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Recently, increased frequency of costly floods has prompted consideration of whether offsetting benefits might accrue from management of floodplains for ecosystem services. We employed a simple inundation model for 800 km of the Lower Missouri River, USA, to evaluate spatial and temporal distributions of ecological floodplain inundation metrics and how those distributions might vary with levee removal and climatic change. The model evaluates inundation at 30 × 30 m resolution on a daily basis over 82 years of record. We quantified provisioning of waterfowl habitat and potential denitrification. Spatial variability is affected by ongoing geomorphic adjustments that affect floodplain connectivity. Statistical models indicate that available floodplain area and recent aggradation are predictive of most inundation metrics. Connectivity is sensitive to climate-change scenarios that predict increased floodplain inundation during spring waterfowl migrations; the greatest sensitivity to future climate exists where channel-floodplain geomorphology presently enhances floodplain connectivity. Evaluation of floodplain denitrification indicates that on average, the nonleveed part of the floodplain could denitrify 0.05%–1.7% of the mean annual nitrogen load of the river. Levee removal could increase this rate to only 3.6% of the nitrogen load. The capacity of floodplain connectivity to influence certain ecosystem services is highly variable in space along the Lower Missouri River and may be appreciably influenced by climate change. Hence, decisions to optimize management of large-river floodplains are likely to be highly location dependent.","language":"English","publisher":"John Wiley & Sons","doi":"10.1029/2021WR031204","usgsCitation":"Jacobson, R., Bouska, K.L., Bulliner, E., Lindner, G., and Paukert, C., 2022, Geomorphic controls on floodplain connectivity, ecosystem services, and sensitivity to climate change: An example from the lower Missouri River: Water Resources Research, v. 58, no. 6, e2021WR031204, 26 p., https://doi.org/10.1029/2021WR031204.","productDescription":"e2021WR031204, 26 p.","ipdsId":"IP-133183","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":447047,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021wr031204","text":"Publisher Index 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,{"id":70233466,"text":"70233466 - 2022 - Land cover change effects on stormflow characteristics across broad hydroclimate representative urban watersheds in the United States","interactions":[],"lastModifiedDate":"2022-07-21T13:24:14.694312","indexId":"70233466","displayToPublicDate":"2022-07-21T08:09:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Land cover change effects on stormflow characteristics across broad hydroclimate representative urban watersheds in the United States","docAbstract":"<p>Urban development alters stormflow characteristics and is associated with increasing flood risks. The long-term evaluation of stormflow characteristics that exacerbate floods, such as peak stormflow and time-to-peak stormflow at varying levels of urbanization across different hydroclimates, is limited. This study investigated the long-term (1980s to 2010s) effects of increasing urbanization on key stormflow characteristics using observed 15 min streamflow data across six broad hydroclimate representative urban watersheds in the conterminous United States. The results indicate upward trends in peak stormflow and downward trends in time-to-peak stormflow at four out of six watersheds. The watershed in the Great Plains region had the largest annual increasing (decreasing) percent change in peak stormflow (time-to-peak stormflow). With the current change rates, peak stormflow in the Great Plains region watershed is expected to increase by 55.4% and have a 2.71 h faster time-to-peak stormflow in the next decade.</p>","language":"English","publisher":"MDPI","doi":"10.3390/w14142256","usgsCitation":"Khand, K., and Senay, G.B., 2022, Land cover change effects on stormflow characteristics across broad hydroclimate representative urban watersheds in the United States: Water, v. 14, no. 14, 2256, 11 p., https://doi.org/10.3390/w14142256.","productDescription":"2256, 11 p.","ipdsId":"IP-134427","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":447048,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w14142256","text":"Publisher Index Page"},{"id":435762,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91X5I6L","text":"USGS data release","linkHelpText":"Unit hydrographs of evolving urban watersheds across the United States"},{"id":404207,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia, Illinois, Texas, Utah, Virginia, Washington","city":"Atlanta; Chicago; Houston; Salt Lake City; Seattle; Washington, D. 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,{"id":70233448,"text":"fs20223062 - 2022 - Mississippi and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T11:55:07.537702","indexId":"fs20223062","displayToPublicDate":"2022-07-20T21:20:22","publicationYear":"2022","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":"2022-3062","displayTitle":"Mississippi and Landsat","title":"Mississippi and Landsat","docAbstract":"<p>Mississippi holds a significant place in the cultural and economic history of the United States. For example, the Magnolia State was a hotbed for the Delta blues, an early 20th century musical genre with tremendous cultural effects in the United States and around the world. The “Delta” in Delta blues is the Mississippi River Delta, the largest delta in the United States. The Mississippi River is one of the longest in the Nation, having 1,800 navigable miles that serve as a natural transportation corridor that is foundational to America’s economic fortunes.</p><p>This important waterway forms Mississippi’s western border, and the fertile alluvial soils that fan out across its western third served to bolster its early economy and remain key drivers of its agriculture sector. These soils continue to support cotton, soybeans, corn, and agricultural products for which Mississippi is a national leader: rice and farmed catfish.</p><p>Such heavy reliance on the land necessitates a strong understanding of the health of—and threats to—the landscape. Spikes in extreme heat and associated wildfire dangers, the increasing frequency of powerful hurricanes on the Gulf Coast, and annual tornadoes all stand as challenges to the Magnolia State’s land resources.</p><p>The U.S. Geological Survey Landsat program serves a critical role in mapping, monitoring, and understanding changes across Mississippi. 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 \"}}]}","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/programs/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/programs/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Monitoring Forest Health</li><li>Cataloging Crop Health, Crop Types</li><li>Tracking Damage from Extreme Weather</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-07-20","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":147999,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":847112,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70233445,"text":"fs20223064 - 2022 - Delaware and Landsat","interactions":[],"lastModifiedDate":"2022-12-07T20:07:32.258065","indexId":"fs20223064","displayToPublicDate":"2022-07-20T20:04:53","publicationYear":"2022","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":"2022-3064","displayTitle":"Delaware and Landsat","title":"Delaware and Landsat","docAbstract":"<p>Delaware’s status as the first State to ratify the U.S. Constitution is a well-known point of pride. “The First State” is among Delaware’s nicknames, alongside “the Blue Hen State,” “the Diamond State,” and “the Small Wonder,” the last of which relates to Delaware’s diminutive land area—larger only than Rhode Island.&nbsp;</p><p>Less well known, perhaps, is Delaware’s geographic distinction as the State with the lowest average elevation. Most of its land area rises no more than 80 feet above sea level. In fact, about 32,000 acres of Cypress Swamp, sometimes called the Great Cypress Swamp, stretch across its southern border.</p><p>These low elevations put Delaware at particular risk of sea level rise associated with climate change. Sea levels are rising more quickly than average for the Mid-Atlantic Region, which includes Delaware. The State has seen its coastal waters rise more than 1 foot over the past century.</p><p>The Landsat Program’s 50-year archive of repeat Earth observations offers an indispensable record of land change along the Nation’s coastlines. Imagery collected by Landsat satellites can inform studies of the coastline losses, flooding extents, and land cover conversions that affect climate resilience in Delaware. Landsat data also can support plans to mitigate those effects. Here are a few examples of the ways Delaware benefits from Landsat.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223064","usgsCitation":"U.S. Geological Survey, 2022, Delaware and Landsat: U.S. Geological Survey Fact Sheet 2022–3064, 2 p., https://doi.org/10.3133/fs20223064.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-143113","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":404508,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3064/images"},{"id":404180,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3064/fs20223064.pdf","text":"Report","size":"4.24 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3064"},{"id":406522,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223064/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":404507,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3064/fs20223064.XML"},{"id":404179,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3064/coverthb.jpg"}],"country":"United 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 \"}}]}","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/programs/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/programs/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Tracking Coastal Change</li><li>Watching over Wetlands</li><li>Documenting Deluges</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-07-20","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":210377,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":847109,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70233441,"text":"fs20223058 - 2022 - New Hampshire and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T11:59:45.633299","indexId":"fs20223058","displayToPublicDate":"2022-07-20T16:16:00","publicationYear":"2022","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":"2022-3058","displayTitle":"New Hampshire and Landsat","title":"New Hampshire and Landsat","docAbstract":"<p>At its widest point, a mere 80 miles separate the eastern and western borders of New Hampshire. Its northern and southern borders are just 175 miles apart. Even so, few States can boast as much rugged natural beauty per mile as the Nation’s fifth smallest.</p><p>Nestled within New Hampshire are 93 State parks teeming with moose, <i>Ursus americanus</i> (Pallas, 1780; black bears), coyotes, beavers, river otters, and foxes. The largest section of White Mountain National Forest cuts across north-central New Hampshire, drawing visitors to its lakes, streams, mountain peaks, and hardwood forests. New Hampshire also is home to Lake Winnipesaukee, the State’s largest lake, notable for its floating post offices, the annual “ice-out” contest that sees residents vying to guess the date its surface ice dissipates, and its supporting role in films such as “On Golden Pond” and “What About Bob?” However, the scenic forests of New Hampshire face challenges in the form of invasive species such as <i>Lymantria dispar</i> (Linnaeus, 1758; spongy moth), <i>Adelges piceae</i> (balsam woolly adelgid), and <i>Agrilus planipennis</i> (emerald ash borer). In recent years, New Hampshire’s lakes and streams have seen more cyanobacterial blooms as well.</p><p>The U.S. Geological Survey Landsat Program offers a consistent, reliable, and historically unmatched source of Earth observations that can aid in the mapping, monitoring, and management of New Hampshire’s land and water resources. Here are a few ways Landsat data have been used in the Granite State.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223058","usgsCitation":"U.S. Geological Survey, 2022, New Hampshire and Landsat: U.S. Geological Survey Fact Sheet 2022–3058, 2 p., https://doi.org/10.3133/fs20223058.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-143117","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":406525,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223058/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":404499,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3058/images"},{"id":404498,"rank":3,"type":{"id":31,"text":"Publication 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Hampshire\",\"nation\":\"USA  \"}}]}","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/programs/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/programs/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Monitoring Water Quality</li><li>A Watchful Eye on Forests</li><li>Mapping Land Use, Land Cover</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-07-20","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":202815,"corporation":true,"usgs":false,"organization":"U.S. Geological 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,{"id":70233275,"text":"sir20225039 - 2022 - Geohydrology and water quality of the northern and central parts of the Tug Hill glacial aquifer, Jefferson and Oswego Counties, north-central New York","interactions":[],"lastModifiedDate":"2026-04-09T17:27:29.792829","indexId":"sir20225039","displayToPublicDate":"2022-07-20T15:18:00","publicationYear":"2022","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":"2022-5039","displayTitle":"Geohydrology and Water Quality of the Northern and Central Parts of the Tug Hill Glacial Aquifer, Jefferson and Oswego Counties, North-Central New York","title":"Geohydrology and water quality of the northern and central parts of the Tug Hill glacial aquifer, Jefferson and Oswego Counties, north-central New York","docAbstract":"<p>The northern and central parts of the Tug Hill glacial aquifer consist of a 29-mile-long, crescent-shaped, mixture of glaciofluvial, glaciolacustrine, and recent alluvial deposits of predominantly sand and gravel on the western side of the Tug Hill Plateau in Jefferson and Oswego Counties in north-central New York. Approximately 11,400 people are supplied by groundwater that is withdrawn from municipal and nonmunicipal wells in the northern and central parts of the aquifer. In addition, many farms, several industries, and a large New York State fish hatchery also rely on the water from the aquifer.