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Transport and fate of dissolved organic carbon (DOC) in rivers are important aspects of the carbon cycle and the critical linkage between terrestrial, aquatic, and marine systems. Recent studies have quantified fluvial export to the marine environment in many systems, but in-stream losses of DOC are poorly constrained. This study compares DOC yields (kg C/ha) between the area-weighted averages of several tributaries within larger watersheds with the DOC yields of the larger watersheds to gain insight on in-stream losses in larger river systems. Four large watersheds, 22 tributaries to those watersheds, and 5 additional main stem locations in Maine were studied during 1 April to 15 November in 2011 through 2013. There were no significant differences in the area-weighted average DOC yield of the tributaries and the larger watersheds indicating little net in-stream loss in the main stems of the larger rivers. It is unlikely that inputs of DOC from un-gauged areas compensated for losses from gauged tributaries based on similarity in DOC yield longitudinally along the main stems of two of the rivers. In addition, wetland abundance, which is associated with higher DOC yield in this environment, did not consistently increase from tributaries to the larger watershed or longitudinally along the main stems. This geographic distribution of wetlands therefore also indicates that it is unlikely that inputs of DOC from un-gauged areas compensated for losses from gauged tributaries. These findings suggest that in-stream losses of DOC in these larger river systems are minimal and that the vast majority of DOC in major rivers in Maine is transported conservatively to the coastal ocean.
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States\"}}]}","edition":"Version 1.1: May 28, 2019","contact":"Chief, EcoScience Synthesis
Director of BISON and ITIS
Core Science Systems Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive, Mailstop 302
Reston, Virginia 20192
bison@usgs.gov
The “National Field Manual for the Collection of Water-Quality Data” (NFM) provides guidelines and procedures for U.S. Geological Survey (USGS) personnel who collect data used to assess the quality of the Nation’s surface-water and groundwater resources. This chapter, NFM A6.3, provides guidance and protocols for the measurement of specific conductance of a water sample, which include the scientific basis of the measurement, selection and maintenance of equipment, calibration, troubleshooting, and procedures for measurement and reporting. It updates and supersedes USGS Techniques of Water-Resources Investigations, book 9, chapter A6.3, version 1.2, by D.B. Radtke, J.V. Davis, and F.D. Wilde.
Specific conductance is routinely measured when water samples are collected, is often measured continually at USGS streamgages, and is a parameter regularly measured during laboratory and field experiments. The field method for measuring specific conductance described in this chapter is applicable to most natural waters.
Before 2017, the NFM chapters were released in the USGS Techniques of Water-Resources Investigations series. Effective in 2018, new and revised NFM chapters are being released in the USGS Techniques and Methods series; this series change does not affect the content and format of the NFM. More information is in the general introduction to the NFM (USGS Techniques and Methods, book 9, chapter A0) at https://doi.org/10.3133/tm9A0. The authoritative current versions of NFM chapters are available in the USGS Publications Warehouse at https://pubs.er.usgs.gov. Comments, questions, and suggestions related to the NFM can be addressed to nfm@usgs.gov.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: National field manual for the collection of water-quality data in Book 9: Handbooks for water-resources investigations","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm9A6.3","usgsCitation":"U.S. Geological Survey, 2019, Chapter A6.3. Specific Conductance: U.S. Geological Survey Techniques and Methods 9-A6.3, vi, 15 p., https://doi.org/10.3133/tm9A6.3.","productDescription":"vi, 15 p.","costCenters":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":361085,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/tm9A0","text":"Techniques and Methods 9-A0","linkHelpText":"General Introduction for the “National Field Manual for the Collection of Water-Quality Data\""},{"id":360655,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/09/a6.3/tm9-a6_3.pdf","text":"Report","size":"1.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 9A63"},{"id":360656,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/tm/09/a6.3//versionHist.txt","size":"2.91 KB","linkFileType":{"id":2,"text":"txt"}},{"id":360654,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/09/a6.3/coverthb.jpg"}],"contact":"Chief, Office of Quality Assurance
U.S. Geological Survey
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Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
Chlorinated volatile organic compounds have affected groundwater beneath a former 9-acre landfill at Operable Unit 1 (OU 1) of Naval Base Kitsap (NBK) Keyport, in Keyport, Washington. The landfill was the primary disposal area for domestic and industrial waste generated by NBK Keyport from the 1930s through 1973. Naval Facilities Engineering Command Northwest, in conjunction with the Environmental Protection Agency, Washington State Department of Ecology, and the Suquamish Tribe, is charged with collecting necessary data to monitor the contamination left in place and to ensure that the site does not pose a risk to human health or the environment.
To support these efforts, refined information was collected on how groundwater levels throughout OU 1 respond to tidal fluctuations at this nearshore site adjacent to Liberty Bay, an inlet of Puget Sound. The information was analyzed to determine the optimal times during the semidiurnal and the neap-spring tidal cycles to sample groundwater for contaminants associated with fresh groundwater originating from OU 1. The optimal times for sampling are presumed to be when fresh groundwater flowing seaward is least impeded by elevated tides, and those times are related to predicted tide levels by tidal lags, the durations between low tides, and corresponding low groundwater levels. Discrete groundwater-specific conductance data also were collected to determine if a seawater/freshwater interface was present at any of the monitoring wells, and to inform decisions on the depth at which groundwater should be sampled in existing wells.
