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
Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike
Lawrenceville, NJ 08648
Sedimentary deposits in midlatitude continental basins often preserve a paleoclimate record complementary to marine-based records. However, deriving that paleoclimate record depends on having well-exposed deposits and establishing a sufficiently robust geochronology. After decades of research, we have been able to correlate 77 tephra beds exposed in multiple stratigraphic sections in the Death Valley area, California, United States. These correlations identify 25 different tephra beds that erupted from at least five different volcanic centers from older than 3.58 Ma to ca. 32 ka. We have informally named and determined the ages for seven previously unrecognized beds: ca. 3.54 Ma tuff of Curry canyon, ca. 3.45 Ma tuff of Furnace Creek, ca. 3.1 Ma tuff of Kit Fox Hills, ca. 3.1 Ma tuff of Mesquite Flat, ca. 3.15 Ma tuff of Texas Spring, 3.117 ± 0.011 Ma tuff of Echo Canyon, and the ca. 1.3 Ma Amargosa ash bed. Several of these tephra beds are found as far northeast as central Utah and could be important marker beds in western North America.
Our tephrochronologic data, combined with magnetic polarity data and 40Ar/39Ar age determinations, redefine Neogene sedimentary deposits exposed across 175 km2 of the Death Valley area. The alluvial/lacustrine Furnace Creek Formation is a time-transgressive sedimentary sequence ranging from ca. 6.0 to 2.5 Ma in age. The ca. 2.5–1.7 Ma Funeral Formation is typically exposed as a proximal alluvial-fan facies overlying the Furnace Creek Formation. We have correlated deposits in the Kit Fox Hills, Salt Creek, Nova Basin, and southern Death Valley with the informally named ca. 1.3–0.5 Ma Mormon Point formation. In addition, our correlation of the late Pleistocene Wilson Creek ash bed 15 in the Lake Rogers deposits represents the first unambiguous sequences deposited during the Last Glacial Maximum (marine isotope stage [MIS] 2) in Death Valley.
Based on this new stratigraphic framework, we show that the Pliocene and Pleistocene climate in Death Valley is consistent with the well-established marine tropical/subtropical record. Pluvial lakes in Death Valley and Searles Valley began to form ca. 3.5–3.4 Ma in the late Pliocene during MIS MG5. Initiation of lakes in these two hydrologically separated valleys at the same time at the beginning of a cooling trend in the marine climate record suggests a link to a cooler, wetter (glacial) regional climate in North America. The Death Valley lake persisted until ca. 3.30 Ma, at the peak of the M2 glaciation, after which there is no evidence of Pliocene lacustrine deposition, even at the peak of the Northern Hemisphere Glaciation (ca. 2.75 Ma). If pluvial lakes in the Pliocene are an indirect record of glacial climate conditions, as they are for the Pleistocene, then a glacial climate was present in western North America for ∼200,000 yr during the Pliocene, encompassing MIS MG5–M2.
Pleistocene pluvial lakes in Death Valley that formed ca. 1.98–1.78 Ma, 1.3–1.0 Ma, and ca. 0.6 Ma (MIS 16) are consistent with other regional climate records that indicate a regional glacial climate; however, Death Valley was relatively dry at ca. 0.77 Ma (MIS 19), when large lakes existed in other basins. The limited extent of the MIS 2 marsh/shallow lake in the Lake Rogers basin of northern Death Valley reflects the well-known regional glacial climate at that time; however, Death Valley received relatively lower inflow and rainfall in comparison.
Lake trout stocking began in the 1970s as part of a binational effort to restore a self-sustaining population of lake trout in Lake Ontario. Despite 48 years of restoration stocking, lake trout in Lake Ontario have not reestablished a self-sustaining population. Spawning surveys done at Stony Island Reef (SIR) in eastern Lake Ontario in 1987 and 1989 documented lake trout egg deposition and swim-up fry. Bottom trawls in the early 1990s found naturally-reproduced juvenile lake trout in this region of the lake. More recently, naturally-reproduced juveniles have been found in western Lake Ontario, but few have been found near SIR in the eastern basin. In 2017 and 2018, we examined SIR spawning habitat and lake trout egg deposition rates and compared them to historical values. The average interstitial depth observed in 2018 was less than 4 cm, and the maximum depth observed was 15 cm. These interstitial depths are greatly reduced from depths up to 45 cm reported in the 1980s. Only one egg was captured in 95 egg nets deployed during the spawning period, which resulted in a CPUE of 0.00035 eggs/net/day, markedly lower than the egg densities measured at SIR in 1987 and 1989 of (1.27 eggs/net/day and 0.27 eggs/net/day respectively). Observations of the cobble spawning habitat suggested interstitial spaces were more infilled relative to conditions observed in the 1980s. Infill material was heavily comprised of dreissenid mussels shells and shell fragments. These findings indicate that changes in lake trout spawning habitat may be inhibiting lake trout reproduction at SIR.