{"pageNumber":"200","pageRowStart":"4975","pageSize":"25","recordCount":46677,"records":[{"id":70229039,"text":"70229039 - 2021 - Coffee plantations, hurricanes and avian resiliency: Insights from occupancy, and local colonization and extinction rates in Puerto Rico","interactions":[],"lastModifiedDate":"2022-03-01T12:17:19.293477","indexId":"70229039","displayToPublicDate":"2021-06-01T10:31:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Coffee plantations, hurricanes and avian resiliency: Insights from occupancy, and local colonization and extinction rates in Puerto Rico","docAbstract":"Insights on impacts and resiliency of avian species with respect to hurricanes in the Caribbean have largely focused on responses measured in protected habitats.  We assessed avian responses in non-protected habitat, specifically shade-restored coffee plantations, because their structural complexity retains many attributes of secondary forests, and may contribute to landscape scale species resiliency.  We tallied species richness, and estimated occupancy probability of 12 resident avian species, after adjusting for imperfect detection, to assess the impact of hurricane Maria (20 September 2017) in the context of two stages of shade-restored coffee plantations.  For 5 of those species, we also estimated local colonization and extinction probabilities to assess their prospect of rebounding (resiliency).  We used survey data collected March-June 2015-2017 (pre-hurricane) and 2018 (post-hurricane) in 58 coffee farms and satellite imagery to assess vegetation structure.  Restored farms were grouped into two categories based on time-since-restoration: newly-restored and fully-restored. We predicted that mean percent forest cover in fully-restored farms (~30-40%) would revert to levels in newly-restored farms (<15%), with concomitant changes in occupancy by avian species.  As predicted, mean percent forest cover (16.17  4.27%) in fully-restored farms post-hurricane reverted to pre-hurricane levels in newly-restored farms (15.00  5.61%).  The loss represented 30-38% relative to the pre-hurricane cover levels.  Detections of focal species dropped an average of 41% post-hurricane, with associated reductions in occupancy for 9/11 species.  Occupancy of the Puerto Rican Bullfinch and Puerto Rican Spindalis reverted to levels detected in newly-restored plantations prior to the hurricane as predicted.  Prospects of rebounding were more likely for species with invariant or increases in colonization probability (e.g., Yellow-faced Grassquit, Northern Mockingbird, Puerto Rican Spindalis).  Rebounding for frugivores like the Puerto Rican Bullfinch would be protracted given that colonization rates dropped from 0.56  0.12 (pre-hurricane) to 0.04  0.2 (post-hurricane), regardless of restoration stage.  Our work showed that the avian community associated with restored coffee farms exhibited as high degree of ecological resistance as the similarity in species composition before and after the hurricane was 81%, and all 12 focal species continued to occupy farms under both restoration stages.  The prospect of the focal species to rebound (resiliency) was specific-specific, and in some cases, mediated by their affinity to a particular farm restoration stage.  The strength of hurricanes is projected to intensify with global warming.  Pockets of undamaged or partially damaged shade-grown or fully-restored coffee plantations may contribute to species resiliency by increasing landscape level habitat redundancy, and facilitate habitat shifts to secure food resources or harbor source populations to colonize recovering, hurricane-damaged habitat tracts.","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2021.e01579","usgsCitation":"Irizarry, A.D., Collazo, J.A., Vandermeer, J., and Perfecto, I., 2021, Coffee plantations, hurricanes and avian resiliency: Insights from occupancy, and local colonization and extinction rates in Puerto Rico: Global Ecology and Conservation, v. 27, e01579, 12 p., https://doi.org/10.1016/j.gecco.2021.e01579.","productDescription":"e01579, 12 p.","ipdsId":"IP-120553","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":452043,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2021.e01579","text":"Publisher Index Page"},{"id":396563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Puerto Rico","otherGeospatial":"Greater Antilles","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -67.08251953125,\n              17.955219304287816\n            ],\n            [\n              -66.42059326171874,\n              17.955219304287816\n            ],\n            [\n              -66.42059326171874,\n              18.424896202842426\n            ],\n            [\n              -67.08251953125,\n              18.424896202842426\n            ],\n            [\n              -67.08251953125,\n              17.955219304287816\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"27","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Irizarry, Amarilys D.","contributorId":205434,"corporation":false,"usgs":false,"family":"Irizarry","given":"Amarilys","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":836342,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":836343,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vandermeer, J.","contributorId":286880,"corporation":false,"usgs":false,"family":"Vandermeer","given":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":836344,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perfecto, I.","contributorId":286882,"corporation":false,"usgs":false,"family":"Perfecto","given":"I.","email":"","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":836345,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70226922,"text":"70226922 - 2021 - A multi-tracer and well-bore flow profile approach to determine occurrence, movement, and sources of perchlorate in groundwater","interactions":[],"lastModifiedDate":"2021-12-21T14:59:34.058138","indexId":"70226922","displayToPublicDate":"2021-06-01T08:43:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"A multi-tracer and well-bore flow profile approach to determine occurrence, movement, and sources of perchlorate in groundwater","docAbstract":"The purpose of this study is to determine the occurrence, movement and sources of perchlorate in groundwater using a comprehensive set of environmental tracers coupled with discreet borehole data. Potential sources of perchlorate to groundwater at the study site have been attributed to waste disposal and industrial activities as well as to past agricultural operations. Perchlorate concentrations in samples ranged from <1 to 40 g/l, with a median of 6.1 g/l. Concentrations were relativity consistent with depth except at one site where dilution may be occurring due to the infiltration of surface water from Pyrite Creek. Well-bore flow profiles indicated that perchlorate redistribution was occurring via intra-well bore flow at one site where up to 14,000 mg/year of perchlorate could be moving from the shallower to the deeper zones of the alluvial aquifer. Natural attenuation processes of perchlorate do not appear to be widespread in groundwater but does occur in portions of the aquifer adjacent to the Santa Ana River, likely limiting the mobility of perchlorate from the southernmost extent of the mapped plume to areas further down-gradient. Age dating tracers indicate that perchlorate originating from the waste disposal ponds has largely moved through the zones of the aquifer sampled. Age distributions, noble gas temperature, delta neon values and stable isotopes of water indicate that a substantial fraction of perchlorate in groundwater may have been mobilized from the unsaturated zone and/or is from the infiltration of storm water runoff originating from Pyrite Canyon.","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2021.104959","usgsCitation":"Wright, M., Izbicki, J.A., and Jurgens, B.C., 2021, A multi-tracer and well-bore flow profile approach to determine occurrence, movement, and sources of perchlorate in groundwater: Applied Geochemistry, v. 129, p. 1-18, https://doi.org/10.1016/j.apgeochem.2021.104959.","productDescription":"104959, 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-116219","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":452054,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2021.104959","text":"Publisher Index Page"},{"id":393189,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Jurupa Valley","otherGeospatial":"Jurupa Mountains, Mira Loma Hills, Pedley Hills, San Sevaine Channel, Santa Ana River, Stringfellow Superfund Site","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -117.66082763671875,\n              33.945638452963024\n            ],\n            [\n              -117.14241027832031,\n              33.945638452963024\n            ],\n            [\n              -117.14241027832031,\n              34.34343606848294\n            ],\n            [\n              -117.66082763671875,\n              34.34343606848294\n            ],\n            [\n              -117.66082763671875,\n              33.945638452963024\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"129","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Wang, Z. Zimeng","contributorId":270243,"corporation":false,"usgs":false,"family":"Wang","given":"Z.","email":"","middleInitial":"Zimeng","affiliations":[],"preferred":false,"id":828813,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Wright, Michael 0000-0003-0653-6466 mtwright@usgs.gov","orcid":"https://orcid.org/0000-0003-0653-6466","contributorId":151031,"corporation":false,"usgs":true,"family":"Wright","given":"Michael","email":"mtwright@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828799,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Izbicki, John A. 0000-0003-0816-4408 jaizbick@usgs.gov","orcid":"https://orcid.org/0000-0003-0816-4408","contributorId":152474,"corporation":false,"usgs":true,"family":"Izbicki","given":"John","email":"jaizbick@usgs.gov","middleInitial":"A.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828800,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X bjurgens@usgs.gov","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":127842,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","email":"bjurgens@usgs.gov","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828801,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70221599,"text":"70221599 - 2021 - Watersheds and drainage networks","interactions":[],"lastModifiedDate":"2021-06-25T12:49:28.165348","indexId":"70221599","displayToPublicDate":"2021-06-01T07:47:23","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Watersheds and drainage networks","docAbstract":"<div class=\"field field-name-body field-type-text-with-summary field-label-hidden\"><div class=\"field-items\"><div class=\"field-item even\"><p>This topic is&nbsp;an overview of basic concepts about how the distribution of water on the Earth, with specific regard to watersheds, stream and river networks, and waterbodies are represented by geographic data. The flowing and non-flowing bodies of water on the earth’s surface vary in extent largely due to seasonal and annual changes in climate and precipitation. Consequently, modeling the detailed representation of surface water using geographic information is important. The area of land that collects surface runoff and other flowing water and drains to a common outlet location defines a watershed. Terrain and surface features can be naturally divided into watersheds of various sizes. Drainage networks are important data structures for modeling the distribution and movement of surface water over the terrain. &nbsp;Numerous tools and methods exist to extract drainage networks and watersheds from digital elevation models (DEMs). The cartographic representations of surface water are referred to as hydrographic features and consist of a snapshot at a specific time. Hydrographic features can be assigned general feature types, such as lake, pond, river, and ocean. Hydrographic features can be stored, maintained, and distributed for use through vector geospatial databases, such as the National Hydrography Dataset (NHD) for the United States.</p></div></div></div>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The geographic information science & technology body of knowledge","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"University Consortium for Geographic Information Science","doi":"10.22224/gistbok/2021.2.1","usgsCitation":"Stanislawski, L., and Shavers, E.J., 2021, Watersheds and drainage networks, chap. <i>of</i> The geographic information science & technology body of knowledge, https://doi.org/10.22224/gistbok/2021.2.1.","ipdsId":"IP-125926","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":452062,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.22224/gistbok/2021.2.1","text":"Publisher Index Page"},{"id":386732,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Stanislawski, Larry 0000-0002-9437-0576","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":217849,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":818251,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":818252,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70222359,"text":"70222359 - 2021 - Quarterly wildlife mortality report April 2021","interactions":[],"lastModifiedDate":"2023-10-13T13:31:04.206897","indexId":"70222359","displayToPublicDate":"2021-05-31T11:09:23","publicationYear":"2021","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":9359,"text":"Wildlife Disease Association Newsletter","active":true,"publicationSubtype":{"id":30}},"title":"Quarterly wildlife mortality report April 2021","docAbstract":"The USGS National Wildlife Health Center (NWHC) Quarterly Mortality Report provides brief summaries of epizootic mortality and morbidity events by quarter. The write-ups, highlighting epizootic events and other wildlife disease topics of interest, are published in the Wildlife Disease Association quarterly newsletter. A link is provided in this WDA newsletter to the Wildlife Health Information Sharing Partnership event reporting system (WHISPers) so readers can view associated data.","language":"English","publisher":"Wildlife Disease Association","usgsCitation":"Richards, B.J., Russell, R., and Ballmann, A., 2021, Quarterly wildlife mortality report April 2021: Wildlife Disease Association Newsletter, p. 9-12.","productDescription":"4 p.","startPage":"9","endPage":"12","ipdsId":"IP-128295","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":389735,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.wildlifedisease.org/PersonifyEbusiness/Resources/Publications/Newsletter/Archive"},{"id":389741,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Richards, Bryan J. 0000-0001-9955-2523","orcid":"https://orcid.org/0000-0001-9955-2523","contributorId":219535,"corporation":false,"usgs":true,"family":"Richards","given":"Bryan","email":"","middleInitial":"J.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":819751,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russell, Robin E. 0000-0001-8726-7303","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":219536,"corporation":false,"usgs":true,"family":"Russell","given":"Robin E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":819752,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ballmann, Anne 0000-0002-0380-056X aballmann@usgs.gov","orcid":"https://orcid.org/0000-0002-0380-056X","contributorId":140319,"corporation":false,"usgs":true,"family":"Ballmann","given":"Anne","email":"aballmann@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":819753,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70219918,"text":"70219918 - 2021 - 3-D Modeling of the Duluth Complex from geophysical data","interactions":[],"lastModifiedDate":"2021-09-17T15:49:58.370459","indexId":"70219918","displayToPublicDate":"2021-05-31T10:43:45","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"3-D Modeling of the Duluth Complex from geophysical data","docAbstract":"The Mesoproterozoic Duluth Complex in northeastern Minnesota is one of the major plutonic components of the Midcontinent Rift System and hosts a variety of copper-nickel sulfide and platinum-group element deposits. The Duluth Complex is composed of a series of individual mafic and felsic intrusions emplaced 1110-1098 Ma within Paleoproterozoic sedimentary rocks of the Animikie basin and volcanic flows of the Midcontinent Rift. Prior work has included 2-D modeling and qualitative geologic interpretations of gravity and magnetic data (e.g., Chandler, 1990; Chandler and Ferderer, 1989), much of which is still preliminary (V. Chandler, written commun., 2020). Three-dimensional modeling has been limited, with only one 3-D model created using Bouguer gravity data constrained by seismic-reflection interpretations as part of a PhD thesis (Allen, 1994). Given the complex geology of the area, 3-D modeling is useful for providing a complete picture of the variable densities, susceptibilities, and electrical resistivities throughout the Duluth Complex and associated volcanic rocks as well as their depth extent beneath sedimentary cover. Models of these geophysical properties at depth enable more accurate geologic mapping in the subsurface which can lead to an improved understanding of the formation history of the Duluth Complex. \nIn this study, we use aeromagnetic data acquired between 1979-1991 (Chandler, 2007), Bouguer gravity data collected since 1950 (Chandler and Lively, 2019), and magnetotelluric data collected in 2019 to create new 2-D and 3-D geophysical models of the Duluth Complex constrained by seismic reflection, geologic, and rock property data. An inversion of the Bouguer gravity data for thickness of the Duluth Complex using constant densities of 3110 kg/m3 and 2670 kg/m3 for the Duluth Complex and surrounding crustal rocks, respectively, results in thicknesses ranging from ~3-28 km for the Duluth Complex and related intrusions and volcanic rocks (Figure 1A). A 3-D model of the magnetotelluric data reveals low resistivity anomalies at ~5-10 km depth below the northern margin of the Duluth Complex and below the Greenwood Lake intrusion (Figure 1B). We expect to encounter low resistivities at depth associated with the Paleoproterozoic Animikie basin, which makes up the floor of the Duluth Complex, and therefore interpret these anomalies as either the base of the complex or as fragments of Animikie sediments interfingered with igneous intrusive rocks. Finally, 3-D voxel models of density and susceptibility illuminate the subsurface distribution of rock properties below the Duluth Complex which, in combination with resistivity and thickness models, can be used to create a 3-D geologic map of this area.