{"pageNumber":"278","pageRowStart":"6925","pageSize":"25","recordCount":46681,"records":[{"id":70204621,"text":"cir1457 - 2019 - National earthquake information center strategic plan, 2019–23","interactions":[],"lastModifiedDate":"2020-09-01T13:55:24.927546","indexId":"cir1457","displayToPublicDate":"2019-09-13T10:30:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1457","displayTitle":"National Earthquake Information Center Strategic Plan, 2019–23","title":"National earthquake information center strategic plan, 2019–23","docAbstract":"

Executive Summary

Damaging earthquakes occur regularly around the world; since the turn of the 20th century, hundreds of earthquakes have caused significant loss of life and (or) millions of dollars or more in economic losses. While most of these did not directly affect the United States and its Territories, by studying worldwide seismicity we can better understand how to mitigate the effects of earthquakes when they do occur within U.S. borders. Within the U.S. Government, this mandate falls on the U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC), which has the statutory responsibility for monitoring and reporting on earthquakes domestically and globally.

The NEIC has been operating since 1966, and throughout its history has been recognized as a world leader for earthquake information. For much of this time, NEIC has been cooperating with a number of regional seismic networks (RSNs) which operate in areas of heightened seismicity in the United States. In 2000, the Advanced National Seismic System (ANSS) was founded as a cooperative umbrella for earthquake-related data collection, analysis, and dissemination in the United States, thereby promoting advanced interoperability between the NEIC and RSN partners. The NEIC also cooperates and coordinates with dozens of global seismic networks. At present (2019), NEIC acquires real-time waveform data from more than 2,000 seismic stations worldwide, contributed from more than 130 seismic networks.

Since 2006, the NEIC has operated on a 24-hour, 7-days per week (24/7) basis, and reports on about 30,000 earthquakes per year. Soon after the occurrence of a significant global earthquake, notifications are issued to government representatives, aid agencies, the press, and members of the general public by the Earthquake Notification Service (ENS), electronic feeds, and through the USGS Earthquake Hazards Program (EHP) website. Event-specific web pages provide detailed source parameter information outlining the location and magnitude of the earthquake, including more detailed source characteristics like moment magnitude and focal mechanisms and finite fault solutions. Further, NEIC produces a suite of real-time situational awareness products, including ShakeMap, ShakeCast, Did-You-Feel-It? (DYFI?), and Prompt Assessment of Global Earthquakes for Response (PAGER), to characterize the shaking resulting from the earthquake and the impact it is likely to have on nearby populations and infrastructure. All of these products are ultimately archived in the ANSS Comprehensive Catalog (ComCat), hosted and served by the NEIC.

The NEIC also pursues an active research program to improve its ability to characterize earthquakes and understand their hazards. These efforts are all aimed at mitigating the risks of earthquakes to humankind.

To maintain its prominent position in earthquake monitoring, the NEIC must continue to evolve, concurrently improving its operations and 24/7 robustness, streamlining services and infrastructure, and keeping pace with research and innovation in the field of seismology. This document outlines how the NEIC might best achieve such goals, by describing specific avenues and opportunities for development in the next five years (2019–23).

Several key areas of operational and research focus are identified in this plan as being of the highest importance. First, NEIC must finalize improvements to its regional monitoring capabilities, including the implementation of a variety of improved earthquake detection and association algorithms. One of the most exciting avenues of recent research expansion in earthquake monitoring has involved the use of machine learning; NEIC must explore the benefits of machine learning for improved earthquake detection and source characterization. NEIC also needs to address issues related to the timeliness of earthquake information, exploring the benefits of distributing information as it becomes available, rather than when certain quality criteria are met. To that end, the incorporation of real-time Global Positioning System (GPS) data into the NEIC operational workflow will help improve the speed and accuracy of information for moderate-to-large earthquakes. Finally, NEIC should explore how to further expand and improve the quality and content of the products served during earthquake response efforts, including the generation of new earthquake sequence-specific products, adding an evolutionary component to earthquake information, and continued improvements to earthquake impact products.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/cir1457","usgsCitation":"Hayes, G.P., Earle, P.S., Benz, H.M., Wald, D.J., and Yeck, W.L., 2019, National Earthquake Information Center strategic plan, 2019–23: U.S. Geological Survey Circular 1457, 17 p., https://doi.org/10.3133/cir1457.","productDescription":"vi, 20 p.","onlineOnly":"N","ipdsId":"IP-107447","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":367395,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1457/coverthb2.jpg"},{"id":367396,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1457/circ1457.pdf","text":"Report","size":"19.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1457"}],"contact":"

Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS 966
Denver, CO 80225

","tableOfContents":"","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2019-09-13","noUsgsAuthors":false,"publicationDate":"2019-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Hayes, Gavin P. 0000-0003-3323-0112 ghayes@usgs.gov","orcid":"https://orcid.org/0000-0003-3323-0112","contributorId":147556,"corporation":false,"usgs":true,"family":"Hayes","given":"Gavin","email":"ghayes@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":770772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Earle, Paul S. 0000-0002-3500-017X pearle@usgs.gov","orcid":"https://orcid.org/0000-0002-3500-017X","contributorId":173551,"corporation":false,"usgs":true,"family":"Earle","given":"Paul","email":"pearle@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":770773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":767801,"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":767802,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":767803,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202192,"text":"sir20175037 - 2019 - Methods for estimating regional coefficient of skewness for unregulated streams in New England, based on data through water year 2011","interactions":[],"lastModifiedDate":"2026-01-23T16:05:31.669203","indexId":"sir20175037","displayToPublicDate":"2019-09-13T10:26:37","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5037","displayTitle":"Methods for Estimating Regional Coefficient of Skewness for Unregulated Streams in New England, Based on Data Through Water Year 2011","title":"Methods for estimating regional coefficient of skewness for unregulated streams in New England, based on data through water year 2011","docAbstract":"

The magnitude of annual exceedance probability floods is greatly affected by the coefficient of skewness (skew) of the annual peak flows at a streamgage. Standard flood frequency methods recommend weighting the station skew with a regional skew to better represent regional and stable conditions. This study presents an updated analysis of a regional skew for New England developed using a robust Bayesian weighted and generalized least squares regression model. Nineteen explanatory variables for 153 streamgages were tested in the regression analysis, but none were statistically significant and, as a result, a constant model was selected to define the regional skew for New England. The constant model for the New England region has, in log units, a skew of 0.37, a model error variance of 0.13, and an average variance of prediction at a new site of 0.14. An assessment of the selected regional skew model was conducted using a Monte Carlo analysis. The Monte Carlo simulations reveal that the perceived pattern in the station skews among the 153 streamgages is an artifact of the sample variability and the covariance structure of the errors.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175037","usgsCitation":"Veilleux, A.G., Zariello, P.J., Hodgkins, G.A., Ahearn, E.A., Olson, S.A., and Cohn, T.A., 2019, Methods for estimating regional coefficient of skewness for unregulated streams in New England, based on data through water year 2011: U.S. Geological Survey Scientific Investigations Report 2017–5037, 29 p., https://doi.org/10.3133/sir20175037.","productDescription":"Report: iv, 29 p.; Data Release","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-071009","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":367392,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MC98OM","linkHelpText":"Annual peak-flow data and PeakFQ output files for selected streamflow gaging stations operated by the U.S. Geological Survey in the New England region that were used to estimate regional skewness of annual peak flows"},{"id":367390,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5037/sir20175037.pdf","text":"Report","size":"18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2017–5037"},{"id":367389,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5037/coverthb.jpg"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island, Vermont","otherGeospatial":"New England","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.90673828125,\n 44.84808025602074\n ],\n [\n -67.82958984375,\n 46.042735653846506\n ],\n [\n -67.78564453125,\n 47.07012182383309\n ],\n [\n -68.345947265625,\n 47.4057852900587\n ],\n [\n -68.93920898437499,\n 47.2270293988673\n ],\n [\n -69.027099609375,\n 47.44294999517949\n ],\n [\n -69.224853515625,\n 47.45780853075031\n ],\n [\n -69.98291015625,\n 46.77749276376827\n ],\n [\n -70.301513671875,\n 46.210249600187225\n ],\n [\n -70.400390625,\n 45.79816953017265\n ],\n [\n -70.86181640625,\n 45.413876460821086\n ],\n [\n -71.16943359375,\n 45.3444241045224\n ],\n [\n -71.575927734375,\n 45.01141864227728\n ],\n [\n -74.24560546875,\n 44.99588261816546\n ],\n [\n -74.256591796875,\n 40.53050177574321\n ],\n [\n -72.13623046875,\n 40.90520969727358\n ],\n [\n -70.499267578125,\n 41.86956082699455\n ],\n [\n -70.72998046875,\n 42.22851735620852\n ],\n [\n -70.850830078125,\n 42.48830197960227\n ],\n [\n -70.59814453125,\n 42.65012181368022\n ],\n [\n -70.77392578125,\n 42.94838139765314\n ],\n [\n -70.169677734375,\n 43.69965122967144\n ],\n [\n -69.6533203125,\n 43.75522505306928\n ],\n [\n -66.90673828125,\n 44.84808025602074\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director,
Integrated Modeling and Prediction Division
U.S. Geological Survey
MS 415 National Center
12201 Sunrise Valley Drive
Reston, VA 20192

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2019-09-13","noUsgsAuthors":false,"publicationDate":"2019-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Veilleux, Andrea G. 0000-0002-8742-4660 aveilleux@usgs.gov","orcid":"https://orcid.org/0000-0002-8742-4660","contributorId":203278,"corporation":false,"usgs":true,"family":"Veilleux","given":"Andrea","email":"aveilleux@usgs.gov","middleInitial":"G.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":757168,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zarriello, Phillip J. 0000-0001-9598-9904 pzarriel@usgs.gov","orcid":"https://orcid.org/0000-0001-9598-9904","contributorId":1868,"corporation":false,"usgs":true,"family":"Zarriello","given":"Phillip","email":"pzarriel@usgs.gov","middleInitial":"J.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757169,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hodgkins, Glenn A. 0000-0002-4916-5565 gahodgki@usgs.gov","orcid":"https://orcid.org/0000-0002-4916-5565","contributorId":2020,"corporation":false,"usgs":true,"family":"Hodgkins","given":"Glenn","email":"gahodgki@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757170,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ahearn, Elizabeth A. 0000-0002-5633-2640 eaahearn@usgs.gov","orcid":"https://orcid.org/0000-0002-5633-2640","contributorId":194658,"corporation":false,"usgs":true,"family":"Ahearn","given":"Elizabeth","email":"eaahearn@usgs.gov","middleInitial":"A.","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true},{"id":377,"text":"Massachusetts-Rhode Island Water Science Center","active":false,"usgs":true}],"preferred":false,"id":757171,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olson, Scott A. 0000-0002-1064-2125","orcid":"https://orcid.org/0000-0002-1064-2125","contributorId":210173,"corporation":false,"usgs":true,"family":"Olson","given":"Scott A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757172,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cohn, Timothy A. tacohn@usgs.gov","contributorId":213234,"corporation":false,"usgs":true,"family":"Cohn","given":"Timothy","email":"tacohn@usgs.gov","middleInitial":"A.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":757173,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215323,"text":"70215323 - 2019 - Using a mechanistic model to develop management strategies to cool Apache Trout streams under the threat of climate change","interactions":[],"lastModifiedDate":"2020-10-16T14:15:08.962952","indexId":"70215323","displayToPublicDate":"2019-09-13T09:10:51","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Using a mechanistic model to develop management strategies to cool Apache Trout streams under the threat of climate change","docAbstract":"

User‐friendly stream temperature models populated with on‐site data may help in developing strategies to manage temperatures of individual stream reaches that are subject to climate change. We used the field‐tested Stream Segment Temperature model (U.S. Geological Survey) to simulate how altering discharge, groundwater input, channel wetted width, and shade prevents the temperatures of White Mountain, Arizona, stream reaches from exceeding the thermal tolerance of Apache Trout Oncorhynchus apache, both under existing conditions and under a climate change scenario. Simulations suggested increasing shade, either through streamside planting of specific numbers and species of plants or by other means, would be most effective and feasible for cooling the stream reaches we studied. Ponderosa pine Pinus ponderosa and Douglas fir Pseudotsuga menziesii provided the most shade followed in order by Engelman spruce Picea engelmannii, Bebb's willow Salix bebbiana, Arizona alder Alnus oblongifolia, and finally coyote willow Salix exigua. Vegetation survival depends on the appropriateness of site conditions at present and under climate change, and planting in buffer strips minimizes additional water removal from the watershed through evapotranspiration. Alternative shading options, including thick sedge growth, shade cloth, or felled woody vegetation, may be considered when environmental conditions do not support plantings. Increasing groundwater input can cool streams, but additional sources are scarce in the region. Decreasing the width‐to‐depth ratio would succeed best on reaches with widths greater than 2.0 m. Increasing discharge from upstream may lower water temperature on reaches with an initial discharge greater than 0.5 m3/s. Existing models provide suggestions to cool stream reaches. Further development of accessible software packages that incorporate evaporation, fragmentation, and other projected climate change effects into their routines will provide additional tools to help manage climate change effects.

","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10337","usgsCitation":"Baker, J.P., and Bonar, S.A., 2019, Using a mechanistic model to develop management strategies to cool Apache Trout streams under the threat of climate change: North American Journal of Fisheries Management, v. 39, no. 5, p. 849-867, https://doi.org/10.1002/nafm.10337.","productDescription":"19 p.","startPage":"849","endPage":"867","ipdsId":"IP-098411","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":379466,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"White Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.225830078125,\n 33.53681606773302\n ],\n [\n -109.05853271484374,\n 33.53681606773302\n ],\n [\n -109.05853271484374,\n 34.440893571391165\n ],\n [\n -110.225830078125,\n 34.440893571391165\n ],\n [\n -110.225830078125,\n 33.53681606773302\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"5","noUsgsAuthors":false,"publicationDate":"2019-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Baker, Joy Price","contributorId":243199,"corporation":false,"usgs":false,"family":"Baker","given":"Joy","email":"","middleInitial":"Price","affiliations":[{"id":40855,"text":"UA","active":true,"usgs":false}],"preferred":false,"id":801718,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bonar, Scott A. 0000-0003-3532-4067 sbonar@usgs.gov","orcid":"https://orcid.org/0000-0003-3532-4067","contributorId":3712,"corporation":false,"usgs":true,"family":"Bonar","given":"Scott","email":"sbonar@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":801719,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70204961,"text":"sim3439 - 2019 - Potentiometric surface of the Mississippi River Valley alluvial aquifer, spring 2016","interactions":[],"lastModifiedDate":"2019-11-04T06:00:30","indexId":"sim3439","displayToPublicDate":"2019-09-12T17:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3439","displayTitle":"Potentiometric Surface of the Mississippi River Valley Alluvial Aquifer, Spring 2016","title":"Potentiometric surface of the Mississippi River Valley alluvial aquifer, spring 2016","docAbstract":"

A potentiometric surface map for spring 2016 was created for the Mississippi River Valley alluvial (MRVA) aquifer using selected available groundwater-altitude data from wells and surface-water-altitude data from streamgages. Most of the wells were measured annually or one time after installation, but some wells were measured more than one time or continually; streamgages are typically operated continuously. Personnel from the Arkansas Natural Resources Commission, Arkansas Department of Health, Arkansas Geological Survey, Illinois Department of Agriculture, Illinois State Water Survey, Louisiana Department of Natural Resources, Louisiana Department of Transportation and Development, Mississippi Department of Environmental Quality, Yazoo Mississippi Delta Joint Water Management District, U.S. Department of Agriculture–Natural Resources Conservation Service, and the U.S. Geological Survey (USGS) routinely collect groundwater data from wells screened in the MRVA aquifer. The USGS and the U.S. Army Corps of Engineers routinely collect data on river stage and discharge for the rivers overlying the MRVA aquifer.