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"NYSDEC Lake Ontario annual report 2018","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"language":"English","publisher":"New York State Department of Environmental Conservation (NYSDEC)","usgsCitation":"Furgal, S., Lantry, B.F., Weidel, B., Farrell, J.M., Gorsky, D., and Biesinger, Z., 2018, Lake trout spawning and habitat assessment at Stony Island Reef, chap. 20 of NYSDEC Lake Ontario annual report 2018, p. 20-1-20-6.","productDescription":"6 p.","startPage":"20-1","endPage":"20-6","ipdsId":"IP-106482","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":369862,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":369861,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.dec.ny.gov/docs/fish_marine_pdf/lourpt18.pdf"}],"country":"Canada, United States","otherGeospatial":"Lake Ontario, Stony Island Reef","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.3065719604492,\n 43.906055713100805\n ],\n [\n -76.27035140991211,\n 43.906055713100805\n ],\n [\n -76.27035140991211,\n 43.92707729641145\n ],\n [\n -76.3065719604492,\n 43.92707729641145\n ],\n [\n -76.3065719604492,\n 43.906055713100805\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Furgal, Stacy 0000-0001-8828-6290","orcid":"https://orcid.org/0000-0001-8828-6290","contributorId":216791,"corporation":false,"usgs":true,"family":"Furgal","given":"Stacy","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":765537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lantry, Brian F. 0000-0001-8797-3910 bflantry@usgs.gov","orcid":"https://orcid.org/0000-0001-8797-3910","contributorId":3435,"corporation":false,"usgs":true,"family":"Lantry","given":"Brian","email":"bflantry@usgs.gov","middleInitial":"F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":765538,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":765539,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Farrell, John M.","contributorId":172505,"corporation":false,"usgs":false,"family":"Farrell","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":27058,"text":"State University of New York, College of Environmental Science and Forestry, Department of Environmental and Forest Biology, 250 Illick Hall, 1 Forestry Drive, Syracuse, NY 13210, USA","active":true,"usgs":false}],"preferred":false,"id":765540,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gorsky, Dimitry","contributorId":169691,"corporation":false,"usgs":false,"family":"Gorsky","given":"Dimitry","affiliations":[],"preferred":false,"id":765541,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Biesinger, Zy","contributorId":197993,"corporation":false,"usgs":false,"family":"Biesinger","given":"Zy","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":765542,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":97670,"text":"ofr20091103 - 2018 - A practical primer on geostatistics","interactions":[],"lastModifiedDate":"2019-11-25T09:59:18","indexId":"ofr20091103","displayToPublicDate":"2019-11-25T11:05:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-1103","displayTitle":"A Practical Primer on Geostatistics","title":"A practical primer on geostatistics","docAbstract":"The Challenge—Most geological phenomena are extraordinarily complex in their interrelationships and vast in their geographical extension. Ordinarily, engineers and geoscientists are faced with corporate or scientific requirements to properly prepare geological models with measurements involving a small fraction of the entire area or volume of interest. Exact description of a system such as an oil reservoir is neither feasible nor economically possible. The results are necessarily uncertain. Note that the uncertainty is not an intrinsic property of the systems; it is the result of incomplete knowledge by the observer.
The Aim of Geostatistics—The main objective of geostatistics is the characterization of spatial systems that are incompletely known, systems that are common in geology. A key difference from classical statistics is that geostatistics uses the sampling location of every measurement. Unless the measurements show spatial correlation, the application of geostatistics is pointless. Ordinarily the need for additional knowledge goes beyond a few points, which explains the display of results graphically as fishnet plots, block diagrams, and maps.
Geostatistical Methods—Geostatistics is a collection of numerical techniques for the characterization of spatial attributes using primarily two tools: probabilistic models, which are used for spatial data in a manner similar to the way in which time-series analysis characterizes temporal data, or pattern recognition techniques. The probabilistic models are used as a way to handle uncertainty in results away from sampling locations, making a radical departure from alternative approaches like inverse distance estimation methods.
Differences with Time Series—On dealing with time-series analysis, users frequently concentrate their attention on extrapolations for making forecasts. Although users of geostatistics may be interested in extrapolation, the methods work at their best interpolating. This simple difference has significant methodological implications.
Historical Remarks—As a discipline, geostatistics was firmly established in the 1960s by the French engineer Georges Matheron, who was interested in the appraisal of ore reserves in mining. Geostatistics did not develop overnight. Like other disciplines, it has built on previous results, many of which were formulated with different objectives in various fields.
Pioneers—Seminal ideas conceptually related to what today we call geostatistics or spatial statistics are found in the work of several pioneers, including: 1940s: A.N. Kolmogorov in turbulent flow and N. Wiener in stochastic processing; 1950s: D. Krige in mining; 1960s: B. Mathern in forestry and L.S. Gandin in meteorology
Calculations—Serious applications of geostatistics require the use of digital computers. Although for most geostatistical techniques rudimentary implementation from scratch is fairly straightforward, coding programs from scratch is recommended only as part of a practice that may help users to gain a better grasp of the formulations.
Software—For professional work, the reader should employ software packages that have been thoroughly tested to handle any sampling scheme, that run as efficiently as possible, and that offer graphic capabilities for the analysis and display of results. This primer employs primarily the package Stanford Geomodeling Software (SGeMS) - recently developed at the Energy Resources Engineering Department at Stanford University - as a way to show how to obtain results practically. This applied side of the primer should not be interpreted as the notes being a manual for the use of SGeMS. The main objective of the primer is to help the reader gain an understanding of the fundamental concepts and tools in geostatistics.