Groundwater and surface-water levels were monitored at 19 monitoring wells and five adjacent surface-water sites. Specific conductance was monitored in each surface-water site. All time-series data parameters were collected every 15 minutes during a 4-week duration to measure how nearshore groundwater responds to tidal forcing. Time-series data were collected from July 12, 2018, to August 8, 2018, a period that included neap and spring tides. Vertical water-quality profiles were measured once in the screened interval of nine selected monitoring wells. The profiles included measurements at the top, middle, and bottom of each saturated screen interval.
Tidal lag times were determined relative to tidal levels in Liberty Bay (rather than in the more nearby Tide Flats) because the predicted tides for the Poulsbo, Washington Station (National Oceanic and Atmospheric Administration [NOAA] Station 9445719) that are used to schedule groundwater sampling represent open-water conditions in the area; a sill that separates Dogfish Bay from the Tide Flats clearly affects the timing and magnitude of low-low tides in the Tide Flats. Calculated tidal lag times were divided into three general groups: (1) wells where groundwater responded to tidal level changes immediately, (2) wells where groundwater responded to tidal level changes within about 2–5 hours, and (3) wells where groundwater had minimal response to tidal level changes. Groundwater levels in the middle group of wells primarily responded in concert with tidal level changes in the Tide Flats rather than tidal level changes in Liberty Bay.
An intended sampling depth refinement based on an assessment of transient seawater intrusion was not completed because of a failure to collect specific-conductance time-series data in select wells. Instead, discrete specific-conductance data from this and prior studies were evaluated to determine that the midpoint of well screens in OU 1 wells can be assumed to be a reasonably representative of undiluted groundwater. When sampling during spring (rather than neap) tides (as has generally been the standard practice at OU 1), the optimal time to sample the monitoring wells influenced by tides would be to add the tidal lags presented in this report to the time of the predicted low-low tide for Liberty Bay as measured at NOAA Station 9445719 at Poulsbo, Washington. Sampling schedules for the six wells where groundwater levels were only minimally influenced by tide changes should not be constrained by tidal conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191098","collaboration":"Prepared in cooperation with the Department of the Navy, Naval Facilities Engineering Command, Northwest","usgsCitation":"Opatz, C.C., and Dinicola, R.S., 2019, Analysis of groundwater response to tidal fluctuations, Operable Unit 1, Naval Base Kitsap, Keyport, Washington: U.S. Geological Survey Open-File Report 2019-1098, 36 p., https://doi.org/10.3133/ofr20191098.","productDescription":"vi, 36 p.","onlineOnly":"Y","ipdsId":"IP-107656","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":367168,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1098/coverthb.jpg"},{"id":367169,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1098/ofr20191098.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1098"}],"country":"United States","state":"Washington","city":"Keyport","otherGeospatial":"Naval Base Kitsap","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.62941598892212,\n 47.694699930336995\n ],\n [\n -122.62280702590942,\n 47.694699930336995\n ],\n [\n -122.62280702590942,\n 47.69943693711954\n ],\n [\n -122.62941598892212,\n 47.69943693711954\n ],\n [\n -122.62941598892212,\n 47.694699930336995\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Steep declines in North American monarch butterfly (Danaus plexippus) populations have prompted continent-wide conservation efforts. While monarch monitoring efforts have existed for years, we lack a comprehensive approach to monitoring population vital rates integrated with habitat quality to inform adaptive management and effective conservation strategies. Building a geographically and ecologically representative dataset of monarchs and their habitat will improve these efforts. These data will help track long-term changes in the distribution and abundance of monarchs and their habitats, refine population and habitat models, and illuminate how conservation activities affect monarchs and their habitats. The Monarch Conservation Science Partnership developed the Integrated Monarch Monitoring Program (IMMP) to profile breeding habitats and their use by monarchs in North America. A spatially balanced random sampling framework guides site selection, while also allowing opportunistic inclusion of sites chosen by participants, such as conservation areas. The IMMP weaves new protocols together with those from existing monitoring programs to improve data compatibility for assessing milkweed (Asclepias spp.) density, nectar resources, monarch reproduction and survival, and adult monarch habitat use. Participants may select a protocol subset according to interests or local monitoring objectives, thereby maximizing contributions. Conservation partners, including public and private land managers, academic researchers, and citizen scientists contribute data to a national dataset available for analyses at multiple scales. We describe the program and its development, implementation elements that make the program robust and feasible, participation to date, and how IMMP data can advance research and conservation for monarchs, pollinators, and their habitats.
