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Identifying migration routes and fall stopover sites of Cinnamon Teal (Spatula cyanoptera septentrionalium) can provide a spatial guide to management and conservation efforts, and address vulnerabilities in wetland networks that support migratory waterbirds. Using high spatiotemporal resolution GPS-GSM transmitters, we analyzed 61 fall migration tracks across western North America during our three-year study (2017–2019). We marked Cinnamon Teal primarily during spring/summer in important breeding and molting regions across seven states (California, Oregon, Washington, Idaho, Utah, Colorado, and Nevada). We assessed fall migration routes and timing, detected 186 fall stopover sites, and identified specific North American ecoregions where sites were located. We classified underlying land cover for each stopover site and measured habitat selection for 12 land cover types within each ecoregion. Cinnamon Teal selected a variety of flooded habitats including natural, riparian, tidal, and managed wetlands; wet agriculture (including irrigation ditches, flooded fields, and stock ponds); wastewater sites; and golf and urban ponds. Wet agriculture was the most used habitat type (29.8% of stopover locations), and over 72% of stopover locations were on private land. Relatively scarce habitats such as wastewater ponds, tidal marsh, and golf and urban ponds were highly selected in specific ecoregions. In contrast, dry non-habitat across all ecoregions, and dry agriculture in the Cold Deserts and Mediterranean California ecoregions, was consistently avoided. Resources used by Cinnamon Teal often reflected wetland availability across the west and emphasize their adaptability to dynamic resource conditions in arid landscapes. Our results provide much needed information on spatial and temporal resource use by Cinnamon Teal during migration and indicate important wetland habitats for migrating waterfowl in the western United States.

","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8115","usgsCitation":"Mackell, D.A., Casazza, M.L., Overton, C.T., Donnelly, J.P., Olson, D., McDuie, F., Ackerman, J.T., and Eadie, J.M., 2021, Migration stopover ecology of Cinnamon Teal in western North America: Ecology and Evolution, v. 11, no. 20, p. 14056-14069, https://doi.org/10.1002/ece3.8115.","productDescription":"14 p.","startPage":"14056","endPage":"14069","ipdsId":"IP-127833","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":450738,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.8115","text":"Publisher Index Page"},{"id":436193,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99L4XJ5","text":"USGS data release","linkHelpText":"Migration stopover ecology of cinnamon teal in western North America"},{"id":389946,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.54296874999999,\n 54.57206165565852\n ],\n [\n -122.87109375,\n 55.178867663281984\n ],\n [\n -128.84765625,\n 53.85252660044951\n ],\n [\n -128.84765625,\n 51.72702815704774\n ],\n [\n -127.08984375000001,\n 45.706179285330826\n ],\n [\n -122.51953125000001,\n 35.02999636902568\n ],\n [\n -115.48828125000001,\n 23.07973176244989\n ],\n [\n -105.29296875,\n 17.476432197195532\n ],\n [\n -101.42578125,\n 21.453068633086783\n ],\n [\n -105.8203125,\n 34.30714385628804\n ],\n [\n -108.28125000000001,\n 44.84029065139799\n ],\n [\n -110.91796875,\n 52.05249047600099\n ],\n [\n -116.54296874999999,\n 54.57206165565852\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"20","noUsgsAuthors":false,"publicationDate":"2021-09-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Mackell, Desmond Alexander 0000-0002-1682-2581","orcid":"https://orcid.org/0000-0002-1682-2581","contributorId":266036,"corporation":false,"usgs":true,"family":"Mackell","given":"Desmond","email":"","middleInitial":"Alexander","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824184,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overton, Cory T. 0000-0002-5060-7447 coverton@usgs.gov","orcid":"https://orcid.org/0000-0002-5060-7447","contributorId":3262,"corporation":false,"usgs":true,"family":"Overton","given":"Cory","email":"coverton@usgs.gov","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824185,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Donnelly, J. Patrick","contributorId":266037,"corporation":false,"usgs":false,"family":"Donnelly","given":"J.","email":"","middleInitial":"Patrick","affiliations":[{"id":54869,"text":"Intermountain West Joint Venture – U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":824186,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olson, David","contributorId":265284,"corporation":false,"usgs":false,"family":"Olson","given":"David","affiliations":[{"id":37461,"text":"fws","active":true,"usgs":false}],"preferred":false,"id":824187,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McDuie, Fiona 0000-0002-1948-5613","orcid":"https://orcid.org/0000-0002-1948-5613","contributorId":222936,"corporation":false,"usgs":true,"family":"McDuie","given":"Fiona","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824188,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":202848,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824189,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Eadie, John M.","contributorId":34067,"corporation":false,"usgs":false,"family":"Eadie","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":6961,"text":"Department of Wildlife, Fish & Conservation Biology, University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":824190,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70225668,"text":"70225668 - 2021 - Episodic nutrient addition affects water column nutrient processing rates in river-to-lake transitional zones","interactions":[],"lastModifiedDate":"2021-11-02T12:00:49.683888","indexId":"70225668","displayToPublicDate":"2021-09-21T06:57:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Episodic nutrient addition affects water column nutrient processing rates in river-to-lake transitional zones","docAbstract":"

Storm-driven nutrient loading from tributaries can fuel eutrophication in nearshore and open water areas of lentic ecosystems. However, nutrient processing in river-to-lake transitional zones can substantially alter the amount and composition of nutrients transported to lakes from upstream surface waters. We measured the removal of nutrients and dissolved organic carbon (DOC) from the water column in the Fox rivermouth (Green Bay, Lake Michigan) to evaluate the response of rivermouth plankton to episodic nutrient enrichment. Light and dark water column incubations (8–12 hr) were conducted on four occasions from April through September to measure changes in dissolved nitrogen (N), phosphorus (P), and DOC concentrations in three locations along the Fox rivermouth. Two incubation experiments were conducted on consecutive days, (a) under ambient nutrient concentrations, and (b) under experimentally enriched N and P concentrations. Spatial and temporal variation was observed in nutrient uptake rates, but light incubations consistently had higher nutrient uptake rates than dark incubations. Nutrient enrichment increased total dissolved P and total dissolved N uptake and DOC release in light incubations, but only increased total dissolved P uptake in dark incubations. Moreover, nutrient uptake ratios (N:P) decreased from ambient to nutrient enriched conditions and indicated preferential P uptake by phytoplankton communities in light conditions. Our study substantiates that rivermouths can process nutrients bound for downstream ecosystems and demonstrates the potential of plankton communities to dynamically increase net uptake rates in response to episodic nutrient enrichment.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JG006374","usgsCitation":"Pearce, N.J., Larson, J.H., Evans, M.A., Frost, P., and Xenopoulos, M., 2021, Episodic nutrient addition affects water column nutrient processing rates in river-to-lake transitional zones: Journal of Geophysical Research: Biogeosciences, v. 126, no. 11, e2021JG006374, 16 p., https://doi.org/10.1029/2021JG006374.","productDescription":"e2021JG006374, 16 p.","ipdsId":"IP-125050","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":490083,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021jg006374","text":"Publisher Index Page"},{"id":436194,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PS7X5R","text":"USGS data release","linkHelpText":"Data Release Sediment and water column flux data from the Fox Rivermouth (Green Bay, WI; 2017)"},{"id":391264,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Fox River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.11309814453125,\n 44.42299211572251\n ],\n [\n -87.93869018554686,\n 44.42299211572251\n ],\n [\n -87.93869018554686,\n 44.55133484083592\n ],\n [\n -88.11309814453125,\n 44.55133484083592\n ],\n [\n -88.11309814453125,\n 44.42299211572251\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Pearce, Nolan J.T. 0000-0001-6600-5275","orcid":"https://orcid.org/0000-0001-6600-5275","contributorId":268195,"corporation":false,"usgs":false,"family":"Pearce","given":"Nolan","email":"","middleInitial":"J.T.","affiliations":[{"id":36679,"text":"Trent University","active":true,"usgs":false}],"preferred":false,"id":826134,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826135,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evans, Mary Anne 0000-0002-1627-7210 maevans@usgs.gov","orcid":"https://orcid.org/0000-0002-1627-7210","contributorId":149358,"corporation":false,"usgs":true,"family":"Evans","given":"Mary","email":"maevans@usgs.gov","middleInitial":"Anne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":826136,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frost, Paul C.","contributorId":138622,"corporation":false,"usgs":false,"family":"Frost","given":"Paul C.","affiliations":[{"id":12467,"text":"Department of Biology, Trent University, Peterborough, ON CA","active":true,"usgs":false}],"preferred":false,"id":826137,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xenopoulos, Marguerite A.","contributorId":138623,"corporation":false,"usgs":false,"family":"Xenopoulos","given":"Marguerite A.","affiliations":[{"id":12467,"text":"Department of Biology, Trent University, Peterborough, ON CA","active":true,"usgs":false}],"preferred":false,"id":826138,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224957,"text":"70224957 - 2021 - Integrating regional and local monitoring data and assessment tools to evaluate habitat conditions and inform river restoration","interactions":[],"lastModifiedDate":"2021-10-11T15:55:41.405172","indexId":"70224957","displayToPublicDate":"2021-09-20T10:49:47","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Integrating regional and local monitoring data and assessment tools to evaluate habitat conditions and inform river restoration","docAbstract":"

RRestoring degraded rivers requires initial assessment of the fluvial landscape to identify stressors and riverine features that can be enhanced. We associated local-scale river habitat data collected using standardized national monitoring tools with modeled regional water temperature and flow data on mid-sized northwest U.S. rivers (30–60 m wide). We grouped these rivers according to quartiles of their modeled mean August water temperature and examined their physical habitat structure and flow. We then used principal components analysis to summarize the variation in several dimensions of physical habitat. We also compared local conditions in the Priest River, a river targeted for restoration of native salmonid habitat in northern Idaho, with those in other rivers of the region to infer potential drivers controlling water temperature. The warmest rivers had physical structure and fluvial characteristics typical of thermally degraded rivers, whereas the coldest rivers had higher mean summer flows and greater channel planform complexity. The Priest River sites had approximately twice as many deep residual pools (>50, >75, and >100 cm) and incision that averaged approximately twice that in the coldest rivers. Percentage fines and natural cover in the Priest were also more typical of the higher-temperature river groups. We found generally low instream cover and low levels of large wood both across the region and within the Priest River. Our approach enabled us to consider the local habitat conditions of a river in the context of other similarly sized rivers in the surrounding region. Understanding this context is important for identifying potential influences on river water temperature within the focal basin and for defining attainable goals for management and restoration of thermal and habitat conditions.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.108213","usgsCitation":"Mejia, F.H., Connor, J.M., Kaufmann, P.R., Torgersen, C.E., Berntsen, E.K., and Andersen, T., 2021, Integrating regional and local monitoring data and assessment tools to evaluate habitat conditions and inform river restoration: Ecological Indicators, no. 131, 108213, 14 p., https://doi.org/10.1016/j.ecolind.2021.108213.","productDescription":"108213, 14 p.","ipdsId":"IP-119748","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":450752,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.108213","text":"Publisher Index Page"},{"id":390391,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Washington","otherGeospatial":"Priest River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.158203125,\n 48.191725575618726\n ],\n [\n -116.861572265625,\n 48.191725575618726\n ],\n [\n -116.861572265625,\n 48.49840764096433\n ],\n [\n -117.158203125,\n 48.49840764096433\n ],\n [\n -117.158203125,\n 48.191725575618726\n ]\n ]\n ]\n }\n }\n ]\n}","issue":"131","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mejia, Francine H. 0000-0003-4447-231X","orcid":"https://orcid.org/0000-0003-4447-231X","contributorId":214345,"corporation":false,"usgs":true,"family":"Mejia","given":"Francine","email":"","middleInitial":"H.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":824849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connor, Jason M","contributorId":267258,"corporation":false,"usgs":false,"family":"Connor","given":"Jason","email":"","middleInitial":"M","affiliations":[{"id":40867,"text":"Kalispel Tribe Natural Resources Department","active":true,"usgs":false}],"preferred":false,"id":824850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kaufmann, Phil R","contributorId":267259,"corporation":false,"usgs":false,"family":"Kaufmann","given":"Phil","email":"","middleInitial":"R","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":824851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":146935,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":824852,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Berntsen, Eric K","contributorId":214885,"corporation":false,"usgs":false,"family":"Berntsen","given":"Eric","email":"","middleInitial":"K","affiliations":[{"id":39131,"text":"Kalispel Tribe of Indians","active":true,"usgs":false}],"preferred":false,"id":824853,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Andersen, Todd","contributorId":243418,"corporation":false,"usgs":false,"family":"Andersen","given":"Todd","email":"","affiliations":[{"id":40867,"text":"Kalispel Tribe Natural Resources Department","active":true,"usgs":false}],"preferred":false,"id":824854,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70224266,"text":"sir20215062 - 2021 - Development of regression equations for the estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017","interactions":[],"lastModifiedDate":"2021-09-21T11:32:14.387182","indexId":"sir20215062","displayToPublicDate":"2021-09-20T09:49:44","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5062","displayTitle":"Development of Regression Equations for the Estimation of the Magnitude and Frequency of Floods at Rural, Unregulated Gaged and Ungaged Streams in Puerto Rico Through Water Year 2017","title":"Development of regression equations for the estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017","docAbstract":"

The methods of computation and estimates of the magnitude of flood flows were updated for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent chance exceedance levels for 91 streamgages on the main island of Puerto Rico by using annual peak-flow data through 2017. Since the previous flood frequency study in 1994, the U.S. Geological Survey has collected additional peak flows at additional streamgages, and Puerto Rico has experienced numerous flood events. This updated study was performed using longer annual peak-flow datasets from more stations to provide more representative equations to predict flood flows. Screening criteria for these streamgages included 10 or more years of annual peak-flow data, unregulated flow, and less than 10 percent impervious drainage area.

The magnitude and frequency of floods at selected streamgages in Puerto Rico were estimated using updated methods outlined in Bulletin 17C. The new procedures include a regional skew analysis that incorporates Bayesian regression techniques, the Expected Moments Algorithm to better represent missing record and estimate parameters of the log-Pearson Type III distribution, and the Multiple Grubbs-Beck test for low outlier detection.

Regional regression equations were developed to estimate peak-flow statistics at ungaged locations by using selected basin and climatic characteristics as explanatory variables. These variables were determined from digital spatial datasets and geographic information systems by using the most recent data available. Ordinary least-squares regression techniques were used to filter the basin characteristics and determine two separate regions, region 1 (west) and region 2 (east), based on residuals. A generalized least-squares procedure was used to account for cross-correlation of sites and develop the final set of equations that have drainage area as the only explanatory variable. The average standard errors of prediction ranged from 18.7 to 46.7 percent in region 1 and 33.4 to 57.6 percent in region 2 for all annual exceedance probabilities (AEPs) examined. The updated statistics showed a greater accuracy of prediction when compared to those from the previous study using drainage area as the only explanatory variable for all AEPs examined in region 1 and the 0.01 and 0.002 AEP flows for region 2. When compared to equations developed in the previous study that have drainage area, mean annual rainfall, and (or) depth-to-rock as explanatory variables, the updated statistics show a greater accuracy of prediction in region 1 at AEP flows of 0.02 and lower (that is, higher flows). Those developed for region 2 do not show a greater accuracy of prediction for any AEP flows when compared to the equations having multiple explanatory variables in the previous study.

