The San Juan River is a major water source for communities in the Four Corners Region of the United States (Colorado, Arizona, New Mexico, Utah) and is a vital source of water for the Navajo Nation. The Navajo Nation Environmental Protection Agency (NNEPA) periodically samples surface water on the Navajo Nation and has found that some elements exceed NNEPA surface water standards (the upper limits of an element for consumption or other use of water). Constituents of concern are substances that could be harmful if present in sufficient quantities, and it is important to keep track of the concentrations of these substances in the environment. In the San Juan River, constituents of concern include metals detected in river water, such as arsenic, lead, and aluminum. These metals can come from natural sources or can result from human activities (anthropogenic) and can affect the health of people, plants, and animals. The Animas River is one natural source of metals to the San Juan River because of the types of rock through which the Animas River flows and because of hard rock mining at the headwaters. Other potential sources of metals are oil and gas development, coal mining, coal-fired power plants, urban areas, illegal trash dumping, abandoned uranium mines and mills, overgrazed areas, natural geology, and leaching from subsurface agricultural return flows. Determining how much each of these sources contributes and the relative effect of each source on San Juan River water will help the Navajo Nation in their efforts to protect human health and the environment along the San Juan River.
The U.S. Geological Survey (USGS) is working with the NNEPA to identify sources of metals and trace elements entering the San Juan River from tributaries in the reach flowing through the Navajo Nation and to quantify the contribution from each natural and human-caused source. The USGS and NNEPA worked with local community members to locate tributaries where sampling equipment was installed. The 3-year source-tracking project, starting in spring 2021, will identify where metals at concentrations above safe surface water standards might be entering the river by evaluating the chemical signatures of water in the major tributaries of the San Juan River. Results will provide valuable information to the Navajo Nation, public drinking-water managers, irrigation districts, other stakeholders, scientists, and the public.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213029","collaboration":"Prepared in cooperation with the Navajo Nation Environmental Protection Agency","usgsCitation":"Blake, J.M., Chavarria, S.B., and Matherne, A.M., 2021, Tracking the source of metals to the San Juan River (ver. 1.1, July 2021): U.S. Geological Survey Fact Sheet 2021–3029, 4 p., https://doi.org/10.3133/fs20213029.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-128185","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":386921,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2021/3029/versionHist.txt","text":"Version History","size":"1 kB","linkFileType":{"id":2,"text":"txt"},"description":"FS 2021–3029 Version History"},{"id":386149,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3029/coverthb2.jpg"},{"id":386150,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3029/fs20213029.pdf","text":"Report","size":"13.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3029"}],"country":"United States","state":"Colorado, Arizona, New Mexico, Utah","otherGeospatial":"San Juan River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.4892578125,\n 35.37113502280101\n ],\n [\n -106.61132812499999,\n 35.37113502280101\n ],\n [\n -106.61132812499999,\n 38.151837403006766\n ],\n [\n -111.4892578125,\n 38.151837403006766\n ],\n [\n -111.4892578125,\n 35.37113502280101\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: June 3, 2021; Version 1.1: July 2, 2021","contact":"Director, New Mexico Water Science Center
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6700 Edith Blvd. NE
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A comparison of the presence of additives in airport deicers commonly used in the United States and in airport runoff was conducted with data collected before and after changes in deicer formulations. Three isomers of benzotriazoles (BTs)—4-methyl-1H-benzotriazole (4-MeBT), 5-methyl-1H-benzotriazole (5-MeBT), and 1H-benzotriazole (1H-BT)—are corrosion inhibitors added to some formulations of airport deicers and are reported to be a source of aquatic toxicity in streams receiving airport runoff. Concentrations of BT in aircraft deicers and anti-icing fluids (ADAF) were reduced over time but were not reduced in potassium acetate airfield-pavement deicer material (PDM) that was used throughout the study period. Streams receiving runoff from Milwaukee Mitchell International Airport, Milwaukee, Wisconsin, USA, were monitored from 2004 to 2019 for BTs, with concentrations of 4-MeBT varying from <0.35 to 4600 µg/L, 5-MeBT varying from <0.25 to 6600 µg/L, and 1H-BT varying from <0.25 to 150 µg/L. Median 4-MeBT concentrations at sites downstream from the airport decreased by approximately 74%, 5-MeBT by 69%, and 1H-BT by 82% following reduction in BTs in ADAF formulations, resulting in a reduction in the potential for aquatic toxicity in receiving streams. A change in residuals from regression analysis between freezing point depressants and BTs indicate that the reduction in BT concentrations in airport runoff was a result of BT reduction in ADAF formulations, but PDM may still be a substantial source of BTs in airport runoff. Because BTs are a source of aquatic toxicity in airport deicers, the reductions in BTs in the common deicers observed in this study can be used to demonstrate the potential for a reduction in the effects to aquatic organisms in airport runoff, resulting in greater likelihood of meeting aquatic toxicity requirements in airport stormwater permits, and potentially driving airports, airlines, and permit holders to advocate further reduction or elimination of BTs and other harmful contaminants in airport deicers.
","language":"English","publisher":"Wiley","doi":"10.1002/ieam.4468","usgsCitation":"Olds, H., Corsi, S., and Rutter, T.D., 2021, Benzotriazole concentrations in airport runoff are reduced following changes in airport deicer formulations: Integrated Environmental Assessment and Management, v. 18, no. 1, p. 245-257, https://doi.org/10.1002/ieam.4468.","productDescription":"13 p.","startPage":"245","endPage":"257","ipdsId":"IP-119323","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":467241,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ieam.4468","text":"Publisher Index Page"},{"id":464297,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","city":"Milwaukee","otherGeospatial":"Milwaukee Mitchell International Airport","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.93312340482309,\n 42.95913686851276\n ],\n [\n -87.93312340482309,\n 42.93182296880775\n ],\n [\n -87.87946797516405,\n 42.93182296880775\n ],\n [\n -87.87946797516405,\n 42.95913686851276\n ],\n [\n -87.93312340482309,\n 42.95913686851276\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-05-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Olds, Hayley T. 0000-0002-6701-6459 htolds@usgs.gov","orcid":"https://orcid.org/0000-0002-6701-6459","contributorId":215837,"corporation":false,"usgs":true,"family":"Olds","given":"Hayley","email":"htolds@usgs.gov","middleInitial":"T.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Corsi, Steven R. 0000-0003-0583-5536 srcorsi@usgs.gov","orcid":"https://orcid.org/0000-0003-0583-5536","contributorId":172002,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rutter, Troy D. 0000-0001-5130-204X tdrutter@usgs.gov","orcid":"https://orcid.org/0000-0001-5130-204X","contributorId":2081,"corporation":false,"usgs":true,"family":"Rutter","given":"Troy","email":"tdrutter@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918761,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70221076,"text":"sir20215024 - 2021 - Use of dissolved oxygen monitoring to evaluate phosphorus loading in Connecticut streams, 2015–18","interactions":[],"lastModifiedDate":"2021-06-02T17:25:11.979978","indexId":"sir20215024","displayToPublicDate":"2021-06-02T08:11:17","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":"2021-5024","displayTitle":"Use of Dissolved Oxygen Monitoring to Evaluate Phosphorus Loading in Connecticut Streams, 2015–18","title":"Use of dissolved oxygen monitoring to evaluate phosphorus loading in Connecticut streams, 2015–18","docAbstract":"The Connecticut Department of Energy and Environmental Protection (CT DEEP) has developed an interim phosphorus reduction strategy to establish water-quality-based phosphorus limits in nontidal freshwaters for industrial and municipal water pollution control facilities. A recommendation in the strategy included the addition of diurnal dissolved oxygen (DO) sampling to the sampling of diatom communities collected by CT DEEP. The chemistry data coupled with biological data will help to examine the effects of phosphorus loading in streams. The U.S. Geological Survey (USGS), in cooperation with the CT DEEP and New England Interstate Water Pollution Control Commission, implemented a summer DO monitoring program from 2015 to 2018 to examine the effects of phosphorus loading in streams. Continuous DO data were collected at 18 sites in streams with varying concentrations of phosphorus throughout the State of Connecticut. Discrete water-quality nutrient data were collected by the USGS at 11 of the 18 sites. All continuous and discrete data collected from June to September for the 4 years were examined for all sites. This report documents a pattern of diurnal DO for monitoring sites across 4 years and presents estimated daily gross primary productivity (GPP), ecosystem respiration (ER), and a standardized rate coefficient for gas exchange for selected streams. Relations of phosphorus concentrations to the diurnal DO response and stream metabolism are described. Interannual variability in average annual total phosphorus (TP) concentrations and maximum daily DO concentrations were evaluated among sites in years of the study. Streams identified as impaired by CT DEEP such as Naugatuck River at Beacon Falls (USGS station 01208500), Still River at Route 7 at Brookfield Center (USGS station 01201487), and Quinnipiac River at Wallingford (USGS station 01196500) had higher TP concentrations (greater than 0.10 milligram per liter [mg/L]) throughout the study. Reference streams considered unimpaired had lower concentrations of TP (less than 0.10 mg/L). The range in daily DO concentrations remained less than 4 mg/L for most of the sites during the study except for Naugatuck River at Beacon Falls and Still River at Route 7 at Brookfield Center. Daily GPP and ER were summarized for 11 sites using the maximum likelihood estimation model of the streamMetabolizer package in the R statistical program. The models indicated that most sites had an estimated negative net primary productivity, based on the daily estimates of GPP and ER, which indicates the systems are heterotrophic and dominated by respiration. The high variation of GPP and ER reported for several sites can be affected by many physical, chemical, and biological factors, including the abundance and community composition of phytoplankton, periphyton, and macrophyte algae present. The variability in mean GPP was similar to the variability in maximum DO concentrations when plotted against annual average TP concentrations for the maximum likelihood estimation model in streamMetabolizer. The concept that phosphorus loading can affect the stream metabolism requires more detailed knowledge of stream geomorphic variables (canopy cover, stream velocity, water depth) and algal communities to help improve the scientific basis for managing phosphorus loading.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215024","collaboration":"Prepared in cooperation with the Connecticut Department of Energy and Environmental Protection and New England Interstate Water Pollution Control Commission","usgsCitation":"Izbicki, B., and Morrison, J., 2021, Use of dissolved oxygen monitoring to evaluate phosphorus loading in Connecticut streams, 2015–18: U.S. Geological Survey Scientific Investigations Report 2021–5024, 25 p., https://doi.org/10.3133/sir20215024.","productDescription":"Report: vii, 25 p.; Data Release; Dataset","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-109745","costCenters":[{"id":466,"text":"New England Water Science 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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Streambank erosion and reservoir sedimentation are primary concerns of resource managers in Kansas and throughout many regions of the United States and negatively affect flood control, water supply, and recreation. The Cottonwood and upper Neosho Rivers drain into John Redmond Reservoir, and since reservoir completion in 1964, there has been substantial conservation-pool sedimentation and storage loss in John Redmond Reservoir, causing storage capacity losses more rapidly than most other Federal reservoirs in Kansas. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office, has monitored water quality (temperature, specific conductance, and turbidity) on the Cottonwood River (upstream from the reservoir) and Neosho River (upstream and downstream from the reservoir) since 2007 with additional sites added in 2009. The purpose of this report is to quantify suspended-sediment concentrations, loads, and yields entering and exiting John Redmond Reservoir during January 1, 2010, through December 31, 2019.