</p><p>In the early 2000s, anticipated developmental pressures from potential new industries (including a proposed water-bottling plant in the central part of the Tug Hill glacial aquifer) and expansion of the Fort Drum military base north of Watertown (with the projected increase in population extending into the northern part of the aquifer) prompted the Tug Hill Commission, local municipal officials, and representatives from the New York State Department of Environmental Conservation to initiate a geohydrologic study with the U.S. Geological Survey. The information from this study is intended to help the state, counties, and local communities make sound policy decisions about their use of this large groundwater resource.</p><p>The northern part of the Tug Hill glacial aquifer is a combination of glaciofluvial outwash and alluvial sand and gravel in the Sandy Creek Valley northeast of Adams, New York, and mostly glaciolacustrine beach and deltaic sand or sand and gravel north and south of the village of Adams. The southern and eastern areas of the central part of the aquifer are composed mostly of glaciofluvial sediments such as kames, kame moraines, and kame terraces, whereas most of the western areas of the central part are composed mostly of glaciolacustrine sediments such as deltaic sand and beach sand and gravel.</p><p>The northern and central parts of the aquifer are unconfined. Recharge to the northern and central parts of the aquifer is from three main sources: (1) precipitation that falls directly onto the aquifer; (2) unchannelized runoff (overland flow) and groundwater from till and bedrock in the Tug Hill Plateau that seeps into the eastern side of the aquifer; and (3) streams that drain the Tug Hill Plateau and flow across and lose water to the aquifer. Groundwater discharges to springs, seeps, headwaters of streams, and wetlands in the middle area of the central part of the aquifer and along the entire western boundary of the northern and central parts of the aquifer; pumping wells; artificial ditches; and deeply incised streams in the northern and central parts of the aquifer. The groundwater discharge to such streams is critical in supporting the salmonid fishery in the central part of the aquifer.</p><p>Groundwater levels were measured on July 17, 2014, at 22 wells throughout the northern and central parts of the aquifer. Water-table contours were drawn on the basis of the measured July 2014 water levels, historical water-level data, and surface-water levels where surface water in the channels was expected to be hydraulically connected to the groundwater system. The water table generally slopes from east to west throughout the northern and central parts of the aquifer; this slope also indicates that the direction of groundwater flow is generally from east to west.</p><p>Water-quality samples were collected from 23 stream sites during base-flow conditions, and groundwater-quality and other types of environmental samples were collected from 20 wells in the northern and central parts of the Tug Hill glacial aquifer. The results of the sampling indicate that surface water and groundwater are generally of good quality.</p><p>Comparison of the median concentration values of major ions in groundwater samples indicated that hardness in the northern part of the aquifer was about twice as great, and concentrations of calcium and sodium were more than three times as great, as in the central part of the aquifer. As was the case with surface water, the much greater median concentrations in groundwater of calcium, hardness, and alkalinity in the northern part of the aquifer are due to the dissolution of limestone that underlies most of that area and to the high-carbonate content of the clasts in the sand and gravel. There was little to no difference among the median values for bromide, fluoride, silica, and iron in the two parts of the aquifer. Concentrations of most other major ions were slightly greater in the northern part than in the central part of the Tug Hill glacial aquifer, except for magnesium, whose concentration was greater in the central part. Median concentrations of nutrients were generally greatest in surface water and groundwater in the northern part of the aquifer.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225039","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation, the Tug Hill Commission, the Jefferson County Soil and Water Conservation District, the Oswego County Soil and Water Conservation District, and the Tug Hill Land Trust","usgsCitation":"Miller, T.S., Fisher, B.N., and Kappel, W.M., 2022, Geohydrology and water quality of the northern and central parts of the Tug Hill glacial aquifer, Jefferson and Oswego Counties, north-central New York: U.S. Geological Survey Scientific Investigations Report 2022–5039, 54 p., https://doi.org/10.3133/sir20225039.","productDescription":"Report: ix, 54 p.; 2 Data Releases; 2 Figures; 4 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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>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Data Collection</li><li>Geology</li><li>Geohydrology of the Tug Hill Glacial Aquifer</li><li>Water Quality</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Concentrations of Water-Quality Constituents in Water Samples From the Tug Hill Glacial Aquifer, New York</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2022-07-20","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Todd S.","contributorId":293295,"corporation":false,"usgs":false,"family":"Miller","given":"Todd S.","affiliations":[{"id":63270,"text":"Retired Hydrologist, NY Water Science Center, USGS","active":true,"usgs":false}],"preferred":false,"id":846917,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fisher, Benjamin N. 0000-0003-1308-1906","orcid":"https://orcid.org/0000-0003-1308-1906","contributorId":220916,"corporation":false,"usgs":true,"family":"Fisher","given":"Benjamin","email":"","middleInitial":"N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846918,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kappel, William M. 0000-0002-2382-9757 wkappel@usgs.gov","orcid":"https://orcid.org/0000-0002-2382-9757","contributorId":1074,"corporation":false,"usgs":true,"family":"Kappel","given":"William","email":"wkappel@usgs.gov","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846919,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
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Dairy and cheese factories rely on milk from dairy farms with fields and pastures and generate billions in revenue for the State’s economy. The Wisconsin Dells, Door County, and the Northwoods draw tourists with their natural beauty and recreation opportunities.</p><p>Wisconsin contains more than 17 million acres of hardwood and coniferous forest, much of it on land reforested since the large-scale timber cutting of the 1800s and early 1900s. The Badger State boasts nearly 15,000 lakes within its borders and touches two Great Lakes—a bit of Lake Superior and a considerable length of western Lake Michigan. Wisconsin ranks second in the Nation for milk production, but it ranks first for cheese, cranberries, snap beans, and milk goats.</p><p>Data and imagery from Landsat Earth observation systems assist agencies and land managers in monitoring these resources and planning for future management. 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 \"}}]}","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/programs/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/programs/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Monitoring Crops and Other Land Cover</li><li>Watching Water Quality</li><li>Determining Urban Heat Islands</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-07-20","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":147999,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":847104,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70233335,"text":"sir20225068 - 2022 - Assessment of fecal contamination sources to Alley Creek, Queens County, New York, August 2020–June 2021","interactions":[],"lastModifiedDate":"2022-09-27T13:39:37.735468","indexId":"sir20225068","displayToPublicDate":"2022-07-20T13:00:00","publicationYear":"2022","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":"2022-5068","displayTitle":"Assessment of Fecal Contamination Sources to Alley Creek, Queens County, New York, August 2020–June 2021","title":"Assessment of fecal contamination sources to Alley Creek, Queens County, New York, August 2020–June 2021","docAbstract":"Alley Creek, a tributary to Little Neck Bay in Queens County, New York, has been designated by the New York State Department of Environmental Conservation as impaired (Class I) for fecal coliform because of pollution from combined sewer overflow, including stormwater runoff. The U.S. Geological Survey, in cooperation with the New York City Department of Environmental Protection, conducted a 1-year study from August 2020 to June 2021 using microbial source tracking (MST) methods to assess potential host sources of fecal contamination (for example, human, canine, and waterfowl) from the following: three outfall sites, TI–025, TI–008, and TI–024; an artesian well (Q277) adjacent to Alley Creek; and natural waters within the Alley Creek watershed and Little Neck Bay. In addition to analyzing for MST markers, field measurements such as water temperature and specific conductance, samples for total suspended solids, and fecal indicator bacteria (FIB; enterococci and fecal coliform) were collected. Pharmaceutical compounds were also collected for analysis, and the results of sampling were compared spatially and temporally to help support management decisions related to mitigation of fecal sources to Alley Creek. Factors that could affect concentrations, including tidal conditions, seasonality, and weather conditions, also were assessed. A sediment resuspension laboratory experiment was designed to replicate tidal activity in Alley Creek using sediment collected in the sewers and on the shoreline, as well as water collected from Oakland Lake. These sediment samples were assessed to understand the relation between sediment resuspension and FIB in the water column. The human MST markers used for this study, Bacteroides HF183/BacR287, and crAssphage CPQ_056 and CPQ_064, were detected in most samples (27 of 28) collected at the three outfall sites along Alley Creek, whereas the canine marker BacCan was less prevalent (20 of 28 samples) but exhibited a pattern of relative concentrations similar to the human markers. The waterfowl MST GFD marker was detected in 7 of 28 samples collected at the three outfall sites. Human MST markers were not detected at Oakland Lake (which drains through a combined sewer line to Alley Creek at TI–008), indicating minimal or nonexistent influence of sewage contamination in the lake. Groundwater samples collected from Q277 did not contain any MST markers, and concentrations of fecal coliform were less than 10 colony forming units per 100 milliliters. Although FIB did not correlate well with total suspended solids for individual sample sets, samples collected following precipitation and high-turbidity events were typically found to have higher concentrations of FIB than dry-weather samples. Results from the pharmaceutical compounds analysis provided additional evidence for determining known and suspected human sources when coupled with MST markers. Together, the MST, pharmaceutical, and FIB data generated by this study, along with supplementary data such as locations of point sources, locations of wildlife populations, and tidal exchange data, may provide reliable information on source identification and transport mechanisms of fecal contamination to Alley Creek.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225068","collaboration":"Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Fisher, S.C., Kephart, C.M., Cheung, N., and Tagliaferri, T.N., 2022, Assessment of fecal contamination sources to Alley Creek, Queens County, New York, August 2020–June 2021: U.S. Geological Survey Scientific Investigations Report 2022–5068, 35 p., https://doi.org/10.3133/sir20225068.","productDescription":"Report: viii, 35 p.; Data Release","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-132943","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":404047,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SLY4J8","text":"USGS data release","linkHelpText":"Assessment of fecal contamination sources to Alley Creek, Queens County, New York—Results from a sediment resuspension experiment, thermal imagery, and additional sample collection, 2020–2021"},{"id":404055,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5068/sir20225068.XML"},{"id":404054,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5068/images/"},{"id":404045,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5068/sir20225068.pdf","text":"Report","size":"28.