{"pageNumber":"275","pageRowStart":"6850","pageSize":"25","recordCount":46681,"records":[{"id":70204701,"text":"sir20195083 - 2019 - Flood-inundation maps for Nimishillen Creek near North Industry, Ohio, 2019","interactions":[],"lastModifiedDate":"2019-10-10T14:57:22","indexId":"sir20195083","displayToPublicDate":"2019-10-10T13:58:55","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":"2019-5083","displayTitle":"Flood-Inundation Maps for Nimishillen Creek near North Industry, Ohio, 2019","title":"Flood-inundation maps for Nimishillen Creek near North Industry, Ohio, 2019","docAbstract":"<p>Digital flood-inundation maps for a 4-mile reach of Nimishillen Creek near North Industry, Ohio, were created by the U.S. Geological Survey (USGS) in cooperation with the Muskingum Watershed Conservancy District, Ohio, and the Stark County Board of Commissioners. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping (FIM) Program website at <a data-mce-href=\"https://water.usgs.gov/osw/flood_inundation/\" href=\"https://water.usgs.gov/osw/flood_inundation/\">https://water.usgs.gov/osw/flood_inundation/</a>, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on Nimishillen Creek at North Industry, Ohio (station number 03118500). Near-real-time stages at this streamgage can be obtained on the internet from the USGS National Water Information System at <a data-mce-href=\"https://waterdata.usgs.gov/\" href=\"https://waterdata.usgs.gov/\">https://waterdata.usgs.gov/</a> or the National Weather Service Advanced Hydrologic Prediction Service at <a data-mce-href=\"https://water.weather.gov/ahps/\" href=\"https://water.weather.gov/ahps/\">https://water.weather.gov/ahps/</a>, which also forecasts flood hydrographs at this site.</p><p>Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated to the current stage-discharge relation at the streamgage on Nimishillen Creek at North Industry and documented high-water marks from the flood of January 12, 2017.</p><p>The hydraulic model was then used to compute seven water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 8 to 14 ft, which is from “action stage” to above “major flood stage” as reported by the National Weather Service. The simulated water-surface profiles were then used in combination with a geographic information system (GIS) digital elevation model derived from light detection and ranging data to delineate the areas flooded at each water level.</p><p>The availability of these maps, along with internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts. Forecasts for the USGS streamgage on Nimishillen Creek at North Industry, Ohio are issued as needed during times of high water, but are not routinely available (National Weather Service, 2017).</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195083","collaboration":"Prepared in cooperation with the Muskingum Watershed Conservancy District, Ohio, and the Stark County Board of Commissioners","usgsCitation":"Whitehead, M.T., 2019, Flood-inundation maps for Nimishillen Creek near North Industry, Ohio, 2019: U.S. Geological Survey Scientific Investigations Report 2019–5083, 11 p., https://doi.org/10.3133/sir20195083.\n","productDescription":"Report: vi, 11 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-104812","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368076,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WFOVN2","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets and hydraulic model for flood-inundation maps of Nimishillen Creek near North Industry, Ohio:"},{"id":368075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5083/sir20195083.pdf","text":"Report","size":"14.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5083"},{"id":368074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5083/coverthb.jpg"}],"country":"United States","state":"Ohio","county":"Stark County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-81.0864,40.9879],[-81.0865,40.9839],[-81.0866,40.978],[-81.0869,40.9013],[-81.0873,40.728],[-81.0922,40.7285],[-81.1001,40.7281],[-81.1989,40.7292],[-81.1991,40.7224],[-81.2373,40.7237],[-81.241,40.6507],[-81.2755,40.651],[-81.2791,40.6511],[-81.304,40.6518],[-81.3173,40.6519],[-81.4372,40.6529],[-81.4365,40.6584],[-81.4395,40.6625],[-81.4467,40.6657],[-81.4589,40.6654],[-81.4675,40.6555],[-81.6489,40.6346],[-81.6491,40.6681],[-81.6483,40.7371],[-81.648,40.9145],[-81.4201,40.9064],[-81.4164,40.9889],[-81.3932,40.9887],[-81.1059,40.9882],[-81.0925,40.988],[-81.0864,40.9879]]]},\"properties\":{\"name\":\"Stark\",\"state\":\"OH\"}}]}","contact":"<p>Director,&nbsp;<a data-mce-href=\"https://www.usgs.gov/centers/oki-water\" href=\"https://www.usgs.gov/centers/oki-water\">Ohio-Kentucky-Indiana Water Science Center</a><br>U.S. Geological Survey<br>6460 Busch Boulevard <br>Columbus OH 43229–1753 <br></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Creation of Flood-Inundation-Map Library</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2019-10-10","noUsgsAuthors":false,"publicationDate":"2019-10-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Whitehead, Matthew T. 0000-0002-4888-2597 mtwhiteh@usgs.gov","orcid":"https://orcid.org/0000-0002-4888-2597","contributorId":218036,"corporation":false,"usgs":true,"family":"Whitehead","given":"Matthew T.","email":"mtwhiteh@usgs.gov","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768122,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70205929,"text":"70205929 - 2019 - Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley, California","interactions":[],"lastModifiedDate":"2022-07-21T13:48:01.291782","indexId":"70205929","displayToPublicDate":"2019-10-10T13:39:19","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2027,"text":"International Journal of Applied Earth Observation and Geoinformation","active":true,"publicationSubtype":{"id":10}},"title":"Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley, California","docAbstract":"Accurate monitoring of surface water location and extent is critical for the management of diverse water resource phenomena. The multi-decadal archive of Landsat satellite imagery is punctuated by missing data due to cloud cover during acquisition times, hindering the assembly of a continuous time series of inundation dynamics. This study investigated whether streamflow volume measurements could be integrated with satellite data to fill gaps in monthly surface water chronologies for the Central Valley region of California, USA, from 1984 to 2015.  We aggregated measurements of maximum monthly water extent within each of the study area’s 50 8-digit hydrologic unit code [HUC] watersheds from two Landsat-derived datasets: the European Commission’s Joint Research Centre (JRC) Monthly Water History and the U.S. Geological Survey Dynamic Surface Water Extent (DSWE).  We calculated Spearman rank correlation coefficients between water extent values in each HUC and streamflow discharge data.  Linear regression fits of the water extent/streamflow data pairs with the highest correlations served as the basis for interpolation of missing imagery surface water values on a HUC-wise basis.  Results show strong (ρ > 0.7) maximum correlations in 11 (22.4%) and 25 (51.0%) HUCs for the DSWE and JRC time series, respectively, when comparisons were restricted to imagery and gages co-located in each HUC. Strong maximum correlations occurred in 39 (79.6%; DSWE) and 42 (85.7%; JRC) HUCs when imagery was paired with discharge data from any study area gage, providing a solid basis for reconstruction of water extent values. We generated continuous time series of 30+ years in 35 HUCs, demonstrating that this technique can provide quantitative estimates of historical surface water extents and elucidate flooding or drought events over the period of data collection.  Results of a non-parametric trend analysis of the long-term time series on an annual, seasonal, and monthly basis varied among HUCs, though most trends indicate an increase in surface water over the past 30 years.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2019.101973","usgsCitation":"Walker, J., Soulard, C.E., and Petrakis, R.E., 2019, Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley, California: International Journal of Applied Earth Observation and Geoinformation, v. 84, 101973, 13 p.; Data Release, https://doi.org/10.1016/j.jag.2019.101973.","productDescription":"101973, 13 p.; Data Release","ipdsId":"IP-110207","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":459569,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doaj.org/article/4da62e7b2b8b4e95ab645ffcc5de6106","text":"Publisher Index Page"},{"id":368237,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":404209,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XPA5AK"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.87158203125,\n              41.5579215778042\n            ],\n            [\n              -123.53027343749999,\n              41.52502957323801\n            ],\n            [\n              -123.24462890625,\n              39.57182223734374\n            ],\n            [\n              -122.2119140625,\n              37.70120736474139\n            ],\n            [\n              -120.38818359375,\n              36.491973470593685\n            ],\n            [\n              -119.24560546875001,\n              34.70549341022544\n            ],\n            [\n              -116.65283203124999,\n              35.94243575255426\n            ],\n            [\n              -119.42138671875,\n              38.11727165830543\n            ],\n            [\n              -120.43212890625,\n              39.53793974517628\n            ],\n            [\n              -120.38818359375,\n              41.0130657870063\n            ],\n            [\n              -120.87158203125,\n              41.5579215778042\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"84","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Walker, Jessica J. 0000-0002-3225-0317","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":207373,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":772925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":772926,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Petrakis, Roy E. 0000-0001-8932-077X","orcid":"https://orcid.org/0000-0001-8932-077X","contributorId":219707,"corporation":false,"usgs":false,"family":"Petrakis","given":"Roy","email":"","middleInitial":"E.","affiliations":[{"id":27608,"text":"Contractor to the USGS","active":true,"usgs":false}],"preferred":false,"id":772927,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70208293,"text":"70208293 - 2019 - Assessing the feasibility of satellite-based thresholds for hydrologically driven landsliding","interactions":[],"lastModifiedDate":"2020-02-03T12:41:42","indexId":"70208293","displayToPublicDate":"2019-10-10T12:37:49","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":"Assessing the feasibility of satellite-based thresholds for hydrologically driven landsliding","docAbstract":"Elevated soil moisture and heavy precipitation contribute to landslides worldwide. These environmental variables are now being resolved with satellites at spatiotemporal scales that could offer new perspectives on the development of landslide warning systems. However, the application of these data to hydro-meteorological thresholds (which account for antecedent soil moisture and rainfall) first need to be evaluated with respect to proven, direct measurement-based thresholds that use rain gauges and in situ soil moisture sensors. Here, we compare ground-based hydrologic data to overlapping satellite-based data before, during, and after a recent season of widespread shallow landsliding in the San Francisco Bay Area (California, USA). We then explore how the remotely sensed information could be used to empirically define hypothetical thresholds for shallow landsliding. We find that the ground-based thresholds developed with a single monitoring station show superior performance because the in situ soil saturation data better reflect the gravity-dominated subsurface flow conditions that are characteristic of hillslopes during the rainy season. Although the satellite-based thresholds can identify most of the landslide days, they include a greater number of false alarms due to overestimates of soil moisture between major storm events. To avoid the type of false alarms that are characteristic of our satellite-based thresholds, further post-processing of the near-surface hydrologic response data to better reflect gravity-dominated drainage should be integrated into satellite-based model outputs. Our results encourage further deployment of ground stations in landslide-prone terrain and cautious exploration of satellite-based hydro-meteorological thresholds where in situ networks are nonexistent.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019WR025577","usgsCitation":"Thomas, M.A., Collins, B.D., and Mirus, B.B., 2019, Assessing the feasibility of satellite-based thresholds for hydrologically driven landsliding: Water Resources Research, v. 55, no. 11, p. 9006-9023, https://doi.org/10.1029/2019WR025577.","productDescription":"18 p.","startPage":"9006","endPage":"9023","ipdsId":"IP-110185","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":459570,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019wr025577","text":"Publisher Index Page"},{"id":371947,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"East Bay Hills","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.63238525390626,\n              37.6359849542696\n            ],\n            [\n              -122.09930419921876,\n              38.05674222065296\n            ],\n            [\n              -122.26409912109375,\n              38.05674222065296\n            ],\n            [\n              -122.420654296875,\n              37.96152331396614\n            ],\n            [\n              -122.34649658203124,\n              37.898697801966094\n            ],\n            [\n              -121.89331054687499,\n              37.505368263398104\n            ],\n            [\n              -121.63238525390626,\n              37.6359849542696\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"55","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Thomas, Matthew A. 0000-0002-9828-5539 matthewthomas@usgs.gov","orcid":"https://orcid.org/0000-0002-9828-5539","contributorId":200616,"corporation":false,"usgs":true,"family":"Thomas","given":"Matthew","email":"matthewthomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":781289,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collins, Brian D. 0000-0003-4881-5359 bcollins@usgs.gov","orcid":"https://orcid.org/0000-0003-4881-5359","contributorId":149278,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":781290,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":781291,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70205614,"text":"fs20193061 - 2019 - Real-time assessments of water quality—A nowcast for <i>Escherichia coli</i> and cyanobacterial toxins","interactions":[],"lastModifiedDate":"2019-10-10T13:13:22","indexId":"fs20193061","displayToPublicDate":"2019-10-10T11:16:46","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-3061","displayTitle":"Real-Time Assessments of Water Quality—A Nowcast for <i>Escherichia coli</i> and Cyanobacterial Toxins","title":"Real-time assessments of water quality—A nowcast for <i>Escherichia coli</i> and cyanobacterial toxins","docAbstract":"<p>Threats to our recreational and drinking waters include disease-causing (pathogenic) organisms from fecal contamination and toxins produced by some species of cyanobacteria (cyanotoxins) that can cause acute and (or) chronic illnesses. Because traditional laboratory methods for detecting these threats take too long for prompt public health protection, tools for real-time assessments are needed to protect public health. To address this need, the U.S. Geological Survey is collaborating with State and local partners to develop models that provide real-time estimates of <i>Escherichia coli</i> (<i>E</i><i>. coli</i>) (for pathogens) and (or) microcystin (for freshwater cyanotoxins) levels at inland and Great Lakes beaches and drinking-water intakes. Model results are then used to inform the public of water-quality conditions in near-real time through the Great Lakes NowCast (<a href=\"https://ny.water.usgs.gov/maps/nowcast/\" data-mce-href=\"https://ny.water.usgs.gov/maps/nowcast/\">https://ny.water.usgs.gov/maps/nowcast/</a>). Behind the scenes, the NowCast provides speed and efficiency for managers by automating data management and standardizing methods among agencies.