Organization of the Primer—The chapters of greatest importance are those covering kriging and simulation. All other materials are peripheral and are included for better comprehension of these main geostatistical modeling tools. The choice of kriging versus simulation is often a big puzzle to the uninitiated, let alone the different variants of both of them. Chapters 14, 18, and 19 are intended to shed light on those subjects. The critical aspect of assessing and modeling spatial correlation is covered in chapter 7. Chapters 2 and 3 review relevant concepts in classical statistics.
Course Objectives—This course offers stochastic solutions to common problems in the characterization of complex geological systems. At the end of the course, participants should have: an understanding of the theoretical foundations of geostatistics; a good grasp of its possibilities and limitations; and reasonable familiarity with the SGeMS software, thus opening the possibility of practically applying geostatistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20091103","usgsCitation":"Olea, R., 2018, A practical primer on geostatistics (Version 1.0: Originally posted July 6, 2009; Version 1.1: January 2010; Version 1.2: July 2017, Version 1.3: November 2017; Version 1.4: December 2018): U.S. Geological Survey Open-File Report 2009-1103, ii, 346 p., https://doi.org/10.3133/ofr20091103.","productDescription":"ii, 346 p.","numberOfPages":"348","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":344191,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2009/1103/versionHist_1_4.txt","size":"4.74 KB","linkFileType":{"id":2,"text":"txt"}},{"id":344186,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2009/1103/ofr20091103.pdf","text":"Report","size":"10.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2009-1103"},{"id":125462,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2009/1103/coverthb4.jpg"}],"edition":"Version 1.0: Originally posted July 6, 2009; Version 1.1: January 2010; Version 1.2: July 2017, Version 1.3: November 2017; Version 1.4: December 2018","contact":"Eastern Energy Resources Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Batrachochytrium salamandrivorans (Bsal), a pathogenic chytrid fungus, is nonnative to the United States and poses a disease threat to vulnerable amphibian hosts. The Bsal fungus may lead to increases in threatened, endangered, and sensitive status listings at State, Tribal, and Federal levels, resulting in financial costs associated with implementing the Endangered Species Act of 1973. The United States is a global biodiversity hotspot for salamanders, an order of amphibians that is particularly vulnerable to developing a disease called chytridiomycosis when exposed to Bsal. Published Bsal risk assessments for North America have suggested that salamanders within the Appalachian region of the United States are at a high risk. In May 2017, a workshop was facilitated by the Department of the Interior’s Strategic Sciences Group. During the workshop, a discussion-based incident-response exercise focused on a hypothetical Bsal disease outbreak in Appalachia was led by U.S. Geological Survey staff members. Participants included representatives of the Eastern Band of the Cherokee Indians, U.S. Fish and Wildlife Service, National Park Service, Appalachian Landscape Conservation Cooperative, Tennessee Wildlife Resources Agency, and U.S. Department of Agriculture’s U.S. Forest Service. Scenario building was used to brainstorm cascading consequences (social, economic, and ecological) of a Bsal disease outbreak in the Appalachian region. This report highlights the management and science actions that could be undertaken to ensure an effective, rapid response to a Bsal introduction into the United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181150","usgsCitation":"Hopkins, M.C., Adams, M.J., Super, P.E., Olson, D.H., Hickman, C.R., English, P., Sprague, L., Maska, I.B., Pennaz, A.B., and Ludwig, K.A., 2018, Batrachochytrium salamandriovrans (Bsal) in Appalachia—Using scenario building to proactively prepare for a wildlife disease outbreak caused by an invasive amphibian chytrid fungus: U.S. Geological Survey Open-File Report 2018–1150, 31 p., https://doi.org/10.3133/ofr20181150.","productDescription":"Report: v, 9 p.; Appendixes","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-092683","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":359122,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1150/coverthb.jpg"},{"id":359123,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1150/ofr20181150.pdf","text":"Report","size":"7.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OF 2018-1150"}],"contact":"Associate Director, Ecosystems Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive, Suite 300
Reston, VA 20192
The use of water in the United States is arguably one of the most important factors determining water availability at any specific place and time. Numerous local, State, and Federal entities develop, compile, and report water-use data, which can lead to confusing or conflicting information. This report was authored jointly by the U.S. Geological Survey (USGS) and Bureau of Reclamation (Reclamation) to compare and contrast the two agencies’ water-use information programs in the Colorado River Basin. The report also describes the legal drivers for each program, clarifies confusing terminology, compares the methods used, and contrasts the information reported by each agency. This detailed comparison demonstrates that these two Federal agencies have different missions, different programmatic drivers, and different user communities, all of which lead to different approaches to water-use data collection, analysis, and reporting. This report highlights those differences and explains why the USGS and Reclamation programs exist and how the data serve different user communities. Even though the two water-use programs are different by design and purpose, the program comparison presented in this report has identified opportunities for closer coordination and sharing of information between the USGS and Reclamation, as well as program components where agency collaboration can improve water-use estimate methodologies. This comparison effort emphasizes that it is incumbent upon each agency to clearly define the meaning of the terms used and the appropriate application of the reported information to avoid confusion or the accidental misuse of the information. An additional benefit of this comparison effort is the formation of a joint USGS/Reclamation water-use team that will continue to investigate opportunities to expand and coordinate future water-use data compilation and reporting.