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5068"},{"id":404042,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5068/coverthb.jpg"},{"id":404046,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225068/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5068"}],"country":"United States","state":"New York","county":"Queens County","otherGeospatial":"Alley Creek","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -73.80237579345705,\n              40.75323899431278\n            ],\n            [\n              -73.72993469238281,\n              40.75323899431278\n            ],\n            [\n              -73.72993469238281,\n              40.80679319175187\n            ],\n            [\n              -73.80237579345705,\n              40.80679319175187\n            ],\n            [\n              -73.80237579345705,\n              40.75323899431278\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/new-york-water-science-center\" data-mce-href=\"https://www.usgs.gov/centers/new-york-water-science-center\">New York Water Science Center</a><br>U.S. Geological Survey<br>425 Jordan Road<br>Troy, NY 12180–8349</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results</li><li>Discussion</li><li>Summary</li><li>References Cited</li><li>Appendix 1. List of Pharmaceutical Compounds Analyzed to Assess Fecal Contamination Sources to Alley Creek, Queens County, New York, August 2020–June 2021</li><li>Appendix 2. Sediment Resuspension Data and Statistical Summary of Microbiological and Water-Chemistry Data, Alley Creek, Queens County, New York, August 2020–June 2021</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2022-07-20","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"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":846935,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kephart, Christopher M. 0000-0002-3369-5596 ckephart@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-5596","contributorId":1932,"corporation":false,"usgs":true,"family":"Kephart","given":"Christopher","email":"ckephart@usgs.gov","middleInitial":"M.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846936,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":846937,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tagliaferri, Tristen N. 0000-0001-7408-7899","orcid":"https://orcid.org/0000-0001-7408-7899","contributorId":202904,"corporation":false,"usgs":true,"family":"Tagliaferri","given":"Tristen N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846938,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70233310,"text":"sir20225070 - 2022 - Seasonal and long-term clarity trend assessment of Lake Tahoe, California–Nevada","interactions":[],"lastModifiedDate":"2022-07-21T11:42:06.569222","indexId":"sir20225070","displayToPublicDate":"2022-07-20T12:11:18","publicationYear":"2022","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":"2022-5070","displayTitle":"Seasonal and Long-Term Clarity Trend Assessment of Lake Tahoe, California–Nevada","title":"Seasonal and long-term clarity trend assessment of Lake Tahoe, California–Nevada","docAbstract":"<p>The clarity of Lake Tahoe, observed using a Secchi disk on a regular basis since the late 1960s, continues to be a sentinel metric of lake health. Water clarity is influenced by physical and biological processes and has declined in the five decades of monitoring, revealing differences between summer (June–September) and winter (December–March). This document summarizes key findings of a study of Lake Tahoe water clarity, including long-term variability and the relative importance of several influencing variables and processes.</p><p>This study, prepared in cooperation with the Nevada Division of Environmental Protection, focused on (1) an apparent divergence in clarity trends between summer and winter periods, (2) observed changes in in-lake physical and ecological variables that may influence or control seasonal and annual clarity trends, and (3) five research hypotheses regarding lake clarity that were developed by Lake Tahoe management agencies. Previously collected data were used to complete this study. Trend analysis confirmed that winter clarity stabilized (that is, there is no longer a statistically significant trend up or down) during the last 20 years. Evaluation of clarity for selected months in the 50-year Secchi disk clarity dataset showed that only two summer months, July and August, had statistically significant decreases in clarity from 2000–19. Different subsets of available data were analyzed to reveal the presence or absences of trends for each season, decade, and month.</p><p>Five hypotheses related to lake clarity were part of the study described by this report. Hypothesis 1 stated that clarity is controlled predominantly by the distribution and volumetric density of fine particles in suspension. This hypothesis was studied using available data describing in-lake fine (0–20 micrometers) particles from 2008–19. Water clarity was negatively correlated with in-lake particle abundance, with particles in the 1.0-4.6 μm range having the greatest effect, consistent with light-scattering theory. Estimated abundances of diatoms of the genus <i>Cyclotella</i> also were found to be negatively correlated with clarity.</p><p>Data limitations precluded a complete investigation of hypothesis 2, which stated that the observed improvements in winter water clarity are a response to decreasing fine suspended-sediment concentrations in the lake resulting from load reductions from upland sources in and near urbanized areas. Data describing fine-sediment loading from urban areas to the lake were only available since 2014, and only once or twice per month. A slight, statistically significant, negative correlation was identified between urban fine-particle loading and monthly lake clarity with a 4-month lag. Particle abundance in monitored streams is highly correlated with simultaneous particle abundance in the lake.</p><p>Hypothesis 3 stated that changing hydrodynamic conditions in the lake are increasing thermal stability and resistance to mixing. Trend analyses performed on stability index and buoyancy frequency time series computed from long-term observations of lake temperatures support the hypothesis that hydrodynamic conditions have evolved since 1969 to increase the lake’s resistance to mixing. The date of maximum mixing in winter has become progressively earlier in the year. Lake density stratification, defined using the stability index, is commencing earlier in the year and extending a month longer than in the early years of the monitoring program.</p><p>Hypothesis 4 stated that the trend of decreasing summer clarity is a result of earlier, prolonged, and more intense stratification. Statistically significant correlations were found between summer clarity and (1) date of onset of stratification, (2) duration of stratification, and (3) buoyancy frequency.</p><p>Hypothesis 5 stated that ecological (food web) interactions are causing changes in the trends of seasonal or annual clarity; data supporting hypothesis 5 were limited to examples from other systems and to intermittent monitoring of Lake Tahoe and Emerald Bay. The resulting narrative assessment was motivated by a 6-year study of <i>Mysis</i> shrimp disappearance and return in Emerald Bay. The available data and a large body of published literature are consistent with the inference that <i>Mysis</i> shrimp-induced food web changes are causing changes in the trends of seasonal or annual clarity. This food-web study focused on the relations between introduced <i>Mysis</i> shrimp, the native cladocerans (<i>Daphnia</i> and <i>Bosmina</i>) that were largely eliminated following <i>Mysis</i> introduction, and the effect on fine particles within the lake. The records of <i>Mysis</i> and other zooplankton data for Lake Tahoe are episodic and have large gaps. Consequently, statistical analyses could not be conducted to compare zooplankton data with other variables. The long-term record, however, indicates that the key effect was a change to the phytoplankton assemblage, where larger diatoms disappeared, likely due to <i>Mysis</i> grazing, only to be replaced by <i>Cyclotella</i> that are an order-of-magnitude smaller and have increased the abundance and volumetric density of total fine particles in suspension (biotic and abiotic).</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225070","collaboration":"Prepared in cooperation with the Nevada Division of Environmental Protection","usgsCitation":"Naranjo, R., Work, P., Heyvaert, A., Schladow, G., Cortes, A., Watanabe, S., Tanaka, L., and Elci, S., 2022, Seasonal and long-term clarity trend assessment of Lake Tahoe, California–Nevada: U.S. Geological Survey Scientific Investigations Report 2022–5070, 86 p., https://doi.org/10.3133/sir20225070.","productDescription":"x, 86 p.","numberOfPages":"86","onlineOnly":"Y","ipdsId":"IP-120090","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":404038,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5070/covrthb.jpg"},{"id":404039,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5070/sir20225070.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":404040,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5070/sir20225070.xml"},{"id":404041,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5070/images"}],"country":"United States","state":"California","otherGeospatial":"Lake Tahoe","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.201416015625,\n              38.90813299596705\n            ],\n            [\n              -119.90753173828125,\n              38.90813299596705\n            ],\n            [\n              -119.90753173828125,\n              39.30029918615029\n            ],\n            [\n              -120.201416015625,\n              39.30029918615029\n            ],\n            [\n              -120.201416015625,\n              38.90813299596705\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a 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>Acknowledgments&nbsp;&nbsp;</li><li>Abstract&nbsp;&nbsp;</li><li>A. Introduction&nbsp;&nbsp;</li><li>B. Trends in Lake Tahoe Water Clarity&nbsp;&nbsp;</li><li>C. Hypothesis1: Clarity is Controlled Predominantly by the Distribution and (Volumetric) Density of Fine Particles in Suspension&nbsp;&nbsp;</li><li>D. Hypothesis 2: The Change in Trend of Winter Clarity is a Response to Decreasing Fine Suspended-Sediment Concentrations Resulting from Load Reductions&nbsp;&nbsp;</li><li>E. Hypothesis 3: Changing Hydrodynamic Conditions in the Lake are Increasing Thermal Stability and Resistance to Mixing&nbsp;&nbsp;</li><li>F. Hypothesis 4: The Trend in Summer Clarity is a Result of Earlier, Prolonged, and More Intense Stratification&nbsp;&nbsp;</li><li>G. Hypothesis 5: Ecological (Food Web) Interactions are Causing Changes in the Trends of Seasonal or Annual Clarity&nbsp;&nbsp;</li><li>H. Variables That Influence Winter and Summer Lake Clarity&nbsp;&nbsp;</li><li>I. Limitations&nbsp;&nbsp;</li><li>A-I. Summary&nbsp;&nbsp;</li><li>References Cited&nbsp;&nbsp;</li><li>Appendix 1. Supplemental Information About Hypothesis 2 for Lake Tahoe Water-Clarity Trends&nbsp;&nbsp;</li><li>Appendix 2. Supplemental Information on Hypothesis 3&nbsp;&nbsp;</li><li>Appendix 3. Description of Variables Used in the Correlation Analysis&nbsp;</li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2022-07-20","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Naranjo, Ramon C. 0000-0003-4469-6831 rnaranjo@usgs.gov","orcid":"https://orcid.org/0000-0003-4469-6831","contributorId":3391,"corporation":false,"usgs":true,"family":"Naranjo","given":"Ramon","email":"rnaranjo@usgs.gov","middleInitial":"C.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846927,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Work, Paul 0000-0002-2815-8040","orcid":"https://orcid.org/0000-0002-2815-8040","contributorId":220041,"corporation":false,"usgs":true,"family":"Work","given":"Paul","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846928,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heyvaert, Alan","contributorId":145509,"corporation":false,"usgs":false,"family":"Heyvaert","given":"Alan","email":"","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":846929,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schladow, Geoffrey","contributorId":10312,"corporation":false,"usgs":true,"family":"Schladow","given":"Geoffrey","email":"","affiliations":[],"preferred":false,"id":846930,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cortes, Alicia","contributorId":293333,"corporation":false,"usgs":false,"family":"Cortes","given":"Alicia","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":true,"id":846931,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Watanabe, Shohei","contributorId":293334,"corporation":false,"usgs":false,"family":"Watanabe","given":"Shohei","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":true,"id":846932,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tanaka, Lidia","contributorId":293335,"corporation":false,"usgs":false,"family":"Tanaka","given":"Lidia","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":true,"id":846933,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Elci, Sebnem","contributorId":293336,"corporation":false,"usgs":false,"family":"Elci","given":"Sebnem","email":"","affiliations":[],"preferred":true,"id":846934,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70233266,"text":"sir20225067 - 2022 - Occurrence of per- and polyfluoroalkyl substances and inorganic analytes in groundwater and surface water used as sources for public water supply in West Virginia","interactions":[],"lastModifiedDate":"2022-09-27T13:40:14.016525","indexId":"sir20225067","displayToPublicDate":"2022-07-20T11:40:00","publicationYear":"2022","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":"2022-5067","displayTitle":"Occurrence of Per- and Polyfluoroalkyl Substances and Inorganic Analytes in Groundwater and Surface Water Used as Sources for Public Water Supply in West Virginia","title":"Occurrence of per- and polyfluoroalkyl substances and inorganic analytes in groundwater and surface water used as sources for public water supply in West Virginia","docAbstract":"<p>Per- and polyfluoroalkyl substances (PFAS) are widely observed anthropogenic compounds found in water supplies worldwide and increasingly linked with adverse health effects in humans. In 2019, the West Virginia Legislature recognized the contamination risk to public source-water supplies posed by PFAS and passed a resolution that required a statewide PFAS study. The purpose of the resolution was to understand the occurrence and distribution of PFAS contamination throughout the State’s rivers, lakes, and groundwater aquifers. The U.S. Geological Survey has worked in cooperation with the West Virginia Department of Environmental Protection and West Virginia Department of Health and Human Resources to collect raw-water samples at 279 public-water systems across West Virginia. Public-water systems sampled for this study were identified by the West Virginia Department of Health and Human Resources and included all community water systems in the State and all daycares and schools that operate their own water systems.