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193061","collaboration":"Prepared in cooperation with U.S. Environmental Protection Agency, Great Lakes Restoration Initiative","usgsCitation":"Francy, D.S., Brady, A.M., and Zimmerman, T.M., 2019, Real-time assessments of water quality—A nowcast for Escherichia coli and cyanobacterial toxins: U.S. Geological Survey Fact Sheet 2019–3061, 4 p., https://doi.org/10.3133/fs20193061.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-111133","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368188,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3061/coverthb.jpg"},{"id":368189,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3061/fs20193061.pdf","text":"Report","size":"970 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3061"}],"contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/oki-water\" href=\"https://www.usgs.gov/centers/oki-water\">Ohio-Kentucky-Indiana Water Science Center</a> <br>U.S. Geological Survey <br>6460 Busch Boulevard, Suite 100 <br>Columbus, OH 43229</p>","tableOfContents":"<ul><li>Why Do We Need a Nowcast?</li><li>What Is a Nowcast and How Does It Work?</li><li>What Are the Data Requirements to Develop a Nowcast Model?</li><li>What Are the Steps for Nowcast Model Development, Testing, and Implementation?</li><li>What Are the Benefits of a Nowcast?</li><li>Operational Nowcast—The Great Lakes NowCast</li><li>What Is Next for the Great Lakes NowCast?</li><li>What Training Is Available to Develop a Nowcast?</li><li>How Well Does a Nowcast Perform as Compared to the Traditional Method for Determining Water-Quality Conditions?</li><li>References</li></ul>","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2019-10-10","noUsgsAuthors":false,"publicationDate":"2019-10-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Francy, Donna S. 0000-0001-9229-3557 dsfrancy@usgs.gov","orcid":"https://orcid.org/0000-0001-9229-3557","contributorId":1853,"corporation":false,"usgs":true,"family":"Francy","given":"Donna","email":"dsfrancy@usgs.gov","middleInitial":"S.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771873,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brady, Amie M.G. 0000-0002-7414-0992 amgbrady@usgs.gov","orcid":"https://orcid.org/0000-0002-7414-0992","contributorId":2544,"corporation":false,"usgs":true,"family":"Brady","given":"Amie","email":"amgbrady@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771874,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zimmerman, Tammy M. 0000-0003-0842-6981","orcid":"https://orcid.org/0000-0003-0842-6981","contributorId":219288,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Tammy M.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771875,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70205963,"text":"70205963 - 2019 - Improving Darwin Core for research and management of alien species","interactions":[],"lastModifiedDate":"2019-10-15T08:13:43","indexId":"70205963","displayToPublicDate":"2019-10-10T11:11:12","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5872,"text":"Biodiversity Information Science and Standards","onlineIssn":"2535–0897","active":true,"publicationSubtype":{"id":10}},"title":"Improving Darwin Core for research and management of alien species","docAbstract":"<p>To improve the suitability of the Darwin Core standard for the research and management of alien species, the standard needs to express the native status of organisms, how well established they are and how they came to occupy a location. To facilitate this, we propose: 1. To adopt a controlled vocabulary for the existing Darwin Core term dwc:establishmentMeans 2. To elevate the pathway term from the Invasive Species Pathways extension to become a new Darwin Core term dwc:pathway maintained as part of the Darwin Core standard 3. To adopt a new Darwin Core term dwc:degreeOfEstablishment with an associated controlled vocabulary These changes to the standard will allow users to clearly state whether an occurrence of a species is native to a location or not, how it got there (pathway), and to what extent the species has become a permanent feature of the location. By improving Darwin Core for capturing and sharing these data, we aim to improve the quality of occurrence and checklist data in general and to increase the number of potential uses of these data.</p>","language":"English","publisher":"Pensoft","doi":"10.3897/biss.3.38084","usgsCitation":"Groom, Q.J., Desmet, P., Reyserhove, L., Adriaens, T., Oldoni, D., Vanderhoeven, S., Baskauf, S.J., Chapman, A., McGeoch, M., Walls, R., Wieczorek, J., Wilson, J.R., Zermoglio, P.F., and Simpson, A., 2019, Improving Darwin Core for research and management of alien species: Biodiversity Information Science and Standards, p. 1-24, https://doi.org/10.3897/biss.3.38084.","productDescription":"38084, 24 p.","startPage":"1","endPage":"24","ipdsId":"IP-103524","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":459572,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/biss.3.38084","text":"Publisher Index Page"},{"id":368312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":368282,"type":{"id":15,"text":"Index Page"},"url":"https://biss.pensoft.net/article/38084/list/9/"}],"edition":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Groom, Quentin J.","contributorId":171401,"corporation":false,"usgs":false,"family":"Groom","given":"Quentin","email":"","middleInitial":"J.","affiliations":[{"id":26893,"text":"Botanic Garden Meise, Belgium","active":true,"usgs":false}],"preferred":false,"id":773077,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Desmet, Peter","contributorId":197999,"corporation":false,"usgs":false,"family":"Desmet","given":"Peter","email":"","affiliations":[],"preferred":false,"id":773078,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reyserhove, Lien","contributorId":219746,"corporation":false,"usgs":false,"family":"Reyserhove","given":"Lien","email":"","affiliations":[{"id":40059,"text":"The Research Institute for Nature & Forest (INBO), Brussels, Belgium","active":true,"usgs":false}],"preferred":false,"id":773079,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Adriaens, Tim","contributorId":171412,"corporation":false,"usgs":false,"family":"Adriaens","given":"Tim","email":"","affiliations":[{"id":26902,"text":"Research Institute for Nature and Forest, Belgium","active":true,"usgs":false}],"preferred":false,"id":773080,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oldoni, Damiano","contributorId":219747,"corporation":false,"usgs":false,"family":"Oldoni","given":"Damiano","email":"","affiliations":[{"id":40059,"text":"The Research Institute for Nature & Forest (INBO), Brussels, Belgium","active":true,"usgs":false}],"preferred":false,"id":773081,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vanderhoeven, Sonia","contributorId":171413,"corporation":false,"usgs":false,"family":"Vanderhoeven","given":"Sonia","email":"","affiliations":[{"id":26903,"text":"Belgian Biodiversity Platform, Belgium","active":true,"usgs":false}],"preferred":false,"id":773082,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Baskauf, Steven J","contributorId":219748,"corporation":false,"usgs":false,"family":"Baskauf","given":"Steven","email":"","middleInitial":"J","affiliations":[{"id":40060,"text":"Vanderbilt University, Nashville, Tennessee, United States of America","active":true,"usgs":false}],"preferred":false,"id":773083,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chapman, Arthur","contributorId":219749,"corporation":false,"usgs":false,"family":"Chapman","given":"Arthur","email":"","affiliations":[{"id":40061,"text":"Australian Biodiversity Information Services, Ballan, Australia","active":true,"usgs":false}],"preferred":false,"id":773084,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McGeoch, Melodie","contributorId":219750,"corporation":false,"usgs":false,"family":"McGeoch","given":"Melodie","affiliations":[{"id":40062,"text":"Monash University, School of Biological Sciences, Clayton, Australia","active":true,"usgs":false}],"preferred":false,"id":773085,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Walls, Ramona","contributorId":219751,"corporation":false,"usgs":false,"family":"Walls","given":"Ramona","email":"","affiliations":[{"id":40063,"text":"CyVerse, Tucson, Arizona, United States of America","active":true,"usgs":false}],"preferred":false,"id":773086,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wieczorek, John","contributorId":219752,"corporation":false,"usgs":false,"family":"Wieczorek","given":"John","email":"","affiliations":[{"id":40064,"text":"Museum of Vertebrate Zoology, University of California, Berkeley, United States of America","active":true,"usgs":false}],"preferred":false,"id":773087,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Wilson, John RU 0000-0001-6752-4069 jtwilson@usgs.gov","orcid":"https://orcid.org/0000-0001-6752-4069","contributorId":219753,"corporation":false,"usgs":false,"family":"Wilson","given":"John","email":"jtwilson@usgs.gov","middleInitial":"RU","affiliations":[{"id":40065,"text":"South African National Biodiversity Institute, Kirstenbosch, South Africa; and Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch, South Africa","active":true,"usgs":false}],"preferred":false,"id":773088,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Zermoglio, Paula FF","contributorId":219754,"corporation":false,"usgs":false,"family":"Zermoglio","given":"Paula","email":"","middleInitial":"FF","affiliations":[{"id":40066,"text":"Instituto de Ecología, Genética y Evolución de Buenos Aires (IEGEBA-CONICET), University of Buenos Aires, Buenos Aires, Argentina","active":true,"usgs":false}],"preferred":false,"id":773089,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Simpson, Annie 0000-0001-8338-5134","orcid":"https://orcid.org/0000-0001-8338-5134","contributorId":206062,"corporation":false,"usgs":true,"family":"Simpson","given":"Annie","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":773076,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}}
,{"id":70205870,"text":"70205870 - 2019 - Prioritizing chemicals of ecological concern in Great Lakes tributaries using high-throughput screening data and adverse outcome pathways","interactions":[],"lastModifiedDate":"2019-12-05T09:46:00","indexId":"70205870","displayToPublicDate":"2019-10-09T08:01:16","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":"Prioritizing chemicals of ecological concern in Great Lakes tributaries using high-throughput screening data and adverse outcome pathways","docAbstract":"Chemical monitoring data were collected in surface waters from 57 Great Lakes tributaries from 2010-13 to identify chemicals of potential biological relevance and sites at which these chemicals occur. Traditional water-quality benchmarks for aquatic life based on in vivo toxicity data were available for 34 of 67 evaluated chemicals. To expand evaluation of potential biological effects, measured chemical concentrations were compared to chemical-specific biological activities determined in high-throughput (ToxCast) in vitro assays. Resulting exposure-activity ratios (EARs) were used to prioritize the chemicals of greatest potential concern: 4-nonylphenol, bisphenol A, metolachlor, atrazine, DEET, caffeine, tris(2-butoxyethyl) phosphate, tributyl phosphate, triphenyl phosphate, benzo(a)pyrene, fluoranthene, and benzophenone. Water-quality benchmarks were unavailable for five of these chemicals, but for the remaining seven, EAR-based prioritization was consistent with that based on toxicity quotients calculated from benchmarks. Water-quality benchmarks identified three additional PAHs (anthracene, phenanthrene, and pyrene) not prioritized using EARs. Through this analysis, an EAR of 10-3 was identified as a reasonable threshold above which a chemical might be of potential concern. To better understand apical hazards potentially associated with biological activities captured in ToxCast assays, in vitro bioactivity data were matched with available adverse outcome pathway (AOP) information. The 49 ToxCast assays prioritized via EAR analysis aligned with 23 potentially-relevant AOPs present in the AOP-Wiki. Mixture effects at monitored sites were estimated by summation of EAR values for multiple chemicals by individual assay or individual AOP. Commonly predicted adverse outcomes included impacts on reproduction and mitochondrial function. The EAR approach provided a screening-level assessment for evidence-based prioritization of chemicals and sites with potential for adverse biological effects. The approach aids prioritization of future monitoring activities and provides testable hypotheses to help focus those efforts. This also expands the fraction of detected chemicals for which biologically-based benchmark concentrations are available to help contextualize chemical monitoring results.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.05.457","usgsCitation":"Corsi, S., DeCicco, L.A., Villeneuve, D., Blackwell, B., Fay, K., Ankley, G., and Baldwin, A.K., 2019, Prioritizing chemicals of ecological concern in Great Lakes tributaries using high-throughput screening data and adverse outcome pathways: Science of the Total Environment, v. 686, p. 995-1009, https://doi.org/10.1016/j.scitotenv.2019.05.457.","productDescription":"15 p.","startPage":"995","endPage":"1009","ipdsId":"IP-106774","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":459589,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2019.05.457","text":"Publisher Index Page"},{"id":368146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -93.2080078125,\n              40.212440718286466\n            ],\n            [\n              -75.7177734375,\n              40.212440718286466\n            ],\n            [\n              -75.7177734375,\n              49.1242192485914\n            ],\n            [\n              -93.2080078125,\n              49.1242192485914\n            ],\n            [\n              -93.2080078125,\n              40.212440718286466\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"686","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Corsi, Steven R. 0000-0003-0583-5536 srcorsi@usgs.gov","orcid":"https://orcid.org/0000-0003-0583-5536","contributorId":172002,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":772732,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeCicco, Laura A. 0000-0002-3915-9487 ldecicco@usgs.gov","orcid":"https://orcid.org/0000-0002-3915-9487","contributorId":174716,"corporation":false,"usgs":true,"family":"DeCicco","given":"Laura","email":"ldecicco@usgs.gov","middleInitial":"A.