In 1996, at the request of the Government of the Islamic Republic of Mauritania, a team of U.S. Geological Survey (USGS) scientists produced a strategic plan for the acquisition, improvement and modernization of multidisciplinary sets of data to support the growth of the Mauritanian minerals sector and to highlight the geological and mineral exploration potential of the country. In 1999, the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania implemented a program for the acquisition of the recommended basic geoscientific information, termed the first Projet de Renforcement Institutionnel du Secteur Minier (Project for Institutional Capacity Building in the Mining Sector, PRISM-I). As a result of the PRISM-I efforts, a great deal of new geological, geophysical, geochemical, remote sensing, and hydrological data became available for evaluation and synthesis. However, the Ministry of Petroleum, Energy, and Mines recognized that additional work was required to extract the full benefit of the data before it could be of greatest use to the international community and of benefit to the Mauritanian minerals and development sector.
\nTo achieve this benefit, the Ministry of Petroleum, Energy, and Mines implemented a second Projet de Renforcement Institutionnel du Secteur Minier (PRISM-II) in 2006 to consolidate, synthesize, and interpret all of the existing data, create a new 1:1,000,000 scale geologic map, and define the mineral resource potential of the country. A consortium in which the USGS was the lead scientific agency carried out the majority of the PRISM-II work. In 2008, the USGS Mauritania Minerals Project was interrupted due to political changes in Mauritania. PRISM-II work resumed in 2011, and was completed in 2013 with the delivery of over 40 separate written reports and plates, an access file containing the Mauritanian National Mineral Deposits Database, and an interactive GIS containing all of the multi-disciplinary data and interpretive areas of mineral resource potential in Mauritania.
\nThis report contains the USGS results of the PRISM-II Mauritania Minerals Project and is presented in cooperation with the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania. The Report is composed of separate chapters consisting of multidisciplinary interpretive reports with accompanying plates on the geology, structure, geochronology, geophysics, hydrogeology, geochemistry, remote sensing (Landsat TM and ASTER), and SRTM and ASTER digital elevation models of Mauritania. The syntheses of these multidisciplinary data formed the basis for additional chapters containing interpretive reports on 12 different commodities and deposit types known to occur in Mauritania, accompanied by countrywide mineral resource potential maps of each commodity/deposit type. The commodities and deposit types represented include: (1) Ni, Cu, PGE, and Cr deposits hosted in ultramafic rocks; (2) orogenic, Carlin-like, and epithermal gold deposits; (3) polymetallic Pb-Zn-Cu vein deposits; (4) sediment-hosted Pb-Zn-Ag deposits of the SEDEX and Mississippi Valley-type; (5) sediment-hosted copper deposits; ( 6) volcanogenic massive sulfide deposits; (7) iron oxide copper-gold deposits; (8) uranium deposits; (9) Algoma-, Superior-, and oolitic-type iron deposits; (10) shoreline Ti-Zr placer deposits; (11) incompatible element deposits hosted in pegmatites, alkaline rocks, and carbonatites, and; (12) industrial mineral deposits. Additional chapters include the Mauritanian National Mineral Deposits Database are accompanied by an explanatory text and the Mauritania Minerals Project GIS that contains all of the interpretive layers created by USGS scientists. Raw data not in the public domain may be obtained from the Ministry of Petroleum, Energy, and Mines in Nouakchott, Mauritania.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II) (Open File Report 2013-1280)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131280A1","collaboration":"Prepared in cooperation with the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania","usgsCitation":"Bradley, D., Motts, H., Horton, J.D., Giles, S.A., and Taylor, C.D., 2015, Geologic map of Mauritania (phase V, deliverables 51a, 51b, and 51c): U.S. Geological Survey Open-File Report 2013-1280, 3 Plates: 57.99 x 60.00 inches or smaller; Data; Metadata, https://doi.org/10.3133/ofr20131280A1.","productDescription":"3 Plates: 57.99 x 60.00 inches or smaller; Data; Metadata","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056943","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":319009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":319008,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1280/GIS_and_Maps/Chapter_A1_deliverable_51-Geology/","text":"Map, Data, and Metadata"}],"country":"Mauritania","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-12.17075,14.61683],[-12.83066,15.30369],[-13.43574,16.03938],[-14.09952,16.3043],[-14.57735,16.59826],[-15.13574,16.58728],[-15.62367,16.36934],[-16.12069,16.45566],[-16.4631,16.13504],[-16.54971,16.67389],[-16.27055,17.16696],[-16.14635,18.10848],[-16.25688,19.09672],[-16.37765,19.59382],[-16.27784,20.09252],[-16.53632,20.56787],[-17.06342,20.99975],[-16.84519,21.33332],[-12.9291,21.32707],[-13.11875,22.77122],[-12.87422,23.28483],[-11.93722,23.37459],[-11.96942,25.93335],[-8.68729,25.88106],[-8.6844,27.39574],[-4.92334,24.97457],[-6.45379,24.95659],[-5.97113,20.64083],[-5.48852,16.3251],[-5.31528,16.20185],[-5.53774,15.50169],[-9.55024,15.4865],[-9.70026,15.26411],[-10.08685,15.33049],[-10.65079,15.13275],[-11.3491,15.41126],[-11.66608,15.38821],[-11.83421,14.7991],[-12.17075,14.61683]]]},\"properties\":{\"name\":\"Mauritania\"}}]}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56ed26b3e4b0f59b85db0a0c","contributors":{"authors":[{"text":"Bradley, Dwight 0000-0001-9116-5289 bradleyorchard2@gmail.com","orcid":"https://orcid.org/0000-0001-9116-5289","contributorId":2358,"corporation":false,"usgs":true,"family":"Bradley","given":"Dwight","email":"bradleyorchard2@gmail.com","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":581945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Motts, Holly","contributorId":149746,"corporation":false,"usgs":false,"family":"Motts","given":"Holly","affiliations":[{"id":17810,"text":"Previously USGS, Anchorage, Alaska","active":true,"usgs":false}],"preferred":false,"id":581946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horton, John D. 0000-0003-2969-9073 jhorton@usgs.gov","orcid":"https://orcid.org/0000-0003-2969-9073","contributorId":1227,"corporation":false,"usgs":true,"family":"Horton","given":"John","email":"jhorton@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":581947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Giles, Stuart A. 0000-0002-8696-5078 sgiles@usgs.gov","orcid":"https://orcid.org/0000-0002-8696-5078","contributorId":1233,"corporation":false,"usgs":true,"family":"Giles","given":"Stuart","email":"sgiles@usgs.gov","middleInitial":"A.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":581948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Taylor, Cliff D. 0000-0001-6376-6298 ctaylor@usgs.gov","orcid":"https://orcid.org/0000-0001-6376-6298","contributorId":1283,"corporation":false,"usgs":true,"family":"Taylor","given":"Cliff","email":"ctaylor@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":581949,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70140618,"text":"70140618 - 2015 - Impacts of fire management on aboveground tree carbon stocks in Yosemite and Sequoia & Kings Canyon National Parks","interactions":[],"lastModifiedDate":"2016-09-04T15:23:44","indexId":"70140618","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Impacts of fire management on aboveground tree carbon stocks in Yosemite and Sequoia & Kings Canyon National Parks","docAbstract":"Forest biomass on Sierra Nevada landscapes constitutes one of the largest carbon stocks in California, and its stability is tightly linked to the factors driving fire regimes. Research suggests that fire suppression, logging, climate change, and present management practices in Sierra Nevada forests have altered historic patterns of landscape carbon storage, and over a century of fire suppression and the resulting accumulation in surface fuels have been implicated in contributing to recent increases in high severity, stand-replacing fires. For over 30 years, fire management at Yosemite (YOSE) and Sequoia & Kings Canyon (SEKI) national parks has led the nation in restoring fire to park landscapes; however, the impacts on the stability and magnitude of carbon stocks have not been thoroughly examined.
\nThe purpose of this study is to quantify relationships between recent fire patterns and aboveground tree carbon stocks in YOSE and SEKI. Our approach focuses on evaluating fire effects on 1) amounts of aboveground tree carbon on the landscape, and 2) rates of carbon accumulation by individual trees. In 2010, we compiled a database of existing plot data for our analyses. In 2011, our field crews acquired vegetation data and collected tree growth cores from 105 plots. In 2012, we completed an interpretive component and began data analyses. In 2013, processing of tree cores began. In 2014, final processing of tree cores, data analyses, and manuscript preparation was conducted. The work for this project was facilitated through an interagency agreement between the National Park Service and the U.S. Geological Survey, and through a Cooperative Ecosystems Studies Unit (CESU) agreement with the University of Washington.
\nIn order to accurately quantify landscape-level carbon stocks, our analyses accounted for major sources of measurement errors, propagating those errors as we scaled plot-based carbon density estimates up to landscape-level totals. Using Monte Carlo simulation methods, we found that vegetation type mapping error was the largest source of uncertainty, while measurement uncertainties contributed by tree diameter measurements and tree diameter–biomass allometry equations were relatively minor.
\nFor some forest types, we found differences in aboveground tree carbon densities between burned and unburned areas. For example, mean carbon density in burned red fir forests was estimated to be ~29% lower versus unburned areas. Alternative measures of fire history, such as time since fire and number of times burned, were poorly related to carbon densities.
\nWithin YOSE, we evaluated the stability of landscape carbon pools by quantifying carbon stocks in areas of varying degrees of departure from historic fire return intervals. Of the ~25 Tg of total aboveground tree carbon in YOSE, ~10 Tg is contained within relatively stable areas (the next fire is unlikely to be high severity and stand-replacing), ~10 Tg occurs in areas deemed moderately stable, and the remaining ~5 Tg within relatively unstable areas.
\nWe compared our landscape carbon estimates in YOSE to remotely-sensed carbon estimates from the NASA–CASA project and found that the two methods roughly agree. Our analysis and comparisons suggest, however, that fire severity should be integrated into future carbon mapping efforts. We illustrate this with an example using the 2013 Rim Fire, which we estimate burned an area containing over 5 Tg of aboveground tree carbon, but likely left a large fraction of that carbon on the landscape if one accounts for fire severity.
