{"pageNumber":"274","pageRowStart":"6825","pageSize":"25","recordCount":46681,"records":[{"id":70215406,"text":"70215406 - 2019 - Identifying salt marsh shorelines from remotely sensed elevation data and imagery","interactions":[],"lastModifiedDate":"2020-10-20T13:58:45.477548","indexId":"70215406","displayToPublicDate":"2019-10-19T11:04:46","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Identifying salt marsh shorelines from remotely sensed elevation data and imagery","docAbstract":"Salt marshes are valuable ecosystems that are vulnerable to lateral erosion, submergence, and internal disintegration due to sea-level rise, storms, and sediment deficits. Because many salt marshes are losing area in response to these factors, it is important to monitor their lateral extent at high resolution over multiple timescales. In this study we describe two methods to calculate the location of the salt marsh shoreline. The Marsh Edge from Elevation Data (MEED) method uses remotely sensed elevation data to calculate an objective proxy for the shoreline of a salt marsh. This proxy is the abrupt change in elevation that usually characterizes the seaward edge of a salt marsh, designated the “marsh scarp.” It is detected as the maximum slope along a cross-shore transect between Mean High Water and Mean Tide Level. The method was tested using lidar topobathymetric and photogrammetric elevation data from Massachusetts, USA.  The other method to calculate the salt marsh shoreline is the Marsh Edge by Image Processing (MEIP) method which finds the unvegetated/vegetated line. This method applies image classification techniques to multispectral imagery and elevation datasets for edge detection. The method was tested using aerial imagery and coastal elevation data from the Plum Island Estuary in Massachusetts, USA. Both methods calculate a line that closely follows the edge of vegetation seen in imagery. The root-mean-square deviation between the two methods within the test area is 0.6 meter. The two methods were compared to each other using high resolution Unmanned Aircraft Systems (UAS) data and to a heads-up digitized shoreline. The root-mean-square deviation was 0.6 meters between the two methods and less than 0.43 meters from the digitized shoreline. MEIP method was also applied to a lower resolution dataset to investigate the effect of horizontal resolution on the results. Both methods provide an accurate, efficient, and objective way to track salt marsh shorelines with spatially intensive data over large spatial scales, which is necessary to evaluate geomorphic change and wetland vulnerability","language":"English","publisher":"MDPI AG","doi":"10.3390/rs11151795","usgsCitation":"Farris, A.S., Defne, Z., and Ganju, N., 2019, Identifying salt marsh shorelines from remotely sensed elevation data and imagery: Remote Sensing, v. 11, no. 15, 1795, 17 p., https://doi.org/10.3390/rs11151795.","productDescription":"1795, 17 p.","ipdsId":"IP-109869","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":459466,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11151795","text":"Publisher Index Page"},{"id":379518,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","city":"Buzzards Bay, Orleans, Quincy","otherGeospatial":"Broad Meadows Marsh, Brant Island Cove, Pleasant Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -71.092529296875,\n              42.21224516288584\n            ],\n            [\n              -70.81787109374999,\n              42.21224516288584\n            ],\n            [\n              -70.81787109374999,\n              42.391008609205045\n            ],\n            [\n              -71.092529296875,\n              42.391008609205045\n            ],\n            [\n              -71.092529296875,\n              42.21224516288584\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -69.98703002929688,\n              41.66367910784373\n            ],\n            [\n              -69.89639282226562,\n              41.66367910784373\n            ],\n            [\n              -69.89639282226562,\n              41.84501267270689\n            ],\n            [\n              -69.98703002929688,\n              41.84501267270689\n            ],\n            [\n              -69.98703002929688,\n              41.66367910784373\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -70.95245361328125,\n              41.59285100004952\n            ],\n            [\n              -70.78765869140625,\n              41.59285100004952\n            ],\n            [\n              -70.78765869140625,\n              41.68111756290652\n            ],\n            [\n              -70.95245361328125,\n              41.68111756290652\n            ],\n            [\n              -70.95245361328125,\n              41.59285100004952\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"11","issue":"15","noUsgsAuthors":false,"publicationDate":"2019-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Farris, Amy S. 0000-0002-4668-7261 afarris@usgs.gov","orcid":"https://orcid.org/0000-0002-4668-7261","contributorId":196866,"corporation":false,"usgs":true,"family":"Farris","given":"Amy","email":"afarris@usgs.gov","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":802065,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Defne, Zafer 0000-0003-4544-4310 zdefne@usgs.gov","orcid":"https://orcid.org/0000-0003-4544-4310","contributorId":5520,"corporation":false,"usgs":true,"family":"Defne","given":"Zafer","email":"zdefne@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":802066,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":802067,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70206507,"text":"70206507 - 2019 - Review of indicators for comparing environmental effects across energy sources","interactions":[],"lastModifiedDate":"2019-11-07T13:58:45","indexId":"70206507","displayToPublicDate":"2019-10-18T13:57:03","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Review of indicators for comparing environmental effects across energy sources","docAbstract":"Robust, quantitative comparisons of environmental effects across energy sources can support development of energy planning strategies that meet growing demand while managing and minimizing undesirable effects on environmental resources. Multicriteria analyses of energy systems often use a suite of indicators to make such comparisons, but those indicators and their units of measure vary among studies. We reviewed 179 papers that described or applied energy indicators to compare environmental effects of different primary energy sources to answer four questions: (1) what environmental indicators have been used in multicriteria energy-source comparisons? (2) across studies, how consistently are these indicators used to quantify effects? (3) to what degree are different effects accounted for across energy sources and locations? and (4) how comprehensive are indicators in terms of assessing known environmental effects? For reviewed studies, we quantified the number of unique indicators and the diversity of indicators used to measure different effects. We also recorded the specific measurement units applied to quantify each indicator, the energy sources evaluated, and the continent where each study was conducted. While we found that many environmental effects of energy development have been analyzed across multiple sources, indicators were frequently measured, interpreted, and applied in ways that are not directly comparable, and some known environmental effects were infrequently assessed. We also found an emphasis on applying indicators to renewable energy sources; assessing current and potential energy sources, both renewable and nonrenewable, would greatly clarify the full suite of tradeoffs among sources and can inform energy development strategies that minimize adverse environmental effects. Overall, our review indicated that making comprehensive comparisons of the effects of energy development across sources may require efforts to standardize how effects are measured, synthesize effects literature into an open-source database, expand the range of environmental effects analyzed, and establish consistent frameworks for comparison.","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ab402d","usgsCitation":"Dorning, M., Diffendorfer, J., Loss, S.R., and Bagstad, K.J., 2019, Review of indicators for comparing environmental effects across energy sources: Environmental Research Letters, v. 14, no. 10, 103002, 15 p., https://doi.org/10.1088/1748-9326/ab402d.","productDescription":"103002, 15 p.","ipdsId":"IP-103123","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":459469,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ab402d","text":"Publisher Index Page"},{"id":369053,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Dorning, Monica 0000-0002-7576-1256 mdorning@usgs.gov","orcid":"https://orcid.org/0000-0002-7576-1256","contributorId":191772,"corporation":false,"usgs":true,"family":"Dorning","given":"Monica","email":"mdorning@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":774835,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":774836,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loss, Scott R","contributorId":220356,"corporation":false,"usgs":false,"family":"Loss","given":"Scott","email":"","middleInitial":"R","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":774837,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":774838,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70208579,"text":"70208579 - 2019 - Annual winter site fidelity of Barrow's goldeneyes in the Pacific","interactions":[],"lastModifiedDate":"2020-02-19T20:14:06","indexId":"70208579","displayToPublicDate":"2019-10-17T20:09:48","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Annual winter site fidelity of Barrow's goldeneyes in the Pacific","docAbstract":"Coastal regions on the Pacific north coast of North America provide important wintering habitat for many species of sea ducks. Although winter range and habitat preferences are well described for most species, fidelity to coastal wintering sites is generally undocumented. Fidelity is an important factor necessary for understanding interactions with coastal developments and activities and corresponding management strategies. We used data from Barrow's goldeneyes (Bucephala islandica), a sea duck that winters predominantly in nearshore habitats along the Pacific north coast, to investigate inter‐annual fidelity to, and intra‐annual fidelity within, coastal wintering sites. Between 2006 and 2015, we marked goldeneyes on breeding, molting, and wintering sites with satellite transmitters. We retained 4,931 locations in coastal habitats from 221 goldeneyes across 4 coastal regions for our analyses. These birds demonstrated high inter‐annual fidelity to coastal wintering sites; 75% of selected wintering sites were within 29 km of sites used the previous winter. Inter‐annual fidelity to wintering sites was similar between sex and age classes but differed by coastal region. Goldeneyes from southcentral Alaska, USA, expressed greater inter‐annual fidelity relative to birds from northern or southern British Columbia, Canada, and southeast Alaska. Goldeneyes also expressed high intra‐annual fidelity within wintering sites, with 75% of individuals averaging within‐season movements of ≤9 km. Intra‐annual fidelity was lesser for female than male goldeneyes but did not differ between hatch‐year and after‐hatch‐year birds. We found regional variation in intra‐annual fidelity, with goldeneyes from southcentral Alaska expressing greater intra‐annual fidelity compared to birds from other regions. High inter‐ and intra‐annual winter site fidelity by Barrow's goldeneyes suggests that, at a population level, habitat use is predictable and can be used to inform risk assessment or to evaluate factors affecting habitat choice. Also, low dispersal among wintering sites suggests that recovery from population perturbations, whether caused by natural or anthropogenic events, will be protracted.","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21767","usgsCitation":"Willie, M., Esler, D., Boyd, W.S., Bowman, T.D., Schamber, J., and Thompson, J., 2019, Annual winter site fidelity of Barrow's goldeneyes in the Pacific: Journal of Wildlife Management, v. 84, no. 1, p. 161-171, https://doi.org/10.1002/jwmg.21767.","productDescription":"11 p.","startPage":"161","endPage":"171","ipdsId":"IP-106368","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":372431,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Pacific north coast","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -126.2548828125,\n              38.89103282648846\n            ],\n            [\n              -119.970703125,\n              38.89103282648846\n            ],\n            [\n              -119.970703125,\n              48.777912755501845\n            ],\n            [\n              -126.2548828125,\n              48.777912755501845\n            ],\n            [\n              -126.2548828125,\n              38.89103282648846\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"84","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Willie, Megan","contributorId":199404,"corporation":false,"usgs":false,"family":"Willie","given":"Megan","email":"","affiliations":[{"id":12437,"text":"Simon Fraser University, Centre for Wildlife Ecology","active":true,"usgs":false}],"preferred":false,"id":782582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Esler, Daniel 0000-0001-5501-4555 desler@usgs.gov","orcid":"https://orcid.org/0000-0001-5501-4555","contributorId":5465,"corporation":false,"usgs":true,"family":"Esler","given":"Daniel","email":"desler@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":12437,"text":"Simon Fraser University, Centre for Wildlife Ecology","active":true,"usgs":false}],"preferred":true,"id":782581,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyd, W. Sean","contributorId":199405,"corporation":false,"usgs":false,"family":"Boyd","given":"W.","email":"","middleInitial":"Sean","affiliations":[{"id":35539,"text":"Science and Technology Branch, Environment and Climate Change Canada, Delta, BC, Canada","active":true,"usgs":false}],"preferred":false,"id":782583,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bowman, Timothy D.","contributorId":80779,"corporation":false,"usgs":false,"family":"Bowman","given":"Timothy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":782584,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schamber, Jason","contributorId":190328,"corporation":false,"usgs":false,"family":"Schamber","given":"Jason","affiliations":[],"preferred":false,"id":782585,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thompson, Jonathan","contributorId":222570,"corporation":false,"usgs":false,"family":"Thompson","given":"Jonathan","affiliations":[{"id":40562,"text":"Golder Associates","active":true,"usgs":false}],"preferred":false,"id":782586,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70204610,"text":"ofr20191086 - 2019 - Estimated use of water in Georgia for 2015 and water-use trends, 1985–2015","interactions":[],"lastModifiedDate":"2019-10-17T14:32:56","indexId":"ofr20191086","displayToPublicDate":"2019-10-17T15:50:00","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":"2019-1086","displayTitle":"Estimated Use of Water in Georgia for 2015 and Water-Use Trends, 1985–2015","title":"Estimated use of water in Georgia for 2015 and water-use trends, 1985–2015","docAbstract":"<p>Water-withdrawal, water-use, and water-return information have been collected and compiled for each county in Georgia every 5 years since 1980 using data obtained from various Federal, State, and private agencies, as well as additional online sources. For 2015, water use, water withdrawal, and water returns were estimated for each county, water-planning region, major river basin, and principal aquifer in Georgia. Offstream water use in 2015 is estimated for the categories of domestic, commercial, industrial processing, mining, irrigation (subdivided into crop and golf course irrigation), livestock, aquaculture, and thermoelectric power cooling.</p><p>According to the U.S. Census Bureau, approximately 10.2 million people in Georgia needed water resources to meet their personal, commercial, and recreational needs in 2015. Public water suppliers provided water to about 85 percent of the population of Georgia. Estimated total water withdrawals from both surface-water and groundwater sources were about 3,384 million gallons per day (Mgal/d) in 2015, which is a 27-percent reduction from 2010, a 48.1-percent reduction from 2000, and a 49.7-percent reduction from 1980. In 2015, surface-water withdrawals were greatest for thermoelectric power cooling (839.8 Mgal/d), and groundwater withdrawals were greatest for irrigating crops (547.9 Mgal/d). Water needs in northern Georgia are typically met by withdrawing a larger percentage of water from surface-water than groundwater sources; conversely, counties in southern Georgia withdraw more water from groundwater sources. About 1,571 Mgal/d of water were returned to Georgia streams and lakes in 2015, which represents about 46 percent of the total water withdrawn from all sources in 2015.</p><p>Water users in the Apalachicola River Basin, in 2015, withdrew the highest percentage of water (35 percent) and returned the highest percentage of water to surface-water bodies (almost 40 percent) compared to other major river basins in Georgia. Withdrawals in the Apalachicola River Basin are primarily extracted by public-supply systems (43 percent) and irrigation (34 percent). The aquifer from which 68 percent of statewide groundwater withdrawals were extracted was the Floridan aquifer system, and the majority of the water was used for irrigation (57 percent).