Three water-quality monitoring sites were upstream from the reservoir (Cottonwood River near Plymouth, Kansas [USGS site 07182250; hereinafter referred to as “Cottonwood”]; Neosho River at Burlingame Road near Emporia, Kans. [USGS site 07179750; hereinafter referred to as “Burlingame”]; and Neosho River at Neosho Rapids, Kans. [USGS site 07182390; hereinafter referred to as “Neosho Rapids”]), and one water-quality monitoring site was downstream from the reservoir (Neosho River at Burlington, Kans. [USGS site 07182510; hereinafter referred to as “Burlington”]). The Neosho Rapids streamgage is downstream from the confluence of the Cottonwood and upper Neosho Rivers and has a contributing drainage area accounting for 91 percent of the total contributing drainage area to John Redmond Reservoir.
Continuously measured streamflow, water quality, and discrete water-quality data were used to develop updated regression models to compute suspended-sediment concentrations, loads, and yields upstream and downstream from John Redmond Reservoir in east-central Kansas. Several turbidity sensors were deployed during the analysis period, and there are no established relations between the sensors; therefore, individual models for each sensor were developed. Model statistics for the turbidity and suspended-sediment concentration linear regression models were better (based on the coefficient of determination, root mean square error, and model standard percentage error) than the streamflow and suspended-sediment concentration linear regression models, indicating better model performance. Computed concentrations, loads, and yields do not account for the ungaged 9 percent of the drainage basin downstream from the Neosho Rapids streamgage.
Mean daily suspended-sediment loads upstream from the reservoir were largest at Neosho Rapids (2,250 tons), second largest at Cottonwood (2,180 tons), and smallest at Burlingame (624 tons). Streamflow at Burlington was predominately regulated by reservoir releases, and mean daily suspended-sediment loads were smaller (286 tons) than at upstream sites. Among the upstream sites, Cottonwood had the largest mean daily suspended-sediment concentration (179 milligrams per liter [mg/L]), followed by Neosho Rapids (162 mg/L), and Burlingame (108 mg/L). Burlington had the smallest mean daily suspended-sediment concentration of all sites (46 mg/L).
Annual reservoir trapping efficiency ranged from 82 to 94 percent, and the largest sediment mass trapped was during 2019 (2,230,000 tons). Reservoir storage decreased an estimated 7,750 acre-feet during 2010 and 2014–19. Using the mean trapping efficiency to estimate suspended-sediment loads during years with missing data (2011–13), the total estimated reservoir storage lost to sedimentation for the analysis period (2010–19) was 8,690 acre-feet, about 17 percent of the remaining storage space reported in 2007. The mean annual sedimentation rate during the analysis period (747 acre-feet per year) was about 85 percent larger than the design sedimentation rate (404 acre-feet per year) originally projected during construction. Different reservoir outflow management strategies, including operating near normal capacity as opposed to higher flood pool levels, could reduce the total reservoir storage lost by 3 percent (about 261 acre-feet), which is equal to 14 percent of the total sediment removed during the dredging operation in 2016.
During the study period, about 56 percent of the total suspended-sediment load was transported during streamflows greater than the National Weather Service flood action stage at the upstream sites (0.1–5 percent of the record; Cottonwood mean: 48 percent; Burlingame mean: 40 percent; Neosho Rapids mean: 78 percent). Disproportionately large sediment loads were delivered during short periods of time, and localized efforts of stream erosion protection (streambank stabilization, riparian buffers) were likely to be overwhelmed. Precipitation frequency and intensity are projected to continue to increase in this region; therefore, future sediment reduction strategies that account for extreme episodic events may be beneficial. Changes to reservoir outflow management could also minimize sediment accumulation while still preserving flood control. Continued investigation of sediment reduction measures is necessary for future mitigation with the understanding that sedimentation rate is largely driven by high flows. Results from this study can be used to calibrate sediment models, explore sediment reduction strategies, highlight the importance of continued water-quality monitoring to determine effectiveness and changes in sediment transport, and assess the ability of John Redmond Reservoir to support designated uses into the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215037","collaboration":"Prepared in cooperation with the Kansas Water Office","usgsCitation":"Kramer, A.R., Peterman-Phipps, C.L., Mahoney, M.D., and Lukasz, B.S., 2021, Sediment concentrations and loads upstream from and through John Redmond Reservoir, east-central Kansas, 2010–19: U.S. Geological Survey Scientific Investigations Report 2021–5037, 49 p., https://doi.org/10.3133/sir20215037.","productDescription":"Report: ix, 50 p; Appendixes: 12; Dataset","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119997","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":386084,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5037/sir20215037_appendix09.pdf","text":"Appendix 9","size":"457 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5037 Appendix 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November 13, 2015, through December 31, 2019"},{"id":386085,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5037/sir20215037_appendix10.pdf","text":"Appendix 10","size":"418 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5037 Appendix 10","linkHelpText":"— Model Archive Summary for Suspended-Sediment Concentration at U.S. Geological Survey Site 07179750, Neosho River at Burlingame Road near Emporia, Kansas, during January 1, 2010, through December 31, 2019"},{"id":386080,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5037/sir20215037_appendix05.pdf","text":"Appendix 5","size":"376 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5037 Appendix 5","linkHelpText":"— Model Archive Summary for Suspended-Sediment Concentration at U.S. Geological Survey Site 07182250, Cottonwood River near Plymouth, Kansas, during April 22, 2015, through December 31, 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Redmond Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.52838134765624,\n 38.01131226070673\n ],\n [\n -95.49041748046875,\n 38.01131226070673\n ],\n [\n -95.49041748046875,\n 39.27266344858914\n ],\n [\n -97.52838134765624,\n 39.27266344858914\n ],\n [\n -97.52838134765624,\n 38.01131226070673\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
The Greater Yellowstone Area (GYA) is one of the last remaining large and nearly intact temperate ecosystems on Earth. GYA was originally defined in the 1970s as the Greater Yellowstone Ecosystem, which encompassed the minimum range of the grizzly bear. The boundary now includes about 22 million acres (8.9 million ha) in northwestern Wyoming, south central Montana, and eastern Idaho (Figure ES-1). Two national parks, five national forests, three wildlife refuges, 20 counties, and state and private lands lie within the GYA boundary (Figure ES-1). The Tribal Nations of the Eastern Shoshone, Northern Arapaho, Apsa´alooke/Crow, Northern Cheyenne, Shoshone, and Bannock have reservations in and near the Greater Yellowstone Area, and 27 Tribes are formally recognized to have historical connections to the lands and resources of the region. Natural resources sensitive to climate change connect many of the major economic activities of the GYA, including tourism and recreation, agriculture, and energy development.
