Improving access to data and fostering open exchange of water information is foundational to solving water resources issues. In this vein, the Department of the Interior's Assistant Secretary for Water and Science put forward the charge to undertake an Open Water Data Initiative (OWDI) that would prioritize and accelerate work toward better water data infrastructure. The goal of the OWDI is to build out the Open Water Web (OWW). We therefore considered the OWW in terms of four conceptual functions: water data cataloging, water data as a service, enriching water data, and community for water data. To describe the current state of the OWW and identify areas needing improvement, we conducted an analysis of existing systems using a standard model for describing distributed systems and their business requirements. Our analysis considered three OWDI-focused use cases—flooding, drought, and contaminant transport—and then examined the landscape of other existing applications that support the Open Water Web. The analysis, which includes a discussion of observed successful practices of cataloging, serving, enriching, and building community around water resources data, demonstrates that we have made significant progress toward the needed infrastructure, although challenges remain. The further development of the OWW can be greatly informed by the interpretation and findings of our analysis.
","language":"English","publisher":"American Water Resources Associaton","publisherLocation":"Herndon, VA","doi":"10.1111/1752-1688.12417","usgsCitation":"Blodgett, D.L., Read, E.K., Lucido, J., Slawecki, T., and Young, D., 2016, An analysis of water data systems to inform the Open Water Data Initiative: Journal of the American Water Resources Association, v. 52, no. 4, p. 845-858, https://doi.org/10.1111/1752-1688.12417.","productDescription":"14 p.","startPage":"845","endPage":"858","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071340","costCenters":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"links":[{"id":471083,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12417","text":"Publisher Index Page"},{"id":319943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-08","publicationStatus":"PW","scienceBaseUri":"570ccaace4b0ef3b7ca14703","contributors":{"authors":[{"text":"Blodgett, David L. 0000-0001-9489-1710 dblodgett@usgs.gov","orcid":"https://orcid.org/0000-0001-9489-1710","contributorId":3868,"corporation":false,"usgs":true,"family":"Blodgett","given":"David","email":"dblodgett@usgs.gov","middleInitial":"L.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":626327,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Read, Emily K. 0000-0002-9617-9433 eread@usgs.gov","orcid":"https://orcid.org/0000-0002-9617-9433","contributorId":5815,"corporation":false,"usgs":true,"family":"Read","given":"Emily","email":"eread@usgs.gov","middleInitial":"K.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":false,"id":626328,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lucido, Jessica M. jlucido@usgs.gov","contributorId":4695,"corporation":false,"usgs":true,"family":"Lucido","given":"Jessica M.","email":"jlucido@usgs.gov","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":true,"id":626329,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Slawecki, Tad","contributorId":168540,"corporation":false,"usgs":false,"family":"Slawecki","given":"Tad","email":"","affiliations":[{"id":25325,"text":"LimnoTech, 501 Avis Drive, Ann Arbor, Michigan, USA 48108","active":true,"usgs":false}],"preferred":false,"id":626330,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Young, Dwane","contributorId":168541,"corporation":false,"usgs":false,"family":"Young","given":"Dwane","affiliations":[{"id":25326,"text":"U.S. Environmental Protection Agency, 1200 Pennsylvania Ave., NW, Washington, DC, USA 20460","active":true,"usgs":false}],"preferred":false,"id":626331,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70170157,"text":"70170157 - 2016 - From \"Duck Factory\" to \"Fish Factory\": Climate induced changes in vertebrate communities of prairie pothole wetlands and small lakes","interactions":[],"lastModifiedDate":"2017-01-03T16:18:55","indexId":"70170157","displayToPublicDate":"2016-04-07T09:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"From \"Duck Factory\" to \"Fish Factory\": Climate induced changes in vertebrate communities of prairie pothole wetlands and small lakes","docAbstract":"The Prairie Pothole Region’s myriad wetlands and small lakes contribute to its stature as the “duck factory” of North America. The fishless nature of the region’s aquatic habitats, a result of frequent drying, freezing, and high salinity, influences its importance to waterfowl. Recent precipitation increases have resulted in higher water levels and wetland/lake freshening. In 2012–13, we sampled chemical characteristics and vertebrates (fish and salamanders) of 162 Prairie Pothole wetlands and small lakes. We used non-metric multidimensional scaling, principal component analysis, and bootstrapping techniques to reveal relationships. We found fish present in a majority of sites (84 %). Fish responses to water chemistry varied by species. Fathead minnows (Pimephales promelas) and brook sticklebacks (Culaea inconstans) occurred across the broadest range of conditions. Yellow perch (Perca flavescens) occurred in a smaller, chemically defined, subset. Iowa darters (Etheostoma exile) were restricted to the narrowest range of conditions. Tiger salamanders (Ambystoma mavortium) rarely occurred in lakes with fish. We also compared our chemical data to similar data collected in 1966–1976 to explore factors contributing to the expansion of fish into previously fishless sites. Our work contributes to a better understanding of relationships between aquatic biota and climate-induced changes in this ecologically important area.
","language":"English","publisher":"Society of Wetland Scientists","publisherLocation":"McClean, VA","doi":"10.1007/s13157-016-0766-3","usgsCitation":"McLean, K.I., Mushet, D.M., and Stockwell, C., 2016, From \"Duck Factory\" to \"Fish Factory\": Climate induced changes in vertebrate communities of prairie pothole wetlands and small lakes: Wetlands, v. 36, no. s2, p. 407-421, https://doi.org/10.1007/s13157-016-0766-3.","productDescription":"15 p.","startPage":"407","endPage":"421","numberOfPages":"15","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068956","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":319944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","county":"Kidder County, Stutsman County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-100.1129,47.3272],[-100.0347,47.327],[-99.6498,47.3274],[-99.6077,47.3267],[-99.5248,47.3275],[-99.4801,47.3267],[-99.2669,47.3268],[-98.8466,47.327],[-98.8392,47.327],[-98.8232,47.3272],[-98.8152,47.3271],[-98.4991,47.327],[-98.467,47.3266],[-98.4677,47.2402],[-98.4685,46.9788],[-98.4412,46.9789],[-98.4396,46.6296],[-98.7894,46.6294],[-99.0379,46.6309],[-99.1616,46.6317],[-99.4122,46.6316],[-99.4498,46.6319],[-99.912,46.6319],[-100.0799,46.6316],[-100.0794,46.705],[-100.0794,46.7123],[-100.0792,46.7454],[-100.0791,46.7513],[-100.0777,46.9794],[-100.1162,46.98],[-100.1139,47.1567],[-100.1129,47.3272]]]},\"properties\":{\"name\":\"Kidder\",\"state\":\"ND\"}}]}","volume":"36","issue":"s2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-07","publicationStatus":"PW","scienceBaseUri":"570ccab2e4b0ef3b7ca14718","contributors":{"authors":[{"text":"McLean, Kyle I. kmclean@usgs.gov","contributorId":147397,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","middleInitial":"I.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":626292,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":626291,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stockwell, Craig A.","contributorId":55257,"corporation":false,"usgs":true,"family":"Stockwell","given":"Craig A.","affiliations":[],"preferred":false,"id":626293,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70169354,"text":"fs20163018 - 2016 - Landsat International Cooperators and Global Archive Consolidation","interactions":[],"lastModifiedDate":"2023-04-26T15:38:43.560081","indexId":"fs20163018","displayToPublicDate":"2016-04-07T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3018","displayTitle":"Landsat International Cooperators and Global Archive Consolidation","title":"Landsat International Cooperators and Global Archive Consolidation","docAbstract":"Landsat missions have always been an important component of U.S. foreign policy, as well as science and technology policy. The Landsat program’s longstanding network of International Cooperators (ICs), which operates numerous International Ground Stations (IGS) around the world, embodies the United States’ policy of peaceful use of outer space and the worldwide dissemination of civil space technology for public benefit. Thus, the ICs provide an essential dimension to the Landsat mission.