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"67th Institute on Lake Superior Geology Proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Institute on Lake Superior Geology","usgsCitation":"Peterson, D.E., Bedrosian, P.A., and Finn, C., 2021, 3-D Modeling of the Duluth Complex from geophysical data, <i>in</i> 67th Institute on Lake Superior Geology Proceedings, p. 52-53.","productDescription":"2 p.","startPage":"52","endPage":"53","ipdsId":"IP-128596","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":389395,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389394,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.lakesuperiorgeology.org/Virtual2021/index.html"}],"country":"United States","state":"Minnesota","otherGeospatial":"Duluth Complex","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -93.31787109374999,\n              47.29413372501023\n            ],\n            [\n              -88.48388671874999,\n              47.29413372501023\n            ],\n            [\n              -88.48388671874999,\n              48.46563710044979\n            ],\n            [\n              -93.31787109374999,\n              48.46563710044979\n            ],\n            [\n              -93.31787109374999,\n              47.29413372501023\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peterson, Dana E. 0000-0002-1941-265X","orcid":"https://orcid.org/0000-0002-1941-265X","contributorId":225536,"corporation":false,"usgs":true,"family":"Peterson","given":"Dana","email":"","middleInitial":"E.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":814394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":814395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finn, Carol A. 0000-0002-6178-0405","orcid":"https://orcid.org/0000-0002-6178-0405","contributorId":205010,"corporation":false,"usgs":true,"family":"Finn","given":"Carol A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":814396,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70223180,"text":"70223180 - 2021 - Alaska landbird montoring survey: Alaska regional protocol framework for monitoring landbirds using point counts","interactions":[],"lastModifiedDate":"2021-08-17T14:13:12.185743","indexId":"70223180","displayToPublicDate":"2021-05-31T09:03:14","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5856,"text":"Regional Protocol Framework","active":true,"publicationSubtype":{"id":1}},"title":"Alaska landbird montoring survey: Alaska regional protocol framework for monitoring landbirds using point counts","docAbstract":"<p>Alaska provides habitat for 143 species of landbirds that occur regularly in the state, about half of which breed predominantly north of the border between the contiguous United States and Canada. The road-based North American Breeding Bird Survey (BBS) provides some data on population trends in Alaska, but most northern populations are inadequately monitored by this program because of a paucity of roads. To remedy this deficiency, Boreal Partners in Flight developed the Alaska Landbird Monitoring Survey (ALMS) to monitor breeding populations of landbirds in off-road areas of Alaska in tandem with data collected from the roadside BBS. The primary objective of ALMS is to monitor long-term population trends of landbirds and other species that can be monitored by diurnal point counts during the breeding season, including many shorebirds and aquatic birds. A secondary objective is to estimate landbird densities by habitat, which can be used to model avian distribution and abundance across Alaska. ALMS is a collaborative program whereby agencies and other entities conduct standardized surveys of breeding birds and their habitats on the lands they manage and then contribute the data to the U.S. Geological Survey Alaska Science Center for storage and analysis. </p><p>The short-term implementation goal of ALMS is to monitor birds systematically within each of 100 randomly selected survey blocks, thereby matching the number of BBS surveys conducted in each of Alaska's five Bird Conservation Regions (BCRs). Each block has a mini-grid of 15−25 points that are surveyed biennially, with half of the blocks surveyed in alternating years. Survey blocks are stratified by accessibility and cost-effectiveness. Refuges may opt to limit sites to those accessible by foot, vehicle, boat, or fixed-wing aircraft, as these can be surveyed more inexpensively and reliably over time. Observers survey each point within a survey block for birds using a 10-min point count once per summer on a biennial basis. They collect corresponding habitat data during the first visit and at subsequent 10-year intervals or whenever a disturbance (e.g., fire, wind) has caused a significant change. USGS analyzes ALMS data jointly with BBS data to test for differences between off-road and roadside areas and to increase power to detect statewide trends. Additional blocks can be surveyed in areas that are more difficult and expensive to access as resources become available in the future. Long-term monitoring enables detection of change in bird populations in relation to fire, disease and insect damage, resource development, climate-related change, and other landscape-level disturbances across Alaska. Results from ALMS can also help prioritize conservation and research towards species before they become endangered and require expensive recovery programs.</p>","language":"English","publisher":"U.S. Fish & Wildlife Service","usgsCitation":"Handel, C.M., Matsuoka, S.M., Cady, M.N., and Granfors, D.A., 2021, Alaska landbird montoring survey: Alaska regional protocol framework for monitoring landbirds using point counts: Regional Protocol Framework, vi, 66 p.","productDescription":"vi, 66 p.","ipdsId":"IP-113748","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":387999,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":387969,"type":{"id":15,"text":"Index Page"},"url":"https://ecos.fws.gov/ServCat/Reference/Profile/114719"}],"country":"United 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,{"id":70221725,"text":"70221725 - 2021 - Improving species status assessments under the U.S. Endangered Species Act and implications for multispecies conservation challenges worldwide","interactions":[],"lastModifiedDate":"2021-12-10T16:36:17.553183","indexId":"70221725","displayToPublicDate":"2021-05-31T07:50:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Improving species status assessments under the U.S. Endangered Species Act and implications for multispecies conservation challenges worldwide","docAbstract":"<p>Despite its successes, the U.S. Endangered Species Act (ESA) has proven challenging to implement due to funding limitations, workload backlog, and other problems. As threats to species survival intensify and as more species come under threat, the need for the ESA and similar conservation laws and policies in other countries to function efficiently has grown. Attempts by the U.S. Fish and Wildlife Service (USFWS) to streamline ESA decisions include multispecies recovery plans and habitat conservation plans. We address species status assessment (SSA), a USFWS process to inform ESA decisions from listing to recovery, within the context of multispecies and ecosystem planning. Although existing SSAs have a single-species focus, ecosystem-based research can efficiently inform multiple SSAs within a region and provide a foundation for transition to multispecies SSAs in the future. We considered at-risk grassland species and ecosystems within the southeastern United States, where a disproportionate number of rare and endemic species are associated with grasslands. To initiate our ecosystem-based approach, we used a combined literature-based and structured World Café workshop format to identify science needs for SSAs. Discussions concentrated on 5 categories of threats to grassland species and ecosystems, consistent with recommendations to make shared threats a focus of planning under the ESA: (1) habitat loss, fragmentation, and disruption of functional connectivity; (2) climate change; (3) altered disturbance regimes; (4) invasive species; and (5) localized impacts. For each threat, workshop participants identified science and information needs, including database availability, research priorities, and modeling and mapping needs. Grouping species by habitat and shared threats can make the SSA process and other planning processes for conservation of at-risk species worldwide more efficient and useful. We found a combination of literature review and structured discussion effective for identifying the scientific information and analysis needed to support the development of multiple SSAs.</p>","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/cobi.13777","usgsCitation":"Noss, R., Cartwright, J.M., Estes, D., Witsell, T., Elliott, G., Adams, D.S., Albrecht, M.A., Boyles, R., Comer, P., Doffitt, C., Hill, J.G., Hunter, W.C., Knapp, W.M., Marshall, M., Singhurst, J.R., Tracey, C., Walck, J.L., and Weakley, A., 2021, Improving species status assessments under the U.S. Endangered Species Act and implications for multispecies conservation challenges worldwide: Conservation Biology, v. 35, no. 6, p. 1715-1724, https://doi.org/10.1111/cobi.13777.","productDescription":"10 p.","startPage":"1715","endPage":"1724","ipdsId":"IP-122143","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science 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Theo","contributorId":258187,"corporation":false,"usgs":false,"family":"Witsell","given":"Theo","email":"","affiliations":[],"preferred":false,"id":818522,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Elliott, Gregg","contributorId":260712,"corporation":false,"usgs":false,"family":"Elliott","given":"Gregg","email":"","affiliations":[{"id":52648,"text":"Southeastern Grasslands Initiative","active":true,"usgs":false}],"preferred":false,"id":818523,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Adams, Daniel S. 0000-0001-9695-0577","orcid":"https://orcid.org/0000-0001-9695-0577","contributorId":258189,"corporation":false,"usgs":false,"family":"Adams","given":"Daniel","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":818524,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Albrecht, Matthew A. 0000-0002-1079-1630","orcid":"https://orcid.org/0000-0002-1079-1630","contributorId":213559,"corporation":false,"usgs":false,"family":"Albrecht","given":"Matthew","email":"","middleInitial":"A.","affiliations":[{"id":38790,"text":"Missouri Botanical Garden","active":true,"usgs":false}],"preferred":false,"id":818525,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Boyles, Ryan 0000-0001-9272-867X","orcid":"https://orcid.org/0000-0001-9272-867X","contributorId":221983,"corporation":false,"usgs":true,"family":"Boyles","given":"Ryan","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":818526,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Comer, Patrick","contributorId":191654,"corporation":false,"usgs":false,"family":"Comer","given":"Patrick","affiliations":[],"preferred":false,"id":818527,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Doffitt, Chris","contributorId":258191,"corporation":false,"usgs":false,"family":"Doffitt","given":"Chris","email":"","affiliations":[],"preferred":false,"id":818528,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hill, JoVonn G. 0000-0002-1892-7117","orcid":"https://orcid.org/0000-0002-1892-7117","contributorId":258193,"corporation":false,"usgs":false,"family":"Hill","given":"JoVonn","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":818529,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hunter, William C.","contributorId":258194,"corporation":false,"usgs":false,"family":"Hunter","given":"William","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":818530,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Knapp, Wesley M. 0000-0002-5289-5649","orcid":"https://orcid.org/0000-0002-5289-5649","contributorId":258195,"corporation":false,"usgs":false,"family":"Knapp","given":"Wesley","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":818531,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Marshall, Mike","contributorId":260713,"corporation":false,"usgs":false,"family":"Marshall","given":"Mike","affiliations":[{"id":52649,"text":"U.S Fish and Wildlife Service; Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":818532,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Singhurst, Jason R.","contributorId":258196,"corporation":false,"usgs":false,"family":"Singhurst","given":"Jason","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":818533,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Tracey, Christopher","contributorId":260714,"corporation":false,"usgs":false,"family":"Tracey","given":"Christopher","affiliations":[{"id":52650,"text":"Pennsylvania Natural Heritage Program","active":true,"usgs":false}],"preferred":false,"id":818534,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Walck, Jeffrey L. 0000-0002-8518-9900","orcid":"https://orcid.org/0000-0002-8518-9900","contributorId":258197,"corporation":false,"usgs":false,"family":"Walck","given":"Jeffrey","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":818535,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Weakley, Alan 0000-0003-2093-3767","orcid":"https://orcid.org/0000-0003-2093-3767","contributorId":197982,"corporation":false,"usgs":false,"family":"Weakley","given":"Alan","email":"","affiliations":[],"preferred":false,"id":818536,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}}
,{"id":70220904,"text":"70220904 - 2021 - Surface flow velocities from space: Particle image velocimetry of satellite video of a large, sediment-laden river","interactions":[],"lastModifiedDate":"2021-05-28T18:41:13.32766","indexId":"70220904","displayToPublicDate":"2021-05-28T13:36:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7170,"text":"Frontiers in Water","active":true,"publicationSubtype":{"id":10}},"title":"Surface flow velocities from space: Particle image velocimetry of satellite video of a large, sediment-laden river","docAbstract":"<p><span>Conventional, field-based streamflow monitoring in remote, inaccessible locations such as Alaska poses logistical challenges. Safety concerns, financial considerations, and a desire to expand water-observing networks make remote sensing an appealing alternative means of collecting hydrologic data. In an ongoing effort to develop non-contact methods for measuring river discharge, we evaluated the potential to estimate surface flow velocities from satellite video of a large, sediment-laden river in Alaska via particle image velocimetry (PIV). In this setting, naturally occurring sediment boil vortices produced distinct water surface features that could be tracked from frame to frame as they were advected by the flow, obviating the need to introduce artificial tracer particles. In this study, we refined an end-to-end workflow that involved stabilization and geo-referencing, image preprocessing, PIV analysis with an ensemble correlation algorithm, and post-processing of PIV output to filter outliers and scale and geo-reference velocity vectors. Applying these procedures to image sequences extracted from satellite video allowed us to produce high resolution surface velocity fields; field measurements of depth-averaged flow velocity were used to assess accuracy. Our results confirmed the importance of preprocessing images to enhance contrast and indicated that lower frame rates (e.g., 0.25 Hz) lead to more reliable velocity estimates because longer capture intervals allow more time for water surface features to translate several pixels between frames, given the relatively coarse spatial resolution of the satellite data. Although agreement between PIV-derived velocity estimates and field measurements was weak (</span><i>R</i><sup>2</sup><span>&nbsp;= 0.39) on a point-by-point basis, correspondence improved when the PIV output was aggregated to the cross-sectional scale. For example, the correspondence between cross-sectional maximum velocities inferred via remote sensing and measured in the field was much stronger (</span><i>R</i><sup>2</sup><span>&nbsp;= 0.76), suggesting that satellite video could play a role in measuring river discharge. Examining correlation matrices produced as an intermediate output of the PIV algorithm yielded insight on the interactions between image frame rate and sensor spatial resolution, which must be considered in tandem. Although further research and technological development are needed, measuring surface flow velocities from satellite video could become a viable tool for streamflow monitoring in certain fluvial environments.