","language":"English","publisher":"Frontiers Media SA","doi":"10.3389/fevo.2019.00167","usgsCitation":"Cariveau, A.B., Holt, H.L., Ward, J.P., Lukens, L., Kasten, K., Thieme, J., Caldwell, W., Tuerk, K., Baum, K.A., Drobney, P., Drum, R.G., Grundel, R., Hamilton, K., Hoang, C., Kinkead, K., McIntyre, J., Thogmartin, W.E., Turner, T., Weiser, E.L., and Oberhauser, K., 2019, The integrated monarch monitoring program: From design to implementation: Frontiers in Ecology and Evolution, v. 29, https://doi.org/10.3389/fevo.2019.00167.","ipdsId":"IP-106644","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":468146,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2019.00167","text":"Publisher Index 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Universities","active":true,"usgs":false}],"preferred":false,"id":763544,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ward, James P","contributorId":215963,"corporation":false,"usgs":false,"family":"Ward","given":"James","email":"","middleInitial":"P","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":763545,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lukens, Laura","contributorId":215964,"corporation":false,"usgs":false,"family":"Lukens","given":"Laura","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":763546,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kasten, Kyle","contributorId":215965,"corporation":false,"usgs":false,"family":"Kasten","given":"Kyle","email":"","affiliations":[{"id":6626,"text":"University of 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0000-0003-1598-659X","orcid":"https://orcid.org/0000-0003-1598-659X","contributorId":213770,"corporation":false,"usgs":true,"family":"Weiser","given":"Emily","email":"","middleInitial":"L.","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":763560,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Oberhauser, Karen","contributorId":191431,"corporation":false,"usgs":false,"family":"Oberhauser","given":"Karen","affiliations":[],"preferred":false,"id":763561,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"id":70270060,"text":"70270060 - 2018 - Landsat-8 on-orbit and Landsat-9 pre-launch sensor radiometric characterization","interactions":[],"lastModifiedDate":"2025-08-08T14:25:23.619796","indexId":"70270060","displayToPublicDate":"2025-08-08T09:20:53","publicationYear":"2018","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Landsat-8 on-orbit and Landsat-9 pre-launch sensor radiometric characterization","docAbstract":"Landsat-8 has been operating on-orbit for 5+ years. Its two sensors, the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), are continuing to produce high quality data. The OLI has been radiometrically stable at the better than 0.3% level on a band average basis for all but the shortest wavelength (443 nm) band, which has degraded about 1.3% since launch. All on-board calibration devices continue to perform well and consistently. No gaps in across track coverage exist as 100% operability of the detectors is maintained. The variability over time of detector responsivity within a band relative to the average is better than 0.05% (1 sigma), though there are occasional detectors that jump up to 1.5% in response in the Short-Wave InfraRed (SWIR) bands. Signal-to-Noise performance continues at 2-3x better than requirements, with a small degradation in the 443 nm band commensurate with the loss in sensitivity. Pre-launch error analysis, combined with the stability of the OLI indicates that the absolute reflectance calibration uncertainty is better than 3%; comparisons to ground measurements and comparisons to other sensors are consistent with this. The Landsat-8 TIRS is similarly radiometrically stable, showing changes of at most 0.3% over the mission. The uncertainty in the absolute calibration as well as the detector to detector variability are largely driven by the stray light response of TIRS. The current processing corrects most of the stray light effects, resulting in absolute uncertainties of ~1% and reduced striping. Efforts continue to further reduce the striping. Noise equivalent delta temperature is about 50 mK at typical temperatures and 100% detector operability is maintained. Landsat-9 is currently under development with a launch no earlier than December 2020. The nearly identical OLI-2 and upgraded TIRS-2 sensors have completed integration and are in the process of instrument level performance characterization including spectral, spatial, radiometric and geometric testing. Component and assembly level measurements of the OLI-2, which include spectral response, radiometric response and stray light indicate comparable performance to OLI. The first functional tests occurred in July 2018 and spatial performance testing in vacuum is scheduled for August 2018. Similarly, for TIRS-2, partially integrated instrument level testing indicated spectral and spatial responses comparable to TIRS, with stray light reduced by approximately an order of magnitude from TIRS.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SPIE","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Society of Photo-Optical Instrumentation Engineers (SPIE)","doi":"10.1117/12.2324715","usgsCitation":"Markham, B., Barsi, J., Montanaro, M., McCorkel, J., Gerace, A., Pedelty, J., Hook, S., Raqueno, N., Anderson, C., and Haque, O., 2018, Landsat-8 on-orbit and Landsat-9 pre-launch sensor radiometric characterization, in Proceedings of SPIE, v. 10781, 1078104, https://doi.org/10.1117/12.2324715.","productDescription":"1078104","ipdsId":"IP-101030","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":494179,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/2060/20190002076","text":"External Repository"},{"id":493833,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10781","noUsgsAuthors":false,"publicationDate":"2018-10-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Markham, Brian L.","contributorId":359395,"corporation":false,"usgs":false,"family":"Markham","given":"Brian L.","affiliations":[{"id":79115,"text":"NASA/GSFC","active":true,"usgs":false}],"preferred":false,"id":945252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barsi, Julia A","contributorId":359397,"corporation":false,"usgs":false,"family":"Barsi","given":"Julia A","affiliations":[{"id":85786,"text":"SSAI, NASA/GSFC","active":true,"usgs":false}],"preferred":false,"id":945253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Montanaro, Matthew","contributorId":251778,"corporation":false,"usgs":false,"family":"Montanaro","given":"Matthew","affiliations":[{"id":32390,"text":"Rochester Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":945254,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCorkel, Joel T","contributorId":359399,"corporation":false,"usgs":false,"family":"McCorkel","given":"Joel T","affiliations":[{"id":79115,"text":"NASA/GSFC","active":true,"usgs":false}],"preferred":false,"id":945255,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gerace, Aaron","contributorId":199173,"corporation":false,"usgs":false,"family":"Gerace","given":"Aaron","email":"","affiliations":[],"preferred":false,"id":945256,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pedelty, Jeffrey A.","contributorId":359400,"corporation":false,"usgs":false,"family":"Pedelty","given":"Jeffrey