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":772733,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Villeneuve, Daniel L. 0000-0003-2801-0203","orcid":"https://orcid.org/0000-0003-2801-0203","contributorId":219631,"corporation":false,"usgs":false,"family":"Villeneuve","given":"Daniel L.","affiliations":[{"id":39312,"text":"U.S. EPA","active":true,"usgs":false}],"preferred":false,"id":772734,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blackwell, Brett","contributorId":219632,"corporation":false,"usgs":false,"family":"Blackwell","given":"Brett","email":"","affiliations":[{"id":39312,"text":"U.S. EPA","active":true,"usgs":false}],"preferred":false,"id":772735,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fay, Kellie","contributorId":219633,"corporation":false,"usgs":false,"family":"Fay","given":"Kellie","email":"","affiliations":[{"id":39312,"text":"U.S. EPA","active":true,"usgs":false}],"preferred":false,"id":772736,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ankley, Gerald","contributorId":219634,"corporation":false,"usgs":false,"family":"Ankley","given":"Gerald","email":"","affiliations":[{"id":39312,"text":"U.S. EPA","active":true,"usgs":false}],"preferred":false,"id":772737,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Baldwin, Austin K. 0000-0002-6027-3823 akbaldwi@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3823","contributorId":4515,"corporation":false,"usgs":true,"family":"Baldwin","given":"Austin","email":"akbaldwi@usgs.gov","middleInitial":"K.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":772738,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70223513,"text":"70223513 - 2019 - Pathogen prevalence in American black bears (Ursus americanus) of the Jemez Mountains, New Mexico, USA","interactions":[],"lastModifiedDate":"2021-08-31T12:50:06.561276","indexId":"70223513","displayToPublicDate":"2019-10-09T07:46:43","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2507,"text":"Journal of Wildlife Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Pathogen prevalence in American black bears (Ursus americanus) of the Jemez Mountains, New Mexico, USA","docAbstract":"<div class=\"div0\"><div class=\"row ArticleContentRow\"><p id=\"ID0EF\" class=\"first\">Informed management of American black bears (<i>Ursus americanus</i>) requires knowledge of the distribution and pathology of diseases affecting the species. Little information is available on pathogen prevalence from black bear populations in the Southwest, US, and it is unknown how these infections may influence black bear populations or disease transmission. We captured New Mexico black bears (<i>Ursus americanus amblyceps</i>) during 2016–17 as part of a long-term monitoring project and opportunistically collected 36 blood samples from 12 female and 17 male black bears. We wanted to determine prior exposure to canine distemper virus, canine parvovirus,<span>&nbsp;</span><i>Yersinia pestis</i>,<span>&nbsp;</span><i>Francisella tularensis</i>, West Nile virus,<span>&nbsp;</span><i>Toxoplasma gondii</i>, and the tick-borne pathogens,<span>&nbsp;</span><i>Anaplasma</i><span>&nbsp;</span>spp.,<span>&nbsp;</span><i>Ehrlichia</i><span>&nbsp;</span>spp.,<span>&nbsp;</span><i>Borrelia burgdorferi</i>,<span>&nbsp;</span><i>Rickettsia</i><span>&nbsp;</span>spp., and<span>&nbsp;</span><i>Babesia</i><span>&nbsp;</span>spp. Approximately half (55%, 16/29) of the individuals sampled had antibodies to<span>&nbsp;</span><i>Y. pestis</i>, and 37% (10/27) had antibodies to<span>&nbsp;</span><i>T. gondii</i>. Prevalence of antibodies to West Nile virus,<span>&nbsp;</span><i>F. tularensis</i>, and canine parvovirus were lower (i.e., 11, 10, and 3%, respectively). We detected no antibodies to canine distemper,<span>&nbsp;</span><i>B. burgdorferi</i>,<span>&nbsp;</span><i>Rickettsia</i><span>&nbsp;</span>spp., or<span>&nbsp;</span><i>Babesia</i><span>&nbsp;</span>spp. We documented changes in antibody titer levels for both sexes of several recaptured black bears. Our data will inform managers of pathogen prevalence and distribution in black bears in north-central New Mexico and provide a vital baseline dataset for future pathogen monitoring. Additionally, these data support actions to minimize exposure through handling wild individuals or through hunter harvest activities.</p></div></div>","language":"English","publisher":"BioOne","doi":"10.7589/2018-12-286","usgsCitation":"Bard, S.M., and Cain, J.W., 2019, Pathogen prevalence in American black bears (Ursus americanus) of the Jemez Mountains, New Mexico, USA: Journal of Wildlife Diseases, v. 55, no. 4, p. 745-754, https://doi.org/10.7589/2018-12-286.","productDescription":"10 p.","startPage":"745","endPage":"754","ipdsId":"IP-104311","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":388682,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Jemez Mountains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -107.10296630859374,\n              35.47185482057798\n            ],\n            [\n              -105.90270996093749,\n              35.47185482057798\n            ],\n            [\n              -105.90270996093749,\n              36.37706783983685\n            ],\n            [\n              -107.10296630859374,\n              36.37706783983685\n            ],\n            [\n              -107.10296630859374,\n              35.47185482057798\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"55","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bard, Susan M.","contributorId":264967,"corporation":false,"usgs":false,"family":"Bard","given":"Susan","email":"","middleInitial":"M.","affiliations":[{"id":27575,"text":"NMSU","active":true,"usgs":false}],"preferred":false,"id":822249,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":822250,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70205114,"text":"sir20195097 - 2019 - Groundwater/surface-water interactions along Ellerbe Creek in Durham, North Carolina, 2016–18","interactions":[],"lastModifiedDate":"2019-10-08T12:19:15","indexId":"sir20195097","displayToPublicDate":"2019-10-08T09:15:00","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":"2019-5097","displayTitle":"Groundwater/Surface-Water Interactions Along Ellerbe Creek in Durham, North Carolina, 2016–18","title":"Groundwater/surface-water interactions along Ellerbe Creek in Durham, North Carolina, 2016–18","docAbstract":"<p>An assessment of groundwater/surface-water interactions along Ellerbe Creek, a major tributary to upper Falls Lake in Durham County, North Carolina, was conducted from July 2016 to March 2018 to determine if groundwater is a likely source of elevated nitrate input to the stream. Groundwater/surface-water interactions were characterized by synoptic streamflow measurements, groundwater-level monitoring, hydrograph-separation methods, and a continuous streambed temperature survey to aid in the collection and interpretation of water-quality data. A streamflow gain-loss survey identified gaining and losing reaches within the stream and found that surface-water inflow, including that from a treated wastewater outfall, provided much of the streamflow gain within the study reach. Through the use of two hydrograph-separation methods, base flow for the Ellerbe Creek study reach was estimated to be between 14.0 and 17.7 cubic feet per second during the study period, contributing up to 57 percent of mean streamflow, with the remaining contributions coming from surface runoff to the stream. The effluent discharge accounted for most of the estimated base-flow contribution to the stream below the North Durham Water Reclamation Facility outfall. Hydraulic gradients within the groundwater were determined to flow upward and toward the stream during base-flow conditions and reverse during storm events. Nitrate concentrations ranged from below the method detection level to 2.69 milligrams per liter, with the highest concentrations just downstream from the wastewater outfall. Bank seeps and groundwater samples had lower nitrate concentrations than surface-water samples, ranging from below the method detection level to 1.04 milligrams per liter, with the highest concentration at the piezometer within the stream. Results indicate that groundwater is not a large component of streamflow within Ellerbe Creek nor a major source of nitrate within the study reach.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195097","collaboration":"Prepared in cooperation with the City of Durham Public Works Department, Stormwater and GIS Services Division","usgsCitation":"Antolino, D.J., 2019, Groundwater/surface-water interactions along Ellerbe Creek in Durham, North Carolina, 2016–18: U.S. Geological Survey Scientific Investigations Report 2019–5097, 32 p., https://doi.org/10.3133/sir20195097.","productDescription":"viii, 32 p.","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-097853","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":437312,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YFET78","text":"USGS data release","linkHelpText":"Groundwater-Surface Water Interactions in Ellerbe Creek in Durham, North Carolina, 2016-2018"},{"id":368078,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5b6630abe4b006a11f75221b","text":"USGS data release","linkHelpText":"Groundwater-Surface Water Interactions in Ellerbe Creek in Durham, North Carolina, 2016-2018"},{"id":368058,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5097/coverthb.jpg"},{"id":368059,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5097/sir20195097.pdf","text":"Report","size":"4.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5097"}],"country":"United States","state":"North Carolina","county":"Durham County, Wake County","city":"Durham","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-78.8019,36.2361],[-78.8059,36.0928],[-78.8059,36.0878],[-78.7986,36.085],[-78.7957,36.0858],[-78.7923,36.0854],[-78.7919,36.0772],[-78.7879,36.0758],[-78.7852,36.0703],[-78.7749,36.0707],[-78.7498,36.0718],[-78.7088,36.0768],[-78.6895,36.0752],[-78.5922,36.0378],[-78.5465,36.0218],[-78.4307,35.9795],[-78.3969,35.9387],[-78.3567,35.9318],[-78.351,35.909],[-78.3385,35.9052],[-78.3347,35.8997],[-78.3302,35.896],[-78.3245,35.896],[-78.3177,35.8963],[-78.3137,35.8976],[-78.3081,35.8935],[-78.2948,35.8797],[-78.292,35.8792],[-78.2893,35.8741],[-78.2859,35.8713],[-78.2831,35.8681],[-78.2782,35.8631],[-78.2749,35.8567],[-78.2756,35.8494],[-78.2707,35.843],[-78.2657,35.8361],[-78.2652,35.8325],[-78.2613,35.8315],[-78.2591,35.826],[-78.2599,35.8183],[-78.3731,35.7523],[-78.4635,35.7072],[-78.4686,35.7087],[-78.4709,35.7078],[-78.4732,35.7046],[-78.4778,35.7011],[-78.5716,35.6255],[-78.708,35.5191],[-78.9196,35.5857],[-78.9956,35.6104],[-78.9796,35.6656],[-78.9439,35.7515],[-78.9421,35.756],[-78.9403,35.7615],[-78.9337,35.7859],[-78.9191,35.8216],[-78.9096,35.8506],[-78.9076,35.8678],[-78.9144,35.8674],[-78.9332,35.8667],[-78.9587,35.866],[-78.986,35.8644],[-78.9985,35.8641],[-79.011,35.8633],[-79.0161,35.8633],[-79.0142,35.8755],[-79.0124,35.886],[-78.9507,36.2393],[-78.8019,36.2361]]]},\"properties\":{\"name\":\"Durham\",\"state\":\"NC\"}}]}","contact":"<p><a href=\"mailto: dc_sc@usgs.gov\" data-mce-href=\"mailto: dc_sc@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/sa-water\" data-mce-href=\"https://www.usgs.gov/centers/sa-water\">South Atlantic Water Science Center</a><br>U.S. Geological Survey<br>720 Gracern Road<br>Stephenson Center, Suite 129<br>Columbia, SC 29210</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Groundwater/Surface-Water Interactions</li><li>Water-Quality Results</li><li>Summary and Conclusions</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2019-10-08","noUsgsAuthors":false,"publicationDate":"2019-10-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Antolino, Dominick J. 0000-0001-7838-5279 dantolin@usgs.gov","orcid":"https://orcid.org/0000-0001-7838-5279","contributorId":179174,"corporation":false,"usgs":true,"family":"Antolino","given":"Dominick","email":"dantolin@usgs.gov","middleInitial":"J.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":false,"id":770086,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70215198,"text":"70215198 - 2019 - Earthquake arrival association with backprojection and graph theory","interactions":[],"lastModifiedDate":"2020-10-13T22:47:56.450096","indexId":"70215198","displayToPublicDate":"2019-10-08T08:06:13","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Earthquake arrival association with backprojection and graph theory","docAbstract":"<p><span>The association of seismic‐wave arrivals with causative earthquakes becomes progressively more challenging as arrival detection methods become more sensitive, and particularly when earthquake rates are high. For instance, seismic waves arriving across a monitoring network from several sources may overlap in time, false arrivals may be detected, and some arrivals may be of unknown phase (e.g.,&nbsp;</span><i>P</i><span>&nbsp;or&nbsp;</span><i>S</i><span>&nbsp;waves). We propose an automated method to associate arrivals with earthquake sources and obtain source locations applicable to such situations. To do so, we use a pattern detection metric based on the principle of backprojection to reveal candidate sources followed by graph‐theory‐based clustering and an integer linear optimization routine to associate arrivals with the minimum number of sources necessary to explain the data. This method solves for all sources and phase assignments simultaneously, rather than in a sequential greedy procedure as is common in other association routines. We demonstrate our method on both synthetic and real data from the Integrated Plate Boundary Observatory Chile seismic network of northern Chile. For the synthetic tests, we report results for cases with varying complexity, including rates of 500 earthquakes/day and 500 false arrivals/station/day, for which we measure true positive detection accuracy of&nbsp;</span><span class=\"inline-formula no-formula-id\"><span id=\"MathJax-Element-1-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=\">&gt;95%</span></span><span>. For the real data, we develop a new catalog between 1 January 2010 and 31 December 2017 containing 817,548 earthquakes, with detection rates on average 279 earthquakes/day and a magnitude‐of‐completion of&nbsp;</span><span class=\"inline-formula no-formula-id\"><span id=\"MathJax-Element-2-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=\">M∼1.8</span>⁠</span><span>. A subset of detections are identified as sources related to quarry and industrial site activity, and we also detect thousands of foreshocks and aftershocks of the 1 April 2014&nbsp;</span><span class=\"inline-formula no-formula-id\"><span id=\"MathJax-Element-3-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=\">M<sub>w</sub></span></span><span>&nbsp;8.2 Iquique earthquake. During the highest rate of aftershock activity,&nbsp;</span><span class=\"inline-formula no-formula-id\"><span id=\"MathJax-Element-4-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=\">&gt;600</span></span><span>&nbsp;earthquakes/day are detected in the vicinity of the Iquique earthquake rupture zone.