</span></p>","language":"English","publisher":"Frontiers Media","doi":"10.3389/frwa.2021.652213","usgsCitation":"Legleiter, C.J., and Kinzel, P.J., 2021, Surface flow velocities from space: Particle image velocimetry of satellite video of a large, sediment-laden river: Frontiers in Water, v. 3, 652213, 20 p., https://doi.org/10.3389/frwa.2021.652213.","productDescription":"652213, 20 p.","ipdsId":"IP-125455","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":452077,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frwa.2021.652213","text":"Publisher Index Page"},{"id":436332,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZY5LK1","text":"USGS data release","linkHelpText":"Satellite video and field measurements of flow velocity acquired from the Tanana River in Alaska and used for particle image velocimetry (PIV)"},{"id":386020,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Nenana","otherGeospatial":"Tanana River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -149.21218872070312,\n              64.53486288126804\n            ],\n            [\n              -148.92929077148438,\n              64.53486288126804\n            ],\n            [\n              -148.92929077148438,\n              64.61387025268262\n            ],\n            [\n              -149.21218872070312,\n              64.61387025268262\n            ],\n            [\n              -149.21218872070312,\n              64.53486288126804\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"3","noUsgsAuthors":false,"publicationDate":"2021-05-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":816651,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":816652,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70220894,"text":"sir20215044 - 2021 - Characterization of historical and stochastically generated climate and streamflow conditions in the Souris River Basin, United States and Canada","interactions":[],"lastModifiedDate":"2021-05-28T19:05:24.819834","indexId":"sir20215044","displayToPublicDate":"2021-05-28T10:53:21","publicationYear":"2021","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":"2021-5044","displayTitle":"Characterization of Historical and Stochastically Generated Climate and Streamflow Conditions in the Souris River Basin, United States and Canada","title":"Characterization of historical and stochastically generated climate and streamflow conditions in the Souris River Basin, United States and Canada","docAbstract":"<p>The Souris River Basin is a 61,000-square-kilometer basin in the Provinces of Saskatchewan and Manitoba in Canada and the State of North Dakota in the United States. Greater than average snowpack during the winter of 2010–11, along with record-setting rains in May and June 2011, resulted in historically unprecedented flooding in the Souris River Basin. The severity of the 2011 flood led the United States and Canada to request a review of the operating plan for any improvements of reservoir operations and flood control measures in the basin, and the Souris River Basin Task Force was formed. The International Souris River Study Board was then formed in 2017 to carry out the recommendations of the Souris River Basin Task Force laid out in a plan of study. To support the International Souris River Study Board, the U.S. Geological Survey (USGS), in cooperation with the North Dakota State Water Commission and the International Joint Commission, used the previously developed unregulated and regulated streamflow models and data for stochastic streamflow in the Souris River Basin to characterize climate and streamflow and support selection of streamflow traces based on their characterization. Components of the original stochastic hydrology models and their outputs were used in this phase of the study to (1) characterize historical and stochastic climate and streamflow for the Souris River Basin, (2) disaggregate monthly stochastic streamflow spatially and temporally to meet the needs of the U.S. Army Corps of Engineers, Hydrologic Engineering Center, Reservoir System Simulation model for the Souris River Basin, and (3) discuss selection of disaggregated streamflow traces (simulations) using the characteristics of climate and streamflow. A trace is a time series of a stochastic variable such as streamflow, potential evapotranspiration, or precipitation.</p><p>To characterize climate conditions, precipitation, potential evapotranspiration (PET), and moisture deficit for the Souris River Basin and individual points at Rafferty, Grant Devine, and Lake Darling Reservoirs were determined annually and seasonally. The annual basin (November 1–October 31) precipitation for the 50-percent nonexceedance probability is 452 millimeters (mm). Spring (March–May) is the wettest season, followed by summer (June–August), fall (September–November), and winter (December–February). Annual moisture deficit was largest at Lake Darling Reservoir, followed by Rafferty Reservoir, and then Grant Devine Reservoir.</p><p>Annual maximum monthly mean streamflow was determined for the Souris River below Rafferty Reservoir, Saskatchewan (Canadian streamgage 05NB036); Long Creek near Noonan (above Boundary Reservoir), North Dakota (USGS streamgage 05113600); Moose Mountain Creek near Oxbow, Saskatchewan (Canadian streamgage 05ND004); the Souris River near Sherwood, N. Dak. (USGS streamgage 05114000); the Des Lacs River at Foxholm, N. Dak. (USGS streamgage 05116500); and the Souris River above Minot, N. Dak. (USGS streamgage 05117500). When the seasonal maximum monthly mean streamflows are evaluated in contrast to annual maximum monthly mean streamflows separated by their seasonal occurrence, summer months of annual maximum monthly mean streamflows have a higher 50-percent exceedance probability of streamflow compared to annual maximum monthly mean streamflows that occur in spring, seasonal maximum monthly mean streamflows that occur in spring, and seasonal maximum monthly mean streamflows that occur in summer. When annual maximum monthly mean streamflows in summer are compared to annual maximum monthly mean streamflows in spring, they are consistently higher in streamflow but occur in less than 4.2 percent of years. Evaluation of whether the annual maximum monthly mean streamflows that occur in summer can be described as a separate population from annual maximum monthly mean streamflows that occur in spring was outside the scope of this study, and the summer and spring annual maximum monthly mean streamflows were not tested for statistical differences in mean or variance. Further investigation of seasonal weather patterns that induce flooding could lead to a better understanding of the seasonal differences in flooding.</p><p>Long-term hydrologic drought was characterized by evaluating multiyear mean streamflow. Shorter averaging periods have greater streamflow variability than longer periods and hence have a wider range of values. As the averaging period is extended to a longer period, the variability of mean streamflow decreases, and the more extreme streamflow volumes seen in shorter averaging periods cannot be sustained. Stochastic streamflow time series were disaggregated spatially and temporally for use in a HEC–ResSim model. The combination of monthly and daily stochastic streamflow data was used to select traces with qualities that could be used to test alternatives focused on water supply, summer flooding, and apportionment.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215044","collaboration":"Prepared in cooperation with the North Dakota State Water Commission and the International Joint Commission","usgsCitation":"Gregory, A., and Galloway, J.M., 2021, Characterization of historical and stochastically generated climate and streamflow conditions in the Souris River Basin, United States and Canada: U.S. Geological Survey Scientific Investigations Report 2021–5044, 36 p., https://doi.org/10.3133/sir20215044.","productDescription":"Report: viii, 36 p.; Data Release; Dataset","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-120682","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":386014,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":386011,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5044/coverthb.jpg"},{"id":386012,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5044/sir20215044.pdf","text":"Report","size":"5.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021—5044"},{"id":386013,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93AOWFL","text":"USGS data release","linkHelpText":"Historical and stochastically generated climate and streamflow data for the Souris River Basin, United States and Canada"}],"country":"Canada, United States","state":"Manitoba, North Dakota, Saskatchewan","otherGeospatial":"Souris River basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -97.55859375,\n              46.6795944656402\n            ],\n            [\n              -98.0859375,\n              50.12057809796008\n            ],\n            [\n              -101.25,\n              51.67255514839674\n            ],\n            [\n              -107.138671875,\n              53.48804553605622\n            ],\n            [\n              -108.6328125,\n              50.958426723359935\n            ],\n            [\n              -102.568359375,\n              48.22467264956519\n            ],\n            [\n              -99.66796875,\n              46.98025235521883\n            ],\n            [\n              -97.55859375,\n              46.6795944656402\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a data-mce-href=\"mailto:%20dc_nd@usgs.gov\" href=\"mailto:%20dc_nd@usgs.gov\">Director</a>, <a data-mce-href=\"https://www.usgs.gov/centers/dakota-water\" href=\"https://www.usgs.gov/centers/dakota-water\">Dakota Water Science Center</a> <br>U.S. Geological Survey<br>821 East Interstate Avenue<br>Bismarck, ND 58503 <br>1608 Mountain View Road<br>Rapid City, SD 57702</p><p><a data-mce-href=\"../contact\" href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods of Analysis</li><li>Historical and Stochastic Climate Characteristics</li><li>Stochastically Generated Natural (Unregulated) Streamflow Characteristics</li><li>Disaggregated Daily Stochastic Streamflow</li><li>Stochastically Generated Regulated Streamflow and Reservoir Volume Characteristics</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-05-28","noUsgsAuthors":false,"publicationDate":"2021-05-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Gregory, Angela 0000-0002-9905-1240","orcid":"https://orcid.org/0000-0002-9905-1240","contributorId":45018,"corporation":false,"usgs":true,"family":"Gregory","given":"Angela","email":"","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816616,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Galloway, Joel M. 0000-0002-9836-9724 jgallowa@usgs.gov","orcid":"https://orcid.org/0000-0002-9836-9724","contributorId":1562,"corporation":false,"usgs":true,"family":"Galloway","given":"Joel","email":"jgallowa@usgs.gov","middleInitial":"M.","affiliations":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816617,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70226594,"text":"70226594 - 2021 - Floral resource selection by wild bees and honey bees in the Midwest United States: Implications for designing pollinator habitat","interactions":[],"lastModifiedDate":"2021-12-01T13:21:30.054747","indexId":"70226594","displayToPublicDate":"2021-05-28T07:19:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Floral resource selection by wild bees and honey bees in the Midwest United States: Implications for designing pollinator habitat","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Many seed mix recommendations for creating pollinator habitat are in part based on anecdotal evidence or field observations of bees visiting forbs (i.e. use). However, there is limited information on what forbs are preferred by bees, particularly in working landscapes where bee forage may be limited. We examined floral resource selection by wild bees and honey bees on grasslands in the Midwest using a 5-year dataset containing over 8,000 plant-bee interactions. We observed wild bees visiting 83 forb species, but only 14 species were significantly selected (i.e. bees visited a plant more than expected based on availability). Approximately 70% of all wild bee visitations were on native flowers, whereas only 20% of all honey bee visitations were on natives. Honey bees visited 70 forb species, but only four forbs were significantly selected. The selection ratio for each forb species was not correlated with proportion of use by wild bees or honey bees, suggesting that bee visitation data alone do not elucidate patterns of forb selection or avoidance. We then compared our resource selection results to forbs recommended by U.S. Department of Agriculture (USDA) for regional pollinator habitat plantings. Many forbs that were selected by bees in our study were also recommended by USDA; however, some USDA-recommended forbs were selected against by bees. A greater understanding of which floral resources are selected by bees can assist land managers in assessing conservation seed mixes and ultimately provide diverse, season-long pollinator forage in working landscapes.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/rec.13456","usgsCitation":"Simanonok, S.C., Otto, C., and Buhl, D.A., 2021, Floral resource selection by wild bees and honey bees in the Midwest United States: Implications for designing pollinator habitat: Restoration Ecology, v. 29, no. 8, e13456, 11 p., https://doi.org/10.1111/rec.13456.","productDescription":"e13456, 11 p.","ipdsId":"IP-121320","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":392299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, North Dakota, South 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 \"}}]}","volume":"29","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-07-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Simanonok, Stacy C. 0000-0002-0287-3871","orcid":"https://orcid.org/0000-0002-0287-3871","contributorId":229607,"corporation":false,"usgs":true,"family":"Simanonok","given":"Stacy","email":"","middleInitial":"C.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":827430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Otto, Clint 0000-0002-7582-3525 cotto@usgs.gov","orcid":"https://orcid.org/0000-0002-7582-3525","contributorId":5426,"corporation":false,"usgs":true,"family":"Otto","given":"Clint","email":"cotto@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":827431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buhl, Deborah A. 0000-0002-8563-5990 dbuhl@usgs.gov","orcid":"https://orcid.org/0000-0002-8563-5990","contributorId":146226,"corporation":false,"usgs":true,"family":"Buhl","given":"Deborah","email":"dbuhl@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":827432,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70221343,"text":"70221343 - 2021 - Use of the smeltCam as an efficient fish sampling alternative within the San Francisco Estuary","interactions":[],"lastModifiedDate":"2021-06-11T12:05:22.631939","indexId":"70221343","displayToPublicDate":"2021-05-28T07:04:41","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Use of the smeltCam as an efficient fish sampling alternative within the San Francisco Estuary","docAbstract":"<p><span>Resource managers often rely on long-term monitoring surveys to detect trends in biological data. However, no survey gear is 100% efficient, and many sources of bias can be responsible for detecting or not detecting biological trends. The SmeltCam is an imaging apparatus developed as a potential sampling alternative to long-term trawling gear surveys within the San Francisco Estuary, California, to reduce handling stress on sensitive species like the Delta Smelt (</span><i>Hypomesus transpacificus</i><span>). Although believed to be a reliable alternative to closed cod-end trawling surveys, no formal test of sampling efficiency has been implemented using the SmeltCam. We used a paired deployment of the SmeltCam and a conventional closed cod-end trawl within the Napa River and San Pablo Bay, a Bayesian binomial&nbsp;</span><i>N</i><span>-mixture model, and data simulations to determine the sampling efficiency of both deployed gear types to capture a Delta Smelt surrogate (Northern Anchovy,&nbsp;</span><i>Engraulis mordax</i><span>) and to test potential bias in our modeling framework. We found that retention efficiency—a component of detection efficiency that estimates the probability a fish is retained by the gear, conditional on gear contact—was slightly higher using the SmeltCam (mean = 0.58) than the conventional trawl (mean = 0.47, Probability SmeltCam retention efficiency &gt; trawl retention efficiency = 94%). We also found turbidity did not affect the SmeltCam’s retention efficiency, although total fish density during an individual tow improved the trawl’s retention efficiency. Simulations also showed the binomial model was accurate when model assumptions were met. Collectively, our results suggest the SmeltCam to be a reliable alternative to sampling with conventional trawling gear, but future tests are needed to confirm whether the SmeltCam is as reliable when applied to taxa other than Northern Anchovy over a greater range of conditions.