</p><p>Historically, statewide water use in Georgia was highest in 1980 (6,735 Mgal/d), decreased to 5,353 Mgal/d in 1990, peaked at 6,531 Mgal/d in 2000, and has been declining since that time. The reduction in water use between 2000 and 2015 came primarily from surface-water withdrawals (90 percent of total reduction) and thermoelectric power cooling use (78 percent of total reduction). Water use for livestock and aquaculture increased between 1985 and 2015, and this increase correlates with the growth of agriculture in Georgia during that period. The driving forces behind the observed water-use changes include (1) shifts in population numbers and locations, (2) five periods of major drought, (3) water conservation efforts and education programs initiated by State and local governments and water utilities, and (4) changing water needs for thermoelectric power cooling, industry, and agricultural activities.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191086","collaboration":"Prepared in cooperation with the Georgia Department of Natural Resources, Environmental Protection Division","usgsCitation":"Painter, J.A., 2019, Estimated use of water in Georgia for 2015 and water-use trends, 1985–2015: U.S. Geological Survey Open-File Report 2019–1086, 216 p., https://doi.org/10.3133/ofr20191086.","productDescription":"vi, 216 p.","numberOfPages":"226","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-096369","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":437300,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V2F373","text":"USGS data release","linkHelpText":"Georgia Water Use Mapper"},{"id":368396,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1086/ofr20191086.pdf","text":"Report","size":"27.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1086"},{"id":367811,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1086/coverthb.jpg"},{"id":367813,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T6P5SM","text":"USGS data release","linkHelpText":"Georgia water-use information by county and water-use trends by water-planning region"}],"country":"United States ","state":"Georgia 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 \"}}]}","contact":"<p><a href=\"mailto:dc_ga@usgs.gov\" data-mce-href=\"mailto:dc_ga@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>1770 Corporate Drive<br>Suite 500<br>Norcross, GA 30093</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Information Sources and Methodology</li><li>Water Withdrawals, Estimated Water Use, and Surface-Water Returns</li><li>Water-Use Trends, 1985–2015</li><li>Discussion and Conclusions</li><li>Summary</li><li>References Cited</li><li>Glossary</li><li>Appendix 1. North American Industrial Classification System Codes</li><li>Appendix 2. Population, Water Withdrawals, and Water Use by Source of Water for Each County in Georgia, 2015</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2019-09-30","noUsgsAuthors":false,"publicationDate":"2019-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Painter, Jaime A. 0000-0001-8883-9158 jpainter@usgs.gov","orcid":"https://orcid.org/0000-0001-8883-9158","contributorId":1466,"corporation":false,"usgs":true,"family":"Painter","given":"Jaime","email":"jpainter@usgs.gov","middleInitial":"A.","affiliations":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":767759,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70207162,"text":"70207162 - 2019 - Characterizing range-wide population divergence in an alpine-endemic bird: A comparison of genetic and genomic approaches","interactions":[],"lastModifiedDate":"2019-12-12T06:27:12","indexId":"70207162","displayToPublicDate":"2019-10-17T15:31:27","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing range-wide population divergence in an alpine-endemic bird: A comparison of genetic and genomic approaches","docAbstract":"The delineation of intraspecific units that are evolutionarily and demographically distinct is an important step in the development of species-specific management plans. Neutral genetic variation has served as the primary data source for delineating “evolutionarily significant units,” but with recent advances in genomic technology, we now have an unprecedented ability to utilize information about neutral and adaptive variation across the entire genome. Here, we use traditional genetic markers (microsatellites) and a newer reduced-representation genomic approach (single nucleotide polymorphisms) to delineate distinct groups of white-tailed ptarmigan (Lagopus leucura), an alpine-obligate species that is distributed in naturally fragmented habitats from Alaska to New Mexico. Five subspecies of white-tailed ptarmigan are currently recognized but their distinctiveness has not been verified with molecular data. Based on analyses of 436 samples at 12 microsatellite loci and 95 samples at 14,866 single nucleotide polymorphism loci, we provide strong support for treating two subspecies as distinct intraspecific units—L. l. altipetens, found in Colorado and neighboring states; and L. l. saxatilis, found on British Columbia’s Vancouver Island—but our findings reveal more moderate patterns of divergence within the remainder of the species’ range. Results based on genetic and genomic datasets generally agreed with one another, indicating that in many cases microsatellite loci may be sufficient for describing major patterns of genetic structure across species’ ranges. This work will inform future conservation and management decisions for the white-tailed ptarmigan, a species that may be vulnerable to future changes in climate.","language":"English","publisher":"Springer","doi":"10.1007/s10592-018-1115-2","usgsCitation":"Langin, K., Aldridge, C.L., Fike, J., Cornman, R.S., Martin, K.M., Wann, G., Seglund, A.E., Schroeder, M.A., Benson, D.P., Fedy, B.C., Young, J.R., Wilson, S.D., Wolfe, D., Braun, C.E., and Oyler-McCance, S.J., 2019, Characterizing range-wide population divergence in an alpine-endemic bird: A comparison of genetic and genomic approaches: Conservation Genetics, v. 19, no. 6, p. 1471-1485, https://doi.org/10.1007/s10592-018-1115-2.","productDescription":"15 p.","startPage":"1471","endPage":"1485","ipdsId":"IP-089058","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":437301,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GM86GZ","text":"USGS data release","linkHelpText":"Sample collection information, single nucleotide polymorphism, and microsatellite data for white-tailed ptarmigan across the species range generated in the Molecular Ecology Lab during 2016"},{"id":370135,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -123.92578125,\n              45.1510532655634\n            ],\n            [\n              -124.45312499999999,\n              44.59046718130883\n            ],\n            [\n              -118.564453125,\n              42.74701217318067\n            ],\n            [\n              -115.400390625,\n              42.8115217450979\n            ],\n            [\n    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0000-0002-1799-1942 klangin@usgs.gov","orcid":"https://orcid.org/0000-0002-1799-1942","contributorId":221128,"corporation":false,"usgs":true,"family":"Langin","given":"Kathryn","email":"klangin@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":777069,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":777070,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fike, Jennifer A. 0000-0001-8797-7823","orcid":"https://orcid.org/0000-0001-8797-7823","contributorId":207268,"corporation":false,"usgs":true,"family":"Fike","given":"Jennifer A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":777071,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":777072,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martin, Kathy M","contributorId":221129,"corporation":false,"usgs":false,"family":"Martin","given":"Kathy","email":"","middleInitial":"M","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":777073,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wann, Greg T","contributorId":221130,"corporation":false,"usgs":false,"family":"Wann","given":"Greg T","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":777074,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Seglund, Amy E.","contributorId":218686,"corporation":false,"usgs":false,"family":"Seglund","given":"Amy","email":"","middleInitial":"E.","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":777075,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schroeder, Michael A","contributorId":221131,"corporation":false,"usgs":false,"family":"Schroeder","given":"Michael","email":"","middleInitial":"A","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":777076,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Benson, David P","contributorId":221132,"corporation":false,"usgs":false,"family":"Benson","given":"David","email":"","middleInitial":"P","affiliations":[{"id":40330,"text":"Marian University","active":true,"usgs":false}],"preferred":false,"id":777077,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Fedy, Brad C.","contributorId":140877,"corporation":false,"usgs":false,"family":"Fedy","given":"Brad","email":"","middleInitial":"C.","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":777078,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Young, Jessica R.","contributorId":200014,"corporation":false,"usgs":false,"family":"Young","given":"Jessica","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":777079,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Wilson, Scott D.","contributorId":181519,"corporation":false,"usgs":false,"family":"Wilson","given":"Scott","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":777080,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Wolfe, Don H","contributorId":221133,"corporation":false,"usgs":false,"family":"Wolfe","given":"Don H","affiliations":[{"id":40331,"text":"G. M. Sutton Avian Research Center","active":true,"usgs":false}],"preferred":false,"id":777081,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Braun, Clait E.","contributorId":200013,"corporation":false,"usgs":false,"family":"Braun","given":"Clait","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":777082,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":777068,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}}
,{"id":70216775,"text":"70216775 - 2019 - Climate-driven state shifts in the Prairie Pothole Region: Assessing future impacts relevant to the management of wetland habitats critical to waterfowl","interactions":[],"lastModifiedDate":"2022-03-07T17:53:15.186194","indexId":"70216775","displayToPublicDate":"2019-10-17T11:44:16","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":7504,"text":"Final Report","active":true,"publicationSubtype":{"id":1}},"title":"Climate-driven state shifts in the Prairie Pothole Region: Assessing future impacts relevant to the management of wetland habitats critical to waterfowl","docAbstract":"<p>Embedded within the North American Prairie Pothole Region (PPR) are millions of small, depressional wetlands that annually support 50–80% of the continent’s waterfowl production. We recently assembled evidence that demonstrates a change towards a wetter climate that is driving a shift in the state of the region’s wetland ecosystems. This ecological state-shift has been primarily the result of a sustained wet climate that has influenced timing and magnitude of surface-water inputs to wetlands, connections to groundwater, and inputs of dissolved salts. As climate influences continue to change in the PPR, it is important to understand the potential of these changes to impact wetland habitats important for waterfowl production. Previous model simulations of prairie-pothole wetlands under future climate scenarios projected decreases in the ability of wetlands to facilitate waterfowl production throughout the majority of what is currently the most productive portion of the region. Results from these modeling efforts also suggested that suitable waterfowl breeding-habitat would be limited mostly to the southeastern portion of the PPR, a portion of the region in which most depressional wetlands (&gt; 90%) have been drained. Thus, if these modeled outcomes materialize, a significant restoration effort would be needed in the southeastern PPR to support waterfowl production. However, the models used in earlier efforts were developed from a small number of wetlands using data from a relatively dry period and did not allow for changing mechanisms influencing surface-water, groundwater and dissolved salt inputs to prairie-pothole wetlands.</p><p>The primary objective of our research is to improve our understanding of future climate change on impacts to wetland ecosystems and breeding waterfowl habitat in the PPR. We used a newly developed Pothole Hydrology Linked Systems Simulator (PHyLiSS) model to estimate wetland ecosystem responses to 32 distinct climate models under 2 different emissions scenarios. Unlike previous wetland hydrology models, the PHyLiSS model allows for shifting hydrological and geochemical mechanisms influencing wetland ecosystems. We modeled one average-sized seasonal wetland basin at 18 different geographic locations (hereafter “sites”) across the PPR with 3 sites represented for each of 6 ecoregions coincident to early research. We applied the PHyLiSS model using historical daily precipitation and temperature data from 1982–2015 and developed linear models relative to ponded water depth in the simulated wetlands and the observed regional WBPHS May Pond count number for 16 of the 18 sites. Based on the output of 32 climate models and 2 emission scenarios we found a projected change in May pond numbers from -23% to +.02% when comparing the most recent climate period (1989–2018) to the end of the 21<sup>st</sup> century (2070–2099). We also found no evidence that the distribution of May ponds will shift in the future. These results suggest that management and conservation strategies for wetlands in the PPR should continue to focus on areas where high densities of intact wetland basins support large numbers of breeding duck pairs.</p>","language":"English","publisher":"Climate Adaptation Science Centers","usgsCitation":"Mushet, D.M., and McKenna, O.P., 2019, Climate-driven state shifts in the Prairie Pothole Region: Assessing future impacts relevant to the management of wetland habitats critical to waterfowl: Final Report, 15 p.","productDescription":"15 p.","ipdsId":"IP-117473","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":396800,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":381010,"type":{"id":15,"text":"Index Page"},"url":"https://cascprojects.org/#/project/4f83509de4b0e84f60868124/5b33be6fe4b040769c172fad"}],"country":"Canada, United States","state":"Alberta, Iowa, 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,{"id":70215004,"text":"70215004 - 2019 - Controls on eolian landscape evolution in fractured bedrock","interactions":[],"lastModifiedDate":"2020-10-06T15:52:57.416752","indexId":"70215004","displayToPublicDate":"2019-10-17T10:40:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Controls on eolian landscape evolution in fractured bedrock","docAbstract":"<p><span>Wind abrasion is important for planetary landscape evolution, and wind‐abraded bedrock landscapes contain many landforms that are difficult to interpret. Here we exploit a natural experiment in Chile where topographic shielding by an upwind lava flow yields diverse erosional landforms in a downwind ignimbrite. Using a 3‐D topographic wind model, we find that low velocities in the wake of a lava lobe coincide with a transition from landforms reflecting fracture‐parallel erosion to flow‐parallel erosion. Erosion rates across these landforms vary with shear velocity and abrasion susceptibility of the windward escarpment. We hypothesize that this morphologic threshold is controlled by whether particles can be lofted in suspension and overcome topographic steering imposed by fractured bedrock blocks. Within a phase space set by Rouse and Stokes numbers, our data illustrate that wind‐abraded landforms reflect a competition between the material skeleton of the landscape and the strength of the flow that shapes it.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GL083955","usgsCitation":"Perkins, J.P., Finnegan, N.J., de Silva, S.L., and Willis, M.J., 2019, Controls on eolian landscape evolution in fractured bedrock: Geophysical Research Letters, v. 46, no. 21, p. 12012-12020, https://doi.org/10.1029/2019GL083955.","productDescription":"9 p.","startPage":"12012","endPage":"12020","ipdsId":"IP-107187","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":459479,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2019gl083955","text":"External Repository"},{"id":379084,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","otherGeospatial":"Antofogasta region, Central Andes Mountains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -70.6640625,\n              -23.94609601499837\n            ],\n            [\n              -70.11474609375,\n              -23.94609601499837\n            ],\n            [\n              -70.11474609375,\n              -23.443088931121775\n            ],\n            [\n              -70.6640625,\n              -23.443088931121775\n            ],\n            [\n              -70.6640625,\n              -23.94609601499837\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"46","issue":"21","noUsgsAuthors":false,"publicationDate":"2019-11-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Perkins, Jonathan P. 0000-0002-6113-338X","orcid":"https://orcid.org/0000-0002-6113-338X","contributorId":237053,"corporation":false,"usgs":true,"family":"Perkins","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":800519,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finnegan, Noah J.","contributorId":198803,"corporation":false,"usgs":false,"family":"Finnegan","given":"Noah","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":800520,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"de Silva, Shanaka L. 0000-0002-0310-5516","orcid":"https://orcid.org/0000-0002-0310-5516","contributorId":242616,"corporation":false,"usgs":false,"family":"de Silva","given":"Shanaka","email":"","middleInitial":"L.