately 80–100 centimeters. The <2-millimeter fraction of each sample was analyzed for a suite of 45 major and trace elements by methods that yield the total or near-total elemental concentration. The major mineralogical components in the samples from the soil A and C horizons were determined by a quantitative X-ray diffraction method using Rietveld refinement. This report presents all the maps and statistical information for each determined element and mineral along with an interpretive section discussing the possible processes that caused the observed national-scale geochemical and mineralogical patterns. Most often, the geochemical and mineralogical patterns reflect the composition of the underlying soil parent material with some modifications caused by leaching of the more mobile elements (for example, calcium and sodium) in the humid areas of the country.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175118","usgsCitation":"Smith, D.B., Solano, Federico, Woodruff, L.G., Cannon, W.F., and Ellefsen, K.J., 2019, Geochemical and mineralogical maps, with interpretation, for soils of the conterminous United States: U.S. Geological Survey Scientific Investigations Report 2017-5118, https://doi.org/10.3133/sir20175118. 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U.S. Geological Survey
Box 25046, MS 973
Denver, CO 80225
The Gulf of Mexico is home to a large proportion of the wintering population of the threatened piping plover (Charadrius melodus), but little is known about the bird's ecology in this region. In Louisiana, the majority of nonbreeding piping plovers are found on the state's rapidly eroding barrier islands. Between August 2013 and May 2014, surveys were conducted to assess the abundance and habitat use of piping plovers, as well as to characterize their invertebrate prey base, on Whiskey and Trinity islands. Seventy-eight percent of piping plovers observed were foraging, 18% roosting, and 4% engaged in other ambulatory activities. Intertidal habitat, such as foreshore beach and tidal flats, was used by 87% of foraging and 96% of roosting piping plovers. Though available, backshore beach, interior sand flats, and dunes were rarely used. The invertebrate community was dominated by haustoriid amphipods (87.5% of individuals collected), followed by bivalves (9.3%) and polychaetes (2.7%). Seasonal patterns and between-island differences were observed in all three invertebrate taxa, but these effects differed between beach habitat and the gulfside and bayside of prominent sand spits. Moisture had a positive effect on amphipod abundance and polychaete presence. There was no association between invertebrate and plover abundance, and prey abundance did not differ between sample sites where piping plovers were observed foraging and random sites. The low abundances of birds and prey, coupled with high variation among samples, are challenges for establishing baseline datasets to evaluate the consequences of coastal restoration activities.
","language":"English","publisher":"BioOne","doi":"10.2112/JCOASTRES-D-17-00147.1","usgsCitation":"Schulz, J.L., and Leberg, P., 2019, Factors affecting prey availability and habitat usage of nonbreeding piping plovers (Charadrius melodus) in coastal Louisiana: Journal of Coastal Research, v. 35, no. 4, p. 861-871, https://doi.org/10.2112/JCOASTRES-D-17-00147.1.","productDescription":"11 p.","startPage":"861","endPage":"871","ipdsId":"IP-089847","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":437485,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RJ4GP7","text":"USGS data release","linkHelpText":"Factors affecting prey availability and habitat use of nonbreeding piping plovers (Charadrius melodus) in coastal Louisiana"},{"id":365573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","county":"Terrebonne Parish","otherGeospatial":"Isles Dernières Barrier Island Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.021728515625,\n 28.954080659357132\n ],\n [\n -90.42022705078125,\n 28.954080659357132\n ],\n [\n -90.42022705078125,\n 29.28160772298835\n ],\n [\n -91.021728515625,\n 29.28160772298835\n ],\n [\n -91.021728515625,\n 28.954080659357132\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"4","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schulz, Jessica L. 0000-0002-8311-9423 jschulz@usgs.gov","orcid":"https://orcid.org/0000-0002-8311-9423","contributorId":200299,"corporation":false,"usgs":true,"family":"Schulz","given":"Jessica","email":"jschulz@usgs.gov","middleInitial":"L.