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185021","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Bruce, B.W., Prairie, J.R., Maupin, M.A., Dodds, J.R., Eckhardt, D.W., Ivahnenko, T.I., Matuska, P.J., Evenson, E.J., and Harrison, A.D., 2018, Comparison of U.S. Geological Survey and Bureau of Reclamation water-use reporting in the Colorado River Basin (ver. 1.1, September 2019): U.S. Geological Survey Scientific Investigations Report 2018–5021, 41 p., https://doi.org/10.3133/sir20185021.\n","productDescription":"vi, 41 p.","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-089692","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":353709,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5021/sir20185021.pdf","text":"Report","size":"10.1 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U.S. Geological Survey
988 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Sediment pulses can cause widespread, complex changes to rivers and coastal regions. Quantifying landscape response to sediment-supply changes is a long-standing problem in geomorphology, but the unanticipated nature of most sediment pulses rarely allows for detailed measurement of associated landscape processes and evolution. The intentional removal of two large dams on the Elwha River (Washington, USA) exposed ~30 Mt of impounded sediment to fluvial erosion, presenting a unique opportunity to quantify source-to-sink river and coastal responses to a massive sediment-source perturbation. Here we evaluate geomorphic evolution during and after the sediment pulse, presenting a 5-year sediment budget and morphodynamic analysis of the Elwha River and its delta. Approximately 65% of the sediment was eroded, of which only ~10% was deposited in the fluvial system. This restored fluvial supply of sand, gravel, and wood substantially changed the channel morphology. The remaining ~90% of the released sediment was transported to the coast, causing ~60 ha of delta growth. Although metrics of geomorphic change did not follow simple time-coherent paths, many signals peaked 1–2 years after the start of dam removal, indicating combined impulse and step-change disturbance responses.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-018-30817-8","usgsCitation":"Ritchie, A.C., Warrick, J.A., East, A.E., Magirl, C.S., Stevens, A.W., Bountry, J.A., Randle, T.J., Curran, C.A., Hilldale, R.C., Duda, J.J., Miller, I.M., Pess, G.R., Eidam, E., Foley, M.M., McCoy, R., and Ogston, A.S., 2018, Morphodynamic evolution following sediment release from the world’s largest dam removal: Scientific Reports, v. 8, 13279, 13 p., https://doi.org/10.1038/s41598-018-30817-8.","productDescription":"13279, 13 p.","ipdsId":"IP-093205","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":468150,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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aritchie@usgs.gov","contributorId":4984,"corporation":false,"usgs":true,"family":"Ritchie","given":"Andrew","email":"aritchie@usgs.gov","middleInitial":"C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":769402,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warrick, Jonathan A. 0000-0002-0205-3814 jwarrick@usgs.gov","orcid":"https://orcid.org/0000-0002-0205-3814","contributorId":167736,"corporation":false,"usgs":true,"family":"Warrick","given":"Jonathan","email":"jwarrick@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":769403,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"East, Amy E. 0000-0002-9567-9460 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Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":769408,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Curran, Christopher A. 0000-0001-8933-416X ccurran@usgs.gov","orcid":"https://orcid.org/0000-0001-8933-416X","contributorId":1650,"corporation":false,"usgs":true,"family":"Curran","given":"Christopher","email":"ccurran@usgs.gov","middleInitial":"A.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":769409,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hilldale, Robert C.","contributorId":139315,"corporation":false,"usgs":false,"family":"Hilldale","given":"Robert","email":"","middleInitial":"C.","affiliations":[{"id":6736,"text":"Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":769410,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Duda, Jeffrey J. 0000-0001-7431-8634 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USA","active":true,"usgs":false}],"preferred":false,"id":769413,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Eidam, Emily","contributorId":139311,"corporation":false,"usgs":false,"family":"Eidam","given":"Emily","email":"","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":769414,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Foley, Melissa M. 0000-0002-5832-6404 mfoley@usgs.gov","orcid":"https://orcid.org/0000-0002-5832-6404","contributorId":4861,"corporation":false,"usgs":true,"family":"Foley","given":"Melissa","email":"mfoley@usgs.gov","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":769415,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"McCoy, 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reservoir connectivity in river networks control nitrogen removal","docAbstract":"Lakes, reservoirs, and other ponded waters are ubiquitous features of the aquatic landscape, yet their cumulative role in nitrogen removal in large river basins is often unclear. Here we use predictive modeling, together with comprehensive river water quality, land use, and hydrography datasets, to examine and explain the influences of more than 18,000 ponded waters on nitrogen removal through river networks of the Northeastern United States. Thresholds in pond density where ponded waters become important features to regional nitrogen removal are identified and shown to vary according to a ponded waters’ relative size, network position, and degree of connectivity to the river network, which suggests worldwide importance of these new metrics. Consideration of the interacting physical and biological factors, along with thresholds in connectivity, reveal where, why, and how much ponded waters function differently than streams in removing nitrogen, what regional water quality outcomes may result, and in what capacity management strategies could most effectively achieve desired nitrogen loading reduction.