</p><p>Raw source water was sampled for both groundwater and surface-water sites at the first available tap in the public-water system, prior to any treatment. One hundred and seventy-three samples were collected from groundwater sources and 106 samples were collected from surface-water sources. Parameters collected at the time of sampling included pH, specific conductance, water temperature, dissolved oxygen, turbidity, and alkalinity. PFAS was analyzed at all 279 sites, major ions and trace elements were analyzed at 272 sites, and nutrients were analyzed at 270 sites.</p><p>The type of source water used for public supply in West Virginia is generally dependent on geology with more groundwater sites sampled in high-yield aquifers such as karst and alluvium. Surface-water sites were more evenly distributed throughout the State and are often the only source used in areas underlain by lower-yielding fractured-rock aquifers. Twenty-four percent of the sites sampled for this study had at least 1 PFAS detected, 47 of which were in groundwater sources and 20 in surface-water sources. Five sites exceeded the U.S. Environmental Protection Agency’s health advisory for combined perfluorooctanoate and perfluorooctanesulfonate concentrations of 70 nanograms per liter. These sites were located in highly susceptible karst and alluvial groundwater aquifers on the east and west sides of the State.</p><p>Higher PFAS concentrations were more commonly found in groundwater than surface-water sources, and high concentrations and PFAS detections were generally concentrated in the Ohio River Valley and West Virginia’s eastern panhandle. PFAS was rarely detected in groundwater sites in fractured-rock aquifers and abandoned underground coal-mine aquifers in the Appalachian Plateaus Physiographic Province had very little PFAS detected. These data represent a baseline summary of source water in West Virginia. Additional studies may be needed to understand exposure to private homeowners with domestic-water sources, variability of PFAS concentrations over time, and PFAS in finished drinking water as evaluated by current and future drinking-water regulations.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225067","collaboration":"Prepared in cooperation with the West Virginia Department of Environmental Protection, Division of Water and Waste Management and the West Virginia Department of Health and Human Resources, Bureau for Public Health","usgsCitation":"McAdoo, M.A., Connock, G.T., and Messinger, T., 2022, Occurrence of per- and polyfluoroalkyl substances and inorganic analytes in groundwater and surface water used as sources for public water supply in West Virginia: U.S. Geological Survey Scientific Investigations Report 2022–5067, 37 p., https://doi.org/10.3133/sir20225067.","productDescription":"Report: ix, 37 p.; Data 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Virginia\",\"nation\":\"USA  \"}}]}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/virginia-and-west-virginia-water-science-center\" data-mce-href=\"https://www.usgs.gov/centers/virginia-and-west-virginia-water-science-center\">Virginia and West Virginia Water Science Center</a><br>U.S. Geological Survey<br>1730 East Parham Road<br>Richmond, VA 23228</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods of Study</li><li>Quality Assurance and Data Validation</li><li>Water Quality of West Virginia’s Public Source-Water Supplies</li><li>Relations between PFAS Concentrations and Source-Water Vulnerability</li><li>Summary and Conclusions</li><li>References Cited</li><li>Appendix 1. Estimated results for PFAS detected between the reporting level and minimum detection level</li><li>Appendix 2. Detections for PFAS analytes over the reporting level organized by result with site information</li></ul>","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2022-07-20","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"McAdoo, Mitchell A. 0000-0002-3895-0816 mmcadoo@usgs.gov","orcid":"https://orcid.org/0000-0002-3895-0816","contributorId":200287,"corporation":false,"usgs":true,"family":"McAdoo","given":"Mitchell","email":"mmcadoo@usgs.gov","middleInitial":"A.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":846911,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connock, Gregory T. 0000-0002-7111-7551","orcid":"https://orcid.org/0000-0002-7111-7551","contributorId":293288,"corporation":false,"usgs":true,"family":"Connock","given":"Gregory","email":"","middleInitial":"T.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846912,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Messinger, Terence 0000-0003-4084-9298 tmessing@usgs.gov","orcid":"https://orcid.org/0000-0003-4084-9298","contributorId":2717,"corporation":false,"usgs":true,"family":"Messinger","given":"Terence","email":"tmessing@usgs.gov","affiliations":[{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846913,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70233189,"text":"ofr20221040 - 2022 - Presented abstracts from the U.S. Geological Survey 2020 Rocky Mountain Region Science Exchange (September 15–17, 2020)","interactions":[],"lastModifiedDate":"2022-09-27T13:40:52.910482","indexId":"ofr20221040","displayToPublicDate":"2022-07-20T10:00:00","publicationYear":"2022","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":"2022-1040","displayTitle":"Presented Abstracts from the U.S. Geological Survey 2020 Rocky Mountain Region Science Exchange (September 15–17, 2020)","title":"Presented abstracts from the U.S. Geological Survey 2020 Rocky Mountain Region Science Exchange (September 15–17, 2020)","docAbstract":"<p>The U.S. Geological Survey Rocky Mountain Region hosted scientists, managers, program coordinators, and leadership team members for a virtual Science Exchange during September 15–17, 2020. The Science Exchange had 216 registered participants and included 48 talks over the 3-day period. Invited speakers presented information about the novel U.S. Geological Survey Earth Monitoring, Analysis, and Prediction (EarthMAP) concept. Scientists showcased their research and participated in discussions related to the EarthMAP concept and EarthMAP applications. In addition, the Colorado River Basin Pilot Project, one of the first EarthMAP Pilot Projects, was unveiled during the Science Exchange. This report provides synopses of session objectives and corresponding abstracts that were presented along with author affiliations and email address of the lead author. In addition, web links are provided for related programs and projects, and associated publications are referenced.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221040","usgsCitation":"Anderson, P.J., and Tillery, A.C., 2022, Presented abstracts from the U.S. Geological Survey 2020 Rocky Mountain Region Science Exchange (September 15–17, 2020): U.S. Geological Survey Open-File Report 2022–1040, 23 p., https://doi.org/10.3133/ofr20221040.","productDescription":"x, 23 p.","onlineOnly":"Y","ipdsId":"IP-132301","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":405563,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221040/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1040"},{"id":403936,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1040/ofr20221040.xml"},{"id":403935,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1040/images"},{"id":403934,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1040/ofr20221040.pdf","text":"Report","size":"2.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1040"},{"id":403933,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1040/coverthb.jpg"}],"contact":"<p>Director, <a href=\"https://www.usgs.gov/unified-interior-regions/region-7/science/colorado-river-basin-building-advanced-strategic?qt-science_%20center_objects=0#qt-science_center_objects/\" data-mce-href=\"https://www.usgs.gov/unified-interior-regions/region-7/science/colorado-river-basin-building-advanced-strategic?qt-science_%20center_objects=0#qt-science_center_objects/\">Region 7 - Upper Colorado Basin</a><br>U.S. Geological Survey<br>P.O. Box 25046, Mail Stop 911<br>Denver, CO 80225</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Session Objectives and Presented Abstracts</li><li>Conclusion</li><li>References Cited</li></ul>","publishedDate":"2022-07-20","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"editors":[{"text":"Anderson, Patrick J. 0000-0003-2281-389X andersonpj@usgs.gov","orcid":"https://orcid.org/0000-0003-2281-389X","contributorId":3590,"corporation":false,"usgs":true,"family":"Anderson","given":"Patrick","email":"andersonpj@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":846746,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Tillery, Anne C. 0000-0002-9508-7908 atillery@usgs.gov","orcid":"https://orcid.org/0000-0002-9508-7908","contributorId":2549,"corporation":false,"usgs":true,"family":"Tillery","given":"Anne","email":"atillery@usgs.gov","middleInitial":"C.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846747,"contributorType":{"id":2,"text":"Editors"},"rank":2}]}}
,{"id":70232646,"text":"dr1160 - 2022 - Concentrations of per- and polyfluoroalkyl substances (PFAS) in selected rivers and streams in Massachusetts, 2020","interactions":[],"lastModifiedDate":"2026-02-04T19:58:00.043538","indexId":"dr1160","displayToPublicDate":"2022-07-20T09:55:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1160","displayTitle":"Concentrations of Per- and Polyfluoroalkyl Substances (PFAS) in Selected Rivers and Streams in Massachusetts, 2020","title":"Concentrations of per- and polyfluoroalkyl substances (PFAS) in selected rivers and streams in Massachusetts, 2020","docAbstract":"<p>Water samples collected from 27 rivers and streams in Massachusetts were analyzed to characterize the presence and concentrations of per- and polyfluoroalkyl substances (collectively known as PFAS) in surface waters across the Commonwealth. Sampling sites were selected in urban rivers where PFAS were expected to be present, such as those that receive treated municipal wastewater, and in rural rivers that were not known to be affected by municipal wastewater. The samples were collected three times in 2020 from 64 sites, and were analyzed for 24 PFAS, 18 of which are included in the U.S. Environmental Protection Agency’s Method 537.1.</p><p>Samples were collected when the instantaneous flow of the rivers and streams were at base-flow condition to minimize PFAS input or dilution from stormwater runoff and overland flow. The analyses detected PFAS in samples from all 27 rivers and streams. The number of PFAS detected in each sample ranged from 2 to 16. Concentrations of individual PFAS ranged from no detectable concentrations (less than 1.74 nanograms per liter) to 109 nanograms per liter. Samples from sites associated with wastewater treatment facilities in urban areas had a larger number and variety of PFAS present, and at higher concentrations, than in samples from the more rural rivers. This report includes a summary of the chemical data and physical properties of both environmental and quality-control samples, and a description of procedures for the collection and processing of the samples.