{"pageNumber":"379","pageRowStart":"9450","pageSize":"25","recordCount":40804,"records":[{"id":70197300,"text":"70197300 - 2018 - Placing the Common Era in a Holocene context: Millennial to centennial patterns and trends in the hydroclimate of North America over the past 2000 years","interactions":[],"lastModifiedDate":"2020-09-01T20:28:07.991747","indexId":"70197300","displayToPublicDate":"2018-05-29T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1250,"text":"Climate of the Past","active":true,"publicationSubtype":{"id":10}},"title":"Placing the Common Era in a Holocene context: Millennial to centennial patterns and trends in the hydroclimate of North America over the past 2000 years","docAbstract":"A synthesis of 93 hydrologic records from across North and Central America, and adjacent tropical and Arctic islands, reveals centennial to millennial trends in the regional hydroclimates of the Common Era (CE; past 2000 years). The hydrological records derive from materials stored in lakes, bogs, caves, and ice from extant glaciers, which have the continuity through time to preserve low-frequency ( > 100 year) climate signals that may extend deeper into the Holocene. The most common pattern, represented in 46 (49 %) of the records, indicates that the centuries before 1000 CE were drier than the centuries since that time. Principal component analysis indicates that millennial-scale trends represent the dominant pattern of variance in the southwestern US, northeastern US, mid-continent, Pacific Northwest, Arctic, and tropics, although not all records within a region show the same direction of change. The Pacific Northwest and the southernmost tier of the tropical sites tended to dry toward present, as many other areas became wetter than before. In 22 records (24 %), the Medieval Climate Anomaly period (800–1300 CE) was drier than the Little Ice Age (1400–1900 CE), but in many cases the difference was part of the longer millennial-scale trend, and, in 25 records (27 %), the Medieval Climate Anomaly period represented a pluvial (wet) phase. Where quantitative records permitted a comparison, we found that centennial-scale fluctuations over the Common Era represented changes of 3–7 % in the modern interannual range of variability in precipitation, but the accumulation of these long-term trends over the entirety of the Holocene caused recent centuries to be significantly wetter, on average, than most of the past 11 000 years.","language":"English","publisher":"Copernicus Publications","doi":"10.5194/cp-14-665-2018","usgsCitation":"Shuman, B., Routson, C.C., McKay, N., Fritz, S., Kaufman, D.S., Kirby, M., Nolan, C., Pederson, G.T., and St. Jacques, J., 2018, Placing the Common Era in a Holocene context: Millennial to centennial patterns and trends in the hydroclimate of North America over the past 2000 years: Climate of the Past, v. 14, no. 5, p. 665-686, https://doi.org/10.5194/cp-14-665-2018.","productDescription":"32 p.","startPage":"665","endPage":"686","ipdsId":"IP-085003","costCenters":[{"id":481,"text":"Northern Rocky 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P.","contributorId":187602,"corporation":false,"usgs":false,"family":"McKay","given":"Nicholas P.","affiliations":[],"preferred":false,"id":736584,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fritz, Sherilyn","contributorId":205233,"corporation":false,"usgs":false,"family":"Fritz","given":"Sherilyn","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":736585,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kaufman, Darrell S.","contributorId":192787,"corporation":false,"usgs":false,"family":"Kaufman","given":"Darrell","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":736586,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kirby, Matthew","contributorId":140654,"corporation":false,"usgs":false,"family":"Kirby","given":"Matthew","affiliations":[{"id":13544,"text":"California State University, Fullerton","active":true,"usgs":false}],"preferred":false,"id":736587,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nolan, Connor","contributorId":197051,"corporation":false,"usgs":false,"family":"Nolan","given":"Connor","affiliations":[],"preferred":false,"id":736588,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pederson, Gregory T. 0000-0002-6014-1425 gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":736581,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"St. Jacques, Jeannine-Marie","contributorId":205234,"corporation":false,"usgs":false,"family":"St. Jacques","given":"Jeannine-Marie","email":"","affiliations":[{"id":6586,"text":"Concordia University","active":true,"usgs":false}],"preferred":false,"id":736589,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70198761,"text":"70198761 - 2018 - Range expansion in unfavorable environments through behavioral responses to microclimatic conditions: Moose (Alces americanus) as the model","interactions":[],"lastModifiedDate":"2018-11-21T15:20:48","indexId":"70198761","displayToPublicDate":"2018-05-26T10:48:16","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2653,"text":"Mammalian Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Range expansion in unfavorable environments through behavioral responses to microclimatic conditions: Moose <i>(Alces americanus)</i> as the model","title":"Range expansion in unfavorable environments through behavioral responses to microclimatic conditions: Moose (Alces americanus) as the model","docAbstract":"<p><span>Wildlife populations&nbsp;occurring at the edge of their range boundaries are thought to be the most sensitive to&nbsp;climate change&nbsp;due to temperatures being at or near the limit of a species’ thermal envelope. Moose (</span><i>Alces americanus</i><span>) are a cold adapted species that are showing&nbsp;population declines&nbsp;in some portions of the southern edge of their range. However, other moose populations are actively expanding southward into thermally stressful areas. The direct effects of temperature on moose have not yet been studied in these southwardly expanding populations and may offer insights into how moose are successfully establishing in areas at the edge of their thermal envelope. We used&nbsp;ambient temperature&nbsp;and&nbsp;GPScollar data from moose to quantify the direct effect of temperature on moose&nbsp;habitat use&nbsp;in Massachusetts, USA, which is one of these southwardly expanding populations. The mean daily temperature in our study area exceeded the reported physiological tolerances of moose in over 90% of daytime and 75% of nighttime locations in summer and in over 80% of daytime and 67% of nighttime locations in winter. Across seasons and times of day, moose preferred regenerating forest, but as&nbsp;ambient air&nbsp;temperatures increased, selection for regenerating forest declined and selection for forested&nbsp;wetlands&nbsp;and&nbsp;coniferous forestincreased. This response indicates moose are altering their behavior to utilize thermal shelters when temperatures are high. We observed higher temperatures and stronger&nbsp;behavioral responses&nbsp;than other studies at the southern edge of moose range. We found habitat for moose in Massachusetts is climatically marginal and loss of habitat, increase in&nbsp;parasites, and further climatic warming may cause population declines in the future.</span></p>","language":"English","publisher":"Elsevier ","doi":"10.1016/j.mambio.2018.05.009","usgsCitation":"Zeller, K.A., Wattles, D.W., and DeStefano, S., 2018, Range expansion in unfavorable environments through behavioral responses to microclimatic conditions: Moose (Alces americanus) as the model: Mammalian Biology, v. 93, p. 189-197, https://doi.org/10.1016/j.mambio.2018.05.009.","productDescription":"9 p.","startPage":"189","endPage":"197","ipdsId":"IP-096685","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":356622,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"93","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a2bbe4b0702d0e842fd1","contributors":{"authors":[{"text":"Zeller, Katherine A.","contributorId":204574,"corporation":false,"usgs":false,"family":"Zeller","given":"Katherine","email":"","middleInitial":"A.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":742886,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wattles, David W.","contributorId":204573,"corporation":false,"usgs":false,"family":"Wattles","given":"David","email":"","middleInitial":"W.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":742885,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeStefano, Stephen 0000-0003-2472-8373 destef@usgs.gov","orcid":"https://orcid.org/0000-0003-2472-8373","contributorId":166706,"corporation":false,"usgs":true,"family":"DeStefano","given":"Stephen","email":"destef@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":742884,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70197250,"text":"70197250 - 2018 - Assessing the impacts of dams and levees on the hydrologic record of the Middle and Lower Mississippi River, USA","interactions":[],"lastModifiedDate":"2018-05-24T10:46:20","indexId":"70197250","displayToPublicDate":"2018-05-24T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the impacts of dams and levees on the hydrologic record of the Middle and Lower Mississippi River, USA","docAbstract":"The impacts of dams and levees on the long-term (>130 years) discharge record was assessed along a ~1200 km segment of the Mississippi River between St. Louis, Missouri, and Vicksburg, Mississippi. To aid in our evaluation of dam impacts, we used data from the U.S. National Inventory of Dams to calculate the rate of reservoir expansion at five long-term hydrologic monitoring stations along the study segment. We divided the hydrologic record at each station into three periods: (1) a pre-rapid reservoir expansion period; (2) a rapid reservoir expansion period; and (3) a post-rapid reservoir expansion period. We then used three approaches to assess changes in the hydrologic record at each station. Indicators of hydrologic alteration (IHA) and flow duration hydrographs were used to quantify changes in flow conditions between the pre- and post-rapid reservoir expansion periods. Auto-regressive interrupted time series analysis (ARITS) was used to assess trends in maximum annual discharge, mean annual discharge, minimum annual discharge, and standard deviation of daily discharges within a given water year. A one-dimensional HEC-RAS hydraulic model was used to assess the impact of levees on flood flows. Our results revealed that minimum annual discharges and low-flow IHA parameters showed the most significant changes. Additionally, increasing trends in minimum annual discharge during the rapid reservoir expansion period were found at three out of the five hydrologic monitoring stations. These IHA and ARITS results support previous findings consistent with the observation that reservoirs generally have the greatest impacts on low-flow conditions. River segment scale hydraulic modeling revealed levees can modestly increase peak flood discharges, while basin-scale hydrologic modeling assessments by the U.S. Army Corps of Engineers showed that tributary reservoirs reduced peak discharges by a similar magnitude (2 to 30%). This finding suggests that the effects of dams and levees on peak flood discharges are in part offsetting one another along the modeled river segments and likely other substantially leveed segments of the Mississippi River.","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2018.01.004","usgsCitation":"Remo, J.W., Ickes, B., Ryherd, J.K., Guida, R.J., and Therrell, M.D., 2018, Assessing the impacts of dams and levees on the hydrologic record of the Middle and Lower Mississippi River, USA: Geomorphology, v. 313, p. 88-100, https://doi.org/10.1016/j.geomorph.2018.01.004.","productDescription":"13 p.","startPage":"88","endPage":"100","ipdsId":"IP-088232","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":468733,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2018.01.004","text":"Publisher Index Page"},{"id":354450,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mississippi River","volume":"313","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d78e4b092d9651e1b38","contributors":{"authors":[{"text":"Remo, Jonathan W.F. 0000-0002-8208-2091","orcid":"https://orcid.org/0000-0002-8208-2091","contributorId":205201,"corporation":false,"usgs":false,"family":"Remo","given":"Jonathan","email":"","middleInitial":"W.F.","affiliations":[{"id":32417,"text":"Southern Illinois University-Carbondale","active":true,"usgs":false}],"preferred":false,"id":736404,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ickes, Brian 0000-0001-5622-3842 bickes@usgs.gov","orcid":"https://orcid.org/0000-0001-5622-3842","contributorId":2925,"corporation":false,"usgs":true,"family":"Ickes","given":"Brian","email":"bickes@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":736403,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ryherd, Julia K.","contributorId":205202,"corporation":false,"usgs":false,"family":"Ryherd","given":"Julia","email":"","middleInitial":"K.","affiliations":[{"id":32417,"text":"Southern Illinois University-Carbondale","active":true,"usgs":false}],"preferred":false,"id":736405,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guida, Ross J.","contributorId":205203,"corporation":false,"usgs":false,"family":"Guida","given":"Ross","email":"","middleInitial":"J.","affiliations":[{"id":37056,"text":"Sam Houston State University","active":true,"usgs":false}],"preferred":false,"id":736406,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Therrell, Matthew D.","contributorId":172810,"corporation":false,"usgs":false,"family":"Therrell","given":"Matthew","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":736407,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70197251,"text":"70197251 - 2018 - Canopy volume removal from oil and gas development activity in the upper Susquehanna River basin in Pennsylvania and New York (USA): An assessment using lidar data","interactions":[],"lastModifiedDate":"2018-05-24T10:43:13","indexId":"70197251","displayToPublicDate":"2018-05-24T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Canopy volume removal from oil and gas development activity in the upper Susquehanna River basin in Pennsylvania and New York (USA): An assessment using lidar data","docAbstract":"<p><span>Oil and gas development is changing the landscape in many regions of the United States and globally. However, the nature, extent, and magnitude of landscape change and development, and precisely how this development compares to other ongoing land conversion (e.g. urban/sub-urban development, timber harvest) is not well understood. In this study, we examine land conversion from oil and gas infrastructure development in the upper Susquehanna River basin in Pennsylvania and New York, an area that has experienced much oil and gas development over the past 10 years. We quantified land conversion in terms of forest canopy geometric volume loss in contrast to previous studies that considered only areal impacts. For the first time in a study of this type, we use fine-scale lidar forest canopy geometric models to assess the volumetric change due to forest clearing from oil and gas development and contrast this land change to clear cut forest harvesting, and urban and suburban development. Results show that oil and gas infrastructure development removed a large volume of forest canopy from 2006 to 2013, and this removal spread over a large portion of the study area. Timber operations (clear cutting) on Pennsylvania State Forest lands removed a larger total volume of forest canopy during the same time period, but this canopy removal was concentrated in a smaller area. Results of our study point to the need to consider volumetric impacts of oil and gas development on ecosystems, and to place potential impacts in context with other ongoing land conversions.