{"pageNumber":"454","pageRowStart":"11325","pageSize":"25","recordCount":165459,"records":[{"id":70223458,"text":"ofr20211083 - 2021 - Evaluation of movement and survival of juvenile steelhead (Oncorhynchus mykiss) and coho salmon (Oncorhynchus kisutch) in the Klickitat River, Washington, 2018–2019","interactions":[],"lastModifiedDate":"2021-08-30T11:46:21.021396","indexId":"ofr20211083","displayToPublicDate":"2021-08-27T08:30:54","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1083","displayTitle":"Evaluation of Movement and Survival of Juvenile Steelhead (<em>Oncorhynchus mykiss</em>) and Coho Salmon (<em>Oncorhynchus kisutch</em>) in the Klickitat River, Washington, 2018–2019","title":"Evaluation of movement and survival of juvenile steelhead (Oncorhynchus mykiss) and coho salmon (Oncorhynchus kisutch) in the Klickitat River, Washington, 2018–2019","docAbstract":"<p class=\"p1\">A 2-year telemetry study was conducted April–July in 2018 and 2019 to evaluate migration behavior and survival of juvenile steelhead (<i>Oncorhynchus mykiss</i>) and coho salmon (<i>O. kisutch</i>) in the Klickitat River, Washington. A total of 612 natural-origin steelhead, collected in a smolt trap on the Klickitat River, were tagged, released, and monitored as they outmigrated through the lower 17 kilometers (km) of the Klickitat River, and in the 52 km reach between the mouth of the Klickitat River and Bonneville Dam. The primary goal of the steelhead study was to estimate survival through the Klickitat River delta, the 2 km reach located at the confluence of the Klickitat and Columbia rivers. A total of 400 hatchery-origin coho salmon were tagged and released at the Klickitat Hatchery and monitored during migration through the lower 68 km of the Klickitat River and in the Columbia River to Bonneville Dam. The primary goals of the coho salmon study were (1) to estimate survival through the Klickitat River delta and (2) to determine residence time in the Klickitat River to assess potential for interactions with rearing natural-origin fish.</p><p class=\"p1\">Many tagged steelhead and coho salmon moved quickly downstream and left the Klickitat River shortly after release. Median elapsed time from release to Klickitat River exit ranged from 1.4 to 1.5 days for steelhead, and from 5.1 to 12.9 days for coho salmon during the two-year study. Ten percent of the tagged coho salmon in 2018 remained in the Klickitat River for 21.9–29.2 days before entering the Columbia River. In 2019, ten percent of the tagged coho salmon remained in the Klickitat River for 36.0–45.5 days before entering the Columbia River. This suggests that some hatchery fish spend considerable time in the river after hatchery release. Migration rates were consistently slow for both species in the Klickitat River delta compared to upstream reaches of the free-flowing Klickitat River and downstream reaches of the Columbia River. Similarly, reach-specific survival was highest in free-flowing reaches of the Klickitat River and lowest near the Klickitat River delta. Cumulative survival from release to sites located downstream of the Klickitat River delta were 0.78 for juvenile steelhead in both 2018 and 2019, and 0.57 and 0.61 for juvenile coho salmon in 2018 and 2019. Standardized survival estimates (survival per 100 river kilometers) were 0.243 in 2018 and 0.302 in 2019 for steelhead, and 0.100 in 2018 and 0.153 in 2019 for coho salmon. These estimates of standardized survival are low compared to similar estimates from other rivers in Washington, Oregon, Idaho, and California. This study provided new information about survival and residence time of juvenile steelhead and coho salmon in the Klickitat River. Additional studies would be helpful to understand factors affecting outmigration survival and overlap between hatchery-origin and natural-original juvenile steelhead and coho salmon in the system.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211083","collaboration":"Prepared in cooperation with Yakama Nation Fisheries","usgsCitation":"Evans, S.D., Lindley, D.S., Kock, T.J., Hansen, A.C., Perry, R.W., Zendt, J.S., and Romero, N., 2021, Evaluation of movement and survival of juvenile steelhead (Oncorhynchus mykiss) and coho salmon (Oncorhynchus kisutch) in the Klickitat River, Washington, 2018–2019: U.S. Geological Survey Open-File Report 2021–1083, 20 p., https://doi.org/10.3133/ofr20211083.","productDescription":"vi, 17 p.","onlineOnly":"Y","ipdsId":"IP-126889","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":388572,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1083/coverthb.jpg"},{"id":388573,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1083/ofr20211083.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1083"}],"country":"United States","state":"Washington","otherGeospatial":"Klickitat River, Columbia River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.70654296874999,\n              45.583289756006316\n            ],\n            [\n              -120.69580078124999,\n              45.583289756006316\n            ],\n            [\n              -120.71777343749997,\n              45.98169518512228\n            ],\n            [\n              -121.75048828124997,\n              45.96642454131025\n            ],\n            [\n              -121.70654296874999,\n              45.583289756006316\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/wfrc\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/wfrc\">Western Fisheries Research Center</a><br>U.S. Geological Survey<br>6505 NE 65th Street<br>Seattle, Washington 98115-5016</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results</li><li>Discussion</li><li>References Cited</li><li>Appendix 1. Travel Time, Survival, and Detection Probability Tables</li></ul>","publishedDate":"2021-08-27","noUsgsAuthors":false,"publicationDate":"2021-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Evans, Scott D. 0000-0003-0452-7726 sdevans@usgs.gov","orcid":"https://orcid.org/0000-0003-0452-7726","contributorId":4408,"corporation":false,"usgs":true,"family":"Evans","given":"Scott","email":"sdevans@usgs.gov","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":822074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lindley, David S.","contributorId":264839,"corporation":false,"usgs":false,"family":"Lindley","given":"David","email":"","middleInitial":"S.","affiliations":[{"id":16959,"text":"Yakama Nation Fisheries Program, Klickitat Field Office, 1575 Horseshoe Bend Road, Klickitat, WA  98628","active":true,"usgs":false}],"preferred":false,"id":822075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kock, Tobias J. 0000-0001-8976-0230 tkock@usgs.gov","orcid":"https://orcid.org/0000-0001-8976-0230","contributorId":3038,"corporation":false,"usgs":true,"family":"Kock","given":"Tobias","email":"tkock@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":822076,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Amy C. 0000-0002-0298-9137 achansen@usgs.gov","orcid":"https://orcid.org/0000-0002-0298-9137","contributorId":4350,"corporation":false,"usgs":true,"family":"Hansen","given":"Amy","email":"achansen@usgs.gov","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":822077,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":822078,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zendt, Joseph S","contributorId":147934,"corporation":false,"usgs":false,"family":"Zendt","given":"Joseph S","affiliations":[{"id":16959,"text":"Yakama Nation Fisheries Program, Klickitat Field Office, 1575 Horseshoe Bend Road, Klickitat, WA  98628","active":true,"usgs":false}],"preferred":false,"id":822079,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Romero, Nicolas","contributorId":73561,"corporation":false,"usgs":true,"family":"Romero","given":"Nicolas","email":"","affiliations":[],"preferred":false,"id":822080,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70223457,"text":"ofr20181094 - 2021 - Development of demographic models to analyze populations with multi-year data—Using Agassiz’s Desert Tortoise (Gopherus agassizii) as a case study","interactions":[],"lastModifiedDate":"2021-08-30T11:40:21.500348","indexId":"ofr20181094","displayToPublicDate":"2021-08-27T08:20:51","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1094","displayTitle":"Development of Demographic Models to Analyze Populations with Multi-Year Data—Using Agassiz’s Desert Tortoise (<i>Gopherus agassizii</i>) as a Case Study","title":"Development of demographic models to analyze populations with multi-year data—Using Agassiz’s Desert Tortoise (Gopherus agassizii) as a case study","docAbstract":"<p>We developed a model for analyzing multi-year demographic data for long-lived animals and used data from a population of Agassiz’s desert tortoise (<i>Gopherus agassizii</i>) at the Desert Tortoise Research Natural Area in the western Mojave Desert of California as a case study. The study area was 7.77 square kilometers and included two locations: inside and outside the fenced boundary. The wildlife-permeable, protective fence was designed to prevent entry from vehicle users and sheep grazing. We collected mark-recapture data from 1,123 tortoises during seven annual surveys consisting of two censuses each over a 34-year period. Additional data were collected when marked tortoises were recovered dead and removed between survey years. We used a Bayesian modeling framework to develop a multistate Jolly-Seber model because of its ability to handle unobserved (latent) states and modified this model to incorporate the additional data from non-survey years. Three size-age states (juvenile, immature, adult), sex (female, male), two location states (inside and outside the fenced boundary), and three survival states (not-yet-entered, entered/alive, and dead/removed) were incorporated into the model. We calculated population densities and estimated probabilities of growth of the tortoises from one size-age state to a larger size-age state, survival after 1 year and 5 years, and detection. Our results show a declining population with low estimates for survival after 1 year and 5 years. The probability for tortoises to move from outside to inside the boundary fence was greater than for tortoises to move from inside the fence to outside. The probability for detecting tortoises differed by size-age state and was lowest for the smallest tortoises and highest for the adult tortoises. The framework for the model can be used to analyze other animal populations where vital rates are expected to vary depending on multiple individual states.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181094","usgsCitation":"Berry, K.H., and Yee, J.L., 2021, Development of demographic models to analyze populations with multi-year data—Using Agassiz’s Desert Tortoise (Gopherus agassizii) as a case study: U.S. Geological Survey Open-File Report 2018–1094, 55 p., https://doi.org/10.3133/ofr20181094.","productDescription":"vi, 55 p.","numberOfPages":"55","onlineOnly":"Y","ipdsId":"IP-086643","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":388564,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2018/1094/images"},{"id":388563,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2018/1094/ofr20181094.xml"},{"id":388562,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1094/ofr20181094.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388561,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1094/covrthb.jpg"}],"contact":"<p>Director,<br><a href=\"https://www.usgs.gov/%20centers/%20werc\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/ centers/ werc\">Western Ecological Research Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov\">U.S. Geological Survey</a><br>3020 State University Drive East<br>Sacramento, California 95819</p>","tableOfContents":"<ul><li>Acknowledgments&nbsp;&nbsp;</li><li>Abstract&nbsp;&nbsp;</li><li>Introduction&nbsp;&nbsp;</li><li>Methods&nbsp;&nbsp;</li><li>Results&nbsp;&nbsp;</li><li>Discussion&nbsp;&nbsp;</li><li>Potential Future Developments of the Models&nbsp;&nbsp;</li><li>References Cited&nbsp;&nbsp;</li><li>Appendix 1&nbsp;</li><li>Appendix 2&nbsp;</li><li>Appendix 3</li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2021-08-27","noUsgsAuthors":false,"publicationDate":"2021-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Berry, Kristin H. 0000-0003-1591-8394 kristin_berry@usgs.gov","orcid":"https://orcid.org/0000-0003-1591-8394","contributorId":437,"corporation":false,"usgs":true,"family":"Berry","given":"Kristin","email":"kristin_berry@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":822069,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":822070,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70224524,"text":"70224524 - 2021 - An efficient Bayesian framework for updating PAGER loss estimates","interactions":[],"lastModifiedDate":"2021-09-27T11:01:08.524796","indexId":"70224524","displayToPublicDate":"2021-08-27T08:03:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7565,"text":"Earthquake Spectra Journal","active":true,"publicationSubtype":{"id":10}},"title":"An efficient Bayesian framework for updating PAGER loss estimates","docAbstract":"<p><span>We introduce a Bayesian framework for incorporating time-varying noisy reported data on damage and loss information to update near real-time loss estimates/alerts for the U.S. Geological Survey’s Prompt Assessment of Global Earthquakes for Response (PAGER) system. Initial loss estimation by PAGER immediately following an earthquake includes several uncertainties. Historically, the PAGER’s alerting on fatality and economic losses has not incorporated location-specific reported data on physical damage or casualties for a given earthquake. The proposed framework provides the ability to include early reports on fatalities at any given time and improve the overall impact forecast for the earthquake. The reported data on fatalities or damage are generally incomplete and noisy, especially in the early hours of the disaster. To address these challenges, we develop a recursive Bayesian updating framework that takes into account the loss projection model and the measurement and model uncertainties. The framework is applied to loss data for three example earthquakes, and the results show that the proposed updating improves the loss estimates and alert level to the correct level within the first day of the earthquake.