</span></p>","language":"English","publisher":"University of California","doi":"10.15447/sfews.2021v19iss2art6","usgsCitation":"Huntsman, B., Feyrer, F.V., and Young, M.J., 2021, Use of the smeltCam as an efficient fish sampling alternative within the San Francisco Estuary: San Francisco Estuary and Watershed Science, v. 19, no. 2, 16 p., https://doi.org/10.15447/sfews.2021v19iss2art6.","productDescription":"16 p.","ipdsId":"IP-123894","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":452096,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2021v19iss2art6","text":"Publisher Index Page"},{"id":386410,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Francisco","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -123.57421875,\n              36.84446074079564\n            ],\n            [\n              -121.86035156249999,\n              36.84446074079564\n            ],\n            [\n              -121.86035156249999,\n              39.40224434029275\n            ],\n            [\n              -123.57421875,\n              39.40224434029275\n            ],\n            [\n              -123.57421875,\n              36.84446074079564\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"19","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-06-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Huntsman, Brock 0000-0003-4090-1949","orcid":"https://orcid.org/0000-0003-4090-1949","contributorId":223101,"corporation":false,"usgs":true,"family":"Huntsman","given":"Brock","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Feyrer, Frederick V. 0000-0003-1253-2349 ffeyrer@usgs.gov","orcid":"https://orcid.org/0000-0003-1253-2349","contributorId":178379,"corporation":false,"usgs":true,"family":"Feyrer","given":"Frederick","email":"ffeyrer@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Matthew J. 0000-0001-9306-6866 mjyoung@usgs.gov","orcid":"https://orcid.org/0000-0001-9306-6866","contributorId":206255,"corporation":false,"usgs":true,"family":"Young","given":"Matthew","email":"mjyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817386,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70221054,"text":"70221054 - 2021 - Ten years of volcanic activity at Mt Etna: High-resolution mapping and accurate quantification of the morphological changes by Pleiades and Lidar data","interactions":[],"lastModifiedDate":"2021-06-01T14:22:02.175408","indexId":"70221054","displayToPublicDate":"2021-05-28T06:52:40","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8912,"text":"International Journal of Applied Earth Observations and Geoinformation","active":true,"publicationSubtype":{"id":10}},"title":"Ten years of volcanic activity at Mt Etna: High-resolution mapping and accurate quantification of the morphological changes by Pleiades and Lidar data","docAbstract":"<p><span>The topography of Mt. Etna, Italy, is subjected to continuous modifications depending on intensity and magnitude of eruptions that frequently occur at the volcano summit and flanks. In order to make high-resolution maps of morphological changes and accurately calculate the overall volume of the erupted products (e.g., lava flows, tephra fall out, scoriae cones) in ten years, we have compared the altimetry models of Mt. Etna derived from 2005 Airborne Laser Scanning data and 2015 Pleiades stereo satellite imagery. Both models cover a common area of 400&nbsp;km</span><sup>2</sup><span>&nbsp;with spatial resolution of 2&nbsp;m and comparable vertical accuracy (RMSE&nbsp;&lt;&nbsp;0.8&nbsp;m). The results show that the area most affected by the erupted products is the mid-upper portion of the volcano with an altitude ranging from 1300&nbsp;m to more than 3300&nbsp;m a.s.l., value reached at the summit of the North East crater. In particular, this portion changes dramatically in the eastern sector due to the birth and growth of the New South-East Crater, the invasion of dozens of lava flows in the Valle del Bove, and the formation of the 2014 scoriae cones and lava field at the base of the North-East Crater. The total volume of products erupted in the investigated period results in 284.3±15.8 x 10</span><sup>6</sup><span>&nbsp;m</span><sup>3</sup><span>&nbsp;with a yearly average volume of 28.4 x 10</span><sup>6</sup><span>&nbsp;m</span><sup>3</sup><span>/y comparable with the previous decades. In addition, the products emitted by the 2014 sub-terminal eruption are mapped and quantified including, for the first time, the volume of the 2014 scoriae cones generated on the eastern flank of North-East Crater This study demonstrates how a rigorous comparison between digital elevation models derived from different remote sensing techniques produce high accurate mapping and quantifications of morphological changes applicable for worldwide active volcanoes. This allows to quantify volumes and areas of erupted products reducing the error estimations, a crucial point to provide precise data often used as key parameters for many volcanic hazard studies.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2021.102369","usgsCitation":"Bisson, M., Spinetti, C., Andronico, D., Palaseanu-Lovejoy, M., Buongiorno, M.F., Alexandrov, O., and Cecere, T., 2021, Ten years of volcanic activity at Mt Etna: High-resolution mapping and accurate quantification of the morphological changes by Pleiades and Lidar data: International Journal of Applied Earth Observations and Geoinformation, v. 102, 102369, 11 p., https://doi.org/10.1016/j.jag.2021.102369.","productDescription":"102369, 11 p.","ipdsId":"IP-121404","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":452102,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jag.2021.102369","text":"Publisher Index Page"},{"id":386026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","otherGeospatial":"Mt. Etna, Sicily","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              14.849395751953125,\n              37.62402129571883\n            ],\n            [\n              15.137786865234377,\n              37.62402129571883\n            ],\n            [\n              15.137786865234377,\n              37.85859141570558\n            ],\n            [\n              14.849395751953125,\n              37.85859141570558\n            ],\n            [\n              14.849395751953125,\n              37.62402129571883\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"102","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bisson, Marina 0000-0002-7104-9210","orcid":"https://orcid.org/0000-0002-7104-9210","contributorId":221724,"corporation":false,"usgs":false,"family":"Bisson","given":"Marina","email":"","affiliations":[{"id":40408,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, via Della Faggiola, Pisa, 56126, Italy","active":true,"usgs":false}],"preferred":false,"id":816657,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spinetti, Claudia 0000-0002-1861-5666","orcid":"https://orcid.org/0000-0002-1861-5666","contributorId":221725,"corporation":false,"usgs":false,"family":"Spinetti","given":"Claudia","email":"","affiliations":[{"id":40409,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione ONT, via di Vigna Murata, Roma, 00143, Italy","active":true,"usgs":false}],"preferred":false,"id":816658,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andronico, Daniele 0000-0002-8333-1547","orcid":"https://orcid.org/0000-0002-8333-1547","contributorId":259163,"corporation":false,"usgs":false,"family":"Andronico","given":"Daniele","email":"","affiliations":[{"id":52323,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo","active":true,"usgs":false}],"preferred":false,"id":816659,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118 mpal@usgs.gov","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":3639,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","email":"mpal@usgs.gov","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":816660,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buongiorno, Maria Fabrizia 0000-0002-6095-6974","orcid":"https://orcid.org/0000-0002-6095-6974","contributorId":221726,"corporation":false,"usgs":false,"family":"Buongiorno","given":"Maria","email":"","middleInitial":"Fabrizia","affiliations":[{"id":40409,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione ONT, via di Vigna Murata, Roma, 00143, Italy","active":true,"usgs":false}],"preferred":false,"id":816661,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Alexandrov, Oleg","contributorId":167662,"corporation":false,"usgs":false,"family":"Alexandrov","given":"Oleg","email":"","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":816662,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cecere, Thomas 0000-0001-5254-8404 tcecere@usgs.gov","orcid":"https://orcid.org/0000-0001-5254-8404","contributorId":221727,"corporation":false,"usgs":true,"family":"Cecere","given":"Thomas","email":"tcecere@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":816663,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70220717,"text":"sir20215033 - 2021 - Overview and methodology for a study to identify fecal contamination sources using microbial source tracking in seven embayments on Long Island, New York","interactions":[],"lastModifiedDate":"2022-09-01T10:06:27.850627","indexId":"sir20215033","displayToPublicDate":"2021-05-27T18:19:54","publicationYear":"2021","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":"2021-5033","displayTitle":"Overview and Methodology for a Study To Identify Fecal Contamination Sources Using Microbial Source Tracking in Seven Embayments on Long Island, New York","title":"Overview and methodology for a study to identify fecal contamination sources using microbial source tracking in seven embayments on Long Island, New York","docAbstract":"<p>Between June 2018 and July 2019, the U.S. Geological Survey collaborated with the New York State Department of Environmental Conservation to analyze water quality in seven embayments on Long Island, New York, for a study to examine fecal contamination using microbial source tracking. This report documents the approach, methodology, and quality-assurance data used in the study. All samples and field data were collected in accordance with U.S. Geological Survey National Field Manual procedures. Samples were analyzed for host-specific deoxyribonucleic acid (DNA) markers, fecal coliform bacteria, inorganic and total organic nitrogen, and stable isotopes of nitrate and ammonium.</p><p>Samples for quality control were collected for microbiological analyses at a rate of 1 per 20 environmental samples. A total of 14 blank and 15 replicate samples were collected for DNA markers, 52 sequential field replicates were analyzed by the Public Environmental Health Laboratory of the Suffolk County Department of Health Services and the New York State Department of Conservation Marine Laboratory for fecal coliform, and 7 blank and 7 replicate samples were collected to be analyzed for nutrients. Results from quality-control samples collected throughout the course of the study confirmed that sampling procedures were adequate and did not disqualify any data from analysis.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215033","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Tagliaferri, T.N., Fisher, S.C., Kephart, C.M., Cheung, N., Reed, A.P., and Welk, R.J., 2021, Overview and methodology for a study to identify fecal contamination sources using microbial source tracking in seven embayments on Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2021–5033, 8 p., https://doi.org/10.3133/sir20215033.","productDescription":"iv, 8 p.","numberOfPages":"8","onlineOnly":"Y","ipdsId":"IP-128174","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":406050,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20215042","text":"Scientific Investigations Report 2021–5042","linkHelpText":"- Using Microbial Source Tracking To Identify Fecal Contamination Sources in an Embayment in Hempstead Harbor on Long Island, New York"},{"id":406049,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20215141","text":"Scientific Investigations Report 2021–5141","linkHelpText":"- Using Microbial Source Tracking To Identify Contamination Sources in Port Jefferson Harbor, Setauket Harbor, and Conscience Bay on Long Island, New York"},{"id":406048,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20225007","text":"Scientific Investigations Report 2022–5007","linkHelpText":"- Using Microbial Source Tracking To Identify Fecal Contamination Sources in Patchogue and Bellport Bays on Long Island, New York"},{"id":406047,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20225028","text":"Scientific Investigations Report 2022–5028","linkHelpText":"- Using Microbial Source Tracking To Identify Fecal Contamination Sources in Sag Harbor on Long Island, New York"},{"id":406046,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20225038","text":"Scientific Investigations Report 2022–5038","linkHelpText":"- Using Microbial Source Tracking To Identify Fecal Contamination Sources in Lake Montauk on Long Island, New York"},{"id":406045,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20225057","text":"Scientific Investigations Report 2022–5057","linkHelpText":"- Using Microbial Source Tracking To Identify Fecal Contamination Sources in Great South Bay on Long Island, New York"},{"id":406044,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20225082","text":"Scientific Investigations Report 2022–5082","linkHelpText":"- Using Microbial Source Tracking To Identify Fecal Contamination Sources in South Oyster Bay on Long Island, New York"},{"id":385932,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5033/sir20215033.pdf","text":"Report","size":"1.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5033"},{"id":385931,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5033/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n            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-73.795166015625,\n              40.82212357516945\n            ],\n            [\n              -73.94073486328125,\n              40.749337730454826\n            ],\n            [\n              -74.0313720703125,\n              40.65980593837852\n            ],\n            [\n              -74.0423583984375,\n              40.60978237983301\n            ],\n            [\n              -74.0313720703125,\n              40.58684239087908\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_ny@usgs.gov\" data-mce-href=\"mailto:dc_ny@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/ny-water\" data-mce-href=\"https://www.usgs.gov/centers/ny-water\">New York Water Science Center</a><br>U.S. Geological Survey<br>425 Jordan Road<br>Troy, NY 12180-8349</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Site Descriptions</li><li>Approach and Methods</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2021-05-27","noUsgsAuthors":false,"publicationDate":"2021-05-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Tagliaferri, Tristen N. 0000-0001-7408-7899 ttagliaferri@usgs.gov","orcid":"https://orcid.org/0000-0001-7408-7899","contributorId":5138,"corporation":false,"usgs":true,"family":"Tagliaferri","given":"Tristen","email":"ttagliaferri@usgs.gov","middleInitial":"N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816437,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fisher, Shawn C. 0000-0001-6324-1061 scfisher@usgs.gov","orcid":"https://orcid.org/0000-0001-6324-1061","contributorId":4843,"corporation":false,"usgs":true,"family":"Fisher","given":"Shawn","email":"scfisher@usgs.gov","middleInitial":"C.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816438,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kephart, Christopher M. 0000-0002-3369-5596 ckephart@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-5596","contributorId":1932,"corporation":false,"usgs":true,"family":"Kephart","given":"Christopher","email":"ckephart@usgs.gov","middleInitial":"M.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816439,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cheung, Natalie 0000-0003-2987-0440 ncheung@usgs.gov","orcid":"https://orcid.org/0000-0003-2987-0440","contributorId":258429,"corporation":false,"usgs":true,"family":"Cheung","given":"Natalie","email":"ncheung@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816441,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, Ariel P. 0000-0002-0792-5204","orcid":"https://orcid.org/0000-0002-0792-5204","contributorId":219992,"corporation":false,"usgs":true,"family":"Reed","given":"Ariel","email":"","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816442,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Welk, Robert J. 0000-0003-0852-5584 rwelk@usgs.gov","orcid":"https://orcid.org/0000-0003-0852-5584","contributorId":194109,"corporation":false,"usgs":true,"family":"Welk","given":"Robert","email":"rwelk@usgs.gov","middleInitial":"J.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816440,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70229737,"text":"70229737 - 2021 - Improving short-term recruitment forecasts for coho salmon using a spatiotemporal integrated population model","interactions":[],"lastModifiedDate":"2022-03-16T16:11:47.02032","indexId":"70229737","displayToPublicDate":"2021-05-27T11:06:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Improving short-term recruitment forecasts for coho salmon using a spatiotemporal integrated population model","docAbstract":"<p><span>Fishery managers often rely on forecasts of future population abundance to set allowable harvest quotas or exploitation rates. While there has been substantial research devoted to identifying environmental factors that can predict recruitment for individual populations, such correlations often degrade over time, thereby limiting their utility for management. Conversely, examining multiple populations at once to detect shared, spatially structured patterns can offer insights into their recruitment dynamics that are advantageous for forecasting. Here, we develop a population dynamics model for natural origin coho salmon (</span><span><i>Oncorhynchus kisutch</i></span><span>) stocks in Washington State that leverages spatial and temporal&nbsp;autocorrelation&nbsp;in marine survival to improve one-year-ahead forecasts of adult returns. Executed in a Bayesian hierarchical integrated modelling framework, our spatiotemporal approach incorporates multiple data types and shares information among stocks to estimate key biological parameters that are informative for forecasting. Retrospective evaluation of one-year-ahead forecast skill indicated that the spatiotemporal integrated population model (ST-IPM) outperformed existing forecasts of Washington State coho salmon returns by 25–38 % on average. Moreover, the ST-IPM estimates parameters that were previously non-identifiable for many stocks, and propagates uncertainty from multiple contributing data sources into model forecasts. Our results add to a growing body of work demonstrating the utility of spatiotemporal and integrated approaches for modelling population dynamics, and the framework developed here has broad applications to the assessment and management of coho salmon in Washington State and elsewhere throughout their range.