The calculated regression equations, basin characteristics, and at-site statistics will be incorporated into the U.S. Geological Survey web application, StreamStats (https://streamstats.usgs.gov/ss/). This application allows users to select a location on a stream, whether gaged or ungaged, to obtain estimates of basin characteristics and flow statistics.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215062","usgsCitation":"Ryan, P.J., Gotvald, A.J., Hazelbaker, C.L., Veilleux, A.G., and Wagner, D.M., 2021, Development of regression equations for the estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017: U.S. Geological Survey Scientific Investigations Report 2021–5062, 37 p., https://doi.org/10.3133/sir20215062.","productDescription":"Report: v, 37 p.; Appendix Tables: 3; Data Release","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-123614","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":389343,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91XT14B","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data files for the development of regression equations for estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017"},{"id":389335,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5062/coverthb.jpg"},{"id":389336,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062.pdf","text":"Report","size":"4.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5062"},{"id":389337,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_2.1.csv","text":"Appendix Table 2.1 (.csv format)","size":"5.89 kB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"— Streamgages operated by the U.S. Geological Survey (USGS) in Puerto Rico that were used in the regional skew analysis"},{"id":389340,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_1.xlsx","text":"Appendix 1 (.xlsx format)","size":"30.9 kB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"— Streamgages considered for development of regional regression equations in Puerto Rico and details of at-site statistic inputs"},{"id":389338,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_2.1.xlsx","text":"Appendix Table 2.1 (.xlsx format)","size":"19.6 kB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"— Streamgages operated by the U.S. Geological Survey (USGS) in Puerto Rico that were used in the regional skew analysis"},{"id":389339,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_1.csv","text":"Appendix 1 (.csv format)","size":"20.7 kB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"— Streamgages considered for development of regional regression equations in Puerto Rico and details of at-site statistic inputs"},{"id":389341,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_3.csv","text":"Appendix 3 (.csv format)","size":"80.4 kB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"— At-site, regression equation, and weighted magnitude, variance, and prediction intervals of annual exceedance probability floods for select unregulated streamgages in Puerto Rico"},{"id":389342,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_3.xlsx","text":"Appendix 3 (.xlsx format)","size":"134 kB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"— At-site, regression equation, and weighted magnitude, variance, and prediction intervals of annual exceedance probability floods for select unregulated streamgages in Puerto Rico"}],"country":"United States","otherGeospatial":"Puerto 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Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
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Lutz, FL 33559

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2021-09-20","noUsgsAuthors":false,"publicationDate":"2021-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Ryan, Patrick J. 0000-0002-1490-4938 pryan@usgs.gov","orcid":"https://orcid.org/0000-0002-1490-4938","contributorId":203974,"corporation":false,"usgs":true,"family":"Ryan","given":"Patrick","email":"pryan@usgs.gov","middleInitial":"J.","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true},{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"preferred":true,"id":823409,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gotvald, Anthony J. 0000-0002-9019-750X agotvald@usgs.gov","orcid":"https://orcid.org/0000-0002-9019-750X","contributorId":1970,"corporation":false,"usgs":true,"family":"Gotvald","given":"Anthony","email":"agotvald@usgs.gov","middleInitial":"J.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hazelbaker, Cody L. 0000-0001-5170-9149","orcid":"https://orcid.org/0000-0001-5170-9149","contributorId":265802,"corporation":false,"usgs":true,"family":"Hazelbaker","given":"Cody","email":"","middleInitial":"L.","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823411,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":823412,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wagner, Daniel M. 0000-0002-0432-450X dwagner@usgs.gov","orcid":"https://orcid.org/0000-0002-0432-450X","contributorId":4531,"corporation":false,"usgs":true,"family":"Wagner","given":"Daniel","email":"dwagner@usgs.gov","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823413,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70230277,"text":"70230277 - 2021 - Stable isotopes used to infer trophic position of green turtles (Chelonia mydas) from Dry Tortugas National Park, Gulf of Mexico, United States","interactions":[],"lastModifiedDate":"2023-06-09T14:07:06.207792","indexId":"70230277","displayToPublicDate":"2021-09-20T09:00:51","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5094,"text":"Regional Studies in Marine Science","onlineIssn":"2352-4855","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Stable isotopes used to infer trophic position of green turtles (Chelonia mydas) from Dry Tortugas National Park, Gulf of Mexico, United States","title":"Stable isotopes used to infer trophic position of green turtles (Chelonia mydas) from Dry Tortugas National Park, Gulf of Mexico, United States","docAbstract":"

Evaluating resource use patterns for imperiled species is critical for understanding what supports their populations. Here we established stable isotope (δ13\">δ13C, δ15\">δ15N) values for the endangered green sea turtle (Chelonia mydas) population found within the boundaries of Dry Tortugas National Park (DRTO), south Florida, USA. There is little gene flow between turtles sampled at DRTO and in other rookeries in Florida, underscoring the need to study this distinct population. Between 2008 and 2015 we collected multiple sample types (skin [homogenized epidermis/dermis], whole blood, red blood cells, plasma, carapace) from 151 unique green turtles, including 43 nesting females and 108 in-water captures; some individuals were resampled multiple times across years to evaluate consistency of isotope signatures. Isotopic ratios ranged from -27.3 to -5.4 for δ13\">δ13C and 3.7 to 10.6 for δ15\">δ15N. Using linear mixed models, we evaluated covariates (sample type, turtle size and year) that best explained the isotope patterns observed in turtle tissues. Predictions from the top model for δ13\">δ13C indicated a slight decrease over time and for δ15\">δ15N a slight increase in the middle sampling years (2010–2012); results indicated that turtle size appeared to be the driver behind the range in δ13\">δ13C and δ15\">δ15N observed in turtle skin. We found a pattern in stable carbon isotope values that are indicative of an ontogenetic change from an omnivorous diet in smaller turtles to a seagrass-based diet in larger turtles. When we compared the stable carbon and nitrogen isotope values of the samples collected from turtles with that of seagrasses found in DRTO, we found that turtles > 65 cm SCL had similar stable carbon isotope values to the seagrass species present. Results of this study suggest stable isotope analysis coupled with data for available resources can be useful for tracking and detecting future changes in green turtle resource shifts in DRTO.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsma.2021.102011","usgsCitation":"Roche, D., Cherkiss, M., Smith, B., Burkholder, D.A., and Hart, K., 2021, Stable isotopes used to infer trophic position of green turtles (Chelonia mydas) from Dry Tortugas National Park, Gulf of Mexico, United States: Regional Studies in Marine Science, v. 48, 102011, 10 p.; Data Release, https://doi.org/10.1016/j.rsma.2021.102011.","productDescription":"102011, 10 p.; Data Release","ipdsId":"IP-113179","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450757,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rsma.2021.102011","text":"Publisher Index Page"},{"id":398210,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417871,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9060E4Q"}],"country":"United States","state":"Florida","otherGeospatial":"Dry Tortugas National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.79640197753906,\n 24.625172168430968\n ],\n [\n -82.76275634765625,\n 24.69194341912649\n ],\n [\n -82.80189514160156,\n 24.728122241065808\n ],\n [\n -82.87811279296875,\n 24.724380091871726\n ],\n [\n -82.96875,\n 24.648889412955334\n ],\n [\n -82.96943664550781,\n 24.56710835257599\n ],\n [\n -82.90008544921875,\n 24.566483864143358\n ],\n [\n -82.79640197753906,\n 24.625172168430968\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Roche, David 0000-0002-3329-2746 droche@usgs.gov","orcid":"https://orcid.org/0000-0002-3329-2746","contributorId":204332,"corporation":false,"usgs":true,"family":"Roche","given":"David","email":"droche@usgs.gov","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839792,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cherkiss, Michael 0000-0002-7802-6791","orcid":"https://orcid.org/0000-0002-7802-6791","contributorId":222180,"corporation":false,"usgs":true,"family":"Cherkiss","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839794,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Brian J. 0000-0002-0531-0492","orcid":"https://orcid.org/0000-0002-0531-0492","contributorId":139672,"corporation":false,"usgs":false,"family":"Smith","given":"Brian J.","affiliations":[{"id":12876,"text":"Cherokee Nation Technology Solutions","active":true,"usgs":false}],"preferred":false,"id":839793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burkholder, Derek A. 0000-0001-6315-6932","orcid":"https://orcid.org/0000-0001-6315-6932","contributorId":289783,"corporation":false,"usgs":false,"family":"Burkholder","given":"Derek","email":"","middleInitial":"A.","affiliations":[{"id":62249,"text":"Halmos College of Natural Sciences and Oceanography, Department of Marine and Environmental Science, Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":839795,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":220333,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839796,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224293,"text":"70224293 - 2021 - A comparison of methods for the long-term harness-based attachment of radio-transmitters to juvenile Japanese quail (Coturnix japonica)","interactions":[],"lastModifiedDate":"2021-09-20T12:52:02.734108","indexId":"70224293","displayToPublicDate":"2021-09-20T07:50:47","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":773,"text":"Animal Biotelemetry","active":true,"publicationSubtype":{"id":10}},"title":"A comparison of methods for the long-term harness-based attachment of radio-transmitters to juvenile Japanese quail (Coturnix japonica)","docAbstract":"

Background

While the period from fledging through first breeding for waterbird species such as terns (e.g., genus Sterna, Sternula) is of great interest to researchers and conservationists, this period remains understudied due in large part to the difficulty of marking growing juveniles with radio transmitters that remain attached for extended periods.

Methods

In an effort to facilitate such research, we examined the impact of various combinations of harness types (backpack, leg-loop, and 3D-printed harnesses), harness materials (Automotive ribbon, Elastic cord, and PFTE ribbon), and transmitter types (center-weighted and rear-weighted) on a surrogate for juvenile terns, 28-day-old Japanese quail (Coturnix japonica; selected due to similarities in adult mass and downy feathering of juveniles), in a 30-day experiment. We monitored for abrasion at points of contact and tag gap issues via daily exams while also recording mass and wing cord as indices of growth. This study was designed to serve as an initial examination of the impacts of marking on the growth and development of young birds and does not account for any impacts of tags on movement or behavior.

Results

While we found that treatment (the specific combination of the transmitter type, harness type, and harness material) had no impact on bird growth relative to unmarked control birds (P ≥ 0.05), we did observe differences in abrasion and tag gap between treatments (P ≤ 0.05). Our results suggest that leg-loop harnesses constructed from elastic cord and backpack harnesses from PFTE ribbon are suitable options for long-term attachment to growing juveniles. Conversely, we found that automotive ribbon led to extensive abrasion with these small-bodied birds, and that elastic cord induced blisters when used to make a backpack harness.

Conclusions

While these results indicate that long-term tagging of juvenile birds is possible with limited impacts on growth, this work does not preclude the need for small-scale studies with individual species. Instead, we hope this provides an informed starting point for further exploration of this topic.

","language":"English","publisher":"Springer Nature","doi":"10.1186/s40317-021-00257-9","usgsCitation":"Buck, E., Sullivan, J.D., Kent, C.M., Mullinax, J.M., and Prosser, D., 2021, A comparison of methods for the long-term harness-based attachment of radio-transmitters to juvenile Japanese quail (Coturnix japonica): Animal Biotelemetry, v. 9, 32, 16 p., https://doi.org/10.1186/s40317-021-00257-9.","productDescription":"32, 16 p.","ipdsId":"IP-126974","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":450759,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-021-00257-9","text":"Publisher Index Page"},{"id":436197,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LZD1V0","text":"USGS data release","linkHelpText":"Testing transmitter types, harness types, and harness materials for attachment of radio transmitters onto avian chicks"},{"id":389472,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2021-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Buck, Evan J","contributorId":265821,"corporation":false,"usgs":false,"family":"Buck","given":"Evan J","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":823482,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":823483,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kent, Cody M.","contributorId":265823,"corporation":false,"usgs":false,"family":"Kent","given":"Cody","email":"","middleInitial":"M.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":823484,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":823485,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":823486,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224270,"text":"sir20215075 - 2021 - Development of a screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States","interactions":[],"lastModifiedDate":"2021-09-20T14:34:44.807541","indexId":"sir20215075","displayToPublicDate":"2021-09-20T06:57:11","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5075","displayTitle":"Development of a Screening Tool To Examine Lake and Reservoir Susceptibility to Eutrophication in Selected Watersheds of the Eastern and Southeastern United States","title":"Development of a screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States","docAbstract":"