","largerWorkTitle":"Natural Resource Report NPS/SIEN/NRR—2015/910","language":"English","publisher":"National Park Service","usgsCitation":"Matchett, J.R., Lutz, J.A., Tarnay, L.W., Smith, D.G., Becker, K.M., and Brooks, M.L., 2015, Impacts of fire management on aboveground tree carbon stocks in Yosemite and Sequoia & Kings Canyon National Parks, Report: ix, 29 p.; Appendixes A-C.","productDescription":"Report: ix, 29 p.; Appendixes A-C","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-053961","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":312660,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":297885,"type":{"id":15,"text":"Index Page"},"url":"https://www.werc.usgs.gov/ProductDetails.aspx?ID=5177"}],"country":"United States","state":"California","otherGeospatial":"Kings Canyon National Park, Sequoia National Park ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.531494140625,\n 36.9795180188502\n ],\n [\n -118.64547729492188,\n 36.986100060204095\n ],\n [\n -118.70315551757812,\n 36.887309668681155\n ],\n [\n -118.76083374023436,\n 36.691547435472636\n ],\n [\n -118.80615234374999,\n 36.64858894203172\n ],\n [\n -118.7677001953125,\n 36.619936625629215\n ],\n [\n -118.72512817382812,\n 36.59458146560139\n ],\n [\n -118.78280639648438,\n 36.54163950596125\n ],\n [\n -118.76495361328125,\n 36.49859745028132\n ],\n [\n -118.77593994140624,\n 36.45221769643571\n ],\n [\n -118.58230590820312,\n 36.41907231092499\n ],\n [\n -118.50540161132812,\n 36.36822190085111\n ],\n [\n -118.45458984375,\n 36.4223874864237\n ],\n [\n -118.40240478515624,\n 36.445589751779174\n ],\n [\n -118.33923339843749,\n 36.488661268293136\n ],\n [\n -118.39004516601562,\n 36.72567681977065\n ],\n [\n -118.51638793945312,\n 36.96744946416931\n ],\n [\n -118.531494140625,\n 36.9795180188502\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"567930cee4b0da412f4fb56f","contributors":{"authors":[{"text":"Matchett, John R. 0000-0002-2905-6468 jmatchett@usgs.gov","orcid":"https://orcid.org/0000-0002-2905-6468","contributorId":1669,"corporation":false,"usgs":true,"family":"Matchett","given":"John","email":"jmatchett@usgs.gov","middleInitial":"R.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":540256,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lutz, James A.","contributorId":139178,"corporation":false,"usgs":false,"family":"Lutz","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":12682,"text":"Utah State University, Logan, UT","active":true,"usgs":false}],"preferred":false,"id":540257,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tarnay, Leland W.","contributorId":139179,"corporation":false,"usgs":false,"family":"Tarnay","given":"Leland","email":"","middleInitial":"W.","affiliations":[{"id":12683,"text":"National Park Service, Yosemite National Park, El Portal, CA","active":true,"usgs":false}],"preferred":false,"id":540258,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Douglas G. dgsmith@usgs.gov","contributorId":1532,"corporation":false,"usgs":true,"family":"Smith","given":"Douglas","email":"dgsmith@usgs.gov","middleInitial":"G.","affiliations":[{"id":476,"text":"North Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":540259,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Becker, Kendall M.L.","contributorId":139180,"corporation":false,"usgs":false,"family":"Becker","given":"Kendall","email":"","middleInitial":"M.L.","affiliations":[{"id":12682,"text":"Utah State University, Logan, UT","active":true,"usgs":false}],"preferred":false,"id":540260,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brooks, Matthew L. 0000-0002-3518-6787 mlbrooks@usgs.gov","orcid":"https://orcid.org/0000-0002-3518-6787","contributorId":393,"corporation":false,"usgs":true,"family":"Brooks","given":"Matthew","email":"mlbrooks@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":540255,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70157453,"text":"70157453 - 2015 - Quantifying stream thermal regimes at management-pertinent scales: combining thermal infrared and stationary stream temperature data in a novel modeling framework.","interactions":[],"lastModifiedDate":"2015-09-24T09:40:08","indexId":"70157453","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying stream thermal regimes at management-pertinent scales: combining thermal infrared and stationary stream temperature data in a novel modeling framework.","docAbstract":"Accurately quantifying stream thermal regimes can be challenging because stream temperatures are often spatially and temporally heterogeneous. In this study, we present a novel modeling framework that combines stream temperature data sets that are continuous in either space or time. Specifically, we merged the fine spatial resolution of thermal infrared (TIR) imagery with hourly data from 10 stationary temperature loggers in a 100 km portion of the Big Hole River, MT, USA. This combination allowed us to estimate summer thermal conditions at a relatively fine spatial resolution (every 100 m of stream length) over a large extent of stream (100 km of stream) during during the warmest part of the summer. Rigorous evaluation, including internal validation, external validation with spatially continuous instream temperature measurements collected from a Langrangian frame of reference, and sensitivity analyses, suggests the model was capable of accurately estimating longitudinal patterns in summer stream temperatures for this system Results revealed considerable spatial and temporal heterogeneity in summer stream temperatures and highlighted the value of assessing thermal regimes at relatively fine spatial and temporal scales. Preserving spatial and temporal variability and structure in abiotic stream data provides a critical foundation for understanding the dynamic, multiscale habitat needs of mobile stream organisms. Similarly, enhanced understanding of spatial and temporal variation in dynamic water quality attributes, including temporal sequence and spatial arrangement, can guide strategic placement of monitoring equipment that will subsequently capture variation in environmental conditions directly pertinent to research and management objectives.
","language":"English","publisher":"Wiley","doi":"10.1002/2014WR015588","usgsCitation":"Vatland, S.J., Gresswell, R.E., and Poole, G., 2015, Quantifying stream thermal regimes at management-pertinent scales: combining thermal infrared and stationary stream temperature data in a novel modeling framework.: Water Resources Research, v. 51, no. 1, p. 31-46, https://doi.org/10.1002/2014WR015588.","productDescription":"16 p.","startPage":"31","endPage":"46","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-055712","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":308481,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Big Hole River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.10012817382812,\n 45.84793427349226\n ],\n [\n -113.05892944335936,\n 45.867062714815475\n ],\n [\n -113.08914184570311,\n 45.89383147810292\n ],\n [\n -113.16741943359374,\n 45.89956596377031\n ],\n [\n -113.23196411132812,\n 45.88905228767688\n ],\n [\n -113.2635498046875,\n 45.865150166790585\n ],\n [\n -113.291015625,\n 45.843151135002834\n ],\n [\n -113.35556030273438,\n 45.823057462282456\n ],\n [\n -113.38851928710938,\n 45.765606992288184\n ],\n [\n -113.44757080078124,\n 45.72823242650626\n ],\n [\n -113.47915649414061,\n 45.693710616454496\n ],\n [\n -113.48190307617188,\n 45.66684557788979\n ],\n [\n -113.46405029296874,\n 45.651488335713594\n ],\n [\n -113.48190307617188,\n 45.612116176517304\n ],\n [\n -113.50662231445312,\n 45.56406391514301\n ],\n [\n -113.51074218749999,\n 45.53040285599187\n ],\n [\n -113.51211547851562,\n 45.48324350868221\n ],\n [\n -113.48739624023436,\n 45.44086267178177\n ],\n [\n -113.45718383789062,\n 45.39941425500784\n ],\n [\n -113.45718383789062,\n 45.36661954708626\n ],\n [\n -113.45581054687499,\n 45.3386325573467\n ],\n [\n -113.43521118164062,\n 45.3386325573467\n ],\n [\n -113.41873168945312,\n 45.369513963007414\n ],\n [\n -113.42422485351561,\n 45.404235401683344\n ],\n [\n -113.43658447265625,\n 45.4437532863259\n ],\n [\n -113.47640991210938,\n 45.493833740092185\n ],\n [\n -113.46817016601562,\n 45.52751668442124\n ],\n [\n -113.4503173828125,\n 45.58713413436409\n ],\n [\n -113.43658447265625,\n 45.64764836702863\n ],\n [\n -113.4393310546875,\n 45.686995566120395\n ],\n [\n -113.38851928710938,\n 45.72439772280638\n ],\n [\n -113.35006713867188,\n 45.7560261558602\n ],\n [\n -113.3349609375,\n 45.79625461321962\n ],\n [\n -113.291015625,\n 45.80295653465527\n ],\n [\n -113.24020385742188,\n 45.838367585245855\n ],\n [\n -113.21548461914062,\n 45.85654288638554\n ],\n [\n -113.17153930664062,\n 45.8766244679252\n ],\n [\n -113.11798095703125,\n 45.87088761346192\n ],\n [\n -113.10012817382812,\n 45.84793427349226\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"51","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-01-07","publicationStatus":"PW","scienceBaseUri":"56051edfe4b058f706e51307","chorus":{"doi":"10.1002/2014wr015588","url":"http://dx.doi.org/10.1002/2014wr015588","publisher":"Wiley-Blackwell","authors":"Vatland Shane J., Gresswell Robert E., Poole Geoffrey C.","journalName":"Water Resources Research","publicationDate":"1/2015","auditedOn":"1/12/2015"},"contributors":{"authors":[{"text":"Vatland, Shane J.","contributorId":147916,"corporation":false,"usgs":false,"family":"Vatland","given":"Shane","email":"","middleInitial":"J.","affiliations":[{"id":16955,"text":"Montana State University, Department of Ecology, P.O. 