","language":"English","publisher":"Montana State University","doi":"10.15788/GYCA2021","usgsCitation":"Hostetler, S.W., Whitlock, C., Shuman, B., Liefert, D., Wolf Drimal, C., and Bischke, S., 2021, Greater Yellowstone climate assessment: Past, present, and future climate change in the greater Yellowstone watersheds: Greater Yellowstone Climate Assessment, 218 p., https://doi.org/10.15788/GYCA2021.","productDescription":"218 p.","ipdsId":"IP-127170","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":452032,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15788/gyca2021","text":"Publisher Index Page"},{"id":436330,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P972JAUC","text":"USGS data release","linkHelpText":"Data release for Greater Yellowstone Climate Assessment (vol 1), Chapter 7. Future Water Projections for the GYA"},{"id":397116,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Greater Yellowstone watersheds","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.54394531249999,\n 42.09822241118974\n ],\n [\n -108.2373046875,\n 42.09822241118974\n ],\n [\n -108.2373046875,\n 45.9511496866914\n ],\n [\n -112.54394531249999,\n 45.9511496866914\n ],\n [\n -112.54394531249999,\n 42.09822241118974\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2021-06-23","publicationStatus":"PW","contributors":{"editors":[{"text":"Alder, Jay R. 0000-0003-2378-2853 jalder@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-2853","contributorId":5118,"corporation":false,"usgs":true,"family":"Alder","given":"Jay","email":"jalder@usgs.gov","middleInitial":"R.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":837847,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Pederson, Gregory T. 0000-0002-6014-1425 gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":837848,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Hostetler, Steven W. 0000-0003-2272-8302 swhostet@usgs.gov","orcid":"https://orcid.org/0000-0003-2272-8302","contributorId":3249,"corporation":false,"usgs":true,"family":"Hostetler","given":"Steven","email":"swhostet@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":837841,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whitlock, Cathy","contributorId":79745,"corporation":false,"usgs":false,"family":"Whitlock","given":"Cathy","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":837842,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shuman, Bryan","contributorId":205232,"corporation":false,"usgs":false,"family":"Shuman","given":"Bryan","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":837843,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Liefert, David 0000-0001-7682-6835","orcid":"https://orcid.org/0000-0001-7682-6835","contributorId":288395,"corporation":false,"usgs":false,"family":"Liefert","given":"David","email":"","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":837844,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wolf Drimal, Charles","contributorId":288398,"corporation":false,"usgs":false,"family":"Wolf Drimal","given":"Charles","email":"","affiliations":[{"id":61749,"text":"Greater Yellowstone Coalition","active":true,"usgs":false}],"preferred":false,"id":837845,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bischke, Scott","contributorId":288399,"corporation":false,"usgs":false,"family":"Bischke","given":"Scott","email":"","affiliations":[{"id":61752,"text":"Mountain Works","active":true,"usgs":false}],"preferred":false,"id":837846,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228972,"text":"70228972 - 2021 - A review of factors affecting PIT tag detection using mobile arrays and use of mobile antennas to detect PIT-tagged suckers in a wadeable Ozark stream","interactions":[],"lastModifiedDate":"2022-02-25T15:35:34.113825","indexId":"70228972","displayToPublicDate":"2021-06-01T09:22:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"A review of factors affecting PIT tag detection using mobile arrays and use of mobile antennas to detect PIT-tagged suckers in a wadeable Ozark stream","docAbstract":"Advantages of passive integrated transponder (PIT) tags are their small size, longevity, and low-cost compared to other tags. PIT tags are often used in fisheries to study movement patterns, survival, or estimate population size. However, PIT tags are limited by their short detection distance. Mobile PIT antennas may increase the utility of PIT tags in fisheries. In this study, we synthesize the current detection efficiency literature on mobile PIT antennas, determine factors influencing PIT-tag detection probability and efficiency for a raft-mounted mobile antenna, and summarize techniques used to increase observations of PIT-tagged fishes with raft-mounted mobile antennas in a wadable stream. Our literature review indicated tag size and orientation were the most-important factors affecting detection probabilities; however, our antenna was primarily influenced by water depth of the tag and distance from the antenna. Detection efficiency was influenced by discharge, turbidity, and sample date. Tracking methods that include targeting key habitats (e.g., rootwads) and using natural features to congregated tagged fishes (e.g., riffles or pinch points) may increase detection efficiency in wadable streams. This is the first formal review of factors affecting mobile PIT antenna detection efficiency. The published literature, combined with our study results, indicate several factors need to be considered prior to mobile PIT antenna tracking.","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10578","usgsCitation":"Zentner, D., Wolf, S., Brewer, S.K., and Shoup, D.E., 2021, A review of factors affecting PIT tag detection using mobile arrays and use of mobile antennas to detect PIT-tagged suckers in a wadeable Ozark stream: North American Journal of Fisheries Management, v. 41, no. 3, p. 697-710, https://doi.org/10.1002/nafm.10578.","productDescription":"14 p.","startPage":"697","endPage":"710","ipdsId":"IP-119648","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":498905,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10578","text":"Publisher Index 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University","active":true,"usgs":false}],"preferred":false,"id":836057,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":836058,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shoup, Daniel E.","contributorId":141325,"corporation":false,"usgs":false,"family":"Shoup","given":"Daniel","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":836059,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70226922,"text":"70226922 - 2021 - A multi-tracer and well-bore flow profile approach to determine occurrence, movement, and sources of perchlorate in groundwater","interactions":[],"lastModifiedDate":"2021-12-21T14:59:34.058138","indexId":"70226922","displayToPublicDate":"2021-06-01T08:43:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"A multi-tracer and well-bore flow profile approach to determine occurrence, movement, and sources of perchlorate in groundwater","docAbstract":"The purpose of this study is to determine the occurrence, movement and sources of perchlorate in groundwater using a comprehensive set of environmental tracers coupled with discreet borehole data. Potential sources of perchlorate to groundwater at the study site have been attributed to waste disposal and industrial activities as well as to past agricultural operations. Perchlorate concentrations in samples ranged from <1 to 40 g/l, with a median of 6.1 g/l. Concentrations were relativity consistent with depth except at one site where dilution may be occurring due to the infiltration of surface water from Pyrite Creek. Well-bore flow profiles indicated that perchlorate redistribution was occurring via intra-well bore flow at one site where up to 14,000 mg/year of perchlorate could be moving from the shallower to the deeper zones of the alluvial aquifer. Natural attenuation processes of perchlorate do not appear to be widespread in groundwater but does occur in portions of the aquifer adjacent to the Santa Ana River, likely limiting the mobility of perchlorate from the southernmost extent of the mapped plume to areas further down-gradient. Age dating tracers indicate that perchlorate originating from the waste disposal ponds has largely moved through the zones of the aquifer sampled. Age distributions, noble gas temperature, delta neon values and stable isotopes of water indicate that a substantial fraction of perchlorate in groundwater may have been mobilized from the unsaturated zone and/or is from the infiltration of storm water runoff originating from Pyrite Canyon.","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2021.104959","usgsCitation":"Wright, M., Izbicki, J.A., and Jurgens, B.C., 2021, A multi-tracer and well-bore flow profile approach to determine occurrence, movement, and sources of perchlorate in groundwater: Applied Geochemistry, v. 129, p. 1-18, https://doi.org/10.1016/j.apgeochem.2021.104959.","productDescription":"104959, 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-116219","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":452054,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2021.104959","text":"Publisher Index Page"},{"id":393189,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Jurupa Valley","otherGeospatial":"Jurupa Mountains, Mira Loma Hills, Pedley Hills, San Sevaine Channel, Santa Ana River, Stringfellow Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.66082763671875,\n 33.945638452963024\n ],\n [\n -117.14241027832031,\n 33.945638452963024\n ],\n [\n -117.14241027832031,\n 34.34343606848294\n ],\n [\n -117.66082763671875,\n 34.34343606848294\n ],\n [\n -117.66082763671875,\n 33.945638452963024\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"129","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Wang, Z. Zimeng","contributorId":270243,"corporation":false,"usgs":false,"family":"Wang","given":"Z.","email":"","middleInitial":"Zimeng","affiliations":[],"preferred":false,"id":828813,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Wright, Michael 0000-0003-0653-6466 mtwright@usgs.gov","orcid":"https://orcid.org/0000-0003-0653-6466","contributorId":151031,"corporation":false,"usgs":true,"family":"Wright","given":"Michael","email":"mtwright@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828799,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Izbicki, John A. 0000-0003-0816-4408 jaizbick@usgs.gov","orcid":"https://orcid.org/0000-0003-0816-4408","contributorId":152474,"corporation":false,"usgs":true,"family":"Izbicki","given":"John","email":"jaizbick@usgs.gov","middleInitial":"A.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828800,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X bjurgens@usgs.gov","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":127842,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","email":"bjurgens@usgs.gov","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828801,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70221599,"text":"70221599 - 2021 - Watersheds and drainage networks","interactions":[],"lastModifiedDate":"2021-06-25T12:49:28.165348","indexId":"70221599","displayToPublicDate":"2021-06-01T07:47:23","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Watersheds and drainage networks","docAbstract":"This topic is an overview of basic concepts about how the distribution of water on the Earth, with specific regard to watersheds, stream and river networks, and waterbodies are represented by geographic data. The flowing and non-flowing bodies of water on the earth’s surface vary in extent largely due to seasonal and annual changes in climate and precipitation. Consequently, modeling the detailed representation of surface water using geographic information is important. The area of land that collects surface runoff and other flowing water and drains to a common outlet location defines a watershed. Terrain and surface features can be naturally divided into watersheds of various sizes. Drainage networks are important data structures for modeling the distribution and movement of surface water over the terrain. Numerous tools and methods exist to extract drainage networks and watersheds from digital elevation models (DEMs). The cartographic representations of surface water are referred to as hydrographic features and consist of a snapshot at a specific time. Hydrographic features can be assigned general feature types, such as lake, pond, river, and ocean. Hydrographic features can be stored, maintained, and distributed for use through vector geospatial databases, such as the National Hydrography Dataset (NHD) for the United States.