This report describes a new screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States using estimated nutrient loading and flushing rates with measures of waterbody morphometry. To that end, the report documents the compiled data and methods (R-script) used to categorize waterbodies by Carlson’s Trophic State Index. Assessments were completed for 232 lakes and reservoirs having a surface area greater than or equal to 0.1 square kilometer in watersheds that drain to the Atlantic and eastern Gulf of Mexico coasts of the United States and in watersheds within the Tennessee River Basin. Waterbodies were categorized by type—natural lakes, headwater reservoirs, and downstream reservoirs—and were assessed independently. Recursive partitioning and the model-based boosting routine were used to create four-node regression trees to group waterbodies into five endpoints from low-to-high measures of Secchi depth, and concentrations of chlorophyll a and microcystin according to shared nutrient loading, flushing rate, and morphometric characteristics. Trophic state designations were assigned based on the average value within each of the five endpoints. An application (procedure) is provided using the tool to examine the susceptibility of a given waterbody of interest to eutrophication. Results of this study can aid water-resource managers in prioritizing lake and reservoir protection and restoration efforts based on the susceptibility of these waterbodies to eutrophication relative to nutrient loading, flushing rate, and morphometric characteristics.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215075","usgsCitation":"Green, W.R., Hoos, A.B., Wilson, A.E., and Heal, E.N., 2021, Development of a screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States: U.S. Geological Survey Scientific Investigations Report 2021–5075, 59 p., https://doi.org/10.3133/sir20215075.","productDescription":"Report: vi, 59 p.; Data Release","numberOfPages":"70","onlineOnly":"Y","ipdsId":"IP-097274","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":389348,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5075/coverthb.jpg"},{"id":389349,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5075/sir20215075.pdf","text":"Report","size":"4.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5075"},{"id":389350,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K7EOH0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Nutrient loading, flushing rate, and lake morphometry data used to identify trophic states in selected watersheds of the eastern and southeastern United States"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.916015625,\n 40.51379915504413\n ],\n [\n -73.71826171874999,\n 40.896905775860006\n ],\n [\n -73.05908203125,\n 41.19518982948959\n ],\n [\n -71.60888671875,\n 41.343824581185686\n ],\n [\n -70.48828125,\n 41.21172151054787\n ],\n [\n -69.7412109375,\n 41.83682786072714\n ],\n [\n -70.48828125,\n 42.69858589169842\n ],\n [\n -70.09277343749999,\n 43.54854811091286\n ],\n [\n -67.1044921875,\n 44.574817404670306\n ],\n [\n -66.884765625,\n 44.824708282300236\n ],\n [\n -67.236328125,\n 45.182036837015886\n ],\n [\n -67.43408203124999,\n 45.259422036351694\n ],\n [\n -67.39013671875,\n 45.583289756006316\n ],\n [\n -67.8515625,\n 45.706179285330855\n ],\n [\n -67.87353515625,\n 47.15984001304432\n ],\n [\n -68.22509765625,\n 47.338822694822\n ],\n [\n -68.90625,\n 47.234489635299184\n ],\n [\n -69.08203125,\n 47.45780853075031\n ],\n [\n -69.2138671875,\n 47.487513008956554\n ],\n [\n -70.048828125,\n 46.694667307773116\n ],\n [\n -70.224609375,\n 46.17983040759436\n ],\n [\n -70.55419921875,\n 45.66012730272194\n ],\n [\n -70.90576171875,\n 45.27488643704891\n ],\n [\n -71.279296875,\n 45.321254361171476\n ],\n [\n -71.47705078125,\n 44.99588261816546\n ],\n [\n -74.86083984375,\n 45.01141864227728\n ],\n [\n -75.73974609375,\n 44.5435052132082\n ],\n [\n -74.8828125,\n 44.18220395771566\n ],\n [\n -74.33349609375,\n 43.91372326852401\n ],\n [\n -75.12451171875,\n 43.46886761482925\n ],\n [\n -75.9375,\n 43.29320031385282\n ],\n [\n -76.88232421875,\n 42.4234565179383\n ],\n [\n -77.431640625,\n 42.65012181368022\n ],\n [\n -77.9150390625,\n 42.22851735620852\n ],\n [\n -77.76123046875,\n 41.902277040963696\n ],\n [\n -78.57421875,\n 40.64730356252251\n ],\n [\n -79.4970703125,\n 39.791654835253425\n ],\n [\n -79.95849609375,\n 38.013476231041935\n ],\n [\n -80.52978515625,\n 37.24782120155428\n ],\n [\n -81.40869140625,\n 35.37113502280101\n ],\n [\n -83.43017578125,\n 35.35321610123823\n ],\n [\n -85.10009765625,\n 35.38904996691167\n ],\n [\n -86.90185546874999,\n 35.38904996691167\n ],\n [\n -87.802734375,\n 35.17380831799959\n ],\n [\n -89.07714843749999,\n 34.63320791137959\n ],\n [\n -89.05517578125,\n 33.37641235124676\n ],\n [\n -88.87939453125,\n 32.80574473290688\n ],\n [\n -89.8681640625,\n 31.85889704445453\n ],\n [\n -89.93408203124999,\n 31.316101383495624\n ],\n [\n -89.736328125,\n 30.694611546632277\n ],\n [\n -89.45068359374999,\n 30.14512718337613\n ],\n [\n -86.8359375,\n 30.088107753367257\n ],\n [\n -85.62744140625,\n 29.935895213372444\n ],\n [\n -84.9462890625,\n 29.401319510041485\n ],\n [\n -84.287109375,\n 29.878755346037977\n ],\n [\n -82.85888671875,\n 29.036960648558267\n ],\n [\n -83.0126953125,\n 27.97499795326776\n ],\n [\n -81.5625,\n 25.403584973186703\n ],\n [\n -80.88134765625,\n 24.607069137709683\n ],\n [\n -79.89257812499999,\n 25.005972656239187\n ],\n [\n -79.5849609375,\n 26.92206991673282\n ],\n [\n -81.2109375,\n 30.939924331023445\n ],\n [\n -77.431640625,\n 34.21634468843463\n ],\n [\n -75.65185546874999,\n 34.97600151317588\n ],\n [\n -75.60791015625,\n 37.142803443716836\n ],\n [\n -75.12451171875,\n 38.35888785866677\n ],\n [\n -73.80615234375,\n 39.99395569397331\n ],\n [\n -73.916015625,\n 40.51379915504413\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
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","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2021-09-20","noUsgsAuthors":false,"publicationDate":"2021-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Green, W. Reed 0000-0002-5778-0955","orcid":"https://orcid.org/0000-0002-5778-0955","contributorId":29856,"corporation":false,"usgs":true,"family":"Green","given":"W.","email":"","middleInitial":"Reed","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823417,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoos, Anne B. 0000-0001-9845-7831","orcid":"https://orcid.org/0000-0001-9845-7831","contributorId":217256,"corporation":false,"usgs":true,"family":"Hoos","given":"Anne B.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823418,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, Alan E.","contributorId":71492,"corporation":false,"usgs":false,"family":"Wilson","given":"Alan","email":"","middleInitial":"E.","affiliations":[],"preferred":true,"id":823419,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heal, Elizabeth N. 0000-0002-1196-4708","orcid":"https://orcid.org/0000-0002-1196-4708","contributorId":265803,"corporation":false,"usgs":true,"family":"Heal","given":"Elizabeth N.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823420,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243281,"text":"70243281 - 2021 - Integrating observations and models to determine the effect of seasonally frozen ground on hydrologic partitioning in alpine hillslopes in the Colorado Rocky Mountains, USA","interactions":[],"lastModifiedDate":"2023-05-05T11:52:18.44271","indexId":"70243281","displayToPublicDate":"2021-09-20T06:49:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Integrating observations and models to determine the effect of seasonally frozen ground on hydrologic partitioning in alpine hillslopes in the Colorado Rocky Mountains, USA","docAbstract":"

This study integrated spatially distributed field observations and soil thermal models to constrain the impact of frozen ground on snowmelt partitioning and streamflow generation in an alpine catchment within the Niwot Ridge Long-Term Ecological Research site, Colorado, USA. The study area was comprised of two contrasting hillslopes with notable differences in topography, snow depth and plant community composition. Time-lapse electrical resistivity surveys and soil thermal models enabled extension of discrete soil moisture and temperature measurements to incorporate landscape variability at scales and depths not possible with point measurements alone. Specifically, heterogenous snowpack thickness (~0–4 m) and soil volumetric water content between hillslopes (~0.1–0.45) strongly influenced the depths of seasonal frost, and the antecedent soil moisture available to form pore ice prior to freezing. Variable frost depths and antecedent soil moisture conditions were expected to create a patchwork of differing snowmelt infiltration rates and flowpaths. However, spikes in soil temperature and volumetric water content, as well as decreases in subsurface electrical resistivity revealed snowmelt infiltration across both hillslopes that coincided with initial decreases in snow water equivalent and early increases in streamflow. Soil temperature, soil moisture and electrical resistivity data from both wet and dry hillslopes showed that initial increases in streamflow occurred prior to deep soil water flux. Temporal lags between snowmelt infiltration and deeper percolation suggested that the lateral movement of water through the unsaturated zone was an important driver of early streamflow generation. These findings provide the type of process-based information needed to bridge gaps in scale and populate physically based cryohydrologic models to investigate subsurface hydrology and biogeochemical transport in soils that freeze seasonally.

","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14374","usgsCitation":"Rey, D., Hinckley, E.S., Walvoord, M.A., and Singha, K., 2021, Integrating observations and models to determine the effect of seasonally frozen ground on hydrologic partitioning in alpine hillslopes in the Colorado Rocky Mountains, USA: Hydrological Processes, v. 35, no. 10, e14374, 17 p., https://doi.org/10.1002/hyp.14374.","productDescription":"e14374, 17 p.","ipdsId":"IP-132727","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":450761,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.14374","text":"Publisher Index Page"},{"id":416751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.38238018570276,\n 40.607505818105096\n ],\n [\n -107.38238018570276,\n 39.0388729281874\n ],\n [\n -104.81268478470398,\n 39.0388729281874\n ],\n [\n -104.81268478470398,\n 40.607505818105096\n ],\n [\n -107.38238018570276,\n 40.607505818105096\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"35","issue":"10","noUsgsAuthors":false,"publicationDate":"2021-10-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":871791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hinckley, Eve-Lyn S. 0000-0002-7081-0530","orcid":"https://orcid.org/0000-0002-7081-0530","contributorId":304865,"corporation":false,"usgs":false,"family":"Hinckley","given":"Eve-Lyn","email":"","middleInitial":"S.","affiliations":[{"id":66177,"text":"Institute of Arctic and Alpine Research","active":true,"usgs":false}],"preferred":false,"id":871792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":871793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Singha, Kamini 0000-0002-0605-3774","orcid":"https://orcid.org/0000-0002-0605-3774","contributorId":191366,"corporation":false,"usgs":false,"family":"Singha","given":"Kamini","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":871794,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227819,"text":"70227819 - 2021 - Tradeoffs in habitat value to maximize natural resource benefits from coastal restoration in a rapidly eroding wetland: Is monitoring land area sufficient?","interactions":[],"lastModifiedDate":"2022-04-26T12:02:37.69253","indexId":"70227819","displayToPublicDate":"2021-09-18T13:35:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Tradeoffs in habitat value to maximize natural resource benefits from coastal restoration in a rapidly eroding wetland: Is monitoring land area sufficient?","docAbstract":"

Louisiana contains nearly 40% of estuarine herbaceous wetlands in the contiguous United States, supporting valuable ecosystem services and providing significant economic benefits to the state and the entire United States. However, coastal Louisiana is a hotspot for rapid land loss from factors including hurricanes, land use change, and high subsidence rates contributing to high relative sea-level rise. The Coastal Protection and Restoration Authority (CPRA) was established after major hurricanes in 2005 to coordinate coastal restoration in Louisiana and develop the Louisiana Coastal Master Plan. The LA Coastal Master Plan uses numerical modeling of multiple scenarios to select a suite of restoration projects based on maximum land area created and flood reduction (as proxies for ecosystem value). Using potential value to aquatic, terrestrial, and social resources, our work compared habitat value of shallow open water areas to emergent wetland. While potential resource benefits varied by emergent wetland salinity type and emergent wetland versus water, they were similar, suggesting that restoration planning based primarily on wetland land area may not achieve the maximum possible ecosystem benefits. After nearly 20 years of integrated restoration planning in coastal Louisiana, a reassessment of restoration planning decision drivers may be beneficial to ensure maximum benefits from coastal restoration. As a result of the Deepwater Horizon oil spill, settlement funds will be a major support to coastal restoration in Louisiana for many years. Assessing potential habitat value to multiple natural and social resources in Louisiana has potential to maximize synergy with large northern Gulf of Mexico restoration programs.

","language":"English","publisher":"Society for Ecological Restoration","doi":"10.1111/rec.13564","usgsCitation":"Carruthers, T., Kiskaddon, E.P., Baustian, M., Darnell, K.M., Moss, L.C., Perry, C.L., and Stagg, C., 2021, Tradeoffs in habitat value to maximize natural resource benefits from coastal restoration in a rapidly eroding wetland: Is monitoring land area sufficient?: Restoration Ecology, v. 30, e13564, 8 p., https://doi.org/10.1111/rec.13564.","productDescription":"e13564, 8 p.","ipdsId":"IP-074120","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450763,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/rec.13564","text":"Publisher Index Page"},{"id":395227,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.922119140625,\n 28.94086176940557\n ],\n [\n -88.9947509765625,\n 28.94086176940557\n ],\n [\n -88.9947509765625,\n 30.439202087235582\n ],\n [\n -93.922119140625,\n 30.439202087235582\n ],\n [\n -93.922119140625,\n 28.94086176940557\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","noUsgsAuthors":false,"publicationDate":"2021-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Carruthers, Tim J. B.","contributorId":140566,"corporation":false,"usgs":false,"family":"Carruthers","given":"Tim J. B.","affiliations":[],"preferred":false,"id":832364,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiskaddon, Erin P.","contributorId":272886,"corporation":false,"usgs":false,"family":"Kiskaddon","given":"Erin","email":"","middleInitial":"P.","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":832365,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baustian, Melissa M.","contributorId":189569,"corporation":false,"usgs":false,"family":"Baustian","given":"Melissa M.","affiliations":[],"preferred":false,"id":832366,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Darnell, Kelly M.","contributorId":272888,"corporation":false,"usgs":false,"family":"Darnell","given":"Kelly","email":"","middleInitial":"M.","affiliations":[{"id":48626,"text":"The Water Institute of the Gulf, Baton Rouge, LA","active":true,"usgs":false}],"preferred":false,"id":832367,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moss, Leland C.","contributorId":272644,"corporation":false,"usgs":false,"family":"Moss","given":"Leland","email":"","middleInitial":"C.","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":832368,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Perry, Carey L.","contributorId":189570,"corporation":false,"usgs":false,"family":"Perry","given":"Carey","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":832369,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stagg, Camille 0000-0002-1125-7253","orcid":"https://orcid.org/0000-0002-1125-7253","contributorId":220330,"corporation":false,"usgs":true,"family":"Stagg","given":"Camille","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":832370,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70225531,"text":"70225531 - 2021 - Stratigraphic and structural controls on groundwater salinity variations in the Poso Creek Oil Field, Kern County, California, USA","interactions":[],"lastModifiedDate":"2021-12-10T17:01:34.289166","indexId":"70225531","displayToPublicDate":"2021-09-18T08:12:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Stratigraphic and structural controls on groundwater salinity variations in the Poso Creek Oil Field, Kern County, California, USA","docAbstract":"

Groundwater total dissolved solids (TDS) distribution was mapped with a three-dimensional (3D) model, and it was found that TDS variability is largely controlled by stratigraphy and geologic structure. General TDS patterns in the San Joaquin Valley of California (USA) are attributed to predominantly connate water composition and large-scale recharge from the adjacent Sierra Nevada. However, in smaller areas, stratigraphy and faulting play an important role in controlling TDS. Here, the relationship of stratigraphy and structure to TDS concentration was examined at Poso Creek Oil Field, Kern County, California. The TDS model was constructed using produced water TDS samples and borehole geophysics. The model was used to predict TDS concentration at discrete locations in 3D space and used a Gaussian process to interpolate TDS over a volume. In the overlying aquifer, TDS is typically <1,000 mg/L and increases with depth to ~1,200–3,500 mg/L in the hydrocarbon zone below the Macoma claystone—a regionally extensive, fine-grained unit—and reaches ~7,000 mg/L in isolated places. The Macoma claystone creates a vertical TDS gradient in the west where it is thickest, but control decreases to the east where it pinches out and allows freshwater recharge. Previously mapped normal faults were found to exhibit inconsistent control on TDS. In one case, high-density faulting appears to prevent recharge from flushing higher-TDS connate water. Elsewhere, the high-throw segments of a normal fault exhibit variable behavior, in places blocking lower-TDS recharge and in other cases allowing flushing. Importantly, faults apparently have differential control on oil and groundwater.