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2018.05.041","usgsCitation":"Young, J.A., Maloney, K.O., Slonecker, E.T., Milheim, L., and Siripoonsup, D., 2018, Canopy volume removal from oil and gas development activity in the upper Susquehanna River basin in Pennsylvania and New York (USA): An assessment using lidar data: Journal of Environmental Management, v. 222, p. 66-75, https://doi.org/10.1016/j.jenvman.2018.05.041.","productDescription":"10 p.","startPage":"66","endPage":"75","ipdsId":"IP-089887","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":354448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York, Pennsylvania","otherGeospatial":"Upper Susquehanna River basin","volume":"222","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d78e4b092d9651e1b36","contributors":{"authors":[{"text":"Young, John A. 0000-0002-4500-3673 jyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-3673","contributorId":3777,"corporation":false,"usgs":true,"family":"Young","given":"John","email":"jyoung@usgs.gov","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":736408,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maloney, Kelly O. 0000-0003-2304-0745 kmaloney@usgs.gov","orcid":"https://orcid.org/0000-0003-2304-0745","contributorId":4636,"corporation":false,"usgs":true,"family":"Maloney","given":"Kelly","email":"kmaloney@usgs.gov","middleInitial":"O.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":736409,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Slonecker, E. Terrence 0000-0002-5793-0503 tslonecker@usgs.gov","orcid":"https://orcid.org/0000-0002-5793-0503","contributorId":168591,"corporation":false,"usgs":true,"family":"Slonecker","given":"E.","email":"tslonecker@usgs.gov","middleInitial":"Terrence","affiliations":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":736410,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Milheim, Lesley E. lmilheim@usgs.gov","contributorId":2560,"corporation":false,"usgs":true,"family":"Milheim","given":"Lesley E.","email":"lmilheim@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":false,"id":736411,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Siripoonsup, David dsiripoonsup@usgs.gov","contributorId":197039,"corporation":false,"usgs":true,"family":"Siripoonsup","given":"David","email":"dsiripoonsup@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":736412,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70197255,"text":"70197255 - 2018 - Projected 21st century coastal flooding in the Southern California Bight. Part 1: Development of the third generation CoSMoS model","interactions":[],"lastModifiedDate":"2018-05-24T10:55:15","indexId":"70197255","displayToPublicDate":"2018-05-24T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2380,"text":"Journal of Marine Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Projected 21st century coastal flooding in the Southern California Bight. Part 1: Development of the third generation CoSMoS model","docAbstract":"<p><span>Due to the effects of climate change over the course of the next century, the combination of rising sea levels, severe storms, and coastal change will threaten the sustainability of coastal communities, development, and ecosystems as we know them today. To clearly identify coastal vulnerabilities and develop appropriate adaptation strategies due to projected increased levels of coastal flooding and erosion, coastal managers need local-scale hazards projections using the best available climate and coastal science. In collaboration with leading scientists world-wide, the USGS designed the Coastal Storm Modeling System (CoSMoS) to assess the coastal impacts of climate change for the California coast, including the combination of sea-level rise, storms, and coastal change. In this project, we directly address the needs of coastal resource managers in Southern California by integrating a vast range of global climate change projections in a thorough and comprehensive numerical modeling framework. In Part 1 of a two-part submission on CoSMoS, methods and the latest improvements are discussed, and an example of hazard projections is presented.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/jmse6020059","usgsCitation":"O'Neill, A., Erikson, L.H., Barnard, P., Limber, P.W., Vitousek, S., Warrick, J.A., Foxgrover, A.C., and Lovering, J., 2018, Projected 21st century coastal flooding in the Southern California Bight. Part 1: Development of the third generation CoSMoS model: Journal of Marine Science and Engineering, v. 6, no. 2, p. 1-31, https://doi.org/10.3390/jmse6020059.","productDescription":"Article 59; 31 p.","startPage":"1","endPage":"31","ipdsId":"IP-096868","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468732,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse6020059","text":"Publisher Index Page"},{"id":354451,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.860595703125,\n              32.27320009948135\n            ],\n            [\n              -116.83959960937499,\n              32.27320009948135\n            ],\n            [\n              -116.83959960937499,\n              34.70549341022544\n            ],\n            [\n              -120.860595703125,\n              34.70549341022544\n            ],\n            [\n              -120.860595703125,\n              32.27320009948135\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"6","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-24","publicationStatus":"PW","scienceBaseUri":"5b155d78e4b092d9651e1b34","contributors":{"authors":[{"text":"O'Neill, Andrea C. 0000-0003-1656-4372 aoneill@usgs.gov","orcid":"https://orcid.org/0000-0003-1656-4372","contributorId":5351,"corporation":false,"usgs":true,"family":"O'Neill","given":"Andrea C.","email":"aoneill@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736422,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736423,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":147147,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick L.","email":"pbarnard@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736424,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Limber, Patrick W. 0000-0002-8207-3750 plimber@usgs.gov","orcid":"https://orcid.org/0000-0002-8207-3750","contributorId":196794,"corporation":false,"usgs":true,"family":"Limber","given":"Patrick","email":"plimber@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736425,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vitousek, Sean","contributorId":190192,"corporation":false,"usgs":false,"family":"Vitousek","given":"Sean","affiliations":[],"preferred":false,"id":736426,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Warrick, Jonathan A. 0000-0002-0205-3814 jwarrick@usgs.gov","orcid":"https://orcid.org/0000-0002-0205-3814","contributorId":167736,"corporation":false,"usgs":true,"family":"Warrick","given":"Jonathan","email":"jwarrick@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736427,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Foxgrover, Amy C. 0000-0003-0638-5776 afoxgrover@usgs.gov","orcid":"https://orcid.org/0000-0003-0638-5776","contributorId":3261,"corporation":false,"usgs":true,"family":"Foxgrover","given":"Amy","email":"afoxgrover@usgs.gov","middleInitial":"C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736428,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lovering, Jessica 0000-0002-0705-9633","orcid":"https://orcid.org/0000-0002-0705-9633","contributorId":204726,"corporation":false,"usgs":true,"family":"Lovering","given":"Jessica","email":"","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736429,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70211480,"text":"70211480 - 2018 - A retrospective look at the February 1993 east rift zone intrusion at Kīlauea volcano, Hawaii","interactions":[],"lastModifiedDate":"2020-07-28T22:49:01.833424","indexId":"70211480","displayToPublicDate":"2018-05-23T17:40:45","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"A retrospective look at the February 1993 east rift zone intrusion at Kīlauea volcano, Hawaii","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0075\"><span>The February 1993 dike intrusion in the East&nbsp;Rift Zone&nbsp;(ERZ) of Kīlauea&nbsp;Volcano, Hawai'i, was recognized from tilt and&nbsp;seismic data, but ground-based&nbsp;geodetic data&nbsp;were too sparse to constrain the characteristics of the intrusion. Analysis of Interferometric Synthetic Aperture Radar (InSAR) from the Japan Aerospace Exploration Agency (JAXA)&nbsp;JERS-1&nbsp;satellite reveals a maximum of ~30 cm of line-of-sight (LOS) displacement occurring near Makaopuhi&nbsp;Crater&nbsp;in the middle ERZ of Kīlauea. We model this deformation signal as a subvertical dike using a 3D-Mixed&nbsp;Boundary Element Method&nbsp;(3D-MBEM) paired with a nonlinear inversion algorithm to find the best-fit model. The best-fit dike is located just to the west of Makaopuhi Crater striking N50°W, extends to within 100 m of the surface, is ~1.3 km in length by ~4.2 km in width along strike, and has a total volume of ~7.4 × 10</span><sup>6</sup> m<sup>3</sup><span>. In addition, a post-intrusion&nbsp;interferogram&nbsp;from JERS-1 spanning 1993–1997 was analyzed. Guided by previous results, our model for the 4-year period consists of opening of the deep rift zones by about 0.5 m at 3–8.5 km depth beneath the Southwest Rift Zone (SWRZ), ERZ and the summit. A sub-horizontal&nbsp;detachment fault&nbsp;is connected to the seaward side of the vertical dike-like source to mimic the&nbsp;décollement&nbsp;known to exist beneath the volcano. We classify the 1993 dike intrusion as a passive intrusion similar to those that occurred in 1997 and 1999. Passive intrusions lack precursory inflation at Kīlauea's summit, and the likely triggering mechanism is persistent deep rift opening combined with seaward motion of the south flank along the basal décollement. Passive intrusions make forecasting and hazard assessment difficult since they are not preceded by inflation nor by large increases in&nbsp;seismicity.</span></p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2018.05.017","usgsCitation":"Conway, S., Wauthier, C., Fukushima, Y., and Poland, M.P., 2018, A retrospective look at the February 1993 east rift zone intrusion at Kīlauea volcano, Hawaii: Journal of Volcanology and Geothermal Research, v. 358, p. 241-251, https://doi.org/10.1016/j.jvolgeores.2018.05.017.","productDescription":"114 p.","startPage":"241","endPage":"251","ipdsId":"IP-090650","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":376803,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea Volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -155.53070068359375,\n              19.235956641468505\n            ],\n            [\n              -154.86190795898438,\n              19.235956641468505\n            ],\n            [\n              -154.86190795898438,\n              19.48536557300507\n            ],\n            [\n              -155.53070068359375,\n              19.48536557300507\n            ],\n            [\n              -155.53070068359375,\n              19.235956641468505\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"358","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Conway, Sarah 0000-0003-1953-5550","orcid":"https://orcid.org/0000-0003-1953-5550","contributorId":215609,"corporation":false,"usgs":true,"family":"Conway","given":"Sarah","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":794228,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wauthier, Christelle","contributorId":81011,"corporation":false,"usgs":true,"family":"Wauthier","given":"Christelle","affiliations":[],"preferred":false,"id":794229,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fukushima, Yo","contributorId":236709,"corporation":false,"usgs":false,"family":"Fukushima","given":"Yo","email":"","affiliations":[],"preferred":false,"id":794230,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Poland, Michael P. 0000-0001-5240-6123 mpoland@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":146118,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","email":"mpoland@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":794231,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70197216,"text":"70197216 - 2018 - Origin of last-glacial loess in the western Yukon-Tanana Upland, central Alaska, USA","interactions":[],"lastModifiedDate":"2018-05-23T10:35:34","indexId":"70197216","displayToPublicDate":"2018-05-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3218,"text":"Quaternary Research","active":true,"publicationSubtype":{"id":10}},"title":"Origin of last-glacial loess in the western Yukon-Tanana Upland, central Alaska, USA","docAbstract":"<p><span>Loess is widespread over Alaska, and its accumulation has traditionally been associated with glacial periods. Surprisingly, loess deposits securely dated to the last glacial period are rare in Alaska, and paleowind reconstructions for this time period are limited to inferences from dune orientations. We report a rare occurrence of loess deposits dating to the last glacial period, ~19 ka to ~12 ka, in the Yukon-Tanana Upland. Loess in this area is very coarse grained (abundant coarse silt), with decreases in particle size moving south of the Yukon River, implying that the drainage basin of this river was the main source. Geochemical data show, however, that the Tanana River valley to the south is also a likely distal source. The occurrence of last-glacial loess with sources to both the south and north is explained by both regional, synoptic-scale winds from the northeast and opposing katabatic winds that could have developed from expanded glaciers in both the Brooks Range to the north and the Alaska Range to the south. Based on a comparison with recent climate modeling for the last glacial period, seasonality of dust transport may also have played a role in bringing about contributions from both northern and southern sources.