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Although scientists benefit from many forms of collaboration on the Web (e.g., blogs, wikis, forums, code sharing, etc.), most collaborative projects are coordinated over email, phone calls, and in-person meetings. Our goal is to develop a collaborative infrastructure for scientists to work on complex science questions that require multi-disciplinary contributions to gather and analyze data, that cannot occur without significant coordination to synthesize findings, and that grow organically to accommodate new contributors as needed as the work evolves over time. Our approach is to develop an organic data science framework based on a task-centered organization of the collaboration, includes principles from social sciences for successful on-line communities, and exposes an open science process. Our approach is implemented as an extension of a semantic wiki platform, and captures formal representations of task decomposition structures, relations between tasks and users, and other properties of tasks, data, and other relevant science objects. All these entities are captured through the semantic wiki user interface, represented as semantic web objects, and exported as linked data.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"ESWC 2015: The semantic web. Latest advances and new domains","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"ESWC 2015: The semantic web. Latest advances and new domains","language":"English","publisher":"Springer","doi":"10.1007/978-3-319-18818-8_36","usgsCitation":"Gil, Y., Michel, F., Ratnakar, V., Read, J.S., Hauder, M., Duffy, C., Hanson, P.C., and Dugan, H., 2015, Supporting open collaboration in science through explicit and linked semantic description of processes, in ESWC 2015: The semantic web. Latest advances and new domains, p. 591-605, https://doi.org/10.1007/978-3-319-18818-8_36.","productDescription":"15 p.","startPage":"591","endPage":"605","ipdsId":"IP-060683","costCenters":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"links":[{"id":339815,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-05-21","publicationStatus":"PW","scienceBaseUri":"58f5d442e4b0f2e20545e421","contributors":{"authors":[{"text":"Gil, Yolanda","contributorId":150186,"corporation":false,"usgs":false,"family":"Gil","given":"Yolanda","email":"","affiliations":[{"id":17935,"text":"Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292, US","active":true,"usgs":false}],"preferred":false,"id":580939,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Michel, Felix","contributorId":150187,"corporation":false,"usgs":false,"family":"Michel","given":"Felix","email":"","affiliations":[{"id":17935,"text":"Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292, US","active":true,"usgs":false}],"preferred":false,"id":580940,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ratnakar, Varun","contributorId":150188,"corporation":false,"usgs":false,"family":"Ratnakar","given":"Varun","email":"","affiliations":[{"id":17935,"text":"Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292, US","active":true,"usgs":false}],"preferred":false,"id":580941,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Read, Jordan S. 0000-0002-3888-6631 jread@usgs.gov","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":4453,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","email":"jread@usgs.gov","middleInitial":"S.","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":580938,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hauder, Matheus","contributorId":150189,"corporation":false,"usgs":false,"family":"Hauder","given":"Matheus","email":"","affiliations":[{"id":17936,"text":"Software Engineering for Business Information Systems, Technical University Munich, Munich, BY 85748, DE","active":true,"usgs":false}],"preferred":false,"id":580942,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duffy, Christopher","contributorId":150190,"corporation":false,"usgs":false,"family":"Duffy","given":"Christopher","affiliations":[{"id":17937,"text":"Civil and Environmental Engineering, Penn State University University Park, PA 16801, US","active":true,"usgs":false}],"preferred":false,"id":580943,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hanson, Paul C.","contributorId":35634,"corporation":false,"usgs":false,"family":"Hanson","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":580944,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dugan, Hilary","contributorId":150191,"corporation":false,"usgs":false,"family":"Dugan","given":"Hilary","affiliations":[{"id":17938,"text":"Center for Limnology University of Wisconsin, Madison, WI 53706, US","active":true,"usgs":false}],"preferred":false,"id":580945,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70192613,"text":"70192613 - 2015 - Combined effects of climate, predation, and density dependence on Greater and Lesser Scaup population dynamics","interactions":[],"lastModifiedDate":"2017-11-10T11:24:34","indexId":"70192613","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Combined effects of climate, predation, and density dependence on Greater and Lesser Scaup population dynamics","docAbstract":"An understanding of species relationships is critical in the management and conservation of populations facing climate change, yet few studies address how climate alters species interactions and other population drivers. We use a long-term, broad-scale data set of relative abundance to examine the influence of climate, predators, and density dependence on the population dynamics of declining scaup (Aythya) species within the core of their breeding range. The state-space modeling approach we use applies to a wide range of wildlife species, especially populations monitored over broad spatiotemporal extents. Using this approach, we found that immediate snow cover extent in the preceding winter and spring had the strongest effects, with increases in mean snow cover extent having a positive effect on the local surveyed abundance of scaup. The direct effects of mesopredator abundance on scaup population dynamics were weaker, but the results still indicated a potential interactive process between climate and food web dynamics (mesopredators, alternative prey, and scaup). By considering climate variables and other potential effects on population dynamics, and using a rigorous estimation framework, we provide insight into complex ecological processes for guiding conservation and policy actions aimed at mitigating and reversing the decline of scaup.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/14-0582.1","usgsCitation":"Ross, B., Hooten, M., DeVink, J., and Koons, D.N., 2015, Combined effects of climate, predation, and density dependence on Greater and Lesser Scaup population dynamics: Ecological Applications, v. 25, no. 6, p. 1606-1617, https://doi.org/10.1890/14-0582.1.","productDescription":"12 p.","startPage":"1606","endPage":"1617","ipdsId":"IP-054162","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":348574,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"25","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a06c8d4e4b09af898c8616a","contributors":{"authors":[{"text":"Ross, Beth E.","contributorId":56124,"corporation":false,"usgs":true,"family":"Ross","given":"Beth E.","affiliations":[],"preferred":false,"id":721585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":716559,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeVink, Jean-Michel","contributorId":127663,"corporation":false,"usgs":false,"family":"DeVink","given":"Jean-Michel","email":"","affiliations":[{"id":6779,"text":"Environment Canada, Burlington, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":721586,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Koons, David N.","contributorId":28137,"corporation":false,"usgs":false,"family":"Koons","given":"David","email":"","middleInitial":"N.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":721587,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70156554,"text":"70156554 - 2015 - Evaluation of vector coastline features extracted from 'structure from motion'-derived elevation data","interactions":[],"lastModifiedDate":"2018-03-29T11:15:00","indexId":"70156554","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Evaluation of vector coastline features extracted from 'structure from motion'-derived elevation data","docAbstract":"For extensive and remote coastlines, the absence of high-quality elevation models—for example, those produced with lidar—leaves some coastal populations lacking one of the essential elements for mapping shoreline positions or flood extents. Here, we compare seven different elevation products in a lowlying area in western Alaska to establish their appropriateness for coastal mapping applications that require the delineation of elevation-based vectors. We further investigate the effective use of a Structure from Motion (SfM)-derived surface model (vertical RMSE<20 cm) by generating a tidal datum-based shoreline and an inundation extent map for a 2011 flood event. Our results suggest that SfM-derived elevation products can yield elevation-based vector features that have horizontal positional uncertainties comparable to those derived from other techniques. We also provide a rule-of-thumb equation to aid in the selection of minimum elevation model specifications based on terrain slope, vertical uncertainties, and desired horizontal accuracy.
We extend the approach underlying the well-known Dix equation in reflection seismology to surface waves. Within the context of surface wave inversion, the Dix-type relation we derive for surface waves allows accurate depth profiles of shear-wave velocity to be constructed directly from phase velocity data, in contrast to perturbational methods. The depth profiles can subsequently be used as an initial model for nonlinear inversion. We provide examples of the Dix-type relation for under-parameterized and over-parameterized cases. In the under-parameterized case, we use the theory to estimate crustal thickness, crustal shear-wave velocity, and mantle shear-wave velocity across the Western U.S. from phase velocity maps measured at 8-, 20-, and 40-s periods. By adopting a thin-layer formalism and an over-parameterized model, we show how a regularized inversion based on the Dix-type relation yields smooth depth profiles of shear-wave velocity. In the process, we quantitatively demonstrate the depth sensitivity of surface-wave phase velocity as a function of frequency and the accuracy of the Dix-type relation. We apply the over-parameterized approach to a near-surface data set within the frequency band from 5 to 40 Hz and find overall agreement between the inverted model and the result of full nonlinear inversion.