A.","affiliations":[{"id":79115,"text":"NASA/GSFC","active":true,"usgs":false}],"preferred":false,"id":945257,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hook, Simon J","contributorId":359401,"corporation":false,"usgs":false,"family":"Hook","given":"Simon J","affiliations":[{"id":35444,"text":"NASA/JPL","active":true,"usgs":false}],"preferred":false,"id":945258,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Raqueno, Nina G.","contributorId":359402,"corporation":false,"usgs":false,"family":"Raqueno","given":"Nina G.","affiliations":[{"id":35443,"text":"RIT","active":true,"usgs":false}],"preferred":false,"id":945259,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Anderson, Cody 0000-0001-5612-1889 chanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":195521,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","email":"chanderson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":945260,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Haque, Obaidul 0000-0002-0914-1446 ohaque@usgs.gov","orcid":"https://orcid.org/0000-0002-0914-1446","contributorId":4691,"corporation":false,"usgs":true,"family":"Haque","given":"Obaidul","email":"ohaque@usgs.gov","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":true,"id":945261,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70227998,"text":"70227998 - 2018 - Fish misidentification and potential implications to monitoring within the San Francisco Estuary, California","interactions":[],"lastModifiedDate":"2022-02-03T17:22:45.733023","indexId":"70227998","displayToPublicDate":"2022-08-14T11:16:36","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Fish misidentification and potential implications to monitoring within the San Francisco Estuary, California","docAbstract":"Fish monitoring programs often rely on the collection, species identification, and counting of individual fish over time to inform natural resource management decisions. Thus, the utility of the data used to inform these decisions can be negatively affected by species misidentification. Fish species misidentification bias can be minimized by confirming identification using genetic techniques, training observers, or adjusting monitoring data using estimates of incomplete detection and false-positive misidentification. Despite the existence of well-established fish identification training and quality control programs, there is considerable uncertainty about fish species false-positive misidentification rates and the effectiveness of fish identification training programs within the San Francisco Estuary. We evaluated the misidentification of fish species among Delta Juvenile Fish Monitoring Program observers by conducting five fish identification exams under controlled conditions at the Lodi Fish and Wildlife Office in Lodi, California, between 2012 and 2014. To assess the variability in false-positive misidentification, we fitted data to species and observer characteristics using hierarchical logistic regression. We found that fish species misidentification was fairly common, averaging 17% among 155 test specimens and 32 observers. False-positive misidentification varied considerably among species and was negatively related to fish size, the abundance of the species within monitoring samples, and observer experience. In addition, observers who were not formally trained or used as full-time observers were, on average, 6.0 times more likely to falsely identify a species. However, false-positive misidentification rates among observers and specimens still varied considerably after controlling for observer experience and training, and species and size, respectively. Our results could be used to improve fish identification training and testing, increase the accuracy of fish occupancy or abundance estimation, and justify the allocation of resources to continually use and formally train full-time observers within long-term monitoring programs operating in the system.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/032018-JFWM-020","usgsCitation":"Kirsch, J.E., Day, J.L., Peterson, J., and Fullerton, D.K., 2018, Fish misidentification and potential implications to monitoring within the San Francisco Estuary, California: Journal of Fish and Wildlife Management, v. 9, no. 2, p. 467-485, https://doi.org/10.3996/032018-JFWM-020.","productDescription":"19 p.","startPage":"467","endPage":"485","ipdsId":"IP-096327","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":468147,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/032018-jfwm-020","text":"Publisher Index Page"},{"id":395371,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.77246093750001,\n 37.85750715625203\n ],\n [\n -121.5911865234375,\n 37.85750715625203\n ],\n [\n -121.5911865234375,\n 38.53527591154413\n ],\n [\n -121.77246093750001,\n 38.53527591154413\n ],\n [\n -121.77246093750001,\n 37.85750715625203\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.43850708007811,\n 37.94852933714952\n ],\n [\n -121.77520751953125,\n 37.94852933714952\n ],\n [\n -121.77520751953125,\n 38.238180119798635\n ],\n [\n -122.43850708007811,\n 38.238180119798635\n ],\n [\n -122.43850708007811,\n 37.94852933714952\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"2","noUsgsAuthors":false,"publicationDate":"2018-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Kirsch, J. E.","contributorId":274270,"corporation":false,"usgs":false,"family":"Kirsch","given":"J.","email":"","middleInitial":"E.","affiliations":[{"id":56194,"text":"fs","active":true,"usgs":false}],"preferred":false,"id":832866,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day, J. L.","contributorId":274271,"corporation":false,"usgs":false,"family":"Day","given":"J.","email":"","middleInitial":"L.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":832867,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832868,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fullerton, D. K.","contributorId":274274,"corporation":false,"usgs":false,"family":"Fullerton","given":"D.","email":"","middleInitial":"K.","affiliations":[{"id":56590,"text":"mwd","active":true,"usgs":false}],"preferred":false,"id":832869,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227841,"text":"70227841 - 2018 - Predicting spatial factors associated with cattle depredations by the Mexican wolf (Canis lupus baileyi) with recommendations for depredation risk modeling","interactions":[],"lastModifiedDate":"2022-02-01T17:56:59.268014","indexId":"70227841","displayToPublicDate":"2021-06-21T11:53:14","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Predicting spatial factors associated with cattle depredations by the Mexican wolf (Canis lupus baileyi) with recommendations for depredation risk modeling","title":"Predicting spatial factors associated with cattle depredations by the Mexican wolf (Canis lupus baileyi) with recommendations for depredation risk modeling","docAbstract":"Predation on livestock is one of the primary concerns for Mexican wolf (Canis lupus baileyi) recovery because it causes economic losses and negative attitudes toward wolves. Our objectives were to develop a spatial risk model of cattle depredation by Mexican wolves in the USA portion of their recovery area to help reduce the potential for future depredations.