</span></p>","language":"English","publisher":"Cambridge University Press","doi":"10.1017/qua.2018.11","usgsCitation":"Muhs, D., Pigati, J.S., Budahn, J.R., Skipp, G.L., Bettis, E.A., and Jensen, B., 2018, Origin of last-glacial loess in the western Yukon-Tanana Upland, central Alaska, USA: Quaternary Research, v. 89, no. 3, p. 797-819, https://doi.org/10.1017/qua.2018.11.","productDescription":"23 p.","startPage":"797","endPage":"819","ipdsId":"IP-086762","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":354407,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -150,\n              65.5\n            ],\n            [\n              -148.75,\n              65.5\n            ],\n            [\n              -148.75,\n              66\n            ],\n            [\n              -150,\n              66\n            ],\n            [\n              -150,\n              65.5\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"89","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-10","publicationStatus":"PW","scienceBaseUri":"5b155d78e4b092d9651e1b3e","contributors":{"authors":[{"text":"Muhs, Daniel R. 0000-0001-7449-251X dmuhs@usgs.gov","orcid":"https://orcid.org/0000-0001-7449-251X","contributorId":168575,"corporation":false,"usgs":true,"family":"Muhs","given":"Daniel R.","email":"dmuhs@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":736257,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pigati, Jeffrey S. 0000-0001-5843-6219 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0001-5843-6219","contributorId":201167,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey","email":"jpigati@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":736258,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Budahn, James R. 0000-0001-9794-8882 jbudahn@usgs.gov","orcid":"https://orcid.org/0000-0001-9794-8882","contributorId":1175,"corporation":false,"usgs":true,"family":"Budahn","given":"James","email":"jbudahn@usgs.gov","middleInitial":"R.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":736259,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skipp, Gary L. 0000-0002-9404-0980","orcid":"https://orcid.org/0000-0002-9404-0980","contributorId":201777,"corporation":false,"usgs":true,"family":"Skipp","given":"Gary","email":"","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":736260,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bettis, E. Arthur III 0000-0002-6137-1433","orcid":"https://orcid.org/0000-0002-6137-1433","contributorId":204005,"corporation":false,"usgs":false,"family":"Bettis","given":"E.","suffix":"III","email":"","middleInitial":"Arthur","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":736261,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jensen, Britta","contributorId":184164,"corporation":false,"usgs":false,"family":"Jensen","given":"Britta","affiliations":[],"preferred":false,"id":736262,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70197221,"text":"70197221 - 2018 - Enhancement of a parsimonious water balance model to simulate surface hydrology in a glacierized watershed","interactions":[],"lastModifiedDate":"2018-10-11T15:00:36","indexId":"70197221","displayToPublicDate":"2018-05-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2318,"text":"Journal of Geophysical Research F: Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Enhancement of a parsimonious water balance model to simulate surface hydrology in a glacierized watershed","docAbstract":"<p><span>The U.S. Geological Survey monthly water balance model (MWBM) was enhanced with the capability to simulate glaciers in order to make it more suitable for simulating cold region hydrology. The new model, MWBMglacier, is demonstrated in the heavily glacierized and ecologically important Copper River watershed in Southcentral Alaska. Simulated water budget components compared well to satellite‐based observations and ground measurements of streamflow, evapotranspiration, snow extent, and total water storage, with differences ranging from 0.2% to 7% of the precipitation flux. Nash Sutcliffe efficiency for simulated and observed streamflow was greater than 0.8 for six of eight stream gages. Snow extent matched satellite‐based observations with Nash Sutcliffe efficiency values of greater than 0.89 in the four Copper River ecoregions represented. During the simulation period 1949 to 2009, glacier ice melt contributed 25% of total runoff, ranging from 12% to 45% in different tributaries, and glacierized area was reduced by 6%. Statistically significant (</span><i>p</i><span>&nbsp;&lt;&nbsp;0.05) decreasing and increasing trends in annual glacier mass balance occurred during the multidecade cool and warm phases of the Pacific Decadal Oscillation, respectively, reinforcing the link between climate perturbations and glacier mass balance change. The simulations of glaciers and total runoff for a large, remote region of Alaska provide useful data to evaluate hydrologic, cryospheric, ecologic, and climatic trends. MWBM glacier is a valuable tool to understand when, and to what extent, streamflow may increase or decrease as glaciers respond to a changing climate.</span></p>","language":"English","publisher":"AGU","doi":"10.1029/2017JF004482","usgsCitation":"Valentin, M.M., Viger, R.J., Van Beusekom, A.E., Hay, L.E., Hogue, T.S., and Foks, N.L., 2018, Enhancement of a parsimonious water balance model to simulate surface hydrology in a glacierized watershed: Journal of Geophysical Research F: Earth Surface, v. 123, no. 5, p. 1116-1132, https://doi.org/10.1029/2017JF004482.","productDescription":"17 p.","startPage":"1116","endPage":"1132","ipdsId":"IP-094374","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":468839,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017jf004482","text":"Publisher Index Page"},{"id":354424,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"123","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-23","publicationStatus":"PW","scienceBaseUri":"5b155d78e4b092d9651e1b3c","contributors":{"authors":[{"text":"Valentin, Melissa M.","contributorId":205172,"corporation":false,"usgs":false,"family":"Valentin","given":"Melissa","email":"","middleInitial":"M.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":736281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Viger, Roland J. 0000-0003-2520-714X rviger@usgs.gov","orcid":"https://orcid.org/0000-0003-2520-714X","contributorId":147818,"corporation":false,"usgs":true,"family":"Viger","given":"Roland","email":"rviger@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":false,"id":736280,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Beusekom, Ashley E. 0000-0002-6996-978X beusekom@usgs.gov","orcid":"https://orcid.org/0000-0002-6996-978X","contributorId":3992,"corporation":false,"usgs":true,"family":"Van Beusekom","given":"Ashley","email":"beusekom@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":736282,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hay, Lauren E. 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":1287,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":736283,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hogue, Terri S.","contributorId":205175,"corporation":false,"usgs":false,"family":"Hogue","given":"Terri","email":"","middleInitial":"S.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":736284,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Foks, Nathan Leon","contributorId":194012,"corporation":false,"usgs":false,"family":"Foks","given":"Nathan","email":"","middleInitial":"Leon","affiliations":[],"preferred":false,"id":736285,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70197211,"text":"70197211 - 2018 - Nanomaterials in the environment: Behavior, fate, bioavailability, and effects—An updated review","interactions":[],"lastModifiedDate":"2018-07-23T12:59:06","indexId":"70197211","displayToPublicDate":"2018-05-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Nanomaterials in the environment: Behavior, fate, bioavailability, and effects—An updated review","docAbstract":"<p><span>The present review covers developments in studies of nanomaterials (NMs) in the environment since our much cited review in 2008. We discuss novel insights into fate and behavior, metrology, transformations, bioavailability, toxicity mechanisms, and environmental impacts, with a focus on terrestrial and aquatic systems. Overall, the findings were that: 1) despite substantial developments, critical gaps remain, in large part due to the lack of analytical, modeling, and field capabilities, and also due to the breadth and complexity of the area; 2) a key knowledge gap is the lack of data on environmental concentrations and dosimetry generally; 3) substantial evidence shows that there are nanospecific effects (different from the effects of both ions and larger particles) on the environment in terms of fate, bioavailability, and toxicity, but this is not consistent for all NMs, species, and relevant processes; 4) a paradigm is emerging that NMs are less toxic than equivalent dissolved materials but more toxic than the corresponding bulk materials; and 5) translation of incompletely understood science into regulation and policy continues to be challenging. There is a developing consensus that NMs may pose a relatively low environmental risk, but because of uncertainty and lack of data in many areas, definitive conclusions cannot be drawn. In addition, this emerging consensus will likely change rapidly with qualitative changes in the technology and increased future discharges.&nbsp;</span></p>","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.4147","usgsCitation":"Lead, J.R., Batley, G.E., Alvarez, P.J., Croteau, M.N., Handy, R.D., McLaughlin, M., Judy, J.D., and Schirmer, K., 2018, Nanomaterials in the environment: Behavior, fate, bioavailability, and effects—An updated review: Environmental Toxicology and Chemistry, v. 37, no. 8, p. 2029-2063, https://doi.org/10.1002/etc.4147.","productDescription":"35 p.","startPage":"2029","endPage":"2063","ipdsId":"IP-094801","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":468738,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.4147","text":"Publisher Index Page"},{"id":354409,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-06","publicationStatus":"PW","scienceBaseUri":"5b155d79e4b092d9651e1b44","contributors":{"authors":[{"text":"Lead, Jamie R.","contributorId":41331,"corporation":false,"usgs":false,"family":"Lead","given":"Jamie","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":736234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Batley, Graeme E.","contributorId":205153,"corporation":false,"usgs":false,"family":"Batley","given":"Graeme","email":"","middleInitial":"E.","affiliations":[{"id":12494,"text":"CSIRO Land and Water, Australia","active":true,"usgs":false}],"preferred":false,"id":736235,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alvarez, Pedro J.J.","contributorId":205154,"corporation":false,"usgs":false,"family":"Alvarez","given":"Pedro","email":"","middleInitial":"J.J.","affiliations":[{"id":37035,"text":"Rice University, Houston, Texas, USA","active":true,"usgs":false}],"preferred":false,"id":736236,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Croteau, Marie Noele 0000-0003-0346-3580 mcroteau@usgs.gov","orcid":"https://orcid.org/0000-0003-0346-3580","contributorId":895,"corporation":false,"usgs":true,"family":"Croteau","given":"Marie","email":"mcroteau@usgs.gov","middleInitial":"Noele","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":736233,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Handy, Richard D.","contributorId":205155,"corporation":false,"usgs":false,"family":"Handy","given":"Richard","email":"","middleInitial":"D.","affiliations":[{"id":37036,"text":"University of Plymouth, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":736237,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McLaughlin, Michael J.","contributorId":205156,"corporation":false,"usgs":false,"family":"McLaughlin","given":"Michael J.","affiliations":[{"id":13368,"text":"University of Adelaide, Australia","active":true,"usgs":false}],"preferred":false,"id":736238,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Judy, Jonathon D.","contributorId":205157,"corporation":false,"usgs":false,"family":"Judy","given":"Jonathon","email":"","middleInitial":"D.","affiliations":[{"id":37037,"text":"University of Florida, Florida USA","active":true,"usgs":false}],"preferred":false,"id":736239,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schirmer, Kristin","contributorId":176360,"corporation":false,"usgs":false,"family":"Schirmer","given":"Kristin","email":"","affiliations":[],"preferred":false,"id":736240,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70197212,"text":"70197212 - 2018 - A biodynamic understanding of dietborne and waterborne Ag uptake from Ag NPs in the sediment-dwelling oligochaete, Tubifex tubifex","interactions":[],"lastModifiedDate":"2018-05-23T10:56:55","indexId":"70197212","displayToPublicDate":"2018-05-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5146,"text":"NanoImpact","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A biodynamic understanding of dietborne and waterborne Ag uptake from Ag NPs in the sediment-dwelling oligochaete, <i>Tubifex tubifex</i>","title":"A biodynamic understanding of dietborne and waterborne Ag uptake from Ag NPs in the sediment-dwelling oligochaete, Tubifex tubifex","docAbstract":"<p><span>Metal&nbsp;nanoparticles&nbsp;(Me-NPs) are increasingly used in various products, such as inks and cosmetics, enhancing the likelihood of their release into&nbsp;aquatic environments. An understanding of the mechanisms controlling their&nbsp;bioaccumulation&nbsp;and&nbsp;ecotoxicity&nbsp;in aquatic biota will help support&nbsp;environmental risk assessment. Here we characterized unidirectional parameters for uptake and elimination of silver (Ag) in the sediment-dwelling oligochaete&nbsp;</span><i>Tubifex tubifex</i><span><span>&nbsp;</span>after waterborne (0.01–47 nmol Ag/L) and dietborne (0.4–482 nmol Ag/g dw sed.) exposures to Ag NPs and AgNO</span><sub>3</sub><span>, respectively. Worms accumulated Ag from AgNO</span><sub>3</sub><span>more efficiently than from Ag NPs during waterborne exposure. The Ag uptake rate constants from water were 8.2 L/g/d for AgNO</span><sub>3</sub><span><span>&nbsp;</span>and 0.34 L/g/d for Ag NPs. Silver accumulated from both forms was efficiently retained in tissues, as no significant loss of Ag was detected after up to 20 days of depuration in clean media. High mortality (~50%) during depuration (i.e. after 17 days) was only observed for worms exposed to waterborne AgNO</span><sub>3</sub><span><span>&nbsp;</span>(3 nmol/L). Sediment exposures to both Ag forms resulted in low accumulation, i.e., the uptake rate constants were 0.002 and 0.005 g/g/d for AgNO</span><sub>3</sub><span><span>&nbsp;</span>and Ag NPs, respectively. Avoidance was only observed for worms exposed to sediment amended with AgNO</span><sub>3</sub><span>. Incorporation of the estimated rate constants into a biodynamic model predicted that sediment is likely the most important route of uptake for Ag in both forms in ecologically relevant aquatic environments. However, inference of<span>&nbsp;</span>bioavailability<span><span>&nbsp;</span>from our estimations of Ag<span>&nbsp;</span>assimilation efficiencies<span>&nbsp;</span>(AE) suggests that Ag (AE: 3–12% for AgNO</span></span><sub>3</sub><span><span>&nbsp;</span>and 0.1–0.8% for Ag NPs) is weakly bioavailable from sediment for this species. Thus, Ag amended to sediment as NPs might not pose greater problems than 'conventional' Ag for benthic organisms such as<span>&nbsp;</span></span><i>T. tubifex</i><span>.