The potentiometric surface map for 2016 was created using existing data as part of the USGS Water Availability and Use Science Program to support investigations that characterize the MRVA aquifer. Sufficient groundwater-altitude data were available to create a potentiometric-surface map for spring 2016 for about 81 percent of the aquifer area. The potentiometric contours ranged from 10 to 340 feet. The regional direction of groundwater flow in the MRVA aquifer was generally towards the south-southwest, except in areas of groundwater-altitude depressions, where groundwater flows into the depressions, and near rivers, where groundwater flow generally parallels the flow in the rivers. There are large depressions in the potentiometric surface of the MRVA aquifer in the lower half of the Cache region and in most of the Grand Prairie and Delta regions.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3439","usgsCitation":"McGuire, V.L., Seanor, R.C., Asquith, W.H., Kress, W.H., and Strauch, K.R., 2019, Potentiometric surface of the Mississippi River Valley alluvial aquifer, spring 2016: U.S. Geological Survey Scientific Investigations Map 3439, 14 p., 5 sheets, https://doi.org/10.3133/sim3439.","productDescription":"Pamphlet: vi, 14 p.; 5 Sheets: 30.0 x 46.0 inches or smaller; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-087587","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":367362,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SV1HMQ","text":"USGS data release","description":"USGS data release","linkHelpText":"Data associated with potentiometric surface, Mississippi River Valley alluvial aquifer, spring 2016"},{"id":367352,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3439/sim3439_sheet1.pdf","text":"Sheet 1—All Mississippi Alluvial Plain (MAP) regions","size":"6.10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3439 Sheet 1"},{"id":367351,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3439/coverthb_sheet1.jpg"},{"id":367356,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3439/sim3439_sheet4.pdf","text":"Sheet 4—Delta MAP region","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3439 Sheet 4"},{"id":367353,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3439/sim3439.pdf","text":"Pamphlet","size":"6.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3439 Pamphlet"},{"id":367354,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3439/sim3439_sheet2.pdf","text":"Sheet 2—St. Francis and Cache MAP regions","size":"1.87 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3439 Sheet 2"},{"id":367355,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3439/sim3439_sheet3.pdf","text":"Sheet 3—Boeuf and Grand Prairie MAP regions","size":"2.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3439 Sheet 3"},{"id":367357,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3439/sim3439_sheet5.pdf","text":"Sheet 5—Atchafalaya and Deltaic and Chenier Plain MAP regions ","size":"2.76 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3439 Sheet 5"}],"country":"United States","state":"Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi River Alluvial Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.56054687499999,\n 38.20365531807149\n ],\n [\n -90.791015625,\n 37.26530995561875\n ],\n [\n -91.845703125,\n 35.746512259918504\n ],\n [\n -92.7685546875,\n 33.578014746143985\n ],\n [\n -92.5048828125,\n 30.06909396443887\n ],\n [\n -92.548828125,\n 29.878755346037977\n ],\n [\n -92.59277343749999,\n 29.420460341013133\n ],\n [\n -89.4287109375,\n 28.69058765425071\n ],\n [\n -88.76953125,\n 28.806173508854776\n ],\n [\n -89.296875,\n 30.675715404167743\n ],\n [\n -88.72558593749999,\n 35.460669951495305\n ],\n [\n -88.2861328125,\n 36.914764288955936\n ],\n [\n -88.857421875,\n 37.78808138412046\n ],\n [\n -89.56054687499999,\n 38.20365531807149\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2019-09-12","noUsgsAuthors":false,"publicationDate":"2019-09-12","publicationStatus":"PW","contributors":{"authors":[{"text":"McGuire, Virginia L. 0000-0002-3962-4158 vlmcguir@usgs.gov","orcid":"https://orcid.org/0000-0002-3962-4158","contributorId":404,"corporation":false,"usgs":true,"family":"McGuire","given":"Virginia","email":"vlmcguir@usgs.gov","middleInitial":"L.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":769286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Seanor, Ronald C. 0000-0001-5735-5580 rcseanor@usgs.gov","orcid":"https://orcid.org/0000-0001-5735-5580","contributorId":3731,"corporation":false,"usgs":true,"family":"Seanor","given":"Ronald","email":"rcseanor@usgs.gov","middleInitial":"C.","affiliations":[],"preferred":true,"id":770676,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Asquith, William H. 0000-0002-7400-1861 wasquith@usgs.gov","orcid":"https://orcid.org/0000-0002-7400-1861","contributorId":1007,"corporation":false,"usgs":true,"family":"Asquith","given":"William","email":"wasquith@usgs.gov","middleInitial":"H.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":769288,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kress, Wade 0000-0002-6833-028X","orcid":"https://orcid.org/0000-0002-6833-028X","contributorId":203539,"corporation":false,"usgs":true,"family":"Kress","given":"Wade","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770677,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Strauch, Kellan R. 0000-0002-7218-2099","orcid":"https://orcid.org/0000-0002-7218-2099","contributorId":208562,"corporation":false,"usgs":true,"family":"Strauch","given":"Kellan R.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":769290,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208498,"text":"70208498 - 2019 - Consistent compensatory growth offsets poor condition in trout populations","interactions":[],"lastModifiedDate":"2020-02-13T08:27:52","indexId":"70208498","displayToPublicDate":"2019-09-12T08:24:36","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Consistent compensatory growth offsets poor condition in trout populations","docAbstract":"1.\tCompensatory growth – when individuals in poor condition grow rapidly to “catch up” to conspecifics – may be a mechanism that allows individuals to tolerate stressful environmental conditions, both abiotic and biotic. This phenomenon has been documented fairly widely in laboratory and field experiments, but evidence for compensatory growth in the wild is scarce. \n2.\tCutthroat trout (Oncorhynchus clarkii subsp) are cold-water specialists that inhabit streams in montane ecosystems where seasonal conditions can be harsh and growth rates vary greatly among seasons. Understanding if individuals compensate for periods of reduced growth and body condition will improve understanding of the requirements of fish throughout their life-cycle and across freshwater habitats.\n3.\tWe quantified compensatory growth of juvenile cutthroat trout using extensive mark-recapture data from 11 stream populations (1,125 individuals) and two subspecies inhabiting a wide range of ecological settings in the northern Rocky Mountains, USA. Our objectives were to determine how growth was linked across seasons and determine if individuals behaviorally compensated for depressed body condition via emigration. \n4.\tFish in relatively poor condition consistently demonstrated compensatory growth in mass during subsequent seasons. In contrast, fish in relatively better condition responded with positive growth in length during the summer signaling these fish may be better suited to headwater environments; no compensatory growth in length was found during the winter. Furthermore, we found no evidence that individual condition mediated migration tendencies of fish to seek more favorable habitat.\n5.\tAcross a wide range of environmental conditions, we found consistent empirical support for compensatory growth in mass in the wild. A critical next step is to quantify how changing abiotic and biotic conditions influence the ability of stream fishes to compensate for locally or seasonally challenging conditions, thereby affecting long-term resiliency, viability, and adaptation in the face of changing environmental conditions.","language":"English","publisher":"Wiley","doi":"10.1111/fwb.13400","usgsCitation":"Al-Chokhachy, R., Kovach, R., Sepulveda, A.J., Strait, J., Shepard, B.B., and Muhlfeld, C.C., 2019, Consistent compensatory growth offsets poor condition in trout populations: Freshwater Biology, v. 64, no. 12, p. 2120 -2130, https://doi.org/10.1111/fwb.13400.","productDescription":"11 p.","startPage":"2120 ","endPage":"2130","ipdsId":"IP-100735","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":372304,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","otherGeospatial":"Flathead River basin, Shields River basin, Duck Creek basin, Spread Creek basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.93847656250001,\n 48.16608541901253\n ],\n [\n -114.2138671875,\n 48.16608541901253\n ],\n [\n -114.2138671875,\n 49.61070993807422\n ],\n [\n -116.93847656250001,\n 49.61070993807422\n ],\n [\n -116.93847656250001,\n 48.16608541901253\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.884765625,\n 45.1510532655634\n ],\n [\n -110.74218749999999,\n 45.1510532655634\n ],\n [\n -110.74218749999999,\n 46.34692761055676\n ],\n [\n -111.884765625,\n 46.34692761055676\n ],\n [\n -111.884765625,\n 45.1510532655634\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.0166015625,\n 43.068887774169625\n ],\n [\n -110.830078125,\n 43.068887774169625\n ],\n [\n -110.830078125,\n 44.276671273775186\n ],\n [\n -112.0166015625,\n 44.276671273775186\n ],\n [\n -112.0166015625,\n 43.068887774169625\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"64","issue":"12","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Al-Chokhachy, Robert","contributorId":222445,"corporation":false,"usgs":true,"family":"Al-Chokhachy","given":"Robert","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":782167,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kovach, Ryan 0000-0001-5402-2123 rkovach@usgs.gov","orcid":"https://orcid.org/0000-0001-5402-2123","contributorId":145914,"corporation":false,"usgs":true,"family":"Kovach","given":"Ryan","email":"rkovach@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":782168,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sepulveda, Adam J. 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":150628,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","middleInitial":"J.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":782171,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Strait, Jeff","contributorId":222446,"corporation":false,"usgs":false,"family":"Strait","given":"Jeff","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":782170,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shepard, Bradley B.","contributorId":145880,"corporation":false,"usgs":false,"family":"Shepard","given":"Bradley","email":"","middleInitial":"B.","affiliations":[{"id":6765,"text":"Montana State University, Department of Land Resources and Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":782169,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Muhlfeld, Clint C. 0000-0002-4599-4059 cmuhlfeld@usgs.gov","orcid":"https://orcid.org/0000-0002-4599-4059","contributorId":924,"corporation":false,"usgs":true,"family":"Muhlfeld","given":"Clint","email":"cmuhlfeld@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":782172,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70201457,"text":"tm1D7 - 2019 - Guidelines and standard procedures for high-frequency groundwater-quality monitoring stations—Design, operation, and record computation","interactions":[],"lastModifiedDate":"2019-09-13T09:42:32","indexId":"tm1D7","displayToPublicDate":"2019-09-11T15:52:46","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1-D7","displayTitle":"Guidelines and Standard Procedures for High-Frequency Groundwater-Quality Monitoring Stations—Design, Operation, and Record Computation","title":"Guidelines and standard procedures for high-frequency groundwater-quality monitoring stations—Design, operation, and record computation","docAbstract":"

High-frequency water-quality monitoring stations measure and transmit data, often in near real-time, from a wide range of aquatic environments to assess the quality of the Nation’s water resources. Common instrumentation for high-frequency water-quality data collection uses a multi-parameter sonde, which typically has sensors that measure and record water temperature, specific conductance, pH, and dissolved oxygen. Nitrate, turbidity, and fluorescent dissolved organic matter can also be monitored at high frequency.

High-frequency groundwater-quality monitoring stations provide high-resolution time-series data to improve understanding of the timing of water-quality changes in the subsurface, especially for aquifer systems with short groundwater-residence times. High-frequency time-series data are used to monitor surface-water to groundwater interaction, quantify contaminant transport rates, and study water-quality variability in relation to variability of precipitation and groundwater pumping rates. High-frequency monitoring for contaminants or their surrogates have the added benefit of providing an early warning to protect valuable or sensitive aquifer resources. High-frequency time-series data also reveal short-term trends in groundwater quality, which may not be identifiable from monthly or annual sampling programs which facilitate the interpretation of decadal conditions. Systematic application of water-quality sonde operational procedures and a standard record-computation process are part of the required quality assurance for producing and documenting complete and accurate high-frequency groundwater-quality monitoring records. To collect quality high-frequency groundwater times-series data, water-quality sondes and sensors require careful field operation, cleaning, and calibration, as well as specific procedures for data computation, evaluation, review, and publication of final records.