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2021.106014","usgsCitation":"DeFilippo, L.B., Buehrens, T., Scheuerell, M.D., Kendall, N.W., and Schindler, D.E., 2021, Improving short-term recruitment forecasts for coho salmon using a spatiotemporal integrated population model: Fisheries Research, v. 242, 106014, 12 p., https://doi.org/10.1016/j.fishres.2021.106014.","productDescription":"106014, 12 p.","ipdsId":"IP-129173","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":452108,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Seattle","active":true,"usgs":true}],"preferred":true,"id":838140,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kendall, Neala W.","contributorId":288624,"corporation":false,"usgs":false,"family":"Kendall","given":"Neala","email":"","middleInitial":"W.","affiliations":[{"id":61815,"text":"wafg","active":true,"usgs":false}],"preferred":false,"id":838143,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schindler, Daniel E.","contributorId":288625,"corporation":false,"usgs":false,"family":"Schindler","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":838144,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70220879,"text":"70220879 - 2021 - Appendix C: Central sands lakes study technical report: Modeling documentation","interactions":[],"lastModifiedDate":"2021-05-27T14:04:05.646141","indexId":"70220879","displayToPublicDate":"2021-05-27T08:51:14","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":8761,"text":"Wisconsin DNR Technical Report","active":true,"publicationSubtype":{"id":2}},"title":"Appendix C: Central sands lakes study technical report: Modeling documentation","docAbstract":"<p>This report provides the necessary documentation of the numerical models developed for the Central Sands Lake study in central Wisconsin and will be included as a technical appendix in the report to the Wisconsin State Legislature by the Wisconsin Department of Natural Resources (WDNR) in response to 2017 Wisconsin Act 10. This legislation directed WDNR to determine whether existing and potential groundwater withdrawals are causing or are likely to cause significant reduction of mean seasonal water levels at Pleasant Lake, Long Lake, and Plainfield Lake (s. 281.34(7m)(2)(b), Wis. Stats.) in Waushara County, Wisconsin. To evaluate the potential hydrologic connection between groundwater withdrawals and the nearby study lakes, hydrologic models were created that focused on the lakes of interest and yet were large enough to cover a broad enough region to extend to the major hydrologic boundaries of the natural flow system. The areas near the lakes require finer-scale grid discretization (or spacing) to better represent the lakes and streams in the model, but also need to cover a large enough area to include the groundwater withdrawal locations that have the potential to cause reduction in water levels in the lakes. To accomplish these goals, three groundwater models were created: a regional model extending to major hydrologic boundaries; and two inset models, inheriting boundaries from the regional model but focused near the lakes. Each of the inset models, in turn, included a detailed area close to the lakes surrounded by an area at the same spatial scale as the regional model (Figure 1). </p><p>To support WDNR in evaluating the connection between groundwater withdrawals and lake levels, a representative time period was required over which to compare land use with and without irrigated agriculture and for WDNR to evaluate potential lake stage and flux changes related to irrigated agriculture. WDNR chose the climate period of 1981-2018 to be representative of a typical period and provided two land use scenarios—one with no irrigated agriculture and one with assumed crop rotations similar to current conditions—to simulate with groundwater models to, then, compare lake responses with. As a result, simulations over this climate record are not intended to recreate the history of 1981-2018 because land use changed over that time. These runs are, instead, intended to provide a basis on which to compare land use with and without irrigation-related groundwater withdrawals based on the current arrangement of land use and a varied climatic record. Groundwater withdrawals focused on irrigated-agriculture-related water use because greater than 95% of groundwater withdrawal in the two inset models around the study lakes is for irrigated agriculture water use. </p><p>The period of 2012-2018 was used for parameter estimation (synonymously referred to as “history matching”) for the groundwater models. This time period was chosen because it includes the most complete water use records to simulate groundwater withdrawals. History matching was performed using groundwater elevations, lake stages, and streamflow observations over the 2012-2018 time period and processed observations derived from those raw data. </p><p>Climatic data were incorporated into the model using a soil-water balance approach. A soil water balance model was constructed at the scale of the regional groundwater model to both calculate recharge based on land use and climate, and in the long-term climate-period runs, to estimate water use required by irrigated agriculture to apply as well boundary conditions in the groundwater model in the absence of reported water use values over that period.</p>","language":"English","publisher":"Wisconsin Department of Natural Resources","usgsCitation":"Fienen, M., Haserodt, M.J., Leaf, A.T., and Westenbroek, S., 2021, Appendix C: Central sands lakes study technical report: Modeling documentation: Wisconsin DNR Technical Report, ix, 137 p.","productDescription":"ix, 137 p.","ipdsId":"IP-127829","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":386002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385990,"type":{"id":15,"text":"Index Page"},"url":"https://dnr.wisconsin.gov/topic/Wells/HighCap/CSLStudy.html"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Central Sands region","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -89.78851318359375,\n              43.58834891179792\n            ],\n            [\n              -89.29962158203125,\n              43.57641143300888\n            ],\n            [\n              -89.219970703125,\n              43.75919263886012\n            ],\n            [\n              -89.54132080078125,\n              44.471031231561845\n            ],\n            [\n              -89.7967529296875,\n              44.41808794374846\n            ],\n            [\n              -89.85443115234375,\n              44.33367180085156\n            ],\n            [\n              -89.98901367187499,\n              44.11125397357155\n            ],\n            [\n              -90.01373291015625,\n              44.03232064275081\n            ],\n            [\n              -89.96978759765625,\n              43.878097874251736\n            ],\n            [\n              -89.8187255859375,\n              43.71156424665851\n            ],\n            [\n              -89.78851318359375,\n              43.58834891179792\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haserodt, Megan J. 0000-0002-8304-090X mhaserodt@usgs.gov","orcid":"https://orcid.org/0000-0002-8304-090X","contributorId":174791,"corporation":false,"usgs":true,"family":"Haserodt","given":"Megan","email":"mhaserodt@usgs.gov","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leaf, Andrew T. 0000-0001-8784-4924 aleaf@usgs.gov","orcid":"https://orcid.org/0000-0001-8784-4924","contributorId":5156,"corporation":false,"usgs":true,"family":"Leaf","given":"Andrew","email":"aleaf@usgs.gov","middleInitial":"T.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Westenbroek, Stephen, M. 0000-0002-6284-8643","orcid":"https://orcid.org/0000-0002-6284-8643","contributorId":206429,"corporation":false,"usgs":true,"family":"Westenbroek","given":"Stephen, M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816550,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70230038,"text":"70230038 - 2021 - Assessing the uncertainties in climatic estimates based on vegetation assemblages: Examples from modern vegetation assemblages in the American Southwest","interactions":[],"lastModifiedDate":"2022-03-29T18:57:47.258287","indexId":"70230038","displayToPublicDate":"2021-05-27T08:35:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the uncertainties in climatic estimates based on vegetation assemblages: Examples from modern vegetation assemblages in the American Southwest","docAbstract":"<p id=\"abspara0010\"><span>Assemblages of fossil plant remains have been widely used to reconstruct past climatic conditions, usually through the application of methods that involve either finding vegetation analogues on the modern landscape (and using the modern associated climatic values as the basis for an estimate) or using the modern climatic ranges of individual taxa in an assemblage to determine the range of a given climate variable that would allow these plant taxa to live together. Although these approaches are relatively straightforward, it is difficult to assess the uncertainties associated with each approach, particularly in regard to their application to plant macrofossil assemblages. To explore the uncertainty that may arise from inaccuracy and imprecision in climate reconstructions and from ecological considerations we used variants of both approaches to estimate climate from two data sets of modern vegetation assemblages from the southwestern United States: (1) 1752 gridded “virtual plant assemblages” based on plant range maps that provide uniform spatial coverage of the presence or absence of major&nbsp;woody plant&nbsp;taxa across the study area; and (2) 43 modern packrat (</span><i>Neotoma</i><span>&nbsp;</span>spp.) midden presence-absence assemblages that are similar to fossil midden assemblages. By comparing observed and estimated climate values, we evaluated the quality of the climate estimates, identified sources of uncertainty, and characterized the nature and magnitude of the effects of these uncertainties on the climate estimates.</p><p id=\"abspara0015\">Uncertainties in estimating climate from vegetation assemblages arise because any given plant taxon (or assemblage) must have the resiliency to survive a range of climatic variability, and because of the strong intercorrelations among climatic variables in the modern climate data. Additional sources of uncertainty in climate estimates from plant assemblages include: (1) the modern climate and plant distribution data that are selected as the basis for estimation; (2) the particular quantitative approach that is used to estimate climate; (3) the sufficiency of the number of taxa in the analysis for providing an unbiased representation of the vegetation community as it existed for each time period in the analysis; and, (4) the location of the assemblage on the climatic and<span>&nbsp;</span>environmental gradients<span>&nbsp;</span>in the calibration data set for each climate variable under consideration.</p><p id=\"abspara0020\">We conclude that vegetation assemblages can provide valid and reproducible estimates of climatic variables and that the primary trends and mapped patterns in the observed climate data can be reconstructed from such estimates. However, many factors may affect the quality of an estimate from a given plant assemblage, including aspects of data selection, data adequacy, methodologies, and the location of the assemblage site relative to gradients in the base climate data. It is particularly difficult to accurately estimate extreme values in the observed climate data, because estimated values from either end of an observed climate gradient necessarily “move toward the middle” of the gradient. In addition, the interval chosen to represent modern climate (here we used 1961 to 1990) may have a large impact on the size of the estimated difference between modern and past climate at a given site.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2021.106880","usgsCitation":"Thompson, R.S., Anderson, K., Pelltier, R.T., Strickland, L.E., Shafer, S., and Bartlein, P.J., 2021, Assessing the uncertainties in climatic estimates based on vegetation assemblages: Examples from modern vegetation assemblages in the American Southwest: Quaternary Science Reviews, v. 262, 106880, 27 p., https://doi.org/10.1016/j.quascirev.2021.106880.","productDescription":"106880, 27 p.","ipdsId":"IP-100351","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":436335,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CKCP22","text":"USGS data release","linkHelpText":"Data release for Assessing the Uncertainties in Climatic Estimates Based on Vegetation Assemblages: Examples from Modern Vegetation Assemblages in the American Southwest"},{"id":397596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Nevada, New Mexico, Texas, Utah","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -102.3046875,\n              29.84064389983441\n            ],\n            [\n              -104.765625,\n              32.32427558887655\n            ],\n            [\n              -106.435546875,\n              35.71083783530009\n            ],\n            [\n              -106.787109375,\n              37.75334401310656\n            ],\n           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rthompson@usgs.gov","orcid":"https://orcid.org/0000-0001-9287-2954","contributorId":891,"corporation":false,"usgs":true,"family":"Thompson","given":"Robert","email":"rthompson@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838827,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Katherine H 0000-0003-2677-6109","orcid":"https://orcid.org/0000-0003-2677-6109","contributorId":289266,"corporation":false,"usgs":false,"family":"Anderson","given":"Katherine H","affiliations":[{"id":62090,"text":"Institute of Arctic and Alpine Research, University of Colorado","active":true,"usgs":false}],"preferred":false,"id":838828,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pelltier, Richard T. 0000-0001-8322-7961 rtpelltier@usgs.gov","orcid":"https://orcid.org/0000-0001-8322-7961","contributorId":4683,"corporation":false,"usgs":true,"family":"Pelltier","given":"Richard","email":"rtpelltier@usgs.gov","middleInitial":"T.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838829,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Strickland, Laura E. 0000-0002-1958-7273 lstrickland@usgs.gov","orcid":"https://orcid.org/0000-0002-1958-7273","contributorId":4682,"corporation":false,"usgs":true,"family":"Strickland","given":"Laura","email":"lstrickland@usgs.gov","middleInitial":"E.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838830,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shafer, Sarah 0000-0003-3739-2637 sshafer@usgs.gov","orcid":"https://orcid.org/0000-0003-3739-2637","contributorId":149866,"corporation":false,"usgs":true,"family":"Shafer","given":"Sarah","email":"sshafer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838831,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bartlein, Patrick J. 0000-0001-7657-5685","orcid":"https://orcid.org/0000-0001-7657-5685","contributorId":211587,"corporation":false,"usgs":false,"family":"Bartlein","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":33397,"text":"U of Oregon","active":true,"usgs":false}],"preferred":false,"id":838832,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70223137,"text":"70223137 - 2021 - Impact of SST and surface waves on Hurricane Florence (2018): A coupled modeling investigation","interactions":[],"lastModifiedDate":"2021-09-21T13:11:40.385044","indexId":"70223137","displayToPublicDate":"2021-05-27T07:58:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3735,"text":"Weather and Forecasting","active":true,"publicationSubtype":{"id":10}},"title":"Impact of SST and surface waves on Hurricane Florence (2018): A coupled modeling investigation","docAbstract":"<div class=\"component component-content-item component-content-summary abstract_or_excerpt\"><div class=\"content-box box border-bottom border-bottom-inherit border-bottom-inherit no-padding no-header vertical-margin-bottom null\"><div class=\"content-box-body null\"><p>Hurricane Florence (2018) devastated the coastal communities of the Carolinas through heavy rainfall that resulted in massive flooding. Florence was characterized by an abrupt reduction in intensity (Saffir-Simpson Category 4 to Category 1) just prior to landfall and synoptic-scale interactions that stalled the storm over the Carolinas for several days. We conducted a series of numerical modeling experiments in coupled and uncoupled configurations to examine the impact of sea surface temperature (SST) and ocean waves on storm characteristics. In addition to experiments using a fully coupled atmosphere-ocean-wave model, we introduced the capability of the atmospheric model to modulate wind stress and surface fluxes by oceanwaves through data from an uncoupled wave model. We examined these experiments by comparing track, intensity, strength, SST, storm structure, wave height, surface roughness, heat fluxes, and precipitation in order to determine the impacts of resolving ocean conditions with varying degrees of coupling. We found differences in the storm’s intensity and strength, with the best correlation coefficient of intensity (r=0.89) and strength (r=0.95) coming from the fully-coupled simulations. Further analysis into surface roughness parameterizations added to the atmospheric model revealed differences in the spatial distribution and magnitude of the largest roughness lengths. Adding ocean andwave features to the model further modified the fluxes due to more realistic cooling beneath the stormwhich in turn modified the precipitation field. Our experiments highlight significant differences in how air-sea processes impact hurricane modeling. The storm characteristics of track, intensity, strength, and precipitation at landfall are crucial to predictability and forecasting of future landfalling hurricanes.