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.impact.2018.01.002","usgsCitation":"Tangaa, S.R., Winther-Nielsen, M., Selck, H., and Croteau, M.N., 2018, A biodynamic understanding of dietborne and waterborne Ag uptake from Ag NPs in the sediment-dwelling oligochaete, Tubifex tubifex: NanoImpact, v. 11, p. 33-41, https://doi.org/10.1016/j.impact.2018.01.002.","productDescription":"9 p.","startPage":"33","endPage":"41","ipdsId":"IP-087267","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":468737,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.impact.2018.01.002","text":"Publisher Index Page"},{"id":354408,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d79e4b092d9651e1b42","contributors":{"authors":[{"text":"Tangaa, Stine Rosendal","contributorId":205159,"corporation":false,"usgs":false,"family":"Tangaa","given":"Stine","email":"","middleInitial":"Rosendal","affiliations":[{"id":37038,"text":"Roskilde University, Dept. of Science and Environment, Roskilde, Denmark","active":true,"usgs":false}],"preferred":false,"id":736242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winther-Nielsen, Margrethe","contributorId":205160,"corporation":false,"usgs":false,"family":"Winther-Nielsen","given":"Margrethe","email":"","affiliations":[{"id":37039,"text":"DHI, Dept. of Environment and Toxicology, Hørsholm, Denmark","active":true,"usgs":false}],"preferred":false,"id":736243,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Selck, Henriette","contributorId":178783,"corporation":false,"usgs":false,"family":"Selck","given":"Henriette","email":"","affiliations":[],"preferred":false,"id":736244,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Croteau, Marie Noele 0000-0003-0346-3580 mcroteau@usgs.gov","orcid":"https://orcid.org/0000-0003-0346-3580","contributorId":895,"corporation":false,"usgs":true,"family":"Croteau","given":"Marie","email":"mcroteau@usgs.gov","middleInitial":"Noele","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":736241,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70200455,"text":"70200455 - 2018 - Alaska snowpack response to climate change: Statewide snowfall equivalent and snowpack water scenarios","interactions":[],"lastModifiedDate":"2018-10-18T13:45:55","indexId":"70200455","displayToPublicDate":"2018-05-22T13:45:44","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Alaska snowpack response to climate change: Statewide snowfall equivalent and snowpack water scenarios","docAbstract":"<p><span>Climatically driven changes in snow characteristics (snowfall, snowpack, and snowmelt) will affect hydrologic and ecological systems in Alaska over the coming century, yet there exist no projections of downscaled future snow pack metrics for the state of Alaska. We updated historical and projected snow day fraction (PSF, the fraction of days with precipitation falling as snow) from McAfee et al. We developed modeled snowfall equivalent (SFE) derived from the product of snow-day fraction (PSF) and existing gridded precipitation for Alaska from Scenarios Network for Alaska and Arctic Planning (SNAP). We validated the assumption that modeled SFE approximates historical decadally averaged snow water equivalent (SWE) observations from snowcourse and Snow Telemetry (SNOTEL) sites. We present analyses of future downscaled PSF and two new products, October–March SFE and ratio of snow fall equivalent to precipitation (SFE:P) based on bias-corrected statistically downscaled projections of Coupled Model Intercomparison Project 5 (CMIP5) Global Climate Model (GCM) temperature and precipitation for the state of Alaska. We analyzed mid-century (2040–2069) and late-century (2070–2099) changes in PSF, SFE, and SFE:P relative to historical (1970–1999) mean temperature and present results for Alaska climate divisions and 12-digit Hydrologic Unit Code (HUC12) watersheds. Overall, estimated historical the SFE is reasonably well related to the observed SWE, with correlations over 0.75 in all decades, and correlations exceeding 0.9 in the 1960s and 1970s. In absolute terms, SFE is generally biased low compared to the observed SWE. PSF and SFE:P decrease universally across Alaska under both Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 emissions scenarios, with the smallest changes for RCP 4.5 in 2040–2069 and the largest for RCP 8.5 in 2070–2099. The timing and magnitude of maximum decreases in PSF vary considerably with regional average temperature, with the largest changes in months at the beginning and end of the snow season. Mean SFE changes vary widely among climate divisions, ranging from decreases between −17 and −58% for late twenty-first century in southeast, southcentral, west coast and southwest Alaska to increases up to 21% on the North Slope. SFE increases most at highest elevations and latitudes and decreases most in coastal southern Alaska. SFE:P ratios indicate a broad switch from snow-dominated to transitional annual hydrology across most of southern Alaska by mid-century, and from transitional to rain-dominated watersheds in low elevation parts of southeast Alaska by the late twenty-first century.&nbsp;</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/w10050668","usgsCitation":"Littell, J., McAfee, S., and Hayward, G.D., 2018, Alaska snowpack response to climate change: Statewide snowfall equivalent and snowpack water scenarios: Water, v. 10, no. 5, p. 1-16, https://doi.org/10.3390/w10050668.","productDescription":"Article 668; 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-097141","costCenters":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"links":[{"id":468739,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w10050668","text":"Publisher Index 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,{"id":70197208,"text":"70197208 - 2018 - Spatial distribution and ecological risk assessment of heavy metals in coastal surface sediments in the Hebei Province offshore area, Bohai Sea, China","interactions":[],"lastModifiedDate":"2018-05-22T15:16:00","indexId":"70197208","displayToPublicDate":"2018-05-22T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2676,"text":"Marine Pollution Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Spatial distribution and ecological risk assessment of heavy metals in coastal surface sediments in the Hebei Province offshore area, Bohai Sea, China","docAbstract":"Seven hundred and nine surface sediment samples, along with deeper sediment samples, were collected from Hebei Province along the coastal section of the Bohai Sea, China, and analyzed for grain size, concentrations of organic carbon (Corg) and heavy metals (Cu, Pb, Zn, Cr, Cd, As, and Hg). Results indicated that the average concentrations in the sediments were 16.1 mg/kg (Cu), 19.4 mg/kg (Pb), 50 mg/kg (Zn), 48.8 mg/kg (Cr), 0.1 mg/kg (Cd), 8.4 mg/kg (As), and 20.3 μg/kg (Hg). These concentrations generally met the China Marine Sediment Quality criteria. However, both pollution assessments indicated moderate to strong Cd and Hg contamination in the study area. The potential ecological risk index suggested that the combined ecological risk of the seven studied metals may be low, but that 24.5% of the sites, where sediments were more finer and higher in Corg concentration, had high ecological risk in Hg and Cd pollution.","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpolbul.2018.04.060","usgsCitation":"Ding, X., Ye, S., Yuan, H., and Krauss, K.W., 2018, Spatial distribution and ecological risk assessment of heavy metals in coastal surface sediments in the Hebei Province offshore area, Bohai Sea, China: Marine Pollution Bulletin, v. 131, no. A, p. 655-661, https://doi.org/10.1016/j.marpolbul.2018.04.060.","productDescription":"7 p.","startPage":"655","endPage":"661","ipdsId":"IP-095668","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":354398,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"Bohai Sea, Hebei Province","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              117.5,\n              38\n            ],\n            [\n              120,\n              38\n            ],\n            [\n              120,\n              40\n            ],\n            [\n              117.5,\n              40\n            ],\n            [\n              117.5,\n              38\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"131","issue":"A","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d79e4b092d9651e1b46","contributors":{"authors":[{"text":"Ding, Xigui","contributorId":173536,"corporation":false,"usgs":false,"family":"Ding","given":"Xigui","email":"","affiliations":[{"id":27244,"text":"Qingdao Institute of Marine Geology, China","active":true,"usgs":false}],"preferred":false,"id":736210,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ye, Siyuan","contributorId":146732,"corporation":false,"usgs":false,"family":"Ye","given":"Siyuan","email":"","affiliations":[{"id":16739,"text":"Qingdao Institute of Marine Geology, Shandong Province, China","active":true,"usgs":false}],"preferred":false,"id":736211,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yuan, Hongming","contributorId":173534,"corporation":false,"usgs":false,"family":"Yuan","given":"Hongming","email":"","affiliations":[{"id":27244,"text":"Qingdao Institute of Marine Geology, China","active":true,"usgs":false}],"preferred":false,"id":736212,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krauss, Ken W. 0000-0003-2195-0729 kraussk@usgs.gov","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":2017,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","email":"kraussk@usgs.gov","middleInitial":"W.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":736209,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70197203,"text":"70197203 - 2018 - A framework for modeling scenario-based barrier island storm impacts","interactions":[],"lastModifiedDate":"2018-05-22T13:22:48","indexId":"70197203","displayToPublicDate":"2018-05-22T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"A framework for modeling scenario-based barrier island storm impacts","docAbstract":"<p><span>Methods for investigating the vulnerability of existing or proposed coastal features to storm impacts often rely on simplified parametric models or one-dimensional process-based modeling studies that focus on changes to a profile across a dune or barrier island. These simple studies tend to neglect the impacts to curvilinear or alongshore varying island planforms, influence of non-uniform nearshore hydrodynamics and sediment transport, irregular morphology of the offshore bathymetry, and impacts from low magnitude wave events (e.g. cold fronts). Presented here is a framework for simulating regionally specific, low and high magnitude scenario-based storm impacts to assess the alongshore variable vulnerabilities of a coastal feature. Storm scenarios based on historic hydrodynamic conditions were derived and simulated using the process-based morphologic evolution model XBeach. Model results show that the scenarios predicted similar patterns of erosion and overwash when compared to observed qualitative morphologic changes from recent storm events that were not included in the dataset used to build the scenarios. The framework model simulations were capable of predicting specific areas of vulnerability in the existing feature and the results illustrate how this storm vulnerability simulation framework could be used as a tool to help inform the decision-making process for scientists, engineers, and stakeholders involved in coastal zone management or restoration projects.