In 2010, the Landsat Global Archive Consolidation (LGAC) initiative began, with goals to consolidate the Landsat data archives of all IGSs, make the data more accessible to the global Landsat user community, and significantly increase the frequency of observations over a given area of interest to contribute to the understanding of a changing Earth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163018","usgsCitation":"U.S. Geological Survey, 2016, Landsat International Cooperators and Global Archive Consolidation (ver. 1.3, April 2023): U.S. Geological Survey Fact Sheet 2016–3018, 2 p., https://doi.org/10.3133/fs20163018.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072463","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":416367,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2016/3018/versionHist.txt","text":"Version History","size":"3.57 kB","linkFileType":{"id":2,"text":"txt"}},{"id":416366,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3018/fs20163018.pdf","text":"Report","size":"572 kB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2016–3018"},{"id":319664,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3018/coverthb4.jpg"}],"edition":"Version 1.0: April 7, 2016; Version 1.1: December 8, 2016; Version 1.2: June 10, 2019; Version 1.3: April 26, 2023","contact":"Landsat User Services
Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
custserv@usgs.gov
https://www.usgs.gov/land-resources/national-land-imaging-program
","tableOfContents":"2015 was another great year for the Department of the Interior (DOI) Climate Science Centers (CSCs) and U.S. Geological Survey (USGS) National Climate Change and Wildlife Science Center (NCCWSC) network. The DOI CSCs and USGS NCCWSC continued their mission of providing the science, data, and tools that are needed for on-the-ground decision making by natural and cultural resource managers to address the effects of climate change on fish, wildlife, ecosystems, and communities. Our many accomplishments in 2015 included initiating a national effort to understand the influence of drought on wildlife and ecosystems; providing numerous opportunities for students and early career researchers to expand their networks and learn more about climate change effects; and working with tribes and indigenous communities to expand their knowledge of and preparation for the impacts of climate change on important resources and traditional ways of living. Here we illustrate some of these 2015 activities from across the CSCs and NCCWSC.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161043","usgsCitation":"Varela Minder, Elda, and Padgett, H.A., 2016, U.S. Department of the Interior Climate Science Centers and U.S. Geological Survey National Climate Change and Wildlife Science Center—Annual report for 2015: U.S. Geological Survey Open-File Report 2016–1043, 10 p., https://dx.doi.org/10.3133/ofr20161043.","productDescription":"10 p.","numberOfPages":"10","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-073088","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":319881,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1043/ofr20161043.pdf","text":"Report","size":"4.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1043"},{"id":319880,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1043/coverthb.jpg"}],"contact":"National Climate Change and Wildlife Science Center (NCCWSC)
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 516
Reston, VA 20192
Sea otters (Enhydra lutris) have exceptionally high energetic requirements, which nearly double during lactation and pup care. Thus, females are extremely vulnerable to caloric insufficiency. Despite a number of compensatory strategies, the metabolic challenge of reproduction culminates in numerous maternal deaths annually. Massive depletion of energy reserves results in a case presentation that we define as end-lactation syndrome (ELS), characterized by moderate to severe emaciation not attributable to a concurrent, independent disease process in females dying during late pup care or postweaning. We compiled detailed data for 108 adult female southern sea otters (Enhydra lutris nereis) examined postmortem that stranded in California, US, 2005–12, and assessed pathology, reproductive status, and the location and timing of stranding. We introduce simple, grossly apparent, standardized physical criteria to assess reproductive stage for female sea otters. We also describe ELS, examine associated risk factors, and highlight female life history strategies that likely optimize reproduction and survival. Our data suggest that females can reset both the timing and energetic demands of reproduction through fetal loss, pup abandonment, or early weaning as part of specific physiologic checkpoints during each reproductive cycle. Females appear to preload nutritionally during delayed implantation and gestation to increase fitness and reproductive success. We found that ELS was a major cause of death, affecting 56% of enrolled adult females. Peak ELS prevalence occurred in late spring, possibly reflecting the population trend toward fall/winter pupping. Increasing age and number of pregnancies were associated with a higher risk of ELS. Although the proportion of ELS females was highest in areas with dense sea otter populations, cases were recovered throughout the range, suggesting that death from ELS is associated with, but not solely caused by, population resource limitation.
","language":"English","publisher":"Wildlife Diseases Association","doi":"10.7589/2015-06-158","usgsCitation":"Chinn, S.S., Miller, M.A., Tinker, M.T., Staedler, M., Batac, F.I., Dodd, E.M., and Henkel, L.A., 2016, The high cost of motherhood: End-lactation syndrome in southern sea otters (Enhydra lutris nereis) on the central California, USA, coast: Journal of Wildlife Diseases, v. 52, no. 2, p. 307-318, https://doi.org/10.7589/2015-06-158.","productDescription":"12 p.","startPage":"307","endPage":"318","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070046","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":319824,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","volume":"52","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"572477b3e4b0b13d3914e15d","contributors":{"authors":[{"text":"Chinn, Sarah S","contributorId":168466,"corporation":false,"usgs":false,"family":"Chinn","given":"Sarah","email":"","middleInitial":"S","affiliations":[{"id":25299,"text":"CA DFW","active":true,"usgs":false}],"preferred":false,"id":626018,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Melissa A.","contributorId":57701,"corporation":false,"usgs":false,"family":"Miller","given":"Melissa","email":"","middleInitial":"A.","affiliations":[{"id":39007,"text":"CA Dept of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":626019,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tinker, M. Tim 0000-0002-3314-839X ttinker@usgs.gov","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":2796,"corporation":false,"usgs":true,"family":"Tinker","given":"M.","email":"ttinker@usgs.gov","middleInitial":"Tim","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":626017,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Staedler, Michelle M.","contributorId":40087,"corporation":false,"usgs":true,"family":"Staedler","given":"Michelle M.","affiliations":[],"preferred":false,"id":626020,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Batac, Francesca I.","contributorId":168467,"corporation":false,"usgs":false,"family":"Batac","given":"Francesca","email":"","middleInitial":"I.","affiliations":[{"id":13632,"text":"CDFW, Bishop, CA","active":true,"usgs":false}],"preferred":false,"id":626021,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dodd, Erin M.","contributorId":168468,"corporation":false,"usgs":false,"family":"Dodd","given":"Erin","email":"","middleInitial":"M.","affiliations":[{"id":13632,"text":"CDFW, Bishop, CA","active":true,"usgs":false}],"preferred":false,"id":626022,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Henkel, Laird A.","contributorId":84288,"corporation":false,"usgs":true,"family":"Henkel","given":"Laird","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":626023,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70170085,"text":"70170085 - 2016 - Semi-arid vegetation response to antecedent climate and water balance windows","interactions":[],"lastModifiedDate":"2016-06-15T16:24:21","indexId":"70170085","displayToPublicDate":"2016-04-06T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":849,"text":"Applied Vegetation Science","active":true,"publicationSubtype":{"id":10}},"title":"Semi-arid vegetation response to antecedent climate and water balance windows","docAbstract":"Can we improve understanding of vegetation response to water availability on monthly time scales in semi-arid environments using remote sensing methods? What climatic or water balance variables and antecedent windows of time associated with these variables best relate to the condition of vegetation? Can we develop credible near-term forecasts from climate data that can be used to prepare for future climate change effects on vegetation?
\nSemi-arid grasslands in Capitol Reef National Park, Utah, USA.
\nWe built vegetation response models by relating the normalized difference vegetation index (NDVI) from MODIS imagery in Mar–Nov 2000–2013 to antecedent climate and water balance variables preceding the monthly NDVI observations. We compared how climate and water balance variables explained vegetation greenness and then used a multi-model ensemble of climate and water balance models to forecast monthly NDVI for three holdout years.
\nWater balance variables explained vegetation greenness to a greater degree than climate variables for most growing season months. Seasonally important variables included measures of antecedent water input and storage in spring, switching to indicators of drought, input or use in summer, followed by antecedent moisture availability in autumn. In spite of similar climates, there was evidence the grazed grassland showed a response to drying conditions 1 mo sooner than the ungrazed grassland. Lead times were generally short early in the growing season and antecedent window durations increased from 3 mo early in the growing season to 1 yr or more as the growing season progressed. Forecast accuracy for three holdout years using a multi-model ensemble of climate and water balance variables outperformed forecasts made with a naïve NDVI climatology.
\nWe determined the influence of climate and water balance on vegetation at a fine temporal scale, which presents an opportunity to forecast vegetation response with short lead times. This understanding was obtained through high-frequency vegetation monitoring using remote sensing, which reduces the costs and time necessary for field measurements and can lead to more rapid detection of vegetation changes that could help managers take appropriate actions.
\nA Decree of the Supreme Court of the United States, entered June 7, 1954, established the position of Delaware River Master within the U.S. Geological Survey (USGS). In addition, the Decree authorizes diversions of water from the Delaware River Basin and requires compensating releases from certain reservoirs, owned by New York City, to be made under the supervision and direction of the River Master. The Decree stipulates that the River Master will furnish reports to the Court, not less frequently than annually. This report is the 56th Annual Report of the River Master of the Delaware River. It covers the 2009 River Master report year, the period from December 1, 2008, to November 30, 2009.
During the report year, precipitation in the upper Delaware River Basin was 50.89 inches (in.) or 116 percent of the long-term average. Combined storage in Pepacton, Cannonsville, and Neversink Reservoirs remained high throughout the year and did not decline below 80 percent of combined capacity at any time. Delaware River operations during the year were conducted as stipulated by the Decree and the Flexible Flow Management Program (FFMP).
Diversions from the Delaware River Basin by New York City and New Jersey were in full compliance with the Decree. Reservoir releases were made as directed by the River Master at rates designed to meet the flow objective for the Delaware River at Montague, New Jersey, on 25 days during the report year. Releases were made at conservation rates—rates designed to relieve thermal stress and protect the fishery and aquatic habitat in the tailwaters of the reservoirs—on all other days.
During the report year, New York City and New Jersey complied fully with the terms of the Decree, and directives and requests of the River Master.
As part of a long-term program, the quality of water in the Delaware Estuary between Trenton, New Jersey, and Reedy Island Jetty, Delaware, was monitored at various locations. Data on water temperature, specific conductance, dissolved oxygen, and pH were collected continuously by electronic instruments at four sites. In addition, selected water-quality data were collected at 22 sites on a monthly basis.