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":766166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leberg, Paul","contributorId":216903,"corporation":false,"usgs":false,"family":"Leberg","given":"Paul","affiliations":[{"id":7155,"text":"University of Louisiana at Lafayette","active":true,"usgs":false}],"preferred":false,"id":766167,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203386,"text":"70203386 - 2019 - Arsenic concentrations after drinking water well installation: Time-varying effects on arsenic mobilization","interactions":[],"lastModifiedDate":"2019-06-18T12:02:06","indexId":"70203386","displayToPublicDate":"2019-04-25T09:33:18","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Arsenic concentrations after drinking water well installation: Time-varying effects on arsenic mobilization","docAbstract":"Chronic exposure to geogenic arsenic via drinking water is a worldwide health concern. However, effects of well installation and operation on arsenic concentrations and mobilization are not well understood. This knowledge gap impacts both reliable detection of arsenic in drinking water and effective public health recommendations to reduce exposure to arsenic. This study examines changes in arsenic and redox geochemistry over one year following installation of 254 new domestic water wells in three regions of the north-central USA that commonly have elevated arsenic concentrations. Our regions' geologic settings share some important characteristics with other high-arsenic aquifers: igneous bedrock aquifers; or late Pleistocene-age glacial sand and gravel aquifers interbedded with aquitards. Over the study, arsenic concentrations increased by 16% or more in 25% of wells in glacial aquifer regions, and the redox conditions changed towards more reducing. In wells in the bedrock region, there was no significant change in arsenic concentrations, and redox conditions changed towards more oxidizing. Our findings illustrate the importance of understanding short- to moderate-term impacts of well installation and operation on arsenic and aqueous chemistry, as it relates to human exposure. Our study informs water quality sampling requirements, which currently do not consider the implications sampling timing with respect to well installation. Evaluating arsenic concentrations in samples from new wells in the context of general regional pH and redox conditions can provide information regarding the degree of disequilibrium created by well drilling. Our analysis approach may be transferable and scalable to similar aquifer settings across the globe.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.04.362","usgsCitation":"Erickson, M., Malenda, H.F., Berquist, E.C., and Ayotte, J.D., 2019, Arsenic concentrations after drinking water well installation: Time-varying effects on arsenic mobilization: Science of the Total Environment, v. 678, p. 681-691, https://doi.org/10.1016/j.scitotenv.2019.04.362.","productDescription":"11 p.","startPage":"681","endPage":"691","ipdsId":"IP-090484","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":467673,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2019.04.362","text":"Publisher Index Page"},{"id":363660,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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","language":"English","publisher":"National Park Service","usgsCitation":"Cullinane Thomas, C., Koontz, L., and Cornachione, E., 2019, 2018 National Park visitor spending effects: Economic contributions to local communities, states, and the nation, v, 55 p.","productDescription":"v, 55 p.","numberOfPages":"64","ipdsId":"IP-107948","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":365359,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":365358,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.nps.gov/subjects/socialscience/vse.htm"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cullinane Thomas, Catherine 0000-0001-8168-1271 ccullinanethomas@usgs.gov","orcid":"https://orcid.org/0000-0001-8168-1271","contributorId":141097,"corporation":false,"usgs":true,"family":"Cullinane Thomas","given":"Catherine","email":"ccullinanethomas@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":765391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koontz, Lynne koontzl@usgs.gov","contributorId":2174,"corporation":false,"usgs":false,"family":"Koontz","given":"Lynne","email":"koontzl@usgs.gov","affiliations":[{"id":7016,"text":"Environmental Quality Division, National Park Service, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":765392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cornachione, Egan 0000-0001-9248-4118","orcid":"https://orcid.org/0000-0001-9248-4118","contributorId":216701,"corporation":false,"usgs":false,"family":"Cornachione","given":"Egan","email":"","affiliations":[{"id":27102,"text":"USGS student contractor","active":true,"usgs":false}],"preferred":false,"id":765393,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70203211,"text":"70203211 - 2019 - Cloud cover and delayed herbivory relative to timing of spring onset interact to dampen climate change impacts on net ecosystem exchange in a coastal Alaskan wetland","interactions":[],"lastModifiedDate":"2019-09-18T15:24:01","indexId":"70203211","displayToPublicDate":"2019-04-25T08:23:14","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Cloud cover and delayed herbivory relative to timing of spring onset interact to dampen climate change impacts on net ecosystem exchange in a coastal Alaskan wetland","docAbstract":"Rapid warming in northern ecosystems over the past four decades has resulted in earlier spring, increased precipitation, and altered timing of plant–animal interactions, such as herbivory. Advanced spring phenology can lead to longer growing seasons and increased carbon (C) uptake. Greater precipitation coincides with greater cloud cover possibly suppressing photosynthesis. Timing of herbivory relative to spring phenology influences plant biomass. None of these changes are mutually exclusive and their interactions could lead to unexpected consequences for Arctic ecosystem function. We examined the influence of advanced spring phenology, cloud cover, and timing of grazing on C exchange in the Yukon–Kuskokwim Delta of western Alaska for three years. We combined advancement of the growing season using passive-warming open-top chambers (OTC) with controlled timing of goose grazing (early, typical, and late season) and removal of grazing. We also monitored natural variation in incident sunlight to examine the C exchange consequences of these interacting forcings. We monitored net ecosystem exchange of C (NEE) hourly using an autochamber system. Data were used to construct daily light curves for each experimental plot and sunlight data coupled with a clear-sky model was used to quantify daily and seasonal NEE over a range of incident sunlight conditions. Cloudy days resulted in the largest suppression of NEE, reducing C uptake by approximately 2 g C m−2 d−1 regardless of the timing of the season or timing of grazing. Delaying grazing enhanced C uptake by approximately 3 g C m−2 d−1. Advancing spring phenology reduced C uptake by approximately 1.5 g C m−2 d−1, but only when plots were directly warmed by the OTCs; spring advancement did not have a long-term influence on NEE. Consequently, the two strongest drivers of NEE, cloud cover and grazing, can have opposing effects and thus future growing season NEE will depend on the magnitude of change in timing of grazing and incident sunlight.
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Using real-time data-driven computation of drift ratios as the parametric indicator of structural deformation and damage to a structure could be of great value to minimize potential judgmental errors in such assessments. Recorded sensor data are an indication of performance, and performance-based design standards stipulate that the amplitude of relative displacement of a building’s roof (with respect to its base) indicates performance. Establishing sound criteria for performance is the most important issue for S2HM process, and since 2000 (in the USA), using real-time computed drift ratios and acceptable threshold criteria form the basis for almost all applications in S2HM.
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0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":854083,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":852330,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70203185,"text":"70203185 - 2019 - Comment on “Particle fluxes in groundwater change subsurface rock chemistry over geologic time”","interactions":[],"lastModifiedDate":"2019-04-25T06:29:42","indexId":"70203185","displayToPublicDate":"2019-04-25T06:23:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Comment on “Particle fluxes in groundwater change subsurface rock chemistry over geologic time”","docAbstract":"Over the last decade, studies at the Shale Hills Critical Zone Observatory (Shale Hills) have greatly expanded knowledge of weathering in previously understudied, shale-mantled terrains, as well as Earth's Critical Zone as a whole. Among the many discoveries made was the importance of redistribution and losses of micron-sized particles during development of shale-derived soils. A geochemical fingerprint of this process for Al and Fe was illustrated quantitatively by Jin et al. (2010). Subsequent papers, too numerous to list in a Comment, built upon this new recognition by evaluating the spatial and temporal aspects element mobilization. Recently, Kim et al. (2018) examined the composition of suspended, generally micron-sized particles in the Shale Hills stream, along with the dissolved load, across seasons and ranges of discharge.