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":800521,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Willis, Michael J. 0000-0003-1308-2888","orcid":"https://orcid.org/0000-0003-1308-2888","contributorId":242617,"corporation":false,"usgs":false,"family":"Willis","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":800522,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70205580,"text":"ofr20191095 - 2019 - Hurricane Matthew: Predictions, observations, and an analysis of coastal change","interactions":[],"lastModifiedDate":"2019-10-16T16:26:40","indexId":"ofr20191095","displayToPublicDate":"2019-10-16T17:40:00","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":"2019-1095","displayTitle":"Hurricane Matthew: Predictions, Observations, and an Analysis of Coastal Change","title":"Hurricane Matthew: Predictions, observations, and an analysis of coastal change","docAbstract":"<p>Hurricane Matthew, the strongest Atlantic hurricane of the 2016 hurricane season, made land-fall south of McClellanville, S.C., around 1500 Coordinated Universal Time (UTC) on October 8, 2016. Hurricane Matthew affected the States of Florida, Georgia, South Carolina, and North Carolina along the U.S. Atlantic coastline. Numerous barrier islands were breached, and the erosion of beaches and dunes occurred along most of the South Atlantic coast. The U.S. Geological Survey (USGS) fore-casted potential coastal-change effects—including dune erosion and overwash that can threaten coastal resources and infrastructure—to assist with pre-storm management decisions. Following the storm, oblique aerial photography was collected, and lidar topographic survey missions were flown. These two datasets were used to document the changes that resulted from the storm and to validate coastal change forecasts. Comparisons of pre- and post-storm photographs were used to characterize the nature, extent, and spatial variability of hurricane-induced coastal changes. Analyses of pre- and post-storm lidar eleva-tions were used to quantify magnitudes of change in shoreline positions, dune elevations, and beach volumes. Erosion was observed along the coast from Florida to North Carolina; however, the coastal response exhibited extensive spatial variability, as would be expected over such a large region.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191095","usgsCitation":"Birchler, J.J., Doran, K.S., Long, J.W., and Stockdon, H.F., 2019, Hurricane Matthew—Predictions, observations, and an analysis of coastal change: U.S. Geological Survey Open-File Report 2019–1095, 37 p., https://doi.org/10.3133/ofr20191095.","productDescription":"x, 37 p.","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-103132","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":437304,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BW6CG6","text":"USGS data release","linkHelpText":"Storm-Induced Overwash 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\"USA  \"}}]}","contact":"<p><a href=\"mailto: dc_fl@usgs.gov\" data-mce-href=\"mailto: dc_fl@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/spcmsc\" data-mce-href=\"https://www.usgs.gov/centers/spcmsc\">St. Petersburg Coastal and Marine Science Center</a><br>U.S. Geological Survey<br>600 4th Street South<br>St. Petersburg, FL 33701</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>1. Abstract</li><li>2. Introduction</li><li>3. Storm Overview</li><li>4. U.S. Geological Survey Coastal Change Predictions</li><li>5. U.S. Geological Survey Coastal-Change Observations</li><li>6. Forecast Validation</li><li>7. Coastal-Change Analysis</li><li>8. Conclusion</li><li>9. 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":"Birchler, Justin J. 0000-0002-0379-2192 jbirchler@usgs.gov","orcid":"https://orcid.org/0000-0002-0379-2192","contributorId":169117,"corporation":false,"usgs":true,"family":"Birchler","given":"Justin","email":"jbirchler@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":771733,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doran, Kara S. 0000-0001-8050-5727 kdoran@usgs.gov","orcid":"https://orcid.org/0000-0001-8050-5727","contributorId":148059,"corporation":false,"usgs":true,"family":"Doran","given":"Kara","email":"kdoran@usgs.gov","middleInitial":"S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":771734,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Long, Joseph W. 0000-0003-2912-1992","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":219235,"corporation":false,"usgs":false,"family":"Long","given":"Joseph","email":"","middleInitial":"W.","affiliations":[{"id":32398,"text":"University of North Carolina Wilmington","active":true,"usgs":false}],"preferred":false,"id":771736,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stockdon, Hilary F. 0000-0003-0791-4676 hstockdon@usgs.gov","orcid":"https://orcid.org/0000-0003-0791-4676","contributorId":2153,"corporation":false,"usgs":true,"family":"Stockdon","given":"Hilary","email":"hstockdon@usgs.gov","middleInitial":"F.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":771735,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70204800,"text":"70204800 - 2019 - Insights into post-Miocene uplift of the western margin of the Colorado Plateau from the stratigraphic record of the lower Colorado River","interactions":[],"lastModifiedDate":"2023-03-27T22:16:32.458393","indexId":"70204800","displayToPublicDate":"2019-10-16T17:15:12","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Insights into post-Miocene uplift of the western margin of the Colorado Plateau from the stratigraphic record of the lower Colorado River","docAbstract":"<p><span>The spatial and temporal distribution of Pliocene to Holocene Colorado River deposits (southwestern USA and northwestern Mexico) form a primary data set that records the evolution of a continental-scale river system and helps to delineate and quantify the magnitude of regional deformation. We focus in particular on the age and distribution of ancestral Colorado River deposits from field observations, geologic mapping, and subsurface studies in the area downstream from Grand Canyon (Arizona, USA). A new 4.73 ± 0.17 Ma age is reported for a basalt that flowed down Grand Wash to near its confluence with the Colorado River at the eastern end of what is now Lake Mead (Arizona and Nevada). That basalt flow, which caps tributary gravels, another previously dated 4.49 ± 0.46 Ma basalt flow that caps Colorado River gravel nearby, and previously dated speleothems (2.17 ± 0.34 and 3.87 ± 0.1 Ma) in western Grand Canyon allow for the calculation of long-term incision rates. Those rates are ~90 m/Ma in western Grand Canyon and ~18–64 m/Ma in the eastern Lake Mead area. In western Lake Mead and downstream, the base of 4.5–3.5 Ma ancestral Colorado River deposits, called the Bullhead Alluvium, is generally preserved below river level, suggesting little if any bedrock incision since deposition. Paleoprofiles reconstructed using ancestral river deposits indicate that the lower Colorado River established a smooth profile that has been graded to near sea level since ca. 4.5 Ma. Steady incision rates in western Grand Canyon over the past 0.6–4 Ma also suggest that the lower Colorado River has remained in a quasi–steady state for millions of years with respect to bedrock incision. Differential incision between the lower Colorado River corridor and western Grand Canyon is best explained by differential uplift across the Lake Mead region, as the overall 4.5 Ma profile of the Colorado River remains graded to Pliocene sea level, suggesting little regional subsidence or uplift. Cumulative estimates of ca. 4 Ma offsets across faults in the Lake Mead region are similar in magnitude to the differential incision across the area during the same approximate time frame. This suggests that in the past ~4 Ma, vertical deformation in the Lake Mead area has been localized along faults, which may be a surficial response to more deep-seated processes. Together these data sets suggest ~140–370m of uplift in the past 2–4 Ma across the Lake Mead region.</span></p>","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02020.1","usgsCitation":"Crow, R.S., Howard, K.A., Beard, L.S., Pearthree, P., House, K., Karlstrom, K., Lisa Peters, McIntosh, W.C., Cassidy, C., Felger, T.J., and Block, D., 2019, Insights into post-Miocene uplift of the western margin of the Colorado Plateau from the stratigraphic record of the lower Colorado River: Geosphere, v. 15, no. 6, p. 1826-1845, https://doi.org/10.1130/GES02020.1.","productDescription":"20 p.","startPage":"1826","endPage":"1845","ipdsId":"IP-072908","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":459490,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02020.1","text":"Publisher Index Page"},{"id":368729,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, California, Nevada","otherGeospatial":"Colorado River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -115.97167968750001,\n              31.541089879585808\n            ],\n            [\n              -112.236328125,\n              31.541089879585808\n            ],\n            [\n              -112.236328125,\n              36.94989178681327\n            ],\n            [\n              -115.97167968750001,\n              36.94989178681327\n            ],\n            [\n              -115.97167968750001,\n              31.541089879585808\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"15","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Crow, Ryan S. 0000-0002-2403-6361 rcrow@usgs.gov","orcid":"https://orcid.org/0000-0002-2403-6361","contributorId":5792,"corporation":false,"usgs":true,"family":"Crow","given":"Ryan","email":"rcrow@usgs.gov","middleInitial":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":768521,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Howard, Keith A. 0000-0002-6462-2947 khoward@usgs.gov","orcid":"https://orcid.org/0000-0002-6462-2947","contributorId":3439,"corporation":false,"usgs":true,"family":"Howard","given":"Keith","email":"khoward@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":768523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beard, L. Sue 0000-0001-9552-1893 sbeard@usgs.gov","orcid":"https://orcid.org/0000-0001-9552-1893","contributorId":152,"corporation":false,"usgs":true,"family":"Beard","given":"L.","email":"sbeard@usgs.gov","middleInitial":"Sue","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pearthree, Phil","contributorId":218167,"corporation":false,"usgs":false,"family":"Pearthree","given":"Phil","email":"","affiliations":[{"id":39771,"text":"AZGS","active":true,"usgs":false}],"preferred":false,"id":768528,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"House, Kyle 0000-0002-0019-8075 khouse@usgs.gov","orcid":"https://orcid.org/0000-0002-0019-8075","contributorId":2293,"corporation":false,"usgs":true,"family":"House","given":"Kyle","email":"khouse@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":768525,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Karlstrom, Karl","contributorId":218165,"corporation":false,"usgs":false,"family":"Karlstrom","given":"Karl","affiliations":[{"id":16658,"text":"UNM","active":true,"usgs":false}],"preferred":false,"id":768522,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lisa Peters","contributorId":179357,"corporation":false,"usgs":false,"family":"Lisa Peters","affiliations":[],"preferred":false,"id":768529,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McIntosh, William C.","contributorId":191163,"corporation":false,"usgs":false,"family":"McIntosh","given":"William","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":768526,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cassidy, Colleen 0000-0003-2963-9185","orcid":"https://orcid.org/0000-0003-2963-9185","contributorId":207193,"corporation":false,"usgs":true,"family":"Cassidy","given":"Colleen","email":"","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":768530,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Felger, Tracey J. 0000-0003-0841-4235 tfelger@usgs.gov","orcid":"https://orcid.org/0000-0003-0841-4235","contributorId":1117,"corporation":false,"usgs":true,"family":"Felger","given":"Tracey","email":"tfelger@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":768531,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Block, Debra 0000-0001-7348-3064 dblock@usgs.gov","orcid":"https://orcid.org/0000-0001-7348-3064","contributorId":198448,"corporation":false,"usgs":true,"family":"Block","given":"Debra","email":"dblock@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":768527,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70227813,"text":"70227813 - 2019 - Evaluating the effects of barriers on Slimy Sculpin movement and population connectivity using novel sibship-based and traditional genetic metrics","interactions":[],"lastModifiedDate":"2022-02-01T20:24:48.776105","indexId":"70227813","displayToPublicDate":"2019-10-16T15:24:21","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the effects of barriers on Slimy Sculpin movement and population connectivity using novel sibship-based and traditional genetic metrics","docAbstract":"<p>Population genetics-based approaches can provide robust and cost-effective ways to assess the effects of potential barriers, including dams and road-stream crossings, on the passage and population connectivity of aquatic organisms. Determining the best way to apply and modify genetic tools for different species and situations is essential for making these genetics-based approaches broadly applicable to fisheries and aquatic habitat management. Here, we used multiple genetic approaches to assess the movement and population structure of Slimy Sculpin Cottus cognatus at two road-stream crossings in Michigan and one dam in Massachusetts, USA. We captured and genotyped individual sculpin and assessed movement and population connectivity by using (1) a sibship-based approach, where the presence and proportional distribution of siblings on either side of a barrier indicates population connectivity and the possible direction of movement (i.e., presumed movement from higher to lower proportions), and (2) two Bayesian genetic assignment approaches (STRUCTURE and BayesAss) to identify migrants across potential barriers based on individual population assignment probabilities. We also used traditional genetic metrics to assess within-population genetic variation and among-population genetic divergence. At all three locations, we found evidence for sculpin movement across the potential barrier based on sibship reconstruction, but small family sizes limited the ability of this approach to provide robust estimates of the rate and direction of movement. At two sites, a lack of genetic differentiation between above- and below-barrier populations limited the effectiveness of the genetic assignment methods for identifying possible migrants. At the third site, reduced upstream allelic diversity and effective number of breeders resulted in high genetic differentiation (FST) between above- and below-barrier populations, and both sibship and genetic assignment methods provided strong evidence of limited connectivity and bias against upstream movement. Overall, combining approaches and metrics may help overcome the limitations of any one method and maximize the value of datasets for genetics-based monitoring and assessment.</p>","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10202","usgsCitation":"Weinstein, S.Y., Coombs, J.A., Nislow, K., Riley, C., Roy, A.H., and Whiteley, A., 2019, Evaluating the effects of barriers on Slimy Sculpin movement and population connectivity using novel sibship-based and traditional genetic metrics: Transactions of the American Fisheries Society, v. 148, no. 6, p. 1117-1131, https://doi.org/10.1002/tafs.10202.","productDescription":"15 p.","startPage":"1117","endPage":"1131","ipdsId":"IP-098904","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":459494,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10202","text":"Publisher Index Page"},{"id":395240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts, Michigan","otherGeospatial":"Arquilla Creek, Fall River, Peterson Creek","volume":"148","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Weinstein, Spencer Y.","contributorId":272869,"corporation":false,"usgs":false,"family":"Weinstein","given":"Spencer","email":"","middleInitial":"Y.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":832352,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coombs, Jason A.","contributorId":270745,"corporation":false,"usgs":false,"family":"Coombs","given":"Jason","email":"","middleInitial":"A.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":832353,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nislow, Keith H.","contributorId":60106,"corporation":false,"usgs":true,"family":"Nislow","given":"Keith H.","affiliations":[],"preferred":false,"id":832354,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Riley, Chris","contributorId":272875,"corporation":false,"usgs":false,"family":"Riley","given":"Chris","email":"","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":832355,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832351,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whiteley, Andrew R.","contributorId":272876,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew R.