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2018.04.012","usgsCitation":"Mickey, R.C., Long, J., Dalyander, P.S., Plant, N.G., and Thompson, D.M., 2018, A framework for modeling scenario-based barrier island storm impacts: Coastal Engineering, v. 138, p. 98-112, https://doi.org/10.1016/j.coastaleng.2018.04.012.","productDescription":"15 p.","startPage":"98","endPage":"112","ipdsId":"IP-092224","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468740,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2018.04.012","text":"Publisher Index Page"},{"id":354386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chandeleur Islands","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -89,\n              28.67\n            ],\n            [\n              -87,\n              28.67\n            ],\n            [\n              -87,\n              30.67\n            ],\n            [\n              -89,\n              30.67\n            ],\n            [\n              -89,\n              28.67\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"138","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d7ae4b092d9651e1b4a","contributors":{"authors":[{"text":"Mickey, Rangley C. 0000-0001-5989-1432 rmickey@usgs.gov","orcid":"https://orcid.org/0000-0001-5989-1432","contributorId":141016,"corporation":false,"usgs":true,"family":"Mickey","given":"Rangley","email":"rmickey@usgs.gov","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736121,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, Joseph W. 0000-0003-2912-1992","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":202183,"corporation":false,"usgs":true,"family":"Long","given":"Joseph W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736122,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dalyander, P. Soupy 0000-0001-9583-0872 sdalyander@usgs.gov","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":141015,"corporation":false,"usgs":true,"family":"Dalyander","given":"P.","email":"sdalyander@usgs.gov","middleInitial":"Soupy","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736123,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736124,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, David M. 0000-0002-7103-5740 dthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-7103-5740","contributorId":3502,"corporation":false,"usgs":true,"family":"Thompson","given":"David","email":"dthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":736125,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70197202,"text":"70197202 - 2018 - Sampling the stream landscape: Improving the applicability of an ecoregion-level capture probability model for stream fishes","interactions":[],"lastModifiedDate":"2023-03-27T22:49:54.298718","indexId":"70197202","displayToPublicDate":"2018-05-22T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Sampling the stream landscape: Improving the applicability of an ecoregion-level capture probability model for stream fishes","docAbstract":"<p><span>Temporal and spatial variability in streams result in heterogeneous gear capture probability (i.e., the proportion of available individuals identified) that confounds interpretation of data used to monitor fish abundance. We modeled tow-barge electrofishing capture probability at multiple spatial scales for nine Ozark Highland stream fishes. In addition to fish size, we identified seven reach-scale environmental characteristics associated with variable capture probability: stream discharge, water depth, conductivity, water clarity, emergent vegetation, wetted width–depth ratio, and proportion of riffle habitat. The magnitude of the relationship between capture probability and both discharge and depth varied among stream fishes. We also identified lithological characteristics among stream segments as a coarse-scale source of variable capture probability. The resulting capture probability model can be used to adjust catch data and derive reach-scale absolute abundance estimates across a wide range of sampling conditions with similar effort as used in more traditional fisheries surveys (i.e., catch per unit effort). Adjusting catch data based on variable capture probability improves the comparability of data sets, thus promoting both well-informed conservation and management decisions and advances in stream-fish ecology.</span></p>","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2016-0422","usgsCitation":"Mollenhauer, R., Mouser, J.B., and Brewer, S.K., 2018, Sampling the stream landscape: Improving the applicability of an ecoregion-level capture probability model for stream fishes: Canadian Journal of Fisheries and Aquatic Sciences, v. 75, no. 10, p. 1614-1625, https://doi.org/10.1139/cjfas-2016-0422.","productDescription":"12 p.","startPage":"1614","endPage":"1625","ipdsId":"IP-079903","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":468741,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1807/87924","text":"External Repository"},{"id":354387,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Missouri, Oklahoma","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -95.09031466610114,\n              36.913206107808435\n            ],\n            [\n              -95.09031466610114,\n              36.02798264227451\n            ],\n            [\n              -93.63459116212798,\n              36.02798264227451\n            ],\n            [\n              -93.63459116212798,\n              36.913206107808435\n            ],\n            [\n              -95.09031466610114,\n              36.913206107808435\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"75","issue":"10","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b155d7ae4b092d9651e1b4c","contributors":{"authors":[{"text":"Mollenhauer, Robert","contributorId":176540,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"Robert","affiliations":[],"preferred":false,"id":735984,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mouser, Joshua B.","contributorId":205087,"corporation":false,"usgs":false,"family":"Mouser","given":"Joshua","email":"","middleInitial":"B.","affiliations":[{"id":37027,"text":"Oklahoma Cooperative Fish and Wildlife Research Unit, Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":735985,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":735983,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70196814,"text":"ofr20181061 - 2018 - Comparison of NEXRAD multisensor precipitation estimates to rain gage observations in and near DuPage County, Illinois, 2002–12","interactions":[],"lastModifiedDate":"2018-05-22T10:19:56","indexId":"ofr20181061","displayToPublicDate":"2018-05-21T15:00:00","publicationYear":"2018","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":"2018-1061","title":"Comparison of NEXRAD multisensor precipitation estimates to rain gage observations in and near DuPage County, Illinois, 2002–12","docAbstract":"<p>In this report, precipitation data from 2002 to 2012 from the hourly gridded Next-Generation Radar (NEXRAD)-based Multisensor Precipitation Estimate (MPE) precipitation product are compared to precipitation data from two rain gage networks—an automated tipping bucket network of 25 rain gages operated by the U.S. Geological Survey (USGS) and 51 rain gages from the volunteer-operated Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network—in and near DuPage County, Illinois, at a daily time step to test for long-term differences in space, time, and distribution. The NEXRAD–MPE data that are used are from the fifty 2.5-mile grid cells overlying the rain gages from the other networks. Because of the challenges of measuring of frozen precipitation, the analysis period is separated between days with or without the chance of freezing conditions. The NEXRAD–MPE and tipping-bucket rain gage precipitation data are adjusted to account for undercatch by multiplying by a previously determined factor of 1.14. Under nonfreezing conditions, the three precipitation datasets are broadly similar in cumulative depth and distribution of daily values when the data are combined spatially across the networks. However, the NEXRAD–MPE data indicate a significant trend relative to both rain gage networks as a function of distance from the NEXRAD radar just south of the study area. During freezing conditions, of the USGS network rain gages only the heated gages were considered, and these gages indicate substantial mean undercatch of 50 and 61 percent compared to the NEXRAD–MPE and the CoCoRaHS gages, respectively. The heated USGS rain gages also indicate substantially lower quantile values during freezing conditions, except during the most extreme (highest) events. Because NEXRAD precipitation products are continually evolving, the report concludes with a discussion of recent changes in those products and their potential for improved precipitation estimation. An appendix provides an analysis of spatially combined NEXRAD–MPE precipitation data as a function of temperature at an hourly time scale and indicates, among other results, that most precipitation in the study area occurs at moderate temperatures of 30 to 74 degrees Fahrenheit. However, when precipitation does occur, its intensity increases with temperature to about 86 degrees Fahrenheit.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181061","collaboration":"Prepared in cooperation with the DuPage County Stormwater Management Department","usgsCitation":"Spies, R.R., Over, T.M., and Ortel, T.W., 2018, Comparison of NEXRAD multisensor precipitation estimates to rain gage observations in and near DuPage County, Illinois, 2002–12: U.S. Geological Survey Open-File Report 2018–1061, 30 p., https://doi.org/10.3133/ofr20181061. ","productDescription":"v, 30 p.","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-057485","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":354281,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1061/coverthb.jpg","text":"Report"},{"id":354282,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1061/ofr20181061.pdf","text":"Report","size":"5.61 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1061"}],"country":"United States","state":"Illinois","county":"DuPage County","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -88.33,\n              41.5833\n            ],\n            [\n              -87.8333,\n              41.5833\n            ],\n            [\n              -87.8333,\n              42.1667\n            ],\n            [\n              -88.33,\n              42.1667\n            ],\n            [\n              -88.33,\n              41.5833\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_il@usgs.gov\" data-mce-href=\"mailto:dc_il@usgs.gov\">Director</a>, <a href=\"https://il.water.usgs.gov/\" data-mce-href=\"https://il.water.usgs.gov/\">Central Midwest Water Science Center</a><br> U.S. Geological Survey<br> 405 North Goodwin Avenue<br> Urbana, IL 61801</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Data Used in This Study</li><li>Methods</li><li>Results</li><li>Summary and Conclusions</li><li>References Cited</li><li>Appendix 1. Daily Precipitation Quantile Comparisons by Year</li><li>Appendix 2. Dependence of NEXRAD–MPE Precipitation on Temperature</li></ul>","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2018-05-21","noUsgsAuthors":false,"publicationDate":"2018-05-21","publicationStatus":"PW","scienceBaseUri":"5b155d7ae4b092d9651e1b50","contributors":{"authors":[{"text":"Spies, Ryan R. rspies@usgs.gov","contributorId":204652,"corporation":false,"usgs":false,"family":"Spies","given":"Ryan","email":"rspies@usgs.gov","middleInitial":"R.","affiliations":[{"id":36969,"text":"Lynker Technologies","active":true,"usgs":false}],"preferred":false,"id":734571,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Over, Thomas M. 0000-0001-8280-4368","orcid":"https://orcid.org/0000-0001-8280-4368","contributorId":204650,"corporation":false,"usgs":true,"family":"Over","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":734569,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ortel, Terry 0000-0001-9647-4259","orcid":"https://orcid.org/0000-0001-9647-4259","contributorId":204651,"corporation":false,"usgs":true,"family":"Ortel","given":"Terry","email":"","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":734570,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70196226,"text":"70196226 - 2018 - Effect of river confinement on depth and spatial extent of bed disturbance affecting salmon redds","interactions":[],"lastModifiedDate":"2018-11-16T11:49:52","indexId":"70196226","displayToPublicDate":"2018-05-21T11:00:54","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5513,"text":"Journal of Ecohydraulics","active":true,"publicationSubtype":{"id":10}},"title":"Effect of river confinement on depth and spatial extent of bed disturbance affecting salmon redds","docAbstract":"<p><span>Human impacts on rivers threaten the natural function of riverine ecosystems. This paper assesses how channel confinement affects the scour depth and spatial extent of bed disturbance and discusses the implications of these results for salmon-redd disturbance in gravel-bedded rivers. Two-dimensional hydrodynamic models of relatively confined and unconfined reaches of the Cedar River in Washington State, USA, were constructed with surveyed bathymetry and available airborne lidar data then calibrated and verified with field observations of water-surface elevation and streamflow velocity. Simulations showed greater water depths and velocities in the confined reach and greater areas of low-velocity inundation in the unconfined reach at high flows. Data on previously published scour depth of bed disturbance during high flows were compared to simulated bed shear stress to construct a probabilistic logistic-regression model of bed disturbance, which was applied to spatial patterns of simulated bed shear stress to quantify the extent of likely bed disturbance to the burial depth of sockeye and Chinook salmon redds. The disturbance depth was not observed to differ between confined and unconfined reaches; however, results indicated the spatial extent of disturbance to a given depth in the confined reach was roughly twice as large as in the unconfined reach.</span></p>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/24705357.2018.1457986","usgsCitation":"Christiana R. Czuba, Czuba, J.A., Magirl, C.S., Gendaszek, A.S., and Konrad, C.P., 2018, Effect of river confinement on depth and spatial extent of bed disturbance affecting salmon redds: Journal of Ecohydraulics, v. 2, no. 2, p. 1-14, https://doi.org/10.1080/24705357.2018.1457986.","productDescription":"14 p.","startPage":"1","endPage":"14","ipdsId":"IP-066545","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":359514,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Cedar River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.29499816894531,\n              47.357431944587034\n            ],\n            [\n              -121.62620544433592,\n              47.357431944587034\n            ],\n            [\n              -121.62620544433592,\n              47.58717856130287\n            ],\n            [\n              -122.29499816894531,\n              47.58717856130287\n            ],\n            [\n              -122.29499816894531,\n              47.357431944587034\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"2","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-21","publicationStatus":"PW","scienceBaseUri":"5befe5bde4b045bfcadf7f44","contributors":{"authors":[{"text":"Christiana R. Czuba","contributorId":203544,"corporation":false,"usgs":false,"family":"Christiana R. Czuba","affiliations":[{"id":36650,"text":"not affiliated, formerly with WAWSC","active":true,"usgs":false}],"preferred":false,"id":731752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Czuba, Jonathan A. 0000-0002-9485-2604","orcid":"https://orcid.org/0000-0002-9485-2604","contributorId":150072,"corporation":false,"usgs":true,"family":"Czuba","given":"Jonathan","email":"","middleInitial":"A.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731749,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Magirl, Christopher S. 0000-0002-9922-6549 magirl@usgs.gov","orcid":"https://orcid.org/0000-0002-9922-6549","contributorId":1822,"corporation":false,"usgs":true,"family":"Magirl","given":"Christopher","email":"magirl@usgs.gov","middleInitial":"S.