Despite its successes, the U.S. Endangered Species Act (ESA) has proven challenging to implement due to funding limitations, workload backlog, and other problems. As threats to species survival intensify and as more species come under threat, the need for the ESA and similar conservation laws and policies in other countries to function efficiently has grown. Attempts by the U.S. Fish and Wildlife Service (USFWS) to streamline ESA decisions include multispecies recovery plans and habitat conservation plans. We address species status assessment (SSA), a USFWS process to inform ESA decisions from listing to recovery, within the context of multispecies and ecosystem planning. Although existing SSAs have a single-species focus, ecosystem-based research can efficiently inform multiple SSAs within a region and provide a foundation for transition to multispecies SSAs in the future. We considered at-risk grassland species and ecosystems within the southeastern United States, where a disproportionate number of rare and endemic species are associated with grasslands. To initiate our ecosystem-based approach, we used a combined literature-based and structured World Café workshop format to identify science needs for SSAs. Discussions concentrated on 5 categories of threats to grassland species and ecosystems, consistent with recommendations to make shared threats a focus of planning under the ESA: (1) habitat loss, fragmentation, and disruption of functional connectivity; (2) climate change; (3) altered disturbance regimes; (4) invasive species; and (5) localized impacts. For each threat, workshop participants identified science and information needs, including database availability, research priorities, and modeling and mapping needs. Grouping species by habitat and shared threats can make the SSA process and other planning processes for conservation of at-risk species worldwide more efficient and useful. We found a combination of literature review and structured discussion effective for identifying the scientific information and analysis needed to support the development of multiple SSAs.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/cobi.13777","usgsCitation":"Noss, R., Cartwright, J.M., Estes, D., Witsell, T., Elliott, G., Adams, D.S., Albrecht, M.A., Boyles, R., Comer, P., Doffitt, C., Hill, J.G., Hunter, W.C., Knapp, W.M., Marshall, M., Singhurst, J.R., Tracey, C., Walck, J.L., and Weakley, A., 2021, Improving species status assessments under the U.S. Endangered Species Act and implications for multispecies conservation challenges worldwide: Conservation Biology, v. 35, no. 6, p. 1715-1724, https://doi.org/10.1111/cobi.13777.","productDescription":"10 p.","startPage":"1715","endPage":"1724","ipdsId":"IP-122143","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":452069,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/cobi.13777","text":"External 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0000-0001-9695-0577","orcid":"https://orcid.org/0000-0001-9695-0577","contributorId":258189,"corporation":false,"usgs":false,"family":"Adams","given":"Daniel","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":818524,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Albrecht, Matthew A. 0000-0002-1079-1630","orcid":"https://orcid.org/0000-0002-1079-1630","contributorId":213559,"corporation":false,"usgs":false,"family":"Albrecht","given":"Matthew","email":"","middleInitial":"A.","affiliations":[{"id":38790,"text":"Missouri Botanical Garden","active":true,"usgs":false}],"preferred":false,"id":818525,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Boyles, Ryan 0000-0001-9272-867X","orcid":"https://orcid.org/0000-0001-9272-867X","contributorId":221983,"corporation":false,"usgs":true,"family":"Boyles","given":"Ryan","affiliations":[{"id":565,"text":"Southeast Climate Science 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important sportfish and within the Chesapeake Bay watershed has experienced a high prevalence of external lesions, infectious disease, mortality events, reproductive endocrine disruption and population declines. To date, no clear or consistent associations with contaminants measured in fish tissue or surface water have been found. Therefore, plasma samples from two sites in the Potomac River and two in the Susquehanna River drainage basins, differing in land-use characteristics, were utilized to determine if perfluoroalkyl substances were present. Four compounds, perfluorooctane sulphonic acid (PFOS), perfluoroundecanoic acid (PFUnA), perfluorodecanoic acid (PFDA) and perfluorododecanoic acid (PFDoA), were detected in every fish. Two additional compounds, perfluorooctane sulphonamide (PFOSA) and perfluorononanoic acid (PFNA), were less commonly detected at lower concentrations, depending on the site. Concentrations of PFOS (up to 574 ng/mL) were the highest detected and varied significantly among sites. No seasonal differences (spring versus fall) in plasma concentrations were observed. Concentrations of PFOS were not significantly different between the sexes. However, PFUnA and PFDoA concentrations were higher in males than females. Both agricultural and developed land-use appeared to be associated with exposure. Further research is needed to determine if these compounds could be affecting the health of smallmouth bass and identify sources.
","language":"English","publisher":"MDPI","doi":"10.3390/ijerph18115881","usgsCitation":"Blazer, V., Gordon, S.E., Walsh, H.L., and Smith, C.R., 2021, Perfluoroalkyl substances in plasma of smallmouth bass from the Chesapeake Bay Watershed: International Journal of Environmental Research and Public Health, v. 11, no. 18, 5881, 13 p., https://doi.org/10.3390/ijerph18115881.","productDescription":"5881, 13 p.","ipdsId":"IP-126689","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":452074,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/ijerph18115881","text":"Publisher Index Page"},{"id":436331,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H8DW78","text":"USGS data release","linkHelpText":"Morphometric, Age and Concentrations of Perfluoroalkyl Substances in Smallmouth Bass Plasma from Four Sites in the Chesapeake Bay 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Center","active":true,"usgs":true}],"preferred":true,"id":816655,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Cheyenne R. 0000-0002-7226-1774","orcid":"https://orcid.org/0000-0002-7226-1774","contributorId":219236,"corporation":false,"usgs":true,"family":"Smith","given":"Cheyenne","email":"","middleInitial":"R.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":816656,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70220904,"text":"70220904 - 2021 - Surface flow velocities from space: Particle image velocimetry of satellite video of a large, sediment-laden river","interactions":[],"lastModifiedDate":"2021-05-28T18:41:13.32766","indexId":"70220904","displayToPublicDate":"2021-05-28T13:36:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7170,"text":"Frontiers in Water","active":true,"publicationSubtype":{"id":10}},"title":"Surface flow velocities from space: Particle image velocimetry of satellite video of a large, sediment-laden river","docAbstract":"Conventional, field-based streamflow monitoring in remote, inaccessible locations such as Alaska poses logistical challenges. Safety concerns, financial considerations, and a desire to expand water-observing networks make remote sensing an appealing alternative means of collecting hydrologic data. In an ongoing effort to develop non-contact methods for measuring river discharge, we evaluated the potential to estimate surface flow velocities from satellite video of a large, sediment-laden river in Alaska via particle image velocimetry (PIV). In this setting, naturally occurring sediment boil vortices produced distinct water surface features that could be tracked from frame to frame as they were advected by the flow, obviating the need to introduce artificial tracer particles. In this study, we refined an end-to-end workflow that involved stabilization and geo-referencing, image preprocessing, PIV analysis with an ensemble correlation algorithm, and post-processing of PIV output to filter outliers and scale and geo-reference velocity vectors. Applying these procedures to image sequences extracted from satellite video allowed us to produce high resolution surface velocity fields; field measurements of depth-averaged flow velocity were used to assess accuracy. Our results confirmed the importance of preprocessing images to enhance contrast and indicated that lower frame rates (e.g., 0.25 Hz) lead to more reliable velocity estimates because longer capture intervals allow more time for water surface features to translate several pixels between frames, given the relatively coarse spatial resolution of the satellite data. Although agreement between PIV-derived velocity estimates and field measurements was weak (R2 = 0.39) on a point-by-point basis, correspondence improved when the PIV output was aggregated to the cross-sectional scale. For example, the correspondence between cross-sectional maximum velocities inferred via remote sensing and measured in the field was much stronger (R2 = 0.76), suggesting that satellite video could play a role in measuring river discharge. Examining correlation matrices produced as an intermediate output of the PIV algorithm yielded insight on the interactions between image frame rate and sensor spatial resolution, which must be considered in tandem. Although further research and technological development are needed, measuring surface flow velocities from satellite video could become a viable tool for streamflow monitoring in certain fluvial environments.