Bat day-roost selection often is described through comparisons of day-roosts with randomly selected, and assumed unused, trees. Relatively few studies, however, look at patterns of multi-year selection or compare day-roosts used across years. We explored day-roost selection using 2 years of roost selection data for female northern long-eared bats (Myotis septentrionalis) on the Fort Knox Military Reservation, Kentucky, USA. We compared characteristics of randomly selected non-roost trees and day-roosts using a multinomial logistic model and day-roost species selection using chi-squared tests. We found that factors differentiating day-roosts from non-roosts and day-roosts between years varied. Day-roosts differed from non-roosts in the first year of data in all measured factors, but only in size and decay stage in the second year. Between years, day-roosts differed in size and canopy position, but not decay stage. Day-roost species selection was non-random and did not differ between years. Although bats used multiple trees, our results suggest that there were additional unused trees that were suitable as roosts at any time. Day-roost selection pattern descriptions will be inadequate if based only on a single year of data, and inferences of roost selection based only on comparisons of roost to non-roosts should be limited.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.gecco.2015.03.008","usgsCitation":"Silvis, A., Ford, W., and Britzke, E.R., 2015, Day-roost tree selection by northern long-eared bats - What do non-roost tree comparisons and one year of data really tell us?: Global Ecology and Conservation, v. 3, p. 756-763, https://doi.org/10.1016/j.gecco.2015.03.008.","productDescription":"8 p.","startPage":"756","endPage":"763","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-040382","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":472624,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2015.03.008","text":"Publisher Index Page"},{"id":325401,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"Fort Knox Military Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.00749969482422,\n 37.86455455760559\n ],\n [\n -86.00749969482422,\n 37.98993962366689\n ],\n [\n -85.81558227539062,\n 37.98993962366689\n ],\n [\n -85.81558227539062,\n 37.86455455760559\n ],\n [\n -86.00749969482422,\n 37.86455455760559\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"578dfdb0e4b0f1bea0e0f82c","contributors":{"authors":[{"text":"Silvis, Alexander","contributorId":171585,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","email":"","affiliations":[{"id":26923,"text":"Virginia Polytechnic Institute, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":642785,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":641002,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Britzke, Eric R.","contributorId":8327,"corporation":false,"usgs":true,"family":"Britzke","given":"Eric","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":642786,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70176445,"text":"70176445 - 2015 - Evaluation of stream flow effects on smolt survival in the Yakima River Basin, Washington, 2012-2014","interactions":[],"lastModifiedDate":"2017-02-27T12:47:53","indexId":"70176445","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"Evaluation of stream flow effects on smolt survival in the Yakima River Basin, Washington, 2012-2014","docAbstract":"The influence of stream flow on survival of emigrating juvenile (smolts) Pacific salmon Oncorhynchus spp. and steelhead trout O. mykiss is of key management interest. However, few studies have quantified flow effects on smolt migration survival, and available information does not indicate a consistent flow-survival relationship within the typical range of flows under\r\nmanagement control. It is hypothesized that smolt migration and dam passage survival are positively correlated with stream flow because higher flows increase migration rates, potentially reducing exposure to predation, and reduce delays in reservoirs. However, available empirical data are somewhat equivocal concerning the influence of flow on smolt survival and the underlying mechanisms driving this relationship. Stream flow effects on survival of emigrating anadromous salmonids in the Yakima Basin have concerned water users and fisheries managers for over 20 years, and previous studies do not provide sufficient information at the resolution\r\nnecessary to inform water operations, which typically occur on a small spatiotemporal scale. Using a series of controlled flow releases from 2012-2014, combined with radio telemetry, we quantified the relationship between flow and smolt survival from Roza Dam 208 km downstream\r\nto the Yakima River mouth, as well as for specific routes of passage at Roza Dam. A novel multistate mark-recapture model accounted for weekly variation in flow conditions experienced by radio-tagged fish.\r\n\r\nGroups of fish were captured and radio-tagged at Roza Dam and released at two locations, upstream at the Big Pines Campground (river kilometer [rkm] 211) and downstream in the Roza Dam tailrace (rkm 208). A total of 904 hatchery-origin yearling Chinook salmon O. tshawytscha were captured in the Roza Dam fish bypass, radio-tagged and released upstream of Roza Dam.\r\nTwo hundred thirty seven fish were released in the tailrace of Roza Dam. Fish released in the tailrace of Roza Dam were tagged concurrently with fish released upstream of the dam using identical tagging methods. Tagging and release events were conducted to target a range of flow conditions indicative of flows observed during the typical migration period (March-May) for\r\njuvenile spring Chinook salmon in the Yakima River. Three, five and four separate upstream releases were conducted in 2012, 2013, and 2014 respectively, and at least 43 fish were released alive on each occasion. The release sample sizes in 2014 were much larger (~130) compared to previous years for the purpose of increasing precision of survival estimates across the range of flows tested.\r\n\r\nMigration movements of radio-tagged spring Chinook salmon smolts were monitored with an array of telemetry receiver stations (fixed sites) that extended 208 rkm downstream from the forebay of Roza Dam to the mouth of the Yakima River. Fixed monitoring sites included the forebay of Roza Dam (rkm 208), the tailrace of Roza Dam (rkm 207.9), the mouth of Wenas Creek (rkm 199.2), the mouth of the Naches River (two sites, rkm 189.4), Sunnyside Dam (two sites, rkm 169.1), Prosser Dam (rkm 77.2), and the mouth of the Yakima River (two sites, rkm2 3). This array segregated the study area into four discrete reaches in which survival of tagged fish was estimated. Aerial and underwater antennas were also used to monitor tagged fish at Roza Dam. Aerial antennas were located in the forebay, on the East gate, on the West gate, and in the tailrace of Roza Dam. Underwater antennas were located in the fish bypass, upstream of the East gate, and upstream of the West gate to collect route-specific passage data for tagged fish.\r\n\r\nAdditional years of data collection and analysis could alter or improve our understanding of the influence of flow and other environmental factors on smolt survival in the Yakima River. Nevertheless, during 2012-2014, yearling hatchery Chinook salmon smolt emigration survival was significantly associated with stream flow in the","language":"English","publisher":"U.S. Bureau of Reclamation ","collaboration":"Cramer Fish Sciences","usgsCitation":"Courter, I., Garrison, T., Kock, T.J., and Perry, R.W., 2015, Evaluation of stream flow effects on smolt survival in the Yakima River Basin, Washington, 2012-2014, 67 p. .","productDescription":"67 p. ","startPage":"1","endPage":"67","ipdsId":"IP-066202","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":336269,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":328633,"type":{"id":15,"text":"Index Page"},"url":"https://www.fishsciences.net/reports/2015/FinalRozaTechReport9-23-15.pdf"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b548c3e4b01ccd54fddfd2","contributors":{"authors":[{"text":"Courter, Ian","contributorId":173188,"corporation":false,"usgs":false,"family":"Courter","given":"Ian","affiliations":[{"id":27180,"text":"Mount Hood Environmental","active":true,"usgs":false}],"preferred":false,"id":648787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garrison, Tommy","contributorId":174619,"corporation":false,"usgs":false,"family":"Garrison","given":"Tommy","email":"","affiliations":[{"id":27482,"text":"Cramer Fish Sciences, 600 NW Fariss Rd., Gresham, OR 97030","active":true,"usgs":false}],"preferred":false,"id":648788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kock, Tobias J. 0000-0001-8976-0230 tkock@usgs.gov","orcid":"https://orcid.org/0000-0001-8976-0230","contributorId":3038,"corporation":false,"usgs":true,"family":"Kock","given":"Tobias","email":"tkock@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":648786,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":648789,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70182726,"text":"70182726 - 2015 - Climate change and vulnerability of bull trout (Salvelinus confluentus) in a fire-prone landscape.","interactions":[],"lastModifiedDate":"2017-11-20T14:24:45","indexId":"70182726","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Climate change and vulnerability of bull trout (Salvelinus confluentus) in a fire-prone landscape.","docAbstract":"Linked atmospheric and wildfire changes will complicate future management of native coldwater fishes in fire-prone landscapes, and new approaches to management that incorporate uncertainty are needed to address this challenge. We used a Bayesian network (BN) approach to evaluate population vulnerability of bull trout (Salvelinus confluentus) in the Wenatchee River basin, Washington, USA, under current and future climate and fire scenarios. The BN was based on modeled estimates of wildfire, water temperature, and physical habitat prior to, and following, simulated fires throughout the basin. We found that bull trout population vulnerability depended on the extent to which climate effects can be at least partially offset by managing factors such as habitat connectivity and fire size. Moreover, our analysis showed that local management can significantly reduce the vulnerability of bull trout to climate change given appropriate management actions. Tools such as our BN that explicitly integrate the linked nature of climate and wildfire, and incorporate uncertainty in both input data and vulnerability estimates, will be vital in effective future management to conserve native coldwater fishes.
Rapid land subsidence was recently measured using multiple methods in two areas of the San Joaquin Valley (SJV): between Merced and Fresno (El Nido), and between Fresno and Bakersfield (Pixley). Recent land-use changes and diminished surface-water availability have led to increased groundwater pumping, groundwater-level declines, and land subsidence. Differential land subsidence has reduced the flow capacity of water-conveyance systems in these areas, exacerbating flood hazards and affecting the delivery of irrigation water.
Vertical land-surface changes during 2007–2014 were determined by using Interferometric Synthetic Aperture Radar (InSAR), Continuous Global Positioning System (CGPS), and extensometer data. Results of the InSAR analysis indicate that about 7600 km2 subsided 50–540 mm during 2008–2010; CGPS and extensometer data indicate that these rates continued or accelerated through December 2014. The maximum InSAR-measured rate of 270 mm yr−1 occurred in the El Nido area, and is among the largest rates ever measured in the SJV. In the Pixley area, the maximum InSAR-measured rate during 2008–2010 was 90 mm yr−1. Groundwater was an important part of the water supply in both areas, and pumping increased when land use changed or when surface water was less available. This increased pumping caused groundwater-level declines to near or below historical lows during the drought periods 2007–2009 and 2012–present.
Long-term groundwater-level and land-subsidence monitoring in the SJV is critical for understanding the interconnection of land use, groundwater levels, and subsidence, and evaluating management strategies that help mitigate subsidence hazards to infrastructure while optimizing water supplies.
State-and-transition simulation models (STSMs) are known for their ability to explore the combined effects of multiple disturbances, ecological dynamics, and management actions on vegetation. However, integrating the additional impacts of climate change into STSMs remains a challenge. We address this challenge by combining an STSM with species distribution modeling (SDM). SDMs estimate the probability of occurrence of a given species based on observed presence and absence locations as well as environmental and climatic covariates. Thus, in order to account for changes in habitat suitability due to climate change, we used SDM to generate continuous surfaces of species occurrence probabilities. These data were imported into ST-Sim, an STSM platform, where they dictated the probability of each cell transitioning between alternate potential vegetation types at each time step. The STSM was parameterized to capture additional processes of vegetation growth and disturbance that are relevant to a keystone species in the Greater Yellowstone Ecosystem—whitebark pine (Pinus albicaulis). We compared historical model runs against historical observations of whitebark pine and a key disturbance agent (mountain pine beetle, Dendroctonus ponderosae), and then projected the simulation into the future. Using this combination of correlative and stochastic simulation models, we were able to reproduce historical observations and identify key data gaps. Results indicated that SDMs and STSMs are complementary tools, and combining them is an effective way to account for the anticipated impacts of climate change, biotic interactions, and disturbances, while also allowing for the exploration of management options.