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731748,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gendaszek, Andrew S. 0000-0002-2373-8986 agendasz@usgs.gov","orcid":"https://orcid.org/0000-0002-2373-8986","contributorId":3509,"corporation":false,"usgs":true,"family":"Gendaszek","given":"Andrew","email":"agendasz@usgs.gov","middleInitial":"S.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731750,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Konrad, Christopher P. 0000-0002-7354-547X cpkonrad@usgs.gov","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":1716,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher","email":"cpkonrad@usgs.gov","middleInitial":"P.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731751,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70196405,"text":"sir20185055 - 2018 - Effects of surface-water and groundwater inflows and outflows on the hydrology of the Tsala Apopka Lake Basin in Citrus County, Florida","interactions":[],"lastModifiedDate":"2018-09-25T06:21:32","indexId":"sir20185055","displayToPublicDate":"2018-05-21T00:00:00","publicationYear":"2018","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":"2018-5055","title":"Effects of surface-water and groundwater inflows and outflows on the hydrology of the Tsala Apopka Lake Basin in Citrus County, Florida","docAbstract":"<p>The U.S. Geological Survey, in cooperation with the Southwest Florida Water Management District, initiated a study to quantify the inflows and outflows in the Floral City, Inverness, and Hernando pools of the Tsala Apopka Lake Basin in Citrus County, Florida. This study assesses hydrologic changes in pool stages, groundwater levels, spring flows, and streamflows caused by the diversion of streamflow from the Withlacoochee River to the Tsala Apopka Lake Basin through water-control structures. A surface-water/groundwater flow model was developed using hydraulic parameters for lakes, streams, the unsaturated zone, and the underlying surficial and Upper Floridan aquifers estimated using an inverse modeling calibration technique. After calibration, the model was used to assess the relation between inflows and outflows in the Tsala Apopka Lake Basin and changes in pool stages.</p><p>Simulation results using the calibrated surface-water/groundwater flow model showed that leakage rates from the pools to the Upper Floridan aquifer were largest at the deep lake cells and that these leakage rates to the Upper Floridan aquifer were the highest in the model area. Downward leakage to the Upper Floridan aquifer occurred beneath most of the extent of the Floral City, Inverness, and Hernando pools. These leakage rates depended on the lakebed leakance and the difference between lake stages and heads in the Upper Floridan aquifer. Leakage rates were higher for the Floral City pool than for the Inverness pool, and higher for the Inverness pool than for the Hernando pool. Lakebed leakance was higher for the Floral City pool than for the Hernando pool, and higher for the Hernando pool than for the Inverness pool.</p><p>Simulation results showed that the average recharge rate to the surficial aquifer was 10.3 inches per year for the 2004 to 2012 simulation period. Areas that recharge the surficial aquifer covered about 86 percent of the model area. Simulations identified areas along segments of the Withlacoochee River and within land-surface depressions that receive water from the surficial aquifer. Recharge rates were largest in physiographic regions having a deep water table. Simulated heads in the Upper Floridan aquifer indicated the general flow directions in the active flow model area were from the northeast toward the southwest and then westward toward the coast, and from the southeast toward the northwest and then westward toward the coast, consistent with flow directions inferred from the estimated potentiometric surface map for May 2010. The largest inflow in the water budget of the Upper Floridan aquifer was downward leakage from the overlying hydrogeologic unit. The largest outflow in the water budget of the Upper Floridan aquifer was spring flow.</p><p>The calibrated surface-water and groundwater flow model was used to simulate hydrologic scenarios that included changes in rainfall rates, projected increases in groundwater pumping rates for 2025 and 2035, no flow for the 2004–12 period through the eight water-control structures in the Tsala Apopka Lake Basin, and the removal of the Inglis Dam and the Inglis Bypass Spillway on Lake Rousseau. Scenario simulation results were compared to annual average calibrated water levels and flows from 2004 to 2012. Simulated declines in the Tsala Apopka Lake pool stages under the 10-percent lower rainfall scenario were about 0.8, 0.3, and 1.3 feet (ft) for the Floral City, Inverness, and Hernando pools, respectively. Simulated groundwater levels under the same scenario declined up to 5.4 ft in the surficial aquifer and up to 2.9 ft in the Upper Floridan aquifer. Under the projected increases in groundwater pumping rates for 2035 that represented an increase of 36 percent from average 2004 to 2012 pumping rates, the simulated declines in the Floral City, Inverness, and Hernando pool stages were, in downstream order, 0.02, 0.06, and 0.04 ft. The largest drawdown under the projected increases in groundwater pumping rates for 2035 was 2.1 ft in the surficial aquifer and about 1.8 ft in the Upper Floridan aquifer. A scenario of decreased rainfall by 10 percent caused&nbsp;greater declines in water levels and pool stages than projected increases in groundwater pumping rates. The simulation with no flow through the eight Tsala Apopka Lake water-control structures resulted in simulated declines in average pool stage of 1.8, 1.9, and 0.5 ft in the Floral City, Inverness, and Hernando pools, respectively. The simulated removal of the two water-control structures in Lake Rousseau caused flow to increase at Rainbow Springs by 28 cubic feet per second, an increase of 4.7 percent from the average calibrated flow for 2004 to 2012.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185055","collaboration":"Prepared in cooperation with the Southwest Florida Water Management District","usgsCitation":"Sepúlveda, N., Fulkerson, M., Basso, R., and Ryan, P.J., 2018, Effects of surface-water and groundwater inflows and outflows on the hydrology of the Tsala Apopka Lake Basin in Citrus County, Florida: U.S. Geological Survey Scientific Investigations Report 2018–5055, 137 p., https://doi.org/10.3133/sir20185055.","productDescription":"Report: vii, 137 p.; Data Release","numberOfPages":"150","onlineOnly":"Y","ipdsId":"IP-066230","costCenters":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"links":[{"id":354255,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QF8RS2","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data sets for simulation of effects of surface-water and groundwater inflows and outflows on the hydrology of the Tsala Apopka Lake Basin in Citrus County, Florida"},{"id":354253,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5055/coverthb2.jpg"},{"id":354254,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5055/sir20185055.pdf","text":"Report","size":"41.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5055"}],"country":"United States","state":"Florida","county":"Citrus County","otherGeospatial":"Tsala Apopka Lake Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -82.81494140625,\n              28.168875180063345\n            ],\n            [\n              -81.650390625,\n              28.168875180063345\n            ],\n            [\n              -81.650390625,\n              29.16895060109228\n            ],\n            [\n              -82.81494140625,\n              29.16895060109228\n            ],\n            [\n              -82.81494140625,\n              28.168875180063345\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www2.usgs.gov/water/caribbeanflorida/index.html\" data-mce-href=\"https://www2.usgs.gov/water/caribbeanflorida/index.html\">Caribbean-Florida Water Science Center</a>&nbsp;<br>U.S. Geological Survey<br>4446 Pet Lane, Suite 108<br>Lutz, FL 33559</p>","tableOfContents":"<ul><li>Abstract<br></li><li>Introduction<br></li><li>Summary of Model Results<br></li><li>Simulation of Hydrologic Changes Effected by Variations in the Hydrologic System<br></li><li>Model Limitations<br></li><li>Summary and Conclusions<br></li><li>References Cited<br></li><li>Appendix 1. Model Construction and Calibration<br></li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2018-05-21","noUsgsAuthors":false,"publicationDate":"2018-05-21","publicationStatus":"PW","scienceBaseUri":"5b155d7ae4b092d9651e1b58","contributors":{"authors":[{"text":"Sepulveda, Nicasio 0000-0002-6333-1865 nsepul@usgs.gov","orcid":"https://orcid.org/0000-0002-6333-1865","contributorId":1454,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Nicasio","email":"nsepul@usgs.gov","affiliations":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"preferred":true,"id":732787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fulkerson, Mark","contributorId":203972,"corporation":false,"usgs":false,"family":"Fulkerson","given":"Mark","affiliations":[{"id":36777,"text":"SWFWMD","active":true,"usgs":false}],"preferred":false,"id":732788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Basso, Ron","contributorId":203973,"corporation":false,"usgs":false,"family":"Basso","given":"Ron","email":"","affiliations":[{"id":36777,"text":"SWFWMD","active":true,"usgs":false}],"preferred":false,"id":732789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ryan, Patrick J. 0000-0002-1490-4938 pryan@usgs.gov","orcid":"https://orcid.org/0000-0002-1490-4938","contributorId":203974,"corporation":false,"usgs":true,"family":"Ryan","given":"Patrick","email":"pryan@usgs.gov","middleInitial":"J.","affiliations":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"preferred":true,"id":732790,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70198551,"text":"70198551 - 2018 - Longevity and population age structure of the arroyo southwestern toad (Anaxyrus californicus) with drought implications","interactions":[],"lastModifiedDate":"2018-08-07T12:26:46","indexId":"70198551","displayToPublicDate":"2018-05-20T11:50:18","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Longevity and population age structure of the arroyo southwestern toad (<i> Anaxyrus californicus</i>) with drought implications","title":"Longevity and population age structure of the arroyo southwestern toad (Anaxyrus californicus) with drought implications","docAbstract":"<p><span>The arroyo southwestern toad is a specialized and federally endangered amphibian endemic to the coastal plains and mountains of central and southern California and northwestern Baja California. It is largely unknown how long these toads live in natural systems, how their population demographics vary across occupied drainages, and how hydrology affects age structure. We used skeletochronology to estimate the ages of adult arroyo toads in seven occupied drainages with varying surface water hydrology in southern California. We processed 179 adult toads with age estimates between 1 and 6&nbsp;years. Comparisons between skeletochronological ages and known ages of PIT tagged toads showed that skeletochronology likely underestimated toad age by up to 2&nbsp;years, indicating they may live to 7 or 8&nbsp;years, but nonetheless major patterns were evident. Arroyo toads showed sexual size dimorphism with adult females reaching a maximum size of 12&nbsp;mm greater than males. Population age structure varied among the sites. Age structure at sites with seasonally predictable surface water was biased toward younger individuals, which indicated stable recruitment for these populations. Age structures at the ephemeral sites were biased toward older individuals with cohorts roughly corresponding to higher rainfall years. These populations are driven by surface water availability, a stochastic process, and thus more unstable. Based on our estimates of toad ages, climate predictions of extreme and prolonged drought events could mean that the number of consecutive dry years could surpass the maximum life span of toads making them vulnerable to extirpation, especially in ephemeral freshwater systems. Understanding the relationship between population demographics and hydrology is essential for predicting species resilience to projected changes in weather and rainfall patterns. The arroyo toad serves as a model for understanding potential responses to climatic and hydrologic changes in Mediterranean stream systems. We recommend development of adaptive management strategies to address these threats.