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1160","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Savoie, J.G., and Argue, D.M., 2022, Concentrations of per- and polyfluoroalkyl substances (PFAS) in selected rivers and streams in Massachusetts, 2020 (ver. 2.0, October 2023): U.S. Geological Survey Data Report 1160, 18 p., https://doi.org/10.3133/dr1160.","productDescription":"Report: vi, 18 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-129431","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":421269,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1160/full","linkFileType":{"id":5,"text":"html"}},{"id":421265,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P967NOOZ","text":"USGS data release","linkHelpText":"Environmental and quality-control data for per- and polyfluoroalkyl substances (PFAS) measured in selected rivers and streams in Massachusetts, 2020 (ver. 2.0, July 2023)"},{"id":421264,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/dr/1160/versionHist.txt","text":"Version History","size":"3.1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":499549,"rank":8,"type":{"id":36,"text":"NGMDB Index 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 \"}}]}","edition":"Version 1.0: July 20, 2022; Version 1.1: February 17, 2023; Version 2.0: October 2, 2023","contact":"<p><a href=\"mailto:dc_nweng%40usgs.gov?subject=\" data-mce-href=\"mailto:dc_nweng%40usgs.gov?subject=\">Director</a>, <a href=\"https://www.usgs.gov/centers/new-england-water\" data-mce-href=\"https://www.usgs.gov/centers/new-england-water\">New England Water Science Center</a><br>U.S. Geological Survey<br>10 Bearfoot Road<br>Northborough, MA 01532</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Study Design</li><li>Results of Analyses of Environmental and Quality-Control Samples</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Alpha Analytical, Inc. Sample Processing Information and Data Qualifiers With Corresponding U.S. Geological Survey Remark and Value Qualifier Codes</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2022-07-20","revisedDate":"2023-10-02","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Savoie, Jennifer G. 0000-0002-3906-6782 jsavoie@usgs.gov","orcid":"https://orcid.org/0000-0002-3906-6782","contributorId":194101,"corporation":false,"usgs":true,"family":"Savoie","given":"Jennifer","email":"jsavoie@usgs.gov","middleInitial":"G.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846181,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Argue, Denise M. 0000-0002-1096-5362","orcid":"https://orcid.org/0000-0002-1096-5362","contributorId":217252,"corporation":false,"usgs":true,"family":"Argue","given":"Denise","email":"","middleInitial":"M.","affiliations":[],"preferred":true,"id":846182,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70233355,"text":"ofr20221036 - 2022 - Intake efficiency field results for Federal Interagency Sedimentation Project bag samplers","interactions":[],"lastModifiedDate":"2022-07-21T10:55:22.5234","indexId":"ofr20221036","displayToPublicDate":"2022-07-20T08:30:00","publicationYear":"2022","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":"2022-1036","displayTitle":"Intake Efficiency Field Results for Federal Interagency Sedimentation Project Bag Samplers","title":"Intake efficiency field results for Federal Interagency Sedimentation Project bag samplers","docAbstract":"<p>The Federal Interagency Sedimentation Project (FISP) standardizes and advances sediment science among federal agencies. It is important to ensure that the FISP bag samplers perform isokinetically under all tested and approved conditions and collect samples that are representative of the stream or river cross-section. A measure of a sampler’s isokinetic behavior is its intake efficiency, which is defined as the ratio of the velocity through the nozzle entrance of the sampler to the ambient stream velocity. The intake efficiencies of all FISP bag samplers and nozzle sizes were evaluated for this report. Samples were obtained across 31 U.S. Geological Survey streamflow-gaging stations between July 15, 2013, and June 17, 2020, where data were collected with all four bag samplers (US D-96, D-96-A1, D-99, and DH-2), each using various 3/16-inch, 1/4-inch, or 5/16-inch diameter nozzles.</p><p>Water temperature and ambient stream velocity outside the nozzle are two of several factors that are known to affect the intake efficiency of bag samplers. A regression curve was fitted to these data through LOWESS (locally weighted scatterplot smoothing), and a Kruskal-Wallis test was executed for the various samplers and nozzle sizes. Based on these results, there is no statistical evidence to indicate that water temperature and stream velocity have a noticeable effect on intake efficiency when the samplers are deployed under isokinetic conditions. Likewise, there is no statistical evidence to indicate that the type of bag sampler and nozzle diameter have a direct effect on intake efficiency.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221036","usgsCitation":"Manaster, A.E., Landers, M.N., and Straub, T.D., 2022, Intake efficiency field results for Federal Interagency Sedimentation Project bag samplers: U.S. Geological Survey Open-File Report 2022–1036, 27 p., https://doi.org/10.3133/ofr20221036.","productDescription":"Report: iv, 27 p.; Database","onlineOnly":"Y","ipdsId":"IP-134358","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science 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Discussion</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li><li>Appendix 1. Intake Efficiency Field Data and Additional Figure</li></ul>","publishedDate":"2022-07-20","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Manaster, Adam E. 0000-0001-8183-4274","orcid":"https://orcid.org/0000-0001-8183-4274","contributorId":215663,"corporation":false,"usgs":true,"family":"Manaster","given":"Adam E.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846943,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landers, Mark N. 0000-0002-3014-0480","orcid":"https://orcid.org/0000-0002-3014-0480","contributorId":204323,"corporation":false,"usgs":true,"family":"Landers","given":"Mark","email":"","middleInitial":"N.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846944,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Straub, Timothy D. 0000-0002-5896-0851","orcid":"https://orcid.org/0000-0002-5896-0851","contributorId":215662,"corporation":false,"usgs":true,"family":"Straub","given":"Timothy","email":"","middleInitial":"D.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846945,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70233537,"text":"70233537 - 2022 - Predicting larval alewife transport in Lake Michigan using hydrodynamic and Lagrangian particle dispersion models","interactions":[],"lastModifiedDate":"2022-09-15T14:17:56.583793","indexId":"70233537","displayToPublicDate":"2022-07-20T06:53:02","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Predicting larval alewife transport in Lake Michigan using hydrodynamic and Lagrangian particle dispersion models","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Several species of fish in large lakes and marine environments have a pelagic larval stage, and are subject to variable transport that can ultimately regulate survival and recruitment success. Alewife,<span>&nbsp;</span><i>Alosa pseudoharengus</i>, are subject to transport by complex coastal currents during their pelagic larval stage (~ 30 d). We assessed backward-trajectory simulations, consisting of a Lagrangian particle dispersion model linked to the Finite Volume Community Ocean Model, to estimate likely hatch locations of aged larval alewife collected from locations on both the eastern and western sides of Lake Michigan during July 2015. We used four deployments of three satellite-tracked drifter buoys in coastal waters to assess model skill in estimating the origin of a drifter from its final location. We found that the trajectories of drifters varied greatly, depending on wind events and associated coastal transport processes, including upwelling/downwelling and coastal jet currents. In 2 of 12 cases, the backward trajectory simulations failed to predict the drifter origin, associated with transport of 170 km in a narrow coastal jet current. In the remaining 10 cases, the known drifter origin was within 3.5 km of the spatial patch of predicted possible origins for a scenario of horizontal diffusivity (188 m<sup>2</sup>&nbsp;s<sup>−1</sup>) consistent with the offshore model grid resolution. Modeled backward trajectories estimated that alewife originated from the same side of the lake where they were collected, within ~ 100 km of the collection site. Our paper demonstrates the utility of hydrodynamic models to estimate a region of origin for aged larval fish.</p></div></div>","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lno.12186","usgsCitation":"Rowe, M.D., Prendergast, S.E., Alofs, K., Bunnell, D.B., Rutherford, E.S., and Anderson, E.J., 2022, Predicting larval alewife transport in Lake Michigan using hydrodynamic and Lagrangian particle dispersion models: Limnology and Oceanography, v. 67, no. 9, p. 2042-2058, https://doi.org/10.1002/lno.12186.","productDescription":"17 p.","startPage":"2042","endPage":"2058","ipdsId":"IP-135521","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":447056,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/lno.12186","text":"External 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M","contributorId":293588,"corporation":false,"usgs":false,"family":"Alofs","given":"Karen M","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":847365,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bunnell, David B. 0000-0003-3521-7747","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":216540,"corporation":false,"usgs":true,"family":"Bunnell","given":"David","middleInitial":"B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":847366,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rutherford, Edward S.","contributorId":175426,"corporation":false,"usgs":false,"family":"Rutherford","given":"Edward","email":"","middleInitial":"S.","affiliations":[{"id":12789,"text":"NOAA Great Lakes Environmental Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":847367,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Eric J.","contributorId":140817,"corporation":false,"usgs":false,"family":"Anderson","given":"Eric","email":"","middleInitial":"J.","affiliations":[{"id":12789,"text":"NOAA Great Lakes Environmental Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":847368,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70243025,"text":"70243025 - 2022 - Fitness homeostasis across an experimental water gradient predicts species' geographic range and climatic breadth","interactions":[],"lastModifiedDate":"2023-04-27T11:33:02.701764","indexId":"70243025","displayToPublicDate":"2022-07-20T06:31:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Fitness homeostasis across an experimental water gradient predicts species' geographic range and climatic breadth","docAbstract":"<div class=\"abstract-group  metis-abstract\"><div class=\"article-section__content en main\"><p>Species range sizes and realized niche breadths vary tremendously. Understanding the source of this variation has been a long-term aim in evolutionary ecology and is a major tool in efforts to ameliorate the impacts of changing climates on species distributions. Species ranges that span a large climatic envelope can be achieved by a collection of specialized genotypes locally adapted to a small range of conditions, by genotypes with stable fitness across variable environments, or a combination of these factors. We asked whether fitness expressed along a key niche axis, water availability, could explain a species' realized niche breadth, its geographic range and climate breadth, in 11 species from a clade of jewelflowers whose range sizes vary by two orders of magnitude. Specifically, we explored whether the range size of a species was related to the ability of genotypes (maternal families) to maintain fitness across a range of experimental water availabilities based on 30-year historical field precipitation regimes. We operationally characterized fitness homeostasis through the coefficient of variation in fitness of a genotype (family) across the experimental water gradient. We found that species with genotypes that had high fitness homeostasis, low variation in fitness over our treatments, had larger climatic niche breadth and geographic range in their field distributions. The result was robust to alternate measures of fitness homeostasis. Our results show that the fitness homeostasis of genotypes can be a major factor contributing to niche breadth and range size in this clade. Fitness homeostasis can buffer species from loss of genetic diversity and under changing climates, provides time for adaptation to future conditions.</p></div></div>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3827","usgsCitation":"Pearse, I.S., McIntyre, P.J., Cacho, N.I., and Strauss, S.Y., 2022, Fitness homeostasis across an experimental water gradient predicts species' geographic range and climatic breadth: Ecology, v. 103, no. 12, e3827, 11 p., https://doi.org/10.1002/ecy.3827.","productDescription":"e3827, 11 p.","ipdsId":"IP-122725","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":416427,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-09-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":216680,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":870625,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McIntyre, Patrick J.","contributorId":182343,"corporation":false,"usgs":false,"family":"McIntyre","given":"Patrick","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":870626,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cacho, N. Ivalu","contributorId":304482,"corporation":false,"usgs":false,"family":"Cacho","given":"N.","email":"","middleInitial":"Ivalu","affiliations":[{"id":25354,"text":"Universidad Nacional Autónoma de México","active":true,"usgs":false}],"preferred":false,"id":870627,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Strauss, Sharon Y","contributorId":304483,"corporation":false,"usgs":false,"family":"Strauss","given":"Sharon","email":"","middleInitial":"Y","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":870628,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70233191,"text":"ofr20221056 - 2022 - Relative contributions of  suspended sediment between the upper Suiattle River Basin and a non-glacial tributary, Washington, May 2016–September 2017","interactions":[],"lastModifiedDate":"2026-03-27T20:27:07.048789","indexId":"ofr20221056","displayToPublicDate":"2022-07-19T12:01:14","publicationYear":"2022","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":"2022-1056","displayTitle":"Relative Contributions of Suspended Sediment between the Upper Suiattle River Basin and a Non-Glacial Tributary, Washington, May 2016–September 2017","title":"Relative contributions of  suspended sediment between the upper Suiattle River Basin and a non-glacial tributary, Washington, May 2016–September 2017","docAbstract":"<p class=\"p1\">Concentrations of suspended sediment were measured in discrete samples and turbidity was continuously monitored at four U.S. Geological Survey streamgages in western Washington State, including one gage on the Sauk River; two gages on the Suiattle River, a tributary to the Sauk River; and one gage on Downey Creek, a tributary to the Suiattle River. The Suiattle River is a sediment-rich stream with headwaters on Glacier Peak, a glaciated volcano in the northern Cascade Range.