","language":"English","publisher":"Springer","doi":"10.1007/s10040-021-02381-5","usgsCitation":"Stephens, M.J., Shimabukuro, D.H., Chang, W., Gillespie, J.M., and Levinson, Z., 2021, Stratigraphic and structural controls on groundwater salinity variations in the Poso Creek Oil Field, Kern County, California, USA: Hydrogeology Journal, v. 29, p. 2803-2820, https://doi.org/10.1007/s10040-021-02381-5.","productDescription":"18 p.","startPage":"2803","endPage":"2820","ipdsId":"IP-113290","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":450766,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-021-02381-5","text":"Publisher Index Page"},{"id":390661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Kern County","otherGeospatial":"Poso Creek Oil Field","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-120.1945,35.788],[-120.1842,35.789],[-120.1655,35.7891],[-120.1474,35.7887],[-120.0816,35.7886],[-119.9688,35.7896],[-119.852,35.7891],[-119.7618,35.7906],[-119.6472,35.7895],[-119.5395,35.79],[-119.4301,35.7905],[-119.3308,35.7899],[-119.2169,35.7906],[-119.1182,35.7903],[-118.9027,35.789],[-118.6504,35.7897],[-118.6441,35.7896],[-118.5885,35.7897],[-118.5233,35.7892],[-118.4785,35.7915],[-118.4706,35.7919],[-118.4502,35.7908],[-118.2716,35.7896],[-118.2562,35.7894],[-118.2387,35.7897],[-118.2137,35.7894],[-118.1956,35.7896],[-118.1632,35.7893],[-118.0839,35.7865],[-118.0697,35.7859],[-118.009,35.7861],[-117.9234,35.7863],[-117.9249,35.7986],[-117.9005,35.7983],[-117.8738,35.7988],[-117.8523,35.7985],[-117.6362,35.7958],[-117.6355,35.7086],[-117.6537,35.7085],[-117.6527,35.6776],[-117.6176,35.6775],[-117.6166,35.6493],[-117.6353,35.6487],[-117.6354,35.6233],[-117.6352,35.5807],[-117.6356,35.5666],[-117.6351,35.5639],[-117.6346,35.4472],[-117.6352,35.3755],[-117.6353,35.3464],[-117.6351,35.3319],[-117.6343,35.3174],[-117.6341,35.3028],[-117.6345,35.2874],[-117.6343,35.2742],[-117.6341,35.2588],[-117.6339,35.2447],[-117.6342,35.2302],[-117.634,35.2157],[-117.6338,35.2011],[-117.6336,35.1861],[-117.6334,35.1707],[-117.6338,35.1562],[-117.6336,35.1417],[-117.6333,35.1271],[-117.6331,35.1126],[-117.6329,35.098],[-117.6352,35.0981],[-117.636,35.0872],[-117.6358,35.0727],[-117.6356,35.0581],[-117.6357,35.0295],[-117.6361,35.015],[-117.6357,34.985],[-117.6351,34.8233],[-117.6519,34.8227],[-117.6704,34.8221],[-117.7757,34.8229],[-118.1408,34.8195],[-118.1493,34.8195],[-118.5995,34.8175],[-118.8946,34.8181],[-118.8945,34.818],[-118.8825,34.791],[-118.9772,34.7902],[-118.9771,34.8126],[-119.2462,34.8147],[-119.2461,34.857],[-119.2797,34.858],[-119.2779,34.8793],[-119.3844,34.8794],[-119.385,34.884],[-119.3849,34.899],[-119.4382,34.8999],[-119.4438,34.8999],[-119.4544,34.8999],[-119.4571,34.9],[-119.4746,34.9004],[-119.4746,34.9005],[-119.4746,34.9136],[-119.474,34.9367],[-119.474,34.9499],[-119.474,34.9576],[-119.474,34.9721],[-119.4746,35.0184],[-119.4746,35.0325],[-119.4745,35.077],[-119.4908,35.077],[-119.4914,35.092],[-119.5004,35.0915],[-119.5088,35.0906],[-119.5628,35.0883],[-119.5583,35.1369],[-119.5566,35.1601],[-119.5549,35.1791],[-119.5769,35.1787],[-119.6095,35.1773],[-119.6675,35.1749],[-119.6675,35.1908],[-119.6675,35.2049],[-119.6688,35.2617],[-119.7397,35.2629],[-119.7572,35.2633],[-119.7746,35.2633],[-119.8113,35.2641],[-119.8122,35.3508],[-119.8815,35.3501],[-119.8824,35.41],[-119.8824,35.4246],[-119.8831,35.4377],[-119.9999,35.4396],[-120.0007,35.4695],[-120.0171,35.469],[-120.0194,35.4835],[-120.0358,35.4834],[-120.0359,35.497],[-120.0523,35.4974],[-120.053,35.5124],[-120.0699,35.5128],[-120.0711,35.5268],[-120.0875,35.5276],[-120.0876,35.6139],[-120.1951,35.6151],[-120.1947,35.7481],[-120.1942,35.7626],[-120.1945,35.788]]]},\"properties\":{\"name\":\"Kern\",\"state\":\"CA\"}}]}","volume":"29","noUsgsAuthors":false,"publicationDate":"2021-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Stephens, Michael J. 0000-0001-8995-9928","orcid":"https://orcid.org/0000-0001-8995-9928","contributorId":205895,"corporation":false,"usgs":true,"family":"Stephens","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shimabukuro, David H. 0000-0002-6106-5284","orcid":"https://orcid.org/0000-0002-6106-5284","contributorId":208209,"corporation":false,"usgs":false,"family":"Shimabukuro","given":"David","email":"","middleInitial":"H.","affiliations":[{"id":37762,"text":"California State University, Sacramento","active":true,"usgs":false}],"preferred":false,"id":825461,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chang, Will","contributorId":267870,"corporation":false,"usgs":false,"family":"Chang","given":"Will","affiliations":[],"preferred":false,"id":825462,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gillespie, Janice M. 0000-0003-1667-3472","orcid":"https://orcid.org/0000-0003-1667-3472","contributorId":219675,"corporation":false,"usgs":true,"family":"Gillespie","given":"Janice","email":"","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825463,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Levinson, Zack","contributorId":267875,"corporation":false,"usgs":false,"family":"Levinson","given":"Zack","email":"","affiliations":[],"preferred":false,"id":825464,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241450,"text":"70241450 - 2021 - Distribution, abundance and spatial variability of microplastic pollution on the surface of Lake Superior","interactions":[],"lastModifiedDate":"2023-03-21T11:44:20.818634","indexId":"70241450","displayToPublicDate":"2021-09-18T06:42:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Distribution, abundance and spatial variability of microplastic pollution on the surface of Lake Superior","docAbstract":"

In 2014, 94 paired neuston net samples (0.5 mm mesh) were collected from the surface waters of Lake Superior. These samples comprise the most comprehensive surface water survey for microplastics of any of the Great Lakes to date, and the first to employ double net trawls. Microplastic abundance estimates showed wide variability, ranging between 4000 to more than 100,000 particles/km2 with most locations having abundances between 20,000 to 50,000 particles/km2. The average abundance in Lake Superior was ~30,000 particles/km2 which was similar to previous estimates within this Laurentian Great Lake and suggests a total count of more than 2.4 billion (1.7 to 3.3 billion, 95% confidence interval) particles across the lake’s surface. Distributions of plastic particles, characterized by size fraction and type, differed between nearshore and offshore samples, and between samples collected in the eastern versus western portion of the lake. Most of the particles found were fibers (67%), and most (62%) were contained in the smallest classified size fraction (0.50–1 mm). The most common type of polymer found was polyethylene (51%), followed by polypropylene (19%). This is consistent with global plastics production and results obtained from other studies. No statistically significant difference was detected between the paired net samples, indicating that single net sampling should produce a representative estimate of microplastic particle abundance and distribution within a body of water.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.08.005","usgsCitation":"Cox, K., Brocious, E., Courtenay, S., Vinson, M., and Mason, S.J., 2021, Distribution, abundance and spatial variability of microplastic pollution on the surface of Lake Superior: Journal of Great Lakes Research, v. 47, no. 5, p. 1358-1364, https://doi.org/10.1016/j.jglr.2021.08.005.","productDescription":"6 p.","startPage":"1358","endPage":"1364","ipdsId":"IP-130920","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":450773,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://scholarsphere.psu.edu/resources/d0390b54-5948-4007-83f2-4498b00919e9","text":"External Repository"},{"id":414422,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.36493471418689,\n 32.14383279973586\n ],\n [\n -111.36493471418689,\n 30.417046183219966\n ],\n [\n -104.51237611072469,\n 30.417046183219966\n ],\n [\n -104.51237611072469,\n 32.14383279973586\n ],\n [\n -111.36493471418689,\n 32.14383279973586\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.87929721575556,\n 46.241105586985555\n ],\n [\n -83.610772437996,\n 46.241105586985555\n ],\n [\n -83.610772437996,\n 49.22397055892117\n ],\n [\n -92.87929721575556,\n 49.22397055892117\n ],\n [\n -92.87929721575556,\n 46.241105586985555\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cox, K","contributorId":303233,"corporation":false,"usgs":false,"family":"Cox","given":"K","email":"","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":866875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brocious, E.","contributorId":303234,"corporation":false,"usgs":false,"family":"Brocious","given":"E.","email":"","affiliations":[{"id":65723,"text":"Penn State Erie","active":true,"usgs":false}],"preferred":false,"id":866876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Courtenay, S","contributorId":303235,"corporation":false,"usgs":false,"family":"Courtenay","given":"S","email":"","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":866877,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vinson, Mark R. 0000-0001-5256-9539 mvinson@usgs.gov","orcid":"https://orcid.org/0000-0001-5256-9539","contributorId":3800,"corporation":false,"usgs":true,"family":"Vinson","given":"Mark","email":"mvinson@usgs.gov","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":866878,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mason, Seth J. K.","contributorId":191535,"corporation":false,"usgs":false,"family":"Mason","given":"Seth","email":"","middleInitial":"J. K.","affiliations":[],"preferred":false,"id":866879,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223904,"text":"sir20215036 - 2021 - Estimates of public-supply, domestic, and irrigation water withdrawal, use, and trends in the Upper Rio Grande Basin, 1985 to 2015","interactions":[],"lastModifiedDate":"2021-09-20T11:38:52.269074","indexId":"sir20215036","displayToPublicDate":"2021-09-17T12:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5036","displayTitle":"Estimates of Public-Supply, Domestic, and Irrigation Water Withdrawal, Use, and Trends in the Upper Rio Grande Basin, 1985 to 2015","title":"Estimates of public-supply, domestic, and irrigation water withdrawal, use, and trends in the Upper Rio Grande Basin, 1985 to 2015","docAbstract":"

The Rio Grande flows approximately 670 miles from its headwaters in the San Juan Mountains of south-central Colorado to Fort Quitman, Texas, draining the Upper Rio Grande Basin (URGB) study area of 32,000 square miles that includes parts of Colorado, New Mexico, and Texas. Parts of the basin extend into the United Mexican States (hereafter “Mexico”), where the Rio Grande forms the international boundary between Texas and the State of Chihuahua, Mexico. The URGB was chosen as a focus area study (FAS) for the U.S. Geological Survey (USGS) National Water Census (NWC) as part of the WaterSMART initiative. The objective of the USGS NWC under WaterSMART is to focus on the technical aspects of providing information and tools to stakeholders so that they can make informed decisions on water availability.

This report contains water-use withdrawal estimates of groundwater and surface water for public-supply, self-supplied domestic, and irrigation water use for years 1985–2015 at 5-year intervals for the 22 drainage basins at the subbasin 8-digit hydrologic unit code (HUC-8) level. Data for additional categories of self-supplied industrial, mining, livestock, aquaculture, thermoelectric, and hydroelectric water use are provided in the accompanying data release to illustrate total withdrawals for the URGB. The additional category data are provided in this report only for the year 2015. Deliveries of water from public-supply systems to domestic users are included and are the only water-delivery data presented in this report. Consumptive use for irrigation is reported for all HUC-8 subbasins for 2015 and for select HUC-8s in the other years beginning in 1985 (the irrigation category includes irrigation for both crop and golf). Water transported outside of the URGB (interbasin transfers) is not included as part of the withdrawals and are not accounted for in any category of use within the URGB.

Estimated total withdrawals for all the water-use categories (including hydroelectric) in 2015 as reported in the USGS compilations in the URGB were 3,152.10 million gallons per day (Mgal/d). Surface water was the dominant source of water used in the URGB, providing about 71 percent of total withdrawals. Nearly all withdrawals were from freshwater sources; there was a small amount of saline groundwater that was used for public supply and self-supplied industrial, which were all reported in Texas. The proportions of total 2015 withdrawals from States in the URGB are 46 percent each in Colorado and New Mexico and 8 percent in Texas. A comparison of 2015 water withdrawals for the URGB—for the categories of public supply, self-supplied domestic, self-supplied industrial, thermoelectric, irrigation, livestock, mining, aquaculture, and hydroelectric—showed that irrigation is the dominant water use, at 74 percent of total withdrawals. Other water-use categories in the URGB that use about 1 percent or greater of the total water use by volume are public supply (9 percent) and self-supplied domestic and aquaculture (each about 1 percent). This report focuses on the higher volume, consumptively used categories of public supply, self-supplied domestic, and irrigation. A discussion on basin population provides context for the categories of public-supply and self-supplied domestic water use.

The population in the part of the basin in the United States grew from 1.36 to 2.26 million people between 1985 and 2015. With the city of Ciudad Juarez, Chihuahua, Mexico, included, the total population of the URGB grew from an estimated 2.01 to 3.66 million people between 1985 and 2015. The largest concentrations of population are in New Mexico, Texas, and Chihuahua, with 98 percent of the total number of people in the basin in 1985 and 99 percent of the total in 2015 residing in these states. Albuquerque, El Paso, and Ciudad Juarez are the largest cities in the basin.

Total withdrawals for public supply in the URGB averaged 277 Mgal/d from 1985 to 2015. About 60 percent of the URGB total public-supply withdrawals occurred in New Mexico, which averaged 170 Mgal/d. Groundwater provided 92 and 70 percent of the total withdrawals for public supply in 1985 and 2015, respectively. Deliveries to domestic users from public suppliers are reported for all drainage basins and years, and account for part of the total public-supply withdrawals. In the URGB, domestic deliveries from public suppliers increased from 1985 to 1995; since 2005, domestic deliveries from public supply have declined. The total populations served by public suppliers in the URGB increased by 90 percent from 1985 to 2015. In the URGB, more people were served by public-supply systems than were self-supplied, and the percentage of people on public-supply systems ranged from 81 to 92 percent from 1985 to 2015. Total domestic withdrawals in the URGB (deliveries plus self-supply withdrawals) ranged from 177.49 to 234.83 Mgal/d and peaked in 2005. Domestic use decreased from 2005 to 2010 by 17 percent and remained less than 200 Mgal/d in 2015. The per-capita daily use for the entire URGB fluctuated between the reporting years, but overall, domestic per-capita use across the basin has declined 46 percent from 145 gallons per capita daily (gpcd) in 1985 to 79 gpcd in 2015.

Total irrigation withdrawals in the URGB had a mean value of 2,767.66 Mgal/d from 1985 to 2015 and withdrawals peaked in 1995 at 3,416.84 Mgal/d. Over the 30-year period, irrigation source water in the URGB has ranged from 69 to 84 percent surface water, and the most surface water diverted in the basin for irrigation was in 1995. Groundwater withdrawals for irrigation have fluctuated but overall decreased by 13 percent between 2005 and 2015. Slightly more than one-half of total irrigation withdrawals within the URGB occurred in Colorado, with a mean of 57 percent from 1985 to 2015. From the peak of water withdrawals in 1995 to the conclusion of this study in 2015, total irrigation withdrawals across the study area decreased by 32 percent.

The total number of irrigated lands in the URGB from 1985 to 2015 had a mean of about 800 thousand acres, and more irrigated lands were consistently located in the headwaters of the URGB in the San Luis Valley, Colorado than the remainder of the study basin. In the 30-year period, Colorado had a mean of 68 percent of total irrigated lands whereas irrigated acres in New Mexico had a mean of 26 percent and the remaining 7 percent were in Texas. Since 2000, the number of irrigated acres in the URGB has fluctuated, but overall has decreased by 12 percent.

More land was irrigated with surface systems (surface irrigation includes flood, furrow, and gated pipe systems, hereafter collectively termed “surface”) in the URGB than with other irrigation system types. Across the 30-year period, from 62 to 88 percent of total irrigated lands had surface-system irrigation, and surface systems covered a mean of 69 percent of the URGB’s acres. Microirrigation systems, predominantly in New Mexico and Texas, compose 0.2 percent or less of the irrigated acres in the basin and were first reported in 1995. From 1985 to 2015, the surface systems decreased in the basin by about 38 percent, and the number of acres of sprinkler and microirrigation systems increased. Acres irrigated by sprinkler systems (predominately center pivot systems) have increased 179 percent from about 99 thousand acres in 1985 to 275 thousand acres in 2015. In this dataset, the number of sprinkler acres surpassed the number of surface irrigated acres in 2000. Within the San Luis Valley in Colorado, the acreage of surface irrigation has decreased, and sprinkler irrigation has increased over the 30-year period. In the New Mexico part of the URGB, surface irrigation is reported as the dominant system type, where irrigation by surface systems accounts for 97–98 percent of how water is provided to crops. As in New Mexico, crops in Texas are irrigated primarily by surface systems.