</span></p>","language":"English","publisher":"Sage Journals","doi":"10.1177/8755293020944177","usgsCitation":"Noh, H.Y., Jaiswal, K.S., Engler, D.T., and Wald, D.J., 2021, An efficient Bayesian framework for updating PAGER loss estimates: Earthquake Spectra Journal, v. 36, no. 4, p. 1719-1742, https://doi.org/10.1177/8755293020944177.","productDescription":"24 p.","startPage":"1719","endPage":"1742","ipdsId":"IP-118585","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":389706,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Noh, Hae Young","contributorId":265961,"corporation":false,"usgs":false,"family":"Noh","given":"Hae","email":"","middleInitial":"Young","affiliations":[{"id":54844,"text":"Carnegie Mellon University (now at Stanford University)","active":true,"usgs":false}],"preferred":false,"id":823863,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823864,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Engler, Davis T. 0000-0002-7133-3545","orcid":"https://orcid.org/0000-0002-7133-3545","contributorId":265962,"corporation":false,"usgs":true,"family":"Engler","given":"Davis","email":"","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823865,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823866,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70224924,"text":"70224924 - 2021 - Flooding duration and volume more important than peak discharge in explaining 18 years of gravel–cobble river change","interactions":[],"lastModifiedDate":"2022-01-06T17:24:33.238441","indexId":"70224924","displayToPublicDate":"2021-08-27T07:22:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Flooding duration and volume more important than peak discharge in explaining 18 years of gravel–cobble river change","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Floods play a critical role in geomorphic change, but whether peak magnitude, duration, volume, or frequency determines the resulting magnitude of erosion and deposition is a question often proposed in geomorphic effectiveness studies. This study investigated that question using digital elevation model differencing to compare and contrast three hydrologically distinct epochs of topographic change spanning 18 years in the 37-km gravel–cobble lower Yuba River in northern California, USA. Scour and fill were analysed by volume at segment and geomorphic reach scales. Each epoch's hydrology was characterized using 15-min and daily averaged flow to obtain distinct peak and recurrence, duration, and volume metrics. Epochs 1 (1999–2008) and 3 (2014–2017) were wetter than average with large floods reaching 3206 and 2466 m<sup>3</sup>/s, respectively, though of different flood durations. Epoch 2 (2008–2014) was a drought period with only four brief moderate floods (peak of 1245 m<sup>3</sup>/s). Total volumetric changes showed that major geomorphic response occurred primarily during large flood events; however, total scour and net export of sediment varied greatly, with 20 times more export in epoch 3 compared to epoch 1. The key finding was that greater peak discharge was not correlated with greater net and total erosion; differences were better explained by duration and volume above floodway-filling stage. This finding highlights the importance of considering flood duration and volume, along with peak, to assess flood magnitude in the context of flood management, frequency analysis, and resulting geomorphic changes.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1002/esp.5230","usgsCitation":"Gervasi, A., Pasternack, G., and East, A.E., 2021, Flooding duration and volume more important than peak discharge in explaining 18 years of gravel–cobble river change: Earth Surface Processes and Landforms, v. 46, no. 15, p. 3194-3212, https://doi.org/10.1002/esp.5230.","productDescription":"9 p.","startPage":"3194","endPage":"3212","ipdsId":"IP-129882","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":390233,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"lower Yuba River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.695556640625,\n              38.78406349514289\n            ],\n            [\n              -120.17944335937499,\n              38.78406349514289\n            ],\n            [\n              -120.17944335937499,\n              39.6606850221923\n            ],\n            [\n              -121.695556640625,\n              39.6606850221923\n            ],\n            [\n              -121.695556640625,\n              38.78406349514289\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"46","issue":"15","noUsgsAuthors":false,"publicationDate":"2021-10-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Gervasi, Arielle","contributorId":267178,"corporation":false,"usgs":false,"family":"Gervasi","given":"Arielle","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":824622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pasternack, Gregory","contributorId":267179,"corporation":false,"usgs":false,"family":"Pasternack","given":"Gregory","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":824623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":824624,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70223456,"text":"fs20213047 - 2021 - Michigan and Landsat","interactions":[],"lastModifiedDate":"2023-01-24T11:48:11.924537","indexId":"fs20213047","displayToPublicDate":"2021-08-26T14:49:39","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3047","displayTitle":"Michigan and Landsat","title":"Michigan and Landsat","docAbstract":"<p>Water means a lot to Michigan, often called the Great Lakes State. The name “Michigan” comes from an Ojibwe word meaning large, or great, water or lake. As the only State touching four of the five Great Lakes—Michigan, Superior, Huron, and Erie—it claims the longest freshwater coastline in the United States.</p><p>Yet Michigan is not just about water—forests, agriculture, mines, cities, and even sand dunes stretch across the State’s landscape. Much of what happens on the land does connect in some way with Michigan’s inland and coastal waters. Michigan relies on a healthy environment to support its residents, abundant tourists, and diverse species of wildlife that call the State and its surrounding waters home. From hundreds of miles above, Landsat satellites provide a clearer picture of the connections among land, water, and the people and wildlife that inhabit the State.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213047","usgsCitation":"U.S. Geological Survey, 2021, Michigan and Landsat (ver. 1.1, January 2023): U.S. Geological Survey Fact Sheet 2021–3047, 2 p., https://doi.org/10.3133/fs20213047.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-126134","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":412223,"rank":6,"type":{"id":39,"text":"HTML 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,{"id":70223431,"text":"70223431 - 2021 - Pollinator communities vary with vegetation structure and time since management within regenerating timber harvests of the Central Appalachian Mountains","interactions":[],"lastModifiedDate":"2021-08-27T13:15:05.97235","indexId":"70223431","displayToPublicDate":"2021-08-26T11:08:38","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Pollinator communities vary with vegetation structure and time since management within regenerating timber harvests of the Central Appalachian Mountains","docAbstract":"Native pollinator populations across the United States are increasingly threatened by a multitude of ecological stressors. Although the drivers behind pollinator population declines are varied, habitat loss/degradation remains one of the most important threats. Forested landscapes, where the impacts of habitat loss/degradation are minimized, are known to support robust pollinator populations in eastern North America. Within heavily forested landscapes, timber management is already implemented as a means for improving forest health and enhancing wildlife habitat, however, little is known regarding the characteristics within regenerating timber harvests that affect forest pollinator populations. In 2018-19, we monitored insect pollinators in 143 regenerating (≤ 9 growing seasons post-harvest) timber harvest sites across Pennsylvania. During 1,129 survey events, we observed over 9,100 bees and butterflies, 220 blooming plant taxa, and collected over 2,200 pollinator specimens. Bee and butterfly abundance were positively associated with season-wide floral abundance and negatively associated with dense vegetation that inhibits the growth of understory floral resources. Particularly in late summer, few pollinators were observed in stands > 6 years post-harvest, with models predicting five times more bees in 1-year-old harvests than in 9-year-old harvests. Pollinator species diversity was positively associated with floral diversity and percent forb cover, and negatively associated with percent tall (>1m) sapling cover. These results suggest that regenerating timber harvests promote abundant and diverse pollinator communities in the Appalachian Mountains, though pollinator abundance declined quickly as woody stems regenerated. Ultimately, our findings contribute to a growing body of literature suggesting that dynamic forest management producing an even mix of age classes would benefit forest pollinator populations in the Central Appalachian Mountains.","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2021.119373","usgsCitation":"Mathis, C.L., McNeil, D.J., Lee, M.R., Grozinger, C.M., King, D.I., Otto, C., and Larkin, J., 2021, Pollinator communities vary with vegetation structure and time since management within regenerating timber harvests of the Central Appalachian Mountains: Forest Ecology and Management, v. 495, 119373, 12 p., https://doi.org/10.1016/j.foreco.2021.119373.","productDescription":"119373, 12 p.","onlineOnly":"N","ipdsId":"IP-127927","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":451052,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Jr.","contributorId":37620,"corporation":false,"usgs":false,"family":"McNeil","given":"Darin","suffix":"Jr.","email":"","middleInitial":"J.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":822062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lee, Monica R.","contributorId":264824,"corporation":false,"usgs":false,"family":"Lee","given":"Monica","email":"","middleInitial":"R.","affiliations":[{"id":54565,"text":"Indiana Un of Penns","active":true,"usgs":false}],"preferred":false,"id":822063,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grozinger, Christina M.","contributorId":214374,"corporation":false,"usgs":false,"family":"Grozinger","given":"Christina","email":"","middleInitial":"M.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":822064,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"King, David I.","contributorId":34390,"corporation":false,"usgs":false,"family":"King","given":"David","email":"","middleInitial":"I.","affiliations":[{"id":18918,"text":"Department of Environmental Conservation, University of Massachusetts, Amherst, MA, 01003, USA","active":true,"usgs":false},{"id":13259,"text":"USDA Forest Service Northern Research Station","active":true,"usgs":false}],"preferred":false,"id":822065,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Otto, Clint 0000-0002-7582-3525 cotto@usgs.gov","orcid":"https://orcid.org/0000-0002-7582-3525","contributorId":5426,"corporation":false,"usgs":true,"family":"Otto","given":"Clint","email":"cotto@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":822066,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Larkin, Jeffery A.","contributorId":210725,"corporation":false,"usgs":false,"family":"Larkin","given":"Jeffery A.","affiliations":[{"id":38140,"text":"Department of Biology, Indiana University of Pennsylvania, Indiana, PA 15705, US","active":true,"usgs":false}],"preferred":false,"id":822067,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70223408,"text":"70223408 - 2021 - Negligible evidence for detrimental effects of Leucocytozoon infections among Emperor Geese (Anser canagicus) breeding on the Yukon-Kuskokwim Delta, Alaska","interactions":[],"lastModifiedDate":"2021-08-26T15:50:24.140426","indexId":"70223408","displayToPublicDate":"2021-08-26T10:40:41","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2025,"text":"International Journal for Parasitology: Parasites and Wildlife","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Negligible evidence for detrimental effects of <i>Leucocytozoon</i> infections among Emperor Geese (<i>Anser canagicus</i>) breeding on the Yukon-Kuskokwim Delta, Alaska","title":"Negligible evidence for detrimental effects of Leucocytozoon infections among Emperor Geese (Anser canagicus) breeding on the Yukon-Kuskokwim Delta, Alaska","docAbstract":"<p><span>Emperor Geese (</span><i>Anser canagicus</i><span>) are iconic&nbsp;waterfowl&nbsp;endemic to Alaska and adjacent areas of northeastern Russia that are considered to be near threatened by the International Union for Conservation. This species has been identified as harboring diverse viruses and parasites which have, at times, been associated with disease in other avian taxa. To better assess if disease represents a vulnerability for Emperor Geese breeding on the Yukon-Kuskokwim Delta, Alaska, we evaluated if&nbsp;haemosporidian&nbsp;parasites were associated with decreased mass or survival among adult female nesting birds captured during 2006–2016. Through molecular analyses, we detected genetically diverse&nbsp;</span><span><i>Leucocytozoon</i></span><span>,&nbsp;</span><span><i>Haemoproteus</i></span><span>, and&nbsp;</span><i>Plasmodium</i><span>&nbsp;parasites in 28%, 1%, and 1% of 607 blood samples screened in triplicate, respectively. Using regression analysis, we found evidence for a small effect of&nbsp;</span><i>Leucocytozoon</i><span>&nbsp;infection on the mass of incubating adult female Emperor Geese. The estimated mass of infected individuals was approximately 43&nbsp;g (95% CI: 20–67&nbsp;g), or approximately 2%, less than uninfected birds when captured during the second half of incubation (days 11–25). We did not, however, find support for an effect of&nbsp;</span><i>Leucocytozoon</i><span>&nbsp;infection on survival of adult female nesting Emperor Geese using a multi-state hidden Markov framework to analyze mark-resight and recapture data. Using parasite mitochondrial DNA&nbsp;cytochrome&nbsp;</span><i>b</i><span>&nbsp;sequences, we identified 23&nbsp;haplotypes&nbsp;among infected Emperor Geese.&nbsp;</span><i>Leucocytozoon</i><span>&nbsp;haplotypes clustered into three phylogenetically supported clades designated as ‘</span><i>L. simondi</i><span>&nbsp;clade A’, ‘</span><i>L. simondi</i><span>&nbsp;clade B’, and ‘other&nbsp;</span><i>Leucocytozoon</i><span>’. We did not find evidence that parasites assigned to any of these clades were associated with differential mass measures among nesting adult female Emperor Geese. Collectively, our results provide negligible evidence for&nbsp;</span><i>Leucocytozoon</i><span>&nbsp;parasites as causing detrimental effects to adult female Emperor Geese breeding on the Yukon-Kuskokwim Delta.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijppaw.2021.08.006","usgsCitation":"Ramey, A.M., Bucheit, R., Uher-Koch, B.D., Reed, J., Pacheco, M.A., Escalante, A., and Schmutz, J., 2021, Negligible evidence for detrimental effects of Leucocytozoon infections among Emperor Geese (Anser canagicus) breeding on the Yukon-Kuskokwim Delta, Alaska: International Journal for Parasitology: Parasites and Wildlife, v. 16, p. 103-112, https://doi.org/10.1016/j.ijppaw.2021.08.006.","productDescription":"10 p.","startPage":"103","endPage":"112","ipdsId":"IP-130428","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":451055,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijppaw.2021.08.006","text":"Publisher Index Page"},{"id":436223,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B5JUBW","text":"USGS data release","linkHelpText":"Blood Parasite Infection, Body Mass, and Survival Data from Emperor Geese (Anser canagicus), Yukon-Kuskokwim Delta, Alaska, 2006-2016"},{"id":388545,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon-Kuskokwim Delta","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -166.728515625,\n              60.646262316136976\n            ],\n            [\n              -163.23486328125,\n              60.646262316136976\n            ],\n            [\n              -163.23486328125,\n              63.28800124531419\n            ],\n            [\n              -166.728515625,\n              63.28800124531419\n            ],\n            [\n              -166.728515625,\n              60.646262316136976\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"16","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":821973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bucheit, Raymond","contributorId":264772,"corporation":false,"usgs":false,"family":"Bucheit","given":"Raymond","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":821974,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Uher-Koch, Brian D. 0000-0002-1885-0260 buher-koch@usgs.gov","orcid":"https://orcid.org/0000-0002-1885-0260","contributorId":5117,"corporation":false,"usgs":true,"family":"Uher-Koch","given":"Brian","email":"buher-koch@usgs.gov","middleInitial":"D.