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-018-05156-x","usgsCitation":"Schmadel, N.M., Harvey, J., Alexander, R., Schwarz, G., Moore, R., Eng, K., Gomez-Velez, J., Boyer, E.W., and Scott, D., 2018, Thresholds of lake and reservoir connectivity in river networks control nitrogen removal: Nature Communications, v. 9, 2779, 10 p., https://doi.org/10.1038/s41467-018-05156-x.","productDescription":"2779, 10 p.","ipdsId":"IP-093046","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":468151,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-018-05156-x","text":"Publisher Index Page"},{"id":367536,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Delaware, District of Columbia, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode island, Vermont, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.884765625,\n 44.37098696297173\n ],\n [\n -66.884765625,\n 44.99588261816546\n ],\n [\n -67.67578124999999,\n 45.82879925192134\n ],\n [\n -67.8515625,\n 47.368594345213374\n ],\n [\n -69.2138671875,\n 47.517200697839414\n ],\n [\n -71.19140625,\n 45.398449976304086\n ],\n [\n -71.8505859375,\n 44.99588261816546\n ],\n [\n -74.3994140625,\n 45.182036837015886\n ],\n [\n -76.81640625,\n 42.74701217318067\n ],\n [\n -77.6953125,\n 40.81380923056958\n ],\n [\n -79.40917968749999,\n 39.40224434029275\n ],\n [\n -79.27734374999999,\n 37.50972584293751\n ],\n [\n -78.9697265625,\n 36.914764288955936\n ],\n [\n -75.234375,\n 36.38591277287651\n ],\n [\n -73.125,\n 40.111688665595956\n ],\n [\n -69.2578125,\n 41.11246878918088\n ],\n [\n -66.884765625,\n 44.37098696297173\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmadel, Noah M.","contributorId":219098,"corporation":false,"usgs":false,"family":"Schmadel","given":"Noah","email":"","middleInitial":"M.","affiliations":[{"id":39961,"text":"USGS Post-Doc","active":true,"usgs":false}],"preferred":false,"id":771257,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Judson","contributorId":219097,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":771256,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, Richard 0000-0001-9166-0626 ralex@usgs.gov","orcid":"https://orcid.org/0000-0001-9166-0626","contributorId":219099,"corporation":false,"usgs":true,"family":"Alexander","given":"Richard","email":"ralex@usgs.gov","affiliations":[],"preferred":true,"id":771258,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":219100,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory E.","email":"gschwarz@usgs.gov","affiliations":[],"preferred":false,"id":771259,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moore, Richard","contributorId":219101,"corporation":false,"usgs":true,"family":"Moore","given":"Richard","affiliations":[],"preferred":true,"id":771260,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Eng, Ken 0000-0001-6838-5849 keng@usgs.gov","orcid":"https://orcid.org/0000-0001-6838-5849","contributorId":3580,"corporation":false,"usgs":true,"family":"Eng","given":"Ken","email":"keng@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":771261,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gomez-Velez, Jesus D.","contributorId":219103,"corporation":false,"usgs":false,"family":"Gomez-Velez","given":"Jesus D.","affiliations":[{"id":39962,"text":"Department of Earth & Environmental Science, New Mexico Institute of Mining and Technology, Socorro, New Mexico, USA","active":true,"usgs":false}],"preferred":false,"id":771262,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Boyer, Elizabeth W.","contributorId":44659,"corporation":false,"usgs":false,"family":"Boyer","given":"Elizabeth","email":"","middleInitial":"W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":771263,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Scott, Durelle","contributorId":219088,"corporation":false,"usgs":false,"family":"Scott","given":"Durelle","affiliations":[{"id":39959,"text":"Virginia Tech.","active":true,"usgs":false}],"preferred":false,"id":771264,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70195984,"text":"tm4B5 - 2018 - Guidelines for determining flood flow frequency — Bulletin 17C","interactions":[],"lastModifiedDate":"2024-03-28T13:15:23.826606","indexId":"tm4B5","displayToPublicDate":"2019-07-16T10:55:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"4-B5","title":"Guidelines for determining flood flow frequency — Bulletin 17C","docAbstract":"Accurate estimates of flood frequency and magnitude are a key component of any effective nationwide flood risk management and flood damage abatement program. In addition to accuracy, methods for estimating flood risk must be uniformly and consistently applied because management of the Nation’s water and related land resources is a collaborative effort involving multiple actors including most levels of government and the private sector.
Flood frequency guidelines have been published in the United States since 1967, and have undergone periodic revisions. In 1967, the U.S. Water Resources Council presented a coherent approach to flood frequency with Bulletin 15, “A Uniform Technique for Determining Flood Flow Frequencies.” The method it recommended involved fitting the log-Pearson Type III distribution to annual peak flow data by the method of moments.