One prominent conclusion from Kim et al. (2018) is that Zr is essentially immobile at Shale Hills. Such a broad conclusion is in direct contradiction with one from Bern and Yesavage (2018) that Zr has been mobilized from soils at Shale Hills, and the losses relative to soil parent material are significant (median 41%). The point is important, because assuming Zr immobility is necessary to index gains and losses of other elements using the open-chemical-system transport function (τ). Both papers draw upon patterns and calculations using elemental concentration data from Shale Hills and attempt to construct conceptual frameworks to explain the results. Here, the argument is made that the understanding of substantial Zr mobility from soils at Shale Hills described by Bern and Yesavage (2018) is more accurate. Additionally, issues with adaptations of the standard τ equations used in Kim et al. (2018) and some previous papers are also addressed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2019.02.014","usgsCitation":"Bern, C.R., and Yesavage, T., 2019, Comment on “Particle fluxes in groundwater change subsurface rock chemistry over geologic time”: Earth and Planetary Science Letters, v. 514, p. 166-168, https://doi.org/10.1016/j.epsl.2019.02.014.","productDescription":"3 p.","startPage":"166","endPage":"168","ipdsId":"IP-102182","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":363221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Shale Hills Critical Zone Observatory ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.17596435546875,\n 40.4323142901375\n ],\n [\n -77.33001708984375,\n 40.4323142901375\n ],\n [\n -77.33001708984375,\n 40.967455873296714\n ],\n [\n -78.17596435546875,\n 40.967455873296714\n ],\n [\n -78.17596435546875,\n 40.4323142901375\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"514","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bern, Carleton R. 0000-0002-8980-1781 cbern@usgs.gov","orcid":"https://orcid.org/0000-0002-8980-1781","contributorId":201152,"corporation":false,"usgs":true,"family":"Bern","given":"Carleton","email":"cbern@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":761537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yesavage, Tiffany 0000-0001-9433-763X","orcid":"https://orcid.org/0000-0001-9433-763X","contributorId":215057,"corporation":false,"usgs":false,"family":"Yesavage","given":"Tiffany","email":"","affiliations":[{"id":39167,"text":"USGS Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":false}],"preferred":false,"id":761538,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203164,"text":"70203164 - 2019 - Calcrete uranium deposits in the Southern High Plains, USA","interactions":[],"lastModifiedDate":"2019-04-25T05:57:51","indexId":"70203164","displayToPublicDate":"2019-04-25T05:53:27","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2954,"text":"Ore Geology Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Calcrete uranium deposits in the Southern High Plains, USA","docAbstract":"The Southern High Plains (SHP) is a new and emerging U.S. uranium province. Here, uranyl vanadates form deposits in Pliocene to Pleistocene sandstone, dolomite, and limestone. Fifteen calcrete uranium occurrences are identified; two of these, the Buzzard Draw and Sulfur Springs Draw deposits, have combined in-place resources estimated at about 4 million pounds of U3O8. Ore minerals carnotite and finchite are hosted in dolomite at the Sulfur Springs Draw deposit, with accessory fluorite, celestine, smectite/illite, autunite, and strontium carbonate. Host carbonate at the Sulfur Springs Draw deposit is ∼190 ka and mineralization mobilized as recently as 3.8 ka. Ash collected near the deposit is 631 ka and erupted from the Yellowstone caldera complex. The Triassic Dockum Group that contains sandstone-hosted uranium deposits throughout the region and underlies the SHP is a potential source for uranium and vanadium. Regional uplift and dissection reintroduced oxygenated groundwater into the Dockum Group, mobilizing uranium. Additional uranium may have been contributed to groundwater by weathering of volcanic ash in Pliocene and Pleistocene host rocks. The locations of the uranium occurrences are mostly in modern drainage systems in the southeast portion of the SHP. Modelling of modern groundwater in the SHP carried out in a parallel study shows that a single fluid could form carnotite through evaporation, and that fluids of the requisite composition are more prevalent in the southern portion of the SHP. The southeastern portion of the SHP hosts more uranium occurrences due to a variety of factors including (1) upward transport of groundwater and connectivity between source and host rock, (2) higher uranium and vanadium content of groundwater, (3) higher rates of groundwater recharge in this region to drive the mineralizing system, and (4) shallower groundwater facilitating surface evaporation. Ongoing erosion of host rocks challenges preservation of deposits and may limit their size.