Arizona and New Mexico, USA.
We used a presence-only maximum entropy modeling approach (Maxent) to develop a risk model based on confirmed depredation incidents on public lands. In addition to landscape and human variables, we developed a model for annual livestock density using linear regression analysis of Animal Unit Month (AUM), and models for abundance of elk (Cervus canadensis), mule deer (Odocoileus hemionus) and white-tailed deer (Odocoileus virginiana) using Maxent, to include them as biotic variables in the risk model. We followed current recommendations for controlling model complexity and other sources of bias.
The primary factors associated with increased risk of depredation by Mexican wolf were higher canopy cover variation and higher relative abundance of elk. Additional factors with increased risk but smaller effect were gentle and open terrain, and greater distances from roads and developed areas.
The risk map revealed areas with relatively high potential for cattle depredations that can inform future expansion of Mexican wolf distribution (e.g., by avoiding hotspots) and prioritize areas for depredation risk mitigation including the implementation of active non-lethal methods in depredation hotspots. We suggest that livestock be better protected in or moved from potential hotspots, especially during periods when they are vulnerable to depredation (e.g. calving season). Our approach to create natural prey and livestock abundance variables can facilitate the process of spatial risk modeling when limitations in availability of abundance data are a challenge, especially in large-scale studies.
Proxy species, which represent suites of organisms with similar habitat requirements, are common in conservation. Landscape Capability (LC) models aim to quantify the spatially-explicit capability of landscapes to support proxy species that represent suites of forest birds.
We evaluated the North Atlantic Landscape Conservation Cooperative (NALCC) proxy models of LC and represented species framework across 13 states in the northeastern United States from Virginia to Maine. We validated a suite of questions related to co-occurrence of proxy and represented species with a compilation of independent datasets.
We tested proxy species LC models ability to explain represented species’ occurrences, including using multiple proxies together, and benchmarked against empirical data and land cover type classifications. We tested effect of several factors on predictive ability including relative range overlap and ecological and taxonomic dissimilarity between proxy and represented species.
LC models performed variably, but represented species occurrences were rarely predicted as accurately as proxy species. Models improved predictions over macrohabitat classifications. Using multiple proxies together occasionally improved predictions of represented species. Considerable range overlap was needed for models to be predictive of represented species. Ecological and taxonomic similarity had no effect on predictive ability. LC models worked similarly to using empirical observations, suggesting shortcomings were because of imperfect surrogacy.
Conservation proxies as representatives of species groups that are associated with macrohabitats are useful, but empirical data are necessary to evaluate proxy species’ effectiveness. Habitat-based models can provide similar predictive ability as empirical observations of proxies and represent a useful tool in conservation planning.
This report provides data collected by the climate monitoring array of the U.S. Department of the Interior on Federal lands in Arctic Alaska over the period August 1998 to July 2019; this array is part of the Global Terrestrial Network for Permafrost (DOI/GTN-P). In addition to presenting data, this report also describes monitoring, data collection, and quality-control methods. The array of 16 monitoring stations spans lat 68.5°N. to 70.5°N. and long 142.5°W. to 161°W., an area of approximately 150,000 square kilometers. Climate summaries are presented along with quality-controlled data. Data collection is ongoing and includes the following climate- and permafrost-related variables: air temperature, wind speed and direction, ground temperature, soil moisture, snow depth, rainfall totals, up- and downwelling shortwave radiation, and atmospheric pressure. These data were collected by the U.S. Geological Survey in close collaboration with the Bureau of Land Management and the U.S. Fish and Wildlife Service.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1092","usgsCitation":"Urban, F.E., and Clow, G.D., 2018, DOI/GTN-P climate and active-layer data acquired in the National Petroleum Reserve–Alaska and the Arctic National Wildlife Refuge, 1998–2019 (ver. 1.2, June 2021), U.S. Geological Survey Data Series 1092, 71 p., https://doi.org/10.3133/ds1092. [Supersedes USGS Data Series 1021.]","productDescription":"Report: vi, 71 p.; Data Release; Version History","onlineOnly":"Y","ipdsId":"IP-088564","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":356509,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1092/coverthb3.jpg"},{"id":356510,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1092/ds1092.pdf","text":"Report","size":"19.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1092"},{"id":356546,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VX0FGB","text":"USGS data release","linkHelpText":"Data Release associated with Data Series - DOI/GTN-P Climate and Active-Layer Data Acquired in the National Petroleum Reserve-Alaska and the Arctic National Wildlife Refuge, 1998-2019 (ver. 3.0, March 2021)"},{"id":375378,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/ds/1092/versionHist2.txt","text":"Version History","size":"4.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"DS 1092 version history"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.45458984375,\n 66.95587684341999\n ],\n [\n -140.99853515625,\n 66.95587684341999\n ],\n [\n -140.99853515625,\n 71.56664127895979\n ],\n [\n -163.45458984375,\n 71.56664127895979\n ],\n [\n -163.45458984375,\n 66.95587684341999\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.2: June 2021: Version 1.1: June 2020; Version 1.0: August 2018","contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS 980
Denver, CO 80225