</p></div></div></div>","language":"English","publisher":"American Meteorological Society","doi":"10.1175/WAF-D-20-0171.1","usgsCitation":"Zambon, J., He, R., Warner, J.C., and Hegermiller, C., 2021, Impact of SST and surface waves on Hurricane Florence (2018): A coupled modeling investigation: Weather and Forecasting, v. 36, no. 5, p. 1713-1734, https://doi.org/10.1175/WAF-D-20-0171.1.","productDescription":"22 p.","startPage":"1713","endPage":"1734","ipdsId":"IP-131401","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":452119,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/waf-d-20-0171.1","text":"Publisher Index Page"},{"id":387898,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zambon, Joseph","contributorId":264200,"corporation":false,"usgs":false,"family":"Zambon","given":"Joseph","affiliations":[{"id":54401,"text":"Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina","active":true,"usgs":false}],"preferred":false,"id":821095,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"He, Ruoying 0000-0001-6158-2292","orcid":"https://orcid.org/0000-0001-6158-2292","contributorId":202189,"corporation":false,"usgs":false,"family":"He","given":"Ruoying","email":"","affiliations":[],"preferred":false,"id":821096,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":258015,"corporation":false,"usgs":true,"family":"Warner","given":"John","email":"jcwarner@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":821097,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hegermiller, Christie 0000-0002-6383-7508 chegermiller@usgs.gov","orcid":"https://orcid.org/0000-0002-6383-7508","contributorId":149010,"corporation":false,"usgs":true,"family":"Hegermiller","given":"Christie","email":"chegermiller@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":821098,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70222119,"text":"70222119 - 2021 - N supply mediates the radiative balance of N2O emissions and CO2 sequestration driven by N-fixing vs. non-fixing trees","interactions":[],"lastModifiedDate":"2021-08-17T15:11:39.429132","indexId":"70222119","displayToPublicDate":"2021-05-27T06:47:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"N supply mediates the radiative balance of N<sub>2</sub>O emissions and CO<sub>2</sub> sequestration driven by N-fixing vs. non-fixing trees","title":"N supply mediates the radiative balance of N2O emissions and CO2 sequestration driven by N-fixing vs. non-fixing trees","docAbstract":"<p><span>Forests are a significant CO</span><sub>2</sub><span>&nbsp;sink. However, CO</span><sub>2</sub><span>&nbsp;sequestration in forests is radiatively offset by emissions of nitrous oxide (N</span><sub>2</sub><span>O), a potent greenhouse gas, from forest soils. Reforestation, an important strategy for mitigating climate change, has focused on maximizing CO</span><sub>2</sub><span>&nbsp;sequestration in plant biomass without integrating N</span><sub>2</sub><span>O emissions from soils. Although nitrogen (N)-fixing trees are often recommended for reforestation because of their rapid growth on N-poor soil, they can stimulate significant N</span><sub>2</sub><span>O emissions from soils. Here, we first used a field experiment to show that a N-fixing tree (</span><i>Robinia pseudoacacia</i><span>) initially mitigated climate change more than a non-fixing tree (</span><i>Betula nigra</i><span>). We then used our field data to parameterize a theoretical model to investigate these effects over time. Under lower N supply, N-fixers continued to mitigate climate change more than non-fixers by overcoming N limitation of plant growth. However, under higher N supply, N-fixers ultimately mitigated climate change less than non-fixers by enriching soil N and stimulating N</span><sub>2</sub><span>O emissions from soils. These results have implications for reforestation, suggesting that N-fixing trees are more effective at mitigating climate change at lower N supply, whereas non-fixing trees are more effective at mitigating climate change at higher N supply.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/ecy.3414","usgsCitation":"Kou-Giesbrecht, S., Funk, J.L., Perakis, S.S., Wolf, A.A., and Menge, D., 2021, N supply mediates the radiative balance of N2O emissions and CO2 sequestration driven by N-fixing vs. non-fixing trees: Ecology, v. 102, no. 8, e03414, 8 p., https://doi.org/10.1002/ecy.3414.","productDescription":"e03414, 8 p.","ipdsId":"IP-123004","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":452122,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.3414","text":"Publisher Index Page"},{"id":387284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Black Rock Forest","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -74.0584945678711,\n              41.3791271230665\n            ],\n            [\n              -73.99017333984375,\n              41.3791271230665\n            ],\n            [\n              -73.99017333984375,\n              41.41737138589576\n            ],\n            [\n              -74.0584945678711,\n              41.41737138589576\n            ],\n            [\n              -74.0584945678711,\n              41.3791271230665\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"102","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-07-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Kou-Giesbrecht, Sian 0000-0002-4086-0561","orcid":"https://orcid.org/0000-0002-4086-0561","contributorId":261258,"corporation":false,"usgs":false,"family":"Kou-Giesbrecht","given":"Sian","email":"","affiliations":[{"id":52786,"text":"Columbia U","active":true,"usgs":false}],"preferred":false,"id":819602,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Funk, Jennifer L.","contributorId":260668,"corporation":false,"usgs":false,"family":"Funk","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":819603,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perakis, Steven S. 0000-0003-0703-9314 sperakis@usgs.gov","orcid":"https://orcid.org/0000-0003-0703-9314","contributorId":145528,"corporation":false,"usgs":true,"family":"Perakis","given":"Steven","email":"sperakis@usgs.gov","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":819604,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wolf, Amelia A.","contributorId":190685,"corporation":false,"usgs":false,"family":"Wolf","given":"Amelia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":819605,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Menge, Duncan 0000-0003-4736-9844","orcid":"https://orcid.org/0000-0003-4736-9844","contributorId":241126,"corporation":false,"usgs":false,"family":"Menge","given":"Duncan","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":819606,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70220727,"text":"sir20215028 - 2021 - Flow characteristics and salinity patterns in tidal rivers within the northern Ten Thousand Islands, southwest Florida, water years 2007–19","interactions":[],"lastModifiedDate":"2021-05-27T11:52:45.64841","indexId":"sir20215028","displayToPublicDate":"2021-05-26T13:37:00","publicationYear":"2021","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":"2021-5028","displayTitle":"Flow Characteristics and Salinity Patterns in Tidal Rivers Within the Northern Ten Thousand Islands, Southwest Florida, Water Years 2007–19","title":"Flow characteristics and salinity patterns in tidal rivers within the northern Ten Thousand Islands, southwest Florida, water years 2007–19","docAbstract":"<p>Freshwater flow to the Ten Thousand Islands (TTI) estuary has been altered by the construction of the Tamiami Trail and construction of features in the now defunct Southern Golden Gate Estates development. This development included four associated canals that combine into the Faka Union Canal, which discharges into the TTI estuary. The Picayune Strand Restoration Project (PSRP) was initiated in 2007 to improve freshwater delivery to the TTI estuary by removing hundreds of miles of roads, emplacing hundreds of canal plugs, removing exotic vegetation, and constructing three pump stations. Quantifying the tributary flows and salinity patterns prior to, during, and after the restoration is essential to assessing the effectiveness of upstream restoration efforts. The U.S. Geological Survey, in cooperation with U.S. Army Corps of Engineers, initiated an ongoing study in 2006 to assess flow and salinity patterns in the TTI estuary. This is the second report by the U.S. Geological Survey describing flow characteristics and salinity patterns in the TTI area as part of the PSRP. This report describes flow characteristics and salinity patterns for the monitoring stations at Faka Union River, Pumpkin River, and East River and includes an assessment of salinity data from the Faka Union Boundary and Blackwater River water-quality stations for water years 2007–19. A water year is defined as the 12-month period from October 1 for any given year to September 30 of the following year.</p><p>Annual and monthly variations in flow and salinity are often related to variations in rainfall with high and low annual flows (and below average and above average salinities) typically occurring during years with high and low annual rainfall, respectively. Monthly flows typically begin increasing in June and peak in September. Over the study period, positive trends in rainfall-adjusted monthly flow were detected at Faka Union River and East River, whereas no significant trend in flow was detected at Pumpkin River. Faka Union River is the largest contributor of freshwater to the TTI estuary, providing over 80 percent of the annual freshwater inflow to the estuary. The Faka Union Canal is expected to be the largest contributor of freshwater because until the PSRP is completed, the Faka Union Canal receives substantial drainage from multiple canals, which is not the case for Pumpkin and East Rivers. East River was the second largest contributor, followed by Pumpkin River. East River is downstream of the Fakahatchee Stand, which is a larger contributing area than the current contributing area for Pumpkin River. Monthly mean salinities were lowest at Faka Union River and East River, indicating that they received a greater amount of freshwater than the stations to the west. Negative trends in rainfall-adjusted salinity monthly means were observed at all monitoring stations during the study period. Increased trends in flow and decreased trends in salinity are attributed to increases in flow from upstream canals.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215028","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Booth, A.C., and Knight, T.M., 2021, Flow characteristics and salinity patterns in tidal rivers within the northern Ten Thousand Islands, southwest Florida, water years 2007–19: U.S. Geological Survey Scientific Investigations Report 2021–5028, 21 p., https://doi.org/10.3133/sir20215028.","productDescription":"vii, 21 p.","numberOfPages":"34","ipdsId":"IP-122818","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":385935,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5028/images"},{"id":385934,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5028/sir20215028.pdf","text":"Report","size":"4.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5028"},{"id":385933,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5028/coverthb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Northern Ten Thousand Islands","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -81.67304992675781,\n              25.852426562716428\n            ],\n            [\n              -81.47941589355469,\n              25.852426562716428\n            ],\n            [\n              -81.47941589355469,\n              25.972243398901558\n            ],\n            [\n              -81.67304992675781,\n              25.972243398901558\n            ],\n            [\n              -81.67304992675781,\n              25.852426562716428\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www2.usgs.gov/water/caribbeanflorida/index.html\" href=\"https://www2.usgs.gov/water/caribbeanflorida/index.html\">Caribbean-Florida Water Science Center (CFWSC)</a> <br><a data-mce-href=\"https://www.usgs.gov/\" href=\"https://www.usgs.gov/\">U.S. Geological Survey</a> <br>4446 Pet Lane, Suite 108 <br>Lutz, FL 33559</p><p><a href=\"../contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods of Investigation</li><li>Annual and Monthly Variability and Trends in Rainfall</li><li>Flow Characteristics and Salinity Patterns in the Ten Thousand Islands</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2021-05-26","noUsgsAuthors":false,"publicationDate":"2021-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Booth, Amanda C. 0000-0002-2666-2366 acbooth@usgs.gov","orcid":"https://orcid.org/0000-0002-2666-2366","contributorId":258448,"corporation":false,"usgs":true,"family":"Booth","given":"Amanda C.","email":"acbooth@usgs.gov","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816443,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knight, Travis M. 0000-0002-0472-8141 tknight@usgs.gov","orcid":"https://orcid.org/0000-0002-0472-8141","contributorId":5433,"corporation":false,"usgs":true,"family":"Knight","given":"Travis","email":"tknight@usgs.gov","middleInitial":"M.","affiliations":[{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true}],"preferred":true,"id":816444,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70221849,"text":"70221849 - 2021 - Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales","interactions":[],"lastModifiedDate":"2021-07-12T17:45:19.948532","indexId":"70221849","displayToPublicDate":"2021-05-26T12:41:34","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales","docAbstract":"<p><span>Cyanobacterial blooms can have negative effects on human health and local ecosystems. Field monitoring of cyanobacterial blooms can be costly, but&nbsp;<a class=\"topic-link\" title=\"Learn more about satellite remote sensing from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/satellite-remote-sensing\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/satellite-remote-sensing\">satellite remote sensing</a>&nbsp;has shown utility for more efficient spatial and temporal monitoring across the United States. Here, satellite imagery was used to assess the annual frequency of surface cyanobacterial blooms, defined for each satellite pixel as the percentage of images for that pixel throughout the year exhibiting detectable&nbsp;</span><a class=\"topic-link\" title=\"Learn more about cyanobacteria from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/cyanobacteria\" data-mce-href=\"https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/cyanobacteria\">cyanobacteria</a><span>. Cyanobacterial frequency was assessed across 2,196 large lakes in 46 states across the continental United States (CONUS) using imagery from the European Space Agency’s Ocean and Land Colour Instrument for the years 2017 through 2019. In 2019, across all satellite pixels considered, annual bloom frequency had a median value of 4% and a maximum value of 100%, the latter indicating that for those satellite pixels, a cyanobacterial bloom was detected by the&nbsp;<a class=\"topic-link\" title=\"Learn more about satellite sensor from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/satellite-sensor\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/satellite-sensor\">satellite sensor</a>&nbsp;for every satellite image considered. In addition to annual pixel-scale cyanobacterial frequency, results were summarized at the lake- and state-scales by averaging annual pixel-scale results across each lake and state. For 2019, average annual lake-scale frequencies also had a maximum value of 100%, and Oregon and Ohio had the highest average annual state-scale frequencies at 65% and 52%. Pixel-scale frequency results can assist in identifying portions of a lake that are more prone to cyanobacterial blooms, while lake- and state-scale frequency results can assist in the prioritization of sampling resources and mitigation efforts. Satellite imagery is limited by the presence of snow and ice, as imagery collected in these conditions are quality flagged and discarded. Thus, annual bloom frequencies within nine climate regions were investigated to determine whether missing data biased results in climate regions more prone to snow and ice, given that their annual summaries would be weighted toward the summer months when cyanobacterial blooms tend to occur. Results were unbiased by the time period selected in most climate regions, but a large bias was observed for the Northwest Rockies and Plains climate region. Moderate biases were observed for the Ohio Valley and the Southeast climate regions. Finally, a clustering analysis was used to identify areas of high and low cyanobacterial frequency across CONUS based on average annual lake-scale cyanobacterial frequencies for 2019. Several clusters were identified that transcended state, watershed, and eco-regional boundaries. Combined with additional data, results from the clustering analysis may offer insight regarding large-scale drivers of cyanobacterial blooms.