","language":"English","publisher":"AIM Press","doi":"10.3934/environsci.2015.2.400","usgsCitation":"Miller, B.W., Frid, L., Chang, T., Piekielek, N.B., Hansen, A.J., and Morisette, J.T., 2015, Combining state-and-transition simulations and species distribution models to anticipate the effects of climate change: AIMS Environmental Science, v. 2, no. 2, p. 400-426, https://doi.org/10.3934/environsci.2015.2.400.","productDescription":"27 p.","startPage":"400","endPage":"426","ipdsId":"IP-065083","costCenters":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"links":[{"id":472396,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3934/environsci.2015.2.400","text":"Publisher Index Page"},{"id":345943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59c37e3be4b091459a631703","contributors":{"authors":[{"text":"Miller, Brian W. 0000-0003-1716-1161","orcid":"https://orcid.org/0000-0003-1716-1161","contributorId":196603,"corporation":false,"usgs":true,"family":"Miller","given":"Brian","email":"","middleInitial":"W.","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":710899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frid, Leonardo","contributorId":196604,"corporation":false,"usgs":false,"family":"Frid","given":"Leonardo","email":"","affiliations":[],"preferred":false,"id":710900,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chang, Tony","contributorId":191992,"corporation":false,"usgs":false,"family":"Chang","given":"Tony","email":"","affiliations":[],"preferred":false,"id":710901,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Piekielek, N. B.","contributorId":127648,"corporation":false,"usgs":false,"family":"Piekielek","given":"N.","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":710902,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hansen, Andrew J.","contributorId":196605,"corporation":false,"usgs":false,"family":"Hansen","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":710903,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morisette, Jeffrey T. 0000-0002-0483-0082 morisettej@usgs.gov","orcid":"https://orcid.org/0000-0002-0483-0082","contributorId":307,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","email":"morisettej@usgs.gov","middleInitial":"T.","affiliations":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":710898,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70182177,"text":"70182177 - 2015 - High-speed limnology: Using advanced sensors to investigate spatial variability in biogeochemistry and hydrology","interactions":[],"lastModifiedDate":"2023-03-13T17:30:15.126103","indexId":"70182177","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"High-speed limnology: Using advanced sensors to investigate spatial variability in biogeochemistry and hydrology","docAbstract":"Advanced sensor technology is widely used in aquatic monitoring and research. Most applications focus on temporal variability, whereas spatial variability has been challenging to document. We assess the capability of water chemistry sensors embedded in a high-speed water intake system to document spatial variability. This new sensor platform continuously samples surface water at a range of speeds (0 to >45 km h–1) resulting in high-density, mesoscale spatial data. These novel observations reveal previously unknown variability in physical, chemical, and biological factors in streams, rivers, and lakes. By combining multiple sensors into one platform, we were able to detect terrestrial–aquatic hydrologic connections in a small dystrophic lake, to infer the role of main-channel vs backwater nutrient processing in a large river and to detect sharp chemical changes across aquatic ecosystem boundaries in a stream/lake complex. Spatial sensor data were verified in our examples by comparing with standard lab-based measurements of selected variables. Spatial fDOM data showed strong correlation with wet chemistry measurements of DOC, and optical NO3 concentrations were highly correlated with lab-based measurements. High-frequency spatial data similar to our examples could be used to further understand aquatic biogeochemical fluxes, ecological patterns, and ecosystem processes, and will both inform and benefit from fixed-site data.
","language":"English","publisher":"ACS Publications","doi":"10.1021/es504773x","usgsCitation":"Crawford, J.T., Loken, L., Casson, N.J., Smith, C., Stone, A.G., and Winslow, L.A., 2015, High-speed limnology: Using advanced sensors to investigate spatial variability in biogeochemistry and hydrology: Environmental Science & Technology, v. 49, no. 1, p. 442-450, https://doi.org/10.1021/es504773x.","productDescription":"9 p.","startPage":"442","endPage":"450","ipdsId":"IP-060665","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":472436,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/es504773x","text":"Publisher Index Page"},{"id":335832,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Lake Mendota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.40173033599879,\n 43.07347645438287\n ],\n [\n -89.38023693230257,\n 43.078382654645424\n ],\n [\n -89.36176603850066,\n 43.09800352757304\n ],\n [\n -89.36848272715585,\n 43.11614723923495\n ],\n [\n -89.37721442240766,\n 43.115656939360235\n ],\n [\n -89.36411687953026,\n 43.12938385089856\n ],\n [\n -89.37284857478208,\n 43.13575601287684\n ],\n [\n -89.4020661704318,\n 43.1313445868372\n ],\n [\n -89.40508868032673,\n 43.15486851517488\n ],\n [\n -89.41986539536839,\n 43.1507034790599\n ],\n [\n -89.43766462030436,\n 43.148253325220736\n ],\n [\n -89.439343792468,\n 43.130364226724964\n ],\n [\n -89.45479217637498,\n 43.114921482182126\n ],\n [\n -89.4866964474873,\n 43.10707605564116\n ],\n [\n -89.48568894418901,\n 43.08868439660202\n ],\n [\n -89.4645313749251,\n 43.07642022167718\n ],\n [\n -89.43262710381278,\n 43.08917491253359\n ],\n [\n -89.42826125618721,\n 43.07936384756135\n ],\n [\n -89.42053706423373,\n 43.074948355706226\n ],\n [\n -89.40173033599879,\n 43.07347645438287\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2014-12-08","publicationStatus":"PW","scienceBaseUri":"58ac0e30e4b0ce4410e7d600","chorus":{"doi":"10.1021/es504773x","url":"http://dx.doi.org/10.1021/es504773x","publisher":"American Chemical Society (ACS)","authors":"Crawford John T., Loken Luke C., Casson Nora J., Smith Colin, Stone Amanda G., Winslow Luke A.","journalName":"Environmental Science & Technology","publicationDate":"1/6/2015","auditedOn":"3/4/2016","publiclyAccessibleDate":"12/8/2014"},"contributors":{"authors":[{"text":"Crawford, John T. 0000-0003-4440-6945 jtcrawford@usgs.gov","orcid":"https://orcid.org/0000-0003-4440-6945","contributorId":4081,"corporation":false,"usgs":true,"family":"Crawford","given":"John","email":"jtcrawford@usgs.gov","middleInitial":"T.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":669891,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loken, Luke C. lloken@usgs.gov","contributorId":169218,"corporation":false,"usgs":true,"family":"Loken","given":"Luke C.","email":"lloken@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":669892,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Casson, Nora J.","contributorId":169271,"corporation":false,"usgs":false,"family":"Casson","given":"Nora","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":669893,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Colin","contributorId":181885,"corporation":false,"usgs":false,"family":"Smith","given":"Colin","affiliations":[],"preferred":false,"id":669894,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stone, Amanda G.","contributorId":181886,"corporation":false,"usgs":false,"family":"Stone","given":"Amanda","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":669895,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Winslow, Luke A. 0000-0002-8602-5510 lwinslow@usgs.gov","orcid":"https://orcid.org/0000-0002-8602-5510","contributorId":5919,"corporation":false,"usgs":true,"family":"Winslow","given":"Luke","email":"lwinslow@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":false,"id":669896,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70189826,"text":"70189826 - 2015 - Analysis and selection of magnitude relations for the Working Group on Utah Earthquake Probabilities","interactions":[],"lastModifiedDate":"2017-07-27T16:10:12","indexId":"70189826","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Analysis and selection of magnitude relations for the Working Group on Utah Earthquake Probabilities","docAbstract":"Prior to calculating time-independent and -dependent earthquake probabilities for faults in the Wasatch Front region, the Working Group on Utah Earthquake Probabilities (WGUEP) updated a seismic-source model for the region (Wong and others, 2014) and evaluated 19 historical regressions on earthquake magnitude (M). These regressions relate M to fault parameters for historical surface-faulting earthquakes, including linear fault length (e.g., surface-rupture length [SRL] or segment length), average displacement, maximum displacement, rupture area, seismic moment (Mo ), and slip rate. These regressions show that significant epistemic uncertainties complicate the determination of characteristic magnitude for fault sources in the Basin and Range Province (BRP). For example, we found that M estimates (as a function of SRL) span about 0.3–0.4 units (figure 1) owing to differences in the fault parameter used; age, quality, and size of historical earthquake databases; and fault type and region considered.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Basin and Range Province Seismic Hazards Summit III, Utah Geological Survey Miscellaneous Publication 15-5","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"conferenceTitle":"Basin and Range Province Seismic Hazards Summit III","language":"English","publisher":"Utah Geological Survey","usgsCitation":"DuRoss, C., Olig, S., and Schwartz, D., 2015, Analysis and selection of magnitude relations for the Working Group on Utah Earthquake Probabilities, in Basin and Range Province Seismic Hazards Summit III, Utah Geological Survey Miscellaneous Publication 15-5, 30 p.","productDescription":"30 p.","ipdsId":"IP-064153","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":344412,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":344373,"type":{"id":15,"text":"Index Page"},"url":"https://ugspub.nr.utah.gov/publications/misc_pubs/mp-15-5/mp-15-5_technical_sessions1-2.pdf"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"597afba7e4b0a38ca2750b6a","contributors":{"authors":[{"text":"DuRoss, Christopher 0000-0002-6963-7451 cduross@usgs.gov","orcid":"https://orcid.org/0000-0002-6963-7451","contributorId":152321,"corporation":false,"usgs":true,"family":"DuRoss","given":"Christopher","email":"cduross@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":706480,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olig, Susan","contributorId":195184,"corporation":false,"usgs":false,"family":"Olig","given":"Susan","affiliations":[],"preferred":false,"id":706481,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwartz, David","contributorId":195185,"corporation":false,"usgs":false,"family":"Schwartz","given":"David","affiliations":[],"preferred":false,"id":706482,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70193151,"text":"70193151 - 2015 - Observational changes to the natural flow regime in Lee Creek in relation to altered precipitation patterns and its implication for fishes","interactions":[],"lastModifiedDate":"2017-11-21T12:46:10","indexId":"70193151","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3894,"text":"Proceedings of the Oklahoma Academy of Science","active":true,"publicationSubtype":{"id":10}},"title":"Observational changes to the natural flow regime in Lee Creek in relation to altered precipitation patterns and its implication for fishes","docAbstract":"The natural flow regime is important for structuring streams and their resident ichthyofauna and alterations to this regime can have cascading consequences. We sought to determine if changes in hydrology could be attributed to changes in precipitation in a minimally altered watershed (Lee Creek). The stream flow regime was analyzed using Indicators of Hydrologic Alteration (IHA) software, and data from a nearby climate station were used to summarize concurrent precipitation patterns. We discovered that Lee Creek hydrology had become flashier (i.e., increased frequency of extreme events of shorter duration) since 1992 coincident with changes in precipitation patterns. Specifically, our results show fewer but more intense rain events within the Lee Creek watershed. Our research provides evidence that climate-induced changes to the natural flow regime are currently underway and additional research on its effects on the fish community is warranted.