This report provides guidelines for the use of water-quality sondes and sensors for high-frequency groundwater-quality monitoring and updates the guidance pertaining to standardized records computation procedures for a wide range of groundwater environments. This report builds on previous continuous surface-water-quality monitoring guidance documentation for water temperature, specific conductance, pH, dissolved oxygen, and nitrate. The specific groundwater-quality monitoring guidelines presented in this report address station selection, design, installation, and operations; sonde and sensor inspections and cleaning and calibration methods; troubleshooting procedures; data evaluations, data corrections, and record computations; and record review, approval, and auditing procedures for the groundwater environment.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm1D7","usgsCitation":"Mathany, T.M., Saraceno, J.F., and Kulongoski, J.T., 2019, Guidelines and standard procedures for high-frequency groundwater-quality monitoring stations—Design, operation, and record computation: U.S. Geological Survey Techniques and Methods 1–D7, 54 p., https://doi.org/10.3133/tm1D7.","productDescription":"Report: vii, 54; 3 Appendices; Data Release","numberOfPages":"66","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-088740","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":367299,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/01/d7/coverthb.jpg"},{"id":367364,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/01/d7/tm1d7_appendix2_field_form.xlsx","text":"Appendix 2","size":"60 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"TM 1D7","linkHelpText":" — U.S. Geological Survey High-Frequency Groundwater-Quality Field Form"},{"id":367323,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QLWSBS","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Electrical conductivity, pH, and dissolved oxygen time-series data generated from the short-term precision experiment and the long-term field precision analysis to characterize water-quality sondes for the Guidelines and Standard Procedures for High-Frequency Groundwater-Quality Monitoring Station Techniques and Methods Report."},{"id":367348,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/01/d7/tm1d7_fig_6-1ab_form_.pdf","text":"Appendix 6","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 1D7","linkHelpText":" — Example of a High-Frequency Groundwater-Quality Record Approver Checklist"},{"id":367300,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/01/d7/tm1d7_.pdf","text":"Report","size":"7.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 1D7"},{"id":367347,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/01/d7/tm1d7_fig_5-1ab_form_.pdf","text":"Appendix 5","size":"2.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 1D7","linkHelpText":" — Example of a High-Frequency Groundwater-Quality Record Analyst Checklist"}],"contact":"

Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2019-09-11","noUsgsAuthors":false,"publicationDate":"2019-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Mathany, Timothy M. 0000-0002-4747-5113 tmathany@usgs.gov","orcid":"https://orcid.org/0000-0002-4747-5113","contributorId":191771,"corporation":false,"usgs":true,"family":"Mathany","given":"Timothy","email":"tmathany@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":767365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saraceno, John Franco 0000-0003-0064-1820","orcid":"https://orcid.org/0000-0003-0064-1820","contributorId":217534,"corporation":false,"usgs":false,"family":"Saraceno","given":"John Franco","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":770516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770517,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70206677,"text":"70206677 - 2019 - Evidence of region‐wide bat population decline from long‐term monitoring and Bayesian occupancy models with empirically informed priors","interactions":[],"lastModifiedDate":"2019-11-15T16:03:38","indexId":"70206677","displayToPublicDate":"2019-09-11T15:46:47","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Evidence of region‐wide bat population decline from long‐term monitoring and Bayesian occupancy models with empirically informed priors","docAbstract":"

Strategic conservation efforts for cryptic species, especially bats, are hindered by limited understanding of distribution and population trends. Integrating long‐term encounter surveys with multi‐season occupancy models provides a solution whereby inferences about changing occupancy probabilities and latent changes in abundance can be supported. When harnessed to a Bayesian inferential paradigm, this modeling framework offers flexibility for conservation programs that need to update prior model‐based understanding about at‐risk species with new data. This scenario is exemplified by a bat monitoring program in the Pacific Northwestern United States in which results from 8 years of surveys from 2003 to 2010 require updating with new data from 2016 to 2018. The new data were collected after the arrival of bat white‐nose syndrome and expansion of wind power generation, stressors expected to cause population declines in at least two vulnerable species, little brown bat (Myotis lucifugus) and the hoary bat (Lasiurus cinereus). We used multi‐season occupancy models with empirically informed prior distributions drawn from previous occupancy results (2003–2010) to assess evidence of contemporary decline in these two species. Empirically informed priors provided the bridge across the two monitoring periods and increased precision of parameter posterior distributions, but did not alter inferences relative to use of vague priors. We found evidence of region‐wide summertime decline for the hoary bat (\"urn:x-wiley:20457758:media:ece35612:ece35612-math-0001\" = 0.86 ± 0.10) since 2010, but no evidence of decline for the little brown bat (\"urn:x-wiley:20457758:media:ece35612:ece35612-math-0002\" = 1.1 ± 0.10). White‐nose syndrome was documented in the region in 2016 and may not yet have caused regional impact to the little brown bat. However, our discovery of hoary bat decline is consistent with the hypothesis that the longer duration and greater geographic extent of the wind energy stressor (collision and barotrauma) have impacted the species. These hypotheses can be evaluated and updated over time within our framework of pre–post impact monitoring and modeling. Our approach provides the foundation for a strategic evidence‐based conservation system and contributes to a growing preponderance of evidence from multiple lines of inquiry that bat species are declining.

","language":"English","publisher":"Wiley","doi":"10.1002/ece3.5612","usgsCitation":"Rodhouse, T.J., Rodriguez, R.M., Banner, K.M., Ormsbee, P.C., Barnett, J., and Irvine, K., 2019, Evidence of region‐wide bat population decline from long‐term monitoring and Bayesian occupancy models with empirically informed priors: Ecology and Evolution, v. 9, no. 19, p. 11078-11088, https://doi.org/10.1002/ece3.5612.","productDescription":"11 p.","startPage":"11078","endPage":"11088","ipdsId":"IP-107039","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":459850,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.5612","text":"Publisher Index Page"},{"id":369257,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.8486328125,\n 41.75492216766298\n ],\n [\n -116.806640625,\n 41.75492216766298\n ],\n [\n -116.806640625,\n 49.081062364320736\n ],\n [\n -124.8486328125,\n 49.081062364320736\n ],\n [\n -124.8486328125,\n 41.75492216766298\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"19","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Rodhouse, Thomas J.","contributorId":173361,"corporation":false,"usgs":false,"family":"Rodhouse","given":"Thomas","email":"","middleInitial":"J.","affiliations":[{"id":6711,"text":"University of Idaho, Moscow ID","active":true,"usgs":false}],"preferred":false,"id":775348,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rodriguez, Rogelio M.","contributorId":220628,"corporation":false,"usgs":false,"family":"Rodriguez","given":"Rogelio","email":"","middleInitial":"M.","affiliations":[{"id":40195,"text":"Oregon State University-Cascades Campus","active":true,"usgs":false}],"preferred":false,"id":775349,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Banner, Katharine M.","contributorId":220630,"corporation":false,"usgs":false,"family":"Banner","given":"Katharine","email":"","middleInitial":"M.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":775350,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ormsbee, Patricia C.","contributorId":173426,"corporation":false,"usgs":false,"family":"Ormsbee","given":"Patricia","email":"","middleInitial":"C.","affiliations":[{"id":27227,"text":"U.S. Forest Service, Willamette National Forest","active":true,"usgs":false}],"preferred":false,"id":775351,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnett, Jenny","contributorId":220629,"corporation":false,"usgs":false,"family":"Barnett","given":"Jenny","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":775352,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Irvine, Kathryn 0000-0002-6426-940X","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":220632,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":775347,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70203470,"text":"pp1837B - 2019 - Evaluation of chemical and hydrologic processes in the eastern Snake River Plain Aquifer based on results from geochemical modeling, Idaho National Laboratory, eastern Idaho","interactions":[],"lastModifiedDate":"2023-04-14T16:58:11.822101","indexId":"pp1837B","displayToPublicDate":"2019-09-11T15:03:14","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1837-B","displayTitle":"Evaluation of Chemical and Hydrologic Processes in the Eastern Snake River Plain Aquifer Based on Results from Geochemical Modeling, Idaho National Laboratory, Eastern Idaho","title":"Evaluation of chemical and hydrologic processes in the eastern Snake River Plain Aquifer based on results from geochemical modeling, Idaho National Laboratory, eastern Idaho","docAbstract":"

Nuclear research activities at the U.S. Department of Energy (DOE) Idaho National Laboratory (INL) produced liquid and solid chemical and radiochemical wastes that were disposed to the subsurface resulting in detectable concentrations of some waste constituents in the eastern Snake River Plain (ESRP) aquifer. These waste constituents may affect the water quality of the aquifer and may pose risks to the eventual users of the aquifer water. To understand these risks to water quality the U.S. Geological Survey, in cooperation with the DOE, conducted geochemical mass-balance modeling of the ESRP aquifer to improve the understanding of chemical reactions, sources of recharge, mixing of water, and groundwater flow directions in the shallow (upper 250 feet) aquifer at the INL.

Modeling was conducted using the water chemistry of 127 water samples collected from sites at and near the INL. Water samples were collected between 1952 and 2017 with most of the samples collected during the mid-1990s. Geochemistry and isotopic data used in geochemical modeling consisted of dissolved oxygen, carbon dioxide, major ions, silica, aluminum, iron, and the stable isotope ratios of hydrogen, oxygen, and carbon.

Geochemical modeling results indicated that the primary chemical reactions in the aquifer were precipitation of calcite and dissolution of plagioclase (An60) and basalt volcanic glass. Secondary minerals other than calcite included calcium montmorillonite and goethite. Reverse cation exchange, consisting of sodium exchanging for calcium on clay minerals, occurred near site facilities where large amounts of sodium were released to the ESRP aquifer in wastewater discharge. Reverse cation exchange acted to retard the movement of wastewater-derived sodium in the aquifer.

Regional groundwater inflow was the primary source of recharge to the aquifer underlying the Northeast and Southeast INL Areas. Birch Creek (BC), the Big Lost River (BLR), and groundwater from BC valley provided recharge to the North INL Area, and the BLR and groundwater from BC and Little Lost River (LLR) valleys provided recharge to the Central INL Area. The BLR, groundwater from the BLR and LLR valleys and the Lost River Range, and precipitation provided recharge to the Northwest and Southwest INL Areas. The primary source of recharge west and southwest of the INL was groundwater inflow from BLR valley. Upwelling geothermal water was a small source of recharge at two wells. Aquifer recharge from surface water in the northern, central, and western parts of the INL indicated that the aquifer in these areas was a dynamic, open system, whereas the aquifer in the eastern part of the INL, which receives little recharge from surface water, was a relatively static and closed system.

Sources of recharge identified from isotope ratios and geochemical modeling (major ion concentrations) were nearly identical for the North, Northeast, Southeast, and Central INL Areas, which indicated that both methods probably accurately identified the sources of recharge in these areas. Conversely, isotope ratios indicated that the BLR and groundwater from the LLR valley provided most recharge to the western parts of the Northwest and Southwest INL Areas, whereas geochemical modeling results indicated a smaller area of recharge from the BLR and groundwater from the LLR valley, a larger area of recharge from the Lost River Range, and recharge of groundwater from the BLR valley that extended to the west INL boundary. The results from geochemical modeling probably were more accurate because major ion concentrations, but not isotope ratios, were available to characterize groundwater from the BLR valley and the Lost River Range. 

Sources of recharge identified with a groundwater flow model (using particle tracking) and geochemical modeling were similar for the Northeast and Southeast INL Areas. However, differences between the models were that the geochemical model represented (1) recharge of groundwater from the Lost River Range in the western part of the INL, whereas the flow model did not, (2) recharge of groundwater from the BC and BLR valleys extending farther south and east, respectively, than the flow model, and (3) more recharge from the BLR in the Southwest INL Area than the flow model.

Mixing of aquifer water beneath the INL included (1) mixing of regional groundwater and water from the BC valley in the Northeast and Southeast INL Areas and (2) mixing of surface water (primarily from the BLR) and groundwater across much of the North, Central, Northwest, and Southwest INL Areas. Localized recharge from precipitation mixed with groundwater in the Northwest and Southwest INL Areas, and localized upwelling geothermal water mixed with groundwater in the Central and Northeast INL Areas. Flow directions of regional groundwater were south in the eastern part of the INL and south-southwest at downgradient locations. Groundwater from the BC and LLR valleys initially flowed southeast before changing to south-southwest flow directions that paralleled regional groundwater, and groundwater from the BLR valley initially flowed south before changing to a southsouthwest direction.

Wastewater-contaminated groundwater flowed south from the Idaho Nuclear Technology and Engineering Center (INTEC) infiltration ponds in a narrow plume, with the percentage of wastewater in groundwater decreasing due to dilution, dispersion, and (or) degradation from about 60‒80 percent wastewater 0.7‒0.8 mile (mi) south of the INTEC infiltration ponds to about 1.4 percent wastewater about 15.5 mi south of the INTEC infiltration ponds. Wastewater contaminated groundwater flowed southeast and then southwest from the Naval Reactors Facility industrial waste ditch, with the percentage of wastewater in groundwater decreasing from about 100 percent wastewater adjacent to the waste ditch to about 2 percent wastewater about 0.6 mi south of the waste ditch.


","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1837B","collaboration":"Prepared in cooperation with the U.S. Department of Energy","usgsCitation":"Rattray, G.W., 2019, Evaluation of chemical and hydrologic processes in the eastern Snake River Plain aquifer based on results from geochemical modeling, Idaho National Laboratory, eastern Idaho: U.S. Geological Survey Professional Paper 1837-B (DOE/ID-22248), 85 p., https://doi.org/10.3133/pp1837B.","productDescription":"viii, 85 p.","ipdsId":"IP-098993","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":415799,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1837D","text":"PP 1837 Chapter D","description":"PP 1837 Chapter D"},{"id":415798,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1837C","text":"PP 1837 Chapter C","description":"PP 1837 Chapter C"},{"id":415797,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1837A","text":"PP 1837 Chapter A","description":"PP 1837 Chapter A"},{"id":367371,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1837/b/pp1837b.pdf","text":"Report","size":"13.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1837B"},{"id":367370,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1837/b/coverthb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Idaho National Laboratory","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.16629028320312,\n 43.402054267905655\n ],\n [\n -111.87515258789062,\n 43.402054267905655\n ],\n [\n -111.87515258789062,\n 43.68872888432795\n ],\n [\n -112.16629028320312,\n 43.68872888432795\n ],\n [\n -112.16629028320312,\n 43.402054267905655\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702

","tableOfContents":"","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2019-09-11","noUsgsAuthors":false,"publicationDate":"2019-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Rattray, Gordon W. 0000-0002-1690-3218 grattray@usgs.gov","orcid":"https://orcid.org/0000-0002-1690-3218","contributorId":2521,"corporation":false,"usgs":true,"family":"Rattray","given":"Gordon","email":"grattray@usgs.gov","middleInitial":"W.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":762788,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70205273,"text":"70205273 - 2019 - Using social-context matching to improve spatial function-transfer performance for cultural ecosystem service models","interactions":[],"lastModifiedDate":"2019-09-11T11:41:28","indexId":"70205273","displayToPublicDate":"2019-09-11T11:32:19","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1477,"text":"Ecosystem Services","active":true,"publicationSubtype":{"id":10}},"title":"Using social-context matching to improve spatial function-transfer performance for cultural ecosystem service models","docAbstract":"Recreational and aesthetic enjoyment of public lands is increasing across a wide range of activities, highlighting the need to assess and adapt management to accommodate these uses. Despite a growing number of studies on mapping cultural ecosystem services, most are local-scale assessments that rely on costly and time-consuming primary data collection. As a result, the availability of spatial information on non-market values associated with cultural ecosystem services (social values) remains limited. Spatial function transfer, if it could be justified for social-value models, would expedite the development of social-value information and promote its more regular inclusion in ecosystem service assessments. We used survey data from six national forests in Colorado and Wyoming to explore the potential for transferring cultural ecosystem service models between forests and specifically to test the hypothesis that transfer performance increases with social-context similarity between transferring and receiving areas. Results confirm this relationship but fall just short of being able to predict with certainty when transferred models will meet the minimum performance criterion needed for defensible use by managers. Social values are highly variable and can be difficult to predict, but our results suggest that with the right combination of indicators spatial function transfer can become a defensible means of generating social-value information when primary data collection is not feasible.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoser.2019.100945","usgsCitation":"Semmens, D.J., Sherrouse, B.C., and Ancona, Z.H., 2019, Using social-context matching to improve spatial function-transfer performance for cultural ecosystem service models: Ecosystem Services, v. 38, https://doi.org/10.1016/j.ecoser.2019.100945.","onlineOnly":"Y","ipdsId":"IP-091558","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":467325,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoser.2019.100945","text":"Publisher Index Page"},{"id":437343,"rank":0,"type":{"id":30,"text":"Data 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bcsherrouse@usgs.gov","orcid":"https://orcid.org/0000-0002-5102-5895","contributorId":2445,"corporation":false,"usgs":true,"family":"Sherrouse","given":"Benson","email":"bcsherrouse@usgs.gov","middleInitial":"C.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":770641,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ancona, Zachary H. 0000-0001-5430-0218 zancona@usgs.gov","orcid":"https://orcid.org/0000-0001-5430-0218","contributorId":5578,"corporation":false,"usgs":true,"family":"Ancona","given":"Zachary","email":"zancona@usgs.gov","middleInitial":"H.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":770642,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215104,"text":"70215104 - 2019 - Detection of rock bridges by infrared thermal imaging and modeling","interactions":[],"lastModifiedDate":"2020-10-07T15:48:15.017733","indexId":"70215104","displayToPublicDate":"2019-09-11T10:39:37","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Detection of rock bridges by infrared thermal imaging and modeling","docAbstract":"