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120190081","usgsCitation":"McBrearty, I., Gomberg, J.S., Delorey, A., and Johnson, P., 2019, Earthquake arrival association with backprojection and graph theory: Bulletin of the Seismological Society of America, v. 109, no. 6, p. 2510-2531, https://doi.org/10.1785/0120190081.","productDescription":"22 p.","startPage":"2510","endPage":"2531","ipdsId":"IP-106212","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":459595,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://arxiv.org/abs/1904.00980","text":"External Repository"},{"id":379299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"109","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-10-08","publicationStatus":"PW","contributors":{"authors":[{"text":"McBrearty, Ian","contributorId":242933,"corporation":false,"usgs":false,"family":"McBrearty","given":"Ian","email":"","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":801143,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":801144,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Delorey, Andrew","contributorId":189149,"corporation":false,"usgs":false,"family":"Delorey","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":801145,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Paul","contributorId":189150,"corporation":false,"usgs":false,"family":"Johnson","given":"Paul","email":"","affiliations":[],"preferred":false,"id":801146,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70215092,"text":"70215092 - 2019 - Spatial and temporal variation in river corridor exchange across a 5th order mountain stream network","interactions":[],"lastModifiedDate":"2020-10-07T20:21:30.905358","indexId":"70215092","displayToPublicDate":"2019-10-07T15:14:05","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal variation in river corridor exchange across a 5th order mountain stream network","docAbstract":"Although most field and modeling studies of river corridor exchange have been conducted a scales ranging from 10’s to 100’s of meters; results of these studies are used to predict their ecological and hydrological influences at the scale of river networks. Further complicating prediction, exchanges are expected to vary with hydrologic forcing and the local geomorphic setting. While we desire predictive power, we lack a complete spatiotemporal relationship relating discharge to the variation in geologic setting and hydrologic forcing that are expected across a river basin. Indeed, Wondzell’s (2011) conceptual model predicts systematic variation in river corridor exchange as a function of (1) variation in baseflow over time at a fixed location, (2) variation in discharge with location in the river network, and (3) local geomorphic setting. To test this conceptual model we conducted more than 60 solute tracer studies including a synoptic campaign in the 5th order river network of the H.J. Andrews Experimental Forest (Oregon, USA) and replicate-in-time experiments in four watersheds. We interpret the data using a series of metrics describing river corridor exchange and solute transport, testing for consistent direction and magnitude of relationships relating these metrics to discharge and local geomorphic setting. We confirmed systematic decrease in river corridor exchange space through the river networks, from headwaters to the larger mainstem. However, we did not find systematic variation with changes in discharge through time, nor with local geomorphic setting. While interpretation of our results is complicated by problems with the analytical methods, they are sufficiently robust for us to conclude that space-for-time and time-for-space substitutions are not appropriate in our study system. Finally, we suggest two strategies that will improve the interpretability of tracer test results and help the hyporheic community develop robust data sets that will enable comparisons across multiple sites and/or discharge conditions.","language":"English","publisher":"European Geosciences Union","doi":"10.5194/hess-23-5199-2019","usgsCitation":"Ward, A.S., Wondzell, S., Schmadel, N., Herzog, S., Zarnetske, J.P., Baranov, V., Blaen, P.J., Brekenfeld, N., Chu, R., Derelle, R., Drummond, J.D., Fleckenstein, J., Garayburu-Caruso, V., Graham, E.B., Hannah, D., Harman, C., Hixson, J., Knapp, J., Krause, S., Kurz, M., Lewandowski, J., Li, A., Martí, E., Miller, M., Milner, A., Neil, K., Orsini, L., Packman, A.I., Plont, S., Renteria, L., Roche, K., Royer, T.V., Segura, C., Stegen, J., Toyoda, J., Wells, J., and Wisnoski, N., 2019, Spatial and temporal variation in river corridor exchange across a 5th order mountain stream network: Hydrology and Earth System Sciences, v. 23, no. 12, p. 5199-5225, https://doi.org/10.5194/hess-23-5199-2019.","productDescription":"27 p.","startPage":"5199","endPage":"5225","ipdsId":"IP-111385","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":459599,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-23-5199-2019","text":"Publisher Index Page"},{"id":379198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"12","noUsgsAuthors":false,"publicationDate":"2019-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Ward, Adam S","contributorId":191363,"corporation":false,"usgs":false,"family":"Ward","given":"Adam","email":"","middleInitial":"S","affiliations":[],"preferred":false,"id":800776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wondzell, Steven","contributorId":242771,"corporation":false,"usgs":false,"family":"Wondzell","given":"Steven","affiliations":[{"id":37019,"text":"USDA Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":800777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmadel, Noah M. 0000-0002-2046-1694","orcid":"https://orcid.org/0000-0002-2046-1694","contributorId":219105,"corporation":false,"usgs":true,"family":"Schmadel","given":"Noah","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":800778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herzog, Skuyler","contributorId":242772,"corporation":false,"usgs":false,"family":"Herzog","given":"Skuyler","affiliations":[{"id":48520,"text":"O’Neill School of Public and Environmental Affairs, Indiana University, Bloomington, Indiana, USA","active":true,"usgs":false}],"preferred":false,"id":800779,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zarnetske, Jay P.","contributorId":210073,"corporation":false,"usgs":false,"family":"Zarnetske","given":"Jay","email":"","middleInitial":"P.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":800780,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baranov, Viktor","contributorId":242773,"corporation":false,"usgs":false,"family":"Baranov","given":"Viktor","email":"","affiliations":[{"id":48521,"text":"LMU Munich Biocenter, Department of Biology II","active":true,"usgs":false}],"preferred":false,"id":800781,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Blaen, Phillip J","contributorId":242774,"corporation":false,"usgs":false,"family":"Blaen","given":"Phillip","email":"","middleInitial":"J","affiliations":[{"id":48522,"text":"School of Geography, Earth & Environmental 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,{"id":70205296,"text":"fs20193042 - 2019 - Hydrologic conditions in Kansas, water year 2018","interactions":[],"lastModifiedDate":"2019-10-15T07:58:21","indexId":"fs20193042","displayToPublicDate":"2019-10-07T14:46:46","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-3042","displayTitle":"Hydrologic Conditions in Kansas, Water Year 2018","title":"Hydrologic conditions in Kansas, water year 2018","docAbstract":"<p>The U.S.&nbsp;Geological Survey, in cooperation with Federal, State, and local agencies, maintains a long-term network of hydrologic monitoring stations in Kansas. In water year 2018, this network included 219&nbsp;real-time streamgages. A water year is the 12-month period from October&nbsp;1 through September&nbsp;30 and is designated by the calendar year in which it ends. Real-time data are calibrated and validated by U.S.&nbsp;Geological Survey personnel throughout the year with regular measurements of streamflow, streamgage height, and lake levels. These data and accompanying analyses provide an overview of hydrologic conditions in Kansas and help advance the understanding of water resources in the State. Annual assessments of hydrologic conditions are made by comparing statistical analyses of current and past water year data for the period of record. Long-term monitoring of hydrologic conditions in Kansas provides imperative information for protecting human life and property, managing water supplies, forecasting floods, operating reservoirs, designing bridges and culverts, processing interstate and intrastate water rights claims, forecasting ecological conditions, and many other uses.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193042","usgsCitation":"Unrein, A.H., 2019, Hydrologic conditions in Kansas, water year 2018: U.S. Geological Survey Fact Sheet 2019–3042, 4 p., https://doi.org/10.3133/fs20193042.\n","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-106493","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":368023,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3042/fs20193042.pdf","text":"Report","size":"4.29 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 \"}}]}","contact":"<p><a data-mce-href=\"mailto:%20dc_ks@usgs.gov\" href=\"mailto:%20dc_ks@usgs.gov\">Director</a>, <a data-mce-href=\"https://www.usgs.gov/centers/kswsc\" href=\"https://www.usgs.gov/centers/kswsc\">Kansas Water Science Center</a> <br>U.S. Geological Survey<br>1217 Biltmore Drive <br>Lawrence, KS 66049 </p>","tableOfContents":"<ul><li>Preceding Conditions/Precipitation Overview/Drought Conditions</li><li>Streamflow Conditions and Drainage Basin Runoff</li><li>Cumulative Streamflow at Selected Streamgages</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2019-10-07","noUsgsAuthors":false,"publicationDate":"2019-10-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Unrein, Angela H. 0000-0002-9038-7493","orcid":"https://orcid.org/0000-0002-9038-7493","contributorId":218959,"corporation":false,"usgs":true,"family":"Unrein","given":"Angela","email":"","middleInitial":"H.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":770771,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70205815,"text":"ofr20191111 - 2019 - Geology of the Monte Blanco borate deposits, Furnace Creek Wash, Death Valley, California","interactions":[],"lastModifiedDate":"2019-10-08T06:50:10","indexId":"ofr20191111","displayToPublicDate":"2019-10-07T14:03:06","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":"2019-1111","displayTitle":"Geology of the Monte Blanco Borate Deposits, Furnace Creek Wash, Death Valley, California","title":"Geology of the Monte Blanco borate deposits, Furnace Creek Wash, Death Valley, California","docAbstract":"<p>The Monte Blanco borate deposits are located along the southern margin of Death Valley’s Furnace Creek Wash, south of Twenty Mule Team Canyon road in California. Topographic and geologic mapping by S. Muessig and F.M. Byers, Jr., in 1954 documented these deposits’ geologic settings, geometries, mineralogies, and chemical characteristics. They estimated borate resources at the time to be in excess of 550,000 tons B<sub><span>2</span></sub>O<sub><span>3</span></sub>.<span>&nbsp;</span></p><p>The borate bodies are composed of predominantly ulexite and colemanite. They lie beneath Monte Blanco itself and along a northwest-trending series of conspicuous, white hills and mounds formed by northeasterly dipping, fine-grained sedimentary beds and basaltic volcanic rocks of the Miocene and Pliocene Furnace Creek Formation.<span>&nbsp;</span></p><p>Geologic data suggest that in Miocene and Pliocene time, fine-grained sediments, volcanic debris and flows, and volcanically associated, boron-rich fluids gradually filled a fairly flat playa-like environment. At times, thick beds of felty crystals of ulexite developed and were interlayered as lenses in a thick series of mudstones as is seen today at the Eagle Borax works. After burial, the exterior of the ulexite deposit was altered to massive colemanite by ground water, which produced the “shell” of colemanite that typically surrounds the presently outcropping ulexite bodies.<span>&nbsp;</span></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191111","usgsCitation":"Muessig, S.J., Pennell, W.M, Knott, J.R., and Calzia, J.P., 2019, Geology of the Monte Blanco borate deposits, Furnace Creek Wash, Death Valley, California: U.S. Geological Survey Open-File Report 2019–1111, 35, p., 2 plates, scales 1:2,400 and 1: 2,000, https://doi.org/10.3133/ofr20191111.","productDescription":"Report: v, 30 p.; 2 Plates: 28.00 x 29.75 and 18.11 x 24.96 inches","numberOfPages":"37","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-088268","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":368047,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1111/coverthb.jpg"},{"id":368050,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1111/ofr20191111_plate2.pdf","text":"Plate 2","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1111"},{"id":368048,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1111/ofr20191111_pamphlet.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1111"},{"id":368049,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1111/ofr20191111_plate1.pdf","text":"Plate 1","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1111"}],"country":"United States","state":"California","otherGeospatial":"Death Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -117.76245117187499,\n              35.60818490437746\n            ],\n            [\n              -116.06506347656251,\n              35.60818490437746\n            ],\n            [\n              -116.06506347656251,\n              37.19095471582605\n            ],\n            [\n              -117.76245117187499,\n              37.19095471582605\n            ],\n            [\n              -117.76245117187499,\n              35.60818490437746\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://geomaps.wr.usgs.gov/gmeg/staff.htm\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://geomaps.wr.usgs.gov/gmeg/staff.htm\">Director</a>,<br><a href=\"https://geomaps.wr.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://geomaps.wr.usgs.gov/\">Geology, Minerals, Energy, &amp; Geophysics Science Center</a><br><a href=\"https://geomaps.wr.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://geomaps.wr.usgs.gov/\">Menlo Park, California</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov/\">U.S. Geological Survey</a><br>345 Middlefield Road<br>Menlo Park, CA 94025-3591</p>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2019-10-07","noUsgsAuthors":false,"publicationDate":"2019-10-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Muessig, S.J.","contributorId":219528,"corporation":false,"usgs":false,"family":"Muessig","given":"S.J.","email":"","affiliations":[{"id":40021,"text":"U.S. Geological Survey (deceased)","active":true,"usgs":false}],"preferred":false,"id":772470,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pennell, W.M.","contributorId":219529,"corporation":false,"usgs":false,"family":"Pennell","given":"W.M.","email":"","affiliations":[{"id":40022,"text":"U.S.Borax (ret)","active":true,"usgs":false}],"preferred":false,"id":772471,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knott, Jeffrey R.","contributorId":81408,"corporation":false,"usgs":true,"family":"Knott","given":"Jeffrey","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":772472,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Calzia, James P. 0000-0003-0536-106X jcalzia@usgs.gov","orcid":"https://orcid.org/0000-0003-0536-106X","contributorId":219527,"corporation":false,"usgs":true,"family":"Calzia","given":"James","email":"jcalzia@usgs.gov","middleInitial":"P.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":772469,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70204846,"text":"sir20195087 - 2019 - Regression models for estimating sediment and nutrient concentrations and loads at the Iroquois River near Foresman, Indiana, March 2015 through July 2018","interactions":[],"lastModifiedDate":"2020-06-03T11:16:43.088546","indexId":"sir20195087","displayToPublicDate":"2019-10-07T12:42:41","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":"2019-5087","displayTitle":"Regression Models for Estimating Sediment and Nutrient Concentrations and Loads at the Iroquois River near Foresman, Indiana, March 2015 through July 2018","title":"Regression models for estimating sediment and nutrient concentrations and loads at the Iroquois River near Foresman, Indiana, March 2015 through July 2018","docAbstract":"<p>In 2015, the U.S. Geological Survey, in cooperation with the Iroquois River Conservancy District, deployed continuous water-quality monitors and began collecting representative discrete water-quality samples at the Iroquois River near Foresman, Indiana, streamflow-gaging station (U.S. Geological Survey station 05524500). By relating continuously monitored water-quality data and discrete water-quality samples collected from April 2015 through July 2018, regression models that estimate concentrations of suspended sediment, total nitrogen, and total phosphorus were developed. Developed regression models indicated a strong correlation between turbidity and streamflow with suspended-sediment concentration (adjusted coefficient of determination equals 0.84, predicted residual error sum of squares equals 0.493), nitrate plus nitrite and streamflow with total nitrogen (adjusted coefficient of determination equals 0.99, predicted residual error sum of squares equals 0.0202), and specific conductance and turbidity with total phosphorus (adjusted coefficient of determination equals 0.84, predicted residual error sum of squares equals 0.0935).