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":821975,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reed, John 0000-0002-3239-6906","orcid":"https://orcid.org/0000-0002-3239-6906","contributorId":214852,"corporation":false,"usgs":true,"family":"Reed","given":"John","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":821976,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pacheco, M. Andreina","contributorId":264773,"corporation":false,"usgs":false,"family":"Pacheco","given":"M.","email":"","middleInitial":"Andreina","affiliations":[{"id":12547,"text":"Temple University","active":true,"usgs":false}],"preferred":false,"id":821977,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Escalante, Ananias","contributorId":264774,"corporation":false,"usgs":false,"family":"Escalante","given":"Ananias","email":"","affiliations":[{"id":12547,"text":"Temple University","active":true,"usgs":false}],"preferred":false,"id":821978,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schmutz, Joel 0000-0002-6516-0836","orcid":"https://orcid.org/0000-0002-6516-0836","contributorId":264776,"corporation":false,"usgs":false,"family":"Schmutz","given":"Joel","affiliations":[{"id":54549,"text":"retired from USGS Alaska Science Center","active":true,"usgs":false}],"preferred":false,"id":821979,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70228842,"text":"70228842 - 2021 - Insect pathogenic fungi for biocontrol of plague vector fleas: A review","interactions":[],"lastModifiedDate":"2022-02-23T16:36:17.916881","indexId":"70228842","displayToPublicDate":"2021-08-26T10:32:01","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10126,"text":"Journal of Integrated Pest Management","active":true,"publicationSubtype":{"id":10}},"title":"Insect pathogenic fungi for biocontrol of plague vector fleas: A review","docAbstract":"<p class=\"chapter-para\">Bubonic plague is a lethal bacterial disease of great historical importance. The plague organism,<span>&nbsp;</span><i>Yersinia pestis</i>, is primarily transmitted by fleas (Siphonaptera). In natural settings, where its range expands,<span>&nbsp;</span><i>Y. pestis</i><span>&nbsp;</span>resides in association with wild rodents and their fleas (sylvatic plague). While chemical insecticides are used against plague vector fleas, biological approaches have not been as critically evaluated. Benign and cost-effective control methods are sorely needed, particularly where imperiled species are at risk. Here we explore the potential of two representative insect pathogenic fungi,<span>&nbsp;</span><i>Beauveria bassiana</i><span>&nbsp;</span>Vuillemin 1912 (Hypocreales: Cordycipitaceae) and<span>&nbsp;</span><i>Metarhizium anisopliae</i><span>&nbsp;</span>Metschnikoff 1879 (Hypocreales: Clavicipitaceae), each already used commercially worldwide in large-scale agricultural applications, as candidate biopesticides for application against fleas. We review the life cycles, flea virulence, commercial production, and field application of these fungi, and ecological and safety considerations. Pathogenic fungi infections among natural flea populations suggest that conditions within at least some rodent burrows are favorable, and laboratory studies demonstrate lethality of these fungi to at least some representative flea species. Continued study and advancements with these fungi, under appropriate safety measures, may allow for effective biocontrol of plague vector fleas to protect imperiled species, decrease plague outbreaks in key rodent species, and limit plague in humans.</p>","language":"English","publisher":"Oxford University Press","doi":"10.1093/jipm/pmab028","usgsCitation":"Eads, D.A., Jaronski, S., Biggins, D.E., and Wimsatt, J., 2021, Insect pathogenic fungi for biocontrol of plague vector fleas: A review: Journal of Integrated Pest Management, v. 12, no. 1, p. 1-10, https://doi.org/10.1093/jipm/pmab028.","productDescription":"30, 10 p.","startPage":"1","endPage":"10","ipdsId":"IP-127322","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":451057,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jipm/pmab028","text":"Publisher Index Page"},{"id":396355,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Eads, David A. 0000-0002-4247-017X deads@usgs.gov","orcid":"https://orcid.org/0000-0002-4247-017X","contributorId":173639,"corporation":false,"usgs":true,"family":"Eads","given":"David","email":"deads@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":835684,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaronski, Stefan 0000-0002-7789-0406","orcid":"https://orcid.org/0000-0002-7789-0406","contributorId":279882,"corporation":false,"usgs":false,"family":"Jaronski","given":"Stefan","email":"","affiliations":[{"id":37295,"text":"USDA APHIS","active":true,"usgs":false}],"preferred":false,"id":835685,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Biggins, Dean E. 0000-0003-2078-671X bigginsd@usgs.gov","orcid":"https://orcid.org/0000-0003-2078-671X","contributorId":2522,"corporation":false,"usgs":true,"family":"Biggins","given":"Dean","email":"bigginsd@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":835686,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wimsatt, Jeffrey","contributorId":173421,"corporation":false,"usgs":false,"family":"Wimsatt","given":"Jeffrey","email":"","affiliations":[],"preferred":false,"id":835687,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70223403,"text":"sir20215090 - 2021 - Estimates of water use associated with continuous oil and gas development in the Permian Basin, Texas and New Mexico, 2010–19","interactions":[],"lastModifiedDate":"2021-12-14T12:26:17.570498","indexId":"sir20215090","displayToPublicDate":"2021-08-26T10:24:57","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5090","displayTitle":"Estimates of Water Use Associated with Continuous Oil and Gas Development in the Permian Basin, Texas and New Mexico, 2010–19","title":"Estimates of water use associated with continuous oil and gas development in the Permian Basin, Texas and New Mexico, 2010–19","docAbstract":"<p>In 2015, the U.S. Geological Survey started a topical study to quantify water use in areas of continuous oil and gas (COG) development. The first phase of the study was completed in 2019 and analyzed the Williston Basin. The second phase of the study analyzed the Permian Basin using the same techniques and approaches used for the Williston Basin analysis. The Permian Basin was selected for the second phase of water-use analysis for the following reasons: (1) the basin has the largest undiscovered technically recoverable oil and gas resource in the United States, (2) the basin has a continuous resource in tight shale that primarily produces oil, and (3) the basin is within the contiguous United States. This study used data from 60 counties in Texas and New Mexico with spatial coverage based on the Permian Basin extent defined by the U.S. Energy Information Administration, a representation of the geologically defined Permian Basin.</p><p>Data from several sources were used in the analysis of direct, indirect, and ancillary water use associated with COG development in the Permian Basin and are available in an associated data release. Hydraulic fracturing water-use data were used to determine the start of the recent (before 2019) COG development boom in oil production in the Permian Basin in the same way that the data were used for the Williston Basin study. Water-use data were aggregated by county and year, which were the sampling units used in the analysis.</p><p>The water-use analysis of the Permian Basin contained three elements: (1) estimates of water use, in million gallons, by county and year; (2) coefficients of water use from regression models, in million gallons per developed oil and gas well; and (3) performance (based on goodness-of-fit metrics) of the regression models in estimating the observed water use.</p><p>Coefficients from the linear and quantile regression models of direct, indirect, and ancillary water use in the Permian Basin were produced as aggregate values for the counties and years. The mean estimate of direct water use had a 95-percent confidence interval of 4.13–5.45 million gallons (Mgal) per developed oil and gas well. The coefficient from the linear regression model of indirect water use was 0.111 Mgal per well, with a 95-percent confidence interval of 0.104–0.117 Mgal per well. The mean estimate of ancillary water use in the Permian Basin was 1.09 Mgal per well, with a 95-percent confidence interval of 1.05–1.13 Mgal per well. Model performance was evaluated with goodness-of-fit metrics including coefficient of determination (<i>R</i><sup>2</sup>), root mean square error, and the ratio of root mean square error to standard deviation of observations computed from leave-one-out cross validation of the linear and quantile regression models of direct, indirect, and ancillary water use. Model performance for direct water use was acceptable, with an <i>R</i><sup>2</sup> value of 0.91. The model performance of indirect water use was acceptable, with an <i>R</i><sup>2</sup> value of 0.89. Values of <i>R</i><sup>2</sup> for the ancillary water-use categories were at least 0.89.</p><p>Annual mean estimates for hydraulic fracturing, cementing, drilling, indirect, and ancillary water use per well for the years 2010–17 were comparable between the Permian and Williston Basins. Hydraulic fracturing water use increased similarly from 2010 to 2015 in the Permian Basin and the Williston Basin, increasing from 0.6 Mgal per well in 2010 to 5.4 Mgal per well in 2015 in the Permian Basin and from 1.4 Mgal per well in 2010 to 4.7 Mgal per well in 2015 in the Williston Basin.</p><p>By design, the Permian water-use assessment is a simplification of a complex and continually developing system and therefore has uncertainty and limitations in the interpretation of results. Despite the modeling limitations, the results summarized in the report, when compared to other studies, compare well with water-use estimations. The favorable comparison highlights the transferability of the water-use methodology to other areas of COG development.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215090","programNote":"Water Availability and Use Science Program","usgsCitation":"Valder, J.F., McShane, R.R., Thamke, J.N., McDowell, J.S., Ball, G.P., Houston, N.A., and Galanter, A.E., 2021, Estimates of water use associated with continuous oil and gas development in the Permian Basin, Texas and New Mexico, 2010–19: U.S. Geological Survey Scientific Investigations Report 2021–5090, 27 p., https://doi.org/10.3133/sir20215090.","productDescription":"Report: vii, 27 p.; Data Releases: 3; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-126972","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water 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continuous oil and gas development, Permian Basin, United States, 1980–2019"},{"id":391022,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/fs20213053","text":"FS 2021–3053","size":"4.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3053","linkHelpText":"— Estimates of Water Use Associated with Continuous Oil and Gas Development in the Permian Basin, Texas and New Mexico, 2010–19, with Comparisons to the Williston Basin, North Dakota and Montana"},{"id":388523,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the 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Basin</li><li>Limitations of Water-Use Analysis of the Permian Basin</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-08-26","noUsgsAuthors":false,"publicationDate":"2021-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Valder, Joshua F. 0000-0003-3733-8868","orcid":"https://orcid.org/0000-0003-3733-8868","contributorId":220912,"corporation":false,"usgs":true,"family":"Valder","given":"Joshua F.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821955,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McShane, Ryan R. 0000-0002-3128-0039 rmcshane@usgs.gov","orcid":"https://orcid.org/0000-0002-3128-0039","contributorId":195581,"corporation":false,"usgs":true,"family":"McShane","given":"Ryan","email":"rmcshane@usgs.gov","middleInitial":"R.","affiliations":[{"id":5050,"text":"WY-MT Water Science 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,{"id":70223402,"text":"fs20213045 - 2021 - Hydrologic conditions in Kansas, water year 2020","interactions":[],"lastModifiedDate":"2021-08-30T12:00:28.9731","indexId":"fs20213045","displayToPublicDate":"2021-08-26T09:02:09","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3045","displayTitle":"Hydrologic Conditions in Kansas, Water Year 2020","title":"Hydrologic conditions in Kansas, water year 2020","docAbstract":"<p>The U.S. Geological Survey, in cooperation with Federal, State, and local agencies, maintains a long-term network of hydrologic monitoring stations in Kansas. 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 \"}}]}","contact":"<p><a data-mce-href=\"mailto:%20dc_ks@usgs.gov\" href=\"mailto:%20dc_ks@usgs.gov\">Director</a>, <a data-mce-href=\"https://www.usgs.gov/centers/kswsc\" href=\"https://www.usgs.gov/centers/kswsc\">Kansas Water Science Center</a><br>U.S. Geological Survey<br>1217 Biltmore Drive<br>Lawrence, KS 66049</p>","tableOfContents":"<ul><li>Preceding Conditions and Precipitation</li><li>Drainage Basin Runoff and Streamflow Conditions</li><li>Reservoirs</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-08-26","noUsgsAuthors":false,"publicationDate":"2021-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Chantelle 0000-0001-6415-7320","orcid":"https://orcid.org/0000-0001-6415-7320","contributorId":225019,"corporation":false,"usgs":true,"family":"Davis","given":"Chantelle","email":"","affiliations":[{"id":353,"text":"Kansas Water Science 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,{"id":70224579,"text":"70224579 - 2021 - Marine distribution and foraging habitat highlight potential threats at sea for Endangered Bermuda Petrel Pterodroma cahow","interactions":[],"lastModifiedDate":"2021-09-29T13:45:54.460103","indexId":"70224579","displayToPublicDate":"2021-08-26T08:45:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1497,"text":"Endangered Species Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Marine distribution and foraging habitat highlight potential threats at sea for Endangered Bermuda Petrel <i>Pterodroma cahow</i>","title":"Marine distribution and foraging habitat highlight potential threats at sea for Endangered Bermuda Petrel Pterodroma cahow","docAbstract":"<p><span>Marine spatial planning relies on detailed spatial information of marine areas to ensure effective conservation of species. To enhance our understanding of marine habitat use by the highly pelagic Bermuda petrel&nbsp;</span><i>Pterodroma cahow</i><span>, we deployed GPS tags on 6 chick-rearing adults in April 2019 and constructed a habitat suitability model using locations classified as foraging to explore functional responses to a selection of marine environmental variables. We defined 15 trips for 5 individuals, ranging from 1-6 trips per bird, that included both short and long foraging excursions indicative of a dual foraging strategy that optimizes chick feeding and self maintenance. The maximum distance birds flew from Bermuda during foraging trips ranged from 61 to 2513 km (total trip lengths: 186-14051 km). Behaviourally deduced foraging habitat was best predicted at shorter distances from the colony, under warmer sea surface temperature, greater sea surface height, and in deeper water compared to transiting locations; our model results indicated that suitable foraging habitat exists beyond the core home range of the population, as far north as the highly productive Gulf Stream frontal system, and within the territorial waters of both the USA and Canada. Our results are crucial to inform management decisions and international conservation efforts by better identifying potential threats encountered at sea by this globally rare seabird and highlighting jurisdictions potentially responsible for mitigating those threats.