","language":"English","publisher":"Oklahoma Academy of Science","usgsCitation":"Gatlin, M.R., Long, J.M., and Turton, D.J., 2015, Observational changes to the natural flow regime in Lee Creek in relation to altered precipitation patterns and its implication for fishes: Proceedings of the Oklahoma Academy of Science, v. 95, p. 135-146.","productDescription":"12 p.","startPage":"135","endPage":"146","ipdsId":"IP-063504","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":349202,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Oklahoma","otherGeospatial":"Lee Creek watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.62661743164062,\n 35.464025575544674\n ],\n [\n -94.28123474121094,\n 35.464025575544674\n ],\n [\n -94.28123474121094,\n 35.74874138089811\n ],\n [\n -94.62661743164062,\n 35.74874138089811\n ],\n [\n -94.62661743164062,\n 35.464025575544674\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"95","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fec8e4b06e28e9c2535b","contributors":{"authors":[{"text":"Gatlin, Michael R.","contributorId":141324,"corporation":false,"usgs":false,"family":"Gatlin","given":"Michael","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":723033,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":718099,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Turton, Donald J.","contributorId":200683,"corporation":false,"usgs":false,"family":"Turton","given":"Donald","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":723034,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70117684,"text":"70117684 - 2015 - Global Cropland Area Database (GCAD) derived from Remote Sensing in Support of Food Security in the Twenty-first Century: Current Achievements and Future Possibilities","interactions":[],"lastModifiedDate":"2015-10-16T16:20:40","indexId":"70117684","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Global Cropland Area Database (GCAD) derived from Remote Sensing in Support of Food Security in the Twenty-first Century: Current Achievements and Future Possibilities","docAbstract":"The precise estimation of the global agricultural cropland- extents, areas, geographic locations, crop types, cropping intensities, and their watering methods (irrigated or rainfed; type of irrigation) provides a critical scientific basis for the development of water and food security policies (Thenkabail et al., 2012, 2011, 2010). By year 2100, the global human population is expected to grow to 10.4 billion under median fertility variants or higher under constant or higher fertility variants (Table 1) with over three quarters living in developing countries, in regions that already lack the capacity to produce enough food. With current agricultural practices, the increased demand for food and nutrition would require in about 2 billion hectares of additional cropland, about twice the equivalent to the land area of the United States, and lead to significant increases in greenhouse gas productions (Tillman et al., 2011). For example, during 1960-2010 world population more than doubled from 3 billion to 7 billion. The nutritional demand of the population also grew swiftly during this period from an average of about 2000 calories per day per person in 1960 to nearly 3000 calories per day per person in 2010. The food demand of increased population along with increased nutritional demand during this period (1960-2010) was met by the “green revolution” which more than tripled the food production; even though croplands decreased from about 0.43 ha/capita to 0.26 ha/capita (FAO, 2009). The increase in food production during the green revolution was the result of factors such as: (a) expansion in irrigated areas which increased from 130 Mha in 1960s to 278.4 Mha in year 2000 (Siebert et al., 2006) or 399 Mha when you do not consider cropping intensity (Thenkabail et al., 2009a, 2009b, 2009c) or 467 Mha when you consider cropping intensity (Thenkabail et al., 2009a; Thenkabail et al., 2009c); (b) increase in yield and per capita food production (e.g., cereal production from 280 kg/person to 380 kg/person and meat from 22 kg/person to 34 kg/person (McIntyre, 2008); (c) new cultivar types (e.g., hybrid varieties of wheat and rice, biotechnology); and (d) modern agronomic and crop management practices (e.g., fertilizers, herbicide, pesticide applications). However, some of the factors that lead to the green revolution have stressed the environment to limits leading to salinization and decreasing water quality. For example, from 1960 to 2000, the phosphorous use doubled from 10 million tons to 20 MT, pesticide use tripled from near zero to 3 MT, and nitrogen use as fertilizer increased to a staggering 80 MT from just 10 MT (Foley et al., 2007; Khan and Hanjra, 2008). Further, diversion of croplands to bio-fuels is already taking water away from food production; the economics, carbon sequestration, environmental, and food security impacts of biofuel production are net negative (Lal and Pimentel, 2009), leaving us with a carbon debt (Gibbs et al., 2008; Searchinger et al., 2008). Climate models predict that in most regions of the world the hottest seasons on record will become the norm by the end of the century-an outcome that bodes ill for feeding the world (Kumar and Singh, 2005). Also, crop yield increases of the green revolution era have now stagnated (Hossain et al., 2005). Thereby, further increase in food production through increase in cropland areas and\\or increased allocations of water for croplands are widely considered unsustainable and\\or infeasible. Indeed, cropland areas have even begun to decrease in many 3 parts of the World due to factors such as urbanization, industrialization, and salinization. Furthermore, ecological and environmental imperatives such as biodiversity conservation and atmospheric carbon sequestration have put a cap on the possible expansion of cropland areas to other lands such as forests and rangelands. Other important factors limit food security. These include factors such as diversion of croplands to biofuels (Bindraban et al., 2009), limited water resources for irrigation expansion (Turral et al., 2009), limits on agricultural intensifications, loss of croplands to urbanization (Khan and Hanjra, 2008), increasing meat consumption (and associated demands on land and water) (Vinnari and Tapio, 2009), environmental infeasibility for cropland expansion (Gordon et al., 2009), and changing climate have all put pressure on our continued ability to sustain global food security in the twenty-first century. So, how does the World continue to meet its food and nutrition needs?. Solutions may come from bio-technology and precision farming, however developments in these fields are not currently moving at rates that will ensure global food security over next few decades. Further, there is a need for careful consideration of possible harmful effects of bio-technology. We should not be looking back 30– 50 years from now, like we have been looking back now at many mistakes made during the green revolution. During the green revolution the focus was only on getting more yield per unit area. Little thought was put about serious damage done to our natural environments, water resources, and human health as a result of detrimental factors such as uncontrolled use of herbicides-pesticides-nutrients, drastic groundwater mining, and salinization of fertile soils due to over irrigation. Currently, there is talk of a “second green revolution” or even an “ever green revolution”, but clear ideas on what these terms actually mean are still debated and are evolving. One of the biggest issues that are not given adequate focus is the use of large quantities of water for food production. Indeed, an overwhelming proportion (60-90%) of all human water use in India goes for producing their food (Falkenmark, M., & Rockström, 2006). But such intensive water use for food production is no longer tenable due to increasing pressure for water use alternatives such as increasing urbanization, industrialization, environmental flows, bio-fuels, and recreation. This has brought into sharp focus the need to grow more food per drop of water leading to a “blue revolution”
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Mean long-term fluxes were calculated in this study for precipitation, surface runoff, infiltration, total evapotranspiration (ET), riparian ET, recharge, base flow (or groundwater discharge) and net total outflow using long-term estimates of mean ET and precipitation and the assumption that the relative change in storage over that 30-year period is small compared to the total ET or precipitation. Fluxes of these components were first estimated on a number of real-time-gaged watersheds across Virginia. Specific conductance was used to distinguish and separate surface runoff from base flow. Specific-conductance (SC) data were collected every 15 minutes at 75 real-time gages for approximately 18 months between March 2007 and August 2008. Precipitation was estimated for 1971-2000 using PRISM climate data. Precipitation and temperature from the PRISM data were used to develop a regression-based relation to estimate total ET. The proportion of watershed precipitation that becomes surface runoff was related to physiographic province and rock type in a runoff regression equation. A new approach to estimate riparian ET using seasonal SC data gave results consistent with those from other methods. Component flux estimates from the watersheds were transferred to flux estimates for counties and independent cities using the ET and runoff regression equations. Only 48 of the 75 watersheds yielded sufficient data, and data from these 48 were used in the final runoff regression equation. Final results for the study are presented as component flux estimates for all counties and independent cities in Virginia. The method has the potential to be applied in many other states in the U.S. or in other regions or countries of the world where climate and stream flow data are plentiful.
","language":"English","publisher":"OMICS International","doi":"10.4172/2157-7587.1000191","usgsCitation":"Sanford, W.E., Nelms, D.L., Pope, J.P., and Selnick, D.L., 2015, Estimating mean long-term hydrologic budget components for watersheds and counties: An application to the commonwealth of Virginia, USA: Hydrology: Current Research, v. 6, p. 1-22, https://doi.org/10.4172/2157-7587.1000191.","productDescription":"Article 191; 22 p.","startPage":"1","endPage":"22","ipdsId":"IP-061320","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":488622,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.4172/2157-7587.1000191","text":"Publisher Index Page"},{"id":340439,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5901b1bee4b0c2e071a99baa","contributors":{"authors":[{"text":"Sanford, Ward E. 0000-0002-6624-0280 wsanford@usgs.gov","orcid":"https://orcid.org/0000-0002-6624-0280","contributorId":2268,"corporation":false,"usgs":true,"family":"Sanford","given":"Ward","email":"wsanford@usgs.gov","middleInitial":"E.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":692978,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelms, David L. 0000-0001-5747-642X dlnelms@usgs.gov","orcid":"https://orcid.org/0000-0001-5747-642X","contributorId":1892,"corporation":false,"usgs":true,"family":"Nelms","given":"David","email":"dlnelms@usgs.gov","middleInitial":"L.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":692979,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pope, Jason P. 0000-0003-3199-993X jpope@usgs.gov","orcid":"https://orcid.org/0000-0003-3199-993X","contributorId":2044,"corporation":false,"usgs":true,"family":"Pope","given":"Jason","email":"jpope@usgs.gov","middleInitial":"P.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true},{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":692980,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Selnick, David L.","contributorId":13480,"corporation":false,"usgs":true,"family":"Selnick","given":"David","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":692981,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70174571,"text":"70174571 - 2015 - Characterization of stormwater runoff from bridges in North Carolina and the effects of bridge runoff on receiving streams","interactions":[],"lastModifiedDate":"2016-11-30T15:08:12","indexId":"70174571","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"title":"Characterization of stormwater runoff from bridges in North Carolina and the effects of bridge runoff on receiving streams","docAbstract":"The presentation will provide an overview of a collaborative study between USGS, NC Department of Transportation and URS Corporation to characterize stormwater runoff from bridges in North Carolina and the effects of bridge runoff on receiving streams. This investigation measured bridge deck runoff from 15 bridges for 12-15 storms, stream water-quality data for baseflow and storm conditions at four of the bridge deck sites and streambed sediment chemistry upstream and downstream of 30 bridges across North Carolina. Background on why the study was conducted, objectives and scope and a general summary of the major results and conclusions will be presented.