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.107822","usgsCitation":"Coffer, M.M., Schaeffer, B., Salls, W.B., Urquhart, E., Loftin, K.A., Stumpf, R.P., Werdell, P.J., and Darling, J., 2021, Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales: Ecological Indicators, v. 128, 107822, 12 p., https://doi.org/10.1016/j.ecolind.2021.107822.","productDescription":"107822, 12 p.","ipdsId":"IP-126524","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":452125,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.107822","text":"Publisher Index Page"},{"id":387135,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"128","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coffer, Megan M. 0000-0003-3188-4729","orcid":"https://orcid.org/0000-0003-3188-4729","contributorId":260857,"corporation":false,"usgs":false,"family":"Coffer","given":"Megan","email":"","middleInitial":"M.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818980,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schaeffer, Blake 0000-0001-9794-3977","orcid":"https://orcid.org/0000-0001-9794-3977","contributorId":245603,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Blake","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818981,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Salls, Wilson B. 0000-0001-7505-0828","orcid":"https://orcid.org/0000-0001-7505-0828","contributorId":260858,"corporation":false,"usgs":false,"family":"Salls","given":"Wilson","email":"","middleInitial":"B.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818982,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Urquhart, Erin 0000-0001-7141-9499","orcid":"https://orcid.org/0000-0001-7141-9499","contributorId":260859,"corporation":false,"usgs":false,"family":"Urquhart","given":"Erin","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":818983,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loftin, Keith A. 0000-0001-5291-876X","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":221964,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":818984,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stumpf, Richard P. 0000-0001-5531-6860","orcid":"https://orcid.org/0000-0001-5531-6860","contributorId":222357,"corporation":false,"usgs":false,"family":"Stumpf","given":"Richard","email":"","middleInitial":"P.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":818985,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Werdell, P. Jeremy 0000-0002-3592-0152","orcid":"https://orcid.org/0000-0002-3592-0152","contributorId":222358,"corporation":false,"usgs":false,"family":"Werdell","given":"P.","email":"","middleInitial":"Jeremy","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":818986,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Darling, John A. 0000-0002-4776-9533","orcid":"https://orcid.org/0000-0002-4776-9533","contributorId":260860,"corporation":false,"usgs":false,"family":"Darling","given":"John A.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818987,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70220698,"text":"ofr20211008 - 2021 - Initial estimates of net infiltration and irrigation from a soil-water-balance model of the Mississippi Embayment Regional Aquifer Study Area","interactions":[],"lastModifiedDate":"2021-05-27T11:45:45.293897","indexId":"ofr20211008","displayToPublicDate":"2021-05-26T08:07:50","publicationYear":"2021","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":"2021-1008","displayTitle":"Initial Estimates of Net Infiltration and Irrigation from a Soil-Water-Balance Model of the Mississippi Embayment Regional Aquifer Study Area","title":"Initial estimates of net infiltration and irrigation from a soil-water-balance model of the Mississippi Embayment Regional Aquifer Study Area","docAbstract":"<p>The Mississippi embayment encompasses about 100,000 square miles and covers parts of eight States. In 2016, the U.S. Geological Survey began updating previous work for a part of the embayment known as the Mississippi Alluvial Plain to support informed water use and agricultural policy in the region. Groundwater, water use, economic, and other related models are being combined with field surveys and observations to create a quantitative framework for evaluating regional groundwater withdrawals and their effects on long-term water availability in the Mississippi Alluvial Plain.</p><p>As part of this effort, the U.S. Geological Survey’s Soil-Water-Balance code (version 2.0) is being used to model potential groundwater recharge and irrigation water use, as necessary inputs to the long-term groundwater modeling efforts. The Soil-Water-Balance code is designed to estimate the distribution and timing of net infiltration leaving the root zone. Soil-Water-Balance makes use of gridded datasets of elevation, soils, land use (including specific crop types), and daily weather datasets to calculate other components of the root-zone water balance, including soil moisture, reference, actual evapotranspiration, snowfall, snowmelt, and canopy interception. Parameters on plant height and growing-season water needs are used to estimate crop-water demand and potential irrigation water use.</p><p>This report documents the initial construction, calibration, and application of a Soil-Water-Balance model of the Mississippi Embayment Regional Aquifer Study area for simulations running from 1915 to 2017. Further refinements of the model calibration for an expanded model area are planned.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211008","programNote":"Water Availability and Use Science Program","usgsCitation":"Westenbroek, S.M., Nielsen, M.G., and Ladd, D.E., 2021, Initial estimates of net infiltration and irrigation from a soil-water-balance model of the Mississippi Embayment Regional Aquifer Study Area: U.S. Geological Survey Open-File Report 2021-1008, 29 p., https://doi.org/10.3133/ofr20211008.","productDescription":"Report: v, 29 p.; 2 Data Releases","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-108908","costCenters":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":385921,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U484X5","text":"USGS data release","description":"USGS data release","linkHelpText":"OFR 2021–1008 MODEL OUTPUT—Soil-Water-Balance net infiltration and irrigation water use output datasets for the Mississippi Embayment Regional Aquifer System, 1915 to 2018"},{"id":385920,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98PBR8O","text":"USGS data release","description":"USGS data release","linkHelpText":"OFR 2021–1008 MODEL ARCHIVE—Soil-Water-Balance model developed to simulate net infiltration and irrigation water use for the Mississippi Embayment Regional Aquifer System, 1915 to 2018"},{"id":385919,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1008/ofr20211008.pdf","text":"Report","size":"11.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1008"},{"id":385918,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1008/coverthb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi Embayment Regional Aquifer Study Area","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -89.6044921875,\n              37.16031654673677\n            ],\n            [\n              -90.4833984375,\n              36.527294814546245\n            ],\n            [\n              -91.2744140625,\n              35.71083783530009\n            ],\n            [\n              -91.7138671875,\n              35.31736632923788\n            ],\n            [\n              -92.4169921875,\n              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       [\n              -87.5390625,\n              31.80289258670676\n            ],\n            [\n              -88.5498046875,\n              32.69486597787505\n            ],\n            [\n              -88.505859375,\n              33.8339199536547\n            ],\n            [\n              -88.5498046875,\n              34.59704151614417\n            ],\n            [\n              -88.3740234375,\n              35.60371874069731\n            ],\n            [\n              -88.5498046875,\n              36.94989178681327\n            ],\n            [\n              -89.6044921875,\n              37.16031654673677\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/umid-water\" href=\"https://www.usgs.gov/centers/umid-water\">Upper Midwest Water Science Center</a><br><a data-mce-href=\"https://www.usgs.gov/\" href=\"https://www.usgs.gov/\">U.S. Geological Survey</a><br>8505 Research Way<br>Middleton, WI 53562</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Data Sources and Preparation</li><li>Parameter Estimation and Observation Data</li><li>Simulations of Net Infiltration and Irrigation, 1915–2017</li><li>Possible Improvements for Future Work</li><li>Summary and Conclusions</li><li>References Cited</li><li>Appendix 1. Spatial Subset Creation</li><li>Appendix 2. Incorporating Observations into PEST++ Workflow</li></ul>","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2021-05-26","noUsgsAuthors":false,"publicationDate":"2021-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Westenbroek, Stephen, M. 0000-0002-6284-8643","orcid":"https://orcid.org/0000-0002-6284-8643","contributorId":206429,"corporation":false,"usgs":true,"family":"Westenbroek","given":"Stephen, M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816423,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nielsen, Martha G. 0000-0003-3038-9400 mnielsen@usgs.gov","orcid":"https://orcid.org/0000-0003-3038-9400","contributorId":4169,"corporation":false,"usgs":true,"family":"Nielsen","given":"Martha","email":"mnielsen@usgs.gov","middleInitial":"G.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816424,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ladd, David E. 0000-0002-9247-7839 deladd@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7839","contributorId":1646,"corporation":false,"usgs":true,"family":"Ladd","given":"David","email":"deladd@usgs.gov","middleInitial":"E.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816425,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70220820,"text":"sir20215019 - 2021 - Status and understanding of groundwater quality in the northern Sierra Nevada foothills domestic-supply aquifer study units, 2015–17—California GAMA Priority Basin Project","interactions":[],"lastModifiedDate":"2021-05-26T11:57:49.296302","indexId":"sir20215019","displayToPublicDate":"2021-05-25T15:11:55","publicationYear":"2021","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":"2021-5019","displayTitle":"Status and Understanding of Groundwater Quality in the Northern Sierra Nevada Foothills Domestic-Supply Aquifer Study Units, 2015–17: California GAMA Priority Basin Project","title":"Status and understanding of groundwater quality in the northern Sierra Nevada foothills domestic-supply aquifer study units, 2015–17—California GAMA Priority Basin Project","docAbstract":"<p>Groundwater quality in the northern Sierra Nevada foothills region of California was investigated as part of California State Water Resources Control Board (SWRCB) Groundwater Ambient Monitoring Assessment Priority Basin Project (GAMA-PBP). The region was divided into two study units: the Yuba-Bear watersheds (YBW) study unit and the American-Cosumnes-Mokelumne watersheds (ACMW) study unit. The GAMA-PBP made a spatially unbiased assessment of aquifer systems used for domestic drinking-water supply in the study region, which are predominantly composed of fractured, hard-rock aquifers of varying lithology. These assessments characterized the quality of raw groundwater to evaluate ambient conditions in the domestic-supply aquifer and not the quality of treated drinking water.</p><p>The study included three components: (1) <i>a status assessment</i>, which characterized the quality of groundwater resources used for domestic drinking-water supply in the YBW and ACMW study units; (2) <i>an understanding assessment</i>, which evaluated natural and anthropogenic explanatory factors that could potentially affect groundwater quality in the study region; and (3) <i>a comparative assessment</i> between the groundwater resources used for domestic and public drinking-water supply in the study region.</p><p>The status assessment was based on data collected by the GAMA-PBP from 74 sites in the YBW study unit during 2015–16 and 67 sites in the ACMW study unit from 2016 to 2017. To contextualize water-quality results, concentrations of water-quality constituents in ambient groundwater were compared to regulatory and non-regulatory benchmarks typically used by the State of California and Federal agencies as health-based or aesthetic standards for public drinking water. The status assessment used a grid-based method to estimate proportions of groundwater resources with concentrations approaching or exceeding benchmark thresholds. This method provides spatially unbiased results and allows inter-comparability with similar groundwater-quality assessments.</p><p>Inorganic constituents with health-based benchmarks were present at high relative concentration (RC), meaning they exceeded the benchmark threshold, in 5.4 and 10 percent of domestic-supply aquifer systems in the YBW and ACMW study units, respectively. Inorganic constituents with aesthetic-based benchmarks were detected at high-RCs in 20 and 28 percent of the YBW and ACMW study units, respectively. The inorganic constituents present at high RC were arsenic, barium, boron, molybdenum, strontium, nitrate, adjusted gross-alpha particle activity, chloride, total dissolved solids, specific conductance, iron, manganese, and hardness. Groundwater samples were tested for presence or absence of three microbial indicators (total coliform, <i>Escherichia coli</i>, and <i>Enterococci</i>). At least one microbial indicator was present in 26 and 28 percent of the YBW and ACMW study units, respectively. At least one organic constituent was detected in 30 and 42 percent of the YBW and ACMW study units, respectively. Organic constituents were not present at high RC, but tetrachloroethene (PCE), trichloroethene (TCE), and toluene were detected in the YBW study unit at moderate RC (between the benchmark concentration and one-tenth of the benchmark concentration). Methyl <i>tert</i>-butyl ether (MTBE) and chloroform were present at low RC (less than one-tenth of the benchmark concentration) in the YBW and ACMW study units with detection frequencies greater than 10 percent. Perchlorate, a constituent of special interest, was detected in 31 and 41 percent of the YBW and ACMW study units, respectively, at either low or moderate RCs.</p><p>Relations among select water-quality constituents and potential explanatory factors were evaluated using statistical and graphical approaches. Nitrate, microbial indicators, and perchlorate were all correlated to elevation-dependent variables relating to climate, land use, and recharge condition. Isotopic and dissolved noble-gas tracers indicated these water-quality constituents are associated with recharge conditions associated with irrigation during the summer dry-season, which is common in areas of rural-residential or agricultural land uses. Higher concentrations of iron and manganese were primarily associated with anoxic groundwater in aquifers of metasedimentary lithology. Increased hardness was primarily associated with anoxic groundwater in aquifers of mafic-ultramafic or metavolcanics lithologies at lower elevations in the study region in the Melones fault zone. Chloroform and MTBE were associated with shallow groundwater (wells depths less than 130 m) under oxic and anoxic redox conditions, respectively.</p><p>The comparative assessment evaluated differences between the aquifer systems used for domestic- and public-supply in study region based on (1) well-construction characteristics, and (2) water quality. Analysis of over 60,000 well-completion reports in the study region showed that although domestic-supply wells span the deepest depth zones in regional aquifers, median depths for public-supply wells were significantly greater than those of domestic-supply wells in both study units. Water-quality data from more than 300 public-supply wells in the study region were assessed using a spatially weighted method for calculation aquifer-scale proportions and compared with the domestic-supply assessment results. Detections of inorganic constituents at high RC and detection frequencies for organic constituents were generally similar between the domestic- and public-supply aquifer systems in both study units, with a few notable exceptions in the ACMW study unit: nitrate was greater for the public- compared to domestic-supply aquifer system and both manganese, hardness, and MTBE were greater in the domestic- compared to public-supply aquifer system. These differences are likely related to contrasting land uses, aquifer lithologies, landscape positions, and depths characterizing domestic- and public-supply wells in the ACMW study unit.