</span></p>","language":"English","publisher":"Inter-Research","doi":"10.3354/esr01139","usgsCitation":"Raine, A., Gjerdrum, C., Pratte, I., Madeiros, J., Felis, J.J., and Adams, J., 2021, Marine distribution and foraging habitat highlight potential threats at sea for Endangered Bermuda Petrel Pterodroma cahow: Endangered Species Research, v. 45, p. 337-356, https://doi.org/10.3354/esr01139.","productDescription":"20 p.","startPage":"337","endPage":"356","ipdsId":"IP-124810","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":451059,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01139","text":"Publisher Index Page"},{"id":389951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Bermuda, Canada, United States","otherGeospatial":"Nonsuch Island, Horn Rock","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -63.54492187500001,\n              31.39115752282472\n            ],\n            [\n              -49.7021484375,\n              43.77109381775651\n            ],\n            [\n              -46.8896484375,\n              48.86471476180277\n            ],\n            [\n              -55.06347656249999,\n              45.182036837015886\n            ],\n            [\n              -61.962890625,\n              43.004647127794435\n            ],\n            [\n              -69.345703125,\n              40.613952441166596\n            ],\n            [\n              -72.99316406249999,\n              38.34165619279595\n            ],\n            [\n              -72.99316406249999,\n              34.34343606848294\n            ],\n            [\n              -66.4013671875,\n              30.90222470517144\n            ],\n            [\n              -63.54492187500001,\n              31.39115752282472\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"45","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Raine, André F","contributorId":266026,"corporation":false,"usgs":false,"family":"Raine","given":"André F","affiliations":[{"id":54862,"text":"Archipelago Research and Conservation, Kauai, Hawai’i 96716, USA","active":true,"usgs":false}],"preferred":false,"id":824149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gjerdrum, Carina","contributorId":266027,"corporation":false,"usgs":false,"family":"Gjerdrum","given":"Carina","email":"","affiliations":[{"id":54863,"text":"Canadian Wildlife Service, Dartmouth, Nova Scotia B2Y 2N6, Canada","active":true,"usgs":false}],"preferred":false,"id":824150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pratte, Isabeau","contributorId":266028,"corporation":false,"usgs":false,"family":"Pratte","given":"Isabeau","email":"","affiliations":[{"id":54863,"text":"Canadian Wildlife Service, Dartmouth, Nova Scotia B2Y 2N6, Canada","active":true,"usgs":false}],"preferred":false,"id":824151,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Madeiros, Jeremy","contributorId":196171,"corporation":false,"usgs":false,"family":"Madeiros","given":"Jeremy","email":"","affiliations":[],"preferred":false,"id":824152,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Felis, Jonathan J. 0000-0002-0608-8950 jfelis@usgs.gov","orcid":"https://orcid.org/0000-0002-0608-8950","contributorId":4825,"corporation":false,"usgs":true,"family":"Felis","given":"Jonathan","email":"jfelis@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824153,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Adams, Josh 0000-0003-3056-925X","orcid":"https://orcid.org/0000-0003-3056-925X","contributorId":213442,"corporation":false,"usgs":true,"family":"Adams","given":"Josh","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824154,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70223467,"text":"70223467 - 2021 - Rebounds, regresses, and recovery: A 15-year study of the coral reef community at Pila‘a, Kaua‘i after decades of natural and anthropogenic stress events","interactions":[],"lastModifiedDate":"2021-10-06T15:57:00.3995","indexId":"70223467","displayToPublicDate":"2021-08-26T08:29:51","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2676,"text":"Marine Pollution Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Rebounds, regresses, and recovery: A 15-year study of the coral reef community at Pila‘a, Kaua‘i after decades of natural and anthropogenic stress events","docAbstract":"<p><span>Pila‘a reef on the north shore of Kaua‘i, Hawai‘i was subjected to a major flood event in 2001 that deposited extensive sediment on the reef flat, resulting in high coral mortality. To document potential recovery, this study replicated benthic and sediment surveys conducted immediately following the event and 15 years later. Coral cores were analyzed to determine coral growth rates and density. Our results suggest that significant reduction in terrigenous sediments has led to partial ecosystem recovery based on coral species and colony increases, more balanced size frequency distributions, improved coral condition, and enhanced coral recruitment despite lack of recovery of large dead coral colonies. However, within this 15-year period, episodic storms and a bleaching event impeded the recovery process, preventing full recovery and continuously threatening the coral reef community. As climate change progresses, the intensity and frequency of these disturbances are predicted to increase.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpolbul.2021.112306","usgsCitation":"Rodgers, K., Richards Dona, A., Stender, Y.O., Tsang, A.O., Han, J.H., Weible, R., Prouty, N.G., Storlazzi, C.D., and Graham, A.M., 2021, Rebounds, regresses, and recovery: A 15-year study of the coral reef community at Pila‘a, Kaua‘i after decades of natural and anthropogenic stress events: Marine Pollution Bulletin, v. 171, 112306, 16 p., https://doi.org/10.1016/j.marpolbul.2021.112306.","productDescription":"112306, 16 p.","ipdsId":"IP-098759","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":451062,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marpolbul.2021.112306","text":"Publisher Index Page"},{"id":388580,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kaua'i Island, Pila'a reef","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -159.58602905273438,\n              22.119357730123134\n            ],\n            [\n              -159.2523193359375,\n              22.119357730123134\n            ],\n            [\n              -159.2523193359375,\n              22.317683823893706\n            ],\n            [\n              -159.58602905273438,\n              22.317683823893706\n            ],\n            [\n              -159.58602905273438,\n              22.119357730123134\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"171","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rodgers, Ku'ulei S.","contributorId":131044,"corporation":false,"usgs":false,"family":"Rodgers","given":"Ku'ulei S.","affiliations":[{"id":7212,"text":"University of Hawai‘i, Hawai‘i Institute of Marine Biology","active":true,"usgs":false}],"preferred":false,"id":822105,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richards Dona, A.","contributorId":264856,"corporation":false,"usgs":false,"family":"Richards Dona","given":"A.","email":"","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":822110,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stender, Y. O.","contributorId":264855,"corporation":false,"usgs":false,"family":"Stender","given":"Y.","email":"","middleInitial":"O.","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":822109,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tsang, A. O.","contributorId":264854,"corporation":false,"usgs":false,"family":"Tsang","given":"A.","email":"","middleInitial":"O.","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":822107,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Han, J. H. J.","contributorId":264853,"corporation":false,"usgs":false,"family":"Han","given":"J.","email":"","middleInitial":"H. J.","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":822106,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Weible, Rebecca","contributorId":264858,"corporation":false,"usgs":false,"family":"Weible","given":"Rebecca","email":"","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":822111,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Prouty, Nancy G. 0000-0002-8922-0688 nprouty@usgs.gov","orcid":"https://orcid.org/0000-0002-8922-0688","contributorId":3350,"corporation":false,"usgs":true,"family":"Prouty","given":"Nancy","email":"nprouty@usgs.gov","middleInitial":"G.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":822108,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":822127,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Graham, Andrew M.","contributorId":178896,"corporation":false,"usgs":false,"family":"Graham","given":"Andrew","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":822128,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70223401,"text":"ofr20211030J - 2021 - System characterization report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A)","interactions":[{"subject":{"id":70223401,"text":"ofr20211030J - 2021 - System characterization report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A)","indexId":"ofr20211030J","publicationYear":"2021","noYear":false,"chapter":"J","displayTitle":"System Characterization Report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A)","title":"System characterization report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A)"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2024-11-06T15:36:02.779518","indexId":"ofr20211030J","displayToPublicDate":"2021-08-26T08:13:17","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1030","chapter":"J","displayTitle":"System Characterization Report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A)","title":"System characterization report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A)","docAbstract":"<h1>Executive Summary</h1><p>This report addresses system characterization of the China-Brazil Earth Resources Satellite-4A (CBERS–4A) multispectral remote sensing satellite and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.</p><p>CBERS–4A is a joint Chinese-Brazilian medium-resolution satellite launched in December 2019 by the China National Space Agency/National Institute for Space Research (Brazil) on a Chang Zheng 4B rocket from the Taiyuan Satellite Launch Center for Earth resources monitoring. The CBERS–4A mission continues the CBERS mission that has been in continual operation since the launch of CBERS–1 in 1999.</p><p>The CBERS–4A satellite was designed and built by Academia Chinesa de Tecnologia Espacial/National Institute for Space Research and uses the Phoenix-Eye bus. CBERS–4A carries the multispectral camera and wide field imager sensors for medium-resolution land imaging and the wide swath panchromatic and multispectral camera sensor for high-resolution land imaging. This assessment focused on the multispectral camera sensor only. More information on CBERS sensors is available in the “<a data-mce-href=\"https://doi.org/10.3133/cir1468\" href=\"https://doi.org/10.3133/cir1468\" target=\"_blank\" rel=\"noopener\">2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium</a>” and at <a href=\"https://www.gov.br/pt-br/servicos/obter-imagens-de-sensoriamento-remoto-da-terra-geradas-pelo-satelite-cbers-04a\" data-mce-href=\"https://www.gov.br/pt-br/servicos/obter-imagens-de-sensoriamento-remoto-da-terra-geradas-pelo-satelite-cbers-04a\">https://www.gov.br/pt-br/servicos/obter-imagens-de-sensoriamento-remoto-da-terra-geradas-pelo-satelite-cbers-04a</a>.</p><p>The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that CBERS–4A provides an interior (band-to-band) geometric performance in the range of −0.02 to −0.16 pixel; an exterior geometric accuracy performance of −22.02 (−1.47 pixels) to −16.06 meters (−1.07 pixels); a radiometric accuracy performance of –0.006 to 0.925 (offset and slope); and a spatial performance for relative edge response in the range of 0.39 to 0.44, for full width at half maximum in the range of 2.38 to 2.56 pixels, and for a modulation transfer function at a Nyquist frequency in the range of 0.001 to 0.013.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030J","usgsCitation":"Vrabel, J.C., Stensaas, G.L., Anderson, C., Christopherson, J., Kim, M., Park, S., and Cantrell, S., 2021, System characterization report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A), chap. J <i>of</i> Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 35 p., https://doi.org/10.3133/ofr20211030J.","productDescription":"v, 35 p.","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-130782","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":388510,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/j/ofr20211030j.pdf","text":"Report","size":"12.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030J"},{"id":388509,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/j/coverthb.jpg"}],"contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/eros\" data-mce-href=\"https://www.usgs.gov/centers/eros\">Earth Resources Observation and Science Center</a> <br>U.S. Geological Survey<br>47914 252nd Street <br>Sioux Falls, SD 57198</p><p><a href=\"../contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Executive Summary</li><li>Introduction</li><li>System Description</li><li>Procedures</li><li>Measurements</li><li>Analysis</li><li>Summary and Conclusions</li><li>Selected References</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-08-26","noUsgsAuthors":false,"publicationDate":"2021-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Vrabel, James C. 0000-0002-0120-4721","orcid":"https://orcid.org/0000-0002-0120-4721","contributorId":264751,"corporation":false,"usgs":false,"family":"Vrabel","given":"James C.","affiliations":[{"id":27608,"text":"Contractor to the USGS","active":true,"usgs":false}],"preferred":false,"id":821947,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stensaas, Gregory L. 0000-0001-6679-2416 stensaas@usgs.gov","orcid":"https://orcid.org/0000-0001-6679-2416","contributorId":2551,"corporation":false,"usgs":true,"family":"Stensaas","given":"Gregory","email":"stensaas@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":821948,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Cody 0000-0001-5612-1889 chanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":195521,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","email":"chanderson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":821949,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christopherson, Jon 0000-0002-2472-0059 jonchris@usgs.gov","orcid":"https://orcid.org/0000-0002-2472-0059","contributorId":2552,"corporation":false,"usgs":true,"family":"Christopherson","given":"Jon","email":"jonchris@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":821950,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kim, Minsu 0000-0003-4472-0926 minsukim@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-4472-0926","contributorId":216429,"corporation":false,"usgs":true,"family":"Kim","given":"Minsu","email":"minsukim@contractor.usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":821951,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Park, Seonkyung 0000-0003-3203-1998","orcid":"https://orcid.org/0000-0003-3203-1998","contributorId":223182,"corporation":false,"usgs":true,"family":"Park","given":"Seonkyung","email":"","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":821952,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cantrell, Simon J. 0000-0001-6909-1973","orcid":"https://orcid.org/0000-0001-6909-1973","contributorId":259304,"corporation":false,"usgs":false,"family":"Cantrell","given":"Simon J.","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":821953,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70223356,"text":"ofr20211069 - 2021 - Changes in forest connectivity from beech bark disease in Pictured Rocks National Lakeshore in the Upper Peninsula of Michigan","interactions":[],"lastModifiedDate":"2021-08-26T14:23:27.409191","indexId":"ofr20211069","displayToPublicDate":"2021-08-25T16:00:16","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1069","displayTitle":"Changes in Forest Connectivity from Beech Bark Disease in Pictured Rocks National Lakeshore in the Upper Peninsula of Michigan","title":"Changes in forest connectivity from beech bark disease in Pictured Rocks National Lakeshore in the Upper Peninsula of Michigan","docAbstract":"<p>Within the forests of Pictured Rocks National Lakeshore, biologists are trying to understand the effects beech bark disease has on wildlife species, especially species that need forest connectivity to thrive. This project used aerial imagery collected in 2005, shortly after beech bark disease infestation, and satellite imagery from 2018. The 2018 imagery represents present day conditions and was used to locate forest canopy gaps through object-based image analysis. Forest canopy gaps were identified using the multiresolution segmentation algorithm within Trimble’s eCognition software. A time change analysis was completed to understand how the forest canopy had changed from 2005 to 2018. The analysis showed areas that had maintained forest canopy, maintained a forest canopy gap, created a new canopy gap (closed forest canopy in 2005 but open canopy gap in 2018), or created new forest canopy (open canopy gap in 2005 but closed forest canopy in 2018). There were 9,127 acres of forest canopy lost, and 72.8 percent of that lost canopy occurred in a forest type where Fagus grandifolia Ehrh. (American beech) is a common tree species. The datasets developed through this project can enhance knowledge of where canopy gaps exist and help place focus on certain areas for wildlife studies. In addition, these datasets can be used in future studies to monitor the health of the forest and conduct additional change analyses.