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Much of this sand is currently stabilized by vegetation, although many drier parts of these Native lands also have active and partly active dunes. Current prolonged drought conditions that started in the mid-1990s are producing significant changes in dune mobility. Reactivation of regional aeolian deposits due to drought or increasing aridity from rising temperatures resulting from climate change could have serious consequences for human and animal populations, agriculture, grazing, and infrastructure. To understand and document the current and future potential for mobility, seasonally repeated surveys were used to track the location of multiple active barchan dunes. By utilizing Real-Time Kinematic GPS field surveys and simultaneously collecting in-situ meteorological data, it is possible to examine climatic parameters and seasonal variations that affect dune mobility and their relative influences. Through analysis of the recorded data, we examined the fit of various climate parameters, and demonstrate that under the current prolonged drought, wind power is the dominant factor controlling dune mobility.
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Annual patterns in A1, A2 and A3 wound stages did not track well in plots of wounding rates for the USGS/NYSDEC SGNS and correlations between A1 and later stages did not exist. A1 rates were not correlated to either Lake Trout abundance or Sea Lamprey numbers when considered alone, but were strongly correlated to the ratio between Sea Lamprey numbers and Lake Trout abundance (parasite/host ratio). While A2 and A3 rates were correlated to each other, neither was consistently correlated to any of the Lake Trout abundance or Sea Lamprey abundance parameters and sums of A1 to A3 rates did not improve correlations over those for A1 rates considered alone. Our analysis of the strain-specific susceptibility of Lake Trout to attack by Sea Lampreys extended the previous Schneider et al. (1996) analysis of three strains (SUP, CWL, and SEN) and 11 years of data 1982-1992 to an analysis of seven strains (SUP, CWL, SEN, JEN, LEW, ONT, and OXS) and two groups of unmarked fish (1983-1995 and 1996-2010) and included 18 more years of data through 2010. The susceptibility to attack for CWLs and SENs were below SUPs and nearly identical to the earlier values, new values for LEWs were greater than SUPS and values of unmarked Lake Trout prior to 1996 were unexpectedly greater than SUPs. By reexamining the Schneider et al. (1996) regression relationship between A1 wounding on Lake Trout and the incidence of Lake Trout carcasses recovered in fall bottom trawls (including three additional years of data), and substituting A1 wounding rate for total numbers of A1s observed which was used as the independent variable in the previous version, we were able to increase the variance explained by the relationship from an r2 of 0.60 to 0.88. Healing rate of wounds was explored by examining the monthly incidence of A1 and A2 wounds on Lake Trout from the OMNR CIS. Because wounding intensity varied between years and monthly sample size was frequently low, the ratio of A2 to A1s wounds was used to index how wounds accumulated or disappeared from the Lake Trout populations across seasons. The A2/A1 ratio decreased between June/July and October. A simple wounding model for Lake Trout was constructed to simulate the pattern of ratios by inflicting wounds on the population each month according to a predetermined distribution and including parameters for lethality (fixed distribution) and healing rate of A1 and A2 wounds (discretely varied between simulations). The best simulated representation (ratio size and monthly pattern) of the OMNR CIS data came from an A1 healing rate of 0.5 months and an A2 rate of 2 months. To examine whether alternate hosts provided reliable data to index damage caused by Sea Lampreys we compared September values of abundance and A1 wounding rates on Lake Trout, Sea Lamprey abundance, and the parasite/host ratio with NYSDEC Creel data for observations of Sea lampreys attached to sport-caught salmonids and to wounding observations for salmonids sampled in OMNR and NYSDEC spawning run assessments. Attachment frequency on NYSDEC Creel Chinook Salmon, Brown Trout, and Rainbow Trout were strongly correlated to all measures of Lake Trout abundance and wounding and to the parasite/host ratio. Chinook Salmon and Coho Salmon wounding observations for the NYSDEC Salmon River spawning run assessments and Rainbow Trout from the OMNR Ganaraska River spawning run assessment were strongly correlated to wounding measures for nearly all salmonids and to the parasite/host ratio.
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Our algorithm partitions a time series of water levels into discrete recharge episodes and intervals of no episodic recharge. It correlates each recharge episode with a specific interval of rainfall, so storm characteristics such as intensity and duration can be associated with the amount of recharge that results. To be useful in humid climates, the algorithm evaluates the separability of events, so that those whose recharge cannot be associated with a single storm can be appropriately lumped together. Elements of this method that are subject to subjectivity in the application of hydrologic judgment are values of lag time, fluctuation tolerance, and master recession parameters. Because these are determined once for a given site, they do not contribute subjective influences affecting episode-to-episode comparisons. By centralizing the elements requiring scientific judgment, our method facilitates such comparisons by keeping the most subjective elements openly apparent, making it easy to maintain consistency. If applied to a period of data long enough to include recharge episodes with broadly diverse characteristics, the method has value for predicting how climatic alterations in the distribution of storm intensities and seasonal duration may affect recharge.
","language":"English","publisher":"Wiley-Blackwell Publishing, Inc.","doi":"10.1111/gwat.12177","usgsCitation":"Nimmo, J.R., Horowittz, C., and Mitchell, L., 2015, Discrete-storm water-table fluctuation method to estimate episodic recharge.: Groundwater, v. 53, no. 2, p. 282-292, https://doi.org/10.1111/gwat.12177.","productDescription":"11 p.","startPage":"282","endPage":"292","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-046105","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":297223,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2014-03-03","publicationStatus":"PW","scienceBaseUri":"54dd2a6ce4b08de9379b3050","chorus":{"doi":"10.1111/gwat.12177","url":"http://dx.doi.org/10.1111/gwat.12177","publisher":"Wiley-Blackwell","authors":"Nimmo John R., Horowitz Charles, Mitchell Lara","journalName":"Groundwater","publicationDate":"3/3/2014","auditedOn":"3/4/2015"},"contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":538091,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Horowittz, Charles","contributorId":138611,"corporation":false,"usgs":false,"family":"Horowittz","given":"Charles","email":"","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":538090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mitchell, Lara","contributorId":138612,"corporation":false,"usgs":false,"family":"Mitchell","given":"Lara","email":"","affiliations":[{"id":12466,"text":"Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":538092,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70141018,"text":"70141018 - 2015 - An updated conceptual model of Delta Smelt biology: Our evolving understanding of an estuarine fish","interactions":[],"lastModifiedDate":"2020-03-10T06:55:47","indexId":"70141018","displayToPublicDate":"2014-12-31T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":414,"text":"Technical Report","active":false,"publicationSubtype":{"id":9}},"seriesNumber":"90","title":"An updated conceptual model of Delta Smelt biology: Our evolving understanding of an estuarine fish","docAbstract":"The main purpose of this report is to provide an up-to-date assessment and conceptual model of factors affecting Delta Smelt (Hypomesus transpacificus) throughout its primarily annual life cycle and to demonstrate how this conceptual model can be used for scientific and management purposes. The Delta Smelt is a small estuarine fish that only occurs in the San Francisco Estuary. Once abundant, it is now rare and has been protected under the federal and California Endangered Species Acts since 1993. The Delta Smelt listing was related to a step decline in the early 1980s; however, population abundance decreased even further with the onset of the “pelagic organism decline” (POD) around 2002. A substantial, albeit short-lived, increase in abundance of all life stages in 2011 showed that the Delta Smelt population can still rebound when conditions are favorable for spawning, growth, and survival. In this report, we update previous conceptual models for Delta Smelt to reflect new data and information since the release of the last synthesis report about the POD by the Interagency Ecological Program for the San Francisco Estuary (IEP) in 2010. Specific objectives include: 1. Provide decision makers with a practical tool for evaluating difficult trade-offs associated with management and policy decisions. 2. Provide scientists with a framework from which they can formulate and evaluate hypotheses using qualitative or quantitative models. 3. Provide the general public with a new way of learning about Delta Smelt and their habitat. Our updated conceptual model describes the habitat conditions and ecosystem drivers affecting each Delta Smelt life stage, across seasons and how the seasonal effects contribute to the annual success of the species. The conceptual model consists of two nested and linked levels of increasing specificity. The general life cycle conceptual model for four Delta Smelt life stages (adults, eggs and larvae, juveniles, and subadults) includes stationary ecosystem components and dynamic environmental drivers, habitat attributes, and Delta Smelt responses. The more detailed life stage transition conceptual models for each of the four Delta Smelt life stages describe relationships between environmental drivers, key habitat attributes, and the responses of Delta Smelt to habitat attributes as they transition from one life stage to the next. Our analyses and conceptual model show that good larval recruitment is essential for setting the stage for a strong year class; however, increased growth and survival through subsequent life stages are also needed to achieve and sustain higher population abundance. We used our conceptual model to generate 16 hypotheses about the factors that may have contributed to the 2011 increase in Delta Smelt relative abundance. We then evaluated these hypotheses by comparing habitat conditions and Delta Smelt responses in the wet year 2011 to those in the prior wet year 2006 and in the drier years 2005 and 2010. Larval recruitment was similarly high in both wet years and lower in the drier antecedent years, but juvenile and adult abundance increased only in 2011. In 2005 and 2006, the population was limited by very poor survival from the larval to the juvenile life stage. We found that in 2011, Delta Smelt may have benefitted from a combination of favorable habitat conditions throughout the year, including: 1. Adults and larvae benefitted from prolonged cool spring water temperatures, high 2011 winter and spring outflows which reduced entrainment risk and possibly improved other habitat conditions, and possibly enhanced food availability in late spring. 2. Juveniles benefitted from cool water temperatures in late spring and early summer as well as from improved food availability and low levels of harmful Microcystis. 3. Subadults also benefitted from improved food availability and from favorable habitat conditions in the large, low salinity zone (salinity 1-6) located more toward Suisun Bay,