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frwa.2021.652213","usgsCitation":"Legleiter, C.J., and Kinzel, P.J., 2021, Surface flow velocities from space: Particle image velocimetry of satellite video of a large, sediment-laden river: Frontiers in Water, v. 3, 652213, 20 p., https://doi.org/10.3389/frwa.2021.652213.","productDescription":"652213, 20 p.","ipdsId":"IP-125455","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":452077,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frwa.2021.652213","text":"Publisher Index Page"},{"id":436332,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZY5LK1","text":"USGS data release","linkHelpText":"Satellite video and field measurements of flow velocity acquired from the Tanana River in Alaska and used for particle image velocimetry (PIV)"},{"id":386020,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Nenana","otherGeospatial":"Tanana River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -149.21218872070312,\n 64.53486288126804\n ],\n [\n -148.92929077148438,\n 64.53486288126804\n ],\n [\n -148.92929077148438,\n 64.61387025268262\n ],\n [\n -149.21218872070312,\n 64.61387025268262\n ],\n [\n -149.21218872070312,\n 64.53486288126804\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","noUsgsAuthors":false,"publicationDate":"2021-05-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":816651,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"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":816652,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70220871,"text":"sir20205057 - 2021 - Flood-inundation maps for the Blue River near Red Bridge Road, Kansas City, Missouri, 2019","interactions":[],"lastModifiedDate":"2021-05-28T19:21:03.271116","indexId":"sir20205057","displayToPublicDate":"2021-05-28T11:11:37","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":"2020-5057","displayTitle":"Flood-Inundation Maps for the Blue River near Red Bridge Road, Kansas City, Missouri, 2019","title":"Flood-inundation maps for the Blue River near Red Bridge Road, Kansas City, Missouri, 2019","docAbstract":"Digital flood-inundation maps for a 4.6-mile reach of the Blue River near Red Bridge Road in Kansas City, Missouri, were created by the U.S. Geological Survey (USGS), in cooperation with the City of Kansas City, Missouri. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage 06893195, Blue River at Red Bridge Road, Kansas City, Mo. Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or the Johnson County, Kansas, StormWatch Automated Local Elevation in Real Time Flood Warning System at https://www.stormwatch.com.
Flood profiles were computed for the Blue River reach by means of a one-dimensional model for simulating water-surface profiles with steady-state flow computations. The model was calibrated by using the current stage-streamflow relations at the upstream USGS streamgage 06893150, Blue River at Blue Ridge Boulevard Extension, Kansas City, Mo., and the downstream streamgage 06893500, Blue River at Kansas City, Mo.
The hydraulic model was then used to compute 37 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 11 ft, or near bankfull, to 47 ft at the reference streamgage 06893195. The upper stage for the map library exceeds the stage corresponding to the estimated 0.2-percent annual exceedance probability flood (500-year recurrence interval flood) in the model reach. The simulated water-surface profiles were then combined with a geographic information system digital elevation model with a maximum 10-centimeter vertical root mean square error and 4.0-ft horizontal resolution to delineate the area flooded at each water level.
The availability of these maps, along with real-time internet information regarding current stage from the USGS streamgage, will help guide emergency management personnel and residents in flood mitigation, preparedness and planning, flood-response activities such as evacuations and road closures, and any postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205057","collaboration":"Prepared in cooperation with the City of Kansas City, Missouri","usgsCitation":"Heimann, D.C., Voss, J.D., and Rydlund, P.H., Jr., 2021, Flood-inundation maps for the Blue River near Red Bridge Road, Kansas City, Missouri, 2019: U.S. Geological Survey Scientific Investigations Report 2020–5057, 14 p., https://doi.org/10.3133/sir20205057.","productDescription":"Report: vi, 14 p.; Data Release; Dataset","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-117597","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":385983,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90MH291","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets for the flood-inundation study of the Blue River near Red Bridge Road, Kansas City, Missouri, 2019"},{"id":385984,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":385981,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5057/coverthb.jpg"},{"id":385982,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5057/sir20205057.pdf","text":"Report","size":"1.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5057"}],"country":"United States","state":"Kansas, Missouri","otherGeospatial":"Blue River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.83333,\n 38.8333\n ],\n [\n -94.45,\n 38.8333\n ],\n [\n -94.45,\n 39.1666\n ],\n [\n -94.833333,\n 39.1666\n ],\n [\n -94.833333,\n 38.8333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
The Souris River Basin is a 61,000-square-kilometer basin in the Provinces of Saskatchewan and Manitoba in Canada and the State of North Dakota in the United States. Greater than average snowpack during the winter of 2010–11, along with record-setting rains in May and June 2011, resulted in historically unprecedented flooding in the Souris River Basin. The severity of the 2011 flood led the United States and Canada to request a review of the operating plan for any improvements of reservoir operations and flood control measures in the basin, and the Souris River Basin Task Force was formed. The International Souris River Study Board was then formed in 2017 to carry out the recommendations of the Souris River Basin Task Force laid out in a plan of study. To support the International Souris River Study Board, the U.S. Geological Survey (USGS), in cooperation with the North Dakota State Water Commission and the International Joint Commission, used the previously developed unregulated and regulated streamflow models and data for stochastic streamflow in the Souris River Basin to characterize climate and streamflow and support selection of streamflow traces based on their characterization. Components of the original stochastic hydrology models and their outputs were used in this phase of the study to (1) characterize historical and stochastic climate and streamflow for the Souris River Basin, (2) disaggregate monthly stochastic streamflow spatially and temporally to meet the needs of the U.S. Army Corps of Engineers, Hydrologic Engineering Center, Reservoir System Simulation model for the Souris River Basin, and (3) discuss selection of disaggregated streamflow traces (simulations) using the characteristics of climate and streamflow. A trace is a time series of a stochastic variable such as streamflow, potential evapotranspiration, or precipitation.
To characterize climate conditions, precipitation, potential evapotranspiration (PET), and moisture deficit for the Souris River Basin and individual points at Rafferty, Grant Devine, and Lake Darling Reservoirs were determined annually and seasonally. The annual basin (November 1–October 31) precipitation for the 50-percent nonexceedance probability is 452 millimeters (mm). Spring (March–May) is the wettest season, followed by summer (June–August), fall (September–November), and winter (December–February). Annual moisture deficit was largest at Lake Darling Reservoir, followed by Rafferty Reservoir, and then Grant Devine Reservoir.
Annual maximum monthly mean streamflow was determined for the Souris River below Rafferty Reservoir, Saskatchewan (Canadian streamgage 05NB036); Long Creek near Noonan (above Boundary Reservoir), North Dakota (USGS streamgage 05113600); Moose Mountain Creek near Oxbow, Saskatchewan (Canadian streamgage 05ND004); the Souris River near Sherwood, N. Dak. (USGS streamgage 05114000); the Des Lacs River at Foxholm, N. Dak. (USGS streamgage 05116500); and the Souris River above Minot, N. Dak. (USGS streamgage 05117500). When the seasonal maximum monthly mean streamflows are evaluated in contrast to annual maximum monthly mean streamflows separated by their seasonal occurrence, summer months of annual maximum monthly mean streamflows have a higher 50-percent exceedance probability of streamflow compared to annual maximum monthly mean streamflows that occur in spring, seasonal maximum monthly mean streamflows that occur in spring, and seasonal maximum monthly mean streamflows that occur in summer. When annual maximum monthly mean streamflows in summer are compared to annual maximum monthly mean streamflows in spring, they are consistently higher in streamflow but occur in less than 4.2 percent of years. Evaluation of whether the annual maximum monthly mean streamflows that occur in summer can be described as a separate population from annual maximum monthly mean streamflows that occur in spring was outside the scope of this study, and the summer and spring annual maximum monthly mean streamflows were not tested for statistical differences in mean or variance. Further investigation of seasonal weather patterns that induce flooding could lead to a better understanding of the seasonal differences in flooding.