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":832356,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70205687,"text":"ofr20191112 - 2019 - Economic valuation of Landsat imagery","interactions":[],"lastModifiedDate":"2025-08-12T18:44:44.520151","indexId":"ofr20191112","displayToPublicDate":"2019-10-16T10:00:00","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":"2019-1112","displayTitle":"Economic Valuation of Landsat Imagery","title":"Economic valuation of Landsat imagery","docAbstract":"<p>Landsat satellites have been operating since 1972, providing a continuous global record of the Earth’s land surface. The imagery is currently available at no cost through the U.S. Geological Survey (USGS). A previous USGS study estimated that Landsat imagery provided users an annual benefit of <abbr>$</abbr>2.19 billion in 2011, with U.S. users accounting for <abbr>$</abbr>1.79 billion of those benefits. That study, published in 2013, surveyed users in 2012 about Landsat imagery they retrieved in 2011. But since then, many changes have altered the demand for and supply of remotely sensed imagery and have made the analysis complex. This report updates these estimates, surveying users in 2018 about Landsat images they retrieved in 2017. The report discusses changes in the value per scene in 2017 when compared to 2011 and analyzes the potential consequences of charging fees. Landsat imagery has been available at no cost to the public since 2008, resulting in the distribution of millions of scenes each subsequent year. In addition, tens of thousands of Landsat users have registered with the USGS to access the data. Considering the number of Landsat data users worldwide and the broad range of Landsat data applications, it is difficult to quantify the cascading benefits to society provided by Landsat imagery. The value of Landsat imagery to these users was demonstrated by the substantial aggregated annual economic benefit from the imagery. Landsat imagery provided domestic and international users an estimated <abbr>$</abbr>3.45 billion in benefits in 2017 compared to <abbr>$</abbr>2.19 billion in 2011, with U.S. users accounting for <abbr>$</abbr>2.06 billion of those benefits. Much of the societal value of Landsat stems from the free and open data policy that allows users to access as much imagery as is necessary for their analysis at no cost. Charging even small fees would result in a loss of users and, most likely, a steep decline in the amount of imagery downloaded. It is reasonable that more than 50 percent of users will decline to pay. The consequences of charging for Landsat imagery would be felt by downstream users as well, through increased prices for value-added products as well as more intangible effects, such as reduced monitoring of environmental hazards.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191112","usgsCitation":"Straub, C.L., Koontz, S.R., and Loomis, J.B., 2019, Economic valuation of Landsat imagery: U.S. Geological Survey Open-File Report 2019–1112, 13 p., https://doi.org/10.3133/ofr20191112.","productDescription":"iv, 13 p.","onlineOnly":"Y","ipdsId":"IP-110993","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":368280,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1112/coverthb.jpg"},{"id":368281,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1112/ofr20191112.pdf","text":"Report","size":"3.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1112"}],"contact":"<p>Director,&nbsp;<a href=\"https://www.usgs.gov/fort/\" data-mce-href=\"https://www.usgs.gov/fort/\">Fort Collins Science Center</a><br>U.S. Geological Survey<br>2150 Centre Ave., Building C<br>Fort Collins, CO 80526-8118</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Valuation Method</li><li>Sample Frame and Sample Design</li><li>Survey Implementation</li><li>Results</li><li>Annual Value of Landsat</li><li>Conclusion</li><li>Acknowledgments</li><li>References</li></ul>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2019-10-16","noUsgsAuthors":false,"publicationDate":"2019-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Straub, Crista L. 0000-0001-7828-3328","orcid":"https://orcid.org/0000-0001-7828-3328","contributorId":219353,"corporation":false,"usgs":true,"family":"Straub","given":"Crista","email":"","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":773075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koontz, Stephen R.","contributorId":69272,"corporation":false,"usgs":true,"family":"Koontz","given":"Stephen","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":773226,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loomis, John B.","contributorId":197268,"corporation":false,"usgs":false,"family":"Loomis","given":"John","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":772072,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70205989,"text":"70205989 - 2019 - Plant and insect herbivore community variation across the Paleocene–Eocene boundary in the Hanna Basin, southeastern Wyoming","interactions":[],"lastModifiedDate":"2020-01-21T06:33:12","indexId":"70205989","displayToPublicDate":"2019-10-15T10:55:17","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Plant and insect herbivore community variation across the Paleocene–Eocene boundary in the Hanna Basin, southeastern Wyoming","docAbstract":"Ecosystem function and stability are highly affected by internal and external stressors. Utilizing paleobotanical data gives insight into the evolutionary processes an ecosystem undergoes across long periods of time, allowing for a more complete understanding of how plant and insect herbivore communities are affected by ecosystem imbalance. To study how plant and insect herbivore communities change during times of disturbance, we quantified community turnover across the Paleocene­–Eocene boundary in the Hanna Basin, southeastern Wyoming. This particular location is unlike other nearby Laramide basins because it has an abundance of late Paleocene and Eocene coal and carbonaceous shales and paucity of well-developed paleosols, suggesting perpetually high water availability. We sampled approximately 800 semi-intact dicot leaves from five stratigraphic levels, one of which occurs late in the Paleocene–Eocene thermal maximum (PETM). Field collections were supplemented with specimens at the Denver Museum of Nature & Science. Fossil leaves were classified into morphospecies and herbivore damage was documented for each leaf. We tested for changes in plant and insect herbivore damage diversity using rarefaction and community composition using non-metric multidimensional scaling ordinations. We also documented changes in depositional environment at each stratigraphic level to better contextualize the environment of the basin. Plant diversity was highest during the mid-late Paleocene and decreased into the Eocene, whereas damage diversity was highest at the sites with low plant diversity. Plant communities significantly changed during the late PETM and do not return to pre-PETM composition. Insect herbivore communities also changed during the PETM, but, unlike plant communities, rebound to their pre-PETM structure. These results suggest that insect herbivore communities responded more strongly to plant community composition than to the diversity of species present.","language":"English","publisher":"PeerJ, Inc","doi":"10.7717/peerj.7798","usgsCitation":"Schmidt, L.E., Dunn, R.E., Mercer, J.J., Dechesne, M., and Currano, E.D., 2019, Plant and insect herbivore community variation across the Paleocene–Eocene boundary in the Hanna Basin, southeastern Wyoming: PeerJ, no. 7, e7798, 27 p., https://doi.org/10.7717/peerj.7798.","productDescription":"e7798, 27 p.","ipdsId":"IP-105607","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":459519,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.7798","text":"Publisher Index Page"},{"id":368336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","county":"Carbon","otherGeospatial":"Hanna Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -106.35589599609375,\n              41.000629848685385\n            ],\n            [\n              -105.7269287109375,\n              41.000629848685385\n            ],\n            [\n              -105.7269287109375,\n              41.34176252711261\n            ],\n            [\n              -106.35589599609375,\n              41.34176252711261\n            ],\n            [\n              -106.35589599609375,\n              41.000629848685385\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","issue":"7","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmidt, Lauren E","contributorId":219800,"corporation":false,"usgs":false,"family":"Schmidt","given":"Lauren","email":"","middleInitial":"E","affiliations":[{"id":34987,"text":"University of Wyoming, Laramie, WY","active":true,"usgs":false}],"preferred":false,"id":773217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunn, Regan E","contributorId":219801,"corporation":false,"usgs":false,"family":"Dunn","given":"Regan","email":"","middleInitial":"E","affiliations":[{"id":40073,"text":"The Field Museum, Chicago","active":true,"usgs":false}],"preferred":false,"id":773218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mercer, Jason J","contributorId":219802,"corporation":false,"usgs":false,"family":"Mercer","given":"Jason","email":"","middleInitial":"J","affiliations":[{"id":34987,"text":"University of Wyoming, Laramie, WY","active":true,"usgs":false}],"preferred":false,"id":773219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dechesne, Marieke 0000-0002-4468-7495","orcid":"https://orcid.org/0000-0002-4468-7495","contributorId":213936,"corporation":false,"usgs":true,"family":"Dechesne","given":"Marieke","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":773220,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Currano, Ellen D","contributorId":219803,"corporation":false,"usgs":false,"family":"Currano","given":"Ellen","email":"","middleInitial":"D","affiliations":[{"id":34987,"text":"University of Wyoming, Laramie, WY","active":true,"usgs":false}],"preferred":false,"id":773221,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70205944,"text":"sir20195108 - 2019 - Spatial and temporal distribution of bacterial indicators and microbial-source tracking within Tumacácori National Historical Park and the upper Santa Cruz River, southern Arizona and northern Mexico, 2015–2016","interactions":[],"lastModifiedDate":"2019-10-15T14:56:51","indexId":"sir20195108","displayToPublicDate":"2019-10-15T09:30:42","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":"2019-5108","displayTitle":"Spatial and Temporal Distribution of Bacterial Indicators and Microbial-Source Tracking within Tumacácori National Historical Park and the Upper Santa Cruz River, Southern Arizona and Northern Mexico, 2015–2016","title":"Spatial and temporal distribution of bacterial indicators and microbial-source tracking within Tumacácori National Historical Park and the upper Santa Cruz River, southern Arizona and northern Mexico, 2015–2016","docAbstract":"<p>Tumacácori National Historical Park (TUMA) in southern Arizona protects the culturally important Mission San José de Tumacácori, while also managing a part of the ecologically diverse riparian corridor of the Santa Cruz River. The quality of the water flowing through depends solely on upstream watershed activities, and among the water-quality issues concerning TUMA is the microbiological pathogens in the river introduced by human and animal sources that pose a significant human health risk to employees and visitors. The U.S. Geological Survey (USGS) conducted a 3-year study to understand the sources, timing, and distribution of the fecal-indicator bacteria <i>Escherichia coli</i> (<i>E. coli</i>) within TUMA and the upstream watershed.</p><p>The information provided in this investigation is a result of a comprehensive approach to quantify the spatial and temporal variability of <i>E. coli</i> and suspended sediment in the Upper Santa Cruz River Watershed. Several types of flow were sampled from base flow to flood flow and at high frequency intervals (rise, peak, and recession) to determine daily variability, as well as seasonal variability. Hydrologic data collection and estimation techniques were used to establish a hydrologic relation with <i>E. coli</i> and suspended sediment. Furthermore, source tracking was used to describe the potential sources of <i>E. coli</i>. Models were developed that are expected to be useful for predicting <i>E. coli</i> concentrations to help TUMA managers understand instantaneous conditions to keep the public and staff informed about potentially harmful water-quality conditions. In addition, the concentration, flux, and source information will provide more accurate data for other surface-water modeling and can be useful in the development of total maximum daily load standards. This will help TUMA describe the water-quality conditions at the park and waters flowing through the park, as well as prioritize and help carry out future best-management actions to address these issues.<br></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195108","collaboration":"Prepared in cooperation with the National Park Service, Tumacácori National Historical Park","usgsCitation":"Paretti, N.V., Kephart, C.M., Porter, T.J., Hermosillo, E., Cederberg, J.R., Mayo, J.P., Gungle, B., Coes, A.L., Tucci, R.S., and Norman, L.M., 2019, Spatial and temporal distribution of bacterial indicators and microbial-source tracking within Tumacácori National Historical Park and the upper Santa Cruz River, southern Arizona and northern Mexico, 2015–2016: U.S. Geological Survey Scientific Investigations Report 2019–5108, 102 p., https://doi.org/10.3133/sir20195108.","productDescription":"Report: xi, 102 p., Tables 1-6","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-099224","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":368276,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20193065","text":"Fact Sheet 2019-3065","linkHelpText":" - <i>Escherichia coli</i> in the Santa Cruz River in Tumacácori National Historical Park, Arizona"},{"id":368273,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5108/coverthb.jpg"},{"id":368274,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5108/sir20195108.pdf","text":"Report","size":"20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5108"},{"id":368275,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5108/sir20195108_tables.xlsx","text":"Tables 1–6","size":"70 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019-5108"}],"country":"United States, Mexico","otherGeospatial":"Tumacácori National Historical Park, Upper Santa Cruz River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.34368896484374,\n              30.987027960280326\n            ],\n            [\n              -110.53070068359375,\n              30.987027960280326\n            ],\n            [\n              -110.53070068359375,\n              32.02204906495204\n            ],\n            [\n              -111.34368896484374,\n              32.02204906495204\n            ],\n            [\n              -111.34368896484374,\n              30.987027960280326\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:leenhout@usgs.gov\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"mailto:leenhout@usgs.gov\">Director</a>,<br><a href=\"https://az.water.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://az.water.usgs.gov/\">Arizona Water Science Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov/\">U.S. Geological Survey</a><br>520 N. Park Avenue<br>Tucson, AZ 85719</p>","tableOfContents":"<p></p><ul><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Study Area and Watershed Characterization</li><li>Regional Analysis</li><li>Summary</li><li>References Cited</li></ul><p></p>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2019-10-15","noUsgsAuthors":false,"publicationDate":"2019-10-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Paretti, Nicholas V. 0000-0003-2178-4820 nparetti@usgs.gov","orcid":"https://orcid.org/0000-0003-2178-4820","contributorId":173412,"corporation":false,"usgs":true,"family":"Paretti","given":"Nicholas","email":"nparetti@usgs.gov","middleInitial":"V.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773002,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kephart, Christopher M. 0000-0002-3369-5596 ckephart@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-5596","contributorId":1932,"corporation":false,"usgs":true,"family":"Kephart","given":"Christopher","email":"ckephart@usgs.gov","middleInitial":"M.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773003,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Porter, Thomas J. 0000-0003-3956-9467 tjporter@usgs.gov","orcid":"https://orcid.org/0000-0003-3956-9467","contributorId":195160,"corporation":false,"usgs":true,"family":"Porter","given":"Thomas","email":"tjporter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":773004,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hermosillo, Edyth 0000-0003-1648-1016","orcid":"https://orcid.org/0000-0003-1648-1016","contributorId":219723,"corporation":false,"usgs":true,"family":"Hermosillo","given":"Edyth","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773005,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cederberg, Jay R. 0000-0001-6649-7353 cederber@usgs.gov","orcid":"https://orcid.org/0000-0001-6649-7353","contributorId":964,"corporation":false,"usgs":true,"family":"Cederberg","given":"Jay","email":"cederber@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773006,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mayo, Justine P. 0000-0002-2684-5031 jmayo@usgs.gov","orcid":"https://orcid.org/0000-0002-2684-5031","contributorId":197035,"corporation":false,"usgs":true,"family":"Mayo","given":"Justine","email":"jmayo@usgs.gov","middleInitial":"P.