A new U.S. government report finds that climate change is already increasing risks to health, the economy, and ecosystems across the United States. These risks are expected to grow in the coming decades.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018EO110527","usgsCitation":"Jay, A., DeAngelo, B., Barrie, D., and Reidmiller, D., 2018, What specific costs and risks do we face from climate change?: Eos Science News, v. 99, HTML Document, https://doi.org/10.1029/2018EO110527.","productDescription":"HTML Document","ipdsId":"IP-102797","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":468149,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018eo110527","text":"Publisher Index Page"},{"id":381176,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jay, Alexa","contributorId":245575,"corporation":false,"usgs":false,"family":"Jay","given":"Alexa","email":"","affiliations":[{"id":39064,"text":"U.S. Global Change Research Program","active":true,"usgs":false}],"preferred":false,"id":806467,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelo, Ben","contributorId":245576,"corporation":false,"usgs":false,"family":"DeAngelo","given":"Ben","email":"","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":806468,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barrie, Dan","contributorId":245577,"corporation":false,"usgs":false,"family":"Barrie","given":"Dan","email":"","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":806469,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reidmiller, David 0000-0001-9321-7548","orcid":"https://orcid.org/0000-0001-9321-7548","contributorId":212241,"corporation":false,"usgs":true,"family":"Reidmiller","given":"David","email":"","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":806470,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70197210,"text":"sir20185066 - 2018 - Comparison of regression relations of bankfull discharge and channel geometry for the glaciated and nonglaciated settings of Pennsylvania and southern New York","interactions":[],"lastModifiedDate":"2020-07-21T14:14:13.262599","indexId":"sir20185066","displayToPublicDate":"2020-07-20T12:50:00","publicationYear":"2018","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":"2018-5066","title":"Comparison of regression relations of bankfull discharge and channel geometry for the glaciated and nonglaciated settings of Pennsylvania and southern New York","docAbstract":"Streambank erosion in areas of past glacial deposition has been shown to be a dominant source of sediment to streams. Water resource managers are faced with the challenge of developing long and short term (emergency) stream restoration efforts that rely on the most suitable channel geometry for project design. A geomorphic dataset of new (2016, n=5) and previous (1999–2006, n=96) estimates of bankfull discharge and channel dimensions at U.S. Geological Survey streamflow-gaging stations was compiled to present and contrast the glaciated and unglaciated noncarbonate settings of southern New York and Pennsylvania that included selected areas of Maryland. Empirical models were developed by using simple linear regressions that relate bankfull discharge and channel geometry to drainage area (regional curves). Significant relations (p<0.05) were able to explain variability with coefficient of determination (R2 ) values of 0.89 for bankfull discharge, 0.94 for cross-sectional area, 0.87 for bankfull width, and 0.83 for bankfull depth. These regression relations for the glaciated noncarbonate settings of northern Pennsylvania and southern New York were able to provide a slightly better fit than regional curve models developed previously for the entire noncarbonate region of Pennsylvania. Although, the analysis of covariance (ANCOVA) results for comparison between regression equations for the glaciated and unglaciated settings showed that except for the significant intercept of bankfull discharge versus drainage area (F=8.26, p-value<0.005), the regression equations are not significantly different between the glaciated and unglaciated setting of Pennsylvania and southern New York. Therefore, data stratification by glaciation does not improve regional curves relations developed previously for the noncarbonate (glaciated and unglaciated) and carbonate settings of Pennsylvania and Maryland. Further analysis that incorporates data stratification or multivariate approaches based on mean annual runoff, precipitation, slope, stream classification, or other relevant parameters may optimize the accuracy and utility of statewide models. The new estimates of bankfull discharge and channel dimensions at streamflowgaging sites and updated drainage areas from StreamStats were incorporated into previously developed regional curves to produce an updated set of regression relations of bankfull discharge and channel geometry for the noncarbonate and carbonate settings of Pennsylvania and Maryland.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185066","collaboration":"Prepared in cooperation with the Bradford County Conservation District","usgsCitation":"Clune, J.W., Chaplin, J.J., and White, K.E., 2018, Comparison of regression relations of bankfull discharge and channel geometry for the glaciated and nonglaciated settings of Pennsylvania and southern New York (ver. 1.1, July 2020): U.S. Geological Survey Scientific Investigations Report 2018–5066, 20 p., https://doi.org/10.3133/sir20185066.","productDescription":"Report: vi, 20 p.; Data Release","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091919","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":355942,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5066/sir20185066.pdf","text":"Report","size":"13.8 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York\",\"nation\":\"USA \"}}]}","edition":"Version 1.1: July 2020; Version 1.0: July 2018","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
The USGS California Water Science Center is heavily involved in the measurement of flow and water quality parameters in the San Francisco Estuary, with support from many partner agencies. The California Department of Water Resources (DWR), through the Interagency Ecological Program (IEP) is one of those agencies. This article describes the resulting efforts and methodologies and provides examples of some of the uses of the data sets for science and management interests.