Characterization of rock discontinuities and rock bridges is required to define stability conditions of fractured rock masses in both natural and engineered environments. Although remote sensing methods for mapping discontinuities have improved in recent years, remote detection of intact rock bridges on cliff faces remains challenging, with their existence typically confirmed only after failure. In steep exfoliating cliffs, such as El Capitan in Yosemite Valley (California, USA), rockfalls mainly occur along cliff-parallel exfoliation joints, with rock bridges playing a key role in the stability of partially detached exfoliation sheets. We employed infrared thermal imaging (i.e., thermography) as a new means of detecting intact rock bridges prior to failure. An infrared thermal panorama of El Capitan revealed cold thermal signatures for the surfaces of two granitic exfoliation sheets, consistent with the expectation that air circulation cools the back of the partially detached sheets. However, we also noted small areas of warm thermal anomalies on these same sheets, even during periods of nocturnal rock cooling. Rock attachment via rock bridges is the likely cause for the warm anomalies in the thermal data. 2-D model simulations of the thermal behavior of one of  the monitored sheets reproduce the observed anomalies and explain the temperature differences detected in the rock bridge area. Based on combined thermal and ground-based lidar imaging, and using geometric and rock fracture mechanics analysis, we are able to quantify the stability of both sheets. Our analysis demonstrates that thermography can remotely detect intact rock bridges and thereby greatly improve rockfall hazard assessment.

","language":"English","publisher":"Nature","doi":"10.1038/s41598-019-49336-1","usgsCitation":"Guerin, A., Jaboyefoff, M., Collins, B.D., Derron, M., Stock, G.M., Matasci, B., Boesiger, M., Lefeuvre, C., and Podladchikov, Y.Y., 2019, Detection of rock bridges by infrared thermal imaging and modeling: Scientific Reports, v. 9, 13138, 19 p., https://doi.org/10.1038/s41598-019-49336-1.","productDescription":"13138, 19 p.","ipdsId":"IP-102814","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":459866,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-019-49336-1","text":"Publisher Index Page"},{"id":379177,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.981689453125,\n 37.13404537126446\n ],\n [\n -118.83911132812499,\n 37.13404537126446\n ],\n [\n -118.83911132812499,\n 38.14319750166766\n ],\n [\n -119.981689453125,\n 38.14319750166766\n ],\n [\n -119.981689453125,\n 37.13404537126446\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2019-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Guerin, Antoine","contributorId":236904,"corporation":false,"usgs":false,"family":"Guerin","given":"Antoine","affiliations":[{"id":37010,"text":"University of Lausanne, Switzerland","active":true,"usgs":false}],"preferred":false,"id":800883,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaboyefoff, Michel","contributorId":242812,"corporation":false,"usgs":false,"family":"Jaboyefoff","given":"Michel","email":"","affiliations":[{"id":37010,"text":"University of Lausanne, Switzerland","active":true,"usgs":false}],"preferred":false,"id":800884,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Brian D. 0000-0003-4881-5359 bcollins@usgs.gov","orcid":"https://orcid.org/0000-0003-4881-5359","contributorId":149278,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":800885,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Derron, Marc-Henri","contributorId":236906,"corporation":false,"usgs":false,"family":"Derron","given":"Marc-Henri","email":"","affiliations":[{"id":37010,"text":"University of Lausanne, Switzerland","active":true,"usgs":false}],"preferred":false,"id":800886,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stock, Greg M.","contributorId":202873,"corporation":false,"usgs":false,"family":"Stock","given":"Greg","email":"","middleInitial":"M.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":800887,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Matasci, Battista","contributorId":204938,"corporation":false,"usgs":false,"family":"Matasci","given":"Battista","email":"","affiliations":[{"id":37010,"text":"University of Lausanne, Switzerland","active":true,"usgs":false}],"preferred":false,"id":800888,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boesiger, Martin","contributorId":242813,"corporation":false,"usgs":false,"family":"Boesiger","given":"Martin","email":"","affiliations":[{"id":37010,"text":"University of Lausanne, Switzerland","active":true,"usgs":false}],"preferred":false,"id":800889,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lefeuvre, Caroline","contributorId":242814,"corporation":false,"usgs":false,"family":"Lefeuvre","given":"Caroline","email":"","affiliations":[{"id":37010,"text":"University of Lausanne, Switzerland","active":true,"usgs":false}],"preferred":false,"id":800890,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Podladchikov, Yury Y.","contributorId":242815,"corporation":false,"usgs":false,"family":"Podladchikov","given":"Yury","email":"","middleInitial":"Y.","affiliations":[{"id":37010,"text":"University of Lausanne, Switzerland","active":true,"usgs":false}],"preferred":false,"id":800891,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70228039,"text":"70228039 - 2019 - How characteristic is the species characteristic selection scale?","interactions":[],"lastModifiedDate":"2022-02-03T16:15:10.817312","indexId":"70228039","displayToPublicDate":"2019-09-11T10:12:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"How characteristic is the species characteristic selection scale?","docAbstract":"

Aim

The importance of framing investigations of organism–environment relationships to interpret patterns at relevant spatial scales is increasingly recognized. However, most research related to environmental relationships is single-scaled, implicitly or explicitly assuming that a “species characteristic selection scale” exists. We tested the premise that a single characteristic scale exists to understand species–environment relationships within species by asking (a) what are the characteristic scales of species’ relationships with environmental predictors, and (b) is within-species, cross-predictor consistency in characteristic scales a general phenomenon.

Location

Nebraska, USA.

Time period

2016.

Major taxa studied

Birds.

Methods

We used data from 86 species at > 500 locations to build hierarchical N-mixture models relating species abundance to land cover variables. By incorporating Bayesian latent indicator scale selection, we identified the spatial scales that best explain species–environment relationships with each land cover predictor. We quantified the extent of cross-predictor consistency in characteristic scales, and contrasted this to the expectation given a single species’ characteristic scale.

Results

We found no evidence for a characteristic spatial scale explaining all abundance–environment relationships within species, rather we found substantial variation in scale-dependence across multiple environmental attributes. Furthermore, 33% of species displayed evidence of multiple important spatial scales within environmental attributes.

Major conclusions

Within species there is little evidence for a single characteristic scale of environmental relationships and considerable variation in species’ scale dependencies. Because species may respond to multiple environmental attributes at different spatial scales, or single environmental attributes at multiple scales, we caution against any unoptimized single-scale studies. Our results demonstrate that until a framework is developed to predict the scales at which species respond to environmental characteristics, multi-scale investigations must be performed to identify and account for multi-scale dependencies. Natural selection acting on species’ response to distinct environmental attributes, rather than natural selection acting on species’ perception of spatial scales per se, may have shaped patterns of scale dependency and is an area ripe for investigation.

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\"}}]}","volume":"28","issue":"12","noUsgsAuthors":false,"publicationDate":"2019-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Stuber, Erica F.","contributorId":198581,"corporation":false,"usgs":false,"family":"Stuber","given":"Erica","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":832950,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fontaine, Joseph J. 0000-0002-7639-9156 jfontaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-9156","contributorId":3820,"corporation":false,"usgs":true,"family":"Fontaine","given":"Joseph","email":"jfontaine@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832951,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70205246,"text":"70205246 - 2019 - Factors affecting post-release survival of coded-wire tagged Lake Trout Salvelinus namaycush in Lake Michigan at four historical spawning locations","interactions":[],"lastModifiedDate":"2019-10-28T10:20:26","indexId":"70205246","displayToPublicDate":"2019-09-10T09:51:24","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Factors affecting post-release survival of coded-wire tagged Lake Trout Salvelinus namaycush in Lake Michigan at four historical spawning locations","docAbstract":"Since the 1950s, fishery agencies on Lake Michigan have pursued Lake Trout Salvelinus namaycush rehabilitation through Sea Lamprey Petromyzon marinus control, harvest regulations, and by stocking millions of fish annually. Stocking was prioritized at four historically important spawning locations beginning in 1985, and coded wire tags (CWTs) were used to help evaluate performance. We used data from CWT fish captured in fishery-independent surveys from 1998 – 2014 to evaluate relative post-release survival of Lake Trout, estimated by catch-per-unit-effort and corrected for the number of fish stocked (CPUE), across 173 CWT tag lots of the 1994 – 2003 year classes stocked at these four locations. Boosted regression tree (BRT) models were used to assess the relative influence of four variables on Lake Trout CPUE in two age groups (age 4-5 years and 6-10 years) and paired with analyses of variance to test for statistical significance. Genetic strain (29.1%), stocking location (27.8%), mortality at release (23.1%) and predator density (19.9%) had similar influence on the relative survival of younger fish, whereas relative survival of older fish was heavily influenced by stocking location (79.8%). Survival of both age groups was lowest for fish stocked in the Northern Refuge, where the age structure was truncated due to fishery harvest and Sea Lamprey predation. Survival of stocked fish was higher at the Southern Refuge, Clay Banks, and Julian’s Reef, where mortality from sea lamprey and harvest was lower, and where increases in wild Lake Trout have been observed in recent years. Stocked Lake Michigan remnant genetic strains also appeared to survive better than strains from other lakes at these three locations, but strain effects could not be fully disentangled from effects of stocking location, and continued stocking of multiple genetic strains may provide resiliency toward future selection pressures. Continued progress toward rehabilitation will require reducing fishing and lamprey-induced mortality in northern Lake Michigan to build parental stocks of advanced ages as well as balancing efforts among competing management goals.","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10338","usgsCitation":"Kornis, M.S., Bronte, C.R., Holey, M.E., Hanson, S.D., Treska, T.J., Jonas, J.L., Madenjian, C.P., Claramunt, R.M., Robillard, S.R., Breidert, B., Donner, K.C., Lenart, S.J., Martell, A.W., McKee, P.C., and Olson, E., 2019, Factors affecting post-release survival of coded-wire tagged Lake Trout Salvelinus namaycush in Lake Michigan at four historical spawning locations: North American Journal of Fisheries Management, v. 39, no. 5, p. 868-895, https://doi.org/10.1002/nafm.10338.","productDescription":"28 p.","startPage":"868","endPage":"895","ipdsId":"IP-104527","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":367309,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.88037109375,\n 46.10370875598026\n ],\n [\n -86.3525390625,\n 46.164614496897094\n ],\n [\n -87.51708984375,\n 45.90529985724799\n ],\n [\n -88.2861328125,\n 44.55916341529182\n ],\n [\n -88.04443359375,\n 43.88205730390537\n ],\n [\n -88.06640625,\n 42.84375132629021\n ],\n [\n -88.06640625,\n 41.86956082699455\n ],\n [\n -87.29736328125,\n 41.541477666790286\n ],\n [\n -86.66015624999999,\n 41.5579215778042\n ],\n [\n -86.0009765625,\n 42.24478535602799\n ],\n [\n -85.869140625,\n 42.87596410238256\n ],\n [\n -86.02294921875,\n 44.166444664458595\n ],\n [\n -85.84716796875,\n 44.449467536006935\n ],\n [\n -85.18798828125,\n 44.762336674810996\n ],\n [\n -84.7705078125,\n 45.166547157856016\n ],\n [\n -84.88037109375,\n 46.10370875598026\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"5","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Kornis, Matthew S.","contributorId":201252,"corporation":false,"usgs":false,"family":"Kornis","given":"Matthew","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":770502,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bronte, Charles R.","contributorId":190727,"corporation":false,"usgs":false,"family":"Bronte","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":770503,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holey, Mark E.","contributorId":212699,"corporation":false,"usgs":false,"family":"Holey","given":"Mark","email":"","middleInitial":"E.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":770504,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanson, S. Dale","contributorId":218843,"corporation":false,"usgs":false,"family":"Hanson","given":"S.","email":"","middleInitial":"Dale","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":770505,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Treska, Theodore J.","contributorId":218844,"corporation":false,"usgs":false,"family":"Treska","given":"Theodore","email":"","middleInitial":"J.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":770506,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jonas, Jory L.","contributorId":215449,"corporation":false,"usgs":false,"family":"Jonas","given":"Jory","email":"","middleInitial":"L.","affiliations":[{"id":6983,"text":"Michigan DNR","active":true,"usgs":false}],"preferred":false,"id":770507,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":770501,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Claramunt, Randall M.","contributorId":190497,"corporation":false,"usgs":false,"family":"Claramunt","given":"Randall","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":770508,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Robillard, Steven R.","contributorId":218845,"corporation":false,"usgs":false,"family":"Robillard","given":"Steven","email":"","middleInitial":"R.","affiliations":[{"id":33955,"text":"Illinois Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":770509,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Breidert, Brian","contributorId":195539,"corporation":false,"usgs":false,"family":"Breidert","given":"Brian","email":"","affiliations":[{"id":34295,"text":"Indiana DNR","active":true,"usgs":false}],"preferred":false,"id":770510,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Donner, Kevin C.","contributorId":218846,"corporation":false,"usgs":false,"family":"Donner","given":"Kevin","email":"","middleInitial":"C.","affiliations":[{"id":39923,"text":"Little Traverse Bay Band of Odawa Indians","active":true,"usgs":false}],"preferred":false,"id":770511,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lenart, Stephen J.","contributorId":218847,"corporation":false,"usgs":false,"family":"Lenart","given":"Stephen","email":"","middleInitial":"J.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":770512,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Martell, Archie W.","contributorId":218848,"corporation":false,"usgs":false,"family":"Martell","given":"Archie","email":"","middleInitial":"W.","affiliations":[{"id":34298,"text":"Little River Band of Ottawa Indians","active":true,"usgs":false}],"preferred":false,"id":770513,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"McKee, Patrick C.","contributorId":218849,"corporation":false,"usgs":false,"family":"McKee","given":"Patrick","email":"","middleInitial":"C.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":770514,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Olson, Erik J.","contributorId":218850,"corporation":false,"usgs":false,"family":"Olson","given":"Erik J.","affiliations":[{"id":34297,"text":"Grand Traverse Band of Ottawa and Chippewa Indians","active":true,"usgs":false}],"preferred":false,"id":770515,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70203786,"text":"sir20195058 - 2019 - Controls on spatial and temporal variations of brine discharge to the Dolores River in the Paradox Valley, Colorado, 2016–18","interactions":[],"lastModifiedDate":"2019-09-10T08:04:36","indexId":"sir20195058","displayToPublicDate":"2019-09-09T15:55:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5058","displayTitle":"Controls on Spatial and Temporal Variations of Brine Discharge to the Dolores River in the Paradox Valley, Colorado, 2016–18","title":"Controls on spatial and temporal variations of brine discharge to the Dolores River in the Paradox Valley, Colorado, 2016–18","docAbstract":"