The Delaware River Basin Commission (DRBC) collects monthly samples from March through October at 22 sites between Biles Channel and South Brown Shoal. Samples were collected and analyzed by the State of Delaware for the DRBC. At each site, water samples were collected at a single point near the center of the channel near the surface and analyzed for selected physical properties, and chemical and biological constituents including routine chemical substances, nutrients and bacteria. These consist of analyses of field measurements and laboratory determinations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151231","usgsCitation":"Krejmas, B.E., Paulachok, G.N., Mason, R.R., Jr., and Owens, Marie, 2016, Report of the River Master of the Delaware River for the period December 1, 2008–November 30, 2009: U.S. Geological Survey Open-File Report 2015–1231, 76 p., https://dx.doi.org/10.3133/ofr20151231.","productDescription":"vi, 76 p.","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2008-12-01","ipdsId":"IP-070813","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":319219,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1231/ofr20151231.pdf","size":"1.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1231"},{"id":319218,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1231/coverthb.jpg"}],"country":"United States","otherGeospatial":"Delaware River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.4815673828125,\n 39.70296052957233\n ],\n [\n -74.498291015625,\n 39.8465036024177\n ],\n [\n -74.4927978515625,\n 40.26695230509781\n ],\n [\n -74.970703125,\n 40.75974059207392\n ],\n [\n -74.6685791015625,\n 40.979898069620155\n ],\n [\n -74.5806884765625,\n 41.335575973123895\n ],\n [\n -74.11376953125,\n 42.13082130188811\n ],\n [\n -74.9432373046875,\n 42.44372793752476\n ],\n [\n -75.574951171875,\n 42.00848901572399\n ],\n [\n -75.8880615234375,\n 41.244772343082104\n ],\n [\n -76.343994140625,\n 40.329795743702064\n ],\n [\n -76.04736328125,\n 39.73253798438173\n ],\n [\n -75.4815673828125,\n 39.70296052957233\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Deputy Delaware River Master
U.S. Geological Survey
10 Buist Road, Bldg. 3, Suite 304
Milford Professional Park
Milford, PA 18337
Fax: 570–296–9414
http://water.usgs.gov/osw/odrm/
The Kansas River is a primary source of drinking water for about 800,000 people in northeastern Kansas. Source-water supplies are treated by a combination of chemical and physical processes to remove contaminants before distribution. Advanced notification of changing water-quality conditions and cyanobacteria and associated toxin and taste-and-odor compounds provides drinking-water treatment facilities time to develop and implement adequate treatment strategies. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office (funded in part through the Kansas State Water Plan Fund), and the City of Lawrence, the City of Topeka, the City of Olathe, and Johnson County Water One, began a study in July 2012 to develop statistical models at two Kansas River sites located upstream from drinking-water intakes. Continuous water-quality monitors have been operated and discrete-water quality samples have been collected on the Kansas River at Wamego (USGS site number 06887500) and De Soto (USGS site number 06892350) since July 2012. Continuous and discrete water-quality data collected during July 2012 through June 2015 were used to develop statistical models for constituents of interest at the Wamego and De Soto sites. Logistic models to continuously estimate the probability of occurrence above selected thresholds were developed for cyanobacteria, microcystin, and geosmin. Linear regression models to continuously estimate constituent concentrations were developed for major ions, dissolved solids, alkalinity, nutrients (nitrogen and phosphorus species), suspended sediment, indicator bacteria (Escherichia coli, fecal coliform, and enterococci), and actinomycetes bacteria. These models will be used to provide real-time estimates of the probability that cyanobacteria and associated compounds exceed thresholds and of the concentrations of other water-quality constituents in the Kansas River. The models documented in this report are useful for characterizing changes in water-quality conditions through time, characterizing potentially harmful cyanobacterial events, and indicating changes in water-quality conditions that may affect drinking-water treatment processes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161040","collaboration":"Prepared in cooperation with the Kansas Water Office, the City of Lawrence, the City of Topeka, the City of Olathe, and Johnson County Water One","usgsCitation":"Foster, G.M., and Graham, J.L., 2016, Logistic and linear regression model documentation for statistical relations between continuous real-time and discrete water-quality constituents in the Kansas River, Kansas, July 2012 through June 2015: U.S. Geological Survey Open-File Report 2016–1040, 27 p., https://dx.doi.org/10.3133/ofr20161040. ","productDescription":"Report: iv, 27 p.; 31 Appendixes","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071163","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":319781,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1040/coverthb.jpg"},{"id":319791,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1040/downloads/","text":"Appendixes 1 through 31","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1040 Appendixes"},{"id":319782,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1040/ofr20161040.pdf","text":"Report","size":"830 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1040"}],"country":"United States","state":"Kansas","otherGeospatial":"Kansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.6142578125,\n 38.81403111409755\n ],\n [\n -94.921875,\n 39.14710270770074\n ],\n [\n -95.185546875,\n 39.45316112807394\n ],\n [\n -95.5810546875,\n 39.90130858574735\n ],\n [\n -95.635986328125,\n 39.95185892663005\n ],\n [\n -95.82275390625,\n 39.707186656826565\n ],\n [\n -96.207275390625,\n 39.690280594818034\n ],\n [\n -96.39404296875,\n 39.487084981687495\n ],\n [\n -96.50390625,\n 39.18117526158749\n ],\n [\n -96.866455078125,\n 39.26628442213066\n ],\n [\n -96.88842773437499,\n 38.976492485539424\n ],\n [\n -96.910400390625,\n 38.60828592850559\n ],\n [\n -96.94335937499999,\n 38.42777351132905\n ],\n [\n -96.624755859375,\n 38.53097889440026\n ],\n [\n -96.075439453125,\n 38.61687046392973\n ],\n [\n -95.020751953125,\n 38.65119833229951\n ],\n [\n -94.68017578125,\n 38.659777730712534\n ],\n [\n -94.6142578125,\n 38.81403111409755\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
The Chicago Department of Transportation’s Sustainable Streetscapes Program is an innovative program that strives to convert Chicago’s neighborhood commercial areas, riverwalks, and bicycle facilities into active, attractive places for Chicagoans to live, work, and play. The objective of each project is to create flourishing public places while improving the ability of infrastructure to support dense urban living. The U.S. Geological Survey (USGS), in cooperation with the Metropolitan Water Reclamation District of Greater Chicago (MWRDGC), and the Chicago Department of Transportation (CDOT), is monitoring the pre- and postconstruction hydrologic characteristics of an urban corridor on the south side of Chicago that is being renovated using sustainable streetscapes technology.
The CDOT Sustainable Streetscapes Program utilizes urban stormwater best-management practices (BMPs) to reduce the storm runoff to the local combined sewer system. The urban stormwater BMPs include permeable pavement, bioswales, infiltration basins, and planters. The urban stormwater BMPs are designed to capture the first flush of storm runoff through features that enhance the infiltration of stormwater runoff to shallow groundwater.
The hydrology of the Sustainable Streetscapes Program area is being monitored to evaluate the impacts and effectiveness of the urban stormwater BMP’s. Continuous monitoring of rainfall, sewer flows, stormwater runoff, soil moisture, and groundwater levels will give engineers and scientists measured data to define baseline pre- and postconstruction conditions for the evaluation of the BMPs.
Three tipping-bucket rain gages are located along the project corridor. The data provide information on the intensity and volume of rainfall. Rainfall can be highly variable even over a small area like the project corridor.
Continuous recording meters are located at specific locations in the combined sewers to record water level and flow during both dry weather (mostly sanitary flow) and wet weather conditions (stormwater runoff in addition to the sanitary flow). Sanitary flow is the largest source of flow in the combined sewers during dry weather, and stormwater runoff and sanitary flow combine during wet weather. The sewer flow data allow engineers and scientists to calculate total runoff volume for selected storm events.
Wells are located within the project corridor to record water levels and help determine the direction of movement of groundwater in response to rainfall and snowmelt. In urban settings with aging sewer systems, groundwater can seep into the sewers or combined sewage can seep from the sewers into the local groundwater system. The groundwater data are also important in evaluating the overall impacts of increased infiltration resulting from BMPs.
Data from wells show the relative water levels of shallow groundwater, water levels in the combined sewer system, and nearby surface-water channels within the project corridor. In some aging urban sewer systems, the local combined sewer system lies below the water table and receives substantial amounts of groundwater inflow, which can significantly reduce the amount of additional water the sewer system can accept.
The bioswale along the south side of West Cermak Road near South Throop Street functions to infiltrate stormwater runoff from the road. Stormwater on the road surface initially drains to the curb and then flows along the curb until it reaches a curb cut-out. Materials within the bioswale allow stormwater to infiltrate and reduce the load to the combined sewer.
A common feature in urban areas are curbside catch basins that collect stormwater runoff from paved streets. Stormwater drains first to the curb and then flows along the curb to the catch basin. Lateral sewer pipe connects the catch basin to the combined sewer beneath the street. The use of permeable pavers along the curbs in the project study reach let stormwater infiltrate before it reaches the curb, thus reducing the amount of stormwater draining to the combined sewers.
Water-level data from catch basins in the project study area show the effects of permeable pavers in reducing the stormwater drainage to the combined sewers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163014","collaboration":"Prepared in cooperation with the Metropolitan Water Reclamation District of Greater Chicago (MWRDGC), and the Chicago Department of Transportation (CDOT)","usgsCitation":"Duncker, J.J., and Morrow, W.S., 2016, Hydrologic monitoring for Chicago’s Sustainable Streetscapes Program. U.S. Geological Survey Fact Sheet 2016–3014, 6 p., https://dx.doi.org/10.3133/fs20163014.","productDescription":"6 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-054040","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":319331,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3014/coverthb.jpg"},{"id":319332,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3014/fs20163014.pdf","text":"Report","size":"3.70 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3014"}],"country":"United States","state":"Illinois","city":"Chicago","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.68531799316406,\n 41.8384904853889\n ],\n [\n -87.68531799316406,\n 41.86035637336661\n ],\n [\n -87.64274597167967,\n 41.86035637336661\n ],\n [\n -87.64274597167967,\n 41.8384904853889\n ],\n [\n -87.68531799316406,\n 41.8384904853889\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 N. Goodwin Ave
Urbana, IL 61801
http://il.water.usgs.gov/
The lower Genesee River and Rochester Embayment of Lake Ontario are a designated Area of Concern (AOC) under the binational Great Lakes Water Quality Agreement. The “degradation of phytoplankton and zooplankton populations” or plankton Beneficial Use Impairment (BUI) was classified as unknown and in need of further assessment in this AOC because water quality data suggested plankton communities could be effected and community data were either unavailable or indicated impacts. The plankton BUI may now be obsolete because local contaminant sources have been largely eliminated. The present study was conducted between July 2013 and August 2014 to assess the BUI-removal criteria: “AOC plankton bioassays confirm that toxicity in ambient waters (i.e., no growth inhibition) is not significantly higher than comparable non-AOC controls”. Acute and chronic toxicity of waters from 13 sites were quantified seasonally using standardized bioassays with the green alga Pseudokirchneriella subcapitata and water flea Ceriodaphnia dubia to test the hypothesis that toxicity of waters from AOC sites was not higher than that of waters from comparable non-AOC reference sites. Survival and reproduction of C. dubia did not differ significantly between site types, systems, or months. The growth of P. subcapitata did not differ between site types, but differed among systems and months. All results indicate that waters from AOC sites were no more toxic to both plankton species than waters from reference sites. Assuming test species represent natural plankton assemblages, water quality should not negatively affect survival and growth of resident plankton populations in the Rochester Embayment AOC.