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211069","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Sattler, S.R., 2021, Changes in forest connectivity from beech bark disease in Pictured Rocks National Lakeshore in the Upper Peninsula of Michigan: U.S. Geological Survey Open-File Report 2021–1069, 12 p., https://doi.org/10.3133/ofr20211069.","productDescription":"Report: vi, 12 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-124452","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":388432,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1069/images"},{"id":388429,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1069/coverthb.jpg"},{"id":388430,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1069/ofr20211069.pdf","text":"Report","size":"6.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1069"},{"id":388431,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EZEAYD","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Effects of beech bark disease on forest connectivity in Pictured Rocks National Lakeshore from 2005 to 2018"}],"country":"United States","state":"Michigan","otherGeospatial":"Pictures Rocks National Lakeshore","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -86.62307739257812,\n              46.42176587242696\n            ],\n            [\n              -86.4349365234375,\n              46.45961954102543\n            ],\n            [\n              -86.20147705078125,\n              46.57585481240773\n            ],\n            [\n              -86.02706909179688,\n              46.619261036171515\n            ],\n            [\n              -86.00509643554686,\n              46.669229446893404\n            ],\n            [\n              -86.08612060546875,\n              46.66545985627255\n            ],\n            [\n              -86.14105224609375,\n              46.677710064644344\n            ],\n            [\n              -86.4459228515625,\n              46.557916007595786\n            ],\n            [\n              -86.48712158203125,\n              46.55602736725248\n            ],\n            [\n              -86.6217041015625,\n              46.44826620185314\n            ],\n            [\n              -86.62307739257812,\n              46.42176587242696\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/umesc\" href=\"https://www.usgs.gov/centers/umesc\">Upper Midwest Environmental Sciences Center</a> <br>U.S. Geological Survey <br>2630 Fanta Reed Road <br>La Crosse, WI 54603</p><p><a data-mce-href=\"../contact\" href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Study Area</li><li>Methods</li><li>Discussion and Conclusions</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2021-08-25","noUsgsAuthors":false,"publicationDate":"2021-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Sattler, Stephanie R. 0000-0003-4417-2480 ssattler@usgs.gov","orcid":"https://orcid.org/0000-0003-4417-2480","contributorId":152030,"corporation":false,"usgs":true,"family":"Sattler","given":"Stephanie","email":"ssattler@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821850,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70263638,"text":"70263638 - 2021 - When Punjab cried wolf: How a rumor triggered an “earthquake” in India","interactions":[],"lastModifiedDate":"2025-02-19T16:57:33.611474","indexId":"70263638","displayToPublicDate":"2021-08-25T10:53:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"When Punjab cried wolf: How a rumor triggered an “earthquake” in India","docAbstract":"<p><span>In recent years, earthquake felt reports contributed via online systems have provided increasingly valuable sources of data to characterize earthquakes and their effects. Contributed felt reports are accompanied by increases in website traffic, which are themselves potentially useful for the early detection of seismic events. In February 2017 the European‐Mediterranean Seismic Centre detected an unusual surge in traffic from the Punjab region in northwestern India, although no nearby seismic event was detected instrumentally. Had crowdsourcing detected a felt earthquake that instruments had missed? Or did Punjab cry wolf? In this Earthquake Lites report, we describe the sleuthing endeavor undertaken to find an answer.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"https://doi.org/10.1785/0220210130","usgsCitation":"Martin, S., Bossu, R., Steed, R., Landes, M., Srinagesh, D., Srinivas, D., and Hough, S.E., 2021, When Punjab cried wolf: How a rumor triggered an “earthquake” in India: Seismological Research Letters, v. 92, no. 6, p. 3887-3898, https://doi.org/https://doi.org/10.1785/0220210130.","productDescription":"12 p.","startPage":"3887","endPage":"3898","ipdsId":"IP-130668","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482227,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","otherGeospatial":"Punjab","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              72.75,\n              32.15\n            ],\n            [\n              72.75,\n              28\n            ],\n            [\n              78,\n              28\n            ],\n            [\n              78,\n              32.15\n            ],\n            [\n              72.75,\n              32.15\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"92","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Martin, S.S.","contributorId":350980,"corporation":false,"usgs":false,"family":"Martin","given":"S.S.","affiliations":[{"id":16807,"text":"Australian National University","active":true,"usgs":false}],"preferred":false,"id":927631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bossu, Remy","contributorId":198780,"corporation":false,"usgs":false,"family":"Bossu","given":"Remy","email":"","affiliations":[],"preferred":false,"id":927632,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Steed, R.","contributorId":350981,"corporation":false,"usgs":false,"family":"Steed","given":"R.","affiliations":[{"id":35319,"text":"EMSC","active":true,"usgs":false}],"preferred":false,"id":927633,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Landes, Matthieu","contributorId":198781,"corporation":false,"usgs":false,"family":"Landes","given":"Matthieu","email":"","affiliations":[],"preferred":false,"id":927634,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Srinagesh, D.","contributorId":18631,"corporation":false,"usgs":true,"family":"Srinagesh","given":"D.","email":"","affiliations":[],"preferred":false,"id":927635,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Srinivas, D.","contributorId":350983,"corporation":false,"usgs":false,"family":"Srinivas","given":"D.","affiliations":[{"id":83893,"text":"NGRI, Hyderabad","active":true,"usgs":false}],"preferred":false,"id":927636,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hough, Susan E. 0000-0002-5980-2986 hough@usgs.gov","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":587,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"hough@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927637,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70223424,"text":"70223424 - 2021 - Coalescent methods reconstruct contributions of natural colonization and stocking to origins of Michigan inland Cisco (Coregonus artedi)","interactions":[],"lastModifiedDate":"2022-01-07T15:57:22.646685","indexId":"70223424","displayToPublicDate":"2021-08-25T10:21:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Coalescent methods reconstruct contributions of natural colonization and stocking to origins of Michigan inland Cisco (<i>Coregonus artedi</i>)","title":"Coalescent methods reconstruct contributions of natural colonization and stocking to origins of Michigan inland Cisco (Coregonus artedi)","docAbstract":"<p><span>Fish population structure in previously glaciated regions is often influenced by natural colonization processes and human-mediated dispersal, including fish stocking. Endemic populations are of conservation interest because they may contain rare and unique genetic variation. While coregonines are native to certain Michigan inland lakes, some were stocked with fish from Great Lakes sources, calling into question the origin of extant populations. While most stocking targeted lake whitefish (</span><i>Coregonus clupeaformis</i><span>), cisco (</span><i>C. artedi</i><span>) were also stocked from the Great Lakes to inland waterbodies. We used&nbsp;population genetic&nbsp;data (microsatellite genotypes and mitochondrial (mt)DNA sequences), coalescent modeling, and approximate Bayesian computation to investigate the origins of 12 inland Michigan cisco populations. The spatial distribution of mtDNA haplotypes suggests Michigan is an&nbsp;introgression&nbsp;zone for two ancestral cisco lineages associated with separate glacial&nbsp;refugia. Low levels of genetic diversity and high levels of genetic divergence were observed for populations located well inland of the Great Lakes relative to populations occupying waterbodies near the Great Lakes. Estimates of recent Great Lakes gene flow ranged from 27 to 48% for populations near the Great Lakes&nbsp;shoreline&nbsp;but were substantially lower (under 8%) for populations further inland. Inland lakes with elevated recent gene flow estimates may have been recipients of stocked coregonine fry, including cisco. Low levels of genetic diversity paired with a high likelihood of&nbsp;endemism&nbsp;as indicated by strong genetic divergence and low Great Lakes population inputs suggest the analyzed cisco populations occupying southern Michigan kettle lakes are of elevated conservation interest.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.08.008","usgsCitation":"Homola, J.J., Robinson, J.D., Kanefsky, J., Stott, W., Whelan, G., and Scribner, K.T., 2021, Coalescent methods reconstruct contributions of natural colonization and stocking to origins of Michigan inland Cisco (Coregonus artedi): Journal of Great Lakes Research, v. 47, no. 6, p. 1781-1792, https://doi.org/10.1016/j.jglr.2021.08.008.","productDescription":"12 p.","startPage":"1781","endPage":"1792","ipdsId":"IP-124168","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":388588,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -88.505859375,\n              41.47566020027821\n            ],\n            [\n              -81.38671875,\n              41.47566020027821\n            ],\n            [\n              -81.38671875,\n              46.830133640447386\n            ],\n            [\n              -88.505859375,\n              46.830133640447386\n            ],\n            [\n              -88.505859375,\n              41.47566020027821\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"47","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Homola, Jared J.","contributorId":264547,"corporation":false,"usgs":false,"family":"Homola","given":"Jared","email":"","middleInitial":"J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":822012,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robinson, John D","contributorId":264810,"corporation":false,"usgs":false,"family":"Robinson","given":"John","email":"","middleInitial":"D","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":822013,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kanefsky, Jeannette","contributorId":243198,"corporation":false,"usgs":false,"family":"Kanefsky","given":"Jeannette","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":822014,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stott, Wendylee 0000-0002-5252-4901 wstott@usgs.gov","orcid":"https://orcid.org/0000-0002-5252-4901","contributorId":191249,"corporation":false,"usgs":true,"family":"Stott","given":"Wendylee","email":"wstott@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":822015,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whelan, Gary","contributorId":146115,"corporation":false,"usgs":false,"family":"Whelan","given":"Gary","email":"","affiliations":[{"id":16584,"text":"Fisheries Division, Michigan Department of Natural Resources, P.O. Box 30446, Lansing, MI 48909","active":true,"usgs":false}],"preferred":false,"id":822016,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scribner, Kim T","contributorId":264811,"corporation":false,"usgs":false,"family":"Scribner","given":"Kim","email":"","middleInitial":"T","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":822017,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70223450,"text":"70223450 - 2021 - An experimental evaluation of the efficacy of imaging flow cytometry (FlowCam) for detecting invasive Dreissened and Corbiculid bivalve veligers","interactions":[],"lastModifiedDate":"2021-12-10T16:40:28.007302","indexId":"70223450","displayToPublicDate":"2021-08-25T10:16:39","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2592,"text":"Lake and Reservoir Management","active":true,"publicationSubtype":{"id":10}},"title":"An experimental evaluation of the efficacy of imaging flow cytometry (FlowCam) for detecting invasive Dreissened and Corbiculid bivalve veligers","docAbstract":"<p><span>Zebra (</span><i>Dreissena polymorpha</i><span>) and quagga (</span><i>D. bugensis</i><span>) mussels, first introduced from central Asia into the Great Lakes of North America in the late 1980s, have crossed the continental divide and more recently spread across western North America. At the same time, several new technologies have been developed for the early detection of dreissenids, including the FlowCam, a digital imaging-in-flow instrument, intended to detect dreissenid planktonic larvae (veligers). However, the efficacy of this technology has rarely been tested. We experimentally evaluated the FlowCam’s ability to capture identifiable images of quagga mussel veligers under 2 different types of conditions: (i) deionized water, and (ii) Columbia River Basin water (CRBW), including natural sediment and native plankton. We further evaluated the FlowCam’s ability to distinguish between dreissenid veligers and corbiculid veligers (Asian clam,&nbsp;</span><i>Corbicula fluminea</i><span>). We interpret our results to indicate that the FlowCam can consistently detect dreissenid veligers across a range of veliger densities. Moreover, the presence of other plankton and detritus only slightly affected dreissenid detection by the FlowCam. However, the orientation of individual bivalve veligers as they were imaged by the FlowCam precluded specific identification of a substantial proportion (24.8%) of veligers as either dreissenid or corbiculid. We suggest that the FlowCam is an important detection tool best utilized as part of a multifaceted approach, including traditional microscopy and possibly environmental DNA.