</p><p>Daily loads of suspended sediment, total nitrogen, and total phosphorus were computed as the product of daily mean regression model concentrations and daily mean streamflow. During periods when regression model concentrations could not be computed, rloadest models, the R programming language version of the LOADEST FORTRAN program, were used to compute daily loads of each constituent. For 2016 and 2017, the estimated annual suspended-sediment loads were 25,000 and 32,100 tons; estimated total nitrogen loads were 4,260 and 5,780 tons; and estimated total phosphorus loads were 104 and 128 tons, respectively.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195087","collaboration":"Prepared in cooperation with the Iroquois River Conservancy District","usgsCitation":"Lathrop, T.R., Bunch, A.R., Downhour, M.S., and Perkins, D.M., 2019, Regression models for estimating sediment and nutrient concentrations and loads at the Iroquois River near Foresman, Indiana, March 2015 through July 2018: U.S. Geological Survey Scientific Investigation Report 2019–5087, 14 p., https://doi.org/10.3133/sir20195087.","productDescription":"Report: vi, 14 p.; Data Releases","numberOfPages":"24","ipdsId":"IP-107470","costCenters":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368030,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YCAELC","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data and regression models for total nitrogen and total phosphorus for the Iroquois River near Foresman, Indiana, March 20, 2015, to July 19, 2018"},{"id":368029,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RFLONI","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data and regression model for suspended sediment for Iroquois River near Foresman, Indiana, March 20, 2015, to July 19, 2018"},{"id":368028,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91FL2GY","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Suspended sediment, total nitrogen, and total phosphorus loads for Iroquois River near Foresman, Indiana, April 2015 to July 2018"},{"id":368027,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5087/sir20195087.pdf","text":"Report","size":"855 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5087"},{"id":368026,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5087/coverthb.jpg"}],"country":"United States","state":"Indiana","county":"Newton County","city":"Foresman","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-87.5263,41.1661],[-87.4801,41.1701],[-87.4587,41.1702],[-87.4484,41.1744],[-87.4466,41.174],[-87.4411,41.1731],[-87.4147,41.1619],[-87.4055,41.1625],[-87.4,41.1625],[-87.394,41.1625],[-87.38,41.1726],[-87.3448,41.1824],[-87.3405,41.1824],[-87.3313,41.1829],[-87.3241,41.1862],[-87.2859,41.2154],[-87.2762,41.2187],[-87.2757,41.1733],[-87.2754,41.0866],[-87.275,40.9991],[-87.2768,40.9405],[-87.2759,40.9133],[-87.268,40.9134],[-87.2664,40.8249],[-87.2655,40.7383],[-87.3807,40.738],[-87.4905,40.7381],[-87.5263,40.7378],[-87.5263,40.741],[-87.5265,40.839],[-87.5262,40.981],[-87.5262,40.9832],[-87.5265,41.0142],[-87.5264,41.1231],[-87.5263,41.1661]]]},\"properties\":{\"name\":\"Newton\",\"state\":\"IN\"}}]}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/oki-water\" href=\"https://www.usgs.gov/centers/oki-water\">Ohio-Kentucky-Indiana Water Science Center</a> <br>U.S. Geological Survey <br>5957 Lakeside Boulevard <br>Indianapolis, IN 46278</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Study Area</li><li>Approach and Methods</li><li>Results of Data Collection—Discrete and Continuous Water-Quality Data</li><li>Regression Models</li><li>Constituent Load Models</li><li>Limitations</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2019-10-07","noUsgsAuthors":false,"publicationDate":"2019-10-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Lathrop, Timothy R. 0000-0002-3568-1286 trlathro@usgs.gov","orcid":"https://orcid.org/0000-0002-3568-1286","contributorId":213061,"corporation":false,"usgs":true,"family":"Lathrop","given":"Timothy","email":"trlathro@usgs.gov","middleInitial":"R.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768724,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bunch, Aubrey R. 0000-0002-2453-3624 aurbunch@usgs.gov","orcid":"https://orcid.org/0000-0002-2453-3624","contributorId":4351,"corporation":false,"usgs":true,"family":"Bunch","given":"Aubrey","email":"aurbunch@usgs.gov","middleInitial":"R.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768726,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Downhour, Myles S. 0000-0001-6677-412X","orcid":"https://orcid.org/0000-0001-6677-412X","contributorId":218220,"corporation":false,"usgs":true,"family":"Downhour","given":"Myles","email":"","middleInitial":"S.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768725,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perkins, Daniel M.","contributorId":218221,"corporation":false,"usgs":false,"family":"Perkins","given":"Daniel","email":"","middleInitial":"M.","affiliations":[{"id":39783,"text":"Jasper County Soil and Water Conservation District","active":true,"usgs":false}],"preferred":false,"id":768727,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70223320,"text":"70223320 - 2019 - Predicting fish species richness and habitat relationships using Bayesian hierarchical multispecies occupancy models","interactions":[],"lastModifiedDate":"2021-08-23T23:16:06.9798","indexId":"70223320","displayToPublicDate":"2019-10-04T18:11:32","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6455,"text":"Canadian Journal Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Predicting fish species richness and habitat relationships using Bayesian hierarchical multispecies occupancy models","docAbstract":"<p><span>Understanding how stream fishes respond to changes in habitat availability is complicated by low occurrence rates of many species, which in turn reduces the ability to quantify species–habitat relationships and account for imperfect detection in estimates of species richness. Multispecies occupancy models have been used sparingly in the analysis of fisheries data, but address the aforementioned deficiencies by allowing information to be shared among ecologically similar species, thereby enabling species–habitat relationships to be estimated for entire fish communities, including rare species. Here, we highlight the utility of hierarchical multispecies occupancy models for the analysis of fish community data and demonstrate the modeling framework on a stream fish community dataset collected in the Delaware Water Gap National Recreation Area, USA. In particular, we demonstrate the ability of the modeling framework to make inferences at the species-, guild-, and community-levels, thereby making it a powerful tool for understanding and predicting how environmental variables influence species occupancy probabilities and structure fish assemblages.</span></p>","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2019-0125","collaboration":"National Park Service","usgsCitation":"White, S., Faulk, E., Tzilkowski, C., Weber, A., Marshall, M., and Wagner, T., 2019, Predicting fish species richness and habitat relationships using Bayesian hierarchical multispecies occupancy models: Canadian Journal Fisheries and Aquatic Sciences, v. 77, no. 3, 9 p., https://doi.org/10.1139/cjfas-2019-0125.","productDescription":"9 p.","ipdsId":"IP-101955","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":388400,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United  States","state":"New York New Jersey, Pennsylvania","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -75.12451171875,\n              40.96330795307353\n            ],\n            [\n              -74.15771484375,\n              40.96330795307353\n            ],\n            [\n              -74.15771484375,\n              41.60722821271717\n            ],\n            [\n              -75.12451171875,\n              41.60722821271717\n            ],\n            [\n              -75.12451171875,\n              40.96330795307353\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"77","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, Shannon","contributorId":264595,"corporation":false,"usgs":false,"family":"White","given":"Shannon","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":821720,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Faulk, Evan","contributorId":264596,"corporation":false,"usgs":false,"family":"Faulk","given":"Evan","email":"","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":821721,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tzilkowski, Caleb","contributorId":264597,"corporation":false,"usgs":false,"family":"Tzilkowski","given":"Caleb","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":821722,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weber, Andrew","contributorId":264598,"corporation":false,"usgs":false,"family":"Weber","given":"Andrew","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":821723,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Marshall, Matt","contributorId":264599,"corporation":false,"usgs":false,"family":"Marshall","given":"Matt","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":821724,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":821719,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70205928,"text":"70205928 - 2019 - Standardizing a non-lethal method for characterizing the reproductive status and larval development of freshwater mussels (Bivalvia: Unionoida)","interactions":[],"lastModifiedDate":"2019-10-10T13:50:07","indexId":"70205928","displayToPublicDate":"2019-10-04T13:48:53","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2498,"text":"Journal of Visualized Experiments","active":true,"publicationSubtype":{"id":10}},"title":"Standardizing a non-lethal method for characterizing the reproductive status and larval development of freshwater mussels (Bivalvia: Unionoida)","docAbstract":"Actively monitoring the timing, development, and reproductive patterns of endangered species is critical when managing for population recovery. Freshwater mussels are among the most imperiled organisms in the world, but information about early larval (glochidial) development and brooding periods is still lacking for many species. Previous studies have focused on the complex life history stage when female mussels are ready to parasitize host fish, but few studies have focused on the brooding period and timing of larval development. The protocol described here allows researchers to non-lethally evaluate the state of gravidity for female mussels. The results of this study show that this method does not affect a female mussel’s ability to stay gravid or become gravid again after sampling has been performed. The advantage of this method may permit its use on federally threatened or endangered species or other populations of high conservation concern. This protocol can be adapted for use on both preserved or live individuals and was tested on a variety of mussel species. The database provided is a repository for a breadth of information on timing of reproductive habits and will facilitate future freshwater mussel research, conservation, and recovery efforts.","language":"English","publisher":"JoVE","doi":"10.3791/60244","usgsCitation":"Beaver, C., Geda, S., and Johnson, N., 2019, Standardizing a non-lethal method for characterizing the reproductive status and larval development of freshwater mussels (Bivalvia: Unionoida): Journal of Visualized Experiments, v. 152, e60244, https://doi.org/10.3791/60244.","productDescription":"e60244","ipdsId":"IP-108167","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":459607,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3791/60244","text":"Publisher Index Page"},{"id":368239,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"152","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Beaver, Caitlin 0000-0002-9269-7604","orcid":"https://orcid.org/0000-0002-9269-7604","contributorId":219705,"corporation":false,"usgs":true,"family":"Beaver","given":"Caitlin","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":772923,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Geda, Susan","contributorId":219706,"corporation":false,"usgs":false,"family":"Geda","given":"Susan","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":772924,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Nathan 0000-0001-5167-1988","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":216876,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":772922,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70208122,"text":"70208122 - 2019 - Estimating sightability of greater sage-grouse at leks using an aerial infrared system and N-mixture models","interactions":[],"lastModifiedDate":"2020-01-29T16:24:01","indexId":"70208122","displayToPublicDate":"2019-10-03T13:36:11","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3766,"text":"Wildlife Biology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating sightability of greater sage-grouse at leks using an aerial infrared system and N-mixture models","docAbstract":"<p><span>Counts of grouse present at leks (breeding grounds) during spring are widely used to monitor population numbers and assess trends. However, only a proportion of birds available to count are detected resulting in a biased population index. We designed a study using an aerial integrated infrared imaging system (AIRIS) and experimental pseudo-leks to quantify sightability (proportion of birds detected) of conventional ground-based visual (GBV) surveys for greater sage-grouse&nbsp;</span><i>Centrocercus urophasianus</i><span>. Specifically, we calibrated AIRIS at pseudo-leks composed of known numbers of captively-raised birds, primarily ring-necked pheasant&nbsp;</span><i>Phasianus colchicus</i><span>. We then carried out AIRIS and GBV surveys, simultaneously, on nearby sage-grouse leks, allowing us to model AIRIS and GBV sightability. AIRIS detected ∼93% of birds on pseudo-leks while GBV detected ∼86% of sage-grouse on leks. Thus, the ground count observation error was –14% from the ‘true' number of male sage-grouse attending the leks. We also found sagebrush cover decreased sightability for GBV counts but did not influence sightability by AIRIS. Because standard GBV protocols typically make repeated counts of sage-grouse in a single morning, we also modeled repeated GBV counts using&nbsp;</span><i>N</i><span>-mixture models and found an 88% sightability, which was nearly the same as GBV sightability from the AIRIS analysis. This suggests that the use of repeated morning counts can potentially account for imperfect detection in the standard GBV surveys currently implemented. We also provide generalized correction values that could be employed by resource managers using either GBV or AIRIS to better estimate ‘true’ numbers of sage-grouse attending leks within similar environments to this study. The findings and interpretation presented can help guide effective monitoring protocols that account for observation error and improve accuracy of data used for population trend and abundance estimation.