The mean of the mean simulated actual evapotranspiration (ETa) for crops in 2015 across the basin was highest for durum wheat at an estimated 36.00 inches per year (in/yr), and lowest for vegetables at an estimated 19.48 in/yr. Alfalfa and irrigated grass pastures mean ETa had a mean of 31.4 and 27.58 in/yr, respectively, for the basin. Pecans and peppers, both signature crops in the Rio Grande Basin, each had a mean ETa of 30.67 and 30.38 in/yr of mean. In general, mean ETa values for crops were lower in the HUCs of the Colorado San Luis Valley (13010001, 13010002, 13010003 and 13010004) which are more northerly and at higher elevations. The mean ETa for crops increased in the HUCs that are more southerly and at lower elevations in the basin.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215036","usgsCitation":"Ivahnenko, T.I., Flickinger, A.K., Galanter, A.E., Douglas-Mankin, K.R., Pedraza, D.E., and Senay, G.B., 2021, Estimates of public-supply, domestic, and irrigation water withdrawal, use, and trends in the Upper Rio Grande Basin, 1985 to 2015: U.S. Geological Survey Scientific Investigations Report 2021–5036, 31 p., https://doi.org/10.3133/sir20215036.","productDescription":"Report: viii, 35 p.:; Data Releases","onlineOnly":"Y","ipdsId":"IP-096649","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":389160,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SQ1Y3T","text":"USGS data release","linkHelpText":"Estimated use of water by subbasin (HUC8) in the Red River Basin, 2010 and 2015"},{"id":389156,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5036/coverthb.jpg"},{"id":389157,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5036/sir20215036.pdf","text":"Report","size":"5.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5036"},{"id":389158,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7SX6CJ2","text":"USGS data release","linkHelpText":"Estimated use of water by subbasin (HUC8) in the Upper Rio Grande Basin, 1985–2015"},{"id":389159,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99OIFYY","text":"USGS data release","linkHelpText":"2015 irrigated acres feature class for the Upper Rio Grande Basin, New Mexico, Texas, United States and Chihuahua, Mexico"}],"country":"United States","state":"New Mexico","otherGeospatial":"Upper Rio Grande Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ],\n [\n -106.8310546875,\n 36.932330061503144\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.973876953125,\n 36.96744946416934\n ],\n [\n -107.09472656249999,\n 36.53612263184686\n ],\n [\n -107.149658203125,\n 35.951329861522666\n ],\n [\n -107.75390625,\n 35.30840140169162\n ],\n [\n -108.25927734375,\n 34.95799531086792\n ],\n [\n -108.204345703125,\n 34.298068350990825\n ],\n [\n -107.852783203125,\n 33.55055114384406\n ],\n [\n -107.70996093749999,\n 33.091541548655215\n ],\n [\n -107.40234375,\n 32.20350534542368\n ],\n [\n -107.314453125,\n 31.85889704445453\n ],\n [\n -107.237548828125,\n 31.70947636001935\n ],\n [\n -106.4794921875,\n 31.793555207271424\n ],\n [\n -106.44653320312499,\n 31.98944183792288\n ],\n [\n -106.336669921875,\n 32.34284135639302\n ],\n [\n -106.226806640625,\n 32.676372772089834\n ],\n [\n -105.99609375,\n 33.0178760185549\n ],\n [\n -105.677490234375,\n 33.65120829920497\n ],\n [\n -105.413818359375,\n 34.21634468843463\n ],\n [\n -105.260009765625,\n 34.939985151560435\n ],\n [\n -105.22705078125,\n 35.27253175660236\n ],\n [\n -105.23803710937499,\n 35.7019167328534\n ],\n [\n -105.16113281249999,\n 36.146746777814364\n ],\n [\n -104.952392578125,\n 36.41244153535644\n ],\n [\n -104.886474609375,\n 36.99377838872517\n ],\n [\n -106.973876953125,\n 36.96744946416934\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046

","tableOfContents":"","publishedDate":"2021-09-17","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Ivahnenko, Tamara I. 0000-0002-1124-7688 ivahnenk@usgs.gov","orcid":"https://orcid.org/0000-0002-1124-7688","contributorId":2050,"corporation":false,"usgs":true,"family":"Ivahnenko","given":"Tamara","email":"ivahnenk@usgs.gov","middleInitial":"I.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true}],"preferred":false,"id":823213,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flickinger, Allison K. 0000-0002-8638-2569","orcid":"https://orcid.org/0000-0002-8638-2569","contributorId":223702,"corporation":false,"usgs":true,"family":"Flickinger","given":"Allison","email":"","middleInitial":"K.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823214,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Galanter, Amy E. 0000-0002-2960-0136","orcid":"https://orcid.org/0000-0002-2960-0136","contributorId":214612,"corporation":false,"usgs":true,"family":"Galanter","given":"Amy E.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823215,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":200849,"corporation":false,"usgs":false,"family":"Douglas-Mankin","given":"Kyle R.","affiliations":[],"preferred":false,"id":823216,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pedraza, Diana E. 0000-0003-4483-8094","orcid":"https://orcid.org/0000-0003-4483-8094","contributorId":217877,"corporation":false,"usgs":true,"family":"Pedraza","given":"Diana E.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823217,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":823218,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70262594,"text":"70262594 - 2021 - The finicky nature of earthquake shaking-triggered submarine sediment slope failures and sediment gravity flows","interactions":[],"lastModifiedDate":"2025-01-21T16:56:03.166383","indexId":"70262594","displayToPublicDate":"2021-09-17T10:52:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"The finicky nature of earthquake shaking-triggered submarine sediment slope failures and sediment gravity flows","docAbstract":"

Since 2011, seafloor temperatures, pressures, and seismic ground motions have been measured by the seafloor cabled Dense Oceanfloor Network system for Earthquakes and Tsunamis (DONET) on the Nankai margin. These measurements, high-resolution bathymetry, and abundant contextual information make the DONET region seem ideally suited to provide constraints on seismic shaking-triggered sediment slope failures and gravity flows, particularly since numerous published studies have linked paleo- to modern earthquakes to failures and flows within the DONET. The occurrences of the local 2016 M6.0 Mie-ken and regional M7.0 Kumamoto earthquakes within and at regional distances, respectively, from the DONET data set provided an opportunity to explore this potential. We used DONET seismic recordings of the posited triggering shaking and to search for submarine slide signals and continuous temperature and pressure data to detect pulses of warm and densified water indicative of passing flows. We developed and applied a variety of analytical methods to eliminate signals generated by water column processes, while leaving slope failures and sediment gravity flow anomalies as residuals. Our explorations yielded no evidence that earthquake shaking initiated either phenomenon, which we suggest reflects the finicky nature both of the detection of and the physical processes that contribute to slope failures and flows (i.e., both require satisfying precise suites of conditions). Nonetheless, this negative result, our analyses, and the estimates of physical properties we derived for them, provide useful lessons and inputs for future studies.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JB022588","usgsCitation":"Gomberg, J.S., Ariyoshi, K., Hautala, S., and Johnson, H., 2021, The finicky nature of earthquake shaking-triggered submarine sediment slope failures and sediment gravity flows: Journal of Geophysical Research, v. 126, e2021JB022588, 26 p., https://doi.org/10.1029/2021JB022588.","productDescription":"e2021JB022588, 26 p.","ipdsId":"IP-123242","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":480836,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Japan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 135.61973524947484,\n 34.29410264461973\n ],\n [\n 135.61973524947484,\n 32.76513344156004\n ],\n [\n 136.82279989673617,\n 32.76513344156004\n ],\n [\n 136.82279989673617,\n 34.29410264461973\n ],\n [\n 135.61973524947484,\n 34.29410264461973\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"126","noUsgsAuthors":false,"publicationDate":"2021-09-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":924641,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ariyoshi, Keisuke","contributorId":349718,"corporation":false,"usgs":false,"family":"Ariyoshi","given":"Keisuke","affiliations":[{"id":40272,"text":"Japan Agency for Marine-Earth Science and Technology","active":true,"usgs":false}],"preferred":false,"id":924642,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hautala, Susan","contributorId":194235,"corporation":false,"usgs":false,"family":"Hautala","given":"Susan","email":"","affiliations":[],"preferred":false,"id":924643,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, H.P.","contributorId":349727,"corporation":false,"usgs":false,"family":"Johnson","given":"H.P.","affiliations":[],"preferred":false,"id":924644,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70224582,"text":"70224582 - 2021 - Evaluation of SWIR crop residue bands for the Landsat Next mission","interactions":[],"lastModifiedDate":"2021-09-29T13:25:56.428904","indexId":"70224582","displayToPublicDate":"2021-09-17T08:22:38","publicationYear":"2021","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":"Evaluation of SWIR crop residue bands for the Landsat Next mission","docAbstract":"
This research reports the findings of a Landsat Next expert review panel that evaluated the use of narrow shortwave infrared (SWIR) reflectance bands to measure ligno-cellulose absorption features centered near 2100 and 2300 nm, with the objective of measuring and mapping non-photosynthetic vegetation (NPV), crop residue cover, and the adoption of conservation tillage practices within agricultural landscapes. Results could also apply to detection of NPV in pasture, grazing lands, and non-agricultural settings. Currently, there are no satellite data sources that provide narrowband or hyperspectral SWIR imagery at sufficient volume to map NPV at a regional scale. The Landsat Next mission, currently under design and expected to launch in the late 2020’s, provides the opportunity for achieving increased SWIR sampling and spectral resolution with the adoption of new sensor technology. This study employed hyperspectral data collected from 916 agricultural field locations with varying fractional NPV, fractional green vegetation, and surface moisture contents. These spectra were processed to generate narrow bands with centers at 2040, 2100, 2210, 2260, and 2230 nm, at various bandwidths, that were subsequently used to derive 13 NPV spectral indices from each spectrum. For crop residues with minimal green vegetation cover, two-band indices derived from 2210 and 2260 nm bands were top performers for measuring NPV (R2 = 0.81, RMSE = 0.13) using bandwidths of 30 to 50 nm, and the addition of a third band at 2100 nm increased resistance to atmospheric correction residuals and improved mission continuity with Landsat 8 Operational Land Imager Band 7. For prediction of NPV over a full range of green vegetation cover, the Cellulose Absorption Index, derived from 2040, 2100, and 2210 nm bands, was top performer (R2 = 0.77, RMSE = 0.17), but required a narrow (≤20 nm) bandwidth at 2040 nm to avoid interference from atmospheric carbon dioxide absorption. In comparison, broadband NPV indices utilizing Landsat 8 bands centered at 1610 and 2200 nm performed poorly in measuring fractional NPV (R2 = 0.44), with significantly increased interference from green vegetation.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13183718","usgsCitation":"Hively, W.D., Lamb, B.T., Daughtry, C.S., Serbin, G., Dennison, P., Kokaly, R.F., Wu, Z., and Masek, J.G., 2021, Evaluation of SWIR crop residue bands for the Landsat Next mission: Remote Sensing, v. 13, no. 18, 3718, 31 p., https://doi.org/10.3390/rs13183718.","productDescription":"3718, 31 p.","ipdsId":"IP-130273","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":450786,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13183718","text":"Publisher Index Page"},{"id":436198,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XK3867","text":"USGS data release","linkHelpText":"Reflectance spectra of agricultural field conditions supporting remote sensing evaluation of non-photosynthetic vegetation cover (ver. 1.1, November 2022)"},{"id":389948,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"18","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824165,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamb, Brian T.","contributorId":211092,"corporation":false,"usgs":false,"family":"Lamb","given":"Brian","email":"","middleInitial":"T.","affiliations":[{"id":38178,"text":"City College of New York","active":true,"usgs":false}],"preferred":false,"id":824166,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":824167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Serbin, Guy 0000-0001-9345-1772","orcid":"https://orcid.org/0000-0001-9345-1772","contributorId":266030,"corporation":false,"usgs":false,"family":"Serbin","given":"Guy","email":"","affiliations":[{"id":54864,"text":"EOAnalytics","active":true,"usgs":false}],"preferred":false,"id":824168,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dennison, Phillip 0000-0002-0241-1917","orcid":"https://orcid.org/0000-0002-0241-1917","contributorId":266031,"corporation":false,"usgs":false,"family":"Dennison","given":"Phillip","email":"","affiliations":[{"id":54865,"text":"Dept. Geography, Utah State University","active":true,"usgs":false}],"preferred":false,"id":824169,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":824170,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":824171,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Masek, Jeffrey G.","contributorId":197725,"corporation":false,"usgs":false,"family":"Masek","given":"Jeffrey","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":824172,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70225495,"text":"70225495 - 2021 - Time-fractional flow equations (t-FFEs) to upscale transient groundwater flow characterized by temporally non-darcian flow due to medium heterogeneity","interactions":[],"lastModifiedDate":"2021-11-16T15:52:35.903673","indexId":"70225495","displayToPublicDate":"2021-09-17T06:40:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Time-fractional flow equations (t-FFEs) to upscale transient groundwater flow characterized by temporally non-darcian flow due to medium heterogeneity","docAbstract":"

Upscaling groundwater flow is a fundamental challenge in hydrogeology. This study proposed time-fractional flow equations (t-FFEs) for upscaling long-term, transient groundwater flow and propagation of pressure heads in heterogeneous media. Monte Carlo simulations showed that, with increasing variance and correlation of the hydraulic conductivity (K), flow dynamics gradually deviated from Darcian flow and exhibit sub-diffusive, time-dependent evolution which can be separated into three major stages. At the early stage, the interconnected high-K zones dominated flow, while at intermediate times, the transverse flow due to mixed high- and low-K zones caused delayed rise of the piezometric head. At late times when flow in the relatively high-K domains reached stability, cells with very low-K continued to block the entry of water and generate “islands” with low piezometric head, significantly extending the temporal evolution of the piezometric head. The elongated water breakthrough curve cannot be quantified by the flow equation with an effective K, the space-fractional flow equation, or the multi-rate mass transfer (MRMT) flow model with a few rates, motivating the development of t-FFEs assuming temporally non-Darcian flow. Model applications showed that both the early and intermediate stages of flow dynamics can be captured by a single-index t-FFE (whose index is the exponent of the power-law probability density function of the random operational time for water parcels), but the overall evolution of flow dynamics, especially the enhanced retention of flow at later times, required a distributed-order t-FFE with variable indexes for different flow phases that can dominate flow dynamics at different stages. Therefore, transient groundwater flow in aquifers with spatially stationary heterogeneity can be temporally non-Darcian and non-stationary, due to the time-sensitive, combined effects of interconnected high-K channels and isolated low-K deposits on flow dynamics (which is the hydrogeological mechanism for the temporally non-Darcian flow and sub-diffusive pressure propagation), whose long-term behavior can be quantified by multi-index stochastic models.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR029554","usgsCitation":"Xia, Y., Zhang, Y., Green, C., and Fogg, G., 2021, Time-fractional flow equations (t-FFEs) to upscale transient groundwater flow characterized by temporally non-darcian flow due to medium heterogeneity: Water Resources Research, v. 57, no. 11, e2020WR029554, 30 p., https://doi.org/10.1029/2020WR029554.","productDescription":"e2020WR029554, 30 p.","ipdsId":"IP-119835","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":450800,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/0pv0z4t0","text":"External Repository"},{"id":390598,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xia, Yuan","contributorId":267790,"corporation":false,"usgs":false,"family":"Xia","given":"Yuan","email":"","affiliations":[{"id":55508,"text":"Guilin University of Technology","active":true,"usgs":false}],"preferred":false,"id":825278,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, Yong","contributorId":214040,"corporation":false,"usgs":false,"family":"Zhang","given":"Yong","email":"","affiliations":[{"id":16675,"text":"U Alabama","active":true,"usgs":false}],"preferred":false,"id":825279,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Green, Christopher 0000-0002-6480-8194","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":201642,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":825280,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fogg, Graham 0000-0003-0676-1911","orcid":"https://orcid.org/0000-0003-0676-1911","contributorId":267791,"corporation":false,"usgs":false,"family":"Fogg","given":"Graham","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":825281,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70224196,"text":"sir20215070 - 2021 - Estimating invertebrate response to changes in total nitrogen, total phosphorus, and specific conductance at sites where invertebrate data are unavailable","interactions":[],"lastModifiedDate":"2021-09-16T16:17:28.171074","indexId":"sir20215070","displayToPublicDate":"2021-09-16T09:50:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5070","displayTitle":"Estimating Invertebrate Response to Changes in Total Nitrogen, Total Phosphorus, and Specific Conductance at Sites Where Invertebrate Data are Unavailable","title":"Estimating invertebrate response to changes in total nitrogen, total phosphorus, and specific conductance at sites where invertebrate data are unavailable","docAbstract":"