The Paradox Valley in southwestern Colorado is a collapsed anticline formed by movement of the salt-rich Paradox Formation at the core of the anticline. The salinity of the Dolores River, a tributary of the Colorado River, increases substantially as it crosses the valley because of discharge of brine-rich groundwater derived from the underlying salts. Although the brine is naturally occurring, it increases the salinity of the Colorado River, which is a major concern to downstream agricultural, municipal, and industrial water users. The U.S. Geological Survey in cooperation with the Bureau of Reclamation conducted a study to improve the characterization of processes controlling spatial and temporal variations in brine discharge to the Dolores River. For the study, three geophysical surveys were conducted in March, May, and September 2017, and water levels were monitored in selected ponds and groundwater wells from November 2016 to May 2018. The study also utilized streamflow and specific conductance data from two U.S. Geological Survey streamflow-gaging stations on the Dolores River to estimate salt load to the river.

River-based continuous resistivity profiling and frequency domain electromagnetic induction surveys made during low-flow conditions indicated a zone of brine-rich groundwater close to the riverbed along an approximately 4-kilometer reach of the river. Under high-flow conditions, the brine was depressed as much as 2 meters below the riverbed, and brine discharge to the river was reduced to a minimum. Direct current electrical resistivity surveys show that the freshwater lens overlying the brine is much thicker (up to 10 meters) on the west bank than on the east bank (less than 5 meters). A large low-conductivity anomaly at river distance 6,800 meters was observed in all surveys and may represent a freshwater discharge zone or a losing reach of the river.

Filling and draining of the wildlife ponds on the west side of the river had a negligible effect on salt loads in the river during the study period. Groundwater monitoring showed there was active exchange of water between the river and the adjacent alluvial aquifer. When river stage was low, groundwater flowed towards the river, and brine discharge to the river increased. When the river stage was high, the gradient was reversed, and fresh surface water recharged the alluvial aquifer minimizing brine discharge. Most of the salt load to the river occurred during the winter and appeared to be enhanced by diurnal stage fluctuations.

A conceptual model of brine discharge to the river is presented at three scales. Groundwater at the regional scale drives dissolution of salt in the Paradox Formation and flow of brine into the base of the alluvial aquifer. Surface water–groundwater interactions at the scale of the alluvial aquifer control brine discharge to the river seasonally and interannually. At the finest scale, diurnal fluctuations in river stage drive exchange of freshwater with saltier pore water in the hyporheic zone, which appears to increase brine discharge to the river during winter.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195058","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Mast, M.A., and Terry, N., 2019, Controls on spatial and temporal variations of brine discharge to the Dolores River in the Paradox Valley, Colorado, 2016–18: U.S. Geological Survey Scientific Investigations Report 2019–5058, 25 p., https://doi.org/10.3133/sir20195058.\n","productDescription":"vi, 25 p.","onlineOnly":"Y","ipdsId":"IP-103865","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":437347,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77080NB","text":"USGS data release","linkHelpText":"Raw Data from Continuous Resistivity Profiles and Electromagnetic Surveys Collected in and adjacent to the Dolores River in the Paradox Valley, Colorado (2017)"},{"id":367271,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5058/sir20195058.pdf","text":"Report","size":"6.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5058"},{"id":367270,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5058/coverthb.jpg"}],"country":"United States","state":"Colorado","county":"Montrose County","otherGeospatial":"Paradox Valley","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-108.3772,38.6678],[-108.1472,38.6675],[-107.965,38.6664],[-107.9279,38.6661],[-107.9084,38.6664],[-107.8589,38.6663],[-107.8206,38.6664],[-107.7782,38.6661],[-107.7658,38.6663],[-107.741,38.6662],[-107.5011,38.6657],[-107.4992,38.6304],[-107.4989,38.6172],[-107.4992,38.5737],[-107.499,38.5356],[-107.4989,38.4717],[-107.4991,38.4531],[-107.4991,38.4504],[-107.4989,38.4445],[-107.4995,38.4404],[-107.4991,38.4246],[-107.4994,38.4096],[-107.4993,38.4033],[-107.4997,38.3656],[-107.4995,38.3248],[-107.4995,38.3008],[-107.5213,38.301],[-107.6333,38.3005],[-107.6358,38.3095],[-107.633,38.3172],[-107.6314,38.3223],[-107.6292,38.3286],[-107.6339,38.3286],[-107.6867,38.3288],[-107.7049,38.329],[-107.7236,38.3287],[-107.7964,38.329],[-107.8146,38.3292],[-107.8522,38.3291],[-107.8715,38.3293],[-107.9079,38.3292],[-107.9449,38.3295],[-107.9631,38.3296],[-108.0007,38.3304],[-108.0206,38.3305],[-108.1127,38.3312],[-108.1274,38.331],[-108.1276,38.3183],[-108.1165,38.3185],[-108.1163,38.3121],[-108.0987,38.312],[-108.0985,38.283],[-108.0815,38.2828],[-108.0807,38.2547],[-108.0085,38.2537],[-108.0084,38.2482],[-107.9814,38.2477],[-107.981,38.2328],[-107.9628,38.2326],[-107.9627,38.2263],[-107.9468,38.2265],[-107.9466,38.2184],[-107.9367,38.2185],[-107.9367,38.1732],[-107.946,38.1731],[-107.946,38.1517],[-107.9654,38.1519],[-108.0549,38.1522],[-108.2235,38.152],[-108.2411,38.1522],[-108.2587,38.1523],[-108.3336,38.1523],[-108.3506,38.1519],[-108.4641,38.1524],[-108.4841,38.1525],[-108.5397,38.1527],[-108.6304,38.153],[-108.6492,38.1531],[-109.041,38.1531],[-109.0409,38.1603],[-109.0607,38.2768],[-109.0608,38.3304],[-109.0608,38.3521],[-109.0607,38.378],[-109.0607,38.4052],[-109.0606,38.4197],[-109.0604,38.4555],[-109.0604,38.4637],[-109.0602,38.4981],[-109.0602,38.4991],[-108.6635,38.4992],[-108.3791,38.4999],[-108.3771,38.6116],[-108.3772,38.6678]]]},\"properties\":{\"name\":\"Montrose\",\"state\":\"CO\"}}]}","contact":"

Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225

","tableOfContents":"","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2019-09-09","noUsgsAuthors":false,"publicationDate":"2019-09-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Mast, M. Alisa 0000-0001-6253-8162 mamast@usgs.gov","orcid":"https://orcid.org/0000-0001-6253-8162","contributorId":827,"corporation":false,"usgs":true,"family":"Mast","given":"M.","email":"mamast@usgs.gov","middleInitial":"Alisa","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":764129,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terry, Neil C. 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":764130,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215387,"text":"70215387 - 2019 - Monitoring drought impact on annual forage production in semi-arid grasslands: A case study of Nebraska sandhills","interactions":[],"lastModifiedDate":"2020-10-18T14:02:49.47461","indexId":"70215387","displayToPublicDate":"2019-09-09T08:58:19","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring drought impact on annual forage production in semi-arid grasslands: A case study of Nebraska sandhills","docAbstract":"
Land management practices and disturbances (e.g. overgrazing, fire) have substantial effects on grassland forage production. When using satellite remote sensing to monitor climate impacts, such as drought stress on annual forage production, minimizing land management practices and disturbance effects sends a clear climate signal to the productivity data. This study investigates the effect of this climate signal by: (1) providing spatial estimates of expected biomass under specific climate conditions, (2) determining which drought indices explain the majority of interannual variability in this biomass, and (3) developing a predictive model that estimates the annual biomass early in the growing season. To address objective 1, this study uses an established methodology to determine Expected Ecosystem Performance (EEP) in the Nebraska Sandhills, US, representing annual forage levels after accounting for non-climatic influences. Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) data were used to approximate actual ecosystem performance. Seventeen years (2000–2016) of annual EEP was calculated using piecewise regression tree models of site potential and climate data. Expected biomass (EB), EEP converted to biomass in kg*ha−1*yr−1, was then used to examine the predictive capacity of several drought indices and the onset date of the growing season. Subsets of these indices were used to monitor and predict annual expected grassland biomass. Independent field-based biomass production data available from two Sandhills locations were used for validation of the EEP model. The EB was related to field-based biomass production (R2 = 0.66 and 0.57) and regional rangeland productivity statistics of the Soil Survey Geographic Database (SSURGO) dataset. The Evaporative Stress Index (ESI), the 3- and 6-month Standardized Precipitation Index (SPI), and the U.S. Drought Monitor (USDM), which represented moisture conditions during May, June and July, explained the majority of the interannual biomass variability in this grassland system (three-month ESI explained roughly 72% of the interannual biomass variability). A new model was developed to use drought indices from early in the growing season to predict the total EB for the whole growing season. This unique approach considers only climate-related drought signal on productivity. The capability to estimate annual EB by the end of May will potentially enable land managers to make informed decisions about stocking rates, hay purchase needs, and other management issues early in the season, minimizing their potential drought losses. View Full-Text
","language":"English","publisher":"MDPI","doi":"10.3390/rs11182106","usgsCitation":"Podebradska, M., Wylie, B., Hayes, M.J., Wardlow, B.D., Bathke, D.J., Bliss, N.B., and Dahal, D., 2019, Monitoring drought impact on annual forage production in semi-arid grasslands: A case study of Nebraska sandhills: Remote Sensing, v. 11, no. 18, 25 p., https://doi.org/10.3390/rs11182106.","productDescription":"25 p.","ipdsId":"IP-110482","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":459881,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11182106","text":"Publisher Index Page"},{"id":437348,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BOIO3D","text":"USGS data release","linkHelpText":"Time Series of expected Nebraska Sandhills livestock forage (2000 - 2016)"},{"id":379492,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.216552734375,\n 40.60561205826018\n ],\n [\n -97.525634765625,\n 40.60561205826018\n ],\n [\n -97.525634765625,\n 42.98053954751642\n ],\n [\n -103.216552734375,\n 42.98053954751642\n ],\n [\n -103.216552734375,\n 40.60561205826018\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"18","noUsgsAuthors":false,"publicationDate":"2019-09-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Podebradska, Marketa 0000-0002-3121-4904","orcid":"https://orcid.org/0000-0002-3121-4904","contributorId":218698,"corporation":false,"usgs":false,"family":"Podebradska","given":"Marketa","email":"","affiliations":[{"id":33286,"text":"School of Natural Resources, University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":801946,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce 0000-0002-7374-1083","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":201929,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":801947,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayes, Michael J. 0000-0001-5006-166X","orcid":"https://orcid.org/0000-0001-5006-166X","contributorId":243284,"corporation":false,"usgs":false,"family":"Hayes","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":48673,"text":"School of Natural Resources, University of Nebraska-Lincoln, 811 Hardin Hall, 3310 Holdrege Street, Lincoln, Nebraska 68583-0988","active":true,"usgs":false}],"preferred":false,"id":801948,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wardlow, Brian D. 0000-0002-4767-581X","orcid":"https://orcid.org/0000-0002-4767-581X","contributorId":191403,"corporation":false,"usgs":false,"family":"Wardlow","given":"Brian","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":801949,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bathke, Deborah J.","contributorId":197224,"corporation":false,"usgs":false,"family":"Bathke","given":"Deborah","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":801950,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bliss, Norman B. 0000-0003-2409-5211 bliss@usgs.gov","orcid":"https://orcid.org/0000-0003-2409-5211","contributorId":1921,"corporation":false,"usgs":true,"family":"Bliss","given":"Norman","email":"bliss@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":801951,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":801952,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70216116,"text":"70216116 - 2019 - Isolation by a hydroelectric dam induces minimal impacts on genetic diversity and population structure in six fish species","interactions":[],"lastModifiedDate":"2020-11-06T14:08:27.769553","indexId":"70216116","displayToPublicDate":"2019-09-09T08:01:32","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Isolation by a hydroelectric dam induces minimal impacts on genetic diversity and population structure in six fish species","docAbstract":"

Reduced connectivity created by artificial barriers can influence the genetic integrity of isolated subpopulations by reducing local population sizes and altering patterns of gene flow. We investigated the genetic impacts of one such barrier, the Prairie du Sac dam, Wisconsin, USA, using microsatellite data from six fish species with varying life history traits sampled above and below the dam. Contrary to many past studies in other systems, we did not detect any significant differences in genetic diversity between populations found above and below the Prairie du Sac dam. Our results also revealed low genetic differentiation (FST = 0–0.008) between populations above and below the dam for all species. In fact, we found that more genetic variation was partitioned among sampling years than between above and below dam populations for all but one of the species. Results from coalescent simulations designed to model our study system indicated that the genetic impacts of the dam will likely be detectable approximately 40–60 generations after the dam was constructed, and that it is possible to largely mitigate these impacts with a fish passage strategy that facilitates a migration rate of ≥ 1% between above and below dam populations. In summary, our findings suggest the genetic impacts of dams can be relatively minimal on short time scales, and that fish passage strategies can significantly reduce genetic impacts if designed appropriately.