</span></p>","language":"English","publisher":"BioONE","doi":"10.2981/wlb.00552","usgsCitation":"Coates, P.S., Wann, G.T., Gillette, G.L., Ricca, M.A., Prochazka, B.G., Severson, J.P., Andrle, K.M., Espinosa, S.P., Casazza, M.L., and Delehanty, D.J., 2019, Estimating sightability of greater sage-grouse at leks using an aerial infrared system and N-mixture models: Wildlife Biology, v. 2019, no. 1, wlb.00552, 11 p., https://doi.org/10.2981/wlb.00552.","productDescription":"wlb.00552, 11 p.","ipdsId":"IP-100795","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":459615,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2981/wlb.00552","text":"Publisher Index 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,{"id":70205995,"text":"70205995 - 2019 - Reducing wet ammonium deposition in Rocky Mountain National Park: The development and evaluation of a pilot early warning system for agricultural operations in eastern Colorado","interactions":[],"lastModifiedDate":"2019-12-04T06:22:06","indexId":"70205995","displayToPublicDate":"2019-10-03T06:56:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Reducing wet ammonium deposition in Rocky Mountain National Park: The development and evaluation of a pilot early warning system for agricultural operations in eastern Colorado","docAbstract":"Agricultural emissions are the primary source of ammonia (NH3) deposition in Rocky Mountain National Park (RMNP), a Class I area, that is granted special air quality protections under the Clean Air Act. Between 2014 and 2016, the pilot phase of the Colorado agricultural nitrogen early warning system (CANEWS) was developed for agricultural producers to voluntarily and temporarily minimize emissions of NH3 during periods of upslope winds. The CANEWS was created using trajectory analyses driven by outputs from an ensemble of numerical weather forecasts together with the climatological expertise of human forecasters. Here, we discuss the methods for the CANEWS and offer preliminary analyses of 33 months of the CANEWS based on atmospheric deposition data from two sites in RMNP as well as responses from agricultural producers after warnings were issued. Results showed that the CANEWS accurately predicted 6 of 9 high N deposition weeks at a lower-elevation observation site, but only 4 of 11 high N deposition weeks at a higher-elevation site. Sixty agricultural producers from 39 of Colorado’s agricultural operations volunteered for the CANEWS, and a two-way line of communication between agricultural producers and scientists was formed. For each warning issued, an average of 23 producers responded to a post-warning survey. Over 75% of responding CANEWS participants altered their practices after an alert. While the current effort was insufficient to reduce atmospheric deposition, we were encouraged by the collaborative spirit between agricultural, scientific, and resource management communities. Solving a broad and complex social-ecological problem requires both a technological approach, such as the CANEWS, and collaboration and trust from all participants, including agricultural producers, land managers, university researchers, and environmental agencies.","language":"English","publisher":"Springer","doi":"10.1007/s00267-019-01209-z","usgsCitation":"Pena, A., Schumacher, R., Denning, S., Faulkner, W., Baron, J., Ham, J., Ojima, D.S., and Collett, J., 2019, Reducing wet ammonium deposition in Rocky Mountain National Park: The development and evaluation of a pilot early warning system for agricultural operations in eastern Colorado: Environmental Management, v. 64, no. 5, p. 626-639, https://doi.org/10.1007/s00267-019-01209-z.","productDescription":"14 p.","startPage":"626","endPage":"639","ipdsId":"IP-099062","costCenters":[{"id":291,"text":"Fort Collins Science 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Jay","contributorId":219811,"corporation":false,"usgs":false,"family":"Ham","given":"Jay","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":773235,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ojima, Dennis S.","contributorId":208511,"corporation":false,"usgs":false,"family":"Ojima","given":"Dennis","email":"","middleInitial":"S.","affiliations":[{"id":37812,"text":"Colorado State University; North Central Climate Science Center","active":true,"usgs":false}],"preferred":false,"id":773236,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Collett, Jeffrey","contributorId":219812,"corporation":false,"usgs":false,"family":"Collett","given":"Jeffrey","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":773237,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70207487,"text":"70207487 - 2019 - Understanding reproductive allometry in turtles: A slippery “slope”","interactions":[],"lastModifiedDate":"2019-12-20T16:39:33","indexId":"70207487","displayToPublicDate":"2019-10-02T16:38:13","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Understanding reproductive allometry in turtles: A slippery “slope”","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Measures of reproductive output in turtles are generally positively correlated with female body size. However, a full understanding of reproductive allometry in turtles requires logarithmic transformation of reproductive and body size variables prior to regression analyses. This allows for slope comparisons with expected linear or cubic relationships for linear to linear and linear to volumetric variables, respectively. We compiled scaling data using this approach from published and unpublished turtle studies (46 populations of 25 species from eight families) to quantify patterns among taxa. Our results suggest that for log–log comparisons of clutch size, egg width, egg mass, clutch mass, and pelvic aperture width to shell length, all scale hypoallometrically despite theoretical predictions of isometry. Clutch size generally scaled at ~1.7 to 2.0 (compared to an isometric expectation of 3.0), egg width at ~0.5 (compared to an expectation of 1.0), egg mass at ~1.1 to 1.3 (3.0), clutch mass at ~2.5 to 2.8 (3.0), and pelvic aperture width at 0.8–0.9 (1.0). We also found preliminary evidence that scaling may differ across years and clutches even in the same population, as well as across populations of the same species. Future investigators should aspire to collect data on all these reproductive parameters and to report log–log allometric analyses to test our preliminary conclusions regarding reproductive allometry in turtles.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.5697","usgsCitation":"Iverson, J.B., Lindeman, P.V., and Lovich, J.E., 2019, Understanding reproductive allometry in turtles: A slippery “slope”: Ecology and Evolution, v. 9, no. 20, p. 11891-11903, https://doi.org/10.1002/ece3.5697.","productDescription":"13 p.","startPage":"11891","endPage":"11903","ipdsId":"IP-105599","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":459618,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.5697","text":"Publisher Index Page"},{"id":370591,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"20","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Iverson, John B.","contributorId":147488,"corporation":false,"usgs":false,"family":"Iverson","given":"John","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":778209,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lindeman, Peter V.","contributorId":140947,"corporation":false,"usgs":false,"family":"Lindeman","given":"Peter","email":"","middleInitial":"V.","affiliations":[{"id":13624,"text":"Edinboro University, Department of Biology and Health Services, 230 Scotland Rd., Edinboro, Pennsylvania 16444, USA","active":true,"usgs":false}],"preferred":false,"id":778210,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":778211,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70205712,"text":"fs20193063 - 2019 - Streamflow—Water year 2018","interactions":[],"lastModifiedDate":"2019-10-02T16:56:52","indexId":"fs20193063","displayToPublicDate":"2019-10-02T16:22:48","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-3063","displayTitle":"Streamflow—Water Year 2018","title":"Streamflow—Water year 2018","docAbstract":"<p><span>The maps and graphs in this summary describe national streamflow conditions for water year 2018 (October 1, 2017, to September 30, 2018) in the context of streamflow ranks relative to the 89-year period of water years 1930–2018. The illustrations are based on observed data from the U.S. Geological Survey National Streamflow Network. Annual runoff in the Nation’s rivers and streams during water year 2018 was higher than the long-term (water years 1930–2018) mean annual runoff of 9.33 inches. Nationwide, the 2018 streamflow ranked 33d highest out of the 89 years.</span></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193063","usgsCitation":"Jian, X., Wolock, D.M., Brady, S.J., and Lins, H.F., 2019, Streamflow—Water year 2018: U.S. Geological Survey Fact Sheet 2019–3063, 6 p.,  https://doi.org/10.3133/fs20193063.\n","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"Y","ipdsId":"IP-109903","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":367937,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System","description":"USGS Water Data for the Nation"},{"id":367936,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3063/fs20193063.pdf","text":"Report","size":"2.10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3063"},{"id":367935,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3063/coverthb.jpg"}],"country":"United 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States\"}}]}","contact":"<p><a data-mce-href=\"https://water.usgs.gov/\" href=\"https://water.usgs.gov/\">U.S. Geological Survey</a> <br>415 National Center <br>Reston, VA 20192&nbsp;</p>","tableOfContents":"<ul><li>Introduction</li><li>National Overview</li><li>Regional Patterns</li><li>Seasonal Characteristics</li><li>High and Low Flows</li><li>Additional Information</li><li>References</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2019-10-02","noUsgsAuthors":false,"publicationDate":"2019-10-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Jian, Xiaodong 0000-0002-9173-3482","orcid":"https://orcid.org/0000-0002-9173-3482","contributorId":207129,"corporation":false,"usgs":true,"family":"Jian","given":"Xiaodong","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":772155,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolock, David M. 0000-0002-6209-938X dwolock@usgs.gov","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":540,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"dwolock@usgs.gov","middleInitial":"M.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":772156,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brady, Steven J. 0000-0002-8527-5227 sbrady@usgs.gov","orcid":"https://orcid.org/0000-0002-8527-5227","contributorId":4071,"corporation":false,"usgs":true,"family":"Brady","given":"Steven","email":"sbrady@usgs.gov","middleInitial":"J.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":772157,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lins, Harry F. 0000-0001-5385-9247","orcid":"https://orcid.org/0000-0001-5385-9247","contributorId":219372,"corporation":false,"usgs":false,"family":"Lins","given":"Harry F.","affiliations":[{"id":34498,"text":"USGS retiree","active":true,"usgs":false}],"preferred":false,"id":772158,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70204655,"text":"sir20195075 - 2019 - Potential interaction of groundwater and surface water including autonomous underwater vehicle reconnaissance at Nolin River Lake, Kentucky, 2016","interactions":[],"lastModifiedDate":"2019-10-02T17:00:48","indexId":"sir20195075","displayToPublicDate":"2019-10-02T13:12:29","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":"2019-5075","displayTitle":"Potential Interaction of Groundwater and Surface Water Including Autonomous Underwater Vehicle Reconnaissance at Nolin River Lake, Kentucky, 2016","title":"Potential interaction of groundwater and surface water including autonomous underwater vehicle reconnaissance at Nolin River Lake, Kentucky, 2016","docAbstract":"<p>The U.S. Geological Survey collaborated with the U.S. Army Corps of Engineers, Louisville District, on a synoptic study of water quality at Nolin River Lake during August 2016. The purpose of the study was to develop a better understanding of the potential for interaction between groundwater and surface water at Nolin River Lake, Kentucky. Groundwater can have properties that are measurably different from those in adjacent surface water, and inflows and outflows can be an important component of water quality and quantity. An improved understanding of potential interaction of groundwater and surface water at Nolin River Lake may be used to refine lake-management strategies. This study (1) compiled and interpreted existing information to characterize the hydrogeological setting and implications for potential interaction of groundwater and surface water in the Nolin River Lake watershed; (2) collected transects of onsite water-quality parameters using an autonomous underwater vehicle (AUV) in areas with potential for interaction of groundwater and surface water, including five sites on Nolin River Lake and one site on the Nolin River; and (3) collected discrete water-quality and phytoplankton community data at the same six sites.</p><p>A review of existing hydrogeologic information did not indicate the presence of karst features adjacent to or beneath Nolin River Lake that would facilitate groundwater interaction with the reservoir. Observations leading to this conclusion include (1) limestone that is adjacent to the shoreline and perhaps beneath the lake, is overlain with siliciclastic rocks and fine-grained sediment that inhibits infiltration and development of karst features that encourage rapid groundwater flow; (2) the geologic deposits surrounding the reservoir are described as having limited or no potential for development of karst features, some exceptions may exist in tributary valleys; (3) very few karst features were mapped within 1 mile of the reservoir or in the area currently occupied by the reservoir; and (4) faults that intersect the reservoir but may not possess hydraulic properties that cause the faults to be conduits for groundwater flow. Groundwater interaction with reservoir tributaries is likely more common in areas of the watershed upstream from Nolin River Lake where karst hydrogeology is prevalent.</p><p>Results of water-quality surveys using an AUV from August 15 to 19, 2016, did not identify areas of anomalous values that might indicate groundwater inflows through preferential flow zones. Spatial distributions of water-quality parameters were generally uniform within each constant-depth layer. The constant-depth layers were selected to be above, within, and below the thermocline and ranged from the water surface to 25 feet. Surveys near the bottom of the reservoir that might have been more sensitive to groundwater inflows were not done because presurvey data were not available to indicate locations of obstacles that could ensnare the AUV. Water-quality data collected with the AUV did identify water-quality anomalies where stream tributaries were discharging to the reservoir.