The purpose of this report is to describe a possible approach to estimate changes in invertebrate taxa richness at sites with known water-quality trends but no invertebrate data. In this study, data from 1,322 sites were used to describe invertebrate response to changes in total nitrogen, total phosphorus, or specific conductance, and to estimate changes in invertebrate taxa richness at 259 sites with reported water-quality trends but no invertebrate data. Sites were stratified using propensity score analysis to control for confounding factors (for example, climate, land use, land cover). Generalized linear models were developed to describe changes in invertebrate taxa richness along gradients of total nitrogen, total phosphorus, and specific conductance values. The magnitude and direction of invertebrate response to gradients of water quality varied among parameters and strata, with changes in invertebrate taxa richness per natural log unit change in concentration ranging from –7 to +6. However, estimated changes in invertebrate taxa richness at sites with known water-quality trends were much less and did not exceed three taxa until changes in concentration were greater than 50 percent. Applying this approach provides (1) a first screening to identify where changes in invertebrate taxa richness are likely to occur and (2) the necessary groundwork to improve estimation of invertebrate response to trends in water quality where biological data are lacking.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20215070","usgsCitation":"Zuellig, R.E., and Carlisle, D.M., 2021, Estimating invertebrate response to changes in total nitrogen, total phosphorus, and specific conductance at sites where invertebrate data are unavailable: U.S. Geological Survey Scientific Investigations Report 2021–5070, 24 p., https://doi.org/10.3133/sir20215070.","productDescription":"Report: v, 24 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119660","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":389267,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SMFACO","text":"USGS data release","linkHelpText":"Datasets for estimating invertebrate response to changes in total nitrogen, total phosphorus, and specific conductance at sites where invertebrate data are unavailable"},{"id":389266,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5070/sir20215070.pdf","text":"Report","size":"5.06 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5070"},{"id":389265,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5070/coverthb.jpg"}],"contact":"

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

","tableOfContents":"","publishedDate":"2021-09-16","noUsgsAuthors":false,"publicationDate":"2021-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Zuellig, Robert E. 0000-0002-4784-2905 rzuellig@usgs.gov","orcid":"https://orcid.org/0000-0002-4784-2905","contributorId":1620,"corporation":false,"usgs":true,"family":"Zuellig","given":"Robert","email":"rzuellig@usgs.gov","middleInitial":"E.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823307,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carlisle, Daren M. 0000-0002-7367-348X","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":223188,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":823308,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70240159,"text":"70240159 - 2021 - Waterborne gradient Self-Potential (WaSP) logging in the Rio Grande to map localized and regional surface and groundwater exchanges across the Mesilla Valley","interactions":[],"lastModifiedDate":"2023-01-31T15:30:33.818125","indexId":"70240159","displayToPublicDate":"2021-09-16T09:24:08","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7446,"text":"FastTIMES","active":true,"publicationSubtype":{"id":10}},"title":"Waterborne gradient Self-Potential (WaSP) logging in the Rio Grande to map localized and regional surface and groundwater exchanges across the Mesilla Valley","docAbstract":"

The Rio Grande is the primary source of recharge to the Mesilla Basin/Conejos-Médanos aquifer system (“Mesilla Basin aquifer system”) in the Mesilla Valley of New Mexico and Texas. The Mesilla Basin aquifer system is the primary source of water supply to several large cities along the United States–Mexico border. Identifying gaining and losing reaches of the Rio Grande in the Mesilla Valley is therefore critical for managing the quality and quantity of surface and groundwater-resources available to stakeholders in the Mesilla Valley and downstream. A Waterborne gradient Self-Potential (WaSP) logging survey was completed in the Rio Grande across the Mesilla Valley between June 26 and July 2, 2020 to identify reaches where surface-water gains and losses were occurring by interpreting an estimate of the streaming-potential component of the electrostatic field in the river, measured during bank-full flow. The WaSP survey, completed as part of the Transboundary Aquifer Assessment Program, began at Leasburg Dam State Park, New Mexico near the northern terminus of the Mesilla Valley and ended ~72 kilometers (km) downstream in Canutillo, Texas. Electric potential data indicated a net losing condition for ~32 km between Leasburg Dam and Mesilla Diversion Dam in New Mexico, with one 200-m long reach showing a localized gaining condition. Downstream from Mesilla Diversion Dam, electric-potential data indicated a neutral-to-mild gaining condition for 12-km that transitioned to a mild-to-moderate gaining condition between 12 and ~22 km from the dam before transitioning back to a losing condition along the remaining 18 km of the survey reach. The interpreted gaining and losing reaches are substantiated by potentiometric surface mapping in hydrostratigraphic units of the Mesilla Basin aquifer system between 2010 and 2011 and streamflow gains and losses quantified from annual streamflow gaging at 16 stations along the survey reach between 1988 and 1998 and between 2004 and 2013. The gaining and losing reaches of the Rio Grande in the Mesilla Valley, interpreted from electric potential data, compare notably well with streamflow gains and losses quantified at 16 locations along the 72-km long survey reach.

","language":"English","publisher":"Environmental and Engineering Geophysical Society","usgsCitation":"Ikard, S., and Teeple, A., 2021, Waterborne gradient Self-Potential (WaSP) logging in the Rio Grande to map localized and regional surface and groundwater exchanges across the Mesilla Valley: FastTIMES, v. 26, no. 3, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-132631","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":412505,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":412478,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://fasttimesonline.co/waterborne-gradient-self-potential-wasp-logging-in-the-rio-grande-to-map-localized-and-regional-surface-and-groundwater-exchanges-across-the-mesilla-valley/"}],"country":"United States","state":"New Mexico, Texas","otherGeospatial":"Mesilla Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.44524373222445,\n 31.760633532310962\n ],\n [\n -106.6117012652096,\n 32.435777766328556\n ],\n [\n -106.97488133717744,\n 32.96635814299131\n ],\n [\n -107.04549968450479,\n 33.44327742390567\n ],\n [\n -107.58018145712424,\n 33.388542989837234\n ],\n [\n -107.50956310979689,\n 32.79267559856237\n ],\n [\n -107.07576469050201,\n 32.350592719244176\n ],\n [\n -106.67223127720436,\n 31.854939592806915\n ],\n [\n -106.4502878998906,\n 31.709153308763334\n ],\n [\n -106.44524373222445,\n 31.760633532310962\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ikard, Scott 0000-0002-8304-4935","orcid":"https://orcid.org/0000-0002-8304-4935","contributorId":201775,"corporation":false,"usgs":true,"family":"Ikard","given":"Scott","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Teeple, Andrew 0000-0003-1781-8354 apteeple@usgs.gov","orcid":"https://orcid.org/0000-0003-1781-8354","contributorId":193061,"corporation":false,"usgs":true,"family":"Teeple","given":"Andrew","email":"apteeple@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862806,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70255559,"text":"70255559 - 2021 - Monthly river temperature trends across the US confound annual changes","interactions":[],"lastModifiedDate":"2024-06-24T11:20:26.360545","indexId":"70255559","displayToPublicDate":"2021-09-16T06:06:47","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Monthly river temperature trends across the US confound annual changes","docAbstract":"

Climate variations and human modifications of the water cycle continue to alter the Earth's surface water and energy exchanges. It is therefore critical to ascertain how these changes impact water quality and aquatic ecosystem habitat metrics such as river temperatures. Though river temperature trend analyses exist in the literature, studies on seasonal trends in river temperatures across large spatial extents, e.g. the contiguous United States (US), are limited. As we show through both annual and monthly trend analyses for 20 year (n = 138 sites) and 40 year (n = 40 sites) periods, annual temperature trends across the US mask extensive monthly variability. While most sites exhibited annual warming trends, these annual trends obscured sub-annual cooling trends at many sites. Monthly trend anomalies were spatially organized, with persistent regional patterns at both reference and human-impacted sites. The largest warming and cooling anomalies happened at human impacted sites and during summer months. Though our analysis points to coherence in trends as well as the overall impact of human activity in driving these patterns, we did not investigate the impact of river temperature observation accuracy on reported trends, an area needed for future work. Overall, these patterns emphasize the need to consider sub-annual behavior when managing the ecological impacts of river temperature throughout lotic networks.

","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ac2289","usgsCitation":"Kelleher, C., Golden, H.E., and Archfield, S.A., 2021, Monthly river temperature trends across the US confound annual changes: Environmental Research Letters, v. 16, 104006, 10 p., https://doi.org/10.1088/1748-9326/ac2289.","productDescription":"104006, 10 p.","ipdsId":"IP-130039","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":450807,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ac2289","text":"Publisher Index Page"},{"id":430440,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -129.57106419384183,\n 51.98412232384288\n ],\n [\n -129.57106419384183,\n 24.426025005896022\n ],\n [\n -65.41090794384175,\n 24.426025005896022\n ],\n [\n -65.41090794384175,\n 51.98412232384288\n ],\n [\n -129.57106419384183,\n 51.98412232384288\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationDate":"2021-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Kelleher, Christa","contributorId":242798,"corporation":false,"usgs":false,"family":"Kelleher","given":"Christa","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":904669,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Golden, Heather E.","contributorId":202423,"corporation":false,"usgs":false,"family":"Golden","given":"Heather","email":"","middleInitial":"E.","affiliations":[{"id":36429,"text":"USEPA ORD","active":true,"usgs":false}],"preferred":false,"id":904670,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Archfield, Stacey A. 0000-0002-9011-3871 sarch@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-3871","contributorId":1874,"corporation":false,"usgs":true,"family":"Archfield","given":"Stacey","email":"sarch@usgs.gov","middleInitial":"A.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":904671,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223926,"text":"sir20215091 - 2021 - Evaluation of hydrologic simulation models for fields with subsurface drainage to mitigated wetlands in Barnes, Dickey, and Sargent Counties, North Dakota","interactions":[],"lastModifiedDate":"2021-09-16T11:37:37.283212","indexId":"sir20215091","displayToPublicDate":"2021-09-15T08:47:15","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5091","displayTitle":"Evaluation of Hydrologic Simulation Models for Fields with Subsurface Drainage to Mitigated Wetlands in Barnes, Dickey, and Sargent Counties, North Dakota","title":"Evaluation of hydrologic simulation models for fields with subsurface drainage to mitigated wetlands in Barnes, Dickey, and Sargent Counties, North Dakota","docAbstract":"

Proper identification of wetlands, along with a better understanding of the hydrology of mitigated wetlands, is needed to assist with conservation efforts aimed at maintaining the productivity and ecological function (wetland mitigation) of agricultural lands. The U.S. Geological Survey, in cooperation with the U.S. Department of Agriculture Natural Resources Conservation Service, completed a study to evaluate two models for simulating hydrologic conditions in fields with subsurface drainage to mitigated wetlands at several sites in North Dakota. These two models were evaluated as possible tools for water resource managers to use for designing wetland mitigation projects in the area in the future.

The Soil-Plant-Atmosphere-Water (SPAW) model simulates the daily hydrologic water budgets of agricultural landscapes by two linked routines, one for farm fields (field hydrology) and one for impoundments such as wetlands and ponds (pond model). The DRAINMOD model was used in conjunction with the SPAW model because although the SPAW model can be used to simulate the hydrology of small drainage basins containing wetlands, the SPAW model does not contain routines to simulate drainage, either subsurface drainage or surface (drainage ditches), that can directly affect the wetland hydrology. The wetlands in the study areas in this report are all downstream from and adjacent to drained agricultural fields. SPAW and DRAINMOD models were developed and calibrated at three study areas (study areas B, D, and S) to evaluate how the models simulated field-scale hydrologic characteristics and the water balance in wetlands from January 1, 2003, through December 31, 2018.

The SPAW model developed for study area B included five modeled fields in the field hydrology portion of SPAW that contributed inflow to one wetland simulated in the pond model portion of SPAW. Simulated wetland water depths were most similar to water depths measured at site BWET1, with an absolute mean error of 0.10 foot and a root mean square error of 0.14 foot. Site BWET2 had slightly larger errors, with an absolute mean error of 0.22 foot and a root mean square error of 0.28 foot. Simulated water depths were similar to the pattern of measured water depths at BWET1 and BWET2 from about mid-April 2018 through about mid-September 2018, but overpredicted water depths in the fall from about mid-September 2018 through about mid-October 2018.

The SPAW model developed for study area D included six modeled fields in the field hydrology portion of SPAW that contributed inflow to five wetlands connected in series in the pond model portion of SPAW. Simulated water depths compared relatively well to water depths in the five wetlands, with the absolute mean error ranging from 0.17 foot (DWET1) to 0.39 foot (DWET2), and the root mean square error ranging from 0.28 foot (DWET1) to 0.56 foot (DWET5).

The SPAW model developed for study area S included one modeled field in the field hydrology portion of SPAW that contributed inflow to one wetland in the pond model portion of SPAW. Among the SPAW models developed for the three study areas, the model for study area S had the best comparison between simulated and measured water depths, with an absolute mean error of 0.06 foot and a root mean square error of 0.10 foot.

DRAINMOD models were developed and calibrated at the three study areas and provided inflow from subsurface drainage discharge to the SPAW models for simulating water levels in wetlands in the study areas. The calibrated DRAINMOD model for study area B showed the variability of hydrologic processes in the modeled field throughout the wide range of hydrologic conditions from January 1, 2003, through December 31, 2018. In general, the discharge through the modeled subsurface drainage system was in the spring and early summer (April through June) most years, with little to no discharge later in the year. Although the subsurface drainage system in study area D was the most complex among the three study areas and was simplified into a uniform system within DRAINMOD, simulated water table depths at study area D correlated better to measured water table depths compared to results from the model applications at the other two study areas. Simulated water table depths had an absolute mean error of 0.30 foot and root mean square error of 0.37 foot at site DGW1 and an absolute mean error of 0.29 foot and a root mean square error of 0.34 foot at site DGW2. Although the subsurface drainage system in study area S was the simplest and the modeled field was the smallest among the three study areas, simulated water table depths at study area S did not correlate as well to measured water table depths compared to results from the model applications at the other two study areas.