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773007,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gungle, Bruce 0000-0001-6406-1206 bgungle@usgs.gov","orcid":"https://orcid.org/0000-0001-6406-1206","contributorId":2237,"corporation":false,"usgs":true,"family":"Gungle","given":"Bruce","email":"bgungle@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773008,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Coes, Alissa L. 0000-0001-6682-5417 alcoes@usgs.gov","orcid":"https://orcid.org/0000-0001-6682-5417","contributorId":4231,"corporation":false,"usgs":true,"family":"Coes","given":"Alissa","email":"alcoes@usgs.gov","middleInitial":"L.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773009,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tucci, Rachel S. 0000-0001-7778-3435","orcid":"https://orcid.org/0000-0001-7778-3435","contributorId":219726,"corporation":false,"usgs":true,"family":"Tucci","given":"Rachel","email":"","middleInitial":"S.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773011,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Norman, Laura M. 0000-0002-3696-8406 lnorman@usgs.gov","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":219725,"corporation":false,"usgs":true,"family":"Norman","given":"Laura","email":"lnorman@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":773010,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70215288,"text":"70215288 - 2019 - Oyster reefs in northern Gulf of Mexico estuaries harbor diverse fish and decapod crustacean assemblages: A meta-synthesis","interactions":[],"lastModifiedDate":"2020-10-16T11:55:34.498702","indexId":"70215288","displayToPublicDate":"2019-10-14T15:07:26","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Oyster reefs in northern Gulf of Mexico estuaries harbor diverse fish and decapod crustacean assemblages: A meta-synthesis","docAbstract":"<p><span>Oyster reefs provide habitat for numerous fish and decapod crustacean species that mediate ecosystem functioning and support vibrant fisheries. Recent focus on the restoration of eastern oyster (</span><i>Crassostrea virginica)</i><span>&nbsp;reefs stems from this role as a critical ecosystem engineer. Within the shallow estuaries of the northern Gulf of Mexico (nGoM), the eastern oyster is the dominant reef building organism. This study synthesizes data on fish and decapod crustacean occupancy of oyster reefs across nGoM with the goal of providing management and restoration benchmarks, something that is currently lacking for the region. Relevant data from 23 studies were identified, representing data from all five U.S. nGoM states over the last 28 years. Cumulatively, these studies documented over 120,000 individuals from 115 fish and 41 decapod crustacean species. Densities as high as 2,800 ind m</span><sup>−2</sup><span>&nbsp;were reported, with individual reef assemblages composed of as many as 52 species. Small, cryptic organisms that occupy interstitial spaces within the reefs, and sampled using trays, were found at an average density of 647 and 20 ind m</span><sup>−2</sup><span>&nbsp;for decapod crustaceans and fishes, respectively. Both groups of organisms were comprised, on average, of 8 species. Larger-bodied fishes captured adjacent to the reef using gill nets were found at an average density of 6 ind m</span><sup>−2</sup><span>, which came from 23 species. Decapod crustaceans sampled with gill nets had a much lower average density, &lt;1 ind m</span><sup>−2</sup><span>, and only contained 2 species. On average, seines captured the greatest number of fish species (</span><i>n</i><span>&nbsp;= 33), which were made up of both facultative residents and transients. These data provide general gear-specific benchmarks, based on values currently found in the region, to assist managers in assessing nekton occupancy of oyster reefs, and assessing trends or changes in status of oyster reef associated nekton support. More explicit reef descriptions (e.g., rugosity, height, area, adjacent habitat) would allow for more precise benchmarks as these factors are important in determining nekton assemblages, and sampling efficiency.</span></p>","language":"English","publisher":"Frontiers","doi":"10.3389/fmars.2019.00666","usgsCitation":"LaPeyre, M.K., Marshall, D., Miller, L.S., and Humphries, A.T., 2019, Oyster reefs in northern Gulf of Mexico estuaries harbor diverse fish and decapod crustacean assemblages: A meta-synthesis: Frontiers in Marine Science, v. 6, 666, 13 p., https://doi.org/10.3389/fmars.2019.00666.","productDescription":"666, 13 p.","ipdsId":"IP-108692","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":459521,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2019.00666","text":"Publisher Index Page"},{"id":379387,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Georgia, Florida, Louisianan, Mississippi, Texas","otherGeospatial":"Northern Gulf of Mexico","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -99.140625,\n              25.085598897064752\n            ],\n            [\n              -79.27734374999999,\n              25.085598897064752\n            ],\n            [\n              -79.27734374999999,\n              31.42866311735861\n            ],\n            [\n              -99.140625,\n              31.42866311735861\n            ],\n            [\n              -99.140625,\n              25.085598897064752\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"6","noUsgsAuthors":false,"publicationDate":"2019-10-25","publicationStatus":"PW","contributors":{"authors":[{"text":"LaPeyre, Megan K. 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":585,"corporation":false,"usgs":true,"family":"LaPeyre","given":"Megan","email":"mlapeyre@usgs.gov","middleInitial":"K.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":801598,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marshall, D. A.","contributorId":243135,"corporation":false,"usgs":false,"family":"Marshall","given":"D. A.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":801599,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, L. S.","contributorId":243136,"corporation":false,"usgs":false,"family":"Miller","given":"L.","email":"","middleInitial":"S.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":801600,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Humphries, A. T.","contributorId":243137,"corporation":false,"usgs":false,"family":"Humphries","given":"A.","email":"","middleInitial":"T.","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":801601,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70215273,"text":"70215273 - 2019 - River water-quality concentration and flux estimation can be improved by accounting for serial correlation through an autoregressive model","interactions":[],"lastModifiedDate":"2020-10-15T13:33:08.421308","indexId":"70215273","displayToPublicDate":"2019-10-14T14:25:04","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":"River water-quality concentration and flux estimation can be improved by accounting for serial correlation through an autoregressive model","docAbstract":"<p><span>Accurate quantification of riverine water‐quality concentration and flux is challenging because monitoring programs typically collect concentration data at lower frequencies than discharge data. Statistical methods are often used to estimate concentration and flux on days without observations. One recently developed approach is the Weighted Regressions on Time, Discharge, and Season (WRTDS), which has been shown to provide among the most accurate estimates compared to other common methods. The main objective of this work was to improve WRTDS estimation by accounting for the autocorrelation structure of model residuals using the first‐order autoregressive model (AR1). This modified approach, called WRTDS‐Kalman Filter (WRTDS‐K), was compared with WRTDS for six constituents including nitrate‐plus‐nitrite (NO</span><sub>x</sub><span>), total phosphorus, total Kjeldahl nitrogen, soluble reactive phosphorus, suspended sediment, and chloride. Near‐daily concentration records at nine sites were used to generate subsets through Monte Carlo sampling for five different sampling scenarios. Results show that WRTDS‐K provided generally better daily estimates of concentration and flux than WRTDS under these sampling scenarios for all constituents, especially NO</span><sub>x</sub><span>. The degree of improvement is strongly affected by the underlying sampling scenario, with WRTDS‐K gaining more advantage when more samples are available, and hence more residuals can be exploited. The performance of WRTDS‐K depends on the AR1 coefficient (ρ) and that relationship varies with constituents and sampling scenarios. These results provided recommendations on the optimal ρ for each constituent and sampling scenario. Overall, WRTDS‐K has the potential for broad applications to monitoring records elsewhere, as demonstrated by a pilot application to Chesapeake Bay tributaries.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019wr025338","usgsCitation":"Zhang, Q., and Hirsch, R.M., 2019, River water-quality concentration and flux estimation can be improved by accounting for serial correlation through an autoregressive model: Water Resources Research, v. 55, no. 11, p. 9705-9723, https://doi.org/10.1029/2019wr025338.","productDescription":"19 p.","startPage":"9705","endPage":"9723","ipdsId":"IP-110106","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":488944,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019wr025338","text":"Publisher Index Page"},{"id":379382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Lake Erie and Ohio River tributaries","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -84.7705078125,\n              40.58058466412761\n            ],\n            [\n              -84.48486328124999,\n              40.01078714046552\n            ],\n            [\n              -83.9794921875,\n              39.53793974517628\n            ],\n            [\n              -83.3642578125,\n              39.90973623453719\n            ],\n            [\n              -82.72705078125,\n              39.2832938689385\n            ],\n            [\n              -82.37548828125,\n              41.04621681452063\n            ],\n            [\n              -83.408203125,\n              40.896905775860006\n            ],\n            [\n              -83.84765625,\n              41.178653972331674\n            ],\n            [\n              -83.7158203125,\n              41.82045509614034\n            ],\n            [\n              -84.3310546875,\n              41.918628865183045\n            ],\n            [\n              -84.6826171875,\n              41.393294288784865\n            ],\n            [\n              -85.1220703125,\n              41.16211393939692\n            ],\n            [\n              -85.0341796875,\n              40.54720023441049\n            ],\n            [\n              -84.7705078125,\n              40.58058466412761\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -80.9912109375,\n              41.72213058512578\n            ],\n            [\n              -81.36474609375,\n              41.343824581185686\n            ],\n            [\n              -81.82617187499999,\n              41.19518982948959\n            ],\n            [\n              -81.5185546875,\n              40.93011520598305\n            ],\n            [\n              -80.96923828125,\n              41.04621681452063\n            ],\n            [\n              -80.6396484375,\n              41.45919537950706\n            ],\n            [\n              -80.6396484375,\n              41.78769700539063\n            ],\n            [\n              -80.9912109375,\n              41.72213058512578\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"55","issue":"11","noUsgsAuthors":false,"publicationDate":"2019-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Qian 0000-0003-0500-5655","orcid":"https://orcid.org/0000-0003-0500-5655","contributorId":174393,"corporation":false,"usgs":false,"family":"Zhang","given":"Qian","email":"","affiliations":[{"id":38802,"text":"University of Maryland Center for Environmental Studies","active":true,"usgs":false}],"preferred":false,"id":801435,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hirsch, Robert M. 0000-0002-4534-075X rhirsch@usgs.gov","orcid":"https://orcid.org/0000-0002-4534-075X","contributorId":2005,"corporation":false,"usgs":true,"family":"Hirsch","given":"Robert","email":"rhirsch@usgs.gov","middleInitial":"M.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":801436,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70208993,"text":"70208993 - 2019 - Calibration of the USGS National Hydrologic Model in ungauged basins using statistical at-site streamflow simulations","interactions":[],"lastModifiedDate":"2020-03-10T14:20:54","indexId":"70208993","displayToPublicDate":"2019-10-14T13:57:26","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2341,"text":"Journal of Hydrologic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Calibration of the USGS National Hydrologic Model in ungauged basins using statistical at-site streamflow simulations","docAbstract":"In the absence of measured streamflow, statistically simulated daily streamflow can be used to support the ability of physical models to represent hydrologic processes at ungauged locations.  The objective of this study was to determine the feasibility of using statistical simulations in place of measured streamflow to calibrate physical models in ungauged basins.  Daily streamflow was simulated at each of the 1,410 gauged watersheds using a cross-validated implementation of pooled ordinary kriging (POK).  In this manner, the streamflow at each gauge was simulated as if no at-site streamflow information were available. The National Hydrologic Model application of the Precipitation-Runoff Modeling System was then calibrated through two separate procedures: (1) with measured streamflow, and (2) with statistically simulated streamflow in lieu of measured streamflow.  Calibrating with statistically simulated streamflow produced performances within 23% of applications with knowledge of at-site measurements.  Furthermore, statistically generated streamflow produced accurate timing information, which, when combined with alternative data sets (e.g., evapotranspiration, recharge, etc.), can be used to improve representation of hydrologic processes at ungauged locations.","language":"English","publisher":"ASCE","doi":"10.1061/(ASCE)HE.1943-5584.0001854","usgsCitation":"Farmer, W., LaFontaine, J., and Hay, L., 2019, Calibration of the USGS National Hydrologic Model in ungauged basins using statistical at-site streamflow simulations: Journal of Hydrologic Engineering, v. 24, no. 11, 04019049, 13 p., https://doi.org/10.1061/(ASCE)HE.1943-5584.0001854.","productDescription":"04019049, 13 p.","ipdsId":"IP-101969","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":437307,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U2A2KU","text":"USGS data release","linkHelpText":"Physical and 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]\n}","volume":"24","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Farmer, William 0000-0002-2865-2196 wfarmer@usgs.gov","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":223175,"corporation":false,"usgs":true,"family":"Farmer","given":"William","email":"wfarmer@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":784444,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"LaFontaine, Jacob 0000-0003-4923-2630 jlafonta@usgs.gov","orcid":"https://orcid.org/0000-0003-4923-2630","contributorId":223176,"corporation":false,"usgs":true,"family":"LaFontaine","given":"Jacob","email":"jlafonta@usgs.gov","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784445,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hay, Lauren 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":223177,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":784446,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70215264,"text":"70215264 - 2019 - Application of a regional climate model to assess changes in the climatology of the Eastern US and Cuba associated with historic landcover change","interactions":[],"lastModifiedDate":"2022-04-14T19:37:38.56995","indexId":"70215264","displayToPublicDate":"2019-10-14T12:02:48","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5998,"text":"JGR Atmospheres","active":true,"publicationSubtype":{"id":10}},"title":"Application of a regional climate model to assess changes in the climatology of the Eastern US and Cuba associated with historic landcover change","docAbstract":"<p><span>We examine the annual, seasonal, monthly, and diurnal climate responses to the land use change (LUC) in eastern United States and Cuba during four epochs (1650, 1850, 1920, and 1992) with ensemble simulations conducted with the RegCM4 regional climate model that includes the Biosphere Atmosphere Transfer Scheme (BATS1e) surface physics package (Dickinson et al., 1993). We derived the land use (LU) data sets by harmonizing a previous reconstruction (Steyaert &amp; Knox, 2008) with updated observations and modeled potential vegetation. The eight‐member ensembles for each epoch were driven with randomly perturbed 1990–2002 atmospheric boundary conditions derived from the National Center for Environmental Prediction global reanalysis. LUC induces statistically significant climate responses across all epochs; the largest changes occur between 1850 and 1920 with the widespread conversion of forests in the United States and forests, grassland, and woody wetlands in Cuba to agriculture. The atmospheric feedback from the aggregated grid‐cell responses attributed to physical and biophysical parameters in BATS1e alters the circulation in the lower atmosphere, thereby propagating the LUC regionally. Depending on the season and location, the altered circulation reinforces, attenuates, or has little effect on surface responses. Relative to pre‐settlement (1650), the 1992 LU produces colder mean annual air temperature (−0.09 ± 0.16 °C) and increased precipitation (0.08 ± 0.09 mm day</span><sup>−1</sup><span>) over the United States, warmer (0.08 °C) and wetter (0.03 mm day</span><sup>−1</sup><span>) conditions over Florida, and warming (0.32 °C) and drying (−0.03 mm day</span><sup>−1</sup><span>) over Cuba, indicating that LUC has played a varying role in climate change over the region.