</p><p>The discrete water-quality samples indicated uniformity among the five reservoir sites. The riverine site that is immediately upstream from Nolin River Lake, however, had some unique water-quality characteristics relative to sites on the reservoir. The highest concentrations of nitrate plus nitrite as nitrogen (0.145 milligrams per liter [mg/L]), total phosphorous (0.07 mg/L), chlorophyll <i>a</i> (36.1 micrograms per liter), and pheophytin <i>a</i> (10.2 micrograms per liter) were measured at the Nolin River Lake riverine site (site 2NRR20034). The concentrations of nutrients and chlorophyll <i>a</i> at the riverine site did exceed the 25th percentile of median concentrations measured by the U.S. Environmental Protection Agency (EPA) at other lakes and reservoirs in EPA level IV ecoregion 71a. Concentrations of most nutrients and chlorophyll a at the five reservoir sites also exceeded the 25th percentile of median concentrations in EPA level IV ecoregion 72h. The exception was the concentrations of total phosphorus as phosphorus at the reservoir sites that were at or below the 25th percentile of median concentrations measured by EPA (0.03 mg/L). Concentrations of orthophosphate as phosphorus were less&nbsp;than the method detection limit of 0.004 mg/L at all sites. The phytoplankton community in Nolin River Lake was almost exclusively (greater than 90 percent of total phytoplankton abundance) cyanobacteria, also known as blue-green algae. A species of <i>Cylindrospermopsis</i> dominated the cyanobacterial community at the five reservoir sites, while <i>Chroococcus microscopicus</i> was most abundant at the riverine site. Cyanobacterial cell densities ranged from 10,000 to 198,067,460 cells per liter in five areas in the reservoir and from 4,800 to 73,751,253 cells per liter at the riverine site.</p><p>Multiple potential sources of water to Nolin River Lake include direct precipitation, overland flow, interflow, groundwater, and surface water. Understanding the exact contribution of each of these components to the water budget at Nolin River Lake may help the U.S. Army Corps of Engineers manage the water quality, water quantity, and biological communities in the reservoir. Additional hydrogeologic and water-quality data that builds on the results of this study may refine the inferences of this study; for example, deeper AUV surveys that target the largest fault zones might further the understanding of the potential for groundwater flow through those features. A complete understanding of the reservoir hydrology, however, may require the use of scientific methods intended for water bodies as large as Nolin River Lake, such as aerial infrared photography and imagery; water mass, chemical, and isotopic balance studies; geophysical measurements; and numerical simulations.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195075","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Louisville District","usgsCitation":"Crain, A.S., Boldt, J.A., Bayless, E.R., Bunch, A.R., Young, J.L., Thomason, J.C., and Wolf, Z.L., 2019, Potential interaction of groundwater and surface water including autonomous underwater vehicle reconnaissance at Nolin River Lake, Kentucky, 2016: U.S. Geological Survey Scientific Investigations Report 2019–5075, 36 p., https://doi.org/10.3133/sir20195075.\n","productDescription":"Report: vi, 36 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-085091","costCenters":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":367882,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5075/sir20195075.pdf","text":"Report","size":"16.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5075"},{"id":367881,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5075/coverthb.jpg"},{"id":367883,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F798857D","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-Quality Datasets from Synoptic Surveys in Nolin River Lake, Kentucky, using an Autonomous Underwater Vehicle, Discrete Sampling, and Depth Profiles, August 2016"}],"country":"United States","state":"Kentucky","otherGeospatial":"Nolin River Lake","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -86.28387451171875,\n              37.25929865437848\n            ],\n            [\n              -86.0504150390625,\n              37.25929865437848\n            ],\n            [\n              -86.0504150390625,\n              37.40780092202727\n            ],\n            [\n              -86.28387451171875,\n              37.40780092202727\n            ],\n            [\n              -86.28387451171875,\n              37.25929865437848\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/oki-water\" href=\"https://www.usgs.gov/centers/oki-water\">Ohio-Kentucky-Indiana Water Science Center</a> <br>U.S. Geological Survey <br>9818 Bluegrass Parkway <br>Louisville, KY 40299–1906</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Description of Study Area</li><li>Methods</li><li>Autonomous Underwater Vehicle Data Processing</li><li>Potential Interaction of Groundwater and Surface Water at Nolin River Lake</li><li>Reservoir Water-Quality Data during August 15–19, 2016</li><li>Limitations</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2019-10-02","noUsgsAuthors":false,"publicationDate":"2019-10-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Crain, Angela S. 0000-0003-0969-6238 ascrain@usgs.gov","orcid":"https://orcid.org/0000-0003-0969-6238","contributorId":3090,"corporation":false,"usgs":true,"family":"Crain","given":"Angela","email":"ascrain@usgs.gov","middleInitial":"S.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":767935,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boldt, Justin A. 0000-0002-0771-3658","orcid":"https://orcid.org/0000-0002-0771-3658","contributorId":207849,"corporation":false,"usgs":true,"family":"Boldt","given":"Justin","email":"","middleInitial":"A.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":767939,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bayless, Randall E. 0000-0002-0357-3635 ebayless@usgs.gov","orcid":"https://orcid.org/0000-0002-0357-3635","contributorId":191766,"corporation":false,"usgs":true,"family":"Bayless","given":"Randall","email":"ebayless@usgs.gov","middleInitial":"E.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":false,"id":767940,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bunch, Aubrey R. 0000-0002-2453-3624 aurbunch@usgs.gov","orcid":"https://orcid.org/0000-0002-2453-3624","contributorId":4351,"corporation":false,"usgs":true,"family":"Bunch","given":"Aubrey","email":"aurbunch@usgs.gov","middleInitial":"R.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":767941,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Young, Jade L.","contributorId":202092,"corporation":false,"usgs":false,"family":"Young","given":"Jade","email":"","middleInitial":"L.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":767936,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thomason, Jennifer C.","contributorId":202093,"corporation":false,"usgs":false,"family":"Thomason","given":"Jennifer","email":"","middleInitial":"C.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":767937,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wolf, Zachary L.","contributorId":202094,"corporation":false,"usgs":false,"family":"Wolf","given":"Zachary","email":"","middleInitial":"L.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":767938,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70206591,"text":"70206591 - 2019 - Effects of ocean climate on the length and condition of forage fish in the Gulf of Alaska","interactions":[],"lastModifiedDate":"2019-11-11T19:13:40","indexId":"70206591","displayToPublicDate":"2019-10-01T19:10:13","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1660,"text":"Fisheries Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Effects of ocean climate on the length and condition of forage fish in the Gulf of Alaska","docAbstract":"Climatic drivers of the size and body condition of forage fish in the North Pacific are poorly known.   We  hypothesized that length and condition of forage fish in the Gulf of Alaska (GoA) should vary in relation to ocean temperature on multiple scales.  To test this hypothesis, we analyzed morphometric data for capelin (Mallotus catervarius) and Pacific sand lance (PSL; Ammodytes personatus) sampled by a seabird (Cerorhinca monocerata) in two regions of the GoA, 1993–2016.  Based on previous studies, we predicted specifically that capelin length and body condition (Fulton’s K) would be negatively related to the Pacific Decadal Oscillation (PDO) and sea surface temperature (SST), whereas PSL length and condition would be positively related.  Interannual variation in length and body condition was evaluated relative to seasonal values of ocean climate using regression.  Forage fish length and condition varied interannually, between sampling regions, and were dependent on the size/age class of the fish sampled.  As predicted, length and body condition of capelin (mostly age 1+) were negatively related to the PDO and SST.  Relationships with ocean climate for PSL varied by size/age class: positive for putative age-0 fish and negative for putative age-1+ fish.  We conclude that our hypothesis was supported for capelin and partially supported for PSL.  This study demonstrates that ocean climate determines key morphometric characteristics of forage fish that may relate to interannual variation in the energetic value of prey, and provides an example of how seabirds can be used to obtain specimens for evaluations of potential prey quality.","language":"English","publisher":"Wiley","doi":"10.1111/fog.12443","usgsCitation":"Thompson, S.A., Garcia-Reyes, M., Sydeman, W., Arimitsu, M.L., Hatch, S., and Piatt, J.F., 2019, Effects of ocean climate on the length and condition of forage fish in the Gulf of Alaska: Fisheries Oceanography, v. 28, no. 6, p. 658-671, https://doi.org/10.1111/fog.12443.","productDescription":"14 p.","startPage":"658","endPage":"671","ipdsId":"IP-104579","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":467318,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/57003","text":"External Repository"},{"id":369116,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -160.576171875,\n              55.99838095535963\n            ],\n            [\n              -127.4853515625,\n              51.590722643120145\n            ],\n            [\n              -129.5947265625,\n              55.55349545845371\n            ],\n            [\n              -137.3291015625,\n              59.84481485969105\n            ],\n            [\n              -144.7998046875,\n              62.451405884537564\n            ],\n            [\n              -152.75390624999997,\n              61.41775026352097\n            ],\n            [\n              -160.576171875,\n              55.99838095535963\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"28","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Sarah Ann","contributorId":220498,"corporation":false,"usgs":false,"family":"Thompson","given":"Sarah","email":"","middleInitial":"Ann","affiliations":[{"id":40179,"text":"Farallon Institute for Advanced Ecosystem Research","active":true,"usgs":false}],"preferred":false,"id":775060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garcia-Reyes, Marisol","contributorId":220499,"corporation":false,"usgs":false,"family":"Garcia-Reyes","given":"Marisol","email":"","affiliations":[{"id":40179,"text":"Farallon Institute for Advanced Ecosystem Research","active":true,"usgs":false}],"preferred":false,"id":775061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sydeman, William","contributorId":220500,"corporation":false,"usgs":false,"family":"Sydeman","given":"William","email":"","affiliations":[{"id":40179,"text":"Farallon Institute for Advanced Ecosystem Research","active":true,"usgs":false}],"preferred":false,"id":775062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arimitsu, Mayumi L. 0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":775059,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hatch, Scott","contributorId":220501,"corporation":false,"usgs":false,"family":"Hatch","given":"Scott","email":"","affiliations":[{"id":35874,"text":"Institute for Seabird Research and Conservation","active":true,"usgs":false}],"preferred":false,"id":775063,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Piatt, John F. 0000-0002-4417-5748 jpiatt@usgs.gov","orcid":"https://orcid.org/0000-0002-4417-5748","contributorId":3025,"corporation":false,"usgs":true,"family":"Piatt","given":"John","email":"jpiatt@usgs.gov","middleInitial":"F.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":775064,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70206007,"text":"70206007 - 2019 - Anthropogenic and geologic causes of anomalously high uranium concentrations in groundwater used for drinking water supply in the southeastern San Joaquin Valley, CA","interactions":[],"lastModifiedDate":"2019-10-17T06:59:53","indexId":"70206007","displayToPublicDate":"2019-10-01T14:50:21","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Anthropogenic and geologic causes of anomalously high uranium concentrations in groundwater used for drinking water supply in the southeastern San Joaquin Valley, CA","docAbstract":"Concentrations of uranium (U) >30 µg/L in groundwater are relatively uncommon in drinking water in the United States but can be of concern in those areas where complex interactions of aquifer materials and anthropogenic alterations of the natural flow regime mobilize uranium.  High concentrations (>30 µg/L) of U in the southeastern San Joaquin Valley, California, USA, have been detected in 24 percent of 257 domestic, irrigation, and public-supply wells sampled across an approximately 110,000 km2 area. The location of high concentrations depends on the interactions of U sources from fluvial fans that originate in the Sierra Nevada to the east and seepage of irrigation water that contains high concentrations of HCO3 that leaches U from the sediments.  In addition, interactions with phosphate (PO4) from fertilized irrigated fields may sequester U in the aquifer.  Principal component analysis of the data demonstrates that HCO3 and ions associated with high total dissolved solids in the aquifer and the percentage of agriculture near the well sampled are associated with high U concentrations. Nitrate concentrations do not appear to control release of U to the aquifer.  Age dating of the groundwater and generally increasing U concentrations of the past 25 years in resampled wells where irrigation is prevalent suggests that high U concentrations are associated with younger water, indicating that irrigation of fields over the past 100 years has significantly contributed to increasing concentrations and mobilizing U.  In some places, the groundwater is supersaturated with uranyl-containing minerals, as would be expected in roll front deposits.  In general, the interaction of natural geological sources high in U, the anthropogenically driven addition of HCO3 and possibly phosphate fertilizer, control the location and concentration of U in each individual fluvial fan, but the addition of nitrate in fertilizer does not appear control the location of high U.  