","language":"English","publisher":"Interagency Ecological Program, California Department of Water Resources","usgsCitation":"Baxter, R., Brown, L.R., Castillo, G., Conrad, L., Culberson, S.D., Dekar, M.P., Dekar, M., Feyrer, F., Hunt, T., Jones, K., Kirsch, J., Mueller-Solger, A., Nobriga, M., Slater, S., Sommer, T., Souza, K., Erickson, G., Fong, S., Gehrts, K., Grimaldo, L., and Herbold, B., 2015, An updated conceptual model of Delta Smelt biology: Our evolving understanding of an estuarine fish: Technical Report 90, xvi, 206 p.","productDescription":"xvi, 206 p.","ipdsId":"IP-052945","costCenters":[{"id":154,"text":"California Water Science 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The LCU classifications produced by the U.S. Geological Survey’s Upper Midwest Environmental Sciences Center (UMESC) include canopy height estimates that are assigned through manual aerial photography interpretation techniques. In an effort to improve upon these techniques, this project investigated the use of high-density lidar data for the Upper Mississippi River System to determine canopy height. An ArcGIS tool was developed to automatically derive height modifier information based on the extent of land cover features for forest classes. The measurement of canopy height included a calculation of the average height from lidar point cloud data as well as the inclusion of a local maximum filter to identify individual tree canopies. Results were compared to original manually interpreted height modifiers and to field survey data from U.S. Forest Service Forest Inventory and Analysis plots. This project demonstrated the effectiveness of utilizing lidar data to more efficiently assign height modifier attributes to LCU classifications produced by the UMESC.","language":"English","publisher":"University of Redlands","usgsCitation":"Hlavacek, E., 2015, Automated lidar-derived canopy height estimates for the Upper Mississippi River System, xviI, 70 p.","productDescription":"xviI, 70 p.","ipdsId":"IP-061233","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":340736,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":309736,"type":{"id":15,"text":"Index Page"},"url":"https://inspire.redlands.edu/gis_gradproj/218/"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.22875213623045,\n 43.57392416032963\n ],\n [\n -91.22188568115234,\n 43.6159452654277\n ],\n [\n -91.22222900390625,\n 43.645019610189216\n ],\n [\n -91.22085571289061,\n 43.6599240747891\n ],\n [\n -91.219482421875,\n 43.68574971761676\n ],\n [\n -91.20712280273438,\n 43.705608034103555\n ],\n [\n -91.20162963867188,\n 43.766135280960974\n ],\n [\n -91.22909545898436,\n 43.78299262890581\n ],\n [\n -91.25106811523438,\n 43.8028187190472\n ],\n [\n -91.24969482421875,\n 43.82660134505382\n ],\n [\n -91.26480102539062,\n 43.86720808597874\n ],\n [\n -91.27578735351561,\n 43.8909650585739\n ],\n [\n -91.33621215820312,\n 43.88502670347002\n ],\n [\n -91.30050659179688,\n 43.85631630786513\n ],\n [\n -91.30050659179688,\n 43.83947964604012\n ],\n [\n -91.307373046875,\n 43.823629034783124\n ],\n [\n -91.29364013671875,\n 43.81372026502735\n ],\n [\n -91.285400390625,\n 43.80876526350847\n ],\n [\n -91.29226684570312,\n 43.79588033566535\n ],\n [\n -91.28952026367188,\n 43.7859669617277\n ],\n [\n -91.28059387207031,\n 43.78447981381514\n ],\n [\n -91.263427734375,\n 43.7824968923812\n ],\n [\n -91.25518798828125,\n 43.77406874252176\n ],\n [\n -91.25724792480469,\n 43.75919263886012\n ],\n [\n -91.26274108886719,\n 43.73736766145917\n ],\n [\n -91.27372741699219,\n 43.71652730498859\n ],\n [\n -91.27372741699219,\n 43.69716905314008\n ],\n [\n -91.27922058105469,\n 43.68326696566219\n ],\n [\n -91.28059387207031,\n 43.67532146891089\n ],\n [\n -91.28196716308594,\n 43.667871610117494\n ],\n [\n -91.27922058105469,\n 43.64899449575356\n ],\n [\n -91.2744140625,\n 43.614205328810954\n ],\n [\n -91.27784729003906,\n 43.601278509333646\n ],\n [\n -91.28368377685545,\n 43.597797737126044\n ],\n [\n -91.28059387207031,\n 43.57591398669178\n ],\n [\n -91.27029418945312,\n 43.561486255932145\n ],\n [\n -91.23115539550781,\n 43.560491112629286\n ],\n [\n -91.22875213623045,\n 43.57392416032963\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59099aafe4b0fc4e44915800","contributors":{"authors":[{"text":"Hlavacek, Enrika 0000-0002-9872-2305 ehlavacek@usgs.gov","orcid":"https://orcid.org/0000-0002-9872-2305","contributorId":149114,"corporation":false,"usgs":true,"family":"Hlavacek","given":"Enrika","email":"ehlavacek@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":576947,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70189610,"text":"70189610 - 2015 - Validation of the SCEC broadband platform V14.3 simulation methods using pseudo spectral acceleration data","interactions":[],"lastModifiedDate":"2017-07-19T10:10:30","indexId":"70189610","displayToPublicDate":"2014-12-31T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Validation of the SCEC broadband platform V14.3 simulation methods using pseudo spectral acceleration data","docAbstract":"This paper summarizes the evaluation of ground motion simulation methods implemented on the SCEC Broadband Platform (BBP), version 14.3 (as of March 2014). A seven-member panel, the authorship of this article, was formed to evaluate those methods for the prediction of pseudo-‐spectral accelerations (PSAs) of ground motion. The panel’s mandate was to evaluate the methods using tools developed through the validation exercise (Goulet et al. ,2014), and to define validation metrics for the assessment of the methods’ performance. This paper summarizes the evaluation process and conclusions from the panel. The five broadband, finite-source simulation methods on the BBP include two deterministic approaches herein referred to as CSM (Anderson, 2014) and UCSB (Crempien and Archuleta, 2014); a band-‐limited stochastic white noise method called EXSIM (Atkinson and Assatourians, 2014); and two hybrid approaches, referred to as G&P (Graves and Pitarka, 2014) and SDSU (Olsen and Takedatsu, 2014), which utilize a deterministic Green’s function approach for periods longer than 1 second and stochastic methods for periods shorter than 1 second. \n\nTwo acceptance tests were defined to validate the broadband finite‐source ground methods (Goulet et al., 2014). Part A compared observed and simulated PSAs for periods from 0.01 to 10 seconds for 12 moderate to large earthquakes located in California, Japan, and the eastern US. Part B compared the median simulated PSAs to published NGA-‐West1 (Abrahamson and Silva, 2008; Boore and Atkinson, 2008; Campbell and Bozorgnia, 2008; and Chiou and Youngs, 2008) ground motion prediction equations (GMPEs) for specific magnitude and distance cases using a pass-‐fail criteria based on a defined acceptable range around the spectral shape of the GMPEs. For the initial Part A and Part B validation exercises during the summer of 2013, the software for the five methods was locked in at version 13.6 (see Maechling et al., 2014). In the spring of 2014, additional moderate events were considered for the Part A validation, and additional magnitude and distance cases were considered for the Part B validation, for the software locked in at version 14.3. Several of the simulation procedures, specifically UCSB and SDSU, changed significantly between versions 13.6 and 14.3. The CSM code was not submitted in time for the v14.3 evaluation and its detailed performance is not addressed in this paper. \n\nAs described in Goulet et al. (2014) and Maechling et al. (2014), the BBP generates a variety of products, including three-‐component acceleration time series. A series of post-‐processing codes were developed to provide individual component PSAs and average median horizontal-‐component PSA (referred to as RotD50; Boore, 2010) for oscillator periods ranging from 0.01 to 10 seconds, as well as median PSA values computed using the NGA-‐West 1 GMPEs. The BBP was also configured to provide statistical analysis of simulation results relative to recordings (Part A) and GMPEs (Part B) as described further in sections below. \n\nAs part of our evaluation, we reviewed documentation provided by each of the developers, which included the technical basis behind the methods and the developer’s self-‐assessments regarding the extrapolation capabilities (in terms of magnitude and distance ranges) of their methods. Two workshops were held in which methods and results were presented, and the panel was given the opportunity to question the developers and to have detailed technical discussions. A SCEC report (Dreger et al., 2013) describes the results of this review for BBP version 13.6. This paper summarizes that work and presents results for the more recent BBP 14.3 validation.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220140118","usgsCitation":"Dreger, D.S., Beroza, G.C., Day, S.M., Goulet, C.A., Jordan, T.H., Spudich, P.A., and Stewart, J.P., 2015, Validation of the SCEC broadband platform V14.3 simulation methods using pseudo spectral acceleration data: Seismological Research Letters, v. 86, no. 1, p. 39-47, https://doi.org/10.1785/0220140118.","productDescription":"9 p.","startPage":"39","endPage":"47","ipdsId":"IP-059822","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":344027,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"86","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2014-12-17","publicationStatus":"PW","scienceBaseUri":"59706fbae4b0d1f9f065a8db","contributors":{"authors":[{"text":"Dreger, Douglas S.","contributorId":55600,"corporation":false,"usgs":false,"family":"Dreger","given":"Douglas","email":"","middleInitial":"S.","affiliations":[{"id":6643,"text":"University of California - Berkeley","active":true,"usgs":false}],"preferred":false,"id":705405,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beroza, Gregory C.","contributorId":191201,"corporation":false,"usgs":false,"family":"Beroza","given":"Gregory","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":705406,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Day, Steven M.","contributorId":194804,"corporation":false,"usgs":false,"family":"Day","given":"Steven","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":705407,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goulet, Christine A. 0000-0002-7643-357X","orcid":"https://orcid.org/0000-0002-7643-357X","contributorId":194805,"corporation":false,"usgs":false,"family":"Goulet","given":"Christine","email":"","middleInitial":"A.","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":705408,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jordan, Thomas H","contributorId":194144,"corporation":false,"usgs":false,"family":"Jordan","given":"Thomas","email":"","middleInitial":"H","affiliations":[],"preferred":false,"id":705409,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spudich, Paul A. 0000-0002-9484-4997 spudich@usgs.gov","orcid":"https://orcid.org/0000-0002-9484-4997","contributorId":2372,"corporation":false,"usgs":true,"family":"Spudich","given":"Paul","email":"spudich@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":705404,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stewart, Jonathan P.","contributorId":100110,"corporation":false,"usgs":false,"family":"Stewart","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":7081,"text":"University of California - Los Angeles","active":true,"usgs":false}],"preferred":false,"id":705410,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}