","language":"English","publisher":"Springer","doi":"10.1007/s10592-019-01220-1","usgsCitation":"Ruzich, J., Turnquist, K., Nye, N., Rowe, D., and Larson, W., 2019, Isolation by a hydroelectric dam induces minimal impacts on genetic diversity and population structure in six fish species: Conservation Genetics, v. 20, p. 1421-1436, https://doi.org/10.1007/s10592-019-01220-1.","productDescription":"16 p.","startPage":"1421","endPage":"1436","ipdsId":"IP-100522","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":380254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Wisconsin River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.51522827148438,\n 43.55352464927332\n ],\n [\n -89.51934814453125,\n 43.51469675271006\n ],\n [\n -89.50698852539062,\n 43.475843857430895\n ],\n [\n -89.60311889648438,\n 43.41701888881103\n ],\n [\n -89.71160888671875,\n 43.389081939117496\n ],\n [\n -89.77340698242188,\n 43.33117156319044\n ],\n [\n -89.77752685546875,\n 43.279204926082784\n ],\n [\n -89.7418212890625,\n 43.2512044908875\n ],\n [\n -89.703369140625,\n 43.2432020009995\n ],\n [\n -89.681396484375,\n 43.28720268480437\n ],\n [\n -89.65530395507812,\n 43.3351671567243\n ],\n [\n -89.60037231445312,\n 43.35713822211053\n ],\n [\n -89.55917358398438,\n 43.33816367935935\n ],\n [\n -89.50561523437499,\n 43.345154990451135\n ],\n [\n -89.47265625,\n 43.401056495052906\n ],\n [\n -89.41635131835938,\n 43.45192195878608\n ],\n [\n -89.37103271484375,\n 43.530629170442424\n ],\n [\n -89.39300537109375,\n 43.56944681061758\n ],\n [\n -89.45892333984374,\n 43.58238046828168\n ],\n [\n -89.50836181640625,\n 43.574421623084234\n ],\n [\n -89.51522827148438,\n 43.55352464927332\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","noUsgsAuthors":false,"publicationDate":"2019-09-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruzich, Jenna","contributorId":244568,"corporation":false,"usgs":false,"family":"Ruzich","given":"Jenna","email":"","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":804180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Turnquist, Keith","contributorId":244569,"corporation":false,"usgs":false,"family":"Turnquist","given":"Keith","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":804181,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nye, Nathan","contributorId":244570,"corporation":false,"usgs":false,"family":"Nye","given":"Nathan","email":"","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":804182,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rowe, David","contributorId":244571,"corporation":false,"usgs":false,"family":"Rowe","given":"David","email":"","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":804183,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Larson, Wesley 0000-0003-4473-3401 wlarson@usgs.gov","orcid":"https://orcid.org/0000-0003-4473-3401","contributorId":199509,"corporation":false,"usgs":true,"family":"Larson","given":"Wesley","email":"wlarson@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":804179,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70205625,"text":"70205625 - 2019 - The landscape of soil carbon data: Emerging questions, synergies and databases","interactions":[],"lastModifiedDate":"2019-10-09T10:15:22","indexId":"70205625","displayToPublicDate":"2019-09-08T10:59:09","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5866,"text":"Progress in Physical Geography: Earth and Environment","active":true,"publicationSubtype":{"id":10}},"title":"The landscape of soil carbon data: Emerging questions, synergies and databases","docAbstract":"

Soil carbon has been measured for over a century in applications ranging from understanding biogeochemical processes in natural ecosystems to quantifying the productivity and health of managed systems. Consolidating diverse soil carbon datasets is increasingly important to maximize their value, particularly with growing anthropogenic and climate change pressures. In this progress report, we describe recent advances in soil carbon data led by the International Soil Carbon Network and other networks. We highlight priority areas of research requiring soil carbon data, including (a) quantifying boreal, arctic and wetland carbon stocks, (b) understanding the timescales of soil carbon persistence using radiocarbon and chronosequence studies, (c) synthesizing long-term and experimental data to inform carbon stock vulnerability to global change, (d) quantifying root influences on soil carbon and (e) identifying gaps in model–data integration. We also describe the landscape of soil datasets currently available, highlighting their strengths, weaknesses and synergies. Now more than ever, integrated soil data are needed to inform climate mitigation, land management and agricultural practices. This report will aid new data users in navigating various soil databases and encourage scientists to make their measurements publicly available and to join forces to find soil-related solutions.

","language":"English","publisher":"Sage","doi":"10.1177/0309133319873309","usgsCitation":"Avni Malhotra, Katherine Todd-Brown, Luke Nave, Batjes, N., Holmquist, J., Alison Hoyt, Colleen Iversen, Jackson, R.B., Lathja, K., Lawrence, C.R., Olga Vinduśková, Wieder, W., Williams, M., Gustaf Hugelias, and Harden, J., 2019, The landscape of soil carbon data: Emerging questions, synergies and databases: Progress in Physical Geography: Earth and Environment, v. 43, no. 5, p. 707-719, https://doi.org/10.1177/0309133319873309.","productDescription":"13 p.","startPage":"707","endPage":"719","ipdsId":"IP-106672","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":459890,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1564214","text":"External 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Nave","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":771922,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Batjes, Niels","contributorId":219295,"corporation":false,"usgs":false,"family":"Batjes","given":"Niels","email":"","affiliations":[{"id":39988,"text":"ISRIC World Soil Information","active":true,"usgs":false}],"preferred":false,"id":771923,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Holmquist, James","contributorId":217021,"corporation":false,"usgs":false,"family":"Holmquist","given":"James","email":"","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":771924,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Alison Hoyt","contributorId":219296,"corporation":false,"usgs":false,"family":"Alison Hoyt","affiliations":[{"id":36389,"text":"Max Planck 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University","active":true,"usgs":false}],"preferred":false,"id":771927,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lawrence, Corey R. 0000-0001-6143-7781","orcid":"https://orcid.org/0000-0001-6143-7781","contributorId":202390,"corporation":false,"usgs":true,"family":"Lawrence","given":"Corey","email":"","middleInitial":"R.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":771919,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Olga Vinduśková","contributorId":219299,"corporation":false,"usgs":false,"family":"Olga Vinduśková","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":771928,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Wieder, William","contributorId":202376,"corporation":false,"usgs":false,"family":"Wieder","given":"William","email":"","affiliations":[{"id":6648,"text":"National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":771929,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Williams, Matt","contributorId":219300,"corporation":false,"usgs":false,"family":"Williams","given":"Matt","email":"","affiliations":[{"id":25497,"text":"University of Edinburgh","active":true,"usgs":false}],"preferred":false,"id":771930,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Gustaf Hugelias","contributorId":219301,"corporation":false,"usgs":false,"family":"Gustaf Hugelias","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":771931,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Harden, Jennifer","contributorId":219302,"corporation":false,"usgs":false,"family":"Harden","given":"Jennifer","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":771932,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70205245,"text":"70205245 - 2019 - Laboratory assessment of alternative stream velocity measurement methods","interactions":[],"lastModifiedDate":"2019-09-10T09:55:38","indexId":"70205245","displayToPublicDate":"2019-09-06T09:54:42","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Laboratory assessment of alternative stream velocity measurement methods","docAbstract":"Understanding streamflow in montane watersheds on regional scales is often incomplete due to a lack of data for small-order streams that link precipitation and snowmelt processes to main stem discharge. This data deficiency is attributed to the prohibitive cost of conventional streamflow measurement methods and the remote location of many small streams. Expedient and low-cost streamflow measurement methods used by resource professionals or citizen scientists can provide scientifically useful solutions to this data deficiency. To this end, four current velocity measurement methods were evaluated in a laboratory flume: the surface float, rising body, velocity head rod, and rising air bubble methods. The methods were tested under a range of stream velocities, cross-sectional depths, and streambed substrates. The resulting measurements provide estimates of precision and bias of each method, as well as method-specific insight and calibration formulas. The velocity head rod and surface float methods were the easiest methods to use, providing greater precision at large (>=0.6 m/s) and small (<0.6 m/s) velocities, respectively. However, the reliance on a velocity ratio for each of these methods can generate inaccuracy in their results. The rising body method is more challenging to execute and of lower precision than the former two methods but provides low bias measurements. The rising air bubble method has a complex instrument assembly that is considered impractical for potential field user groups.","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0222263","usgsCitation":"Hundt, S., and Blasch, K.W., 2019, Laboratory assessment of alternative stream velocity measurement methods: PLoS ONE, v. 14, no. 9, e0222263, https://doi.org/10.1371/journal.pone.0222263.","productDescription":"e0222263","ipdsId":"IP-081975","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":459896,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0222263","text":"Publisher Index Page"},{"id":367310,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":367297,"type":{"id":15,"text":"Index Page"},"url":"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0222263"}],"volume":"14","issue":"9","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Hundt, Stephen A. 0000-0002-6484-0637","orcid":"https://orcid.org/0000-0002-6484-0637","contributorId":204678,"corporation":false,"usgs":true,"family":"Hundt","given":"Stephen","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770499,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blasch, Kyle W. 0000-0002-0590-0724","orcid":"https://orcid.org/0000-0002-0590-0724","contributorId":203415,"corporation":false,"usgs":true,"family":"Blasch","given":"Kyle","email":"","middleInitial":"W.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770500,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227911,"text":"70227911 - 2019 - Effects of distribution, behavior, and climate on mule deer survival","interactions":[],"lastModifiedDate":"2022-02-03T12:06:36.134671","indexId":"70227911","displayToPublicDate":"2019-09-05T14:13:14","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Effects of distribution, behavior, and climate on mule deer survival","docAbstract":"

Mule deer (Odocoileus hemionus hemionus) populations in North America are a valuable economic wildlife resource, with the managed harvest of this species reflecting societal values and recreational opportunities in many parts of the western United States. Managing mule deer populations while allowing for harvest requires an understanding of the species’ population dynamics, including the specific factors associated with population change. We conducted a 7-year (2005–2012) study designed to investigate habitat use and survival of mule deer in eastern Oregon, USA. We used known-fate data for 408 adult female radio-collared mule deer to estimate monthly survival rates and to investigate factors that might affect these rates, including seasonal distribution, temporal effects (seasonal, annual, and trends across season and year), movement behavior, and local weather and regional climatic covariates. Variation in survival rates of female mule deer was best explained by an additive effect of migration behavior, differences in survival during the fall migration period compared to the rest of the annual cycle, and precipitation levels on winter ranges of individual deer. Estimates of annual survival were higher for migrants (0.81–0.82), compared to residents (0.76–0.77). Survival was lower for migrants and residents during fall migration (Oct–Nov) and higher amounts of winter precipitation increased survival of both groups. The results of our study suggest that migrating to potentially higher quality summer foraging areas outweighed the cost of traveling through unfamiliar habitats and energy expenditure associated with migration. © 2018 The Wildlife Society.

","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21558","usgsCitation":"Schuyler, E.M., Dugger, K., and Jackson, D.H., 2019, Effects of distribution, behavior, and climate on mule deer survival: Journal of Wildlife Management, v. 83, no. 1, p. 89-99, https://doi.org/10.1002/jwmg.21558.","productDescription":"11 p.","startPage":"89","endPage":"99","ipdsId":"IP-094663","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":459907,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.21558","text":"Publisher Index Page"},{"id":395301,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.51953124999999,\n 42.06560675405716\n ],\n [\n -122.51953124999999,\n 42.06560675405716\n ],\n [\n -122.51953124999999,\n 42.06560675405716\n ],\n [\n -122.51953124999999,\n 42.06560675405716\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.51953124999999,\n 42.032974332441405\n ],\n [\n -119.35546875000001,\n 42.06560675405716\n ],\n [\n -119.0478515625,\n 42.84375132629021\n ],\n [\n -118.95996093749999,\n 43.229195113965005\n ],\n [\n -119.17968749999999,\n 43.96119063892024\n ],\n [\n -119.61914062499999,\n 44.43377984606822\n ],\n [\n -120.4541015625,\n 44.653024159812\n ],\n [\n -121.5087890625,\n 45.089035564831036\n ],\n [\n -122.56347656249999,\n 44.902577996288876\n ],\n [\n -122.78320312499999,\n 43.96119063892024\n ],\n [\n -122.56347656249999,\n 42.5530802889558\n ],\n [\n -122.51953124999999,\n 42.032974332441405\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"83","issue":"1","noUsgsAuthors":false,"publicationDate":"2018-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Schuyler, Elizabeth M.","contributorId":273895,"corporation":false,"usgs":false,"family":"Schuyler","given":"Elizabeth","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":832756,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832565,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackson, Dewaine H.","contributorId":175029,"corporation":false,"usgs":false,"family":"Jackson","given":"Dewaine","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":832757,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70204608,"text":"sir20195076 - 2019 - Delineation of spatial extent, depth, thickness, and potential volume of aquifers used for domestic and public water-supply in the Central Valley, California","interactions":[],"lastModifiedDate":"2026-04-13T22:40:02.211186","indexId":"sir20195076","displayToPublicDate":"2019-09-05T10:24:35","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5076","displayTitle":"Delineation of Spatial Extent, Depth, Thickness, and Potential Volume of Aquifers Used for Domestic and Public Water-Supply in the Central Valley, California","title":"Delineation of spatial extent, depth, thickness, and potential volume of aquifers used for domestic and public water-supply in the Central Valley, California","docAbstract":"
Identification of the groundwater resources used for drinking-water supplies is essential for the design of strategies to manage those resources. In this study, the spatial extent, depths, thicknesses, and volumes of groundwater aquifers used for domestic and public drinking-water supply were estimated from locations and well-construction data from 11,725 domestic-supply wells and 2,376 public-supply wells in the Central Valley, California. The data were compiled as part of the U.S. Geological Survey National Water Quality Assessment Project and California State Water Resources Control Board Groundwater Ambient Monitoring and Assessment Program Priority Basin Project. The spatial distributions of the depth to top and bottom of well screens were interpolated using Empirical Bayesian Kriging across buffer areas surrounding domestic- and public-supply wells. These surfaces provide a measure of the likely maximum horizontal and vertical extent of the aquifer volume accessed for drinking water in the Central Valley during the past century. Well depth generally increased from north to south, and over time from 1905 to 2010. Well-construction depths were generally more consistent in the Sacramento Valley than in the San Joaquin Valley. The total potential aquifer volume accessed for public supply was calculated to be greater than domestic-supply access, even though the estimated spatial extent of domestic-supply wells was 1.5 times larger than the spatial extent of public-supply wells. Public-supply wells commonly have screen lengths greater than 51 meters, whereas domestic-supply wells typically have shorter screen lengths (overall median of 6 meters). Consequently, the accessed thickness and volume of the aquifer is on average 1.8 and 1.4 times greater for public-supply wells than domestic-supply wells, respectively. Results are presented as maps of areal extent, depth, and thickness of zones in the Central Valley aquifer system used for domestic and public drinking-water supplies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195076","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Voss, S.A., Jurgens, B.C., Fram, M.S., and Bennett, G.L., V, 2019, Delineation of spatial extent, depth, thickness, and potential volume of aquifers used for domestic and public water-supply in the Central Valley, California: U.S. Geological Survey Scientific Investigations Report 2019–5076, 34 p., https://doi.org/10.3133/sir20195076.","productDescription":"Report: vi, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-102126","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":367144,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5076/coverthb.jpg"},{"id":367222,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76Q1V9G","linkHelpText":"Spatial Point Data Sets and Interpolated Surfaces of Well Construction Characteristics for Domestic and Public Supply Wells in the Central Valley, California, USA"},{"id":367145,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5076/sir20195076.pdf","text":"Report","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5076"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.377685546875,\n 36.02244668175846\n ],\n [\n -118.377685546875,\n 35.98689628443789\n ],\n [\n -119.388427734375,\n 37.00255267215955\n ],\n [\n -120.30029296875,\n 38.22091976683121\n ],\n [\n -121.431884765625,\n 39.29179704377487\n ],\n [\n -122.04711914062499,\n 40.713955826286046\n ],\n [\n -122.45361328124999,\n 40.66397287638688\n ],\n [\n -123.057861328125,\n 40.421860362045194\n ],\n [\n -122.6513671875,\n 39.42770738465604\n ],\n [\n -122.244873046875,\n 38.762650338334154\n ],\n [\n -121.343994140625,\n 37.70120736474139\n ],\n [\n -120.66284179687499,\n 36.914764288955936\n ],\n [\n -119.81689453125,\n 35.39800594715108\n ],\n [\n -118.88305664062499,\n 35.505400093441324\n ],\n [\n -118.377685546875,\n 36.02244668175846\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2019-09-05","noUsgsAuthors":false,"publicationDate":"2019-09-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Voss, Stefan 0000-0003-1214-9358","orcid":"https://orcid.org/0000-0003-1214-9358","contributorId":217888,"corporation":false,"usgs":true,"family":"Voss","given":"Stefan","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":767752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X bjurgens@usgs.gov","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":127842,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","email":"bjurgens@usgs.gov","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":767754,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fram, Miranda S. 0000-0002-6337-059X mfram@usgs.gov","orcid":"https://orcid.org/0000-0002-6337-059X","contributorId":1156,"corporation":false,"usgs":true,"family":"Fram","given":"Miranda","email":"mfram@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":767755,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bennett, George L. V 0000-0002-6239-1604 georbenn@usgs.gov","orcid":"https://orcid.org/0000-0002-6239-1604","contributorId":1373,"corporation":false,"usgs":true,"family":"Bennett","given":"George","suffix":"V","email":"georbenn@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":767753,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204915,"text":"sir20195090 - 2019 - Tritium as an indicator of modern, mixed, and premodern groundwater age","interactions":[],"lastModifiedDate":"2019-09-05T09:14:28","indexId":"sir20195090","displayToPublicDate":"2019-09-05T10:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5090","title":"Tritium as an indicator of modern, mixed, and premodern groundwater age","docAbstract":"