These geochemical interactions can be used to determine controls on anomalously high U in alluvial aquifers","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2019.124009","collaboration":"SWRCB","usgsCitation":"Rosen, M.R., Burow, K.R., and Fram, M., 2019, Anthropogenic and geologic causes of anomalously high uranium concentrations in groundwater used for drinking water supply in the southeastern San Joaquin Valley, CA: Journal of Hydrology, v. 577, p. 1-14, https://doi.org/10.1016/j.jhydrol.2019.124009.","productDescription":"124009, 14p.","startPage":"1","endPage":"14","ipdsId":"IP-083522","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":437317,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94ME8S6","text":"USGS data release","linkHelpText":"Water quality and ancillary data for assessment of uranium concentrations in groundwater, San Joaquin Valley, CA (ver. 1.1, December, 2019)"},{"id":368349,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":368344,"type":{"id":15,"text":"Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2019.124009"}],"country":"United States","state":"California","otherGeospatial":"San Joaquin Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.30029296875,\n              36.217687122250574\n            ],\n            [\n              -119.25659179687499,\n              36.217687122250574\n            ],\n            [\n              -119.25659179687499,\n              37.01132594307015\n            ],\n            [\n              -120.30029296875,\n              37.01132594307015\n            ],\n            [\n              -120.30029296875,\n              36.217687122250574\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"577","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rosen, Michael R. 0000-0003-3991-0522 mrosen@usgs.gov","orcid":"https://orcid.org/0000-0003-3991-0522","contributorId":495,"corporation":false,"usgs":true,"family":"Rosen","given":"Michael","email":"mrosen@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773275,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burow, Karen R. 0000-0001-6006-6667 krburow@usgs.gov","orcid":"https://orcid.org/0000-0001-6006-6667","contributorId":1504,"corporation":false,"usgs":true,"family":"Burow","given":"Karen","email":"krburow@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773274,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fram, Miranda 0000-0002-6337-059X mfram@usgs.gov","orcid":"https://orcid.org/0000-0002-6337-059X","contributorId":219833,"corporation":false,"usgs":true,"family":"Fram","given":"Miranda","email":"mfram@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773276,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70217792,"text":"70217792 - 2019 - Burial history reconstruction of the Appalachian Basin in Kentucky, West Virginia, Ohio, Pennsylvania, and New York, using 1D petroleum system models","interactions":[],"lastModifiedDate":"2021-02-02T19:47:44.189506","indexId":"70217792","displayToPublicDate":"2019-10-01T14:47:22","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2789,"text":"Mountain Geologist","active":true,"publicationSubtype":{"id":10}},"title":"Burial history reconstruction of the Appalachian Basin in Kentucky, West Virginia, Ohio, Pennsylvania, and New York, using 1D petroleum system models","docAbstract":"<p class=\"indent\">Nine 1D burial history models were built across the Appalachian Basin to reconstruct the burial, erosional, and thermal maturation histories of contained petroleum source rocks. Models were calibrated to measured downhole temperature and to vitrinite reflectance (% R<sub>o</sub>) data from Devonian through Pennsylvanian petroleum source rocks. The highest levels of thermal maturity in Devonian and Mississippian source rocks are within and proximal to the Rome trough in the deep basin, which is also within the confluence of increased structural complexity and associated faulting, overpressured Devonian shales, and thick intervals of salt in the underlying Silurian Salina Group.</p><p class=\"indent\">Models incorporate minor erosion from 260 to 140 million years ago (Ma) that allows for extended burial and heating of underlying strata and generally results in less eroded thickness, compared to previously published models. Two modeled times of increased erosion, from 140 to 90 Ma and 23 to 5.3 Ma, are followed by less erosion from 5.3 Ma to Present. Absent strata are mainly Permian shales and sandstone; thickness of these removed layers increased from about 6,200 ft (1,890 m) west of the Rome trough to as much as 9,650 ft (2,940 m) within the trough. The onset of oil generation is based on 0.6% R<sub>o</sub><span>&nbsp;</span>for Pennsylvanian coal samples; time of onset for the Ordovician Utica Shale ranges from 387 to 306 Ma, and 359 to 282 Ma for Middle Devonian to basal Mississippian shales. The ∼1.2% R<sub>o</sub><span>&nbsp;</span>onset of wet gas generation ranges from 360 to 281 Ma in the Utica Shale, and 298 to 150 Ma for Middle Devonian to lowermost Mississippian shales.</p>","language":"English","publisher":"The Rocky Mountain Association of Geologists","doi":"10.31582/rmag.mg.56.4.365","usgsCitation":"Higley, D.K., and Enomoto, C.B., 2019, Burial history reconstruction of the Appalachian Basin in Kentucky, West Virginia, Ohio, Pennsylvania, and New York, using 1D petroleum system models: Mountain Geologist, v. 56, no. 4, p. 365-396, https://doi.org/10.31582/rmag.mg.56.4.365.","productDescription":"32 p.","startPage":"365","endPage":"396","ipdsId":"IP-098203","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":382896,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kentucky, New York, Ohio, Pennsylvania, West 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,{"id":70204832,"text":"ofr20191090 - 2019 - Coastal Salinity Index along the southeastern Atlantic coast and the Gulf of Mexico, 1983 to 2018","interactions":[],"lastModifiedDate":"2026-02-10T20:42:13.989011","indexId":"ofr20191090","displayToPublicDate":"2019-10-01T14:05:00","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":"2019-1090","displayTitle":"Coastal Salinity Index Along the Southeastern Atlantic Coast and the Gulf of Mexico, 1983 to 2018","title":"Coastal Salinity Index along the southeastern Atlantic coast and the Gulf of Mexico, 1983 to 2018","docAbstract":"<p>Coastal droughts have a different dynamic than upland droughts, which are typically characterized by agricultural, hydrologic, meteorological, and (or) socioeconomic effects. Drought uniquely affects coastal ecosystems because of changes in the salinity conditions of estuarine creeks and rivers. The location of the freshwater-saltwater interface in surface-water bodies is an important factor in the ecological and socioeconomic dynamics of coastal communities. To address the data and information gap for characterizing coastal drought, the Coastal Salinity Index (CSI) was developed by using salinity data. The CSI uses a computational approach similar to the Standardized Precipitation Index. The CSI can be computed for unique time intervals (for example 1-, 6-, 12-, and 24-month intervals) to characterize short- and long-term drought (saline) conditions, as well as wet (high freshwater inflow) conditions.</p><p>To encourage the use of the CSI in current and future research endeavors, this investigation addressed three activities to enhance the use and application of the CSI. First, a software package was developed for the consistent computation of the CSI that includes preprocessing of salinity data, filling missing data, computing the CSI, post-processing, and generating the supporting metadata. This software package is available for download from the U.S. Geological Survey GitLab repository. Second, the CSI has been computed at sites along the southeastern Atlantic coast (Florida to North Carolina) and the Gulf of Mexico (Texas to Florida) to increase the opportunity for linking the CSI to ecological response data. Third, using telemetered salinity data, the real-time computation of the CSI has been prototyped and disseminated on the web.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191090","collaboration":"Prepared in cooperation with the National Integrated Drought Information System","usgsCitation":"Petkewich, M.D., Lackstrom, K., McCloskey, B.J., Rouen, L.F, and Conrads, P.A., 2019, Coastal Salinity Index along the southeastern Atlantic coast and the Gulf of Mexico, 1983 to 2018 (ver. 1.1, April 2023): U.S. Geological Survey\nOpen-File Report 2019–1090, 26 p., https://doi.org/10.3133/ofr20191090.","productDescription":"Report: vi, 26 p.; Appendix; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105920","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":499716,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109078.htm","linkFileType":{"id":5,"text":"html"}},{"id":415336,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2019/1090/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":415335,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1090/ofr20191090_appendix1.pdf","text":"Appendix 1","size":"1.68 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1090 Appendix 1","linkHelpText":"—Coastal Salinity Index User Guide"},{"id":415334,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1090/ofr20191090.pdf","text":"Report","size":"3.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1090"},{"id":367860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1090/coverthb2.jpg"},{"id":367858,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MQLNL2","text":"USGS data release","linkHelpText":"Coastal Salinity Index for Monitoring Drought"}],"country":"United States","state":"Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, Puerto Rico, South Carolina, Texas","otherGeospatial":"Gulf of Mexico Coast, South Atlantic Coast","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -67.5439453125,\n              17.45547257997284\n            ],\n            [\n              -65.21484375,\n              17.45547257997284\n            ],\n            [\n              -65.21484375,\n              18.95824648598139\n            ],\n            [\n              -67.5439453125,\n              18.95824648598139\n            ],\n            [\n              -67.5439453125,\n              17.45547257997284\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -98.61328125,\n              24.487148563173424\n            ],\n            [\n              -75.0146484375,\n              24.487148563173424\n            ],\n            [\n              -75.0146484375,\n              36.38591277287651\n            ],\n            [\n              -98.61328125,\n              36.38591277287651\n            ],\n            [\n              -98.61328125,\n              24.487148563173424\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","edition":"Version 1.0: October 1, 2019; Version 1.1: April 6, 2023","contact":"<p><a href=\"mailto:dc_sc@usgs.gov\" data-mce-href=\"mailto:dc_sc@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/sa-water\" data-mce-href=\"https://www.usgs.gov/centers/sa-water\">South Atlantic Water Science Center</a><br>U.S. Geological Survey<br>720 Gracern Road<br>Stephenson Center, Suite 129<br>Columbia, SC 29210</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Development of the Coastal Salinity Index</li><li>Use and Interpretation of the Coastal Salinity Index</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Coastal Salinity Index User Guide</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2019-10-01","revisedDate":"2023-04-06","noUsgsAuthors":false,"publicationDate":"2019-10-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Petkewich, Matthew D. 0000-0002-5749-6356 mdpetkew@usgs.gov","orcid":"https://orcid.org/0000-0002-5749-6356","contributorId":982,"corporation":false,"usgs":true,"family":"Petkewich","given":"Matthew","email":"mdpetkew@usgs.gov","middleInitial":"D.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768657,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lackstrom, Kirsten","contributorId":218188,"corporation":false,"usgs":false,"family":"Lackstrom","given":"Kirsten","email":"","affiliations":[{"id":39775,"text":"Carolinas Integrated Science Assessments","active":true,"usgs":false}],"preferred":false,"id":768658,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCloskey, Bryan J. 0000-0003-1975-2440","orcid":"https://orcid.org/0000-0003-1975-2440","contributorId":218190,"corporation":false,"usgs":false,"family":"McCloskey","given":"Bryan J.","affiliations":[{"id":39776,"text":"Cherokee Nation Technologies contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":768660,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rouen, Lauren F.","contributorId":218189,"corporation":false,"usgs":false,"family":"Rouen","given":"Lauren","email":"","middleInitial":"F.","affiliations":[{"id":39775,"text":"Carolinas Integrated Science Assessments","active":true,"usgs":false}],"preferred":false,"id":768659,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Conrads, Paul A.","contributorId":203437,"corporation":false,"usgs":false,"family":"Conrads","given":"Paul A.","affiliations":[{"id":27990,"text":"Deceased","active":true,"usgs":false}],"preferred":false,"id":768661,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70205890,"text":"70205890 - 2019 - Wetland biomass and productivity in Coastal Louisiana: Base line data (1976-2015) and knowledge gaps for the development of spatially explicit models for ecosystem restoration and rehabilitation initiatives","interactions":[],"lastModifiedDate":"2019-10-10T08:13:33","indexId":"70205890","displayToPublicDate":"2019-10-01T13:21:54","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Wetland biomass and productivity in Coastal Louisiana: Base line data (1976-2015) and knowledge gaps for the development of spatially explicit models for ecosystem restoration and rehabilitation initiatives","docAbstract":"Coastal Louisiana host 37% of the coastal wetland area in the conterminous US, including one of the deltaic coastal regions more susceptible to the synergy of human and natural impacts causing wetland loss. As a result of the construction of flood protection infrastructure, dredging of channels across wetlands for oil/gas exploration and maritime transport activities, coastal Coastal Louisiana hosts 37% of the coastal wetland area in the conterminous US, including one of the deltaic coastal regions more susceptible to the synergy of human and natural impacts causing wetland loss. As a result of the construction of flood protection infrastructure, dredging of channels across wetlands for oil/gas exploration and maritime transport activities, coastal Louisiana has lost approximately 4900 km2 of wetland area since the early 1930s. Despite the economic relevance of both wetland biomass and net primary productivity (NPP) as ecosystem services, there is a lack of vegetation simulation models to forecast the trends of those functional attributes at the landscape level as hydrological restoration projects are implemented. Here, we review the availability of peer-reviewed biomass and NPP wetland data (below and aboveground) published during the period 1976–2015 for use in the development, calibration and validation of high spatial resolution (<200 m × 200 m) vegetation process-based ecological models. We discuss and list the knowledge gaps for those species that represent vegetation community associations of ecological importance, including the long-term research issues associated to limited number of paired belowground biomass and productivity studies across hydrological basins currently undergoing different freshwater diversions management regimes and hydrological restoration priorities.","language":"English","publisher":"MDPI","doi":"10.3390/w11102054","usgsCitation":"Rivera-Monroy, V.H., Elliton, C., Narra, S., Meselhe, E., Zhao, X., White, E., Sasser, C.E., Visser, J.M., Meng, X., Wang, H., Xue, Z., and Jaramillo, F., 2019, Wetland biomass and productivity in Coastal Louisiana: Base line data (1976-2015) and knowledge gaps for the development of spatially explicit models for ecosystem restoration and rehabilitation initiatives: Water, v. 11, no. 10, 2054, https://doi.org/10.3390/w11102054.","productDescription":"2054","ipdsId":"IP-108198","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":459655,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w11102054","text":"Publisher Index Page"},{"id":368202,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -93.8671875,\n              30.278044377800153\n            ],\n            [\n              -94.02099609375,\n              29.57345707301757\n            ],\n            [\n              -88.61572265625,\n              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