</p><p class=\"p1\">Contributions of suspended sediment to the Suiattle River from unglaciated tributaries, represented by Downey Creek, were compared to the contributions from Glacier Peak in the upper Suiattle River watershed. During summer 2017, a period for which complete records of discharge and sediment data were available for all three streamgages in the Suiattle River Basin, the suspended-sediment load from Downey Creek (drainage area [DA] 93 square kilometers [km<sup>2</sup>]) was 1,400 metric tons, which is equivalent to a sediment yield of about 15 metric tons per km<sup>2</sup>. During the same period, the suspended-sediment load from the upper Suiattle River (DA 176 km<sup>2</sup>) was 142,000 metric tons, or a sediment yield of about 800 metric tons per km<sup>2</sup>; and the suspended-sediment load from the lower Suiattle River (DA 733 km<sup>2</sup>) was 230,000 metric tons, or a sediment yield of about 300 metric tons per km<sup>2</sup>. The Downey Creek Basin accounts for 13 percent of the drainage area of the Suiattle River watershed but contributed only 0.6 percent of the suspended-sediment load over the summer of 2017 and water year 2017 <span>(October 1, 2016–September 30, 2017).</span> In contrast, the upper Suiattle River Basin, which accounts for 24 percent of the entire Suiattle River watershed, contributed 62 percent of the suspended-sediment load during the summer of 2017.</p><p class=\"p2\">Given the short period for which data were collected, it cannot be known with certainty whether the above values are representative of long-term means. The relatively minor contribution of suspended sediment from Downey Creek, however, is consistent with the expectation that the upper Suiattle River, which drains Glacier Peak, is the dominant contemporary source of suspended sediment to the Sauk River. During summer 2016, the suspended-sediment load in the upper Siuattle River (180,000 metric tons) was more than double the estimated load in the lower Sauk River (80,000 metric tons), even though the upper Suiattle River represents only 10 percent of the total contributing area to the lower Sauk River Basin. This ratio of relative contribution is interpreted as an indication of transient storage of sediment along the Suiattle and Sauk Rivers between the two streamgaging stations. In the glaciated upper Suiattle River Basin, sediment is transported by annual glacial-melt processes in spring and summer months, deposited during the summer base-flow period, and then remobilized by fall and winter floods for delivery to the lower Sauk River.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221056","collaboration":"Prepared in cooperation with the Sauk-Suiattle Indian Tribe","usgsCitation":"Jaeger, K.L., Anderson, S.W., Senter, C.A., Curran, C.A., and Morris, S., 2022, Relative contributions of  suspended sediment between the upper Suiattle River Basin and a non-glacial tributary, Washington, May 2016–September 2017: U.S. Geological Survey Open-File Report 2022–1056, 18 p., https://doi.org/10.3133/ofr20221056.","productDescription":"v, 18 p.","onlineOnly":"Y","ipdsId":"IP-132545","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":403971,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1056/ofr20221056.pdf","text":"Report","size":"8.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1056"},{"id":403970,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1056/coverthb.jpg"},{"id":501781,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113307.htm","linkFileType":{"id":5,"text":"html"}},{"id":403972,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221056/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1056"},{"id":404178,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W74K0K","text":"USGS data release","description":"USGS data release","linkHelpText":"Suspended sediment and water temperature data in the Suiattle River and the Downey Creek Tributary, Washington for select time periods over 2013 - 2017 (ver. 2.0, October 2021)"},{"id":403974,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1056/ofr20221056.XML"},{"id":403973,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1056/images"}],"country":"United States","state":"Washington","otherGeospatial":"Upper Suiattle River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.4,\n              48.0\n            ],\n            [\n              -121.0,\n              48.0\n            ],\n            [\n              -121.0,\n              48.4\n            ],\n            [\n              -121.4,\n              48.4\n            ],\n            [\n              -121.4,\n              48.0\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_wa@usgs.gov\" data-mce-href=\"mailto:dc_wa@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/wa-water\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/wa-water\">Washington Water Science Center</a><br>U.S. Geological Survey<br>934 Broadway, Suite 300<br>Tacoma, Washington 98402</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Description of Study Area</li><li>Study Sites and Discharge Conditions for Study Period</li><li>Study Methods</li><li>Estimates of Turbidity, Suspended-Sediment Load, and Sediment Yield</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li></ul>","publishedDate":"2022-07-19","noUsgsAuthors":false,"publicationDate":"2022-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Jaeger, Kristin L. 0000-0002-1209-8506 kjaeger@usgs.gov","orcid":"https://orcid.org/0000-0002-1209-8506","contributorId":199335,"corporation":false,"usgs":true,"family":"Jaeger","given":"Kristin","email":"kjaeger@usgs.gov","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":false,"id":846749,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Scott W. 0000-0003-1678-5204 swanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-1678-5204","contributorId":107001,"corporation":false,"usgs":true,"family":"Anderson","given":"Scott","email":"swanderson@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":false,"id":846750,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Senter, Craig A. 0000-0002-5479-3080 csenter@usgs.gov","orcid":"https://orcid.org/0000-0002-5479-3080","contributorId":150044,"corporation":false,"usgs":true,"family":"Senter","given":"Craig","email":"csenter@usgs.gov","middleInitial":"A.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846751,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Curran, Christopher A. 0000-0001-8933-416X ccurran@usgs.gov","orcid":"https://orcid.org/0000-0001-8933-416X","contributorId":1650,"corporation":false,"usgs":true,"family":"Curran","given":"Christopher","email":"ccurran@usgs.gov","middleInitial":"A.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846752,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morris, Scott","contributorId":196797,"corporation":false,"usgs":false,"family":"Morris","given":"Scott","affiliations":[],"preferred":false,"id":846753,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70233274,"text":"fs20223054 - 2022 - New Jersey and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T12:02:16.04284","indexId":"fs20223054","displayToPublicDate":"2022-07-19T09:20:57","publicationYear":"2022","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":"2022-3054","displayTitle":"New Jersey and Landsat","title":"New Jersey and Landsat","docAbstract":"<p>New Jersey ranks among the smallest of States but packs a lot within its borders. Of course, that includes the more than 9 million people who make it the most densely populated State, but it also includes diverse landscapes. Ranging from Atlantic Ocean barrier islands and beaches to the Appalachian Mountains, and Pine Barrens forests to swampland, the “Garden State” retains remnants of an agricultural past with produce, horse, and dairy farms and plant nurseries.</p><p>The third State to join the Union has had a strong geographic presence in U.S. history. More than 200 American Revolution battles and skirmishes were fought in New Jersey—more than in any other State. Manufacturing, tourism, and fishing have each had a significant effect on New Jersey’s industrial history. Today, many residents commute from this strategic location to work in New York City, just across the Hudson River to the northeast, or in Philadelphia, just across the Delaware River to the west.</p><p>A dense population and climate change can increase risks for residents and the natural resources around them. Landsat helps officials monitor and plan for resilient cities and landscapes. Here are a few specific ways Landsat benefits New Jersey.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223054","usgsCitation":"U.S. Geological Survey, 2022, New Jersey and Landsat: U.S. Geological Survey Fact Sheet 2022–3054, 2 p., https://doi.org/10.3133/fs20223054.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-140140","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":406518,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223054/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":404012,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3054/coverthb.jpg"},{"id":404013,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3054/fs20223054.pdf","text":"Report","size":"3.67 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Jersey\",\"nation\":\"USA  \"}}]}","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/programs/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/programs/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Identifying City Hotspots</li><li>Reducing Wildfire Risks</li><li>Analyzing Coastal Wetlands</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-07-19","noUsgsAuthors":false,"publicationDate":"2022-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Water Resources Division, U.S. Geological Survey","contributorId":128075,"corporation":true,"usgs":false,"organization":"Water Resources Division, U.S. Geological Survey","id":846920,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70233469,"text":"70233469 - 2022 - Relocated beaver can increase water storage and decrease stream temperature in headwater streams","interactions":[],"lastModifiedDate":"2022-07-21T14:06:28.869203","indexId":"70233469","displayToPublicDate":"2022-07-19T09:01:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Relocated beaver can increase water storage and decrease stream temperature in headwater streams","docAbstract":"<p><span>Many areas are experiencing increasing stream temperatures due to climate change, and some are experiencing reduced summer stream flows and water availability. Because dam building and pond formation by beaver can increase water storage, stream cooling, and riparian ecosystem resilience, beaver have been proposed as a potential climate adaption tool. Despite the large number of studies that have evaluated how beaver activity may affect hydrology and water temperature, few experimental studies have quantified these outcomes following beaver relocation. We evaluated changes in temperature and water storage following the relocation of 69 beaver into 13 headwater stream reaches of the Skykomish River watershed within the Snohomish River basin, Washington, USA. We evaluated how beaver dams affected surface and groundwater storage and stream temperature. Successful relocations created 243 m</span><sup>3</sup><span>&nbsp;of surface water storage per 100 m of stream in the first year following relocation. Dams raised water table elevations by up to 0.33 m and stored approximately 2.4 times as much groundwater as surface water per relocation reach. Stream reaches downstream of dams exhibited an average decrease of 2.3°C during summer base-flow conditions. We also assessed how dam age, condition, maintenance frequency, and pond morphology influenced stream temperature at naturally colonized wetland complexes. Our findings demonstrate that dam building can increase water storage and reduce stream temperatures in the first year following successful beaver relocation. Fluvial and floodplain morphology of candidate reaches for relocation is an important consideration because it determines the type and magnitude of response. Relocation to reaches with existing small, abandoned ponds may address thermal criteria by conversion from warming to cooling reaches, whereas relocation within large, abandoned complexes or vacant habitat may result in greater water storage. Although beaver relocation can be an effective climate adaptation strategy to retain more stable hydrologic regimes and water quality in our study area, there appear to be regionally specific environmental and geomorphic factors that influence how beaver affect water storage and temperature. More research is needed to investigate how and why these regional differences affect water storage and stream temperature response in beaver-influenced systems.