","language":"English","publisher":"International Association for Great Lakes Research","doi":"10.1016/j.jglr.2015.12.007","usgsCitation":"Baldigo, B.P., Duffy, B.T., Smith, A., and George, S.D., 2016, Toxicity of waters from the Rochester Embayment Area of Concern to the plankton species Pseudokirchneriella subcapitata and Ceriodaphnia dubia: Journal of Great Lakes Research, v. 42, no. 2, p. 464-471, https://doi.org/10.1016/j.jglr.2015.12.007.","productDescription":"8 p.","startPage":"464","endPage":"471","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062670","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":471089,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2015.12.007","text":"Publisher Index Page"},{"id":325100,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.7005615234375,\n 42.944360446966314\n ],\n [\n -78.7005615234375,\n 43.44893105587766\n ],\n [\n -76.80541992187499,\n 43.44893105587766\n ],\n [\n -76.80541992187499,\n 42.944360446966314\n ],\n [\n -78.7005615234375,\n 42.944360446966314\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"2","edition":"2016","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"579dd04ee4b0589fa1cbdf45","chorus":{"doi":"10.1016/j.jglr.2015.12.007","url":"http://dx.doi.org/10.1016/j.jglr.2015.12.007","publisher":"Elsevier BV","authors":"Baldigo Barry P., Duffy Brian T., Smith Alexander J., George Scott D.","journalName":"Journal of Great Lakes Research","publicationDate":"4/2016"},"contributors":{"authors":[{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":1234,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":642213,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duffy, Brian T.","contributorId":6352,"corporation":false,"usgs":true,"family":"Duffy","given":"Brian","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":642215,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Alexander J.","contributorId":140345,"corporation":false,"usgs":false,"family":"Smith","given":"Alexander J.","affiliations":[{"id":13464,"text":"Environmental Analyst, NY State Dept of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":642216,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":642214,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168419,"text":"ofr20161016 - 2016 - Estimating the economic impacts of ecosystem restoration—Methods and case studies","interactions":[],"lastModifiedDate":"2016-04-05T09:49:35","indexId":"ofr20161016","displayToPublicDate":"2016-04-05T09:00:00","publicationYear":"2016","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":"2016-1016","title":"Estimating the economic impacts of ecosystem restoration—Methods and case studies","docAbstract":"Federal investments in ecosystem restoration projects protect Federal trusts, ensure public health and safety, and preserve and enhance essential ecosystem services. These investments also generate business activity and create jobs. It is important for restoration practitioners to be able to quantify the economic impacts of individual restoration projects in order to communicate the contribution of these activities to local and national stakeholders. This report provides a detailed description of the methods used to estimate economic impacts of case study projects and also provides suggestions, lessons learned, and trade-offs between potential analysis methods.
\nThis analysis estimates the economic impacts of a wide variety of ecosystem restoration projects associated with U.S. Department of the Interior (DOI) lands and programs. Specifically, the report provides estimated economic impacts for 21 DOI restoration projects associated with Natural Resource Damage Assessment and Restoration cases and Bureau of Land Management lands. The study indicates that ecosystem restoration projects provide meaningful economic contributions to local economies and to broader regional and national economies, and, based on the case studies, we estimate that between 13 and 32 job-years4 and between $2.2 and $3.4 million in total economic output5 are contributed to the U.S. economy for every $1 million invested in ecosystem restoration. These results highlight the magnitude and variability in the economic impacts associated with ecosystem restoration projects and demonstrate how investments in ecosystem restoration support jobs and livelihoods, small businesses, and rural economies. In addition to providing improved information on the economic impacts of restoration, the case studies included with this report highlight DOI restoration efforts and tell personalized stories about each project and the communities that are positively affected by restoration activities. Individual case studies are provided in appendix 1 of this report and are available from an online database at https://www.fort.usgs.gov/economic-impacts-restoration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161016","collaboration":"Prepared in cooperation with the U.S. Department of the Interior Natural Resource Damage Assessment and Restoration Program and Office of Policy Analysis and the Bureau of Land Management Socioeconomics Program","usgsCitation":"Cullinane Thomas, Catherine; Huber, Christopher; Skrabis, Kristin; and Sidon, Joshua, 2016, Estimating the economic impacts of ecosystem restoration—Methods and case studies: U.S. Geological Survey Open-File Report 2016–1016, 98 p., https://dx.doi.org/10.3133/ofr20161016.","productDescription":"v, 98 p.","numberOfPages":"104","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068960","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":318716,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1016/ofr20161016.pdf","text":"Report","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1016"},{"id":318715,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1016/coverthb2.jpg"}],"contact":"Director, Fort Collins Science Center
2150 Centre Ave. Building C
Fort Collins, CO 80526-8118
The U.S. Geological Survey (USGS) provides streamflow characteristics and other related information needed by water-resource managers to protect people and property from floods, plan and manage water-resource activities, and protect water quality. Streamflow characteristics provided by the USGS, such as peak-flow and low-flow frequencies for streamflow-gaging stations, are frequently used by engineers, flood forecasters, land managers, biologists, and others to guide their everyday decisions. In addition to providing streamflow characteristics at streamflow-gaging stations, the USGS also develops regional regression equations and drainage area-adjustment methods for estimating streamflow characteristics at locations on ungaged streams. Regional regression equations can be complex and often require users to determine several basin characteristics, which are physical and climatic characteristics of the stream and its drainage basin. Obtaining these basin characteristics for streamflow-gaging stations and ungaged sites traditionally has been time consuming and subjective, and led to inconsistent results.
StreamStats is a Web-based geographic information system application that was created by the USGS to provide users with access to an assortment of analytical tools that are useful for water-resource planning and management. StreamStats allows users to easily obtain streamflow and basin characteristics for USGS streamflow-gaging stations and user-selected locations on ungaged streams. The USGS, in cooperation with Montana Department of Transportation, Montana Department of Environmental Quality, and Montana Department of Natural Resources and Conservation, completed a study to develop a StreamStats application for Montana, compute streamflow characteristics at streamflow-gaging stations, and develop regional regression equations to estimate streamflow characteristics at ungaged sites. Chapter A of this Scientific Investigations Report describes the Montana StreamStats application and the datasets, streamflow-gaging stations, streamflow characteristics, and regression equations (as described fully in Chapters B through G of this report) that are used for development of the StreamStats application for Montana.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Montana StreamStats (Scientific Investigations Report 2015-5019)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155019A","collaboration":"Prepared in cooperation with the Montana Department of Transportation, Montana Department of Environmental Quality, and Montana Department of Natural Resources and Conservation","usgsCitation":"McCarthy, P.M., Dutton, D.M., Sando, S.K., and Sando, Roy, 2016, Montana StreamStats—A method for retrieving basin and streamflow characteristics in Montana: U.S. Geological Survey Scientific Investigations Report 2015–\n5019–A, 16 p., https://dx.doi.org/10.3133/sir20155019A.","productDescription":"Report: v, 16 p.; 4 Tables","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069671","costCenters":[{"id":5050,"text":"WY-MT Water Science 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\"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
Chapter C of this Scientific Investigations Report documents results from a study by the U.S. Geological Survey, in cooperation with the Montana Department of Transportation and the Montana Department of Natural Resources, to provide an update of statewide peak-flow frequency analyses and results for Montana. The purpose of this report chapter is to present peak-flow frequency analyses and results for 725 streamflow-gaging stations in or near Montana based on data through water year 2011. The 725 streamflow-gaging stations included in this study represent nearly all streamflowgaging stations in Montana (plus some from adjacent states or Canadian Provinces) that have at least 10 years of peak-flow records through water year 2011. For 29 of the 725 streamflow-gaging stations, peak-flow frequency analyses and results are reported for both unregulated and regulated conditions. Thus, peak-flow frequency analyses and results are reported for a total of 754 analyses. Estimates of peak-flow magnitudes for 66.7-, 50-, 42.9-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities are reported. These annual exceedance probabilities correspond to 1.5-, 2-, 2.33-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Montana StreamStats (Scientific Investigations Report 2015-5019)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155019C","collaboration":"Prepared in cooperation with the Montana Department of Transportation and Montana Department of Natural Resources and Conservation","usgsCitation":"Sando, S.K., McCarthy, P.M., and Dutton, D.M., 2016, Peak-flow frequency analyses and results based on data through water year 2011 for selected streamflow-gaging stations in or near Montana: U.S. Geological Survey Scientific Investigations Report 2015–5019–C, 27 p., https://dx.doi.org/10.3133/sir20155019C.","productDescription":"Report: v, 27 p.; 6 Tables","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-061583","costCenters":[{"id":5050,"text":"WY-MT Water Science 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\"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
The U.S. Geological Survey (USGS), in cooperation with the Montana Department of Natural Resources and Conservation, completed a study to update methods for estimating peak-flow frequencies at ungaged sites in Montana based on peak-flow data at streamflow-gaging stations through water year 2011. The methods allow estimation of peak-flow frequencies (that is, peak-flow magnitudes, in cubic feet per second, associated with annual exceedance probabilities of 66.7, 50, 42.9, 20, 10, 4, 2, 1, 0.5, and 0.2 percent) at ungaged sites. The annual exceedance probabilities correspond to 1.5-, 2-, 2.33-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively.
Regional regression analysis is a primary focus of Chapter F of this Scientific Investigations Report, and regression equations for estimating peak-flow frequencies at ungaged sites in eight hydrologic regions in Montana are presented. The regression equations are based on analysis of peak-flow frequencies and basin characteristics at 537 streamflow-gaging stations in or near Montana and were developed using generalized least squares regression or weighted least squares regression.