</span></p>","language":"English","publisher":"Tayor and Francis Group","doi":"10.1080/10402381.2021.1961176","usgsCitation":"Hassett, W., Zimmerman, J., Rollwagen-Bollens, G., Bollens, S.M., and Counihan, T., 2021, An experimental evaluation of the efficacy of imaging flow cytometry (FlowCam) for detecting invasive Dreissened and Corbiculid bivalve veligers: Lake and Reservoir Management, v. 37, no. 4, p. 406-417, https://doi.org/10.1080/10402381.2021.1961176.","productDescription":"12 p.","startPage":"406","endPage":"417","ipdsId":"IP-073274","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":388587,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Hassett, Whitney","contributorId":190161,"corporation":false,"usgs":false,"family":"Hassett","given":"Whitney","email":"","affiliations":[],"preferred":false,"id":822048,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zimmerman, Julie","contributorId":190163,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Julie","affiliations":[],"preferred":false,"id":822049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rollwagen-Bollens, Gretchen","contributorId":190162,"corporation":false,"usgs":false,"family":"Rollwagen-Bollens","given":"Gretchen","email":"","affiliations":[],"preferred":false,"id":822050,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bollens, Stephen M. 0000-0001-9214-9037","orcid":"https://orcid.org/0000-0001-9214-9037","contributorId":148958,"corporation":false,"usgs":false,"family":"Bollens","given":"Stephen","email":"","middleInitial":"M.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":822051,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Counihan, Timothy D. 0000-0003-4967-6514","orcid":"https://orcid.org/0000-0003-4967-6514","contributorId":207532,"corporation":false,"usgs":true,"family":"Counihan","given":"Timothy D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":822052,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70223676,"text":"70223676 - 2021 - Discrete sample introduction module for quantitative and isotopic analysis of methane and other gases by cavity ring-down spectroscopy","interactions":[],"lastModifiedDate":"2021-09-14T16:59:23.317558","indexId":"70223676","displayToPublicDate":"2021-08-25T08:18:12","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Discrete sample introduction module for quantitative and isotopic analysis of methane and other gases by cavity ring-down spectroscopy","docAbstract":"<div class=\"container container_scaled-down\"><div class=\"row\"><div class=\"col-xs-12\"><div id=\"abstractBox\" class=\"article_abstract-content hlFld-Abstract\"><p class=\"articleBody_abstractText\">Carbon dioxide (CO<sub>2</sub>) and methane (CH<sub>4</sub>) are natural and anthropogenic products that play a central role in the global carbon cycle and regulating Earth’s climate. Applications utilizing laser absorption spectroscopy, which continuously measure concentrations and stable isotope ratios of these greenhouse gases, are routinely employed to measure the source and magnitude of atmospheric inputs. We developed a discrete sample introduction module (DSIM) to enable measurements of methane and CO<sub>2</sub><span>&nbsp;</span>concentrations and δ<sup>13</sup>C values from limited volume (5–100 mL) gas samples when interfaced with a commercially available cavity ring-down spectroscopy (CRDS) analyzer. The analysis has a dynamic range that spans six orders of magnitude from 100% analyte to the lower limit of instrument detection (2 ppm). We demonstrate system performance for methane by comparing concentrations and δ<sup>13</sup>C results from the DSIM-CRDS system and traditional methods for a variety of sample types, including low concentration (nanomolar CH<sub>4</sub>) seawater and high concentration (&gt;90% CH<sub>4</sub>) natural gas. The expansive concentration range of the field-portable DSIM-CRDS system can measure enhances analytical performance for investigating methane and CO<sub>2</sub><span>&nbsp;</span>dynamics and, potentially, other gases measured by laser absorption spectroscopy.</p></div></div></div></div>","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.1c01386","usgsCitation":"Pohlman, J., Casso, M., Magen, C., and Bergeron, E., 2021, Discrete sample introduction module for quantitative and isotopic analysis of methane and other gases by cavity ring-down spectroscopy: Environmental Science & Technology, v. 55, no. 17, p. 12066-12074, https://doi.org/10.1021/acs.est.1c01386.","productDescription":"9 p.","startPage":"12066","endPage":"12074","ipdsId":"IP-130600","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":451068,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.1c01386","text":"Publisher Index Page"},{"id":436224,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99B34V1","text":"USGS data release","linkHelpText":"Comparison of methane concentration and stable carbon isotope data for natural samples analyzed by discrete sample introduction module - cavity ring down spectroscopy (DSIM-CRDS) and traditional methods"},{"id":388724,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"17","noUsgsAuthors":false,"publicationDate":"2021-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Pohlman, John 0000-0002-3563-4586","orcid":"https://orcid.org/0000-0002-3563-4586","contributorId":220804,"corporation":false,"usgs":true,"family":"Pohlman","given":"John","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":822288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Casso, Michael 0000-0002-6990-9090 mcasso@usgs.gov","orcid":"https://orcid.org/0000-0002-6990-9090","contributorId":2904,"corporation":false,"usgs":true,"family":"Casso","given":"Michael","email":"mcasso@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":822289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Magen, Cedric","contributorId":265132,"corporation":false,"usgs":false,"family":"Magen","given":"Cedric","email":"","affiliations":[{"id":54603,"text":"University of Maryland Center for Environmental Science, Chesapeake Biological Lab, Solomons MD","active":true,"usgs":false}],"preferred":false,"id":822290,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bergeron, Emile M. ebergeron@usgs.gov","contributorId":3449,"corporation":false,"usgs":true,"family":"Bergeron","given":"Emile M.","email":"ebergeron@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":822329,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70224289,"text":"70224289 - 2021 - Spatiotemporal dynamics of CO2 gas exchange from headwater mountain streams","interactions":[],"lastModifiedDate":"2021-09-20T12:49:53.852729","indexId":"70224289","displayToPublicDate":"2021-08-25T07:43:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal dynamics of CO2 gas exchange from headwater mountain streams","docAbstract":"<div class=\"article-section__content en main\"><p>Mountain streams play an important role in the global carbon cycle by transporting, metabolizing, and exchanging carbon they receive from the terrestrial environment. The rates at which these processes occur remain highly uncertain because of a paucity of observations and the difficulty of measuring gas exchange rates in steep, turbulent mountain streams. This uncertainty is compounded by large temporal and spatial variability in stream carbon dioxide (CO<sub>2</sub>) concentrations in mountain environments. In this study, we measured diel, seasonal, and annual variations in CO<sub>2</sub><span>&nbsp;</span>partial pressure (<i>p</i>CO<sub>2</sub>) in seven headwater streams and a groundwater spring in the Colorado Rocky Mountains to determine how CO<sub>2</sub><span>&nbsp;</span>exchange fluxes (<img class=\"section_image\" src=\"https://agupubs.onlinelibrary.wiley.com/cms/asset/6dcb36c7-fe40-41af-8c8e-768c11738d04/jgrg22024-math-0001.png\" alt=\"urn:x-wiley:21698953:media:jgrg22024:jgrg22024-math-0001\" data-mce-src=\"https://agupubs.onlinelibrary.wiley.com/cms/asset/6dcb36c7-fe40-41af-8c8e-768c11738d04/jgrg22024-math-0001.png\">) vary with time, annual precipitation, and landscape characteristics. Our results show that temporal variability in<span>&nbsp;</span><i>p</i>CO<sub>2</sub><span>&nbsp;</span>and<span>&nbsp;</span><img class=\"section_image\" src=\"https://agupubs.onlinelibrary.wiley.com/cms/asset/e26eac8a-866e-4250-b5d1-dde6672d03d6/jgrg22024-math-0002.png\" alt=\"urn:x-wiley:21698953:media:jgrg22024:jgrg22024-math-0002\" data-mce-src=\"https://agupubs.onlinelibrary.wiley.com/cms/asset/e26eac8a-866e-4250-b5d1-dde6672d03d6/jgrg22024-math-0002.png\"><span>&nbsp;</span>in mountain streams is large and is strongly influenced by solar radiation, the accumulation and melting of seasonal snowpacks, and interannual variations in precipitation. Spatial variations in<span>&nbsp;</span><i>p</i>CO<sub>2</sub><span>&nbsp;</span>and<span>&nbsp;</span><img class=\"section_image\" src=\"https://agupubs.onlinelibrary.wiley.com/cms/asset/c639387c-a043-4d5c-8b9f-0d845a0023aa/jgrg22024-math-0003.png\" alt=\"urn:x-wiley:21698953:media:jgrg22024:jgrg22024-math-0003\" data-mce-src=\"https://agupubs.onlinelibrary.wiley.com/cms/asset/c639387c-a043-4d5c-8b9f-0d845a0023aa/jgrg22024-math-0003.png\"><span>&nbsp;</span>were related to landscape characteristics, with soil organic matter, wetlands, and likely groundwater discharge zones having a positive influence. Periglacial features, such as ice and rock glaciers, had a negative influence on stream<span>&nbsp;</span><i>p</i>CO<sub>2</sub><span>&nbsp;</span>and<span>&nbsp;</span><img class=\"section_image\" src=\"https://agupubs.onlinelibrary.wiley.com/cms/asset/dad80ea0-7b25-43fd-8520-9ddb3318d4da/jgrg22024-math-0004.png\" alt=\"urn:x-wiley:21698953:media:jgrg22024:jgrg22024-math-0004\" data-mce-src=\"https://agupubs.onlinelibrary.wiley.com/cms/asset/dad80ea0-7b25-43fd-8520-9ddb3318d4da/jgrg22024-math-0004.png\">. Estimated<span>&nbsp;</span><img class=\"section_image\" src=\"https://agupubs.onlinelibrary.wiley.com/cms/asset/15dcd4a7-487a-4b6e-a9a4-c3bef5905bd3/jgrg22024-math-0005.png\" alt=\"urn:x-wiley:21698953:media:jgrg22024:jgrg22024-math-0005\" data-mce-src=\"https://agupubs.onlinelibrary.wiley.com/cms/asset/15dcd4a7-487a-4b6e-a9a4-c3bef5905bd3/jgrg22024-math-0005.png\"><span>&nbsp;</span>from streams in an alpine/subalpine region of Colorado was 3.4&nbsp;kg&nbsp;C&nbsp;m<sup>−2</sup>&nbsp;yr<sup>−1</sup><span>&nbsp;</span>normalized to stream surface area (95% CI: 2.1–5.0&nbsp;kg&nbsp;C&nbsp;m<sup>−2</sup>&nbsp;yr<sup>−1</sup>), consistent with recent work on CO<sub>2</sub><span>&nbsp;</span>exchange from mountain streams in the Swiss Alps. Our results highlight the importance of mountain streams as substantial contributors in the global carbon cycle.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JG006509","usgsCitation":"Clow, D.W., Striegl, R.G., and Dornblaser, M., 2021, Spatiotemporal dynamics of CO2 gas exchange from headwater mountain streams: Journal of Geophysical Research: Biogeosciences, v. 126, no. 9, e2021JG006509, 18 p., https://doi.org/10.1029/2021JG006509.","productDescription":"e2021JG006509, 18 p.","ipdsId":"IP-118820","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":451070,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021jg006509","text":"Publisher Index Page"},{"id":436226,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S775Y4","text":"USGS data release","linkHelpText":"Continuous water-quality data for selected streams in Rocky Mountain National Park, Colorado, water years 2011-19 (ver. 2.0, January 2022)"},{"id":436225,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RNS5FP","text":"USGS data release","linkHelpText":"Continuous water-quality data for selected streams in Rocky Mountain National Park, Colorado, water years 2011-19"},{"id":389471,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Loch Vale","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -105.74821472167969,\n              40.16418235037417\n            ],\n            [\n              -105.50102233886719,\n              40.16418235037417\n            ],\n            [\n              -105.50102233886719,\n              40.33660027347341\n            ],\n            [\n              -105.74821472167969,\n              40.33660027347341\n            ],\n            [\n              -105.74821472167969,\n              40.16418235037417\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"126","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Striegl, Robert G. 0000-0002-8251-4659 rstriegl@usgs.gov","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":1630,"corporation":false,"usgs":true,"family":"Striegl","given":"Robert","email":"rstriegl@usgs.gov","middleInitial":"G.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":false,"id":823466,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dornblaser, Mark 0000-0002-6298-3757","orcid":"https://orcid.org/0000-0002-6298-3757","contributorId":220741,"corporation":false,"usgs":true,"family":"Dornblaser","given":"Mark","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":823467,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70231659,"text":"70231659 - 2021 - Mapping wetland burned area from Sentinel-2 across the southeastern United States and its contributions relative to Landsat 8 (2016-2019)","interactions":[],"lastModifiedDate":"2022-05-19T12:16:16.833966","indexId":"70231659","displayToPublicDate":"2021-08-25T07:13:36","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5678,"text":"Fire","active":true,"publicationSubtype":{"id":10}},"title":"Mapping wetland burned area from Sentinel-2 across the southeastern United States and its contributions relative to Landsat 8 (2016-2019)","docAbstract":"<div class=\"art-abstract in-tab hypothesis_container\">Prescribed fires and wildfires are common in wetland ecosystems across the Southeastern United States. However, the wetland burned area has been chronically underestimated across the region due to (1) spectral confusion between open water and burned area, (2) rapid post-fire vegetation regrowth, and (3) high annual precipitation limiting clear-sky satellite observations. We developed a machine learning algorithm specifically for burned area in wetlands, and applied the algorithm to the Sentinel-2 archive (2016–2019) across the Southeastern US (&gt;290,000 km<sup>2</sup>). Combining Landsat-8 imagery with Sentinel-2 increased the annual clear-sky observation count from 17 to 46 in 2016 and from 16 to 78 in 2019. When validated with WorldView imagery, the Sentinel-2 burned area had a 29% and 30% omission and commission rates of error for burned area, respectively, compared to the US Geological Survey Landsat-8 Burned Area Product (L8 BA), which had a 47% and 8% omission and commission rate of error, respectively. The Sentinel-2 algorithm and the L8 BA mapped burned area within 78% and 60% of wetland fire perimeters (<span class=\"html-italic\">n</span><span>&nbsp;</span>= 555) compiled from state and federal agencies, respectively. This analysis demonstrated the potential of Sentinel-2 to support efforts to track the burned area, especially across challenging ecosystem types, such as wetlands.