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4168","usgsCitation":"Dittbrenner, B.J., Schilling, J.W., Torgersen, C.E., and Lawler, J.J., 2022, Relocated beaver can increase water storage and decrease stream temperature in headwater streams: Ecosphere, v. 13, no. 7, e4168, 17 p., https://doi.org/10.1002/ecs2.4168.","productDescription":"e4168, 17 p.","ipdsId":"IP-134665","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447081,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4168","text":"Publisher Index Page"},{"id":404214,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Skykomish River watershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.23800659179686,\n              47.6737103919566\n            ],\n            [\n              -121.15,\n              47.6737103919566\n            ],\n            [\n              -121.15,\n              48.026672195436014\n            ],\n            [\n              -122.23800659179686,\n              48.026672195436014\n            ],\n            [\n              -122.23800659179686,\n              47.6737103919566\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"13","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Dittbrenner, Benjamin J.","contributorId":202890,"corporation":false,"usgs":false,"family":"Dittbrenner","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":847172,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schilling, Jason W.","contributorId":202892,"corporation":false,"usgs":false,"family":"Schilling","given":"Jason","email":"","middleInitial":"W.","affiliations":[{"id":36547,"text":"Tulalip Tribes Natural Resources","active":true,"usgs":false}],"preferred":false,"id":847173,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":146935,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":847174,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lawler, Joshua J.","contributorId":73327,"corporation":false,"usgs":false,"family":"Lawler","given":"Joshua","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":847175,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70236158,"text":"70236158 - 2022 - Prioritizing pharmaceutical contaminants in Great Lakes tributaries using risk-based screening techniques","interactions":[],"lastModifiedDate":"2022-08-30T13:41:54.415555","indexId":"70236158","displayToPublicDate":"2022-07-19T08:37:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Prioritizing pharmaceutical contaminants in Great Lakes tributaries using risk-based screening techniques","docAbstract":"<p><span>In a study of 44 diverse sampling sites across 16 Great Lakes tributaries, 110 pharmaceuticals were detected of 257 monitored. The present study evaluated the ecological relevance of detected chemicals and identified heavily impacted areas to help inform resource managers and guide future investigations. Ten pharmaceuticals (caffeine, nicotine, albuterol, sulfamethoxazole, venlafaxine, acetaminophen, carbamazepine, gemfibrozil, metoprolol, and thiabendazole) were distinguished as having the greatest potential for biological effects based on comparison to screening-level benchmarks derived using information from two biological effects databases, the ECOTOX Knowledgebase and the ToxCast database. Available evidence did not suggest substantial concern for 75% of the monitored pharmaceuticals, including 147 undetected pharmaceuticals and 49 pharmaceuticals with screening-level alternative benchmarks. However, because of a lack of biological effects information, screening values were not available for 51 detected pharmaceuticals. Samples containing the greatest pharmaceutical concentrations and having the highest detection frequencies were from Lake Erie, southern Lake Michigan, and Lake Huron tributaries. Samples collected during low-flow periods had higher pharmaceutical concentrations than those collected during increased-flow periods. The wastewater-treatment plant effluent content in streams correlated positively with pharmaceutical concentrations. However, deviation from this correlation demonstrated that secondary factors, such as multiple pharmaceutical sources, were likely present at some sites. Further research could investigate high-priority pharmaceuticals as well as those for which alternative benchmarks could not be developed.&nbsp;</span></p>","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.5403","usgsCitation":"Pronschinske, M.A., Corsi, S., DeCicco, L.A., Furlong, E., Ankley, G.T., Blackwell, B., Villeneuve, D., Lenaker, P.L., and Nott, M.A., 2022, Prioritizing pharmaceutical contaminants in Great Lakes tributaries using risk-based screening techniques: Environmental Toxicology and Chemistry, v. 41, no. 9, p. 2221-2239, https://doi.org/10.1002/etc.5403.","productDescription":"19 p.","startPage":"2221","endPage":"2239","ipdsId":"IP-138627","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":447090,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.5403","text":"Publisher Index 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,{"id":70236308,"text":"70236308 - 2022 - Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not","interactions":[],"lastModifiedDate":"2022-09-01T12:08:58.021882","indexId":"70236308","displayToPublicDate":"2022-07-19T07:06:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1818,"text":"Geoscientific Model Development","active":true,"publicationSubtype":{"id":10}},"title":"Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not","docAbstract":"<p>The root-mean-squared error (RMSE) and mean absolute error (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide which is more relevant. In a recent reprise to the 200-year debate over their use,&nbsp;Willmott and Matsuura&nbsp;(2005)&nbsp;and&nbsp;Chai and Draxler&nbsp;(2014)&nbsp;give arguments for favoring one metric or the other. However, this comparison can present a false dichotomy. Neither metric is inherently better: RMSE is optimal for normal (Gaussian) errors, and MAE is optimal for Laplacian errors. When errors deviate from these distributions, other metrics are superior.</p>","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-15-5481-2022","usgsCitation":"Hodson, T.O., 2022, Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not: Geoscientific Model Development, v. 15, p. 5481-5487, https://doi.org/10.5194/gmd-15-5481-2022.","productDescription":"7 p.","startPage":"5481","endPage":"5487","ipdsId":"IP-136463","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":447095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-15-5481-2022","text":"Publisher Index Page"},{"id":406059,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationDate":"2022-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Hodson, Timothy O. 0000-0003-0962-5130","orcid":"https://orcid.org/0000-0003-0962-5130","contributorId":78634,"corporation":false,"usgs":true,"family":"Hodson","given":"Timothy","email":"","middleInitial":"O.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":850544,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70233192,"text":"fs20223053 - 2022 - North Dakota and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T12:02:48.50367","indexId":"fs20223053","displayToPublicDate":"2022-07-19T05:41:07","publicationYear":"2022","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":"2022-3053","displayTitle":"North Dakota and Landsat","title":"North Dakota and Landsat","docAbstract":"<p>The State of North Dakota once did not figure prominently in the Nation’s economy. The sparsely populated State supported food production, and hunters and anglers were drawn to its lakes, rivers, and wide-open spaces, but its economy was overshadowed by that of other States. However, the State and its prairie expanses recently rocketed from an economic afterthought to a national energy leader with the soaring production of oil and natural gas in the Bakken oil patch.</p><p>The Bakken development has been transformative for North Dakota’s landscapes in myriad ways. It has boosted economic output, drawn thousands of new residents to cities like Williston and Watford City, and led to a proliferation of oil and gas pads.</p><p>In the past two decades, North Dakota experienced other major changes, such as the expansion of the depressional wetlands of the Prairie Pothole Region on the eastern side of the State. These critical breeding areas for waterfowl, which stretch across Minnesota, South Dakota, North Dakota, and Canada, are home to more than 50 percent of North America’s migratory birds.</p><p>Changes from oil and gas production, urban development, and wetland resurgence can all be tracked over time using the unparalleled Earth observation record of the U.S. Geological Survey Landsat data archive. Its 50-year record of repeat imagery also aids in the monitoring, cataloging, and management of cropland, invasive insect species, and natural or human-made disaster recovery. Here are just a few examples of the benefits offered to North Dakota by the Landsat Program.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223053","usgsCitation":"U.S. Geological Survey, 2022, North Dakota and Landsat: U.S. Geological Survey Fact Sheet 2022–3053, 2 p., https://doi.org/10.3133/fs20223053.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-142198","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":406516,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223053/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":404519,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3053/images"},{"id":404518,"rank":3,"type":{"id":31,"text":"Publication 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Dakota\",\"nation\":\"USA  \"}}]}","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/programs/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/programs/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Tracking Changes from Energy Development</li><li>Monitoring Crops from Above</li><li>Watching Over Wetlands</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-07-19","noUsgsAuthors":false,"publicationDate":"2022-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":127955,"corporation":true,"usgs":false,"organization":"U.S. Geological 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,{"id":70233190,"text":"fs20223049 - 2022 - Indiana and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T12:04:03.759928","indexId":"fs20223049","displayToPublicDate":"2022-07-18T16:34:23","publicationYear":"2022","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":"2022-3049","displayTitle":"Indiana and Landsat","title":"Indiana and Landsat","docAbstract":"<p>Natural resources have always been a strength for Indiana. Once largely covered by forest, the State now includes a mix of forest, farmland, wetlands, and small lakes. In fact, farms and forested areas make up more than 80 percent of the land. The Ohio River forms the southern border, and the south shore of Lake Michigan contrasts with urban development in the northwestern corner. Indiana Dunes National and State Parks reside just east of the Chicago metropolitan area.</p><p>Agriculture adds more than $30 billion to the economy in Indiana, which placed eighth in the country for agricultural exports at $4.6 billion in 2017. Indiana ranks in the top five States nationally for the production of corn and soybeans. The “Hoosier State” also grows sizable crops of popcorn, spearmint, peppermint, pumpkins, tomatoes, and watermelon. Additionally, hogs, cattle, dairy, and poultry contribute to the agricultural economy. Other industries important to Indiana include manufacturing, medicine, energy, and mining. Mineral sources vary from coal, building stone, and gypsum to sand, gravel, and shale.</p><p>Landsat can help monitor the condition of natural resources and the effects of extreme weather events. Here are several ways Landsat has benefited Indiana.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223049","usgsCitation":"U.S. Geological Survey, 2022, Indiana and Landsat: U.S. Geological Survey Fact Sheet 2022–3049, 2 p., https://doi.org/10.3133/fs20223049.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-142164","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":403959,"rank":2,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3049/fs20223049.XML"},{"id":403961,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223049/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":403958,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3049/fs20223049.pdf","text":"Report","size":"6.78 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 \"}}]}","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/programs/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/programs/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey <br>12201 Sunrise Valley Drive <br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Keeping an Eye on Crops</li><li>Assessing Flood Damage</li><li>Monitoring Landscape Change</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-07-18","noUsgsAuthors":false,"publicationDate":"2022-07-18","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":147999,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":846748,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70233247,"text":"70233247 - 2022 - Estratigrafía preliminar del flanco Este del volcán de Santa Ana","interactions":[],"lastModifiedDate":"2024-02-22T15:37:14.957331","indexId":"70233247","displayToPublicDate":"2022-07-18T09:33:57","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Estratigrafía preliminar del flanco Este del volcán de Santa Ana","docAbstract":"<p>We present the eruption sequence for the east flank of Santa Ana volcano, which we divide into the sections above and below the Tierra Blanca Joven (TBJ) formation. The sequence below the TBJ suggests a series of mafic magmatic eruptions that began before 7,800 cal BP and continued until after 5,800 cal BP. These eruptions emplaced tephra-fall and pyroclastic-density-current deposits. The sequence above the TBJ are deposits from recent eruptions during the last 1.5 ky. Most of these eruptions were phreatomagmatic with associated pyroclastic surges. The two sequences demonstrate a shift in eruptive style possibly related to an increase in water volume in the hydrothermal system.