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.4158","usgsCitation":"Fisher, R.N., Brehme, C.S., Hathaway, S.A., Hovey, T.E., Warburton, M.L., and Stokes, D.C., 2018, Longevity and population age structure of the arroyo southwestern toad (Anaxyrus californicus) with drought implications: Ecology and Evolution, v. 8, no. 12, p. 6124-6132, https://doi.org/10.1002/ece3.4158.","productDescription":"9 p.","startPage":"6124","endPage":"6132","ipdsId":"IP-095515","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":468742,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.4158","text":"Publisher Index Page"},{"id":356281,"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      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -118.01239013671874,\n              32.579220642875676\n            ],\n            [\n              -116.68853759765626,\n              32.579220642875676\n            ],\n            [\n              -116.68853759765626,\n              34.04583232505719\n            ],\n            [\n              -118.01239013671874,\n              34.04583232505719\n            ],\n            [\n              -118.01239013671874,\n              32.579220642875676\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"8","issue":"12","noUsgsAuthors":false,"publicationDate":"2018-05-20","publicationStatus":"PW","scienceBaseUri":"5b6fc450e4b0f5d57878ea4d","contributors":{"authors":[{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":741871,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brehme, Cheryl S. 0000-0001-8904-3354 cbrehme@usgs.gov","orcid":"https://orcid.org/0000-0001-8904-3354","contributorId":3419,"corporation":false,"usgs":true,"family":"Brehme","given":"Cheryl","email":"cbrehme@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":741872,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hathaway, Stacie A. 0000-0002-4167-8059 sahathaway@usgs.gov","orcid":"https://orcid.org/0000-0002-4167-8059","contributorId":3420,"corporation":false,"usgs":true,"family":"Hathaway","given":"Stacie","email":"sahathaway@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":741873,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hovey, Tim E.","contributorId":206822,"corporation":false,"usgs":false,"family":"Hovey","given":"Tim","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":741874,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Warburton, Manna L.","contributorId":174875,"corporation":false,"usgs":false,"family":"Warburton","given":"Manna","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":741875,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stokes, Drew C.","contributorId":33836,"corporation":false,"usgs":true,"family":"Stokes","given":"Drew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":741876,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70200911,"text":"70200911 - 2018 - Spatiotemporal analysis of Landsat-8 and Sentinel-2 data to support monitoring of dryland ecosystems","interactions":[],"lastModifiedDate":"2018-12-13T09:13:22","indexId":"70200911","displayToPublicDate":"2018-05-19T11:09:07","publicationYear":"2018","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":"Spatiotemporal analysis of Landsat-8 and Sentinel-2 data to support monitoring of dryland ecosystems","docAbstract":"<p><span>Drylands are the habitat and source of livelihood for about two fifths of the world’s population and are highly susceptible to climate and anthropogenic change. To understand the vulnerability of drylands to changing environmental conditions, land managers need to effectively monitor rates of past change and remote sensing offers a cost-effective means to assess and manage these vast landscapes. Here, we present a novel approach to accurately monitor land-surface phenology in drylands of the Western United States using a regression tree modeling framework that combined information collected by the Operational Land Imager (OLI) onboard Landsat 8 and the Multispectral Instrument (MSI) onboard Sentinel-2. This highly-automatable approach allowed us to precisely characterize seasonal variations in spectral vegetation indices with substantial agreement between observed and predicted values (R</span><sup>2</sup><span>&nbsp;= 0.98; Mean Absolute Error = 0.01). Derived phenology curves agreed with independent eMODIS phenological signatures of major land cover types (average&nbsp;</span><span class=\"html-italic\">r</span><span>-value = 0.86), cheatgrass cover (average&nbsp;</span><span class=\"html-italic\">r</span><span>-value = 0.96), and growing season proxies for vegetation productivity (R</span><sup>2</sup><span>&nbsp;= 0.88), although a systematic bias towards earlier maturity and senescence indicates enhanced monitoring capabilities associated with the use of harmonized Landsat-8 Sentinel-2 data. Overall, our results demonstrate that observations made by the MSI and OLI can be used in conjunction to accurately characterize land-surface phenology and exclusion of imagery from either sensor drastically reduces our ability to monitor dryland environments. Given the declines in MODIS performance and forthcoming decommission with no equivalent replacement planned, data fusion approaches that integrate observations from multispectral sensors will be needed to effectively monitor dryland ecosystems. While the synthetic image stacks are expected to be locally useful, the technical approach can serve a wide variety of applications such as invasive species and drought monitoring, habitat mapping, production of phenology metrics, and land-cover change modeling.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs10050791","usgsCitation":"Pastick, N.J., Wylie, B.K., and Wu, Z., 2018, Spatiotemporal analysis of Landsat-8 and Sentinel-2 data to support monitoring of dryland ecosystems: Remote Sensing, v. 10, no. 5, 15 p., https://doi.org/10.3390/rs10050791.","productDescription":"15 p.","ipdsId":"IP-097826","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468743,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs10050791","text":"Publisher Index Page"},{"id":359420,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"10","issue":"5","noUsgsAuthors":false,"publicationDate":"2018-05-19","publicationStatus":"PW","scienceBaseUri":"5bed4274e4b0b3fc5cf91c92","contributors":{"authors":[{"text":"Pastick, Neal J. 0000-0002-8169-3018 njpastick@usgs.gov","orcid":"https://orcid.org/0000-0002-8169-3018","contributorId":4785,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"njpastick@usgs.gov","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":751236,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":751237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":751238,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70240957,"text":"70240957 - 2018 - Cohesive and mixed sediment in the Regional Ocean Modeling System (ROMS v3.6) implemented in the Coupled Ocean–Atmosphere–Wave–Sediment Transport Modeling System (COAWST r1234)","interactions":[],"lastModifiedDate":"2023-03-02T16:13:57.166938","indexId":"70240957","displayToPublicDate":"2018-05-18T10:02:57","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1818,"text":"Geoscientific Model Development","active":true,"publicationSubtype":{"id":10}},"title":"Cohesive and mixed sediment in the Regional Ocean Modeling System (ROMS v3.6) implemented in the Coupled Ocean–Atmosphere–Wave–Sediment Transport Modeling System (COAWST r1234)","docAbstract":"<p>No abstract available.</p>","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-11-1849-2018","usgsCitation":"Sherwood, C.R., Aretxabaleta, A., Harris, C.K., Rinehimer, J.P., Verney, R., and Ferre, B., 2018, Cohesive and mixed sediment in the Regional Ocean Modeling System (ROMS v3.6) implemented in the Coupled Ocean–Atmosphere–Wave–Sediment Transport Modeling System (COAWST r1234): Geoscientific Model Development, v. 11, p. 1849-1871, https://doi.org/10.5194/gmd-11-1849-2018.","productDescription":"23 p.","startPage":"1849","endPage":"1871","ipdsId":"IP-090028","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468744,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-11-1849-2018","text":"Publisher Index Page"},{"id":413620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2018-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":865485,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":865486,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harris, Courtney K.","contributorId":19620,"corporation":false,"usgs":false,"family":"Harris","given":"Courtney","email":"","middleInitial":"K.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":865487,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rinehimer, J. Paul","contributorId":140081,"corporation":false,"usgs":false,"family":"Rinehimer","given":"J.","email":"","middleInitial":"Paul","affiliations":[{"id":13381,"text":"Center for Coastal Margin Observation & Prediction, Oregon Health and Sciences University, Portland, OR, 97239","active":true,"usgs":false}],"preferred":false,"id":865488,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Verney, Romaric","contributorId":302800,"corporation":false,"usgs":false,"family":"Verney","given":"Romaric","email":"","affiliations":[],"preferred":false,"id":865489,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ferre, Benedicte","contributorId":302801,"corporation":false,"usgs":false,"family":"Ferre","given":"Benedicte","email":"","affiliations":[],"preferred":false,"id":865490,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70208930,"text":"70208930 - 2018 - The genetic network of greater sage-grouse: Range-wide identification of keystone hubs of connectivity","interactions":[],"lastModifiedDate":"2020-03-06T06:48:35","indexId":"70208930","displayToPublicDate":"2018-05-18T06:45:46","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"The genetic network of greater sage-grouse: Range-wide identification of keystone hubs of connectivity","docAbstract":"Genetic networks can characterize complex genetic relationships among groups of\nindividuals, which can be used to rank nodes most important to the overall connectivity\nof the system. Ranking allows scarce resources to be guided toward nodes integral\nto connectivity. The greater sage-grouse (Centrocercus urophasianus) is a species of conservation concern that breeds on spatially discrete leks that must remain connected by genetic exchange for population persistence. We genotyped 5,950 individuals from 1,200 greater sage-grouse leks distributed across the entire species’ geographic range. We found a small-world network composed of 458 nodes connected by 14,481 edges. This network was composed of hubs—that is, nodes facilitating gene flow across the network—and spokes—that is, nodes where connectivity is served by hubs. It is within these hubs that the greatest genetic diversity was housed. Using indices of network centrality, we identified hub nodes of greatest conservation importance. We also identified keystone nodes with elevated centrality\ndespite low local population size. Hub and keystone nodes were found across the\nentire species’ contiguous range, although nodes with elevated importance to\nnetwork-wide connectivity were found more central: especially in northeastern, central,\nand southwestern Wyoming and eastern Idaho. Nodes among which genes are\nmost readily exchanged were mostly located in Montana and northern Wyoming, as\nwell as Utah and eastern Nevada. The loss of hub or keystone nodes could lead to the\ndisintegration of the network into smaller, isolated subnetworks. Protecting both hub\nnodes and keystone nodes will conserve genetic diversity and should maintain network\nconnections to ensure a resilient and viable population over time. Our analysis\nshows that network models can be used to model gene flow, offering insights into its\npattern and process, with application to prioritizing landscapes for conservation.","language":"English","publisher":"Wiley","doi":"10.1002/ece3.4056","usgsCitation":"Cross, T.B., Schwartz, M.D., Naugle, D., Fedy, B.C., Row, J.R., and Oyler-McCance, S.J., 2018, The genetic network of greater sage-grouse: Range-wide identification of keystone hubs of connectivity: Ecology and Evolution, v. 8, no. 11, p. 5394-5412, https://doi.org/10.1002/ece3.4056.","productDescription":"19 p.","startPage":"5394","endPage":"5412","ipdsId":"IP-091562","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":468745,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.4056","text":"Publisher Index Page"},{"id":437891,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73N22PN","text":"USGS data release","linkHelpText":"Genetic data and genetic network attributes for rangewide Greater Sage-grouse network constructed in 2018 (ver. 2.0, December 2022)"},{"id":372986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Wyoming, Montana, Utah, 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 \"}}]}","volume":"8","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Cross, Todd B.","contributorId":189267,"corporation":false,"usgs":false,"family":"Cross","given":"Todd","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":784078,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schwartz, Michael D.","contributorId":174566,"corporation":false,"usgs":false,"family":"Schwartz","given":"Michael","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":784082,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Naugle, David","contributorId":223090,"corporation":false,"usgs":false,"family":"Naugle","given":"David","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":784079,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":784081,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Row, Jeff R","contributorId":140874,"corporation":false,"usgs":false,"family":"Row","given":"Jeff","email":"","middleInitial":"R","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":784080,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":784077,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70197124,"text":"70197124 - 2018 - Do downscaled general circulation models reliably simulate historical climatic conditions?","interactions":[],"lastModifiedDate":"2018-05-18T09:43:34","indexId":"70197124","displayToPublicDate":"2018-05-18T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1421,"text":"Earth Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Do downscaled general circulation models reliably simulate historical climatic conditions?","docAbstract":"The accuracy of statistically downscaled (SD) general circulation model (GCM) simulations of monthly surface climate for historical conditions (1950–2005) was assessed for the conterminous United States (CONUS). The SD monthly precipitation (PPT) and temperature (TAVE) from 95 GCMs from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) were used as inputs to a monthly water balance model (MWBM). Distributions of MWBM input (PPT and TAVE) and output [runoff (RUN)] variables derived from gridded station data (GSD) and historical SD climate were compared using the Kolmogorov–Smirnov (KS) test For all three variables considered, the KS test results showed that variables simulated using CMIP5 generally are more reliable than those derived from CMIP3, likely due to improvements in PPT simulations. At most locations across the CONUS, the largest differences between GSD and SD PPT and RUN occurred in the lowest part of the distributions (i.e., low-flow RUN and low-magnitude PPT). Results indicate that for the majority of the CONUS, there are downscaled GCMs that can reliably simulate historical climatic conditions. But, in some geographic locations, none of the SD GCMs replicated historical conditions for two of the three variables (PPT and RUN) based on the KS test, with a significance level of 0.05. In these locations, improved GCM simulations of PPT are needed to more reliably estimate components of the hydrologic cycle. Simple metrics and statistical tests, such as those described here, can provide an initial set of criteria to help simplify GCM selection.","language":"English","publisher":"American Meteorological Society","doi":"10.1175/EI-D-17-0018.1","usgsCitation":"Bock, A.R., Hay, L.E., McCabe, G., Markstrom, S.L., and Atkinson, R.D., 2018, Do downscaled general circulation models reliably simulate historical climatic conditions?: Earth Interactions, v. 22, p. 1-22, https://doi.org/10.1175/EI-D-17-0018.1.","productDescription":"Paper 10; 22 p.","startPage":"1","endPage":"22","ipdsId":"IP-090110","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":468746,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/ei-d-17-0018.1","text":"Publisher Index Page"},{"id":354299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-17","publicationStatus":"PW","scienceBaseUri":"5afee6b2e4b0da30c1bfbd46","contributors":{"authors":[{"text":"Bock, Andrew R. 0000-0001-7222-6613 abock@usgs.gov","orcid":"https://orcid.org/0000-0001-7222-6613","contributorId":4580,"corporation":false,"usgs":true,"family":"Bock","given":"Andrew","email":"abock@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":735759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hay, Lauren E. 