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019jd030965","usgsCitation":"Hostetler, S.W., Reker, R., Alder, J.R., Loveland, T., Willard, D.A., Bernhardt, C.E., Sundquist, E.T., and Thompson, R., 2019, Application of a regional climate model to assess changes in the climatology of the Eastern US and Cuba associated with historic landcover change: JGR Atmospheres, v. 124, no. 22, p. 11722-11745, https://doi.org/10.1029/2019jd030965.","productDescription":"24 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EROS","active":true,"usgs":false}],"preferred":false,"id":801376,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alder, Jay R. 0000-0003-2378-2853 jalder@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-2853","contributorId":5118,"corporation":false,"usgs":true,"family":"Alder","given":"Jay","email":"jalder@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":801377,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loveland, Thomas 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140611,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":801378,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Willard, Debra A. 0000-0003-4878-0942 dwillard@usgs.gov","orcid":"https://orcid.org/0000-0003-4878-0942","contributorId":2076,"corporation":false,"usgs":true,"family":"Willard","given":"Debra","email":"dwillard@usgs.gov","middleInitial":"A.","affiliations":[{"id":24693,"text":"Climate Research and Development","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":801634,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bernhardt, Christopher E. 0000-0003-0082-4731 cbernhardt@usgs.gov","orcid":"https://orcid.org/0000-0003-0082-4731","contributorId":2131,"corporation":false,"usgs":true,"family":"Bernhardt","given":"Christopher","email":"cbernhardt@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":801635,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sundquist, Eric T. 0000-0002-1449-8802 esundqui@usgs.gov","orcid":"https://orcid.org/0000-0002-1449-8802","contributorId":1922,"corporation":false,"usgs":true,"family":"Sundquist","given":"Eric","email":"esundqui@usgs.gov","middleInitial":"T.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":801380,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thompson, Renee L. rthompson1@usgs.gov","contributorId":2933,"corporation":false,"usgs":true,"family":"Thompson","given":"Renee L.","email":"rthompson1@usgs.gov","affiliations":[],"preferred":true,"id":801636,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70212818,"text":"70212818 - 2019 - Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities","interactions":[],"lastModifiedDate":"2024-05-16T14:56:12.436485","indexId":"70212818","displayToPublicDate":"2019-10-14T08:20:20","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab0005\" class=\"abstract author\"><div id=\"as0005\"><p id=\"sp0060\">Drylands make up roughly 40% of the Earth's land surface, and billions of people depend on services provided by these critically important ecosystems. Despite their relatively sparse vegetation, dryland ecosystems are structurally and functionally diverse, and emerging evidence suggests that these ecosystems play a dominant role in the trend and variability of the terrestrial carbon sink. More, drylands are highly sensitive to climate and are likely to have large, non-linear responses to hydroclimatic change. Monitoring the spatiotemporal dynamics of dryland ecosystem structure (e.g., leaf area index) and function (e.g., primary production and evapotranspiration) is therefore a high research priority. Yet, dryland remote sensing is defined by unique challenges not typically encountered in mesic or humid regions. Major challenges include low vegetation signal-to-noise ratios, high soil background reflectance, presence of photosynthetic soils (i.e., biological soil crusts), high spatial heterogeneity from plot to regional scales, and irregular growing seasons due to unpredictable seasonal rainfall and frequent periods of drought. Additionally, there is a relative paucity of continuous, long-term measurements in drylands, which impedes robust calibration and evaluation of remotely-sensed dryland data products. Due to these issues, remote sensing techniques developed in other ecosystems or for global application often result in inaccurate, poorly constrained estimates of dryland ecosystem structural and functional dynamics. Here, we review past achievements and current progress in remote sensing of dryland ecosystems, including a detailed discussion of the major challenges associated with remote sensing of key dryland structural and functional dynamics. We then identify strategies aimed at leveraging new and emerging opportunities in remote sensing to overcome previous challenges and more accurately contextualize drylands within the broader Earth system. Specifically, we recommend: 1) Exploring novel combinations of sensors and techniques (e.g., solar-induced fluorescence, thermal, microwave, hyperspectral, and LiDAR) across a range of spatiotemporal scales to gain new insights into dryland structural and functional dynamics; 2) utilizing near-continuous observations from new-and-improved geostationary satellites to capture the rapid responses of dryland ecosystems to diurnal variation in water stress; 3) expanding ground observational networks to better represent the heterogeneity of dryland systems and enable robust calibration and evaluation; 4) developing algorithms that are specifically tuned to dryland ecosystems by utilizing expanded ground observational network data; and 5) coupling remote sensing observations with process-based models using data assimilation to improve mechanistic understanding of dryland ecosystem dynamics and to better constrain ecological forecasts and long-term projections.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2019.111401","usgsCitation":"Smith, W.K., Dannenberg, M.P., Yan, D., Herrmann, S., Barnes, M.L., Barron-Gafford, G.A., Biederman, J.A., Ferrenberg, S., Fox, A.M., Hudson, A.R., Knowles, J.F., MacBean, N., Moore, D., Nagler, P.L., Reed, S., Rutherford, W.A., Scott, R.L., Wang, X., and Yang, J., 2019, Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities: Remote Sensing of Environment, v. 233, 111401, 23 p., https://doi.org/10.1016/j.rse.2019.111401.","productDescription":"111401, 23 p.","ipdsId":"IP-103233","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":459542,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2019.111401","text":"Publisher Index Page"},{"id":378007,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"233","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, William K. 0000-0002-5785-6489","orcid":"https://orcid.org/0000-0002-5785-6489","contributorId":239667,"corporation":false,"usgs":false,"family":"Smith","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":47959,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":797546,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dannenberg, Matthew P.","contributorId":239668,"corporation":false,"usgs":false,"family":"Dannenberg","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":47960,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ; Geographical and Sustainability Services, University of Iowa, Iowa City, IA","active":true,"usgs":false}],"preferred":false,"id":797547,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yan, Dong","contributorId":207300,"corporation":false,"usgs":false,"family":"Yan","given":"Dong","email":"","affiliations":[{"id":37515,"text":"University of Arizona School of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":797548,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herrmann, Stephanie","contributorId":239669,"corporation":false,"usgs":false,"family":"Herrmann","given":"Stephanie","email":"","affiliations":[{"id":47961,"text":"Agricultural and Biosystems Engineering, University of Arizona, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":797549,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnes, Mallory L.","contributorId":239670,"corporation":false,"usgs":false,"family":"Barnes","given":"Mallory","email":"","middleInitial":"L.","affiliations":[{"id":39756,"text":"School of Public and Environmental Affairs, Indiana University, Bloomington, IN","active":true,"usgs":false}],"preferred":false,"id":797550,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barron-Gafford, Greg A.","contributorId":19058,"corporation":false,"usgs":false,"family":"Barron-Gafford","given":"Greg","email":"","middleInitial":"A.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":797551,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Biederman, Joel A.","contributorId":201939,"corporation":false,"usgs":false,"family":"Biederman","given":"Joel","email":"","middleInitial":"A.","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":797552,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ferrenberg, Scott","contributorId":217143,"corporation":false,"usgs":false,"family":"Ferrenberg","given":"Scott","affiliations":[{"id":39569,"text":"Department of Biology, New Mexico State University, Las Cruces, NM 88001, USA","active":true,"usgs":false}],"preferred":false,"id":797553,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fox, Andrew M.","contributorId":239671,"corporation":false,"usgs":false,"family":"Fox","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":47963,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ; Joint Center for Satellite Data Assimilation, Boulder, CO","active":true,"usgs":false}],"preferred":false,"id":797554,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hudson, Amy R.","contributorId":202420,"corporation":false,"usgs":false,"family":"Hudson","given":"Amy","email":"","middleInitial":"R.","affiliations":[{"id":36427,"text":"Laboratory of Tree Ring Research, University of Arizona. Tucson, Arizona, USA, School of Natural Resources and the Environment, University of Arizona. Tucson, Arizona, USA","active":true,"usgs":false}],"preferred":false,"id":797555,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Knowles, John F.","contributorId":203853,"corporation":false,"usgs":false,"family":"Knowles","given":"John","email":"","middleInitial":"F.","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":797556,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"MacBean, Natasha","contributorId":239672,"corporation":false,"usgs":false,"family":"MacBean","given":"Natasha","email":"","affiliations":[{"id":47964,"text":"Dept. of Geography, Indiana University, Bloomington, IN","active":true,"usgs":false}],"preferred":false,"id":797557,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Moore, David J.P.","contributorId":239673,"corporation":false,"usgs":false,"family":"Moore","given":"David J.P.","affiliations":[{"id":47959,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":797558,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":797559,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":797560,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Rutherford, William A.","contributorId":239674,"corporation":false,"usgs":false,"family":"Rutherford","given":"William","email":"","middleInitial":"A.","affiliations":[{"id":47959,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":797561,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Scott, Russell L.","contributorId":39875,"corporation":false,"usgs":false,"family":"Scott","given":"Russell","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":797562,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Wang, Xianfeng","contributorId":203491,"corporation":false,"usgs":false,"family":"Wang","given":"Xianfeng","email":"","affiliations":[],"preferred":false,"id":797563,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Yang, Julia","contributorId":239675,"corporation":false,"usgs":false,"family":"Yang","given":"Julia","email":"","affiliations":[{"id":47965,"text":"School of Geography and Development, University of Arizona, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":797564,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}}
,{"id":70207115,"text":"70207115 - 2019 - Mountain-block recharge: A review of current understanding","interactions":[],"lastModifiedDate":"2020-01-08T14:28:09","indexId":"70207115","displayToPublicDate":"2019-10-12T09:31:07","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":"Mountain-block recharge: A review of current understanding","docAbstract":"Mountain-block recharge (MBR) is the subsurface inflow of groundwater to lowland aquifers from adjacent mountains. MBR can be a major component of recharge but remains difficult to characterize and quantify due to limited hydrogeologic, climatic, and other data in the mountain block and at the mountain front. The number of MBR-related studies has increased dramatically in the 15 years since the last review of the topic was conducted by Wilson and Guan (2004), generating important advancements. We review this recent body of literature, summarize current understanding of factors controlling MBR, and provide recommendations for future research priorities. Prior to 2004, most MBR studies were performed in the southwestern United States. Since then, numerous studies have detected and quantified MBR in basins around the world, typically estimating MBR to be 5–50% of basin-fill aquifer recharge. Theoretical studies using generic numerical modeling domains have revealed fundamental hydrogeologic and topographic controls on the amount of MBR and where it originates within the mountain block. Several mountain-focused hydrogeologic studies have confirmed the widespread existence of mountain bedrock aquifers hosting considerable groundwater flow and, in some cases, identified the occurrence of interbasin flow leaving headwater catchments in the subsurface—both of which are required for MBR to occur. Future MBR research should focus on the collection of high-priority data (e.g., subsurface data near the mountain front and within the mountain block) and the development of sophisticated coupled models calibrated to multiple data types to best constrain MBR and predict how it may change in response to climate warming.","language":"English","publisher":"Wiley","doi":"10.1029/2019WR025676","usgsCitation":"Markovich, K.H., Manning, A.H., Condon, L., and Jennifer McIntosh, 2019, Mountain-block recharge: A review of current understanding: Water Resources Research, v. 55, no. 11, p. 8278-8304, https://doi.org/10.1029/2019WR025676.","productDescription":"27 p,","startPage":"8278","endPage":"8304","ipdsId":"IP-108698","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":459550,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019wr025676","text":"Publisher Index Page"},{"id":370078,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Markovich, Katherine H. 0000-0002-4455-8255","orcid":"https://orcid.org/0000-0002-4455-8255","contributorId":221065,"corporation":false,"usgs":false,"family":"Markovich","given":"Katherine","middleInitial":"H.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":776876,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manning, Andrew H. 0000-0002-6404-1237 amanning@usgs.gov","orcid":"https://orcid.org/0000-0002-6404-1237","contributorId":1305,"corporation":false,"usgs":true,"family":"Manning","given":"Andrew","email":"amanning@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":776875,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Condon, Laura 0000-0003-3639-8076","orcid":"https://orcid.org/0000-0003-3639-8076","contributorId":221066,"corporation":false,"usgs":false,"family":"Condon","given":"Laura","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":776877,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jennifer McIntosh","contributorId":176846,"corporation":false,"usgs":false,"family":"Jennifer McIntosh","affiliations":[],"preferred":false,"id":776878,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70227119,"text":"70227119 - 2019 - Do parents synchronise nest visits as an antipredator adaptation in birds of New Zealand and Tasmania?","interactions":[],"lastModifiedDate":"2022-01-03T16:25:25.729256","indexId":"70227119","displayToPublicDate":"2019-10-11T09:38:54","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5993,"text":"Frontiers in Ecology and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Do parents synchronise nest visits as an antipredator adaptation in birds of New Zealand and Tasmania?","docAbstract":"<p><span>Birds with altricial offspring need to feed them regularly, but each feeding visit risks drawing attention to the nest and revealing its location to potential predators. Synchronisation of visits by both parents has been suggested as a behavioural adaptation to reduce the risk of nest predation. Under this hypothesis, higher risk of nest predation favours greater synchrony of parental feeding visits. We investigated this prediction over three timescales using nestling provisioning data from 25 passerine species in Tasmania and New Zealand. We estimated the extent to which parents actively synchronised their visits to the nest by comparing observed patterns of synchrony with those expected to occur at random. We found that in general, species did not synchronise visits more often than expected by chance. Species varied in the tendency to synchronise visits, but this variation was not explained by likely predation pressure in the distant evolutionary past: New Zealand endemic species, which evolved in the absence of mammalian nest predators, synchronised their visits as often as species which evolved with more diverse predatory guilds. Nest predation risk has increased over time in New Zealand due to introduced predators, but synchrony in visits also was not explained by manipulated predation risk: visit synchrony was equivalent between a predator-removal site and a site where predators remained. However, within one New Zealand species, visit synchrony was higher for mainland populations, which have been exposed to predatory mammals for&nbsp;</span><i>c</i><span>.800 years, than for a population on an offshore island to which predatory mammals were never introduced. We conclude that breeding birds may have some capacity to adapt the synchrony with which they provision over short evolutionary timescales. However, the lack of synchrony in most species suggests that either asynchrony provides benefits that outweigh the greater risk of predation, or synchrony incurs costs not compensated by reduced predation.