The DWR/IEP-funded flow and water quality network provides high resolution data in both space and time, over a large portion of the San Francisco Estuary. This measurement network has evolved over the preceding decades, and proven invaluable for both management and science interests, with particular relevance to water supply and endangered species issues. Presented below are a summary of the measurement network, some of the methods being utilized, and some interesting conditions that became evident during the recent drought that spanned Water Years (WY) 2013-2016, and the wet WY2017 (water year spans Oct 1-Sept 30).
","language":"English","publisher":"California Department of Water Resources","usgsCitation":"Work, P.A., and Downing-Kunz, M.A., 2018, State of the network: Long-term, high-frequency flow and water quality data in the San Francisco Estuary, California: Interagency Ecological Program (IEP) Newsletter, v. 32, no. 1, p. 59-64.","productDescription":"6 p.","startPage":"59","endPage":"64","ipdsId":"IP-098804","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":375085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375084,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://www.usgs.gov/center-news/usgs-science-work-san-francisco-bay-estuary"},{"id":375083,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://water.ca.gov/Programs/Environmental-Services/Interagency-Ecological-Program"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.1622314453125,\n 36.756490329505176\n ],\n [\n -120.86608886718749,\n 36.756490329505176\n ],\n [\n -120.86608886718749,\n 38.45789034424927\n ],\n [\n -123.1622314453125,\n 38.45789034424927\n ],\n [\n -123.1622314453125,\n 36.756490329505176\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Work, Paul A. 0000-0002-2815-8040 pwork@usgs.gov","orcid":"https://orcid.org/0000-0002-2815-8040","contributorId":168561,"corporation":false,"usgs":true,"family":"Work","given":"Paul","email":"pwork@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":744156,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Downing-Kunz, Maureen A. 0000-0002-4879-0318 mdowning-kunz@usgs.gov","orcid":"https://orcid.org/0000-0002-4879-0318","contributorId":3690,"corporation":false,"usgs":true,"family":"Downing-Kunz","given":"Maureen","email":"mdowning-kunz@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":744157,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70195971,"text":"sir20185033 - 2018 - Characterization of stormwater runoff from bridge decks in eastern Massachusetts, 2014–16","interactions":[],"lastModifiedDate":"2020-05-19T13:33:40.660565","indexId":"sir20185033","displayToPublicDate":"2020-05-19T09:40:00","publicationYear":"2018","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":"2018-5033","title":"Characterization of stormwater runoff from bridge decks in eastern Massachusetts, 2014–16","docAbstract":"The quality of stormwater runoff from bridge decks (hereafter referred to as “bridge-deck runoff”) was characterized in a field study from August 2014 through August 2016 in which concentrations of suspended sediment (SS) and total nutrients were monitored. These new data were collected to supplement existing highway-runoff data collected in Massachusetts which were deficient in bridge-deck runoff concentration data. Monitoring stations were installed at three bridges maintained by the Massachusetts Department of Transportation in eastern Massachusetts (State Route 2A in the city of Boston, Interstate 90 in the town of Weston, and State Route 20 near Quinsigamond Village in the city of Worcester). The bridges had annual average daily traffic volumes from 21,200 to 124,000 vehicles per day; the land use surrounding the monitoring stations was 25 to 67 percent impervious.
Automatic-monitoring techniques were used to collect more than 160 flow-proportional composite samples of bridge-deck runoff. Samples were analyzed for concentrations of SS, loss on ignition of suspended solids (LOI), particulate carbon (PC), total phosphorus (TP), total dissolved nitrogen (DN), and particulate nitrogen (PN). The distribution of particle size of SS also was determined for composite samples. Samples of bridge-deck runoff were collected year round during rain, mixed precipitation, and snowmelt runoff and with different dry antecedent periods throughout the 2-year sampling period.
At the three bridge-deck-monitoring stations, median concentrations of SS in composite samples of bridge-deck runoff ranged from 1,490 to 2,020 milligrams per liter (mg/L); however, the range of SS in individual composites was vast at 44 to 142,000 mg/L. Median concentrations of SS were similar in composite samples collected from the State Route 2A and Interstate 90 bridge (2,010 and 2,020 mg/L, respectively), and lowest at the State Route 20 bridge (1,490 mg/L). Concentrations of coarse sediment (greater than 0.25 millimeters in diameter) dominated the SS matrix by more than an order of magnitude. Concentrations of LOI and PC in composite samples ranged from 15 to 1,740 mg/L and 6.68 to 1,360 mg/L, respectively, and generally represented less than 10 and 3 percent of the median mass of SS, respectively. Concentrations of TP in composite samples ranged from 0.09 to 7.02 mg/L; median concentrations of TP ranged from 0.505 to 0.69 mg/L and were highest on the bridge on State Route 2A in Boston. Concentrations of total nitrogen (TN) (sum DN and PN) in composite samples were variable (0.36 to 29 mg/L). Median DN (0.64 to 0.90 mg/L) concentrations generally represented about 40 percent of the TN concentration at each bridge and were similar to annual volume-weighted mean concentrations of nitrogen in precipitation in Massachusetts.