The first extension and update of Bulletin 15 was published in 1976 as Bulletin 17, “Guidelines for Determining Flood Flow Frequency” (Guidelines). It extended the Bulletin 15 procedures by introducing methods for dealing with outliers, historical flood information, and regional skew. Bulletin 17A was published the following year to clarify the computation of weighted skew. The next revision of the Bulletin, the Bulletin 17B, provided a host of improvements and new techniques designed to address situations that often arise in practice, including better methods for estimating and using regional skew, weighting station and regional skew, detection of outliers, and use of the conditional probability adjustment.
The current version of these Guidelines are presented in this document, denoted Bulletin 17C. It incorporates changes motivated by four of the items listed as “Future Work” in Bulletin 17B and 30 years of post-17B research on flood processes and statistical methods. The updates include: adoption of a generalized representation of flood data that allows for interval and censored data types; a new method, called the Expected Moments Algorithm, which extends the method of moments so that it can accommodate interval data; a generalized approach to identification of low outliers in flood data; and an improved method for computing confidence intervals.
Federal agencies are requested to use these Guidelines in all planning activities involving water and related land resources. State, local, and private organizations are encouraged to use these Guidelines to assure uniformity in the flood frequency estimates that all agencies concerned with flood risk should use for Federal planning decisions.
This revision is adopted with the knowledge and understanding that review of these procedures will be ongoing. Updated methods will be adopted when warranted by experience and by examination and testing of new techniques.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: Surface water in Book 4: Hydrologic analysis and interpretation","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4B5","isbn":"978-1-4113-4223-1","usgsCitation":"England, J.F., Jr., Cohn, T.A., Faber, B.A., Stedinger, J.R., Thomas, W.O., Jr., Veilleux, A.G., Kiang, J.E., and Mason, R.R., Jr., 2018, Guidelines for determining flood flow frequency — Bulletin 17C (ver. 1.1, May 2019): U.S. Geological Survey Techniques and Methods, book 4, chap. B5, 148 p., https://doi.org/10.3133/tm4B5.","productDescription":"xiii, 148 p.","numberOfPages":"168","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-065340","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":352936,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://acwi.gov/hydrology/Frequency/b17c/","text":"Advisory Committee on Water Information - Bulletin 17C"},{"id":352416,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/b05/tm4b5.pdf","text":"Report","size":"29.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 4-B5"},{"id":352415,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/04/b05/coverthb2.jpg"},{"id":399694,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_107081.htm"},{"id":363942,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/tm/04/b05/versionHist.pdf","size":"153 KB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.5178,\n 33.9703\n ],\n [\n -83.8928,\n 33.9703\n ],\n [\n -83.8928,\n 34.2625\n ],\n [\n -84.5178,\n 34.2625\n ],\n [\n -84.5178,\n 33.9703\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: May 31, 2019","publicComments":"This report is Chapter 5 of Section B: Surface water in Book 4: Hydrologic analysis and interpretation.","contact":"Chief, Analysis and Prediction Branch
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Executive Summary
Drainage for agricultural production over the past 150 years has been an integral component of human-driven change to Minnesota’s rural landscapes.
Benefits of drainage
Historically, poorly drained soils across much of the State would often remain saturated or flooded after spring snowmelt, preventing timely farm operations such as tilling and planting crops (Arneman, 1963). Installation of agricultural drainage, both surface ditches and subsurface drainage, accelerated transport of water off farm fields and imparted producers higher crop yields (Beauchamp, 1987; Stoner and others, 1993). Agricultural drainage offered many other benefits such as preventing crop drown out, aerating the soil profile for improved plant growth, limiting surface runoff and soil erosion, and allowing farmers better access to croplands (Fausey and others, 1987). Without agricultural drainage on much of Minnesota’s croplands, it would have been difficult to realize high enough crop yields to remain economically viable.
Environmental concerns
While drainage of Minnesota croplands provided the benefits mentioned above, several environmental concerns result. These include wetland loss, degradation of downstream water quality, and reduced [potential for] recharge.
Early agricultural drainage efforts (pre-20th century) led to the disappearance of much of Minnesota’s natural wetlands. Increased focus on preventing or mitigating wetland loss over the last 50 years has helped curtail further losses, even as agricultural drainage proceeds. Prior to establishment of Minnesota statehood, wetlands accounted for more than 10 million acres in Minnesota, including prairie wetlands, peatlands, and forest wetlands that comprised approximately 19 percent of the total land area (Palmer, 1915; King, 1980). In 2018, only half of Minnesota’s pre-settlement wetlands remain, mostly in parts of the State that have not experienced widespread drainage, such as northern Minnesota.
Water-quality monitoring has shown that agricultural drainage, in particular the practice of subsurface drainage, provides a direct flow path for nutrient (nitrogen and soluble phosphorus) losses to surface water resources. The negative consequences of agricultural drainage on surface water quality are well documented (for example, Dinnes and others, 2002; Kladivko and others, 2004; Richards and others, 2008; Rozemeijer and others, 2010; Schottler and others, 2013). Agricultural basins with a high percentage of agricultural drainage have been implicated as part of the cause of the Gulf of Mexico hypoxia zone due to excessive nitrogen export (Goolsby and Battaglin, 2001; Randall and Mulla, 2001).