Flux quantification for riverine water-quality constituents has been an active area of research. Statistical approaches are often employed to make estimation for days without observations. One such approach is the Weighted Regressions on Time, Discharge, and Season (WRTDS) method. While WRTDS has been used in many investigations, there is a general lack of effort to identify factors that influence its estimation bias. This work was aimed to (1) synthesize and compare WRTDS estimation bias for constituent concentrations and fluxes for rivers and streams in the Chesapeake Bay watershed (including headwater sites) and (2) identify controlling factors from five broad categories (watershed size, sampling practice, concentration and discharge conditions, land use, and geology). Five major constituents were considered, namely, suspended sediment (SS), total phosphorus (TP), total nitrogen (TN), orthophosphate (PO4), and nitrate-plus-nitrite (NOx). For both concentration and flux, estimation bias follows the general order of SS > TP > PO4 > TN ≈ NOx. Median TN and NOx bias statistics were near zero, with an equal distribution of small positive and negative bias. TP, PO4, and SS each showed a median positive bias across sites of <18% for flux and <7% for concentration. Particulate constituents, especially SS, tend to have larger bias at sites with smaller sampling frequencies, shorter sampling record lengths, and smaller watershed sizes. Results of multivariate models showed that both flux and concentration biases are most affected by concentration and discharge variabilities and the length of concentration record. In comparison, flux bias of particulate constituents is more affected by flow variability, whereas flux bias of dissolved constituents is more affected by concentration variability. Moreover, analysis using classification and regression trees provided additional information on how the factors affected flux bias: when all site-constituent combinations are considered, large flux biases are more likely associated with sites that have large concentration and discharge variabilities, small lengths of concentration record, and small sampling frequencies. These results may be useful for identifying sites with large biases, modifying monitoring practice at existing sites to reduce those biases, and choosing new monitoring locations in the Chesapeake watershed and beyond.
The Little Colorado River in Arizona, U.S.A. has undergone substantial geomorphic change since the early 1900s. We analyzed hydrologic and geomorphic data at different spatial and temporal scales to determine the type, magnitude, and rate of geomorphic change that has occurred since the early 20th century. Since the 1920s, there have been 4 alternating periods of high and low total-annual flow. Peak-flow magnitude, however, has progressively declined. In some reaches, the channel has narrowed between 72 and 88% since the 1930s. Increases in sinuosity in wide alluvial valleys have resulted in reductions in channel slope by ~21 to 32%; channel bed aggradation up to 1.4 m has also occurred in some reaches. Newly developed floodplains have been colonized by dense stands of vegetation that appear to have stabilized these surfaces. Large, long duration floods may cause some channel widening, and meander migration, however, these floods are infrequent, and narrowing resumes shortly thereafter. Channel narrowing, increases in sinuosity, decreases in slope, and increases in vegetative roughness appear to have caused biogeomorphic feedbacks, thereby exacerbating sediment deposition, and disrupting flood conveyance. In recent decades, there has been an increase in the travel time of floods up to ~100% compared to floods of the 1940s and 1950s, and this has likely led to increased flood attenuation, contributing to decreases in peak-flow magnitude. The progressive increase in water development in parts of the basin has also likely played some role in the progressive declines in peak flow over the duration of the study.