<span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span></span></span></div>","language":"English","publisher":"MDPI","doi":"10.3390/fire4030052","usgsCitation":"Vanderhoof, M.K., Hawbaker, T., Teske, C., Ku, A., Noble, J., and Picotte, J., 2021, Mapping wetland burned area from Sentinel-2 across the southeastern United States and its contributions relative to Landsat 8 (2016-2019): Fire, v. 4, no. 3, 52, 25 p., https://doi.org/10.3390/fire4030052.","productDescription":"52, 25 p.","ipdsId":"IP-127398","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":451071,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fire4030052","text":"Publisher Index Page"},{"id":436227,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S8SLEM","text":"USGS data release","linkHelpText":"Wetland burned area extent derived from Sentinel-2 across the southeastern U.S. (2016-2019)"},{"id":400801,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Montana, 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{\"name\":\"Florida\",\"nation\":\"USA  \"}}]}","volume":"4","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":843283,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":843284,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teske, Casey","contributorId":224732,"corporation":false,"usgs":false,"family":"Teske","given":"Casey","email":"","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":843285,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ku, Andrea","contributorId":291889,"corporation":false,"usgs":false,"family":"Ku","given":"Andrea","affiliations":[{"id":27232,"text":"Former USGS Student Contractor","active":true,"usgs":false}],"preferred":false,"id":843286,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Noble, Joe","contributorId":257938,"corporation":false,"usgs":false,"family":"Noble","given":"Joe","email":"","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":843287,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Picotte, Joshua J. 0000-0002-4021-4623","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":202800,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":843288,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70249720,"text":"70249720 - 2021 - Exploring environmental factors that drive diel variations in tree water storage using wavelet analysis","interactions":[],"lastModifiedDate":"2023-10-25T11:59:54.29182","indexId":"70249720","displayToPublicDate":"2021-08-25T06:52:34","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7170,"text":"Frontiers in Water","active":true,"publicationSubtype":{"id":10}},"title":"Exploring environmental factors that drive diel variations in tree water storage using wavelet analysis","docAbstract":"<div class=\"JournalAbstract\"><p>Internal water storage within trees can be a critical reservoir that helps trees overcome both short- and long-duration environmental stresses. We monitored changes in internal tree water storage in a ponderosa pine on daily and seasonal scales using moisture probes, a dendrometer, and time-lapse electrical resistivity imaging (ERI). These data were used to investigate how patterns of in-tree water storage are affected by changes in sapflow rates, soil moisture, and meteorologic factors such as vapor pressure deficit. Measurements of xylem fluid electrical conductivity were constant in the early growing season while inverted sapwood electrical conductivity steadily increased, suggesting that increases in sapwood electrical conductivity did not result from an increase in xylem fluid electrical conductivity. Seasonal increases in stem electrical conductivity corresponded with seasonal increases in trunk diameter, suggesting that increased electrical conductivity may result from new growth. On the daily scale, changes in inverted sapwood electrical conductivity correspond to changes in sapwood moisture. Wavelet analyses indicated that lag times between inverted electrical conductivity and sapflow increased after storm events, suggesting that as soils wetted, reliance on internal water storage decreased, as did the time required to refill daily deficits in internal water storage. We found short time lags between sapflow and inverted electrical conductivity with dry conditions, when ponderosa pine are known to reduce stomatal conductance to avoid xylem cavitation. A decrease in diel amplitudes of inverted sapwood electrical conductivity during dry periods suggest that the ponderosa pine relied on internal water storage to supplement transpiration demands, but as drought conditions progressed, tree water storage contributions to transpiration decreased. Time-lapse ERI- and wavelet-analysis results highlight the important role internal tree water storage plays in supporting transpiration throughout a day and during periods of declining subsurface moisture.</p></div>","language":"English","publisher":"Frontiers","doi":"10.3389/frwa.2021.682285","usgsCitation":"Harmon, R., Barnard, H., Day-Lewis, F., Mao, D., and Singha, K., 2021, Exploring environmental factors that drive diel variations in tree water storage using wavelet analysis: Frontiers in Water, v. 3, 682285, 22 p., https://doi.org/10.3389/frwa.2021.682285.","productDescription":"682285, 22 p.","ipdsId":"IP-130437","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":451074,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frwa.2021.682285","text":"Publisher Index Page"},{"id":422091,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Gordon Gulch","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -105.5213291829561,\n              40.029532063000204\n            ],\n            [\n              -105.5213291829561,\n              39.982722180293365\n            ],\n            [\n              -105.46742751059293,\n              39.982722180293365\n            ],\n            [\n              -105.46742751059293,\n              40.029532063000204\n            ],\n            [\n              -105.5213291829561,\n              40.029532063000204\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"3","noUsgsAuthors":false,"publicationDate":"2021-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Harmon, Ryan","contributorId":331165,"corporation":false,"usgs":false,"family":"Harmon","given":"Ryan","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":886848,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnard, Holly","contributorId":331166,"corporation":false,"usgs":false,"family":"Barnard","given":"Holly","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":886849,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Day-Lewis, Frederick 0000-0003-3526-886X","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":216359,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":886850,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mao, Deqiang","contributorId":331169,"corporation":false,"usgs":false,"family":"Mao","given":"Deqiang","email":"","affiliations":[{"id":79141,"text":"Shandong University","active":true,"usgs":false}],"preferred":false,"id":886851,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singha, Kamini","contributorId":331170,"corporation":false,"usgs":false,"family":"Singha","given":"Kamini","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":886852,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70223331,"text":"sir20215072 - 2021 - Evaluation of actual evapotranspiration rates from the Operational Simplified Surface Energy Balance (SSEBop) model in Florida and parts of Alabama and Georgia, 2000–17","interactions":[],"lastModifiedDate":"2021-08-25T11:39:29.585628","indexId":"sir20215072","displayToPublicDate":"2021-08-24T14:28:01","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5072","displayTitle":"Evaluation of Actual Evapotranspiration Rates from the Operational Simplified Surface Energy Balance (SSEBop) Model in Florida and Parts of Alabama and Georgia, 2000–17","title":"Evaluation of actual evapotranspiration rates from the Operational Simplified Surface Energy Balance (SSEBop) model in Florida and parts of Alabama and Georgia, 2000–17","docAbstract":"<p>Evapotranspiration (ET) is the water-vapor flux transported from the surface of the Earth into the atmosphere and is the sum of surface water directly evaporated and subsurface water transpired by plants. ET rates are commonly estimated by using potential or reference ET, which might differ from actual ET rates. Actual evapotranspiration (ETa) rates can be estimated by using the Operational Simplified Surface Energy Balance (SSEBop) model. This report evaluates SSEBop ETa rates at the point and basin scales in Florida and parts of Alabama and Georgia for 2000–17. ETa rates computed by using data from 24 micrometeorological stations in Florida are referred to as mETa rates and were used to quantify biases in the SSEBop ETa rates, stratified by generalized land-use type. Bias was computed as mETa minus SSEBop ETa rates for given generalized land-use types, and bias-correction equations were computed by using least-squares regressions. In addition to mETa rates at station locations, annual average ETa rates calculated from the application of a water-balance method to 55 basins in Florida and parts of Alabama and Georgia were used to assess the accuracy of the annual SSEBop ETa rates at the basin scale. Another independent model used to simulate ETa rates was based on monthly reference ET from the statewide daily reference evapotranspiration (ETo) gridded dataset for Florida computed by using Geostationary Operational Environmental Satellite estimates of solar radiation (GOES ETo). ETa at grid points was computed as monthly GOES ETo multiplied by ratios of monthly mETa to GOES ETo, computed at micrometeorological stations and stratified by each generalized land-use type.</p><p>The coefficient of determination (R<sup>2</sup>) between monthly mETa and SSEBop ETa rates for all stations combined improved from 0.37 before bias correction of SSEBop ETa rates to 0.79 after the bias correction stratified by land-use type. For individual land-uses types, R<sup>2</sup> varied from 0.59 for the monthly mETa at a station in the land-use type forest to 0.82 for the monthly mETa at stations in the land-use type shallow-water-table pasture. Root-mean-square error (RMSE) was computed as a function of the difference between SSEBop ETa rates and mETa rates. RMSE of monthly SSEBop ETa rates was 1.27 inches per month before the bias corrections improved to 0.73 inch per month after the bias corrections. RMSE for bias-corrected annual SSEBop ETa rates based on micrometeorological stations with complete years of records ranged from 2.01 inches per year (in/yr) for the land-use type of agriculture to 5.73 in/yr for the land-use type of deep water-table pasture, or 4.96 and 21.21 percent errors relative to annual mETa rates, respectively. Bias-corrected annual SSEBop ETa rates were also compared to annual ETa rates computed by using a water-balance method (wbETa) for 55 basins in Florida. Differences in bias-corrected average annual SSEBop ETa rates and average annual wbETa rates for the 55 basins ranged from −3.67 to 5.29 in/yr (−9.24 to 17.36 percent). RMSE when computed as a function of the differences between annual SSEBop ETa rates and wbETa rates decreased, on average, from 4.13 in/yr for the uncorrected bias SSEBop ETa rates to 1.95 in/yr for the bias-corrected SSEBop rates. The average annual bias-corrected SSEBop ETa rates, from all basins, was 36.46 in/yr or 3.41 percent lower than the average annual wbETa rate of 37.79 inches.</p><p>Bias in SSEBop ETa rates varies based on time step (monthly versus annual), scale (point, basin, statewide), and land-use type. Applications to hydrologic models should consider bias relative to the inherent error in models. Bias-corrected SSEBop ETa rates could be used as calibration targets in models of hydrologic processes, such as groundwater models. Annual bias in SSEBop ETa introduced to the model calibration is typically below the margin of error associated with typical residuals in model simulations, depending on scale. Surface-water and groundwater-flow models with RMSEs on the order of a few feet could benefit from bias-corrected SSEBop values of ETa.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215072","collaboration":"Prepared in cooperation with Northwest Florida Water Management District, Suwannee River Water Management District, St. Johns River Water Management District, South Florida Water Management District, Southwest Florida Water Management District, and Tampa Bay Water","usgsCitation":"Sepúlveda, N., 2021, Evaluation of actual evapotranspiration rates from the Operational Simplified Surface Energy Balance (SSEBop) model in Florida and parts of Alabama and Georgia, 2000–17: U.S. Geological Survey Scientific Investigations Report 2021–5072, 66 p., https://doi.org/10.3133/sir20215072.","productDescription":"Report: x, 66 p.; Data Release","numberOfPages":"80","onlineOnly":"Y","ipdsId":"IP-112971","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":388346,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5072/coverthb.jpg"},{"id":388349,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5072/images"},{"id":388347,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5072/sir20215072.pdf","text":"Report","size":"12.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5072"},{"id":388348,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99AB3X4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data sets of actual evapotranspiration rates from 2000 to 2017 for basins in Florida and parts of Alabama and Georgia, calculated using the water-balance method, the bias-corrected Operational Simplified Surface Energy Balance (SSEBop) model, and the land-use crop coefficients model"}],"country":"United States","state":"Alabama, Florida, Georgia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -87.71484375,\n              25.005972656239187\n            ],\n            [\n              -79.98046875,\n              25.005972656239187\n            ],\n            [\n              -79.98046875,\n              31.98944183792288\n            ],\n            [\n              -87.71484375,\n              31.98944183792288\n            ],\n            [\n              -87.71484375,\n              25.005972656239187\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a data-mce-href=\"mailto:gs-w-cfwsc_center_director@usgs.gov\" href=\"mailto:gs-w-cfwsc_center_director@usgs.gov\">Director</a>, <a data-mce-href=\"https://www.usgs.gov/centers/car-fl-water\" href=\"https://www.usgs.gov/centers/car-fl-water\">Caribbean-Florida Water Science Center</a><br>U.S. Geological Survey<br>4446 Pet Lane, Suite 108<br>Lutz, FL 33559 <br> </p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Models Used to Simulate Actual Evapotranspiration</li><li>Evaluation of SSEBop Rates</li><li>Model Limitations</li><li>Summary and Conclusions</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2021-08-24","noUsgsAuthors":false,"publicationDate":"2021-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Sepulveda, Nicasio 0000-0002-6333-1865 nsepul@usgs.gov","orcid":"https://orcid.org/0000-0002-6333-1865","contributorId":1454,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Nicasio","email":"nsepul@usgs.gov","affiliations":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"preferred":true,"id":821783,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70223361,"text":"sir20215080 - 2021 - Estimation of dissolved-solids concentrations using continuous water-quality monitoring and regression models at four sites in the Yuma area, Arizona and California, January 2017 through March 2019","interactions":[],"lastModifiedDate":"2021-08-25T11:44:55.7065","indexId":"sir20215080","displayToPublicDate":"2021-08-24T14:20:10","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5080","displayTitle":"Estimation of Dissolved-Solids Concentrations Using Continuous Water-Quality Monitoring and Regression Models at Four Sites in the Yuma Area, Arizona and California, January 2017 through March 2019","title":"Estimation of dissolved-solids concentrations using continuous water-quality monitoring and regression models at four sites in the Yuma area, Arizona and California, January 2017 through March 2019","docAbstract":"<p>Multiple linear regression models were developed to estimate dissolved-solids concentrations in water at four sites in the Yuma area between Imperial Dam, Arizona and California and the southerly international boundary with Mexico at San Luis, Arizona. Continuous and discrete water-quality data were collected at gaging stations in the Colorado River upstream from Imperial Dam, Arizona-California, the Colorado River below Cooper wasteway near Yuma, Arizona, the Yuma Main Drain above Arizona–Sonora, Mexico boundary, and the 242 lateral above Main Drain at the Arizona–Sonora boundary. Continuous specific conductance and water temperature data were collected at each site between January 2017 and March 2019. Bi-weekly to monthly dissolved-solids water samples were collected during the same period. Continuous specific conductance data collected at the Colorado River below Cooper wasteway were affected by poorly mixed streamflow during periods when the Pilot Knob Hydro-electric Plant was releasing water to the river. The continuous specific conductance data for the site downstream from Cooper wasteway were corrected using mean specific conductance values computed from cross-section measurements collected during site visits. Continuous specific conductance data were affected by sensor fouling issues at the 242 lateral site, and continued operation at the site would require more frequent visits for cleaning and service to ensure data quality.