</p><p>Overall, fewer samples from domestic-supply wells in the northern Sierra Nevada foothills exceeded health-based benchmarks compared to aesthetic-based benchmarks for groundwater quality. Exceedences of health-based benchmarks were primarily caused by nitrate and coliform bacteria, which were associated with recharge from diverted surface water used primarily for irrigation. Exceedences of aesthetic-based benchmarks were primarily caused by iron, managanese, and hardness, which were associated with geologic factors. Regional irrigation practices and aquifer lithology can affect groundwater quality in fractured-rock aquifers in the northern Sierra Nevada foothills used for domestic drinking-water supply.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215019","collaboration":"Prepared in cooperation with the California State Water Resources Control Board <br>A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program</br>","usgsCitation":"Levy, Z.F., and Fram, M.S., 2021, Status and understanding of groundwater quality in the northern Sierra Nevada foothills domestic-supply aquifer study units, 2015–17—California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2021–5019, 120 p., https://doi.org/10.3133/sir20215019.","productDescription":"Report: xv, 120 p.; 5 Data Releases","numberOfPages":"120","onlineOnly":"Y","ipdsId":"IP-087401","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":385968,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20213013","text":"Fact Sheet 2021-3013","linkHelpText":"- Geologic Influences on the Quality of Groundwater Used for Domestic Supply in the Northern Sierra Nevada Foothills"},{"id":385967,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R1V41Q","text":"Attributed California Water Supply Well Completion Report Data for Selected Areas, Derived from CA WCR OSCWR Data"},{"id":385966,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78G8JXP","text":"Groundwater-quality data in the Mokelumne, Cosumnes, and American River Watersheds Shallow Aquifer Study Unit, 2016-2017: Results from the California GAMA Priority Basin Project"},{"id":385965,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YETK9P","text":"Dissolved Noble Gas Concentrations and Modeled Recharge Temperatures for Groundwater from Northern Sierra Nevada Foothills Shallow Aquifer Assessment Study Units, 2015-2017: Results from the California GAMA Priority Basin Project"},{"id":385964,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z3O812","text":"Potential Explanatory factors for Groundwater Quality in the Northern Sierra Nevada Foothills Domestic-Aquifer Assessment Study Units, 2015-2017"},{"id":385963,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73F4MS9","text":"Groundwater-Quality Data in the Yuba and Bear Watersheds Shallow Aquifer Study Unit, 2015-2016: Results from the California GAMA Priority Basin Project"},{"id":385962,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5019/images"},{"id":385961,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5019/sir20215019.xml"},{"id":385960,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5019/sir20215019.pdf","text":"Report","size":"21 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":385959,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5019/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sierra Nevada foothills","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.81640624999999,\n              37.56199695314352\n            ],\n            [\n              -119.06982421874999,\n              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Factors&nbsp;&nbsp;</li><li>Status and Understanding of Groundwater Quality in Aquifers Used for Domestic Drinking-Water Supply&nbsp;&nbsp;</li><li>Comparative Assessment&nbsp;&nbsp;</li><li>Summary&nbsp;&nbsp;</li><li>References Cited&nbsp;&nbsp;</li><li>Appendixes</li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2021-05-25","noUsgsAuthors":false,"publicationDate":"2021-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Levy, Zeno F. 0000-0003-4580-2309 zflevy@usgs.gov","orcid":"https://orcid.org/0000-0003-4580-2309","contributorId":219572,"corporation":false,"usgs":true,"family":"Levy","given":"Zeno","email":"zflevy@usgs.gov","middleInitial":"F.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816471,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fram, Miranda S. 0000-0002-6337-059X mfram@usgs.gov","orcid":"https://orcid.org/0000-0002-6337-059X","contributorId":1156,"corporation":false,"usgs":true,"family":"Fram","given":"Miranda","email":"mfram@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816472,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70220583,"text":"fs20213028 - 2021 - Sampling for Per- and Polyfluoroalkyl Substances (PFAS) by the Groundwater Ambient Monitoring and Assessment Priority Basin Project","interactions":[],"lastModifiedDate":"2021-05-26T11:44:32.403157","indexId":"fs20213028","displayToPublicDate":"2021-05-25T14:43:18","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3028","displayTitle":"Sampling for Per- and Polyfluoroalkyl Substances (PFAS) by the Groundwater Ambient Monitoring and Assessment Priority Basin Project","title":"Sampling for Per- and Polyfluoroalkyl Substances (PFAS) by the Groundwater Ambient Monitoring and Assessment Priority Basin Project","docAbstract":"<p>Per- and polyfluoroalkyl substances (PFAS) are a family of human-made chemicals that can persist in the environment. In 2019, the California State Water Resources Control Board’s Groundwater Ambient Monitoring and Assessment Priority Basin Project (GAMA-PBP) added PFAS to the projects’ on-going assessments of the quality of groundwater used for drinking-water supplies. This fact sheet describes the GAMA-PBP plans for sampling public-supply and domestic wells across California for PFAS and presents preliminary results for data collected in 2019–20.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213028","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Kent, R.H., 2021, Sampling for Per- and Polyfluoroalkyl Substances (PFAS) by the Groundwater Ambient Monitoring and Assessment Priority Basin Project: U.S. Geological Survey Fact Sheet 2021-3028, 4 p., https://doi.org/10.3133/fs20213028.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-124312","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":436338,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92IPRJD","text":"USGS data release","linkHelpText":"Data 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 \"}}]}","contact":"<p><a href=\"mailto:dc_ca@usgs.gov\" data-mce-href=\"mailto:dc_ca@usgs.gov\">Director</a>,<br><a href=\"https://ca.water.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://ca.water.usgs.gov\">California Water Science Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov\">U.S. Geological Survey</a><br>6000 J Street, Placer Hall<br>Sacramento, California 95819</p>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2021-05-25","noUsgsAuthors":false,"publicationDate":"2021-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Kent, Robert H. 0000-0003-4174-9467 rhkent@usgs.gov","orcid":"https://orcid.org/0000-0003-4174-9467","contributorId":175257,"corporation":false,"usgs":true,"family":"Kent","given":"Robert","email":"rhkent@usgs.gov","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816088,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70220713,"text":"sir20215040 - 2021 - Hydrologic and hydraulic analyses of selected streams near the city of Rittman in Wayne and Medina Counties, Ohio","interactions":[],"lastModifiedDate":"2021-05-26T11:40:57.715058","indexId":"sir20215040","displayToPublicDate":"2021-05-25T14:01:34","publicationYear":"2021","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":"2021-5040","displayTitle":"Hydrologic and Hydraulic Analyses of Selected Streams near the City of Rittman in Wayne and Medina Counties, Ohio","title":"Hydrologic and hydraulic analyses of selected streams near the city of Rittman in Wayne and Medina Counties, Ohio","docAbstract":"<p>The U.S. Geological Survey, in cooperation with the Muskingum Watershed Conservancy District and the city of Rittman, Ohio, did a study to provide data to update and expand parts of two Federal Emergency Management Agency Flood Insurance Studies. The study consisted of hydrologic and hydraulic analyses for selected reaches of four streams (Chippewa Creek, Little Chippewa Creek, Styx River, and the unnamed tributary to Styx River) near the city of Rittman in Wayne and Medina Counties, Ohio. The study covered 36.2 miles of stream reaches.</p><p>Instantaneous peak streamflows for floods with 10-, 4-, 2-, 1-, and 0.2-percent and 1-percent plus annual exceedance probabilities were estimated using historical streamflow data from three U.S. Geological Survey streamgages and regional flood-frequency regression equations. The flood-frequency estimates were then used in a Hydrologic Engineering Center River Analysis System step-backwater model to determine water-surface profiles; flood-inundation boundaries for the 10-, 4-, 2-, 1-, and 0.2-percent and 1-percent plus annual exceedance probabilities; and a regulatory floodway for the study reaches. Model inputs included cross sections derived from a digital elevation model supplemented with field surveys of open-channel cross sections and hydraulic structures, field estimates of Manning’s roughness values, and flood estimates determined from regional regression equations and historical streamflow data. Flood-inundation boundaries were mapped for each stream reach for the 1- and 0.2-percent annual exceedance probability floods and a regulatory floodway. All data used in the creation of the flood-inundation boundaries are available through a U.S. Geological Survey data release (Ostheimer, 2021) and will be submitted to the Federal Emergency Management Agency for inclusion in updated Flood Insurance Studies for Wayne and Medina Counties.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215040","collaboration":"Prepared in cooperation with the city of Rittman and the Muskingum Watershed Conservancy District","usgsCitation":"Ostheimer, C.J., 2021, Hydrologic and hydraulic analyses of selected streams near the city of Rittman in Wayne and Medina Counties, Ohio: U.S. Geological Survey Scientific Investigations Report 2021–5040, 30 p., https://doi.org/10.3133/sir20215040.","productDescription":"Report: iv, 30 p.; Data Release","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-117425","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":385929,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W6ROMC","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial data sets and hydraulic models for selected streams near Rittman in Wayne and Medina Counties, Ohio"},{"id":385927,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5040/coverthb.jpg"},{"id":385956,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5040/images"},{"id":385928,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5040/sir20215040.pdf","text":"Report","size":"7.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5040"}],"country":"United States","state":"Ohio","county":"Wayne County, Medina County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-81.6845,41.2772],[-81.6885,40.9887],[-81.6477,40.9884],[-81.648,40.9145],[-81.6483,40.7371],[-81.6491,40.6681],[-82.126,40.6682],[-82.1266,40.778],[-82.1292,40.9921],[-82.1736,40.9922],[-82.1722,41.0435],[-82.1714,41.0639],[-82.1699,41.1251],[-82.1699,41.1369],[-82.0741,41.1362],[-82.0725,41.2001],[-81.9736,41.1998],[-81.9724,41.2747],[-81.8777,41.2747],[-81.7848,41.2765],[-81.6845,41.2772]]]},\"properties\":{\"name\":\"Medina\",\"state\":\"OH\"}}]}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/oki-water\" data-mce-href=\"https://www.usgs.gov/centers/oki-water\">Ohio-Kentucky-Indiana Water Science Center</a><br><a href=\"https://www.usgs.gov/\" data-mce-href=\"https://www.usgs.gov/\">U.S. Geological Survey</a><br>6460 Busch Blvd., Suite 100<br>Columbus, OH 43229<br></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Study Approach</li><li>Hydrologic Analyses</li><li>Hydraulic Analyses</li><li>Development of Flood-Inundation Boundaries</li><li>Data Dissemination</li><li>Summary</li><li>References Cited</li><li>Appendix 1</li></ul>","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2021-05-25","noUsgsAuthors":false,"publicationDate":"2021-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Ostheimer, Chad J. 0000-0002-4528-8867","orcid":"https://orcid.org/0000-0002-4528-8867","contributorId":213950,"corporation":false,"usgs":true,"family":"Ostheimer","given":"Chad","email":"","middleInitial":"J.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816435,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70220590,"text":"sir20215034 - 2021 - Discharge data collection and analysis and implications for surface-water/groundwater interactions in the lower Las Vegas Wash, Clark County, Nevada, 2016–18","interactions":[],"lastModifiedDate":"2021-05-26T11:37:18.903542","indexId":"sir20215034","displayToPublicDate":"2021-05-25T10:39:08","publicationYear":"2021","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":"2021-5034","displayTitle":"Discharge Data Collection and Analysis and Implications for Surface-Water/Groundwater Interactions in the Lower Las Vegas Wash, Clark County, Nevada, 2016–18","title":"Discharge data collection and analysis and implications for surface-water/groundwater interactions in the lower Las Vegas Wash, Clark County, Nevada, 2016–18","docAbstract":"<p>The lower Las Vegas Wash represents the terminal surface drainage for the Las Vegas Valley in southern Nevada. In 1997, high concentrations of perchlorate were found in seeps contributing to discharge in this area and traced to an industrial byproduct from manufacturing operations in the mid-1900s at the nearby Basic Magnesium, Incorporated, plant. The discovery prompted a water-resources investigation by the Nevada Department of Environmental Protection (NDEP) to develop an understanding of the nearby groundwater flow system and the dynamics associated with surface-water flow in the Wash. In 2016, the U.S. Geological Survey was tasked with evaluating surface-water discharge in the lower Las Vegas Wash near locations where perchlorate concentrations from the groundwater system had been detected. Results of this study will assist NDEP with identifying areas of groundwater and surface-water interaction and help guide future cleanup and monitoring efforts.</p><p>Streamflow discharge is evaluated along a 4-mile section of the lower Las Vegas Wash (referred to as the Wash) and used to describe surface-water and groundwater interactions between the Wash channel and bank sediments. Continuous discharge data were collected during a 2-year period (2016–18) at 5 gaging stations along the Wash. Additionally, multiple discrete measurements between gaging stations were collected during 4 synoptic sampling events between 2016 and 2018.</p><p>A diurnal discharge pattern, controlled by upstream treated wastewater releases, provided high- and low-discharge markers that are used to compute downstream time-lags of peak and minimum flows. Computed time-lags are used to establish travel times between measurement sites, and difference in upstream and time-lagged downstream hydrographs are used to compute increases (gain) or decreases (loss) in discharge between gaging stations or between gaging stations and discrete measurements. Tributary surface-water inflows to the lower Las Vegas Wash from wastewater discharge, remediation efforts, and periodic flooding from rainfall runoff are included in computing differences in discharge. Differences between discharge data from delineated reaches are used to define locations of daily, monthly, and yearly streamflow gains from or losses to adjacent bank sediments. Construction of additional channel-stabilization weirs have occurred since the completion of this study and the associated change to streamflow dynamics may limit study results to the period analyzed; however, methods and processes described in this report can be used in future evaluations.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215034","collaboration":"Prepared in cooperation with the Nevada Division of Environmental Protection","usgsCitation":"Wilson, J.W., 2021, Discharge data collection and analysis and implications for surface-water/groundwater interactions in the lower Las Vegas Wash, Clark County, Nevada, 2016–18: U.S. Geological Survey Scientific Investigations Report 2021–5034, 25 p., https://doi.org/10.3133/sir20215034.","productDescription":"Report: vi, 25 p.; Data Release","numberOfPages":"25","onlineOnly":"Y","ipdsId":"IP-091622","costCenters":[{"id":465,"text":"Nevada Water Science 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href=\"mailto:dc_nv@usgs.gov\" data-mce-href=\"mailto:dc_nv@usgs.gov\">Director</a>,<br><a href=\"https://www.usgs.gov/centers/nv-water\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/nv-water\">Nevada Water Science Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov\">U.S. Geological Survey</a><br>2730 N. Deer Run Road<br>Carson City, Nevada 95819</p>","tableOfContents":"<ul><li>Abstract&nbsp;&nbsp;</li><li>Introduction&nbsp;&nbsp;</li><li>Previous Work&nbsp;&nbsp;</li><li>Methods&nbsp;&nbsp;</li><li>Discharge Analysis&nbsp;&nbsp;</li><li>Surface-Water-Groundwater Interaction&nbsp;&nbsp;</li><li>Summary&nbsp;&nbsp;</li><li>References&nbsp;</li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2021-05-25","noUsgsAuthors":false,"publicationDate":"2021-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilson, Jon W. 0000-0003-4391-5318 jwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-4391-5318","contributorId":4574,"corporation":false,"usgs":true,"family":"Wilson","given":"Jon","email":"jwilson@usgs.gov","middleInitial":"W.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816103,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
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