","language":"English","publisher":"The Geological Society of America","doi":"10.1130/B35047.1","usgsCitation":"Dean, D.J., and Topping, D.J., 2019, Geomorphic change and biogeomorphic feedbacks in a dryland river: The Little Colorado River, Arizona, USA: GSA Bulletin, Repository Item: 2019158; 23 p., https://doi.org/10.1130/B35047.1.","productDescription":"Repository Item: 2019158; 23 p.","ipdsId":"IP-099021","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":437486,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XPWIBM","text":"USGS data release","linkHelpText":"Geomorphic Change Data for the Little Colorado River, Arizona, USA"},{"id":363278,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Little Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.412353515625,\n 35.54116627999815\n ],\n [\n -107.830810546875,\n 35.54116627999815\n ],\n [\n -107.830810546875,\n 37.13404537126446\n ],\n [\n -111.412353515625,\n 37.13404537126446\n ],\n [\n -111.412353515625,\n 35.54116627999815\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Dean, David J. 0000-0003-0203-088X djdean@usgs.gov","orcid":"https://orcid.org/0000-0003-0203-088X","contributorId":215067,"corporation":false,"usgs":true,"family":"Dean","given":"David","email":"djdean@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":761569,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Topping, David J. 0000-0002-2104-4577","orcid":"https://orcid.org/0000-0002-2104-4577","contributorId":215068,"corporation":false,"usgs":true,"family":"Topping","given":"David","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":761570,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203195,"text":"70203195 - 2019 - Modeling barrier island habitats using landscape position information","interactions":[],"lastModifiedDate":"2019-08-19T16:53:07","indexId":"70203195","displayToPublicDate":"2019-04-24T16:23:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Modeling barrier island habitats using landscape position information","docAbstract":"Barrier islands are dynamic environments because of their position along the marine–estuarine interface. Geomorphology influences habitat distribution on barrier islands by regulating exposure to harsh abiotic conditions. Researchers have identified linkages between habitat and landscape position, such as elevation and distance from shore, yet these linkages have not been fully leveraged to develop predictive models. Our aim was to evaluate the performance of commonly used machine learning algorithms, including K-nearest neighbor, support vector machine, and random forest, for predicting barrier island habitats using landscape position for Dauphin Island, Alabama, USA. Landscape position predictors were extracted from topobathymetric data. Models were developed for three tidal zones: subtidal, intertidal, and supratidal/upland. We used a contemporary habitat map to identify landscape position linkages for habitats, such as beach, dune, woody vegetation, and marsh. Deterministic accuracy, fuzzy accuracy, and hindcasting were used for validation. The random forest algorithm performed best for intertidal and supratidal/upland habitats, while the K-nearest neighbor algorithm performed best for subtidal habitats. A posteriori application of expert rules based on theoretical understanding of barrier island habitats enhanced model results. For the contemporary model, deterministic overall accuracy was nearly 70%, and fuzzy overall accuracy was over 80%. For the hindcast model, deterministic overall accuracy was nearly 80%, and fuzzy overall accuracy was over 90%. We found machine learning algorithms were well-suited for predicting barrier island habitats using landscape position. Our model framework could be coupled with hydrodynamic geomorphologic models for forecasting habitats with accelerated sea-level rise, simulated storms, and restoration actions.","language":"English","publisher":"MDPI","doi":"10.3390/rs11080976","usgsCitation":"Enwright, N., Lei Wang, Wang, H., Osland, M., Feher, L., Borchert, S., and Day, R., 2019, Modeling barrier island habitats using landscape position information: Remote Sensing, v. 11, no. 8, Article 976; 24 p., https://doi.org/10.3390/rs11080976.","productDescription":"Article 976; 24 p.","ipdsId":"IP-105601","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":460395,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11080976","text":"Publisher Index Page"},{"id":437488,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90MACYS","text":"USGS data release","linkHelpText":"Modeling barrier island habitats using landscape position information for Dauphin Island, Alabama"},{"id":363276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"8","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":215077,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lei Wang","contributorId":215078,"corporation":false,"usgs":false,"family":"Lei Wang","affiliations":[{"id":39170,"text":"Department of Geography and Anthropology, Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":761586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Hongqing 0000-0002-2977-7732 wangh@usgs.gov","orcid":"https://orcid.org/0000-0002-2977-7732","contributorId":215079,"corporation":false,"usgs":true,"family":"Wang","given":"Hongqing","email":"wangh@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":215080,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761588,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Feher, Laura 