Categorical classification of groundwater age is often used for the assessment and understanding of groundwater resources. This report presents a tritium-based age classification system for the conterminous United States based on tritium (3H) thresholds that vary in space and time: modern (recharged in 1953 or later), if the measured value is larger than an upper threshold; premodern (recharged prior to 1953) if the measured value is smaller than a lower threshold; or mixed if the measured value is between the two thresholds. Inclusion of spatially varying thresholds, rather than a single threshold, accounts for the observed systematic variation in 3H deposition across the United States. Inclusion of time-varying thresholds, rather than a single threshold, accounts for the date of sampling given the radioactive decay of 3H.

The efficacy of the tritium-based age classification system was evaluated at national and regional scales. The system was evaluated at a national scale by classifying samples from 1,788 public-supply wells distributed across 19 principal aquifers and comparing those results with expectations based on hydrogeologic principles. The regional-scale data are from five paired networks of shallow and deep wells (287 wells). As expected, modern groundwater is more prevalent in shallow wells than in deeper wells, in fractured-rock and carbonate aquifers as compared to clastic aquifers, in unconfined areas as compared to confined areas, and in humid climates as compared to arid climates. The results from a tritium-based age classification system compared favorably with the results of 14 previous studies of groundwater ages that used different age tracers and analytical methods. The wells and samples from the Cambrian-Ordovician aquifer that had been analyzed using a more complex multi-tracer analysis were also analyzed using the tritium-based age classification system, and there was a close match between the two methods. The results from these various studies suggest that the tritium-based age classification system may be informative as a screening tool prior to selecting more expensive and complex age-dating tracers and methods, or to provide an explanatory variable for other water-quality data where more complex methods or tracers are not available.

This work improves on previous groundwater age classification using 3H by developing methods that (1) determine 3H thresholds for groundwater recharged in 1953 or later that minimize the misclassification of modern samples as mixed; (2) determine a pre-1953 threshold to estimate premodern background concentrations; and (3) add a mixed category to classify samples that are clearly neither entirely modern nor entirely premodern. As with any tritium-based approach, it can fail when the 3H record in precipitation does not accurately reflect the record of 3H in recharge

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20195090","usgsCitation":"Lindsey, B.D., Jurgens, B.C., and Belitz, K., 2019, Tritium as an indicator of modern, mixed, and premodern groundwater age: U.S. Geological Survey Scientific Investigations Report 2019–5090, 18 p., https://doi.org/10.3133/sir20195090.","productDescription":"vii, 18 p.","onlineOnly":"Y","ipdsId":"IP-097386","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes 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Chief Scientist, NAWQA
U.S. Geological Survey
2201 Sunrise Valley Drive, MS 413
Reston, VA 20192-0002

","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2019-09-04","noUsgsAuthors":false,"publicationDate":"2019-09-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Lindsey, Bruce D. 0000-0002-7180-4319 blindsey@usgs.gov","orcid":"https://orcid.org/0000-0002-7180-4319","contributorId":175346,"corporation":false,"usgs":true,"family":"Lindsey","given":"Bruce","email":"blindsey@usgs.gov","middleInitial":"D.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768999,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X bjurgens@usgs.gov","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":127842,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","email":"bjurgens@usgs.gov","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770172,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belitz, Kenneth 0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":201889,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":769001,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70205247,"text":"70205247 - 2019 - Modeling spatially and temporally complex range dynamics when detection is imperfect","interactions":[],"lastModifiedDate":"2023-04-04T13:09:47.297173","indexId":"70205247","displayToPublicDate":"2019-09-05T09:48:15","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Modeling spatially and temporally complex range dynamics when detection is imperfect","docAbstract":"

Species distributions are determined by the interaction of multiple biotic and abiotic factors, which produces complex spatial and temporal patterns of occurrence. As habitats and climate change due to anthropogenic activities, there is a need to develop species distribution models that can quantify these complex range dynamics. In this paper, we develop a dynamic occupancy model that uses a spatial generalized additive model to estimate non-linear spatial variation in occupancy not accounted for by environmental covariates. The model is flexible and can accommodate data from a range of sampling designs that provide information about both occupancy and detection probability. Output from the model can be used to create distribution maps and to estimate indices of temporal range dynamics. We demonstrate the utility of this approach by modeling long-term range dynamics of 10 eastern North American birds using data from the North American Breeding Bird Survey. We anticipate this framework will be particularly useful for modeling species’ distributions over large spatial scales and for quantifying range dynamics over long temporal scales.

","language":"English","publisher":"Nature","doi":"10.1038/s41598-019-48851-5","usgsCitation":"Rushing, C.S., Royle, J.A., Ziolkowski, D., and Pardieck, K.L., 2019, Modeling spatially and temporally complex range dynamics when detection is imperfect: Scientific Reports, v. 9, 12805, 9 p., https://doi.org/10.1038/s41598-019-48851-5.","productDescription":"12805, 9 p.","ipdsId":"IP-098777","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":459911,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-019-48851-5","text":"Publisher Index Page"},{"id":367307,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Rushing, Clark S. 0000-0002-9283-6563","orcid":"https://orcid.org/0000-0002-9283-6563","contributorId":218851,"corporation":false,"usgs":true,"family":"Rushing","given":"Clark","email":"","middleInitial":"S.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":true,"id":770529,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":139626,"corporation":false,"usgs":true,"family":"Royle","given":"J.","email":"aroyle@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":770530,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ziolkowski, David 0000-0002-2500-4417 dziolkowski@usgs.gov","orcid":"https://orcid.org/0000-0002-2500-4417","contributorId":195409,"corporation":false,"usgs":true,"family":"Ziolkowski","given":"David","email":"dziolkowski@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":770531,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pardieck, Keith L. 0000-0003-2779-4392 kpardieck@usgs.gov","orcid":"https://orcid.org/0000-0003-2779-4392","contributorId":4104,"corporation":false,"usgs":true,"family":"Pardieck","given":"Keith","email":"kpardieck@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":770532,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205793,"text":"70205793 - 2019 - Comparison of methods for modeling fractional cover using simulated satellite hyperspectral imager spectra","interactions":[],"lastModifiedDate":"2019-12-09T10:57:03","indexId":"70205793","displayToPublicDate":"2019-09-04T13:57:09","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Comparison of Methods for Modeling Fractional Cover using Simulated Satellite Hyperspectral Imager Spectra","title":"Comparison of methods for modeling fractional cover using simulated satellite hyperspectral imager spectra","docAbstract":"Remotely sensed data can be used to model the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil in natural and agricultural ecosystems. NPV and soil cover are difficult to estimate accurately since absorption by lignin, cellulose, and other organic molecules cannot be resolved by broadband multispectral data. A new generation of satellite hyperspectral imagers will provide contiguous narrowband coverage, enabling new, more accurate, and potentially global fractional cover products. We used six field spectroscopy datasets collected in prior experiments from sites with partial crop, grass, shrub, and low-stature resprouting tree cover to simulate satellite hyperspectral data, including sensor noise and atmospheric correction artifacts. The combined dataset was used to compare hyperspectral index-based and spectroscopic methods for estimating GV, NPV, and soil fractional cover. GV fractional cover was estimated most accurately. NPV and soil fractions were more difficult to estimate, with spectroscopic methods like partial least squares (PLS) regression, spectral feature analysis (SFA), and multiple endmember spectral mixture analysis (MESMA) typically outperforming hyperspectral indices. Using an independent validation dataset, the lowest root mean squared error (RMSE) values were 0.115 for GV using either normalized difference vegetation index (NDVI) or SFA, 0.164 for NPV using PLS, and 0.126 for soil using PLS. PLS also had the lowest RMSE averaged across all three cover types. This work highlights the need for more extensive and diverse fine spatial scale measurements of fractional cover, to improve methodologies for estimating cover in preparation for future hyperspectral global monitoring missions.","language":"English","publisher":"MDPI","doi":"10.3390/rs11182072","usgsCitation":"Dennison, P.E., Qi, Y., Meerdink, S.K., Kokaly, R.F., Thompson, D., Daughtry, C.S., Quemada, M., Roberts, D.A., Gader, P., Wetherley, E., Numata, I., and Roth, K.L., 2019, Comparison of methods for modeling fractional cover using simulated satellite hyperspectral imager spectra: Remote Sensing, v. 11, no. 18, 2072, 23 p., https://doi.org/10.3390/rs11182072.","productDescription":"2072, 23 p.","ipdsId":"IP-102364","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":459915,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11182072","text":"Publisher Index Page"},{"id":367977,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"18","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Dennison, Philip E.","contributorId":105132,"corporation":false,"usgs":true,"family":"Dennison","given":"Philip","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":772400,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Qi, Yi","contributorId":219504,"corporation":false,"usgs":false,"family":"Qi","given":"Yi","email":"","affiliations":[],"preferred":false,"id":772401,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meerdink, Susan K.","contributorId":219505,"corporation":false,"usgs":false,"family":"Meerdink","given":"Susan","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":772402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":205165,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond","email":"","middleInitial":"F.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":772403,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, David R.","contributorId":152638,"corporation":false,"usgs":false,"family":"Thompson","given":"David R.","affiliations":[{"id":18954,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA","active":true,"usgs":false}],"preferred":false,"id":772404,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Daughtry, Craig S.T.","contributorId":214079,"corporation":false,"usgs":false,"family":"Daughtry","given":"Craig","email":"","middleInitial":"S.T.","affiliations":[{"id":38179,"text":"USDA Agricultural Research Service, Hydrology and Remote Sensing Laboratory","active":true,"usgs":false}],"preferred":false,"id":772405,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Quemada, Miguel","contributorId":211094,"corporation":false,"usgs":false,"family":"Quemada","given":"Miguel","email":"","affiliations":[{"id":38180,"text":"School of Agricultural Engineering and CEIGRAM, Technical University of Madrid","active":true,"usgs":false}],"preferred":false,"id":772406,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Roberts, Dar A.","contributorId":100503,"corporation":false,"usgs":false,"family":"Roberts","given":"Dar","email":"","middleInitial":"A.","affiliations":[{"id":12804,"text":"Univ. of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":772407,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gader, Paul","contributorId":219506,"corporation":false,"usgs":false,"family":"Gader","given":"Paul","email":"","affiliations":[],"preferred":false,"id":772408,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wetherley, Erin","contributorId":219507,"corporation":false,"usgs":false,"family":"Wetherley","given":"Erin","email":"","affiliations":[],"preferred":false,"id":772409,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Numata, Izaya","contributorId":219508,"corporation":false,"usgs":false,"family":"Numata","given":"Izaya","email":"","affiliations":[],"preferred":false,"id":772410,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Roth, Keely L.","contributorId":187593,"corporation":false,"usgs":false,"family":"Roth","given":"Keely","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":772411,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70204513,"text":"ds1117 - 2019 - Alaska Geochemical Database Version 3.0 (AGDB3)—Including “Best Value” Data Compilations for Rock, Sediment, Soil, Mineral, and Concentrate Sample Media","interactions":[],"lastModifiedDate":"2019-09-03T16:45:47","indexId":"ds1117","displayToPublicDate":"2019-09-03T14:45:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1117","displayTitle":"Alaska Geochemical Database Version 3.0 (AGDB3)—Including “best value” data compilations for rock, sediment, soil, mineral, and concentrate sample media","title":"Alaska Geochemical Database Version 3.0 (AGDB3)—Including “Best Value” Data Compilations for Rock, Sediment, Soil, Mineral, and Concentrate Sample Media","docAbstract":"