</span></p>","language":"English","publisher":"Frontiers Research Foundation","doi":"10.3389/fevo.2019.00389","usgsCitation":"Khwaja, N., Massaro, M., Martin, T.E., and Briskie, J.V., 2019, Do parents synchronise nest visits as an antipredator adaptation in birds of New Zealand and Tasmania?: Frontiers in Ecology and Environment, v. 7, 389, 11 p., https://doi.org/10.3389/fevo.2019.00389.","productDescription":"389, 11 p.","ipdsId":"IP-107192","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":459556,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2019.00389","text":"Publisher Index Page"},{"id":393649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia, New Zealand","state":"Tasmania","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              144.31640625,\n              -44.087585028245165\n            ],\n            [\n              148.798828125,\n              -44.087585028245165\n            ],\n            [\n              148.798828125,\n              -40.51379915504413\n            ],\n            [\n              144.31640625,\n              -40.51379915504413\n            ],\n            [\n              144.31640625,\n              -44.087585028245165\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              169.716796875,\n              -47.338822694822\n            ],\n            [\n              176.923828125,\n              -40.44694705960048\n            ],\n            [\n              178.9453125,\n              -37.43997405227057\n            ],\n            [\n              176.8359375,\n              -36.87962060502676\n            ],\n            [\n              172.35351562499997,\n              -33.9433599465788\n            ],\n            [\n              173.759765625,\n              -37.5097258429375\n            ],\n            [\n              173.14453125,\n              -39.43619299931407\n            ],\n            [\n              174.19921875,\n              -40.44694705960048\n            ],\n            [\n              171.03515625,\n              -40.044437584608566\n            ],\n            [\n              165.322265625,\n              -46.07323062540835\n            ],\n            [\n              167.6953125,\n              -47.93106634750977\n            ],\n            [\n              169.716796875,\n              -47.338822694822\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"7","noUsgsAuthors":false,"publicationDate":"2019-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Khwaja, Nyil","contributorId":270665,"corporation":false,"usgs":false,"family":"Khwaja","given":"Nyil","email":"","affiliations":[{"id":54468,"text":"uc","active":true,"usgs":false}],"preferred":false,"id":829714,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Massaro, Melanie","contributorId":270666,"corporation":false,"usgs":false,"family":"Massaro","given":"Melanie","affiliations":[{"id":54468,"text":"uc","active":true,"usgs":false}],"preferred":false,"id":829715,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Thomas E. 0000-0002-4028-4867 tmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-4028-4867","contributorId":1208,"corporation":false,"usgs":true,"family":"Martin","given":"Thomas","email":"tmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":829713,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Briskie, James V.","contributorId":270667,"corporation":false,"usgs":false,"family":"Briskie","given":"James","email":"","middleInitial":"V.","affiliations":[{"id":54468,"text":"uc","active":true,"usgs":false}],"preferred":false,"id":829716,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70205952,"text":"70205952 - 2019 - sUAS-based remote sensing of river discharge using thermal particle image velocimetry and bathymetric lidar","interactions":[],"lastModifiedDate":"2019-10-11T09:17:44","indexId":"70205952","displayToPublicDate":"2019-10-11T08:55:22","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"sUAS-based remote sensing of river discharge using thermal particle image velocimetry and bathymetric lidar","docAbstract":"This paper describes a non-contact methodology for computing river discharge based on data collected from small Unmanned Aerial Systems (sUAS). The  approach is complete in that both surface velocity and channel geometry are measured directly under field conditions. The technique does not require introducing artificial tracer particles for computing surface velocity, nor does it rely upon the presence of naturally occurring floating material.  Moreover, no prior knowledge of river bathymetry is necessary. Due to the weight of the sensors and limited payload capacities of the commercially available sUAS used in the study, two sUAS were required. The first sUAS included mid-wave thermal infrared and visible cameras. For the field evaluation described herein, a thermal image time series was acquired and a particle image velocimetry (PIV) algorithm used to track the motion of structures expressed at the water surface as small differences in temperature. The ability to detect these thermal features was significant because the water surface lacked floating material (e.g., foam, debris) that could have been detected with a visible camera and used to perform conventional Large-Scale Particle Image Velocimetry (LSPIV). The second sUAS was devoted to measuring bathymetry with a novel scanning polarizing lidar. We collected field measurements along two channel transects to assess the accuracy of the remotely sensed velocities, depths, and discharges. Thermal PIV provided velocities that agreed closely (R^2 = 0.82 and 0.64) with in situ velocity measurements from an acoustic Doppler current profiler (ADCP). Depths inferred from the lidar closely matched those surveyed by wading in the shallower of the two cross sections (R^2 = 0.95) but the agreement was not as strong for the transect with greater depths (R^2 = 0.61). Incremental discharges computed with the remotely sensed velocities and depths were greater than corresponding ADCP measurements by 22% at the first cross section and < 1% at the second.","language":"English","publisher":"MDPI","doi":"10.3390/rs11192317","usgsCitation":"Kinzel, P.J., and Legleiter, C.J., 2019, sUAS-based remote sensing of river discharge using thermal particle image velocimetry and bathymetric lidar: Remote Sensing, v. 11, no. 19, 2317, 19 p., https://doi.org/10.3390/rs11192317.","productDescription":"2317, 19 p.","onlineOnly":"Y","ipdsId":"IP-111227","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":459558,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11192317","text":"Publisher Index Page"},{"id":437308,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LBGCPT","text":"USGS data release","linkHelpText":"UAS-based remotely sensed data and field measurements of flow depth and velocity from the Blue River, Colorado, October 17-18, 2019"},{"id":368258,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","county":"Grand County","otherGeospatial":"Blue River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -106.40190124511719,\n              40.03977220579366\n            ],\n            [\n              -106.38670921325682,\n              40.03977220579366\n            ],\n            [\n              -106.38670921325682,\n              40.04581742420946\n            ],\n            [\n              -106.40190124511719,\n              40.04581742420946\n            ],\n            [\n              -106.40190124511719,\n              40.03977220579366\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"11","issue":"19","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":773024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":773025,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70202703,"text":"sir20195018 - 2019 - Flood-frequency estimates for Ohio streamgages based on data through water year 2015 and techniques for estimating flood-frequency characteristics of rural, unregulated Ohio streams","interactions":[],"lastModifiedDate":"2019-10-11T06:32:47","indexId":"sir20195018","displayToPublicDate":"2019-10-10T15:41:08","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":"2019-5018","displayTitle":"Flood-Frequency Estimates for Ohio Streamgages Based on Data through Water Year 2015 and Techniques for Estimating Flood-Frequency Characteristics of Rural, Unregulated Ohio Streams","title":"Flood-frequency estimates for Ohio streamgages based on data through water year 2015 and techniques for estimating flood-frequency characteristics of rural, unregulated Ohio streams","docAbstract":"<p>Estimates of the magnitudes of annual peak streamflows with annual exceedance probabilities of 0.5, 0.2, 0.1, 0.04, 0.02, 0.01, and 0.002 (equivalent to recurrence intervals of 2-, 5-, 10-, 25-, 50-, 100-, and 500-years, respectively) were computed for 391 streamgages in Ohio and adjacent states based on data collected through the 2015 water year. The flood-frequency estimates were computed following guidance outlined in Bulletin 17C, developed by the Advisory Committee on Water Information. The Bulletin 17C guidelines retain the basic statistical framework of the superseded Bulletin 17B guidelines; however, the Bulletin 17C guidelines add several enhancements including an improved method of moments approach for fitting the log-Pearson Type III (LPIII) distribution to the flood peaks (called the expected moments algorithm), a generalization of the Grubbs Beck low-outlier test (called the Multiple Grubbs Beck test) that permits identification of multiple potentially influential low floods, and new methods for estimating regional skew and uncertainty.</p><p>Equations for estimating flood-frequency characteristics at ungaged sites on rural, unregulated streams in Ohio were developed with a two-step process involving ordinary least-squares and generalized least-squares regression techniques. Data from 333 streamgages with 10 or more years of unregulated record were screened for redundancy and a regression dataset was selected that was composed of flood-frequency and basin-characteristic data for 275 streamgages in Ohio and adjacent states. Two sets of equations were developed—one set, referred to as the “simple model,” uses regression region and drainage area as regressor variables, and a second set, referred to as the “full model,” uses regression region, drainage area, main-channel slope, and the percentage of the watershed covered by water and wetlands as regressor variables.</p><p>The average standard errors of prediction ranged from about 40.5 to 46.5 percent for the simple-model equations and from about 37.2 to 40.3 percent for the full-model equations. For sites meeting the rural, unregulated criteria, flood-frequency estimates determined by means of LPIII analyses are reported along with weighted flood-frequency estimates, computed as a function of the LPIII estimates and the regression estimates. For sites with homogenous periods of regulation, flood-frequency estimates determined by means of LPIII analyses are reported. Ninety-five percent confidence limits are reported for all estimates.</p><p>Values of regressor variables were determined from digital spatial datasets by means of a geographic information system (GIS). The GIS datasets and the new full-model equations have been incorporated into Ohio’s StreamStats application, a web-based, GIS-backed system designed to facilitate the estimation of streamflow statistics at ungaged locations on streams.</p><p>Seasonal patterns in peak flows were assessed for 295 streamgages in Ohio. Annual peak flows occurred most frequently between January and April, with March having the highest frequency of occurrence. The month with the fewest number of annual peaks was October. Peak-of-record flows occurred most frequently in March, followed by January (months in which two of Ohio’s most severe widespread floods in recent history occurred). None of the peak-of-record flows occurred in October and only two occurred in November.</p><p>Temporal trend in annual peak flows were assessed for 133 streamgages on unregulated streams in Ohio with 30 or more years of systematic record. Trends were assessed by computing the rank correlation (as measured with the two-sided Kendall’s tau statistic) between time and annual peak flows. Weak but statistically significant trends were indicated at 15 of the 133 streamgages. Of the 15 streamgages with significant trend in annual peak flows, 12 had an upward trend (positive tau) and 3 had a downward trend (negative tau). All 12 streamgages with positive tau values were at latitudes north of 40°33', and streamgages with negative tau values were at latitudes south of 40°33'.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195018","collaboration":"Prepared in cooperation with the Ohio Department of Transportation","usgsCitation":"Koltun, G.F., 2019, Flood-frequency estimates for Ohio streamgages based on data through water year 2015 and techniques for estimating flood-frequency characteristics of rural, unregulated Ohio streams: U.S. Geological Survey Scientific Investigations Report 2019–5018, 25 p., https://doi.org/10.3133/sir20195018.","productDescription":"Report: vi, 25 p.; 2 Tables; Appendices 1.1-1.8; Data Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-100946","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science 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streamgages."},{"id":368121,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5018/sir20195018_appendix_tables","text":"Appendix tables 1.1 to 1.8","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5018 Appendix Tables"},{"id":368122,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V3IG0P","text":"USGS data release","description":"USGS Data Release","linkHelpText":"PeakFQ inputs and selected outputs for selected streamgages in Ohio and border areas of adjacent states (through water year 2015)"},{"id":368123,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PJ0O5W","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Model archive—Regional regression models for estimating flood-frequency characteristics of rural, unregulated Ohio streams"}],"country":"United 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 \"}}]}","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 Ste 100<br>Columbus, OH 43229–1737</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Previous Investigations</li><li>Seasonal Patterns of Peak Flows</li><li>Magnitude and Frequency of Floods at Gaged Sites</li><li>Development of Regional Regression Equations</li><li>Weighting Flood-Frequency Estimates at Ungaged Sites with Data for a Nearby Gage</li><li>General Guidelines for Estimating Flood-Frequency Characteristics at Sites on Rural, Unregulated Streams</li><li>Limitations</li><li>Summary</li><li>References Cited</li><li>Appendix 1</li></ul>","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2019-10-10","noUsgsAuthors":false,"publicationDate":"2019-10-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Koltun, G. F. 0000-0003-0255-2960 gfkoltun@usgs.gov","orcid":"https://orcid.org/0000-0003-0255-2960","contributorId":140048,"corporation":false,"usgs":true,"family":"Koltun","given":"G.","email":"gfkoltun@usgs.gov","middleInitial":"F.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759550,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70203785,"text":"sir20195074 - 2019 - Catalog of microscopic organisms of the Everglades, part 2—The desmids of the Arthur R. Marshall Loxahatchee National Wildlife Refuge","interactions":[],"lastModifiedDate":"2019-10-11T06:27:38","indexId":"sir20195074","displayToPublicDate":"2019-10-10T14:28:08","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":"2019-5074","displayTitle":"Catalog of Microscopic Organisms of the Everglades Part 2—The Desmids of the Arthur R. Marshall Loxahatchee National Wildlife Refuge","title":"Catalog of microscopic organisms of the Everglades, part 2—The desmids of the Arthur R. Marshall Loxahatchee National Wildlife Refuge","docAbstract":"<p>The Arthur R. Marshall Loxahatchee National Wildlife Refuge (refuge), Boynton Beach, Florida, contains approximately 147,000 acres southeast of Lake Okeechobee. Water quality in the interior portion of the refuge is strongly influenced by rainfall, resulting in slightly acidic waters with low dissolved ions. Desmids, a unique, ornate group of green algae loosely associated with submerged vascular plants, were photo-documented for the first time in samples from the refuge. The canal system surrounding the refuge contains a high level of ions from agricultural runoff, and intrusion of this water into the refuge interior during high canal water levels may have altered some of the desmid population. A transect from the canal to the interior was sampled every 3 months, and the species present were photographed, identified, and catalogued. Approximately 260 unique taxa from 29 genera were encountered. The interior of the refuge had the greatest diversity of desmids; however, the areas of the refuge adjacent to the canals still contained a rich population of desmids. We postulate that the diversity of desmids indicates that the pristine portions of the refuge may be an important refugium for desmids, particularly for those species restricted to the subtropical parts of the United States. This collection of taxa, identified to species with most specimens, will allow a more detailed examination of water quality issues when co-located water quality data are collected.