</p><p>During the study, instream specific conductance readings ranged from 966 to 3,030 microsiemens per centimeter (μS/cm) at 25 degrees Celsius. Computed dissolved-solids concentrations from discrete samples ranged from 690 to 2,580 milligrams per liter (mg/L). Dissolved-solids concentrations were estimated from regression models using the optimal relation between dissolved solids and environmental factors, such as specific conductance, water temperature, dissolved oxygen, streamflow, and seasonality. Specific conductance was the primary factor at all four sites and explained 87.6 to 94 percent of variation in dissolved solids. Water temperature, as an indicator of seasonality, was determined to be a statistically significant secondary factor at both the Colorado River above Imperial Dam and Colorado River below Cooper wasteway sites explaining an additional 6.9 and 2.1 percent of variation in dissolved solids, respectively. Regression models explained 87.6 to 96.9 percent of the variation in dissolved solids; the root mean square error in the modeled data ranged between about 6 and 27 mg/L.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215080","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Cederberg, J.R., Paretti, N.V., Coes, A.L., Hermosillo, E., Andrade, L., 2021, Estimation of dissolved-solids concentrations using continuous water-quality monitoring and regression models at four sites in the Yuma area, Arizona and California, January 2017 through March 2019: U.S. Geological Survey Scientific Investigations Report 2021–5080, 26 p., https://doi.org/10.3133/sir20215080.","productDescription":"Report: vii, 26 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-111110","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":436228,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SMK908","text":"USGS data release","linkHelpText":"Water-Quality Field Blank and Replicate Sample Data, Instantaneous and Mean Daily Discharge Data, and Dissolved-Solids Concentrations Data Collected in Four Waterways of Southwest Arizona, January 2017-March 2019"},{"id":388445,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/p9SMK908","linkHelpText":"Supplemental streamflow, quality-assurance, and dissolved-solids concentration datasets used for regression model development at four sites in the Yuma area, Arizona and California, January 2017 through March 2019"},{"id":388447,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5080/covrthb.jpg"},{"id":388448,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5080/sir20215080.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Arizona, California","otherGeospatial":"Yuma area","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -114.873046875,\n              32.58384932565662\n            ],\n            [\n              -114.3896484375,\n              32.58384932565662\n            ],\n            [\n              -114.3896484375,\n              32.88881315761995\n            ],\n            [\n              -114.873046875,\n              32.88881315761995\n            ],\n            [\n              -114.873046875,\n              32.58384932565662\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_az@usgs.gov\" data-mce-href=\"mailto:dc_az@usgs.gov\">Director</a>,<br><a href=\"https://www.usgs.gov/centers/az-water\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/az-water\">Arizona Water Science Center</a><br><a href=\"https://www.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/\">U.S. Geological Survey</a><br>520 N. Park Avenue<br>Tucson, AZ 85719</p>","tableOfContents":"<ul><li>Acknowledgments&nbsp;&nbsp;</li><li>Abstract&nbsp;&nbsp;</li><li>Introduction&nbsp;&nbsp;</li><li>Methods&nbsp;&nbsp;</li><li>Results&nbsp; &nbsp;</li><li>Summary&nbsp;&nbsp;</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2021-08-24","noUsgsAuthors":false,"publicationDate":"2021-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Cederberg, Jay R. 0000-0001-6649-7353 cederber@usgs.gov","orcid":"https://orcid.org/0000-0001-6649-7353","contributorId":964,"corporation":false,"usgs":true,"family":"Cederberg","given":"Jay","email":"cederber@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821857,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paretti, Nicholas V. 0000-0003-2178-4820 nparetti@usgs.gov","orcid":"https://orcid.org/0000-0003-2178-4820","contributorId":173412,"corporation":false,"usgs":true,"family":"Paretti","given":"Nicholas","email":"nparetti@usgs.gov","middleInitial":"V.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821858,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coes, Alissa L. 0000-0001-6682-5417 alcoes@usgs.gov","orcid":"https://orcid.org/0000-0001-6682-5417","contributorId":4231,"corporation":false,"usgs":true,"family":"Coes","given":"Alissa","email":"alcoes@usgs.gov","middleInitial":"L.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821859,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hermosillo, Edyth 0000-0003-1648-1016 ehermosillo@usgs.gov","orcid":"https://orcid.org/0000-0003-1648-1016","contributorId":175455,"corporation":false,"usgs":true,"family":"Hermosillo","given":"Edyth","email":"ehermosillo@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821860,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andrade, Lucia 0000-0003-3741-1404","orcid":"https://orcid.org/0000-0003-3741-1404","contributorId":264674,"corporation":false,"usgs":true,"family":"Andrade","given":"Lucia","email":"","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821861,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70223360,"text":"ofr20211074 - 2021 - Assessment of barrier island morphological change in northern Alaska","interactions":[],"lastModifiedDate":"2021-08-25T11:35:00.440129","indexId":"ofr20211074","displayToPublicDate":"2021-08-24T12:41:57","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1074","displayTitle":"Assessment of Barrier Island Morphological Change in Northern Alaska","title":"Assessment of barrier island morphological change in northern Alaska","docAbstract":"<p>Arctic barriers islands are highly dynamic features influenced by a variety of oceanographic, geologic, and environmental factors. Many Alaskan barrier islands and spits serve as habitat and protection for native species, as well as shelter the coast from waves and storms that cause flooding and degradation of coastal villages. This study summarizes changes to barrier morphology in time and space along the North Slope coast of Alaska between the United States-Canadian border and Cape Beaufort from 1947 to 2020. Changes considered in this study include number of barriers, area and perimeter, shoreline length, barrier sinuosity and width, presence and number of relict terminus features, presence and coverage of tundra vegetation, barrier orientation, and elevation metrics. Wave conditions are also summarized and related to changes in barrier morphology. The results in this report help to better predict future barrier evolution and prevalence along Alaska’s coast by increasing our understanding of Arctic barrier development, migration and degradation.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211074","usgsCitation":"Hamilton, A.I., Gibbs, A.E., Erikson, L.H., and Engelstad, A.C., 2021, Assessment of barrier island morphological change in northern Alaska: U.S. Geological Survey Open-File Report 2021–1074, 28 p., https://doi.org/10.3133/ofr20211074.","productDescription":"Report: vi , 28 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-122308","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":388442,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90EQ1H7","linkHelpText":"Historical shorelines and morphological metrics for barrier islands and spits along the north coast of Alaska between Cape Beaufort and the U.S.-Canadian border, 1947 to 2019"},{"id":388441,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1074/ofr20211074.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388440,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1074/covrthb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -160.83984375,\n              69.16255790810499\n            ],\n            [\n              -141.240234375,\n              69.16255790810499\n            ],\n            [\n              -141.240234375,\n              72.01972876525514\n            ],\n            [\n              -160.83984375,\n              72.01972876525514\n            ],\n            [\n              -160.83984375,\n              69.16255790810499\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"http://www.usgs.gov/centers/pcmsc/\" data-mce-href=\"http://www.usgs.gov/centers/pcmsc/\">Pacific Coastal and Marine 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>Pacific Coastal and Marine Science Center<br>2885 Mission St.<br>Santa Cruz, CA 95060</p>","tableOfContents":"<ul><li>Acknowledgments&nbsp;&nbsp;</li><li>Abstract&nbsp;&nbsp;</li><li>Introduction&nbsp;&nbsp;</li><li>Methods&nbsp;&nbsp;</li><li>Results&nbsp;&nbsp;</li><li>Summary&nbsp;&nbsp;</li><li>References Cited&nbsp;&nbsp;</li><li>Appendix 1. Feature Type and Name or Geographical Area of Barrier Island Chains&nbsp;&nbsp;</li><li>Appendix 2. Total Barrier Chain Area&nbsp;&nbsp;</li><li>Appendix 3. Wave Roses for Each Era at Different Locations along Alaska's North Slope</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2021-08-24","noUsgsAuthors":false,"publicationDate":"2021-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Hamilton, Anna I.","contributorId":201415,"corporation":false,"usgs":true,"family":"Hamilton","given":"Anna","email":"","middleInitial":"I.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":16286,"text":"Tetra Tech","active":true,"usgs":false}],"preferred":true,"id":821853,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gibbs, Ann E. 0000-0002-0883-3774 agibbs@usgs.gov","orcid":"https://orcid.org/0000-0002-0883-3774","contributorId":2644,"corporation":false,"usgs":true,"family":"Gibbs","given":"Ann","email":"agibbs@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":821854,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":821855,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Engelstad, Anita C. 0000-0002-0211-4189","orcid":"https://orcid.org/0000-0002-0211-4189","contributorId":24884,"corporation":false,"usgs":true,"family":"Engelstad","given":"Anita C.","affiliations":[],"preferred":true,"id":821856,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70223421,"text":"70223421 - 2021 - Seasonally dynamic nutrient modeling quantifies storage lags and time-varying reactivity across large river basins","interactions":[],"lastModifiedDate":"2021-08-27T15:16:07.4255","indexId":"70223421","displayToPublicDate":"2021-08-24T10:12:21","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Seasonally dynamic nutrient modeling quantifies storage lags and time-varying reactivity across large river basins","docAbstract":"<p><span>Nutrients that have gradually accumulated in soils, groundwaters, and river sediments in the United States over the past century can remobilize and increase current downstream loading, obscuring effects of conservation practices aimed at protecting water resources. Drivers of storage accumulation and release of nutrients are poorly understood at the spatial scale of basins to watersheds. Predicting water quality outcomes in large river basins demands modeling storage lags and time varying reactivity that models of mean conditions typically cannot elucidate. We developed a seasonally dynamic approach to large-scale nutrient modeling based on a multiscale framework and nutrient storage lags were quantified for the nearly 190 000 small catchments that feed the rivers across the northeastern United States where catchment mean transit times were found to be around 4.7 (2–10) years for nitrogen and 1.3 (0.7–2) years for phosphorus. Nutrient loads carried in river flow in the current season contained a significant—and sometimes dominant—portion of mass lagged in its release from catchment storage repositories. Our approach of integrating storage releases with seasonally dynamic hydroclimatic drivers sets the stage to assess the accumulated effects of nutrient storage and lagged releases to the river interacting with seasonally varying nutrient reactivity and societal management actions throughout large river basins.</span></p>","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ac1af4","usgsCitation":"Schmadel, N., Harvey, J., and Schwarz, G.E., 2021, Seasonally dynamic nutrient modeling quantifies storage lags and time-varying reactivity across large river basins: Environmental Research Letters, v. 16, no. 9, 095004, 11 p., https://doi.org/10.1088/1748-9326/ac1af4.","productDescription":"095004, 11 p.","ipdsId":"IP-126236","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":451077,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ac1af4","text":"Publisher Index Page"},{"id":436229,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NRFWOV","text":"USGS data release","linkHelpText":"Mean seasonal SPARROW model inputs and simulated nitrogen and phosphorus loads for the Northeastern United States 2002 base year"},{"id":388586,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmadel, Noah M. 0000-0002-2046-1694","orcid":"https://orcid.org/0000-0002-2046-1694","contributorId":219105,"corporation":false,"usgs":true,"family":"Schmadel","given":"Noah","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":822009,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":822010,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":213621,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory","email":"gschwarz@usgs.gov","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":822011,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
]}