All of the data used in calculating basin characteristics that were included as explanatory variables in the regression equations were developed for and are available through the USGS StreamStats application (http://water.usgs.gov/osw/streamstats/) for Montana. StreamStats is a Web-based geographic information system application that was created by the USGS to provide users with access to an assortment of analytical tools that are useful for water-resource planning and management. The primary purpose of the Montana StreamStats application is to provide estimates of basin characteristics and streamflow characteristics for user-selected ungaged sites on Montana streams. The regional regression equations presented in this report chapter can be conveniently solved using the Montana StreamStats application.
Selected results from this study were compared with results of previous studies. For most hydrologic regions, the regression equations reported for this study had lower mean standard errors of prediction (in percent) than the previously reported regression equations for Montana. The equations presented for this study are considered to be an improvement on the previously reported equations primarily because this study (1) included 13 more years of peak-flow data; (2) included 35 more streamflow-gaging stations than previous studies; (3) used a detailed geographic information system (GIS)-based definition of the regulation status of streamflow-gaging stations, which allowed better determination of the unregulated peak-flow records that are appropriate for use in the regional regression analysis; (4) included advancements in GIS and remote-sensing technologies, which allowed more convenient calculation of basin characteristics and investigation of many more candidate basin characteristics; and (5) included advancements in computational and analytical methods, which allowed more thorough and consistent data analysis.
This report chapter also presents other methods for estimating peak-flow frequencies at ungaged sites. Two methods for estimating peak-flow frequencies at ungaged sites located on the same streams as streamflow-gaging stations are described. Additionally, envelope curves relating maximum recorded annual peak flows to contributing drainage area for each of the eight hydrologic regions in Montana are presented and compared to a national envelope curve. In addition to providing general information on characteristics of large peak flows, the regional envelope curves can be used to assess the reasonableness of peak-flow frequency estimates determined using the regression equations.
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\"}}]}","edition":"Version 1.1: February 2018","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
The U.S. Geological Survey, in cooperation with the Montana Department of Environmental Quality and the Montana Department of Natural Resources and Conservation, developed regional regression equations based on basin and streamflow characteristics for streamflow-gaging stations through water year 2009 that can be used to estimate streamflow characteristics for ungaged sites in western Montana. The regression equations allow estimation of low-flow frequencies; mean annual and mean monthly streamflows; and the 20-, 50-, and 80-percent durations for annual and monthly duration streamflows for ungaged sites in western Montana that are unaffected by regulation.
\nRegression equations were developed for 54 streamflow characteristics in each of the 4 hydrologic regions generally located in western Montana. Exploratory data analyses determined that of the 40 basin characteristics used as explanatory variables, only 3 were significant in the final equations. These three basin characteristics are contributing drainage area, mean annual precipitation of the drainage basin, and percentage of basin with slopes greater than 50 percent. Low-flow frequency data and daily mean streamflow data from 152 streamflow-gaging stations were used to develop the regression equations. These 152 streamflow-gaging stations were considered to be unregulated (or only the unregulated period of record for regulated streamflow-gaging stations were used). For this study, a streamflow-gaging station is considered to be unregulated if cumulative drainage area upstream from all upstream dams is less than 20 percent of the drainage basin for the streamflow-gaging station, and if no large diversion canals are upstream from the streamflow-gaging station.
\nIn addition to the regional regression equations, two methods for estimating streamflow characteristics based on drainage areas of a nearby streamflow-gaging station are provided. Also, spreadsheet tools are provided for calculating streamflow characteristics based on regression equations or the drainage-area ratio method.
\nAll of the data used to calculate basin characteristics were derived from publicly available data sources and are available through the U.S. Geological Survey Streamstats program (http://water.usgs.gov/osw/streamstats/) for Montana. The primary purpose of the Montana StreamStats application is to provide estimates of basin characteristics and streamflow characteristics for user-selected ungaged sites on Montana streams. The regional regression equations presented in this report have been loaded to the Montana StreamStats application and can be used to derive streamflow characteristics for ungaged sites.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Montana StreamStats (Scientific Investigations Report 2015-5019)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155019G","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality and Montana Department of Natural Resources and Conservation","usgsCitation":"McCarthy, P.M., Sando, Roy, Sando, S.K., and Dutton, D.M., 2016, Methods for estimating streamflow characteristics at ungaged sites in western Montana based on data through water year 2009: U.S. Geological Survey Scientific Investigations Report 2015–5019–G, 19 p., https://dx.doi.org/10.3133/sir20155019G.","productDescription":"Report: vii, 19 p.; 10 Tables","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-067085","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":319659,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5019G/coverthb.jpg"},{"id":319661,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2015/5019G/sir20155019G_tables.xlsx","text":"Tables 1 through 4, 1–1 through 1–4, and 2–1 through 2–2","size":"483 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015–5019–G Tables"},{"id":319660,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5019G/sir20155019G.pdf","text":"Report","size":"9.77 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015–5019–G"}],"country":"United States","state":"Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.03759765625,\n 48.99463598353408\n ],\n [\n -116.08154296875001,\n 47.97521412341618\n ],\n [\n -115.6201171875,\n 47.39834920035926\n ],\n [\n -114.64233398437499,\n 46.66451741754235\n ],\n [\n -114.43359375,\n 46.52863469527167\n ],\n [\n -114.53247070312499,\n 46.14178273759234\n ],\n [\n -114.43359375,\n 45.87471224890479\n ],\n [\n -114.53247070312499,\n 45.79050946752472\n ],\n [\n -114.576416015625,\n 45.590978249451936\n ],\n [\n -114.334716796875,\n 45.4524242413431\n ],\n [\n -113.9501953125,\n 45.69850658738848\n ],\n [\n -113.7744140625,\n 45.57560020947799\n ],\n [\n -113.7744140625,\n 45.390735154248894\n ],\n [\n -113.433837890625,\n 45.0502402697946\n ],\n [\n -113.51074218749999,\n 44.933696389694674\n ],\n [\n -113.26904296874999,\n 44.78573392716592\n ],\n [\n -113.08227539062499,\n 44.66865287227321\n ],\n [\n -112.9833984375,\n 44.43377984606825\n ],\n [\n -112.8515625,\n 44.33956524809713\n ],\n [\n -112.796630859375,\n 44.457309801319305\n ],\n [\n -112.47802734375,\n 44.4808302785626\n ],\n [\n -112.379150390625,\n 44.457309801319305\n ],\n [\n -112.291259765625,\n 44.574817404670306\n ],\n [\n -111.8408203125,\n 44.55133484083592\n ],\n [\n -111.52221679687499,\n 44.535674532413196\n ],\n [\n -111.42333984375,\n 44.69989765840318\n ],\n [\n -111.346435546875,\n 44.72332018895825\n ],\n [\n -111.060791015625,\n 44.465151013519616\n ],\n [\n -111.060791015625,\n 44.99588261816546\n ],\n [\n -107.22656249999999,\n 45.02695045318546\n ],\n [\n -107.57812499999999,\n 45.99696161820381\n ],\n [\n -108.12744140625,\n 47.234489635299184\n ],\n [\n -111.42333984375,\n 47.2195681123155\n ],\n [\n -112.7197265625,\n 48.06339653776211\n ],\n [\n -113.35693359375,\n 49.009050809382046\n ],\n [\n -116.03759765625,\n 48.99463598353408\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
Chapter E of this Scientific Investigations Report documents results from a study by the U.S. Geological Survey, in cooperation with the Montana Department of Environmental Quality and the Montana Department of Natural Resources and Conservation, to provide an update of statewide streamflow characteristics based on data through water year 2009 for streamflow-gaging stations in or near Montana. Streamflow characteristics are presented for 408 streamflow-gaging stations in Montana and adjacent areas having 10 or more years of record. Data include the magnitude and probability of annual low and high streamflow, the magnitude and probability of low streamflow for three seasons (March–June, July–October, and November–February), streamflow duration statistics for monthly and annual periods, and mean streamflows for monthly and annual periods. Streamflow is considered to be regulated at streamflow-gaging stations where dams or other large-scale human modifications affect 20 percent or more of the contributing drainage basin. Separate streamflow characteristics are presented for the unregulated and regulated periods of record for streamflow-gaging stations with sufficient data.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Montana StreamStats (Scientific Investigations Report 2015-5019)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155019E","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality and Montana Department of Natural Resources and Conservation","usgsCitation":"McCarthy, P.M., 2016, Streamflow characteristics based on data through water year 2009 for selected streamflow-gaging stations in or near Montana: U.S. Geological Survey Scientific Investigations Report 2015–5019–E, 10 p., https://dx.doi.org/10.3133/sir20155019E.","productDescription":"Report: v, 10 p.; Table","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-061125","costCenters":[{"id":5050,"text":"WY-MT Water Science 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\"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
The climatic conditions of the specific time period during which peak-flow data were collected at a given streamflow-gaging station (hereinafter referred to as gaging station) can substantially affect how well the peak-flow frequency (hereinafter referred to as frequency) results represent long-term hydrologic conditions. Differences in the timing of the periods of record can result in substantial inconsistencies in frequency estimates for hydrologically similar gaging stations. Potential for inconsistency increases with decreasing peak-flow record length. The representativeness of the frequency estimates for a short-term gaging station can be adjusted by various methods including weighting the at-site results in association with frequency estimates from regional regression equations (RREs) by using the Weighted Independent Estimates (WIE) program. Also, for gaging stations that cannot be adjusted by using the WIE program because of regulation or drainage areas too large for application of RREs, frequency estimates might be improved by using record extension procedures, including a mixed-station analysis using the maintenance of variance type I (MOVE.1) procedure. The U.S. Geological Survey, in cooperation with the Montana Department of Transportation and the Montana Department of Natural Resources and Conservation, completed a study to provide adjusted frequency estimates for selected gaging stations through water year 2011.