Long-term hydrologic drought was characterized by evaluating multiyear mean streamflow. Shorter averaging periods have greater streamflow variability than longer periods and hence have a wider range of values. As the averaging period is extended to a longer period, the variability of mean streamflow decreases, and the more extreme streamflow volumes seen in shorter averaging periods cannot be sustained. Stochastic streamflow time series were disaggregated spatially and temporally for use in a HEC–ResSim model. The combination of monthly and daily stochastic streamflow data was used to select traces with qualities that could be used to test alternatives focused on water supply, summer flooding, and apportionment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215044","collaboration":"Prepared in cooperation with the North Dakota State Water Commission and the International Joint Commission","usgsCitation":"Gregory, A., and Galloway, J.M., 2021, Characterization of historical and stochastically generated climate and streamflow conditions in the Souris River Basin, United States and Canada: U.S. Geological Survey Scientific Investigations Report 2021–5044, 36 p., https://doi.org/10.3133/sir20215044.","productDescription":"Report: viii, 36 p.; Data Release; Dataset","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-120682","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":386014,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":386011,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5044/coverthb.jpg"},{"id":386012,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5044/sir20215044.pdf","text":"Report","size":"5.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021—5044"},{"id":386013,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93AOWFL","text":"USGS data release","linkHelpText":"Historical and stochastically generated climate and streamflow data for the Souris River Basin, United States and Canada"}],"country":"Canada, United States","state":"Manitoba, North Dakota, Saskatchewan","otherGeospatial":"Souris River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.55859375,\n 46.6795944656402\n ],\n [\n -98.0859375,\n 50.12057809796008\n ],\n [\n -101.25,\n 51.67255514839674\n ],\n [\n -107.138671875,\n 53.48804553605622\n ],\n [\n -108.6328125,\n 50.958426723359935\n ],\n [\n -102.568359375,\n 48.22467264956519\n ],\n [\n -99.66796875,\n 46.98025235521883\n ],\n [\n -97.55859375,\n 46.6795944656402\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Mars Geological Enigmas From the Late Noachian Epoch to the Present Day","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-820245-6.00021-5","usgsCitation":"Dundas, C., Conway, S.J., and Stillman, D.E., 2021, Introduction: Does water flow on Martian slopes?, chap. of Mars Geological Enigmas From the Late Noachian Epoch to the Present Day, p. 205-206, https://doi.org/10.1016/B978-0-12-820245-6.00021-5.","productDescription":"2 p.","startPage":"205","endPage":"206","ipdsId":"IP-123147","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":392787,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dundas, Colin M. 0000-0003-2343-7224","orcid":"https://orcid.org/0000-0003-2343-7224","contributorId":237028,"corporation":false,"usgs":true,"family":"Dundas","given":"Colin M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":828201,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Susan J.","contributorId":203697,"corporation":false,"usgs":false,"family":"Conway","given":"Susan","email":"","middleInitial":"J.","affiliations":[{"id":36693,"text":"University of Nantes","active":true,"usgs":false}],"preferred":false,"id":828202,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stillman, David E","contributorId":141053,"corporation":false,"usgs":false,"family":"Stillman","given":"David","email":"","middleInitial":"E","affiliations":[{"id":13664,"text":"Southwest Research Institute, Boulder CO","active":true,"usgs":false}],"preferred":false,"id":828203,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70226787,"text":"70226787 - 2021 - Dry formation of recent Martian slope features","interactions":[],"lastModifiedDate":"2021-12-13T13:27:42.656542","indexId":"70226787","displayToPublicDate":"2021-05-28T07:26:38","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"10","title":"Dry formation of recent Martian slope features","docAbstract":"Martian surface conditions are cold and dry, unfavorable for liquid water, yet steep slopes display young and currently active features suggestive of wet processes. These include recurring slope lineae and slope streaks, gully landforms, and small lobate features. Wet origins for these features would imply surprising amounts of liquid water at the surface. However, detailed observations of the morphology and activity of these features have demonstrated that dry processes, some of them unique to the Martian environment, can account for all of them. This reconciles the contradiction between physics and geomorphology and provides a self-consistent model of a Martian surface that is very active today despite having negligible volumes of liquid water.
In Humboldt Bay, tectonic subsidence exacerbates sea-level rise (SLR). To build surface elevations and to keep pace with SLR, the sediment demand created by subsidence and SLR must be balanced by an adequate sediment supply. This study used an ensemble of plausible future scenarios to predict potential climate change impacts on suspended-sediment discharge (Qss) from fluvial sources. Streamflow was simulated using a deterministic water-balance model, and Qss was computed using statistical sediment-transport models. Changes relative to a baseline period (1981–2010) were used to assess climate impacts. For local basins that discharge directly to the bay, the ensemble means projected increases in Qss of 27% for the mid-century (2040–2069) and 58% for the end-of-century (2070–2099). For the Eel River, a regional sediment source that discharges sediment-laden plumes to the coastal margin, the ensemble means projected increases in Qss of 53% for the mid-century and 99% for the end-of-century. Climate projections of increased precipitation and streamflow produced amplified increases in the regional sediment supply that may partially or wholly mitigate sediment demand caused by the combined effects of subsidence and SLR. This finding has important implications for coastal resiliency. Coastal regions with an increasing sediment supply may be more resilient to SLR. In a broader context, an increasing sediment supply from fluvial sources has global relevance for communities threatened by SLR that are increasingly building resiliency to SLR using sediment-based solutions that include regional sediment management, beneficial reuse strategies, and marsh restoration.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-021-00938-x","usgsCitation":"Curtis, J., Flint, L.E., Stern, M.A., Lewis, J., and Klein, R.D., 2021, Amplified impact of climate change on fine-sediment delivery to a subsiding coast, Humboldt Bay, California: Estuaries and Coasts, v. 44, p. 2173-2193, https://doi.org/10.1007/s12237-021-00938-x.","productDescription":"21 p.","startPage":"2173","endPage":"2193","ipdsId":"IP-102755","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":452090,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-021-00938-x","text":"Publisher Index Page"},{"id":436333,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97UBENK","text":"USGS data release","linkHelpText":"Daily Basin Characterization Model (BCM) archive for Humboldt Bay/Eel River"},{"id":386195,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Humboldt County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.49707031249999,\n 39.53793974517623\n ],\n [\n -123.96972656249999,\n 39.53793974517623\n ],\n [\n -123.96972656249999,\n 41.41801503608022\n ],\n [\n -124.49707031249999,\n 41.41801503608022\n ],\n [\n -124.49707031249999,\n 39.53793974517623\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","noUsgsAuthors":false,"publicationDate":"2021-05-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Curtis, Jennifer 0000-0001-7766-994X","orcid":"https://orcid.org/0000-0001-7766-994X","contributorId":212727,"corporation":false,"usgs":true,"family":"Curtis","given":"Jennifer","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816912,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816913,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stern, Michelle A. 0000-0003-3030-7065 mstern@usgs.gov","orcid":"https://orcid.org/0000-0003-3030-7065","contributorId":4244,"corporation":false,"usgs":true,"family":"Stern","given":"Michelle","email":"mstern@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816914,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lewis, Jack","contributorId":189105,"corporation":false,"usgs":false,"family":"Lewis","given":"Jack","email":"","affiliations":[],"preferred":false,"id":816915,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Klein, Randy D.","contributorId":259269,"corporation":false,"usgs":false,"family":"Klein","given":"Randy","email":"","middleInitial":"D.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":816916,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221343,"text":"70221343 - 2021 - Use of the smeltCam as an efficient fish sampling alternative within the San Francisco Estuary","interactions":[],"lastModifiedDate":"2021-06-11T12:05:22.631939","indexId":"70221343","displayToPublicDate":"2021-05-28T07:04:41","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Use of the smeltCam as an efficient fish sampling alternative within the San Francisco Estuary","docAbstract":"Resource managers often rely on long-term monitoring surveys to detect trends in biological data. However, no survey gear is 100% efficient, and many sources of bias can be responsible for detecting or not detecting biological trends. The SmeltCam is an imaging apparatus developed as a potential sampling alternative to long-term trawling gear surveys within the San Francisco Estuary, California, to reduce handling stress on sensitive species like the Delta Smelt (Hypomesus transpacificus). Although believed to be a reliable alternative to closed cod-end trawling surveys, no formal test of sampling efficiency has been implemented using the SmeltCam. We used a paired deployment of the SmeltCam and a conventional closed cod-end trawl within the Napa River and San Pablo Bay, a Bayesian binomial N-mixture model, and data simulations to determine the sampling efficiency of both deployed gear types to capture a Delta Smelt surrogate (Northern Anchovy, Engraulis mordax) and to test potential bias in our modeling framework. We found that retention efficiency—a component of detection efficiency that estimates the probability a fish is retained by the gear, conditional on gear contact—was slightly higher using the SmeltCam (mean = 0.58) than the conventional trawl (mean = 0.47, Probability SmeltCam retention efficiency > trawl retention efficiency = 94%). We also found turbidity did not affect the SmeltCam’s retention efficiency, although total fish density during an individual tow improved the trawl’s retention efficiency. Simulations also showed the binomial model was accurate when model assumptions were met. Collectively, our results suggest the SmeltCam to be a reliable alternative to sampling with conventional trawling gear, but future tests are needed to confirm whether the SmeltCam is as reliable when applied to taxa other than Northern Anchovy over a greater range of conditions.