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195074","usgsCitation":"Rosen, B.H., Stahlhut, K.N., and Hall, J.D., 2019, Catalog of microscopic organisms of the Everglades, part 2—The desmids of the Arthur R. Marshall Loxahatchee National Wildlife Refuge: U.S. Geological Survey Scientific Investigations Report 2019–5074, 277 p., https://doi.org/10.3133/sir20195074.","productDescription":"xii, 277 p.","numberOfPages":"294","onlineOnly":"N","ipdsId":"IP-104022","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":368073,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5074/sir20195074.pdf","text":"Report","size":"73.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5074"},{"id":368077,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5074/coverthb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Arthur R. Marshall Loxahatchee National Wildlife Refuge","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -80.35812377929688,\n              26.311881633667735\n            ],\n            [\n              -80.17684936523438,\n              26.311881633667735\n            ],\n            [\n              -80.17684936523438,\n              26.683048455216138\n            ],\n            [\n              -80.35812377929688,\n              26.683048455216138\n            ],\n            [\n              -80.35812377929688,\n              26.311881633667735\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/wetland-and-aquatic-research-center-warc\" href=\"https://www.usgs.gov/centers/wetland-and-aquatic-research-center-warc\">Wetland and Aquatic Research Center</a><br>U.S. Geological Survey<br>7920 NW 71st St.<br>Gainesville, Florida 32653<br></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results and Discussion</li><li><em>Closterium</em> Nitzsch ex Ralfs</li><li><em>Spinoclosterium</em> Bernard</li><li><em>Actinotaenium</em> (Nägeli) Teiling</li><li><em>Bambusina</em> Kützing</li><li><em>Cosmarium</em> Corda ex Ralfs</li><li><em>Cosmocladium</em> Brébisson</li><li><em>Desmidium</em> C. Agardh ex Ralfs</li><li><em>Docidium</em> Brébisson ex Ralfs</li><li><em>Euastrum</em> Ehrenberg ex Ralfs</li><li><em>Groenbladia</em> Teiling</li><li><em>Haplotaenium</em> Bando</li><li><em>Hyalotheca</em> Ehrenberg ex Ralfs</li><li><em>Micrasterias</em> C. Agardh ex Ralfs</li><li><em>Phymatodocis</em> Nordstedt</li><li><em>Pleurotaenium</em> Nägeli</li><li><em>Sphaerozosma</em> Ralfs</li><li><em>Spinocosmarium</em> Prescott &amp; A.M. Scott</li><li><em>Spondylosium</em> Brébisson ex Kützing</li><li><em>Staurastrum</em> Meyen ex Ralfs</li><li><em>Staurodesmus</em> Teiling</li><li><em>Teilingia</em> Bourrelly</li><li><em>Tetmemorus</em> Ralfs ex Ralfs</li><li><em>Triploceras</em> (Bailey ex Ralfs) Bailey</li><li><em>Xanthidium</em> Ehrenberg ex Ralfs</li><li><em>Gonatozygon</em> De Bary</li><li><em>Penium</em> Brébisson ex Ralfs</li><li><em>Cylindrocystis</em> Meneghini ex De Bary</li><li><em>Spirotaenia</em> Brébisson ex Ralfs</li><li><em>Tortitaenia</em> Brook</li><li><em>Netrium</em> (Nägeli) Itzigsohn &amp; Rothe</li><li>References</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2019-10-10","noUsgsAuthors":false,"publicationDate":"2019-10-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosen, Barry H. 0000-0002-8016-3939","orcid":"https://orcid.org/0000-0002-8016-3939","contributorId":217821,"corporation":false,"usgs":true,"family":"Rosen","given":"Barry H.","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":767589,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stahlhut, Katherine N. 0000-0003-4098-4641","orcid":"https://orcid.org/0000-0003-4098-4641","contributorId":217822,"corporation":false,"usgs":true,"family":"Stahlhut","given":"Katherine N.","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"preferred":true,"id":767590,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hall, John D. 0000-0002-7670-5459","orcid":"https://orcid.org/0000-0002-7670-5459","contributorId":179094,"corporation":false,"usgs":false,"family":"Hall","given":"John","email":"","middleInitial":"D.","affiliations":[{"id":28155,"text":"Century Engineering, Anchorage, AK","active":true,"usgs":false}],"preferred":false,"id":767591,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70207511,"text":"70207511 - 2019 - Characteristics of feeding sites of California Condors (Gymnogyps californianus) in the human-dominated landscape of Southern California","interactions":[],"lastModifiedDate":"2019-12-22T14:07:54","indexId":"70207511","displayToPublicDate":"2019-10-10T14:05:46","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3784,"text":"Wilson Journal of Ornithology","active":true,"publicationSubtype":{"id":10}},"title":"Characteristics of feeding sites of California Condors (Gymnogyps californianus) in the human-dominated landscape of Southern California","docAbstract":"Wildlife conservation is often improved by understanding the movement ecology of species and adapting management strategies to dynamic conditions associated with movement. Despite a remarkable recovery over the past 30 year, the establishment of self-sustaining populations of California Condors (Gymnogyps californianus) has been challenging in the human-dominated landscapes of southern California. Among these challenges are those imposed by condor ground-foraging behavior that exposes them to environmental contamination. These include lead poisoning from the ingestion of spent ammunition and micro-trash ingestion and, during takeoff and landing, collisions with human structures. We tracked 28 California Condors for 24 months with patagially mounted GPS telemetry units to investigate the characteristics of ground sites condors visited and to identify spatiotemporal trends that might aid in conservation of this critically endangered species. Ground sites occurred on a wide variety of land cover types, primarily on steep slopes, and those more frequently used were associated with open cover. Condors concentrated their visits to ground sites around a 3 h period near midday, and usage increased from winter to late summer. Our study is the first to use remotely sensed telemetry data to describe fine-scale ecological correlates of condor ground-foraging ecology and therefore has important relevance for ongoing conservation and management strategies for this species. The descriptions of ground sites we provide can be used to target conservation or management actions.","language":"English","publisher":"BioOne","doi":"10.1676/17-23","usgsCitation":"Hall, J.C., Braham, M.A., Nolan, L.A., Conley, J., Brandt, J., Mendenhall, L.C., Lanzone, M.J., McGann, A.J., and Katzner, T., 2019, Characteristics of feeding sites of California Condors (Gymnogyps californianus) in the human-dominated landscape of Southern California: Wilson Journal of Ornithology, v. 131, no. 3, p. 459-471, https://doi.org/10.1676/17-23.","productDescription":"13 p.","startPage":"459","endPage":"471","ipdsId":"IP-107788","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":370608,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California ","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      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University","active":true,"usgs":false}],"preferred":false,"id":778316,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Braham, Melissa A.","contributorId":199740,"corporation":false,"usgs":false,"family":"Braham","given":"Melissa","email":"","middleInitial":"A.","affiliations":[{"id":34303,"text":"West Virginia University, Department of Geology & Geography","active":true,"usgs":false}],"preferred":false,"id":778317,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nolan, Lee Ann","contributorId":221469,"corporation":false,"usgs":false,"family":"Nolan","given":"Lee","email":"","middleInitial":"Ann","affiliations":[],"preferred":false,"id":778318,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conley, Jamison","contributorId":221470,"corporation":false,"usgs":false,"family":"Conley","given":"Jamison","email":"","affiliations":[],"preferred":false,"id":778319,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brandt, 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,{"id":70205075,"text":"ofr20191094 - 2019 - Baseline environmental monitoring of groundwater, surface water, and soil at the Ammonium Perchlorate Rocket Motor Destruction Facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania, 2016","interactions":[],"lastModifiedDate":"2019-10-16T12:53:02","indexId":"ofr20191094","displayToPublicDate":"2019-10-10T14:05:00","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":"2019-1094","displayTitle":"Baseline Environmental Monitoring of Groundwater, Surface Water, and Soil at the Ammonium Perchlorate Rocket Motor Destruction Facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania, 2016","title":"Baseline environmental monitoring of groundwater, surface water, and soil at the Ammonium Perchlorate Rocket Motor Destruction Facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania, 2016","docAbstract":"<p>Letterkenny Army Depot in Chambersburg, Pennsylvania, built an Ammonium Perchlorate Rocket Motor Destruction (ARMD) facility in 2016. The ARMD Facility was designed to centralize rocket motor destruction and contain or capture all waste during the destruction process. Ideally, there would be no contaminant transport to air, soil, or water from the facility, but the Code of Federal Regulations requires that any hazardous waste disposal facility have an environmental monitoring program in place. In a study by the U.S. Geological Survey, in cooperation with the Letterkenny Army Depot, baseline characterization of constituents in groundwater, surface water, and soil was conducted from September to December 2016 to document site conditions prior to the beginning of operations at the facility in January 2017. Groundwater wells, surface water, and soils were sampled monthly during the baseline characterization period. No sediment transport from the site occurred on days when samples were collected from surface-water sites, so no sediment was collected from the retention basin at the facility during the baseline period. Data collected during the baseline period can be compared to data collected in future years to determine whether there is any contaminant transport from the ARMD Facility to the surrounding environment.</p><p>During the baseline characterization period, monthly samples were collected from 4 groundwater wells and 9 soil sites near the ARMD Facility. The only surface-water site sampled monthly during the baseline period was upgradient from the facility. There was no streamflow at surface-water sites downgradient from the facility on days when surface-water samples were collected during the baseline characterization period.</p><p>Groundwater results for the four wells sampled near the ARMD Facility during the baseline period did not show any major water-quality issues. Mean specific conductance (SC) and pH in groundwater ranged from 220 to 771 microsiemens per centimeter at 25 degrees Celsius (μS/cm) and 6.45 to 6.98, respectively. No constituents in groundwater samples exceeded any U.S. Environmental Protection Agency (EPA) Maximum Contaminant Level (MCL). Dissolved iron (Fe) was the only groundwater constituent that exceeded a Secondary Maximum Contaminant Level (SMCL) established by the EPA. The SMCL for Fe is 300 micrograms per liter (μg/L); samples from three wells had mean dissolved Fe concentrations ranging from 1,100 to 2,600 μg/L. The only volatile organic compounds (VOCs) detected in groundwater samples were bromomethane, acetone, and chloromethane. All VOC detections in groundwater samples were less than the Reporting Detection Levels (RDLs). These three compounds also were detected in blank samples submitted for groundwater samples. Perchlorate was not detected in any groundwater sample collected during the baseline period.</p><p>Surface-water data collected during the baseline period were strictly representative of a stream reach upgradient from the ARMD Facility. Stream discharge ranged from 0.03 to 0.08 cubic feet per second during sample collection. The mean SC and pH were 310 μS/cm and 7.6, respectively. No EPA established MCLs or SMCLs were exceeded for any constituents in samples collected from this upgradient stream. Some VOCs were detected in surface water at less than the RDLs. The VOCs detected in surface water were generally the same VOCs as those detected at less than the RDLs for groundwater. Perchlorate was detected in each sample collected from the stream; the mean concentration was 0.07 μg/L. All perchlorate results were less than the RDL of 0.2 μg/L.</p><p>Soil samples collected during the baseline period did not have any constituent concentrations that exceeded any medium-specific concentrations (MSC) or soil screening levels (SSL) established by either the Commonwealth of Pennsylvania or the EPA. The Commonwealth of Pennsylvania calculates MSCs based on either a function of acceptable concentrations in groundwater or based on health concerns if the soil is directly contacted. The EPA derives acceptable concentrations of constituents (SSLs) in soil based on standardized equations combining exposure information assumptions with EPA toxicity data. The EPA calculates SSLs for residential and industrial sites. Soil sites at the ARMD Facility were considered “industrial” for comparative purposes. There was at least one order of magnitude difference between any inorganic constituent concentration detected in soil and the MSC and (or) SSL for that constituent. Four VOCs were detected in soil samples collected during the baseline period. None of the VOCs detected in the soils were within three orders of magnitude of any established MSCs or SSLs. The VOCs detected in soil were dichloromethane (also known as methylene chloride), methyl tert-butyl ether (MTBE), tetrachloroethene, and acetone (only detected once). Dichloromethane was the only VOC detected at greater than the RDLs; concentrations in all soil samples were greater than the RDLs. Dichloromethane concentrations ranged from 1.9 to 50.1 micrograms per kilogram (μg/kg). MTBE was detected in 50 percent of samples collected but all results were less than the RDLs of 1.7 to 2.6 μg/kg. Tetrachloroethene was detected in 20 percent of soil samples collected, with a maximum estimated value of 1.5 μg/kg. Inorganic constituents with the highest concentrations in soil were Fe and aluminum (Al); mean Fe and Al concentrations ranged from 28,700 to 52,400 and 10,300 to 19,800 milligrams per kilogram (mg/kg), respectively. Data collected during the baseline period in 2016 can be compared to future data to determine whether concentrations in water and soils surrounding the facility have shown any changes that could be caused by the facility operation.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191094","collaboration":"Prepared in Cooperation with the Letterkenny Army Depot","usgsCitation":"Galeone, D.G., 2019, Baseline environmental monitoring of groundwater, surface water, and soil at the Ammonium Perchlorate Rocket Motor Destruction Facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania, 2016: U.S. Geological Survey Open-File Report 2019–1094, 32 p., https://doi.org/10.3133/ofr20191094.","productDescription":"Report: vii; 32 p.; Appendices 1-4","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102807","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":437309,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P973YRPL","text":"USGS data release","linkHelpText":"Quality Control and Soil Quality Data in support of Baseline Environmental Monitoring at the Ammonium Perchlorate Rocket Motor Destruction (ARMD) Facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania, 2016"},{"id":368210,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1094/ofr20191094_appendix3.xlsx","text":"Appendix 3","size":"16.8 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1094"},{"id":368211,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1094/ofr20191094_appendix4.xlsx","text":"Appendix 4","size":"32.3 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 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2016"},{"id":368209,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1094/ofr20191094_appendix2.xlsx","text":"Appendix 2","size":"22.1 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1094"},{"id":368190,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1094/coverthb.jpg"}],"country":"United States","state":"Pennsylvania ","county":"Franklin County","city":"Chambersburg","otherGeospatial":"Letterkenny Army Depot","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -77.71831512451172,\n              40.0013199623656\n            ],\n            [\n              -77.67333984375,\n              40.0013199623656\n            ],\n            [\n              -77.67333984375,\n              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