The purpose of Chapter D of this Scientific Investigations Report is to present adjusted frequency estimates for 504 selected streamflow-gaging stations in or near Montana based on data through water year 2011. Estimates of peak-flow magnitudes for the 66.7-, 50-, 42.9-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities are reported. These annual exceedance probabilities correspond to the 1.5-, 2-, 2.33-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively.
The at-site frequency estimates were adjusted by weighting with frequency estimates from RREs using the WIE program for 438 selected gaging stations in Montana. These 438 selected gaging stations (1) had periods of record less than or equal to 40 years, (2) represented unregulated or minor regulation conditions, and (3) had drainage areas less than about 2,750 square miles.
The weighted-average frequency estimates obtained by weighting with RREs generally are considered to provide improved frequency estimates. In some cases, there are substantial differences among the at-site frequency estimates, the regression-equation frequency estimates, and the weighted-average frequency estimates. In these cases, thoughtful consideration should be applied when selecting the appropriate frequency estimate. Some factors that might be considered when selecting the appropriate frequency estimate include (1) whether the specific gaging station has peak-flow characteristics that distinguish it from most other gaging stations used in developing the RREs for the hydrologic region; and (2) the length of the peak-flow record and the general climatic characteristics during the period when the peak-flow data were collected. For critical structure-design applications, a conservative approach would be to select the higher of the at-site frequency estimate and the weighted-average frequency estimate.
The mixed-station MOVE.1 procedure generally was applied in cases where three or more gaging stations were located on the same large river and some of the gaging stations could not be adjusted using the weighted-average method because of regulation or drainage areas too large for application of RREs. The mixed-station MOVE.1 procedure was applied to 66 selected gaging stations on 19 large rivers.
The general approach for using mixed-station record extension procedures to adjust at-site frequencies involved (1) determining appropriate base periods for the gaging stations on the large rivers, (2) synthesizing peak-flow data for the gaging stations with incomplete peak-flow records during the base periods by using the mixed-station MOVE.1 procedure, and (3) conducting frequency analysis on the combined recorded and synthesized peak-flow data for each gaging station. Frequency estimates for the combined recorded and synthesized datasets for 66 gaging stations with incomplete peak-flow records during the base periods are presented. The uncertainties in the mixed-station record extension results are difficult to directly quantify; thus, it is important to understand the intended use of the estimated frequencies based on analysis of the combined recorded and synthesized datasets. The estimated frequencies are considered general estimates of frequency relations among gaging stations on the same stream channel that might be expected if the gaging stations had been gaged during the same long-term base period. However, because the mixed-station record extension procedures involve secondary statistical analysis with accompanying errors, the uncertainty of the frequency estimates is larger than would be obtained by collecting systematic records for the same number of years in the base period.
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\"}}]}","edition":"Version 1.0: April 6, 2016; Version 1.1: February 26, 2018","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
Montana StreamStats is a Web-based geographic information system (http://water.usgs.gov/osw/streamstats/) application that provides users with access to basin and streamflow characteristics for gaged and ungaged streams in Montana. Montana StreamStats was developed by the U.S. Geological Survey (USGS) in cooperation with the Montana Departments of Transportation, Environmental Quality, and Natural Resources and Conservation. The USGS Scientific Investigations Report consists of seven independent but complementary chapters dealing with various aspects of this effort.
Chapter A describes the Montana StreamStats application, the basin and streamflow datasets, and provides a brief overview of the streamflow characteristics and regression equations used in the study. Chapters B through E document the datasets, methods, and results of analyses to determine streamflow characteristics, such as peak-flow frequencies, low-flow frequencies, and monthly and annual characteristics, for USGS streamflow-gaging stations in and near Montana. The StreamStats analytical toolsets that allow users to delineate drainage basins and solve regression equations to estimate streamflow characteristics at ungaged sites in Montana are described in Chapters F and G.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155019","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality, Montana Department of Natural Resources and Conservation, and the Montana Department of Transportation","productDescription":"Chapters A-G","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":319890,"rank":2,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2015/5019/sir20155019_2.zip","text":"Complete Volume","size":"60.7 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2015"},{"id":319889,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5019/coverthb.jpg"}],"country":"United 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\"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
Developing fishing regulations for previously unexploited populations presents numerous challenges, many of which stem from a scarcity of baseline information about abundance, population productivity, and expected angling pressure. We used simulation models to test the effect of six management strategies (catch and release; trophy, minimum, and maximum length limits; and protected and exploited slot length limits) on an unexploited population of Lake Trout Salvelinus namaycush in Follensby Pond, a 393-ha lake located in New York State’s Adirondack Park. We combined field and literature data and mark–recapture abundance estimates to parameterize an age-structured population model and used the model to assess the effects of each management strategy on abundance, catch per unit effort (CPUE), and harvest over a range of angler effort (0–2,000 angler-days/year). Lake Trout density (3.5 fish/ha for fish ≥ age 13, the estimated age at maturity) was similar to densities observed in other unexploited systems, but growth rate was relatively slow. Maximum harvest occurred at levels of effort ≤ 1,000 angler-days/year in all the scenarios considered. Regulations that permitted harvest of large postmaturation fish, such as New York’s standard Lake Trout minimum size limit or a trophy size limit, resulted in low harvest and high angler CPUE. Regulations that permitted harvest of small and sometimes immature fish, such as a protected slot or maximum size limit, allowed high harvest but resulted in low angler CPUE and produced rapid declines in harvest with increases in effort beyond the effort consistent with maximum yield. Management agencies can use these results to match regulations to management goals and to assess the risks of different management options for unexploited Lake Trout populations and other fish species with similar life history traits.
","language":"English","publisher":"American Fisheries Society","doi":"10.1080/02755947.2015.1131780","usgsCitation":"Lenker, M.A., Weidel, B., Jensen, O.P., and Solomon, C.T., 2016, Developing recreational harvest regulations for an unexploited lake trout population: North American Journal of Fisheries Management, v. 36, no. 2, p. 385-397, https://doi.org/10.1080/02755947.2015.1131780.","productDescription":"13 p.","startPage":"385","endPage":"397","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065857","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":319754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","county":"Franklin County","city":"Harrietstown","otherGeospatial":"Adirondack Park, Follensby Pond","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.3,\n 44.2\n ],\n [\n -74.3,\n 44.1\n ],\n [\n -74.4,\n 44.1\n ],\n [\n -74.4,\n 44.2\n ],\n [\n -74.3,\n 44.2\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-31","publicationStatus":"PW","scienceBaseUri":"5703821ee4b0328dcb81b361","contributors":{"authors":[{"text":"Lenker, Melissa A","contributorId":168438,"corporation":false,"usgs":false,"family":"Lenker","given":"Melissa","email":"","middleInitial":"A","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":625938,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":625937,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jensen, Olaf P.","contributorId":92159,"corporation":false,"usgs":false,"family":"Jensen","given":"Olaf","email":"","middleInitial":"P.","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":625939,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Solomon, Christopher T.","contributorId":34014,"corporation":false,"usgs":false,"family":"Solomon","given":"Christopher","email":"","middleInitial":"T.","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":625940,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70160856,"text":"sir20155186 - 2016 - Hydrogeology and groundwater quality at monitoring wells installed for the Tunnel and Reservoir Plan System and nearby water-supply wells, Cook County, Illinois, 1995–2013","interactions":[],"lastModifiedDate":"2016-05-17T08:51:18","indexId":"sir20155186","displayToPublicDate":"2016-04-04T11:45:00","publicationYear":"2016","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":"2015-5186","title":"Hydrogeology and groundwater quality at monitoring wells installed for the Tunnel and Reservoir Plan System and nearby water-supply wells, Cook County, Illinois, 1995–2013","docAbstract":"Groundwater-quality data collected from 1995 through 2013 from 106 monitoring wells open to the base of the Silurian aquifer surrounding the Tunnel and Reservoir Plan (TARP) System in Cook County, Illinois, were analyzed by the U.S. Geological Survey, in cooperation with the Metropolitan Water Reclamation District of Greater Chicago, to assess the efficacy of the monitoring network and the effects of water movement from the tunnel system to the surrounding aquifer. Groundwater from the Silurian aquifer typically drains to the tunnel system so that analyte concentrations in most of the samples from most of the monitoring wells primarily reflect the concentration of the analyte in the nearby Silurian aquifer. Water quality in the Silurian aquifer is spatially variable because of a variety of natural and non-TARP anthropogenic processes. Therefore, the trends in analyte values at a given well from 1995 through 2013 are primarily a reflection of the spatial variation in the value of the analyte in groundwater within that part of the Silurian aquifer draining to the tunnels. Intermittent drainage of combined sewer flow from the tunnel system to the Silurian aquifer when flow in the tunnel systemis greater than 80 million gallons per day may affect water quality in some nearby monitoring wells. Intermittent drainage of combined sewer flow