The Alaska Geochemical Database Version 3.0 (AGDB3) contains new geochemical data compilations in which each geologic material sample has one “best value” determination for each analyzed species, greatly improving speed and efficiency of use. Like the Alaska Geochemical Database Version 2.0 before it, the AGDB3 was created and designed to compile and integrate geochemical data from Alaska to facilitate geologic mapping, petrologic studies, mineral resource assessments, definition of geochemical baseline values and statistics, element concentrations and associations, environmental impact assessments, and studies in public health associated with geology. This relational database, created from data-bases and published datasets of the U.S. Geological Survey (USGS), Atomic Energy Commission National Uranium Resource Evaluation (NURE), Alaska Division of Geological & Geophysical Surveys (DGGS), U.S. Bureau of Mines, and U.S. Bureau of Land Management serves as a data archive in support of Alaskan geologic and geochemical projects and contains data tables in several different formats describing historical and new quantitative and qualitative geochemical analyses. The analytical results were determined by 112 laboratory and field analytical methods on 396,343 rock, sediment, soil, mineral, heavy-mineral concentrate, and oxalic acid leachate samples. Most samples were collected by personnel of these agencies and analyzed in agency laboratories or, under contracts, in commercial analytical laboratories. These data represent analyses of samples collected as part of various agency programs and projects from 1938 through 2017. In addition, mineralogical data from 18,138 nonmagnetic heavy-mineral concentrate samples are included in this database. The AGDB3 includes historical geochemical data archived in the USGS National Geochemical Database (NGDB) and NURE National Uranium Resource Evaluation-Hydrogeochemical and Stream Sediment Reconnaissance databases, and in the DGGS Geochemistry database. Retrievals from these data-bases were used to generate most of the AGDB data set. These data were checked for accuracy regarding sample location, sample media type, and analytical methods used. In other words, the data of AGDB3 supersedes data in the AGDB and the AGDB2, but the background about the data in these two earlier versions are needed by users of the current AGDB3 to understand what has been done to amend, clean up, correct and format this data. Corrections were entered, resulting in a significantly improved Alaska geochemical dataset, the AGDB3. Data that were not previously in these databases because the data predate the earliest agency geochemical data-bases, or were once excluded for programmatic reasons, are included here in the AGDB3 and will be added to the NGDB and Alaska Geochemistry. The AGDB3 data provided here are the most accurate and complete to date and should be useful for a wide variety of geochemical studies. The AGDB3 data provided in the online version of the database may be updated or changed periodically.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ds1117","usgsCitation":"Granitto, M., Wang, B., Shew, N.B., Karl, S.M., Labay, K.A., Werdon, M.B., Seitz, S.S., and Hoppe, J.E., 2019, Alaska Geochemical Database Version 3.0 (AGDB3)—Including “best value” data compilations for rock, sediment, soil, mineral, and concentrate sample media: U.S. Geological Survey Data Series 1117, 33 p., https://doi.org/10.3133/ds1117.","productDescription":"Report: vii, 33 p.; Data release; Read me","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-099669","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":367033,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98NHRAD","text":"USGS data release","description":"USGS data release","linkHelpText":"Alaska Geochemical Database Version 3.0 (AGDB3) including best value data 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\":{\"name\":\"Alaska\",\"nation\":\"USA \"}}]}","contact":"

Director, Geology, Geophysics and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS-973
Denver, CO 80225-0046

","tableOfContents":"","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2019-09-03","noUsgsAuthors":false,"publicationDate":"2019-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Granitto, Matthew 0000-0003-3445-4863 granitto@usgs.gov","orcid":"https://orcid.org/0000-0003-3445-4863","contributorId":1224,"corporation":false,"usgs":true,"family":"Granitto","given":"Matthew","email":"granitto@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":767352,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wang, Bronwen 0000-0003-1044-2227","orcid":"https://orcid.org/0000-0003-1044-2227","contributorId":217713,"corporation":false,"usgs":true,"family":"Wang","given":"Bronwen","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":767355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shew, Nora B. 0000-0003-0025-7220 nshew@usgs.gov","orcid":"https://orcid.org/0000-0003-0025-7220","contributorId":217712,"corporation":false,"usgs":true,"family":"Shew","given":"Nora B.","email":"nshew@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":767353,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karl, Susan M. 0000-0003-1559-7826 skarl@usgs.gov","orcid":"https://orcid.org/0000-0003-1559-7826","contributorId":502,"corporation":false,"usgs":true,"family":"Karl","given":"Susan","email":"skarl@usgs.gov","middleInitial":"M.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":767354,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Labay, Keith A. 0000-0002-6763-3190 klabay@usgs.gov","orcid":"https://orcid.org/0000-0002-6763-3190","contributorId":217714,"corporation":false,"usgs":true,"family":"Labay","given":"Keith","email":"klabay@usgs.gov","middleInitial":"A.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":769754,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Werdon, Melanie B.","contributorId":193448,"corporation":false,"usgs":false,"family":"Werdon","given":"Melanie","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":767357,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Seitz, Susan S.","contributorId":217716,"corporation":false,"usgs":false,"family":"Seitz","given":"Susan","email":"","middleInitial":"S.","affiliations":[{"id":39689,"text":"Alaska Division of Geological & Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":767359,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hoppe, John E.","contributorId":217715,"corporation":false,"usgs":false,"family":"Hoppe","given":"John","email":"","middleInitial":"E.","affiliations":[{"id":37086,"text":"U.S. Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":767358,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70204416,"text":"sir20195070 - 2019 - Stratigraphic analysis of Corte Madera Creek flood control channel deposits","interactions":[],"lastModifiedDate":"2019-09-03T16:51:36","indexId":"sir20195070","displayToPublicDate":"2019-09-03T14:15:55","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5070","displayTitle":"Stratigraphic Analysis of Corte Madera Creek Flood Control Channel Deposits","title":"Stratigraphic analysis of Corte Madera Creek flood control channel deposits","docAbstract":"

Sedimentation in a channel can reduce flood conveyance capability and potentially place nearby property and life at risk from flooding. In 1998, Marin County Public Works dredged the concrete-lined segment of Corte Madera Creek, which drains a hilly and largely urbanized watershed that terminates in San Francisco Bay, California. From then through 2015, approximately 4,100 cubic meters of sand and gravel infilled the concrete-lined segment. Determining when and under what conditions this material was deposited informs dredging operations for the Corte Madera Creek Flood Control Project and increases understanding of sediment delivery timing and mechanisms from this and other San Francisco Bay tributaries.

Two hypothesized scenarios were investigated: (1) complete flushing during high flows and re-deposition of channel fill afterward and (2) more steady, gradual channel infilling. Stratigraphic analysis of eight sediment cores collected from the flood-control channel deposits in August 2017 was used to identify the most likely scenario. In addition, sediment elevation profiles, grain-size data, and a one-dimensional hydrodynamic model were used to assess the potential for longitudinal-channel scour and deposition following the wet winter of water year 2017 in the intertidal reach of the concrete channel in Corte Madera Creek.

Results indicated the channel is undergoing gradual infilling. Storm flows of water year 2017 did not completely scour the concrete channel fill. Sediment cores, stratigraphic analysis, and sediment elevation profiles indicated 0.23 meter of scour at the downstream end of the concrete-lined section and that roughly 0.5 meter of channel fill remained in the channel. The hydrodynamic model demonstrated that sediment deposition in the concrete channel is expected to start downstream from the point where the channel bed reaches mean lower low-water level. High flows can carry most of the sediment through this segment of channel, depositing the bed-material load downstream from the transition to a wide channel, where velocity and bed shear stress decrease abruptly.

Although the storm flows of 2017 did not completely scour the channel fill, subsequent material deposited in the channel could be transported downstream from the concrete channel if the sediment elevation profile is in equilibrium with present (2019) mean sea level. A calibrated, coupled hydrodynamic-sediment transport model could be used to test the present equilibrium between sediment elevation profiles and mean sea level, such that additional sediment build-up in the concrete channel is remobilized during subsequent wet-season flows and deposited downstream from the concrete-lined segment.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195070","collaboration":"Prepared in cooperation with Marin County Flood Control District","usgsCitation":"Livsey, D., Work, P., and Downing-Kunz, M., 2019, Stratigraphic analysis of Corte Madera Creek flood control channel deposits: U.S. Geological Survey Scientific Investigation Report 2019–5070, 28 p., https://doi.org/10.3133/sir20195070.","productDescription":"vi, 28 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-102889","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":367137,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5070/sir20195070.pdf","text":"Report","size":"7.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5070"},{"id":367136,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5070/coverthb.jpg"}],"country":"United States","state":"California","county":"Marin County","otherGeospatial":"Corte Madera Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.55360603332518,\n 37.95983152006781\n ],\n [\n -122.55401372909544,\n 37.95940856550367\n ],\n [\n -122.55317687988281,\n 37.95847805688854\n ],\n [\n -122.55341291427611,\n 37.957395268386534\n ],\n [\n -122.5513744354248,\n 37.95446827582245\n ],\n [\n -122.54940032958984,\n 37.95382533697663\n ],\n [\n -122.54691123962402,\n 37.95343618704664\n ],\n [\n -122.54618167877197,\n 37.95331774970222\n ],\n [\n -122.54616022109984,\n 37.95120276497281\n ],\n [\n -122.54598855972289,\n 37.95069515957716\n ],\n [\n -122.54487276077272,\n 37.95086436176544\n ],\n [\n -122.54515171051024,\n 37.95292859708327\n ],\n [\n -122.5455379486084,\n 37.9540960487555\n ],\n [\n -122.54740476608275,\n 37.95441751769712\n ],\n [\n -122.55006551742552,\n 37.95548343096359\n ],\n [\n -122.55236148834227,\n 37.957632129735394\n ],\n [\n -122.55236148834227,\n 37.9587487515198\n ],\n [\n -122.55360603332518,\n 37.95983152006781\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2019-09-03","noUsgsAuthors":false,"publicationDate":"2019-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Livsey, Daniel N. 0000-0002-2028-6128 dlivsey@usgs.gov","orcid":"https://orcid.org/0000-0002-2028-6128","contributorId":181870,"corporation":false,"usgs":true,"family":"Livsey","given":"Daniel","email":"dlivsey@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766793,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Work, Paul A. 0000-0002-2815-8040 pwork@usgs.gov","orcid":"https://orcid.org/0000-0002-2815-8040","contributorId":168561,"corporation":false,"usgs":true,"family":"Work","given":"Paul","email":"pwork@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Downing-Kunz, Maureen A. 0000-0002-4879-0318 mdowning-kunz@usgs.gov","orcid":"https://orcid.org/0000-0002-4879-0318","contributorId":3690,"corporation":false,"usgs":true,"family":"Downing-Kunz","given":"Maureen","email":"mdowning-kunz@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766794,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70207028,"text":"70207028 - 2019 - Phosphorus and nitrogen transport in the binational Great Lakes Basin estimated using SPARROW watershed models","interactions":[],"lastModifiedDate":"2020-01-08T14:10:14","indexId":"70207028","displayToPublicDate":"2019-09-03T13:55:39","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Phosphorus and nitrogen transport in the binational Great Lakes Basin estimated using SPARROW watershed models","docAbstract":"

Eutrophication problems in the Great Lakes are caused by excessive nutrient inputs (primarily phosphorus, P, and nitrogen, N) from various sources throughout its basin. In developing protection and restoration plans, it is important to know where and from what sources the nutrients originate. As part of a binational effort, Midcontinent SPARROW (SPAtially Referenced Regression On Watershed attributes) models were developed and used to estimate P and N loading from throughout the entire basin based on nutrient inputs similar to 2002; previous SPARROW models only estimated U.S. contributions. The new models have a higher resolution (~2‐km2 catchments) enabling improved descriptions of where nutrients originate and the sources at various spatial scales. The models were developed using harmonized geospatial datasets describing the stream network, nutrient sources, and environmental characteristics affecting P and N delivery. The models were calibrated using loads from sites estimated with ratio estimator and regression techniques and additional statistical approaches to reduce spatial correlation in the residuals and have all monitoring sites equally influence model development. SPARROW results, along with interlake transfers and direct atmospheric inputs, were used to quantify the entire P and N input to each lake and describe the importance of each nutrient source. Model results can be used to compare loading and yields from various tributaries and jurisdictions.

","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12792","usgsCitation":"Robertson, D.M., Saad, D., Benoy, G.A., Vouk, I., Schwarz, G.E., and Laitta, M.T., 2019, Phosphorus and nitrogen transport in the binational Great Lakes Basin estimated using SPARROW watershed models: Journal of the American Water Resources Association, v. 55, no. 6, p. 1401-1424, https://doi.org/10.1111/1752-1688.12792.","productDescription":"24 p.","startPage":"1401","endPage":"1424","ipdsId":"IP-099596","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":459925,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12792","text":"Publisher Index Page"},{"id":369886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.8125,\n 41.27780646738183\n ],\n [\n -75.8056640625,\n 41.27780646738183\n ],\n [\n -75.8056640625,\n 48.980216985374994\n ],\n [\n -92.8125,\n 48.980216985374994\n ],\n [\n -92.8125,\n 41.27780646738183\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"6","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":204668,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale","email":"","middleInitial":"M.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":776559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saad, David A. 0000-0001-6559-6181","orcid":"https://orcid.org/0000-0001-6559-6181","contributorId":217251,"corporation":false,"usgs":true,"family":"Saad","given":"David A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":776560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benoy, Glenn A. 0000-0001-6530-7220","orcid":"https://orcid.org/0000-0001-6530-7220","contributorId":172405,"corporation":false,"usgs":false,"family":"Benoy","given":"Glenn","email":"","middleInitial":"A.","affiliations":[{"id":13361,"text":"International Joint Commission, Washington DC","active":true,"usgs":false}],"preferred":false,"id":776561,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vouk, Ivana 0000-0002-9134-6933","orcid":"https://orcid.org/0000-0002-9134-6933","contributorId":211795,"corporation":false,"usgs":false,"family":"Vouk","given":"Ivana","email":"","affiliations":[{"id":38321,"text":"National Research Council Canada","active":true,"usgs":false}],"preferred":false,"id":776562,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":776563,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Laitta, Michael T","contributorId":221001,"corporation":false,"usgs":false,"family":"Laitta","given":"Michael","email":"","middleInitial":"T","affiliations":[{"id":40305,"text":"International Joint Commission, U.S. Section","active":true,"usgs":false}],"preferred":false,"id":776564,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}