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":1287,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":735770,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":1453,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory J.","email":"gmccabe@usgs.gov","affiliations":[{"id":218,"text":"Denver Federal Center","active":false,"usgs":true}],"preferred":false,"id":735771,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Markstrom, Steven L. 0000-0001-7630-9547 markstro@usgs.gov","orcid":"https://orcid.org/0000-0001-7630-9547","contributorId":140378,"corporation":false,"usgs":true,"family":"Markstrom","given":"Steven","email":"markstro@usgs.gov","middleInitial":"L.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":735772,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Atkinson, R. Dwight","contributorId":195660,"corporation":false,"usgs":false,"family":"Atkinson","given":"R.","email":"","middleInitial":"Dwight","affiliations":[],"preferred":false,"id":735773,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70196218,"text":"sir20105070P - 2018 - Quartz-pebble-conglomerate gold deposits","interactions":[],"lastModifiedDate":"2024-04-16T16:38:01.913558","indexId":"sir20105070P","displayToPublicDate":"2018-05-17T17:15:00","publicationYear":"2018","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":"2010-5070","chapter":"P","title":"Quartz-pebble-conglomerate gold deposits","docAbstract":"<p>Quartz-pebble-conglomerate gold deposits represent the largest repository of gold on Earth, largely due to the deposits of the Witwatersrand Basin, which account for nearly 40 percent of the total gold produced throughout Earth’s history. This deposit type has had a controversial history in regards to genetic models. However, most researchers conclude that they are paleoplacer deposits that have been modified by metamorphism and hydrothermal fluid flow subsequent to initial sedimentation.</p><p>The deposits are found exclusively within fault-bounded depositional basins. The periphery of these basins commonly consists of granite-greenstone terranes, classic hosts for lode gold that source the detrital material infilling the basin. The gold reefs are typically located along unconformities or, less commonly, at the top of sedimentary beds. Large quartz pebbles and heavy-mineral concentrates are found associated with the gold. Deposits that formed prior to the Great Oxidation Event (circa 2.4 giga-annum [Ga]) contain pyrite, whereas younger deposits contain iron oxides. Uranium minerals and hydrocarbons are also notable features of some deposits.</p><p>Much of the gold in these types of deposits forms crystalline features that are the product of local remobilization. However, some gold grains preserve textures that are undoubtedly of detrital origin. Other heavy minerals, such as pyrite, contain growth banding that is truncated along broken margins, which indicates that they were transported into place as opposed to forming by in situ growth in a hydrothermal setting.</p><p>The ore tailings associated with these deposits commonly contain uranium-rich minerals and sulfides. Oxidation of the sulfides releases sulfuric acid and mobilizes various metals into the environment. The neutralizing potential of the tailings is minimal, since carbonate minerals are rare. The continuity of the tabular ore bodies, such as those of the Witwatersrand Basin, has allowed these mines to be the deepest in the world. The extreme depths create engineering complications and safety issues for the miners, such as rock bursts as a result of pressure release.</p><p>The richness of these deposits makes them a desirable exploration target. However, the likelihood of future discoveries is minimal. Small deposits found in the United States include those found at Nemo in the Black Hills of South Dakota and Deep Lake in the Sierra Madre of Wyoming.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105070P","usgsCitation":"Taylor, R.D., and Anderson, E.D., 2018, Quartz-pebble-conglomerate gold deposits: U.S. Geological Survey Scientific Investigations Report 2010–5070–P, 34 p., https://doi.org/10.3133/sir20105070P","productDescription":"v, 34 p.","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-086517","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":354221,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2010/5070/p/sir20105070p.pdf","text":"Report","size":"6.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2010–5070–P"},{"id":354220,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2010/5070/p/coverthb.jpg"}],"contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/gggsc/\" data-mce-href=\"https://www.usgs.gov/centers/gggsc/\">Geology, Geophysics, and Geochemistry Science Center</a><br>U.S. Geological Survey <br>Box 25046,&nbsp;MS–973<br>Denver, CO 80225</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Deposit Type and Associated Commodities</li><li>Historical Evolution of Descriptive and Genetic Knowledge and Concepts</li><li>Regional Environment</li><li>Physical Description of Deposit</li><li>Geophysical Characteristics</li><li>Ore Characteristics</li><li>Hydrothermal Alteration</li><li>Weathering and Supergene Processes</li><li>Geochemical Characteristics</li><li>Petrology of Associated Sedimentary Rocks</li><li>Theory of Deposit Formation</li><li>Exploration and Resource Assessment Guides</li><li>Geoenvironmental Features and Anthropogenic Mining Effects</li><li>References</li></ul>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2018-05-17","noUsgsAuthors":false,"publicationDate":"2018-05-17","publicationStatus":"PW","scienceBaseUri":"5afee6b4e4b0da30c1bfbd48","contributors":{"authors":[{"text":"Taylor, Ryan D. 0000-0002-8845-5290 rtaylor@usgs.gov","orcid":"https://orcid.org/0000-0002-8845-5290","contributorId":3412,"corporation":false,"usgs":true,"family":"Taylor","given":"Ryan","email":"rtaylor@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":731729,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Eric D. 0000-0002-0138-6166 ericanderson@usgs.gov","orcid":"https://orcid.org/0000-0002-0138-6166","contributorId":1733,"corporation":false,"usgs":true,"family":"Anderson","given":"Eric","email":"ericanderson@usgs.gov","middleInitial":"D.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":731730,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70197076,"text":"70197076 - 2018 - Assessing angler effort, catch, and harvest and the efficacy of a use-estimation system on a multi-lake fishery in middle Georgia","interactions":[],"lastModifiedDate":"2018-08-31T10:58:50","indexId":"70197076","displayToPublicDate":"2018-05-17T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Assessing angler effort, catch, and harvest and the efficacy of a use-estimation system on a multi-lake fishery in middle Georgia","docAbstract":"<p><span>Creel surveys are valuable tools in recreational fisheries management. However, multiple‐impoundment fisheries of complex spatial structure can complicate survey designs and pose logistical challenges for management agencies. Marben Public Fishing Area in Mansfield, GA is a multi‐impoundment fishery with many access points, and these features prevent or complicate use of traditional on‐site contact methods such as standard roving‐ or access‐point designs because many anglers may be missed during the survey process. Therefore, adaptation of a traditional survey method is often required for sampling this special case of multi‐lake fisheries to develop an accurate fishery profile. Accordingly, a modified non‐uniform probability roving creel survey was conducted at the Marben PFA during 2013 to estimate fishery characteristics relating to fishing effort, catch, and fish harvest. Monthly fishing effort averaged 7,523 angler‐hours (h) (SD = 5,956) and ranged from 1,301 h (SD = 562) in December to 21,856 h (SD = 5909) in May. A generalized linear mixed model was used to determine that angler catch and harvest rates were significantly higher in the spring and summer (all&nbsp;</span><i>p</i><span><span>&nbsp;</span>&lt; 0.05) than in the other seasons, but did not vary by fishing location. Our results demonstrate the utility of modifying existing creel methodology for monitoring small, spatially complex, intensely managed impoundments that support quality recreational fisheries and provide a template for the assessment and management of similar regional fisheries.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10179","usgsCitation":"Roop, H.J., Poudyal, N.C., and Jennings, C.A., 2018, Assessing angler effort, catch, and harvest and the efficacy of a use-estimation system on a multi-lake fishery in middle Georgia: North American Journal of Fisheries Management, v. 38, no. 4, p. 833-841, https://doi.org/10.1002/nafm.10179.","productDescription":"9 p.","startPage":"833","endPage":"841","ipdsId":"IP-086163","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":354267,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","volume":"38","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-25","publicationStatus":"PW","scienceBaseUri":"5afee6b8e4b0da30c1bfbd60","contributors":{"authors":[{"text":"Roop, Hunter J.","contributorId":204959,"corporation":false,"usgs":false,"family":"Roop","given":"Hunter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":735563,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Poudyal, Neelam C.","contributorId":204960,"corporation":false,"usgs":false,"family":"Poudyal","given":"Neelam","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":735564,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jennings, Cecil A. 0000-0002-6159-6026 jennings@usgs.gov","orcid":"https://orcid.org/0000-0002-6159-6026","contributorId":874,"corporation":false,"usgs":true,"family":"Jennings","given":"Cecil","email":"jennings@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":735489,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70196934,"text":"70196934 - 2018 - Integrating real-time subsurface hydrologic monitoring with empirical rainfall thresholds to improve landslide early warning","interactions":[],"lastModifiedDate":"2018-09-10T11:32:27","indexId":"70196934","displayToPublicDate":"2018-05-17T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2604,"text":"Landslides","active":true,"publicationSubtype":{"id":10}},"title":"Integrating real-time subsurface hydrologic monitoring with empirical rainfall thresholds to improve landslide early warning","docAbstract":"<p><span>Early warning for rainfall-induced shallow landsliding can help reduce fatalities and economic losses. Although these commonly occurring landslides are typically triggered by subsurface hydrological processes, most early warning criteria rely exclusively on empirical rainfall thresholds and other indirect proxies for subsurface wetness. We explore the utility of explicitly accounting for antecedent wetness by integrating real-time subsurface hydrologic measurements into landslide early warning criteria. Our efforts build on previous progress with rainfall thresholds, monitoring, and numerical modeling along the landslide-prone railway corridor between Everett and Seattle, Washington, USA. We propose a modification to a previously established recent versus antecedent (RA) cumulative rainfall thresholds by replacing the antecedent 15-day rainfall component with an average saturation observed over the same timeframe. We calculate this antecedent saturation with real-time telemetered measurements from five volumetric water content probes installed in the shallow subsurface within a steep vegetated hillslope. Our hybrid rainfall versus saturation (RS) threshold still relies on the same recent 3-day rainfall component as the existing RA thresholds, to facilitate ready integration with quantitative precipitation forecasts. During the 2015–2017 monitoring period, this RS hybrid approach has an increase of true positives and a decrease of false positives and false negatives relative to the previous RA rainfall-only thresholds. We also demonstrate that alternative hybrid threshold formats could be even more accurate, which suggests that further development and testing during future landslide seasons is needed. The positive results confirm that accounting for antecedent wetness conditions with direct subsurface hydrologic measurements can improve thresholds for alert systems and early warning of rainfall-induced shallow landsliding.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10346-018-0995-z","usgsCitation":"Mirus, B.B., Becker, R.E., Baum, R.L., and Smith, J.B., 2018, Integrating real-time subsurface hydrologic monitoring with empirical rainfall thresholds to improve landslide early warning: Landslides, v. 15, no. 10, p. 1909-1919, https://doi.org/10.1007/s10346-018-0995-z.","productDescription":"11 p.","startPage":"1909","endPage":"1919","ipdsId":"IP-093501","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":354283,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-15","publicationStatus":"PW","scienceBaseUri":"5afee6bae4b0da30c1bfbd6e","contributors":{"authors":[{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":735057,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Becker, Rachel E. 0000-0002-2546-1706","orcid":"https://orcid.org/0000-0002-2546-1706","contributorId":204809,"corporation":false,"usgs":false,"family":"Becker","given":"Rachel","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":735058,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baum, Rex L. 0000-0001-5337-1970 baum@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1970","contributorId":1288,"corporation":false,"usgs":true,"family":"Baum","given":"Rex","email":"baum@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":735059,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Joel B. 0000-0001-7219-7875 jbsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-7219-7875","contributorId":4925,"corporation":false,"usgs":true,"family":"Smith","given":"Joel","email":"jbsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":735060,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
]}