from the tunnel system to the Silurian aquifer appears to affect the values of electrical conductivity, hardness, sulfate, chloride, dissolved organic carbon, ammonia, and fecal coliform in samples from many wells but typically during less than 5 percent of the sampling events. Drainage of combined sewer flow into the aquifer is most prevalent in the downstream parts of the tunnel systems because of the hydraulic pressures elevated above background values and long residence time of combined sewer flow in those areas. Elevated values of the analytes emplaced during intermittent migration of combined sewer flow into the Silurian aquifer decrease through time as water from the aquifer drains back into the tunnels in response to typical hydraulic conditions. Of the analytes sampled, fecal coliform provides the clearest indication of the location and timing of combined sewer flow into the Silurian aquifer surrounding the tunnel system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155186","collaboration":"Prepared in cooperation with the Metropolitan Water Reclamation District of Greater Chicago","usgsCitation":"Kay, R.T., 2016, Hydrogeology and groundwater quality at monitoring wells installed for the Tunnel and Reservoir Plan System and nearby water-supply wells, Cook County, Illinois, 1995–2013 (ver 1.1, May 2016): U.S. Geological Survey Scientific Investigations Report 2015–5186, 347 p., https://dx.doi.org/10.3133/sir20155186.","productDescription":"vi, 347 p.","numberOfPages":"357","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"1995-01-01","ipdsId":"IP-063316","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":319409,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5186/coverthb1.jpg"},{"id":321237,"rank":3,"type":{"id":25,"text":"Version 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1.0: Originally posted April 4, 2016; Version 1.1: May 16, 2016","contact":"Director Illinois Water Science Center
U.S. Geological Survey
405 N Goodwin
Urbana, IL 61801
http://il.water.usgs.gov/
Process-based hydrological models have a long history dating back to the 1960s. Criticized by some as over-parameterized, overly complex, and difficult to use, a more nuanced view is that these tools are necessary in many situations and, in a certain class of problems, they are the most appropriate type of hydrological model. This is especially the case in situations where knowledge of flow paths or distributed state variables and/or preservation of physical constraints is important. Examples of this include: spatiotemporal variability of soil moisture, groundwater flow and runoff generation, sediment and contaminant transport, or when feedbacks among various Earth’s system processes or understanding the impacts of climate non-stationarity are of primary concern. These are situations where process-based models excel and other models are unverifiable. This article presents this pragmatic view in the context of existing literature to justify the approach where applicable and necessary. We review how improvements in data availability, computational resources and algorithms have made detailed hydrological simulations a reality. Avenues for the future of process-based hydrological models are presented suggesting their use as virtual laboratories, for design purposes, and with a powerful treatment of uncertainty.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.jhydrol.2016.03.026","usgsCitation":"Fatichi, S., Vivoni, E.R., Odgen, F.L., Ivanov, V.Y., Mirus, B.B., Gochis, D., Downer, C.W., Camporese, M., Davison, J.H., Ebel, B.A., Jones, N., Kim, J., Mascaro, G., Niswonger, R.G., Restrepo, P., Rigon, R., Shen, C., Sulis, M., and Tarboton, D., 2016, An overview of current applications, challenges, and future trends in distributed process-based models in hydrology: Journal of Hydrology, v. 537, p. 45-60, https://doi.org/10.1016/j.jhydrol.2016.03.026.","productDescription":"16 p.","startPage":"45","endPage":"60","numberOfPages":"16","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072365","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":471090,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2016.03.026","text":"Publisher Index Page"},{"id":319747,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"537","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5703821ce4b0328dcb81b35b","contributors":{"authors":[{"text":"Fatichi, Simone","contributorId":152452,"corporation":false,"usgs":false,"family":"Fatichi","given":"Simone","email":"","affiliations":[{"id":18926,"text":"Institute of Environmental Engineering","active":true,"usgs":false}],"preferred":false,"id":589430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vivoni, Enrique R.","contributorId":139052,"corporation":false,"usgs":false,"family":"Vivoni","given":"Enrique","email":"","middleInitial":"R.","affiliations":[{"id":12634,"text":"School of Earth and Space Exploration and School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ","active":true,"usgs":false}],"preferred":false,"id":589431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Odgen, Fred L","contributorId":152453,"corporation":false,"usgs":false,"family":"Odgen","given":"Fred","email":"","middleInitial":"L","affiliations":[{"id":6656,"text":"University of Wyoming, Renewable Resources","active":true,"usgs":false}],"preferred":false,"id":589432,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ivanov, Valeriy Y","contributorId":152454,"corporation":false,"usgs":false,"family":"Ivanov","given":"Valeriy","email":"","middleInitial":"Y","affiliations":[{"id":6649,"text":"University of Michigan, School of Natural Resources and Environment","active":true,"usgs":false}],"preferred":false,"id":589433,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":589434,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gochis, David","contributorId":152455,"corporation":false,"usgs":false,"family":"Gochis","given":"David","email":"","affiliations":[{"id":6648,"text":"National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":589435,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Downer, Charles W","contributorId":152456,"corporation":false,"usgs":false,"family":"Downer","given":"Charles","email":"","middleInitial":"W","affiliations":[{"id":18927,"text":"Engineer Research and Development","active":true,"usgs":false}],"preferred":false,"id":589436,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Camporese, Matteo","contributorId":152457,"corporation":false,"usgs":false,"family":"Camporese","given":"Matteo","email":"","affiliations":[{"id":18928,"text":"University of Padua","active":true,"usgs":false}],"preferred":false,"id":589437,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Davison, Jason H","contributorId":152458,"corporation":false,"usgs":false,"family":"Davison","given":"Jason","email":"","middleInitial":"H","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":589438,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ebel, Brian A. 0000-0002-5413-3963 bebel@usgs.gov","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":2557,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian","email":"bebel@usgs.gov","middleInitial":"A.","affiliations":[{"id":5044,"text":"National Research Program - 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Western Branch","active":true,"usgs":true}],"preferred":false,"id":589443,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Restrepo, Pedro","contributorId":152463,"corporation":false,"usgs":false,"family":"Restrepo","given":"Pedro","affiliations":[{"id":12498,"text":"NOAA National Weather Service, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":589444,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Rigon, Riccardo","contributorId":152464,"corporation":false,"usgs":false,"family":"Rigon","given":"Riccardo","email":"","affiliations":[{"id":18929,"text":"Unversita di Trento","active":true,"usgs":false}],"preferred":false,"id":589445,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Shen, Chaopeng","contributorId":152465,"corporation":false,"usgs":false,"family":"Shen","given":"Chaopeng","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":589446,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Sulis, Mauro","contributorId":152466,"corporation":false,"usgs":false,"family":"Sulis","given":"Mauro","email":"","affiliations":[{"id":18930,"text":"University of Bonn","active":true,"usgs":false}],"preferred":false,"id":589447,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Tarboton, David","contributorId":152467,"corporation":false,"usgs":false,"family":"Tarboton","given":"David","email":"","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":589448,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}} ,{"id":70169099,"text":"70169099 - 2016 - Movement and survival of an amphibian in relation to sediment and culvert design","interactions":[],"lastModifiedDate":"2016-05-27T08:11:41","indexId":"70169099","displayToPublicDate":"2016-04-02T09:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Movement and survival of an amphibian in relation to sediment and culvert design","docAbstract":"Habitat disturbance from stream culverts can affect aquatic organisms by increasing sedimentation or forming barriers to movement. Land managers are replacing many culverts to reduce these negative effects, primarily for stream fishes. However, these management actions are likely to have broad implications for many organisms, including amphibians in small streams. To assess the effects of culverts on movement and survival of the Idaho giant salamander (Dicamptodon aterrimus), we used capture-mark-recapture surveys and measured sediment in streams with 2 culvert types (i.e., unimproved culverts, improved culverts) and in streams without culverts (i.e., reference streams). We predicted culverts would increase stream sediment levels, limit movement, and reduce survival of Idaho giant salamanders. We also determined the effect of sediment levels on survival of salamanders because although sediment is often associated with distribution and abundance of stream amphibians, links with vital rates remain unclear. To estimate survival, we used a spatial Cormack–Jolly–Seber (CJS) model that explicitly incorporated information on movement, eliminating bias in apparent survival estimated from traditional (i.e., non-spatial) CJS models caused by permanent emigration beyond the study area. To demonstrate the importance of using spatial data in studies of wildlife populations, we compared estimates from the spatial CJS to estimates of apparent survival from a traditional CJS model. Although high levels of sediment reduced survival of salamanders, culvert type was unrelated to sediment levels or true survival of salamanders. Across all streams, we documented only 15 movement events between study reaches. All movement events were downstream, and they occurred disproportionately in 1 stream, which precluded measuring the effect of culvert design on movement. Although movement was low overall, the variance among streams was high enough to bias estimates of apparent survival compared to true survival. Our results suggest that where sedimentation occurs from roads and culverts, survival of the Idaho giant salamander could be reduced. Though culverts clearly do not completely block downstream movements of Idaho giant salamanders, the degree to which culvert improvements affect movements under roads in comparison to unimproved culverts remains unclear, especially for rare, but potentially important, upstream movements.