","language":"English","publisher":"University of California","doi":"10.15447/sfews.2021v19iss2art6","usgsCitation":"Huntsman, B., Feyrer, F.V., and Young, M.J., 2021, Use of the smeltCam as an efficient fish sampling alternative within the San Francisco Estuary: San Francisco Estuary and Watershed Science, v. 19, no. 2, 16 p., https://doi.org/10.15447/sfews.2021v19iss2art6.","productDescription":"16 p.","ipdsId":"IP-123894","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":452096,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2021v19iss2art6","text":"Publisher Index Page"},{"id":386410,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Francisco","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.57421875,\n 36.84446074079564\n ],\n [\n -121.86035156249999,\n 36.84446074079564\n ],\n [\n -121.86035156249999,\n 39.40224434029275\n ],\n [\n -123.57421875,\n 39.40224434029275\n ],\n [\n -123.57421875,\n 36.84446074079564\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-06-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Huntsman, Brock 0000-0003-4090-1949","orcid":"https://orcid.org/0000-0003-4090-1949","contributorId":223101,"corporation":false,"usgs":true,"family":"Huntsman","given":"Brock","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Feyrer, Frederick V. 0000-0003-1253-2349 ffeyrer@usgs.gov","orcid":"https://orcid.org/0000-0003-1253-2349","contributorId":178379,"corporation":false,"usgs":true,"family":"Feyrer","given":"Frederick","email":"ffeyrer@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Matthew J. 0000-0001-9306-6866 mjyoung@usgs.gov","orcid":"https://orcid.org/0000-0001-9306-6866","contributorId":206255,"corporation":false,"usgs":true,"family":"Young","given":"Matthew","email":"mjyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817386,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239051,"text":"70239051 - 2021 - Predicting light regime controls on primary productivity across CONUS river networks","interactions":[],"lastModifiedDate":"2022-12-22T13:03:46.202793","indexId":"70239051","displayToPublicDate":"2021-05-28T06:54:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Predicting light regime controls on primary productivity across CONUS river networks","docAbstract":"Solar radiation is a fundamental driver of ecosystem productivity, but widespread estimates of light available for primary producers in rivers are lacking. We developed a model to predict light available for river primary producers and used it to estimate river primary production across the contiguous United States (CONUS). Successively accounting for riparian and water column processes improved predictions of primary production as a function of light. We calculated the ratio of river width to riparian tree height and used this metric to predict whether riparian zones or water column processes most limit productivity for over 2 million reaches. Water column processes limited productivity for 50% of the nation's river length and 80% of its surface area, with variations across ecoregions related to riparian forest cover. Our findings facilitate large-scale predictions of stream and river ecosystem productivity, as well as understanding the processes controlling productivity across networks.
Between June 2018 and July 2019, the U.S. Geological Survey collaborated with the New York State Department of Environmental Conservation to analyze water quality in seven embayments on Long Island, New York, for a study to examine fecal contamination using microbial source tracking. This report documents the approach, methodology, and quality-assurance data used in the study. All samples and field data were collected in accordance with U.S. Geological Survey National Field Manual procedures. Samples were analyzed for host-specific deoxyribonucleic acid (DNA) markers, fecal coliform bacteria, inorganic and total organic nitrogen, and stable isotopes of nitrate and ammonium.
Samples for quality control were collected for microbiological analyses at a rate of 1 per 20 environmental samples. A total of 14 blank and 15 replicate samples were collected for DNA markers, 52 sequential field replicates were analyzed by the Public Environmental Health Laboratory of the Suffolk County Department of Health Services and the New York State Department of Conservation Marine Laboratory for fecal coliform, and 7 blank and 7 replicate samples were collected to be analyzed for nutrients. Results from quality-control samples collected throughout the course of the study confirmed that sampling procedures were adequate and did not disqualify any data from analysis.
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425 Jordan Road
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One of the paramount goals of oyster reef living shorelines is to achieve sustained and adaptive coastal protection, which requires meeting ecological (i.e., develop a self-sustaining oyster population) and engineering (i.e., provide coastal defense) targets. In a large-scale comparison along the Atlantic and Gulf coasts of the United States, the efficacy of various designs of oyster reef living shorelines at providing wave attenuation was evaluated accounting for the ecological limitations of oysters with regard to inundation duration. A critical threshold for intertidal oyster reef establishment is 50% inundation duration. Living shorelines that spent less than one-half of the time (<50%) inundated were not considered suitable habitat for oysters, however, were effective at wave attenuation (68% reduction in wave height). Reefs that experienced >50% inundation were considered suitable habitat for oysters, but wave attenuation was similar to controls (no reef; ~5% reduction in wave height). Many of the oyster reef living shoreline approaches therefore failed to optimize the ecological and engineering goals. In both inundation regimes, wave transmission decreased with an increasing freeboard (difference between reef crest elevation and water level), supporting its importance in the wave attenuation capacity of oyster reef living shorelines. However, given that the reef crest elevation (and thus freeboard) should be determined by the inundation duration requirements of oysters, research needs to be refocused on understanding the implications of other reef parameters (e.g., width) for optimizing wave attenuation. A broader understanding of the reef characteristics and seascape contexts that result in effective coastal defense by oyster reefs is needed to inform appropriate design and implementation of oyster-based living shorelines globally.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2382","usgsCitation":"Morris, R.L., La Peyre, M., Webb, B.M., Marshall, D.A., Bilkovic, D., Cebrian, J., McClenachan, G., Kibler, K.M., Walters, L.J., Bushek, D., Sparks, E.L., Temple, N.A., Moody, J., Angstadt, K., Goff, J., Boswell, M.K., Sacks, P.E., and Swearer, S.E., 2021, Large-scale variation in wave attenuation of oyster reef living shorelines and the influence of inundation duration: Ecological Applications, v. 31, no. 6, e02382, 15 p., https://doi.org/10.1002/eap.2382.","productDescription":"e02382, 15 p.","ipdsId":"IP-113781","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":481103,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://scholarworks.wm.edu/vimsarticles/2082","text":"External Repository"},{"id":396101,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, New Jersey, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.35205078124999,\n 30.278044377800153\n ],\n [\n -88.0224609375,\n 30.278044377800153\n ],\n [\n -88.0224609375,\n 30.751277776257812\n ],\n [\n -88.35205078124999,\n 30.751277776257812\n ],\n [\n -88.35205078124999,\n 30.278044377800153\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.736328125,\n 29.707139348134145\n ],\n [\n -89.31884765624999,\n 29.707139348134145\n ],\n [\n -89.31884765624999,\n 30.20211367909724\n ],\n [\n -89.736328125,\n 30.20211367909724\n ],\n [\n -89.736328125,\n 29.707139348134145\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.14501953125,\n 28.671310915880834\n ],\n [\n -80.5078125,\n 28.671310915880834\n ],\n [\n -80.5078125,\n 29.209713225868185\n ],\n [\n -81.14501953125,\n 29.209713225868185\n ],\n [\n -81.14501953125,\n 28.671310915880834\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.43212890625,\n 39.07890809706475\n ],\n [\n -74.970703125,\n 39.07890809706475\n ],\n [\n -74.970703125,\n 39.50404070558415\n ],\n [\n -75.43212890625,\n 39.50404070558415\n ],\n [\n -75.43212890625,\n 39.07890809706475\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.61865234374999,\n 37.09023980307208\n ],\n [\n -76.0693359375,\n 37.09023980307208\n ],\n [\n -76.0693359375,\n 37.77071473849609\n ],\n [\n -76.61865234374999,\n 37.77071473849609\n ],\n [\n -76.61865234374999,\n 37.09023980307208\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Morris, R. 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A.","affiliations":[],"preferred":false,"id":835215,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Angstadt, K.","contributorId":279613,"corporation":false,"usgs":false,"family":"Angstadt","given":"K.","email":"","affiliations":[{"id":57314,"text":"William & Mary","active":true,"usgs":false}],"preferred":false,"id":835216,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Goff, J.","contributorId":279614,"corporation":false,"usgs":false,"family":"Goff","given":"J.","affiliations":[{"id":48711,"text":"Dauphin Island Sea Lab","active":true,"usgs":false}],"preferred":false,"id":835217,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Boswell, M. 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E.","contributorId":243401,"corporation":false,"usgs":false,"family":"Swearer","given":"S.","email":"","middleInitial":"E.","affiliations":[{"id":13336,"text":"University of Melbourne","active":true,"usgs":false}],"preferred":false,"id":835220,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70220879,"text":"70220879 - 2021 - Appendix C: Central sands lakes study technical report: Modeling documentation","interactions":[],"lastModifiedDate":"2021-05-27T14:04:05.646141","indexId":"70220879","displayToPublicDate":"2021-05-27T08:51:14","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":8761,"text":"Wisconsin DNR Technical Report","active":true,"publicationSubtype":{"id":2}},"title":"Appendix C: Central sands lakes study technical report: Modeling documentation","docAbstract":"This report provides the necessary documentation of the numerical models developed for the Central Sands Lake study in central Wisconsin and will be included as a technical appendix in the report to the Wisconsin State Legislature by the Wisconsin Department of Natural Resources (WDNR) in response to 2017 Wisconsin Act 10. This legislation directed WDNR to determine whether existing and potential groundwater withdrawals are causing or are likely to cause significant reduction of mean seasonal water levels at Pleasant Lake, Long Lake, and Plainfield Lake (s. 281.34(7m)(2)(b), Wis. Stats.) in Waushara County, Wisconsin. To evaluate the potential hydrologic connection between groundwater withdrawals and the nearby study lakes, hydrologic models were created that focused on the lakes of interest and yet were large enough to cover a broad enough region to extend to the major hydrologic boundaries of the natural flow system. The areas near the lakes require finer-scale grid discretization (or spacing) to better represent the lakes and streams in the model, but also need to cover a large enough area to include the groundwater withdrawal locations that have the potential to cause reduction in water levels in the lakes. To accomplish these goals, three groundwater models were created: a regional model extending to major hydrologic boundaries; and two inset models, inheriting boundaries from the regional model but focused near the lakes. Each of the inset models, in turn, included a detailed area close to the lakes surrounded by an area at the same spatial scale as the regional model (Figure 1).