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Memoria, XIV congreso geologica de America Central & VII congreso geologico nacional","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"Spanish","publisher":"COAMSS/OPAMSS","usgsCitation":"Lemus, D., Harpel, C., Garcia, A.V., Escobar, D., Hernandez, A., and Alvarenga, E., 2022, Estratigrafía preliminar del flanco Este del volcán de Santa Ana, <i>in</i> Memoria, XIV congreso geologica de America Central & VII congreso geologico nacional, p. R14-1-R14-5.","productDescription":"5 p.","startPage":"R14-1","endPage":"R14-5","ipdsId":"IP-141070","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":425876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":425875,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://opamss.org.sv/ova_doc/publicaciones-de-congreso-xiv-congreso-geologico-de-america-central/","linkFileType":{"id":5,"text":"html"}}],"country":"El Salvador","otherGeospatial":"Santa Ana volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -89.66204930512441,\n              13.861989677410719\n            ],\n            [\n              -89.66204930512441,\n              13.795862836104703\n            ],\n            [\n              -89.55685050851551,\n              13.795862836104703\n            ],\n            [\n              -89.55685050851551,\n              13.861989677410719\n            ],\n            [\n              -89.66204930512441,\n              13.861989677410719\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lemus, Dennis","contributorId":293267,"corporation":false,"usgs":false,"family":"Lemus","given":"Dennis","email":"","affiliations":[{"id":63262,"text":"Ministerio de Ambiente y Recursos Naturales de El Salvador","active":true,"usgs":false}],"preferred":false,"id":846905,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harpel, Christopher 0000-0001-8587-7845","orcid":"https://orcid.org/0000-0001-8587-7845","contributorId":204746,"corporation":false,"usgs":true,"family":"Harpel","given":"Christopher","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":846906,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garcia, Angela V.","contributorId":293247,"corporation":false,"usgs":false,"family":"Garcia","given":"Angela","email":"","middleInitial":"V.","affiliations":[{"id":63262,"text":"Ministerio de Ambiente y Recursos Naturales de El Salvador","active":true,"usgs":false}],"preferred":false,"id":846907,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Escobar, Demetrio","contributorId":293248,"corporation":false,"usgs":false,"family":"Escobar","given":"Demetrio","email":"","affiliations":[{"id":63262,"text":"Ministerio de Ambiente y Recursos Naturales de El Salvador","active":true,"usgs":false}],"preferred":false,"id":846908,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hernandez, Alexander","contributorId":293268,"corporation":false,"usgs":false,"family":"Hernandez","given":"Alexander","affiliations":[{"id":63269,"text":"Universidad de El Salvador, Facultad Multidisciplinaria de Occidente","active":true,"usgs":false}],"preferred":false,"id":846909,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Alvarenga, Estefany","contributorId":293269,"corporation":false,"usgs":false,"family":"Alvarenga","given":"Estefany","email":"","affiliations":[{"id":63269,"text":"Universidad de El Salvador, Facultad Multidisciplinaria de Occidente","active":true,"usgs":false}],"preferred":false,"id":846910,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70233224,"text":"70233224 - 2022 - Nuevos datos: Avalancha de escombros de Acajutla, volcán Santa Ana","interactions":[],"lastModifiedDate":"2024-02-22T15:33:26.223523","indexId":"70233224","displayToPublicDate":"2022-07-18T09:19:44","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Nuevos datos: Avalancha de escombros de Acajutla, volcán Santa Ana","docAbstract":"<p> The Acajutla debris-avalanche deposit is dated to about 40,000 cal BP. The dating is based on two 14C dates on pieces of wood from the debris-avalanche deposit recovered from a core at the Santa Águeda School Center. The debris-avalanche deposit overlies a 1.2-m-thick paleosol and four ash layers. One of these ash layers is geochemically correlated to the Los Chocoyos ash from Atitlán Caldera, while the others are possibly from eruptions of Coatepeque and Ilopango Calderas. The new data, collected in well reports from the years 1966 to 2019, include deposit thicknesses identified in 25 wells for water monitoring, bathymetric data from nautical charts, GEBCO’s grids, updated topographic data from LIDAR, and a reassessment of the deposit’s lateral limits. This new data will allow us to better constrain the volume of the Acajutla debris-avalanche deposit.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Memoria, XIV congreso geologica de America Central & VII congreso geologico nacional","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"Spanish","publisher":"COAMSS/OPAMSS","usgsCitation":"Garcia, A.V., Harpel, C., Hernandez, W., Escobar, D., Mixco, L.E., Lewis, C., and Scott Cummings, L., 2022, Nuevos datos: Avalancha de escombros de Acajutla, volcán Santa Ana, <i>in</i> Memoria, XIV congreso geologica de America Central & VII congreso geologico nacional, p. R54-1-R54-4.","productDescription":"4 p.","startPage":"R54-1","endPage":"R54-4","ipdsId":"IP-141071","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":425867,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://opamss.org.sv/ova_doc/publicaciones-de-congreso-xiv-congreso-geologico-de-america-central/"},{"id":425868,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"El Salvador","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -89.60202528025114,\n              13.862680525535808\n            ],\n            [\n              -89.68026892718665,\n              13.862680525535808\n            ],\n            [\n              -89.68026892718665,\n              13.783268152881789\n            ],\n            [\n              -89.60202528025114,\n              13.783268152881789\n            ],\n            [\n              -89.60202528025114,\n              13.862680525535808\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Garcia, Angela V.","contributorId":293247,"corporation":false,"usgs":false,"family":"Garcia","given":"Angela","email":"","middleInitial":"V.","affiliations":[{"id":63262,"text":"Ministerio de Ambiente y Recursos Naturales de El Salvador","active":true,"usgs":false}],"preferred":false,"id":846839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harpel, Christopher 0000-0001-8587-7845","orcid":"https://orcid.org/0000-0001-8587-7845","contributorId":204746,"corporation":false,"usgs":true,"family":"Harpel","given":"Christopher","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":846840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hernandez, Walter","contributorId":218214,"corporation":false,"usgs":false,"family":"Hernandez","given":"Walter","email":"","affiliations":[{"id":39782,"text":"Ministerio de Medio Ambiente y Recursos Naturales, San Salvador, El Salvador","active":true,"usgs":false}],"preferred":false,"id":846841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Escobar, Demetrio","contributorId":293248,"corporation":false,"usgs":false,"family":"Escobar","given":"Demetrio","email":"","affiliations":[{"id":63262,"text":"Ministerio de Ambiente y Recursos Naturales de El Salvador","active":true,"usgs":false}],"preferred":false,"id":846842,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mixco, Luis E.","contributorId":293249,"corporation":false,"usgs":false,"family":"Mixco","given":"Luis","email":"","middleInitial":"E.","affiliations":[{"id":63262,"text":"Ministerio de Ambiente y Recursos Naturales de El Salvador","active":true,"usgs":false}],"preferred":false,"id":846843,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lewis, Charles 0000-0001-5848-937X","orcid":"https://orcid.org/0000-0001-5848-937X","contributorId":293250,"corporation":false,"usgs":false,"family":"Lewis","given":"Charles","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":846844,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Scott Cummings, Linda","contributorId":293251,"corporation":false,"usgs":false,"family":"Scott Cummings","given":"Linda","email":"","affiliations":[{"id":63265,"text":"PaleoResearch Institute","active":true,"usgs":false}],"preferred":false,"id":846845,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70233571,"text":"70233571 - 2022 - Assessing spatial transferability of a random forest metamodel for predicting drainage fraction","interactions":[],"lastModifiedDate":"2022-07-26T12:04:54.409941","indexId":"70233571","displayToPublicDate":"2022-07-16T06:59:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Assessing spatial transferability of a random forest metamodel for predicting drainage fraction","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab010\" class=\"abstract author\"><div id=\"as010\"><p id=\"sp0010\"><span>Fully distributed&nbsp;hydrological models&nbsp;are widely used in&nbsp;groundwater management, but model speed and data requirements impede their use for decision support purposes. Metamodels provide a simpler and faster model which emulates the underlying complex model using machine learning techniques. However, metamodel predictions beyond the ranges, in space and/or time, of training data are highly uncertain, and thus it is important to assess the predictive model performance to ranges outside the training data, i.e.,&nbsp;</span><i>model transferability</i>. We present a novel methodology for evaluating model transferability to areas not contained in the training data set, based on various metrics that quantify the differences in covariate distributions between training and testing data. The transferability method can be employed as a screening tool to assess the suitability of a metamodel for spatial prediction beyond its training domain. We evaluated this transferability approach on a Random Forest metamodel of a 1000&nbsp;km<sup>2</sup><span>&nbsp;</span>fully distributed coupled groundwater model for predicting drainage fraction, the partitioning of infiltrating water between drains and groundwater. We conducted spatial cross-validation on 9 holdout sub-basins to assess metamodel transferability beyond sampling locations and compared this estimate with a random split-sample validation test. Using mappable covariates only, the metamodel showed high performance (R<sup>2</sup>&nbsp;=&nbsp;0.79) tested on a 20% randomly sampled holdout. Conversely, metamodel performance significantly decreased for the 9 spatial holdouts (R<sup>2</sup><span>&nbsp;</span>ranging from 0.13 to 0.61). We document that the proposed transferability metric correlates with metamodel predictive performance, and demonstrate its use to assess model transferability to datasets outside the training data spatial domain.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2022.128177","usgsCitation":"Bjerre, E., Fienen, M., Schneider, R., Koch, J., and Højberg, A., 2022, Assessing spatial transferability of a random forest metamodel for predicting drainage fraction: Journal of Hydrology, v. 612, no. Part B, 128177, 11 p., https://doi.org/10.1016/j.jhydrol.2022.128177.","productDescription":"128177, 11 p.","ipdsId":"IP-141041","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":447100,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2022.128177","text":"Publisher Index Page"},{"id":404448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Denmark","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              7.965087890625,\n              55.78892895389262\n            ],\n            [\n              9.5361328125,\n              55.78892895389262\n            ],\n            [\n              9.5361328125,\n              56.71053615360101\n            ],\n            [\n              7.965087890625,\n              56.71053615360101\n            ],\n            [\n              7.965087890625,\n              55.78892895389262\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"612","issue":"Part B","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bjerre, Elisa","contributorId":293621,"corporation":false,"usgs":false,"family":"Bjerre","given":"Elisa","affiliations":[{"id":63347,"text":"Univeristy of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":847440,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":847441,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schneider, Raphael","contributorId":293622,"corporation":false,"usgs":false,"family":"Schneider","given":"Raphael","email":"","affiliations":[{"id":63347,"text":"Univeristy of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":847442,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Koch, Julian","contributorId":293623,"corporation":false,"usgs":false,"family":"Koch","given":"Julian","email":"","affiliations":[{"id":63347,"text":"Univeristy of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":847443,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Højberg, Anker L.","contributorId":187776,"corporation":false,"usgs":false,"family":"Højberg","given":"Anker L.","affiliations":[],"preferred":false,"id":847444,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
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