The SPAW and DRAINMOD model applications at the three study areas in southeast North Dakota adequately simulated the hydrologic processes for fields with subsurface drainage that are connected to adjacent wetlands. However, more measured data would be needed to fully evaluate the models throughout the range of possible climatic conditions.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215091","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture Natural Resources Conservation Service","usgsCitation":"Galloway, J.M., Tatge, W.S., and Wheeling, S.L., 2021, Evaluation of hydrologic simulation models for fields with subsurface drainage to mitigated wetlands in Barnes, Dickey, and Sargent Counties, North Dakota: U.S. Geological Survey Scientific Investigations Report 2021–5091, 58 p., https://doi.org/10.3133/sir20215091.","productDescription":"Report: vi, 58 p.; Dataset","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-128613","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":389200,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5091/images"},{"id":389199,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5091/sir20215091.xml","size":"386 kB","linkFileType":{"id":8,"text":"xml"}},{"id":389198,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":389196,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5091/coverthb.jpg"},{"id":389197,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5091/sir20215091.pdf","text":"Report","size":"5.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5091"}],"country":"United States","state":"North Dakota","county":"Barnes County, Dickey County, Sargent County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-97.961,47.241],[-97.7061,47.2402],[-97.7071,47.1529],[-97.7062,47.0665],[-97.7059,46.9792],[-97.6839,46.9792],[-97.683,46.6294],[-97.81,46.6297],[-97.9059,46.6293],[-97.9357,46.6294],[-98.0349,46.6293],[-98.1889,46.6297],[-98.2868,46.63],[-98.3152,46.63],[-98.4396,46.6296],[-98.4412,46.9789],[-98.4685,46.9788],[-98.4677,47.2402],[-97.9958,47.2411],[-97.9764,47.2412],[-97.961,47.241]]],[[[-98.0095,45.9355],[-98.164,45.9356],[-98.1849,45.9355],[-98.3472,45.9355],[-98.3537,45.9355],[-98.7267,45.9373],[-98.7273,45.9373],[-99.0021,45.9393],[-99.0054,45.9393],[-99.0073,46.0262],[-99.0061,46.1132],[-99.0054,46.2002],[-99.0049,46.2822],[-98.9154,46.2821],[-98.7878,46.2805],[-98.755,46.281],[-98.6622,46.2812],[-98.5359,46.2817],[-98.5024,46.2808],[-98.2859,46.2816],[-98.2524,46.2815],[-98.1616,46.2818],[-98.1314,46.2813],[-98.0366,46.2809],[-98.009,46.2814],[-97.9096,46.2823],[-97.8826,46.2827],[-97.5333,46.2819],[-97.4063,46.2823],[-97.2833,46.2822],[-97.2615,46.2822],[-97.2618,46.196],[-97.2603,45.9985],[-97.231,45.9951],[-97.2313,45.936],[-97.3576,45.936],[-97.3773,45.936],[-97.4826,45.9359],[-97.605,45.9356],[-97.755,45.9356],[-97.9775,45.9351],[-98.0017,45.9355],[-98.0095,45.9355]]]]},\"properties\":{\"name\":\"Barnes\",\"state\":\"ND\"}}]}","contact":"

Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503

1608 Mountain View Road
Rapid City, SD 57702

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-09-15","noUsgsAuthors":false,"publicationDate":"2021-09-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Galloway, Joel M. 0000-0002-9836-9724 jgallowa@usgs.gov","orcid":"https://orcid.org/0000-0002-9836-9724","contributorId":1562,"corporation":false,"usgs":true,"family":"Galloway","given":"Joel","email":"jgallowa@usgs.gov","middleInitial":"M.","affiliations":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823299,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tatge, Wyatt S. 0000-0003-4414-2492","orcid":"https://orcid.org/0000-0003-4414-2492","contributorId":239544,"corporation":false,"usgs":true,"family":"Tatge","given":"Wyatt","email":"","middleInitial":"S.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823300,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wheeling, Spencer L. 0000-0003-4411-6526","orcid":"https://orcid.org/0000-0003-4411-6526","contributorId":221899,"corporation":false,"usgs":true,"family":"Wheeling","given":"Spencer","email":"","middleInitial":"L.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823301,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70232160,"text":"70232160 - 2021 - Fish response to successive clearcuts in a second-growth forest from the central Coast range of Oregon","interactions":[],"lastModifiedDate":"2022-06-09T13:42:26.457253","indexId":"70232160","displayToPublicDate":"2021-09-15T08:25:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Fish response to successive clearcuts in a second-growth forest from the central Coast range of Oregon","docAbstract":"

Research dating back to the 1950 s has documented negative effects from harvesting of primeval forests on stream ecosystems of the Pacific Northwest. By the early 1990 s, state and federal forest practice rules governing timber harvest were modified throughout North America to better protect aquatic habitats and biotic resources, principally salmonids. These rules inspired a generation of studies using a before-after-control-impact (BACI) design to document the capacity of contemporary timber harvest rules to protect salmonids in headwater streams of second-growth forests. One important unanswered question concerns the potential effects of successive clearcuts in second growth forests. Consequently, we used a paired watershed approach to evaluate the effects of two successive clearcut harvests in the Alsea Watershed, site of the seminal Alsea Watershed Study that was conducted from 1958 to 1973, on relative biomass, movement, survival, and distribution of coastal cutthroat trout (Oncorhynchus clarkii clarkii) and three physical habitat characteristics (pool area and depth, and water temperature). Although the total clearcut harvest encompassed 87% of the treatment catchment in six years, no negative effects of logging were detected for either age-1 + coastal cutthroat trout or habitat variables. Comparisons between the harvested and reference catchments suggested the survival of coastal cutthroat trout (>94 mm fork length) and total catchment relative biomass of age-1+ (i.e., > 80 mm) exhibited similar patterns, increasing from the pre-logging period (2006–2009) through the Phase I post-logging period (2009–2014), and decreasing to levels observed in the pre-logging period during the Phase II post-logging period (2014–2017). Additionally, there was no evidence for differences in movement of coastal cutthroat trout related to the harvesting treatment. In terms of habitat variables, there was a relative increase in annual total pool area in the harvested catchment during the Phase II post-logging period, but there was no evidence the 7-day moving mean maximum stream temperature changed after the Phase I and Phase II harvests. Moreover, stream water temperatures never exceeded the criterion designed to protect core coldwater habitat for salmonids (16 °C). As such, it is unlikely that cutthroat trout experienced thermal stress following either harvest. More generally, results from this and other recent studies suggest that forest practice rules developed in conjunction with current best management practices for logging in headwater catchments have substantially improved outcomes for stream biota relative to unregulated forest harvest, at least for short periods of time after logging (i.e., ≤ 8 years).

","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2021.119447","usgsCitation":"Bateman, D.S., Chelgren, N., Gresswell, R.E., Dunham, J.B., Hockman-Wert, D., Leer, D.W., and Bladon, K., 2021, Fish response to successive clearcuts in a second-growth forest from the central Coast range of Oregon: Forest Ecology and Management, v. 496, 119447, 15 p., https://doi.org/10.1016/j.foreco.2021.119447.","productDescription":"119447, 15 p.","ipdsId":"IP-130611","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":450813,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2021.119447","text":"Publisher Index Page"},{"id":401979,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Alsea River Watershed, Drift Creek, Flynn Creek, Needle Branch","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.0143585205078,\n 44.38865427337759\n ],\n [\n -123.79737854003905,\n 44.38865427337759\n ],\n [\n -123.79737854003905,\n 44.524416083679924\n ],\n [\n -124.0143585205078,\n 44.524416083679924\n ],\n [\n -124.0143585205078,\n 44.38865427337759\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"496","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bateman, D. S.","contributorId":292361,"corporation":false,"usgs":false,"family":"Bateman","given":"D.","email":"","middleInitial":"S.","affiliations":[{"id":62882,"text":"Department of Forest Engineering, Resources, and Management, College of Forestry, Oregon State University, Corvallis, OR","active":true,"usgs":false}],"preferred":false,"id":844391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chelgren, Nathan 0000-0003-0944-9165 nchelgren@usgs.gov","orcid":"https://orcid.org/0000-0003-0944-9165","contributorId":3134,"corporation":false,"usgs":true,"family":"Chelgren","given":"Nathan","email":"nchelgren@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gresswell, Robert E. 0000-0003-0063-855X bgresswell@usgs.gov","orcid":"https://orcid.org/0000-0003-0063-855X","contributorId":152031,"corporation":false,"usgs":true,"family":"Gresswell","given":"Robert","email":"bgresswell@usgs.gov","middleInitial":"E.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844393,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":844394,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hockman-Wert, David 0000-0003-2436-6237 dhockman-wert@usgs.gov","orcid":"https://orcid.org/0000-0003-2436-6237","contributorId":3891,"corporation":false,"usgs":true,"family":"Hockman-Wert","given":"David","email":"dhockman-wert@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844395,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leer, D. W.","contributorId":292363,"corporation":false,"usgs":false,"family":"Leer","given":"D.","email":"","middleInitial":"W.","affiliations":[{"id":62882,"text":"Department of Forest Engineering, Resources, and Management, College of Forestry, Oregon State University, Corvallis, OR","active":true,"usgs":false}],"preferred":false,"id":844396,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bladon, K. D.","contributorId":292364,"corporation":false,"usgs":false,"family":"Bladon","given":"K. D.","affiliations":[{"id":62882,"text":"Department of Forest Engineering, Resources, and Management, College of Forestry, Oregon State University, Corvallis, OR","active":true,"usgs":false}],"preferred":false,"id":844397,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223987,"text":"fs20213043 - 2021 - Continuous water-quality and suspended-sediment transport monitoring in the San Francisco Bay, California, water years 2018–19","interactions":[],"lastModifiedDate":"2021-09-15T11:43:03.004281","indexId":"fs20213043","displayToPublicDate":"2021-09-14T13:37:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3043","displayTitle":"Continuous Water-Quality and Suspended-Sediment Transport Monitoring in the San Francisco Bay, California, Water Years 2018–19","title":"Continuous water-quality and suspended-sediment transport monitoring in the San Francisco Bay, California, water years 2018–19","docAbstract":"

Water-Quality in San Francisco Bay

The U.S. Geological Survey (USGS) monitors water quality and suspended-sediment transport in the San Francisco Bay (Bay) as part of a multi-agency effort to address estuary management, water supply, and ecological concerns. The San Francisco Bay area is home to millions of people, and the Bay teems with marine and terrestrial flora and fauna. Freshwater mixes with saltwater in the Bay and is subject to riverine influences (floods, droughts, managed reservoir releases, and freshwater diversions) and marine influences (tides, waves, and effects of saltwater). To understand this environment, the USGS, along with its cooperators (see “Acknowledgments” section), has been monitoring the Bay’s waters continuously since 1988.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213043","usgsCitation":"Einhell, D.C., Davila Olivera, S., and Palm, D.L., 2021, Continuous water-quality and suspended-sediment transport monitoring in the San Francisco Bay, California, water years 2018–19: U.S. Geological Survey Fact Sheet 2021-3043, 4 p., https://doi.org/10.3133/fs20213043.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-129590","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":389209,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3043/covrthb.jpg"},{"id":389210,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3043/fs20213043.pdf","text":"Report","size":"3. 5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":389211,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2021/3043/fs20213043.xml"},{"id":389212,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2021/3043/images"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.98095703125,\n 37.17782559332976\n ],\n [\n -121.4208984375,\n 37.17782559332976\n ],\n [\n -121.4208984375,\n 38.28993659801203\n ],\n [\n -122.98095703125,\n 38.28993659801203\n ],\n [\n -122.98095703125,\n 37.17782559332976\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":"2021-09-14","noUsgsAuthors":false,"publicationDate":"2021-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Einhell, Darin C. 0000-0002-3190-7727 deinhell@usgs.gov","orcid":"https://orcid.org/0000-0002-3190-7727","contributorId":220042,"corporation":false,"usgs":true,"family":"Einhell","given":"Darin","email":"deinhell@usgs.gov","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823303,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davila Olivera, Selina M. 0000-0002-2574-2997","orcid":"https://orcid.org/0000-0002-2574-2997","contributorId":265761,"corporation":false,"usgs":true,"family":"Davila Olivera","given":"Selina","email":"","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823304,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Palm, Danielle L. 0000-0003-3045-5287","orcid":"https://orcid.org/0000-0003-3045-5287","contributorId":265762,"corporation":false,"usgs":true,"family":"Palm","given":"Danielle","email":"","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823305,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70232168,"text":"70232168 - 2021 - Early growth and ecophysiological responses of Koa (Acacia koa A. Gray) seedlings to reduced water and phosphorus","interactions":[],"lastModifiedDate":"2022-06-09T13:18:41.942854","indexId":"70232168","displayToPublicDate":"2021-09-14T08:13:29","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5909,"text":"New Forests","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Early growth and ecophysiological responses of Koa (Acacia koa A. Gray) seedlings to reduced water and phosphorus","title":"Early growth and ecophysiological responses of Koa (Acacia koa A. Gray) seedlings to reduced water and phosphorus","docAbstract":"

Sites in need of restoration typically have one or more environmental factors that limit seedling establishment. Identifying ecophysiological responses to environmental stressors can provide important insights into mitigating measures that would allow seedlings to overcome such constraints to survival. Koa (Acacia koa A. Gray) is a nitrogen-fixing tree species endemic to Hawaiʻi that is highly valued in restoring degraded forest ecosystems, which are often limited in available water and phosphorus. This study examined how koa seedlings respond to conditions of reduced water (65 W) and no phosphorus (0P). After 17 weeks, seedlings subjected to 65 W or 0P accumulated less biomass, smaller root-collar diameters, and lower nitrogen and phosphorus contents. Combined reductions in water and P resulted in seedlings with increased root to shoot dry biomass and shorter shoots. Seedlings subjected to 65 W also had lower instantaneous rates of CO2 assimilation, but higher instantaneous water-use efficiencies following irrigation, suggesting that koa responds to water deficits by decreasing water loss via reduced stomatal conductance. Seedlings subjected to 0P had similar rates of CO2 assimilation relative to those grown with adequate P, suggesting that koa is able to employ strategies to avoid physiological impairment from conditions of inadequate P. Future research should assess whether subjecting koa seedlings to reduced water before planting on water-limited sites cues increased drought resistance and whether uptake and storage of P by seedlings in the nursery better supports growth following outplanting, particularly on sites with anticipated low plant-available water.

","language":"English","publisher":"Springer","doi":"10.1007/s11056-021-09877-8","usgsCitation":"Gerber, K., Ross-Davis, A., Perakis, S.S., and Davis, A.S., 2021, Early growth and ecophysiological responses of Koa (Acacia koa A. Gray) seedlings to reduced water and phosphorus: New Forests, v. 2021, 18 p., https://doi.org/10.1007/s11056-021-09877-8.","productDescription":"18 p.","ipdsId":"IP-126393","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":401969,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"2021","noUsgsAuthors":false,"publicationDate":"2021-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Gerber, Kaitlin","contributorId":292369,"corporation":false,"usgs":false,"family":"Gerber","given":"Kaitlin","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":844416,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ross-Davis, Amy","contributorId":292370,"corporation":false,"usgs":false,"family":"Ross-Davis","given":"Amy","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":844417,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perakis, Steven S. 0000-0003-0703-9314 sperakis@usgs.gov","orcid":"https://orcid.org/0000-0003-0703-9314","contributorId":145528,"corporation":false,"usgs":true,"family":"Perakis","given":"Steven","email":"sperakis@usgs.gov","middleInitial":"S.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844418,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davis, Anthony S.","contributorId":292372,"corporation":false,"usgs":false,"family":"Davis","given":"Anthony","email":"","middleInitial":"S.","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":844419,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70221220,"text":"sir20215053 - 2021 - Analysis of Escherichia coli, total recoverable iron, and dissolved selenium concentrations, loading, and identifying data gaps for selected 303(d) listed streams, Grand Valley, western Colorado, 1980–2018","interactions":[],"lastModifiedDate":"2021-09-13T16:54:19.222516","indexId":"sir20215053","displayToPublicDate":"2021-09-13T11:30:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5053","displayTitle":"Analysis of Escherichia coli, Total Recoverable Iron, and Dissolved Selenium Concentrations, Loading, and Identifying Data Gaps for Selected 303(d) Listed Streams, Grand Valley, Western Colorado, 1980–2018","title":"Analysis of Escherichia coli, total recoverable iron, and dissolved selenium concentrations, loading, and identifying data gaps for selected 303(d) listed streams, Grand Valley, western Colorado, 1980–2018","docAbstract":"

Tributaries to the Colorado River in the Grand Valley in western Colorado (segment COLCLC13b) have been placed on the State of Colorado 303(d) list as impaired for Escherichia coli (E. coli), total recoverable iron, and dissolved selenium. The Colorado Department of Public Health and Environment Water Quality Control Division is required to develop total maximum daily loads for these constituents in these tributaries. The U.S. Geological Survey, in cooperation with the Grand Valley Drainage District and Colorado Water Conservation Board, conducted a study to (1) characterize concentrations, loads, and load reductions for E. coli, total recoverable iron, and dissolved selenium using existing data and (2) identify water-quality data gaps to inform future monitoring strategies. This study analyzed water-quality and streamflow data for 3 main-stem sites (2 sites along the Colorado River and 1 site along the Gunnison River) and 29 selected sites on tributaries to the Colorado River.