0000-0002-5983-6190","orcid":"https://orcid.org/0000-0002-5983-6190","contributorId":215081,"corporation":false,"usgs":true,"family":"Feher","given":"Laura","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761589,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Borchert, Sinéad M. 0000-0002-6665-7115","orcid":"https://orcid.org/0000-0002-6665-7115","contributorId":193278,"corporation":false,"usgs":false,"family":"Borchert","given":"Sinéad M.","affiliations":[],"preferred":false,"id":761590,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Day, Richard 0000-0002-5959-7054","orcid":"https://orcid.org/0000-0002-5959-7054","contributorId":215082,"corporation":false,"usgs":true,"family":"Day","given":"Richard","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761591,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202128,"text":"ofr20191010 - 2019 - Geochemistry and mineralogy of soils collected in the lower Rio Grande valley, Texas","interactions":[],"lastModifiedDate":"2019-04-26T15:38:27","indexId":"ofr20191010","displayToPublicDate":"2019-04-24T14:35:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1010","displayTitle":"Geochemistry and Mineralogy of Soils Collected in the Lower Rio Grande Valley, Texas","title":"Geochemistry and mineralogy of soils collected in the lower Rio Grande valley, Texas","docAbstract":"Presented in this report are the chemical and mineralogical results of a soil study conducted in the lower Rio Grande valley, Texas. Samples were collected from soils formed on Holocene alluvial flood-plain and distributary channel deposits of the Rio Grande, flood plain and meander-belt deposits of the Pliocene Goliad Formation, and the Pleistocene Lissie and Beaumont Formations. The lower Rio Grande valley is located on the old distributary delta of the Rio Grande. The watersheds on the U.S. side of the delta no longer drain into the Rio Grande but are part of a complex system of irrigation channels and wastewater drains that flow into the lower Laguna Madre. The results of the study have been used to map concealed geologic units and identify potential mosquito breeding habitat.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191010","collaboration":" ","usgsCitation":"Whitney, H.A., Solano, F., and Hubbard, B.E., 2019, Geochemistry and mineralogy of soils collected in the lower Rio Grande valley, Texas: U.S. Geological Survey Open-File Report 2019–1010, 92 p., https://doi.org/10.3133/ofr20191010.","productDescription":"Report: v, 92 p.; 6 Tables","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-062701","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":363123,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1010/coverthb.jpg"},{"id":363124,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1010"},{"id":363125,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table01.xlsx","text":"Table 1","size":"70.9 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Geochemical analyses of soil samples collected in 2003–04, by element and method of analysis, lower Rio Grande valley, Texas\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":363126,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table02.xlsx","text":"Table 2","size":"64.1 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Geochemical analyses of soil samples collected in 2007, by element and method of analysis, lower Rio Grande valley, Texas"},{"id":363127,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table03.xlsx","text":"Table 3","size":"18.1 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Univariate statistics and percentiles of analytical results for soil samples collected in 2003 and 2004, lower Rio Grande valley, Texas"},{"id":363128,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table04.xlsx","text":"Table 4","size":"19.1 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Univariate statistics and percentiles of analytical results for soil samples collected in 2007, lower Rio Grande valley, Texas"},{"id":363129,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table05.xlsx","text":"Table 5","size":"31.4 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Mineralogy of all soil samples collected in 2003, 2004, and 2007, lower Rio Grande valley, Texas"},{"id":363130,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table06.xlsx","text":"Table 6","size":"16.4 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Summary statistics of mineral content of soils by geologic formation (Page and others, 2005) as determined by x‐ray diffraction"}],"country":"United States","state":"Texas","otherGeospatial":"Rio Grande Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.1845703125,\n 25.686087780724858\n ],\n [\n -97.1136474609375,\n 25.686087780724858\n ],\n [\n -97.1136474609375,\n 26.76277822801415\n ],\n [\n -99.1845703125,\n 26.76277822801415\n ],\n [\n -99.1845703125,\n 25.686087780724858\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Mineral and Energy Resources Center
U.S. Geological Survey
MS 954 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
The Precipitation-Runoff Modeling System (PRMS) is widely used to simulate the effects of climate, topography, land cover, and soils on landscape-level hydrologic responses and streamflow. The U.S. Geological Survey (USGS), in cooperation with the New Mexico Department of Homeland Security and Emergency Management, developed procedures to apply the PRMS model to simulate the effects of fire on hydrologic responses.