The connection of hydrological effects of agricultural subsurface drainage on groundwater recharge and aquifers, on the other hand, has not been well-established. Agricultural subsurface drainage intercepts infiltrating water below croplands and directly discharges the water to nearby surface waters. However, the size of the water balance shift from drained water that would have evapotranspired or run off the land to drained water that would recharge underlying aquifers has been poorly characterized (Schuh, 2008).
Drain Tiles and Groundwater
Given the poor accounting of subsurface drainage effects on groundwater resources, the Minnesota Ground Water Association (MGWA) deemed it imperative that we document these effects so that groundwater resources in agricultural regions with substantial drainage can be effectively managed. This white paper documents the relations of drain tiles and groundwater resources and discusses the historical significance of agricultural drainage practices, the recognized positive benefits and potential negative consequences of agricultural drainage practices, and the gaps in understanding of the connections between agricultural drainage and groundwater resources.
The major messages emerged from the findings of this white paper are:
No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Free and Open Source Software for Geospatial (FOSS4G)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Free and Open Source Software for Geospatial (FOSS4G)","conferenceDate":"May 14-16, 2018","conferenceLocation":"St Louis, MO","language":"English","publisher":"OSGeo","usgsCitation":"Baumer, W., Powell, L., and Varanka, D.E., 2018, Mapping interactive geospatial linked data, in Free and Open Source Software for Geospatial (FOSS4G), St Louis, MO, May 14-16, 2018, 4 p.","productDescription":"4 p.","ipdsId":"IP-095212","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":369107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Baumer, William 0000-0001-8011-841X","orcid":"https://orcid.org/0000-0001-8011-841X","contributorId":204666,"corporation":false,"usgs":true,"family":"Baumer","given":"William","email":"","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":734627,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Powell, Logan J. 0000-0002-0528-3092 ljpowell@usgs.gov","orcid":"https://orcid.org/0000-0002-0528-3092","contributorId":201294,"corporation":false,"usgs":true,"family":"Powell","given":"Logan J.","email":"ljpowell@usgs.gov","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":false,"id":734626,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Varanka, Dalia E. 0000-0003-2857-9600 dvaranka@usgs.gov","orcid":"https://orcid.org/0000-0003-2857-9600","contributorId":1296,"corporation":false,"usgs":true,"family":"Varanka","given":"Dalia","email":"dvaranka@usgs.gov","middleInitial":"E.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":734625,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202410,"text":"70202410 - 2018 - Water use in Louisiana, 2015","interactions":[],"lastModifiedDate":"2022-06-17T14:44:18.600278","indexId":"70202410","displayToPublicDate":"2019-03-01T12:54:56","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5809,"text":"Water Resources Special Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"18","title":"Water use in Louisiana, 2015","docAbstract":"In 2015, approximately 8,720 million gallons per day (Mgal/d) of water was withdrawn from groundwater and surface-water sources in Louisiana, a 2.6 percent increase from 2010. Total groundwater withdrawals were about 1,750 Mgal/d, an increase of 12 percent from 2010, and total surface-water withdrawals were about 6,970 Mgal/d, an increase of 0.44 percent from 2010 to 2015.
Total water withdrawals, in Mgal/d, in 2015 for the various categories of use were as follows: public supply—715, industry—2,155, power generation—4,265, rural domestic—39, livestock—6, rice irrigation—825, general irrigation—225, and aquaculture—490. From 2010 to 2015, Louisiana’s total withdrawals for public supply decreased by 3.4 percent, industry increased by 5.7 percent, power generation decreased by 3.9 percent, rural domestic decreased by 4.1 percent, livestock decreased by 21 percent, rice irrigation increased by 20 percent, general irrigation decreased by 6.0 percent, and aquaculture increased by 58 percent.
About 48 percent (approximately 850 Mgal/d) of all groundwater withdrawn was from the Chicot aquifer system and 22 percent (approximately 385 Mgal/d) was withdrawn from the Mississippi River alluvial aquifer. Since 2010, withdrawals from the Chicot aquifer system increased by 30 percent and withdrawals from the Mississippi River alluvial aquifer decreased by 2.9 percent.
About 70 percent (4,905 Mgal/d) of all surface water withdrawn was from the Mississippi River mainstem. This value represents a 1.1-percent decrease in withdrawals from 2010 to 2015.