Sample data were available at five sites along Adobe Creek and at six sites along Leach Creek, the two tributaries in the study area that are impaired for E. coli. All geometric mean E. coli concentrations at sites along Adobe Creek and Leach Creek exceeded the State recreational use standard of 126 colony forming units per 100 milliliters (CFU/100 mL). In Adobe Creek, E. coli concentrations in samples ranged from 45.7 to more than 2,420 CFU/100 mL (method upper reporting limit for undiluted samples), and geometric mean concentrations at sites ranged from 301 to 1,180 CFU/100 mL. The E. coli concentrations generally increased in the downstream direction in Adobe Creek; however, increases were not seen between all sites. The largest downstream increase in E. coli concentration was measured between the two most upstream sites. In Leach Creek, concentrations of E. coli in samples ranged from 25.9 to more than 2,420 CFU/100 mL, and geometric mean concentrations at sites ranged from 160 to 259 CFU/100 mL. The E. coli concentrations showed no consistent downgradient increase in Leach Creek. In fact, some of the highest E. coli concentrations were measured at the most upstream site, Leach Creek at Summer Hill Drive.

Total recoverable iron concentrations and loads were evaluated at 15 tributary sites for samples collected from August 1993 to February 2018. Median total recoverable iron concentrations ranged from 211 to 4,670 micrograms per liter (µg/L). The chronic aquatic-life water-quality standard (1,000 µg/L) was exceeded in most irrigation season (April through October) samples but was rarely exceeded in nonirrigation season (November through March) samples. Concentrations were often an order of magnitude higher in samples collected during irrigation season than in samples collected during nonirrigation season. None of the sites had enough concurrent total recoverable iron and streamflow data to compute annual loads. As with E. coli, the lack of concurrent total recoverable iron and streamflow information represents a data gap, which needs to be addressed to compute annual loads.

Dissolved selenium concentrations and loads were evaluated at 20 tributary sites using discrete water-quality data collected 1991–2018. Dissolved selenium concentrations were higher during nonirrigation season than during irrigation season at tributary sites. However, irrigation season dissolved selenium loads were generally higher than nonirrigation selenium loads, because streamflows were higher during irrigation season. Regression analysis was used to estimate daily dissolved selenium concentrations and loads at three main-stem sites for water years (WYs) 1980–2018 (Gunnison River near Grand Junction and Colorado River near Colorado-Utah State Line) and WYs 2002–18 (Colorado River near Cameo). A trend analysis of dissolved selenium concentrations and loads was completed for these sites from the same respective starting dates but ending in 2017. A continuing downward trend in dissolved selenium concentration was observed at all sites and across all seasonal designations of the analysis. The dissolved selenium concentration decreased by 0.12 µg/L from WY 2002 to 2017 at Colorado River near Cameo, representing an 18-percent decrease during the time period. The dissolved selenium concentration at Gunnison River near Grand Junction decreased by 4.2 µg/L from WY 1980 to 2017, representing a 56-percent decrease overall. During the same time period, dissolved selenium concentration at Colorado River near Colorado-Utah State Line decreased by 3.8 µg/L, representing a 56-percent decrease overall. A downward trend in dissolved selenium load was also observed at all sites and across all seasonal designations of the analysis. The relative contribution of dissolved selenium from the Grand Valley near Grand Junction was estimated by comparing loads at main-stem sites bracketing the study area. The two upstream sites, Colorado River near Cameo and Gunnison River near Grand Junction, contributed 60,300 cumulative pounds and 251,000 cumulative pounds, respectively, during WYs 2002–18. At the furthest downstream site, Colorado River near Colorado-Utah State Line, 490,000 cumulative pounds were estimated during the same time period, indicating that the region between Whitewater and State line contributed approximately 179,000 cumulative pounds or a mean annual load of 10,500 lb/yr. Grand Valley dissolved selenium contributions appear to be stable during WYs 2002–18.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215053","collaboration":"Prepared in cooperation with the Grand Valley Drainage District and the Colorado Water Conservation Board","usgsCitation":"Miller, L.D., Gidley, R.G., Day, N.K., and Thomas, J.C., 2021, Analysis of Escherichia coli, total recoverable iron, and dissolved selenium concentrations, loading, and identifying data gaps for selected 303(d) listed streams, Grand Valley, western Colorado, 1980–2018 (ver. 1.1, September 2021): U.S. Geological Survey Scientific Investigations Report 2021-5053, 37 p., https://doi.org/10.3133/sir20215053.","productDescription":"Report: vii, 37 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-106948","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":386290,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5053/sir20215053.pdf","text":"Report","size":"2.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5053"},{"id":386289,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5053/coverthb3.jpg"},{"id":388012,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5053/versionHist.txt","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"Version history"},{"id":386291,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P6WI44","text":"USGS data release","linkHelpText":"Analysis of Escherichia coli, total recoverable iron, and dissolved selenium concentrations and loads for selected 303(d) listed segments in the Grand Valley, western Colorado, 1980–2018 (ver. 3.0, August 2021)"}],"country":"United States","state":"Colorado","otherGeospatial":"Grand Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.083251953125,\n 38.736946065676\n ],\n [\n -107.99560546875,\n 38.736946065676\n ],\n [\n -107.99560546875,\n 39.470125122358176\n ],\n [\n -109.083251953125,\n 39.470125122358176\n ],\n [\n -109.083251953125,\n 38.736946065676\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: June 9, 2021; Version 1.1: September 13, 2021","contact":"

Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046

","tableOfContents":"","publishedDate":"2021-06-09","revisedDate":"2021-09-13","noUsgsAuthors":false,"publicationDate":"2021-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Lisa D. 0000-0002-3523-0768 ldmiller@usgs.gov","orcid":"https://orcid.org/0000-0002-3523-0768","contributorId":1125,"corporation":false,"usgs":true,"family":"Miller","given":"Lisa","email":"ldmiller@usgs.gov","middleInitial":"D.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gidley, Rachel G. 0000-0002-9840-8252","orcid":"https://orcid.org/0000-0002-9840-8252","contributorId":259315,"corporation":false,"usgs":true,"family":"Gidley","given":"Rachel","email":"","middleInitial":"G.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817107,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Day, Natalie K. 0000-0002-8768-5705","orcid":"https://orcid.org/0000-0002-8768-5705","contributorId":207302,"corporation":false,"usgs":true,"family":"Day","given":"Natalie","middleInitial":"K.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817108,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thomas, Judith C. 0000-0001-7883-1419","orcid":"https://orcid.org/0000-0001-7883-1419","contributorId":202706,"corporation":false,"usgs":true,"family":"Thomas","given":"Judith","email":"","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817109,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223869,"text":"sir20215081 - 2021 - Storage capacity and sedimentation characteristics of Loch Lomond Reservoir, California, 2019","interactions":[],"lastModifiedDate":"2021-09-14T16:44:19.744785","indexId":"sir20215081","displayToPublicDate":"2021-09-13T07:29:23","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5081","displayTitle":"Storage Capacity and Sedimentation Characteristics of Loch Lomond Reservoir, California, 2019","title":"Storage capacity and sedimentation characteristics of Loch Lomond Reservoir, California, 2019","docAbstract":"

In May of 2019, Loch Lomond Reservoir was surveyed by the U.S. Geological Survey (USGS) in cooperation with the city of Santa Cruz to assess the current storage capacity and sedimentation rates in the reservoir. Survey methods combined sonar soundings to measure bathymetry and lidar scans with GPS data to measure near-shore topography and sediment bed samples to understand reservoir bed-material
size. The survey data produced a bare-earth digital elevation model (DEM) of the reservoir at a resolution of 1 square meter or better to elevations at or above the reservoir spillway elevation, providing the coverage needed to estimate storage capacity. Additionally, the USGS compared the current survey to storage estimates from historical surveys—particularly the most recent survey in 2009—to evaluate storage capacity trends. Lastly, a hindcast estimate of scaled sediment yield using sediment yields from the San Lorenzo River (USGS station 11160500)—where the San Lorenzo River watershed encompasses the Loch Lomond Reservoir watershed—were used to compare indirect estimates of storage loss to direct storage loss.

The 2019 survey resulted in a measured storage capacity of 8,770±50 acre-feet. The differences in storage between 2009 and 2019 varied substantially by depth. In shallow areas with depths less than 30 ft (at full reservoir), such as the very upstream end of the reservoir, storage loss (sediment deposition) dominated with a loss of about 68 acre-feet from 2009 to 2019. In areas deeper than 30 ft, persistent small storage gains over a wide range of depths totaled 82 acre-feet from 2009 to 2019.

Storage loss estimates derived from estimated watershed sediment yields and reservoir characteristics were similar to storage losses computed from past surveys. This hindcasting produced an estimate of about 500 acre-feet of total storage loss for the history of the reservoir, or an average of about 8–9 acre-feet/year during the 60-year period. For the period 2009–2019, the hindcast produced an estimated total storage loss of 42 acre-feet, which is broadly consistent with the 68 acre-feet of storage loss computed for shallow areas based on the repeat surveys.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215081","collaboration":"Prepared in cooperation with the city of Santa Cruz","programNote":"Water Availability and Use Science Program","usgsCitation":"Whealdon-Haught, D.R., Wright, S.A., and Marineau, M.D., 2021, Storage capacity and sedimentation characteristics of Loch Lomond Reservoir, California, 2019: U.S. Geological Survey Scientific Investigations Report 2021-5081, 28 p., https://doi.org/10.3133/sir20215081.","productDescription":"Report: vii, 28 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-120568","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":389073,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5081/covrthb.jpg"},{"id":389074,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5081/sir20215081.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":389075,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5081/sir20215081.xml"},{"id":389076,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5081/images"},{"id":389147,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91BUQWP","linkHelpText":"Loch Lomond Reservoir 2019 Survey Data"}],"country":"United States","state":"California","otherGeospatial":"Loch Lomond Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.07509994506836,\n 37.10091974583046\n ],\n [\n -122.05415725708008,\n 37.10091974583046\n ],\n [\n -122.05415725708008,\n 37.130897691327746\n ],\n [\n -122.07509994506836,\n 37.130897691327746\n ],\n [\n -122.07509994506836,\n 37.10091974583046\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":"2021-09-13","noUsgsAuthors":false,"publicationDate":"2021-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Whealdon-Haught, Daniel R. 0000-0002-8923-1512","orcid":"https://orcid.org/0000-0002-8923-1512","contributorId":193160,"corporation":false,"usgs":false,"family":"Whealdon-Haught","given":"Daniel","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":823045,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wright, Scott 0000-0002-0387-5713 sawright@usgs.gov","orcid":"https://orcid.org/0000-0002-0387-5713","contributorId":1536,"corporation":false,"usgs":true,"family":"Wright","given":"Scott","email":"sawright@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marineau, Mathieu D. 0000-0002-6568-0743 mmarineau@usgs.gov","orcid":"https://orcid.org/0000-0002-6568-0743","contributorId":4954,"corporation":false,"usgs":true,"family":"Marineau","given":"Mathieu","email":"mmarineau@usgs.gov","middleInitial":"D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823047,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70226498,"text":"70226498 - 2021 - Assessment of multiple ecosystem metabolism methods in an estuary","interactions":[],"lastModifiedDate":"2021-11-22T13:21:39.355414","indexId":"70226498","displayToPublicDate":"2021-09-13T07:12:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9929,"text":"Limnology & Oceanography: Methods","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of multiple ecosystem metabolism methods in an estuary","docAbstract":"

Ecosystem metabolism is a key ecological attribute and easy to describe, but quantifying metabolism in estuaries is challenging. Properly scaling measurements through time and space requires consideration of hydrodynamics and mixing water from heterogeneous sources, making any estimation uncertain. Here, we compared three methods for modeling ecosystem metabolism in a portion of the Sacramento-San Joaquin Delta. Metabolism estimates based on laboratory incubations, continuous in situ buoys, and an oxygen isotope approach all indicated the system was net heterotrophic, and calculated rates were comparable in magnitude when averaged over the 2-month study. Daily metabolic rates based on in situ buoys were the most variable, likely due to horizontal and vertical advection and poor portrayal of the dissolved oxygen budget. After temporally averaging in situ buoy estimates or smoothing the dissolved oxygen time series for tidal effects, rates were more comparable to the other methods, which may be necessary to account for tidal advection and unbalanced contributions from subhabitats within the metabolic footprint. Incubation-based rates represent the finest temporal and spatial scale and only account for pelagic processes, which may explain why incubation-based rates were lower than the other two methods. The oxygen isotope method provided temporally and spatially integrated rates that were bracketed by the other two methods and may be a valuable tool in systems matching the model requirements. Because uncertainty arises in each method from a number of assumptions and scaling calculations, the resolution of metabolic rates in estuaries is likely coarser and more variable than in other aquatic ecosystems.

","language":"English","publisher":"Wiley","doi":"10.1002/lom3.10458","usgsCitation":"Loken, L.C., Van Nieuwenhuyse, E.E., Dahlgren, R.A., Kammel, L., Stumpner, P., Burau, J.R., and Sadro, S., 2021, Assessment of multiple ecosystem metabolism methods in an estuary: Limnology & Oceanography: Methods, v. 19, no. 11, p. 741-757, https://doi.org/10.1002/lom3.10458.","productDescription":"17 p.","startPage":"741","endPage":"757","ipdsId":"IP-128814","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":450828,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/05g263g3","text":"External Repository"},{"id":391974,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.6953125,\n 37.735969208590504\n ],\n [\n -121.124267578125,\n 37.735969208590504\n ],\n [\n -121.124267578125,\n 39.47860556892209\n ],\n [\n -122.6953125,\n 39.47860556892209\n ],\n [\n -122.6953125,\n 37.735969208590504\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Loken, Luke C. 0000-0003-3194-1498 lloken@usgs.gov","orcid":"https://orcid.org/0000-0003-3194-1498","contributorId":195600,"corporation":false,"usgs":true,"family":"Loken","given":"Luke","email":"lloken@usgs.gov","middleInitial":"C.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827108,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Nieuwenhuyse, Erwin E 0000-0002-9032-2681","orcid":"https://orcid.org/0000-0002-9032-2681","contributorId":269423,"corporation":false,"usgs":false,"family":"Van Nieuwenhuyse","given":"Erwin","email":"","middleInitial":"E","affiliations":[{"id":6736,"text":"Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":827109,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dahlgren, Randy A 0000-0002-8961-875X","orcid":"https://orcid.org/0000-0002-8961-875X","contributorId":269424,"corporation":false,"usgs":false,"family":"Dahlgren","given":"Randy","email":"","middleInitial":"A","affiliations":[{"id":7082,"text":"University of California - Davis","active":true,"usgs":false}],"preferred":false,"id":827110,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kammel, Leah 0000-0003-4613-0858","orcid":"https://orcid.org/0000-0003-4613-0858","contributorId":211840,"corporation":false,"usgs":true,"family":"Kammel","given":"Leah","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827111,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stumpner, Paul 0000-0002-0933-7895 pstump@usgs.gov","orcid":"https://orcid.org/0000-0002-0933-7895","contributorId":5667,"corporation":false,"usgs":true,"family":"Stumpner","given":"Paul","email":"pstump@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827112,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burau, Jon R. 0000-0002-5196-5035 jrburau@usgs.gov","orcid":"https://orcid.org/0000-0002-5196-5035","contributorId":1500,"corporation":false,"usgs":true,"family":"Burau","given":"Jon","email":"jrburau@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827113,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sadro, Steven 0000-0002-6416-3840","orcid":"https://orcid.org/0000-0002-6416-3840","contributorId":139662,"corporation":false,"usgs":false,"family":"Sadro","given":"Steven","email":"","affiliations":[{"id":12871,"text":"Marine Science Institute, University of California, Santa Barbara, CA, USA","active":true,"usgs":false}],"preferred":false,"id":827114,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}