To support WDNR in evaluating the connection between groundwater withdrawals and lake levels, a representative time period was required over which to compare land use with and without irrigated agriculture and for WDNR to evaluate potential lake stage and flux changes related to irrigated agriculture. WDNR chose the climate period of 1981-2018 to be representative of a typical period and provided two land use scenarios—one with no irrigated agriculture and one with assumed crop rotations similar to current conditions—to simulate with groundwater models to, then, compare lake responses with. As a result, simulations over this climate record are not intended to recreate the history of 1981-2018 because land use changed over that time. These runs are, instead, intended to provide a basis on which to compare land use with and without irrigation-related groundwater withdrawals based on the current arrangement of land use and a varied climatic record. Groundwater withdrawals focused on irrigated-agriculture-related water use because greater than 95% of groundwater withdrawal in the two inset models around the study lakes is for irrigated agriculture water use.
The period of 2012-2018 was used for parameter estimation (synonymously referred to as “history matching”) for the groundwater models. This time period was chosen because it includes the most complete water use records to simulate groundwater withdrawals. History matching was performed using groundwater elevations, lake stages, and streamflow observations over the 2012-2018 time period and processed observations derived from those raw data.
Climatic data were incorporated into the model using a soil-water balance approach. A soil water balance model was constructed at the scale of the regional groundwater model to both calculate recharge based on land use and climate, and in the long-term climate-period runs, to estimate water use required by irrigated agriculture to apply as well boundary conditions in the groundwater model in the absence of reported water use values over that period.
","language":"English","publisher":"Wisconsin Department of Natural Resources","usgsCitation":"Fienen, M., Haserodt, M.J., Leaf, A.T., and Westenbroek, S., 2021, Appendix C: Central sands lakes study technical report: Modeling documentation: Wisconsin DNR Technical Report, ix, 137 p.","productDescription":"ix, 137 p.","ipdsId":"IP-127829","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":386002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385990,"type":{"id":15,"text":"Index Page"},"url":"https://dnr.wisconsin.gov/topic/Wells/HighCap/CSLStudy.html"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Central Sands region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.78851318359375,\n 43.58834891179792\n ],\n [\n -89.29962158203125,\n 43.57641143300888\n ],\n [\n -89.219970703125,\n 43.75919263886012\n ],\n [\n -89.54132080078125,\n 44.471031231561845\n ],\n [\n -89.7967529296875,\n 44.41808794374846\n ],\n [\n -89.85443115234375,\n 44.33367180085156\n ],\n [\n -89.98901367187499,\n 44.11125397357155\n ],\n [\n -90.01373291015625,\n 44.03232064275081\n ],\n [\n -89.96978759765625,\n 43.878097874251736\n ],\n [\n -89.8187255859375,\n 43.71156424665851\n ],\n [\n -89.78851318359375,\n 43.58834891179792\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haserodt, Megan J. 0000-0002-8304-090X mhaserodt@usgs.gov","orcid":"https://orcid.org/0000-0002-8304-090X","contributorId":174791,"corporation":false,"usgs":true,"family":"Haserodt","given":"Megan","email":"mhaserodt@usgs.gov","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leaf, Andrew T. 0000-0001-8784-4924 aleaf@usgs.gov","orcid":"https://orcid.org/0000-0001-8784-4924","contributorId":5156,"corporation":false,"usgs":true,"family":"Leaf","given":"Andrew","email":"aleaf@usgs.gov","middleInitial":"T.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Westenbroek, Stephen, M. 0000-0002-6284-8643","orcid":"https://orcid.org/0000-0002-6284-8643","contributorId":206429,"corporation":false,"usgs":true,"family":"Westenbroek","given":"Stephen, M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816550,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70221099,"text":"70221099 - 2021 - Long-term shedding from fully convalesced individuals indicates that Pacific herring are a reservoir for viral hemorrhagic septicemia virus","interactions":[],"lastModifiedDate":"2021-06-02T12:13:11.151017","indexId":"70221099","displayToPublicDate":"2021-05-27T07:10:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1396,"text":"Diseases of Aquatic Organisms","active":true,"publicationSubtype":{"id":10}},"title":"Long-term shedding from fully convalesced individuals indicates that Pacific herring are a reservoir for viral hemorrhagic septicemia virus","docAbstract":"Processes that allow viral hemorrhagic septicemia (VHS) virus to persist in the marine environment remain enigmatic, owing largely to the presence of covert and cryptic infections in marine fishes during typical sub-epizootic periods. As such, marine host reservoirs for VHS virus have not been fully demonstrated, nor have the mechanism(s) by which infected hosts contribute to virus perpetuation and transmission. Here, we demonstrate that after surviving VHS, convalesced Pacific herring continue to shed virus at a low rate for extended periods. Further, exposure of previously naïve conspecific sentinels to this shed virus can result in infections for at least 6 mo after cessation of overt disease. This transmission mechanism was not necessarily dependent on the magnitude of the disease outbreak, as prolonged transmission occurred from 2 groups of donor herring that experienced cumulative mortalities of 4 and 29%. The results further suggest that the virus persists in association with the gills of fully recovered individuals, and long-term viral shedding or shedding relapses are related to cooler or decreasing water temperatures. These results provide support for a new VHS virus perpetuation paradigm in the marine environment, whereby the virus can be maintained in convalesced survivors and trafficked from these carriers to sympatric susceptible individuals.
","language":"English","publisher":"Inter-Research","doi":"10.3354/dao03595","usgsCitation":"Hershberger, P., MacKenzie, A., Gregg, J.L., Wilmot, M.D., Powers, R., and Purcell, M.K., 2021, Long-term shedding from fully convalesced individuals indicates that Pacific herring are a reservoir for viral hemorrhagic septicemia virus: Diseases of Aquatic Organisms, v. 144, p. 245-252, https://doi.org/10.3354/dao03595.","productDescription":"8 p.","startPage":"245","endPage":"252","ipdsId":"IP-123287","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":386111,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"144","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hershberger, Paul 0000-0002-2261-7760","orcid":"https://orcid.org/0000-0002-2261-7760","contributorId":203322,"corporation":false,"usgs":true,"family":"Hershberger","given":"Paul","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":816761,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"MacKenzie, Ashley 0000-0002-7402-7877 amackenzie@usgs.gov","orcid":"https://orcid.org/0000-0002-7402-7877","contributorId":150817,"corporation":false,"usgs":true,"family":"MacKenzie","given":"Ashley","email":"amackenzie@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":816762,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gregg, Jacob L. 0000-0001-5328-5482 jgregg@usgs.gov","orcid":"https://orcid.org/0000-0001-5328-5482","contributorId":203912,"corporation":false,"usgs":true,"family":"Gregg","given":"Jacob","email":"jgregg@usgs.gov","middleInitial":"L.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":816763,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilmot, M. D.","contributorId":259184,"corporation":false,"usgs":false,"family":"Wilmot","given":"M.","email":"","middleInitial":"D.","affiliations":[{"id":36303,"text":"unknown","active":true,"usgs":false}],"preferred":false,"id":816764,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Powers, Rachel L. 0000-0001-6901-4361","orcid":"https://orcid.org/0000-0001-6901-4361","contributorId":190182,"corporation":false,"usgs":true,"family":"Powers","given":"Rachel L.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":816765,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Purcell, Maureen K. 0000-0003-0154-8433 mpurcell@usgs.gov","orcid":"https://orcid.org/0000-0003-0154-8433","contributorId":168475,"corporation":false,"usgs":true,"family":"Purcell","given":"Maureen","email":"mpurcell@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":816766,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}