A PRMS model was built of the upper Rio Hondo Basin from the headwaters to approximately 19 miles downstream from the USGS streamgage Rio Hondo above Chavez Canyon near Hondo, New Mexico, by using 24 hydrologic response units (HRUs), or hydrologically similar subareas, from the National Hydrologic Model. A quasi-graphical user interface was created to easily query and analyze published PRMS sensitivity-analysis data. Simulation of mean daily streamflow was most sensitive to parameters related to snowmelt or infiltration throughout the upper Rio Hondo Basin. In the basin’s eastern and northern HRUs, flashiness and timing of streamflow were most sensitive to interflow; in many western-basin HRUs (higher elevations), flashiness of streamflow was most sensitive to soil moisture parameters, and timing of streamflow was most sensitive to infiltration and evapotranspiration parameters.
The PRMS model was calibrated for the fire-affected North Fork Eagle Creek subwatershed by comparing modeled to observed daily streamflow for the nonfrozen (May through October) period for a prefire and postfire time period. The prefire model was calibrated for the period 2007–12 before the 2012 fire, and the postfire model was calibrated for a 2-year (2014–15) period after the fire. Model parameterization combined manual adjustment of 8 parameters on the basis of prior knowledge and automated adjustment of the most sensitive parameters by using the Let Us Calibrate interface. A gridded, daily precipitation dataset that captured the spatial heterogeneity across the study watershed was used as the precipitation input for calibration. Model performance was assessed as satisfactory by using standard statistical measures for prefire and postfire periods.
The calibrated model was run by using data from a single precipitation gage to better represent the effect of localized, extreme storms on postfire hydrologic response. The calibrated models for prefire and postfire conditions simulated streamflows with greater consistency than the uncalibrated model for the corresponding (prefire or postfire) period of hydrographic record. The effect of fire on streamflow was found to be primarily a shift from streamflow dominated by base flow prior to fire to streamflow dominated by surface runoff after fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195022","collaboration":"Prepared in cooperation with the New Mexico Department of Homeland Security and Emergency Management","usgsCitation":"Douglas-Mankin, K.R., and Moeser, C.D., 2019, Calibration of Precipitation-Runoff Modeling System (PRMS) to simulate prefire and postfire hydrologic response in the upper Rio Hondo Basin, New Mexico: U.S. Geological Survey Scientific Investigations Report 2019–5022, 25 p., https://doi.org/10.3133/sir20195022.","productDescription":"Report: vi, 25 p.; Data Release","numberOfPages":"36","ipdsId":"IP-094970","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":363146,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7KD1X7Q","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Model input and output for prefire and postfire hydrologic simulations in the Upper Rio Hondo Basin, New Mexico using the Precipitation-Runoff Modeling System (PRMS)"},{"id":363157,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5022/coverthb2.jpg"},{"id":363145,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5022/sir20195022.pdf","text":"Report","size":"2.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5022"}],"country":"United States","state":"New Mexico","county":"Lincoln County, Otero County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.83610534667969,\n 33.33741240611175\n ],\n [\n -105.74203491210938,\n 33.33741240611175\n ],\n [\n -105.74203491210938,\n 33.465816745730024\n ],\n [\n -105.83610534667969,\n 33.465816745730024\n ],\n [\n -105.83610534667969,\n 33.33741240611175\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, New Mexico 87113