","language":"English","publisher":"Wildlife Society","publisherLocation":"Washington, D.C.","doi":"10.1002/jwmg.1056","usgsCitation":"Honeycutt, R., Lowe, W., and Hossack, B.R., 2016, Movement and survival of an amphibian in relation to sediment and culvert design: Journal of Wildlife Management, v. 80, no. 4, p. 761-770, https://doi.org/10.1002/jwmg.1056.","startPage":"761","endPage":"770","numberOfPages":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065843","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":321812,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.510009765625,\n 45.75219336063106\n ],\n [\n -116.510009765625,\n 48.125767833701666\n ],\n [\n -113.97766113281249,\n 48.125767833701666\n ],\n [\n -113.97766113281249,\n 45.75219336063106\n ],\n [\n -116.510009765625,\n 45.75219336063106\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"80","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-02","publicationStatus":"PW","scienceBaseUri":"57496fb1e4b07e28b665cc7e","contributors":{"authors":[{"text":"Honeycutt, R.K","contributorId":167621,"corporation":false,"usgs":false,"family":"Honeycutt","given":"R.K","email":"","affiliations":[{"id":24785,"text":"Wildlife Biology Program, University of Montana, 32 Campus","active":true,"usgs":false}],"preferred":false,"id":622926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lowe, W.H.","contributorId":91961,"corporation":false,"usgs":true,"family":"Lowe","given":"W.H.","affiliations":[],"preferred":false,"id":622927,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hossack, Blake R. 0000-0001-7456-9564 blake_hossack@usgs.gov","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":1177,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake","email":"blake_hossack@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":622925,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178117,"text":"70178117 - 2016 - Evaluation of six NEHRP B/C crustal amplification models proposed for use in western North America","interactions":[],"lastModifiedDate":"2016-11-03T11:20:58","indexId":"70178117","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of six NEHRP B/C crustal amplification models proposed for use in western North America","docAbstract":"We evaluate six crustal amplification models based on National Earthquake Hazards Reduction Program (NEHRP) B/C crustal profiles proposed for use in western North America (WNA) and often used in other active crustal regions where crustal properties are unknown. One of the models is based on an interpolation of generic rock velocity profiles previously proposed for WNA and central and eastern North America (CENA), in conjunction with material densities based on an updated velocity–density relationship. A second model is based on the velocity profile used to develop amplification factors for the Next Generation Attenuation (NGA)‐West2 project. A third model is based on a near‐surface velocity profile developed from the NGA‐West2 site database. A fourth model is based on velocity and density profiles originally proposed for use in CENA but recently used to represent crustal properties in California. We propose two alternatives to this latter model that more closely represent WNA crustal properties. We adopt a value of site attenuation (κ0) for each model that is either recommended by the author of the model or proposed by us. Stochastic simulation is used to evaluate the Fourier amplification factors and their impact on response spectra associated with each model. Based on this evaluation, we conclude that among the available models evaluated in this study the NEHRP B/C amplification model of Boore (2016) best represents median crustal amplification in WNA, although the amplification models based on the crustal profiles of Kamai et al. (2013, 2016, unpublished manuscript, see Data and Resources) and Yenier and Atkinson (2015), the latter adjusted to WNA crustal properties, can be used to represent epistemic uncertainty.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120150242","usgsCitation":"Boore, D., and Campbell, K.W., 2016, Evaluation of six NEHRP B/C crustal amplification models proposed for use in western North America: Bulletin of the Seismological Society of America, v. 106, no. 2, p. 673-686, https://doi.org/10.1785/0120150242.","productDescription":"14 p.","startPage":"673","endPage":"686","ipdsId":"IP-069474","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":330686,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"106","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-16","publicationStatus":"PW","scienceBaseUri":"581c4cc3e4b09688d6e90fbd","contributors":{"authors":[{"text":"Boore, David 0000-0002-8605-9673 boore@usgs.gov","orcid":"https://orcid.org/0000-0002-8605-9673","contributorId":140502,"corporation":false,"usgs":true,"family":"Boore","given":"David","email":"boore@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":652849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Campbell, Kenneth W.","contributorId":74391,"corporation":false,"usgs":false,"family":"Campbell","given":"Kenneth","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":652850,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70173645,"text":"70173645 - 2016 - Population size and stopover duration estimation using mark–resight data and Bayesian analysis of a superpopulation model","interactions":[],"lastModifiedDate":"2023-03-30T15:33:58.035111","indexId":"70173645","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1039,"text":"Biometrics","active":true,"publicationSubtype":{"id":10}},"title":"Population size and stopover duration estimation using mark–resight data and Bayesian analysis of a superpopulation model","docAbstract":"We present a novel formulation of a mark–recapture–resight model that allows estimation of population size, stopover duration, and arrival and departure schedules at migration areas. Estimation is based on encounter histories of uniquely marked individuals and relative counts of marked and unmarked animals. We use a Bayesian analysis of a state–space formulation of the Jolly–Seber mark–recapture model, integrated with a binomial model for counts of unmarked animals, to derive estimates of population size and arrival and departure probabilities. We also provide a novel estimator for stopover duration that is derived from the latent state variable representing the interim between arrival and departure in the state–space model. We conduct a simulation study of field sampling protocols to understand the impact of superpopulation size, proportion marked, and number of animals sampled on bias and precision of estimates. Simulation results indicate that relative bias of estimates of the proportion of the population with marks was low for all sampling scenarios and never exceeded 2%. Our approach does not require enumeration of all unmarked animals detected or direct knowledge of the number of marked animals in the population at the time of the study. This provides flexibility and potential application in a variety of sampling situations (e.g., migratory birds, breeding seabirds, sea turtles, fish, pinnipeds, etc.). Application of the methods is demonstrated with data from a study of migratory sandpipers.
","language":"English","publisher":"The International Biometric Society","doi":"10.1111/biom.12393","usgsCitation":"Lyons, J., Kendall, W., Royle, J., Converse, S., Andres, B.A., and Buchanan, J.B., 2016, Population size and stopover duration estimation using mark–resight data and Bayesian analysis of a superpopulation model: Biometrics, v. 72, no. 1, p. 262-271, https://doi.org/10.1111/biom.12393.","productDescription":"10 p.","startPage":"262","endPage":"271","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-044684","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":323259,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"72","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-08","publicationStatus":"PW","scienceBaseUri":"57594221e4b04f417c256939","contributors":{"authors":[{"text":"Lyons, James E.","contributorId":35461,"corporation":false,"usgs":true,"family":"Lyons","given":"James E.","affiliations":[],"preferred":false,"id":637860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, William L. 0000-0003-0084-9891 wkendall@usgs.gov","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":166709,"corporation":false,"usgs":true,"family":"Kendall","given":"William L.","email":"wkendall@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":637450,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":138865,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":637451,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Converse, Sarah J.","contributorId":85716,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah J.","affiliations":[],"preferred":false,"id":637861,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andres, Brad A.","contributorId":68811,"corporation":false,"usgs":true,"family":"Andres","given":"Brad","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":637862,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Buchanan, Joseph B.","contributorId":171532,"corporation":false,"usgs":false,"family":"Buchanan","given":"Joseph","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":637863,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70184450,"text":"70184450 - 2016 - RMT focal plane sensitivity to seismic network geometry and faulting style","interactions":[],"lastModifiedDate":"2017-03-09T11:35:11","indexId":"70184450","displayToPublicDate":"2016-04-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"RMT focal plane sensitivity to seismic network geometry and faulting style","docAbstract":"Modern tectonic studies often use regional moment tensors (RMTs) to interpret the seismotectonic framework of an earthquake or earthquake sequence; however, despite extensive use, little existing work addresses RMT parameter uncertainty. Here, we quantify how network geometry and faulting style affect RMT sensitivity. We examine how data-model fits change with fault plane geometry (strike and dip) for varying station configurations. We calculate the relative data fit for incrementally varying geometries about a best-fitting solution, applying our workflow to real and synthetic seismograms for both real and hypothetical station distributions and earthquakes. Initially, we conduct purely observational tests, computing RMTs from synthetic seismograms for hypothetical earthquakes and a series of well-behaved network geometries. We then incorporate real data and station distributions from the International Maule Aftershock Deployment (IMAD), which recorded aftershocks of the 2010 MW 8.8 Maule earthquake, and a set of regional stations capturing the ongoing earthquake sequence in Oklahoma and southern Kansas. We consider RMTs computed under three scenarios: (1) real seismic records selected for high data quality; (2) synthetic seismic records with noise computed for the observed source-station pairings and (3) synthetic seismic records with noise computed for all possible station-source pairings. To assess RMT sensitivity for each test, we observe the ‘fit falloff’, which portrays how relative fit changes when strike or dip varies incrementally; we then derive the ranges of acceptable strikes and dips by identifying the span of solutions with relative fits larger than 90 per cent of the best fit. For the azimuthally incomplete IMAD network, Scenario 3 best constrains fault geometry, with average ranges of 45° and 31° for strike and dip, respectively. In Oklahoma, Scenario 3 best constrains fault dip with an average range of 46°; however, strike is best constrained by Scenario 1, with a range of 26°. We draw two main conclusions from this study. (1) Station distribution impacts our ability to constrain RMTs using waveform time-series; however, in some tectonic settings, faulting style also plays a significant role and (2) increasing station density and data quantity (both the number of stations and the number of individual channels) does not necessarily improve RMT constraint. These results may be useful when organizing future seismic deployments (e.g. by concentrating stations in alignment with anticipated nodal planes), and in computing RMTs, either by guiding a more rigorous data selection process for input data or informing variable weighting among the selected data (e.g. by eliminating the transverse component when strike-slip mechanisms are expected).
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggw141","usgsCitation":"Johnson, K.L., Hayes, G.P., Herrmann, R., Benz, H.M., McNamara, D.E., and Bergman, E.A., 2016, RMT focal plane sensitivity to seismic network geometry and faulting style: Geophysical Journal International, v. 206, no. 1, p. 525-556, https://doi.org/10.1093/gji/ggw141.","productDescription":"32 p.","startPage":"525","endPage":"556","ipdsId":"IP-075457","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":471100,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggw141","text":"Publisher Index Page"},{"id":337167,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"206","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-11","publicationStatus":"PW","scienceBaseUri":"58c277dce4b014cc3a3e76d7","contributors":{"authors":[{"text":"Johnson, Kendra L. kljohnson@usgs.gov","contributorId":4908,"corporation":false,"usgs":true,"family":"Johnson","given":"Kendra","email":"kljohnson@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":681566,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hayes, Gavin P. 0000-0003-3323-0112 ghayes@usgs.gov","orcid":"https://orcid.org/0000-0003-3323-0112","contributorId":147556,"corporation":false,"usgs":true,"family":"Hayes","given":"Gavin","email":"ghayes@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":681567,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herrmann, Robert B.","contributorId":80255,"corporation":false,"usgs":false,"family":"Herrmann","given":"Robert B.","affiliations":[],"preferred":false,"id":681568,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":681569,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McNamara, Daniel E. 0000-0001-6860-0350 mcnamara@usgs.gov","orcid":"https://orcid.org/0000-0001-6860-0350","contributorId":402,"corporation":false,"usgs":true,"family":"McNamara","given":"Daniel","email":"mcnamara@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":681570,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bergman, Eric A. 0000-0002-7069-8286","orcid":"https://orcid.org/0000-0002-7069-8286","contributorId":84513,"corporation":false,"usgs":false,"family":"Bergman","given":"Eric","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":681571,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}