Nonparametric statistical methods were used to test for differences between sample constituent concentrations among the three bridges. These results indicated that there are no statistically significant differences for concentrations of SS, LOI, PC, and TP among the three bridges (one-way analysis of variance test on rank-transformed data, 95-percent confidence level). Test results for concentrations of TN in composite samples indicated that concentrations of TN collected on State Route 20 near Quinsigamond Village were significantly higher than those concentrations collected on State Route 2A in Boston and Interstate 90 near Weston. Median concentrations of TN were about 93 and 55 percent lower at State Route 2A and at Interstate 90, respectively, compared to the median concentrations of TN at State Route 20.
Samples of sediment were collected from five fixed locations on each bridge on three occasions during dry weather to calculate semiquantitative distributions of sediment yields on the bridge surface relative to the monitoring location. Mean yields of bridge-deck sediment during this study for State Route 2A in Boston, Interstate 90 near Weston, and State Route 20 near Quinsigamond Village were 1,500, 250, and 5,700 pounds per curb-mile, respectively. Sediment yields at each sampling location varied widely (26 to 25,000 pounds per curb-mile) but were similar to yields reported elsewhere in Massachusetts and the United States. Yields calculated for each sampling location indicated that the sediment was not evenly distributed across each bridge in this study for plausible reasons such as bridge slope, vehicular tracking, and bridge deterioration.
Bridge-deck sediment quality was largely affected by the distribution of sediment particle size. Concentrations of TP in the fine sediment-size fraction (less than 0.0625 millimeter in diameter) of samples of bridge-deck sediment were about 6 times greater than in the coarse size fraction. Concentrations for many total-recoverable metals were 2 to 17 times greater in the fine size fraction compared to concentrations in the coarse size fraction (greater than or equal to 0.25 millimeter in diameter), and concentrations of total-recoverable copper and lead in the fine size fraction were 2 to 65 times higher compared to concentrations in the intermediate (greater than or equal to 0.0625 to 0.25 millimeter in diameter) or the coarse size fraction. However, the proportion of sediment particles less than 0.0625 millimeter in diameter in composite samples of bridge-deck runoff was small (median values range from 4 to 8 percent at each bridge) compared to the larger sediment particle-size mass. As a result, more than 50 percent of the sediment-associated TP, aluminum, chromium, manganese, and nickel was estimated to be associated with the coarse size fraction of the SS load. In contrast, about 95 percent of the estimated sediment-associated copper concentration was associated with the fine size fraction of the SS load.
Version 1.0.2 of the Stochastic Empirical Loading and Dilution Model was used to simulate long-term (29–30-year) concentrations and annual yields of SS, TP, and TN in bridge-deck runoff and in discharges from a hypothetical stormwater treatment best-management practice structure. Three methods (traditional statistics, robust statistics, and L-moments) were used to calculate statistics for stochastic simulations because the high variability in measured concentration values during the field study resulted in extreme simulated concentrations. Statistics of each dataset, including the average, standard deviation, and skew of the common (base 10) logarithms, for each of the three bridges, and for a lumped dataset, were calculated and used for simulations; statistics representing the median of statistics calculated for the three bridges also were used for simulations. These median statistics were selected for the interpretive simulations so that the simulations could be used to estimate concentrations and yields from other, unmonitored bridges in Massachusetts. Comparisons of the standard and robust statistics indicated that simulation results with either method would be similar, which indicated that the large variability in simulated results was not caused by a few outliers. Comparison to statistics calculated by the L-moments methods indicated that L-moments do not produce extreme concentrations; however, they also do not produce results that represent the bulk of concentration data.
The runoff-quality risk analysis indicated that bridge-deck runoff would exceed discharge standards commonly used for large, advanced wastewater treatment plants, but that commonly used stormwater best-management practices may reduce the percentage of exceedances by one-half. Results of simulations indicated that long-term average yields of TN, TP, and SS may be about 21.4, 6.44, and 40,600 pounds per acre per year, respectively. These yields are about 1.3, 3.4, and 16 times simulated ultra-urban highway yields in Massachusetts; however, simulations indicated that use of a best-management practice structure to treat bridge-deck runoff may reduce discharge yields to about 10, 2.8, and 4,300, pounds per acre per year, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185033","isbn":"978-1-4113-4222-4","usgsCitation":"Smith, K.P., Sorenson, J.R., and Granato, G.E., 2018, Characterization of stormwater runoff from bridge decks in eastern Massachusetts, 2014–16: U.S. Geological Survey Scientific Investigations Report 2018–5033, 73 p., https://doi.org/10.3133/sir20185033.","productDescription":"xiii, 73 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-088034","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":374915,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5033/sir20185033.pdf","text":"Report","size":"4.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5033"},{"id":353906,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5033/coverthb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.98516845703125,\n 41.97582726102573\n ],\n [\n -70.7904052734375,\n 41.97582726102573\n ],\n [\n -70.7904052734375,\n 42.827638636242284\n ],\n [\n -71.98516845703125,\n 42.827638636242284\n ],\n [\n -71.98516845703125,\n 41.97582726102573\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532