All water-withdrawal and water-use data presented in this report should be considered estimates. Because of rounding, totals and percentages presented in the tables, figures, and text in the report may differ slightly from totals or percentages calculated individually
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Pierre 0000-0002-3967-9036 psargent@usgs.gov","orcid":"https://orcid.org/0000-0002-3967-9036","contributorId":1228,"corporation":false,"usgs":true,"family":"Sargent","given":"B.","email":"psargent@usgs.gov","middleInitial":"Pierre","affiliations":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":758608,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70201558,"text":"sim3402 - 2018 - Surficial materials of Massachusetts—A 1:24,000-scale geologic map database","interactions":[],"lastModifiedDate":"2022-02-03T15:37:52.987287","indexId":"sim3402","displayToPublicDate":"2019-03-01T11:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3402","displayTitle":"Surficial Materials of Massachusetts—A 1:24,000-Scale Geologic Map Database","title":"Surficial materials of Massachusetts—A 1:24,000-scale geologic map database","docAbstract":"The surficial materials geologic map database defines the distribution of nonlithified earth materials at the land surface in the 189 7.5-minute, 1:24,000-scale quadrangles that cover the Commonwealth of Massachusetts (index map). Across the State, these materials range in thickness from a few feet to more than 500 feet (ft). In some places, surficial materials are absent where bedrock is at the land surface. The geologic map database differentiates surficial materials of Quaternary age on the basis of their lithologic characteristics (such as grain size and sedimentary structures), constructional geomorphic features, stratigraphic relationships, and age. The mapped distribution of surficial materials defines the areas of exposed bedrock and the boundaries between glacial till, glacial stratified deposits, and overlying postglacial deposits at a 1:24,000-scale level of accuracy.
Most of the surficial materials in Massachusetts are deposits of the last two continental ice sheets that covered all of New England in the latter part of the Pleistocene ice age. The glacial deposits are divided into two broad categories, glacial till and moraine deposits, and glacial stratified deposits. Widespread till deposits were laid down directly on bedrock or on semi-consolidated coastal plain strata by glacier ice. Tills in thick-till (>15 ft thick) drumlin landforms are found in all parts of the State. Areas of shallow bedrock contain thin discontinuous till deposits and numerous bedrock outcrops, and are located chiefly in rocky upland areas. Moraine deposits related to glacial ice lobes of the last ice sheet are located mostly in southeastern Massachusetts. Glacial stratified deposits are concentrated in valleys and lowland areas and were laid down by glacial meltwater in streams, lakes, and the sea in front of the retreating ice margin during the last deglaciation. Postglacial deposits, primarily flood-plain alluvium and swamp deposits, make up a lesser proportion of the unconsolidated materials.
The geodatabase included with this report contains MapUnitPolys, MapUnitOverlayPolys, and OverlayPolys, which show the distribution of geologic units that cover the entire map area and are intended for use at quadrangle scale (1:24,000). These data layers can be clipped by quadrangle or by town boundary. Unlike the units in conventional geologic maps, the digitally defined MapUnitOverlayPolys are arranged in order according to superposition. The polygons for till and bedrock are on the bottom and are overlain by the succeeding stratified deposits; these materials are shown everywhere they occur, including beneath postglacial deposits such as swamp deposits, and also beneath water bodies. The postglacial deposits are on top because these materials overlie the other, older deposits. Instructions for using the digital files are included in the README file. A series of map figures in the pamphlet illustrates the stacking of geologic units in a portion of the Mount Toby quadrangle. The BaseMaps folder contains the 1:24,000-scale topographic base map images (1944–1977 editions) used for this compilation.
This report supersedes U.S. Geological Survey Open-File Reports 2006-1260-A, -B, -C, -D, -E, -F, -G, and -I.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3402","collaboration":"Prepared in cooperation with the Commonwealth of Massachusetts, Massachusetts Geological Survey and Executive Office for Administration and Finance","usgsCitation":"Stone, J.R., Stone, B.D., DiGiacomo-Cohen, M.L., and Mabee, S.B., comps., 2018, Surficial materials of Massachusetts—A 1:24,000-scale geologic map database: U.S. Geological Survey Scientific Investigations Map 3402, 189 sheets, scale 1:24,000; index map, scale 1:250,000; 58-p. pamphlet; and geodatabase files, https://doi.org/10.3133/sim3402.","productDescription":"Pamphlet: iv, 58 p.; Index Map; Quandrangle Map Sheets; Metadata; Read Me; Spatial Data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066389","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":361110,"rank":9,"type":{"id":23,"text":"Spatial 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MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- sim3402_simple.zip contains an automatic translation of most of the contents of SIM3402.gdb into simple flat shapefiles, and contains metadata"},{"id":361232,"rank":10,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3402/sim3402_open.zip","text":"Geodatabase","size":"311 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- sim3402_open.zip contains a complete, automatic translation of SIM3402.gdb into shapefiles and other files, and contains metadata"},{"id":361577,"rank":8,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3402/sim3402_metadata.xml","text":"SIM 3402 - Metadata","size":"29.6 KB xml"},{"id":361200,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3402/sim3402_metadata.txt","text":"SIM 3402 - Metadata","size":"35.1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":361199,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3402/sim3402_quadranglespdfs.zip","text":"Maps of Quadrangles 1–189","size":"3.12 GB","linkFileType":{"id":6,"text":"zip"}},{"id":361334,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3402/sim3402_quadrangle","text":"Individual Quadrangle Map Sheets","size":"938 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":361201,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3402/sim3402_index_map.pdf","text":"Index Map","size":"25.9 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":361100,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3402/sim3402.pdf","text":"Report Pamphlet","size":"22.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3402"}],"country":"United 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\"}}]}","contact":"Florence Bascom Geoscience Center
(Formerly Eastern Geology and Paleoclimate Science Center)
U.S. Geological Survey
926A National Center
12201 Sunrise Valley Drive
Reston, VA 20192