Continuous measurements of sediment transport at reach-bracketing gaging stations allow for the construction of continuous mass-balance sediment budgets for the intervening reach. Although these budgets identify periods of sediment surplus (net deposition) or sediment deficit (net erosion), such analyses cannot identify the locations within the reach where channel change occurs. Because channel change and associated changes in habitat are of greater interest to river managers than the precise value of reach-scale loss or accumulation of sediment, it is important to explicitly link reach-scale changes in sediment mass balance to field measurements of channel change. In this study we will evaluate the relationship between the magnitude of the sediment mass imbalance measured by acoustic-Doppler profilers and the resulting channel change on the Yampa River in Dinosaur National Monument.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SEDHYD 2019","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD 2019 Conference","conferenceDate":"Jun 24-28, 2019","conferenceLocation":"Reno, NV","language":"English","publisher":"Federal Interagency Sedimentation Conference (FISC) and Federal Interagency Hydrologic Modeling Conference (FIHMC)","usgsCitation":"Leonard, C., Schmidt, J.C., Topping, D.J., and Griffiths, R.E., 2019, Interpreting flux-based sediment budgets in a habitat context: Linking precise temporal-resolution measurements of sediment flux to spatially robust characterization of channel change, in Proceedings of SEDHYD 2019, v. 1, Reno, NV, Jun 24-28, 2019, 7 p.","productDescription":"7 p.","ipdsId":"IP-106547","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":377826,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377714,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2019/#sedhyd-2019-proceedings"}],"volume":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Leonard, Christina","contributorId":195596,"corporation":false,"usgs":false,"family":"Leonard","given":"Christina","email":"","affiliations":[],"preferred":true,"id":796952,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmidt, John C.","contributorId":207751,"corporation":false,"usgs":false,"family":"Schmidt","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":37627,"text":"Department of Watershed Sciences, Utah State University, Logan, UT, USA","active":true,"usgs":false}],"preferred":false,"id":796953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Topping, David J. 0000-0002-2104-4577 dtopping@usgs.gov","orcid":"https://orcid.org/0000-0002-2104-4577","contributorId":140985,"corporation":false,"usgs":true,"family":"Topping","given":"David","email":"dtopping@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":796954,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Griffiths, Ronald E. 0000-0003-3620-2926 rgriffiths@usgs.gov","orcid":"https://orcid.org/0000-0003-3620-2926","contributorId":162,"corporation":false,"usgs":true,"family":"Griffiths","given":"Ronald","email":"rgriffiths@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":796955,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204481,"text":"70204481 - 2019 - Hawai‘i Groundwater Recharge Tool","interactions":[],"lastModifiedDate":"2019-12-03T09:36:08","indexId":"70204481","displayToPublicDate":"2019-06-30T09:32:07","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Hawai‘i Groundwater Recharge Tool","docAbstract":"The Hawai‘i Groundwater Recharge Tool allows users to evaluate the potential effects of land-cover and climate changes on groundwater recharge. This website provides a baseline estimate of recharge representing recent conditions of precipitation (1978–2008 average) and land cover (2010). Users can change land cover and rainfall conditions to evaluate the effects on groundwater recharge. Results will be displayed as on-screen maps, and users will be given options to generate interpretive graphics or export results in various formats. Results from this website are based on soil water-balance models. This is a pilot website that is currently limited to the island of O‘ahu, but the website has been designed to be expandable so that other islands and conditions can be added in the future.","language":"English","publisher":"University of Hawaii","usgsCitation":"McLean, J.H., Rotzoll, K., Cleaveland, S.B., and Izuka, S.K., 2019, Hawai‘i Groundwater Recharge Tool, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-104867","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":369857,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":365986,"rank":1,"type":{"id":18,"text":"Project Site"},"url":"https://recharge.ikewai.org/#/workspace"}],"country":"United States","state":"Hawaii","otherGeospatial":"Oahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.33496093749997,\n 21.210019282760218\n ],\n [\n -157.62359619140625,\n 21.210019282760218\n ],\n [\n -157.62359619140625,\n 21.728885873951494\n ],\n [\n -158.33496093749997,\n 21.728885873951494\n ],\n [\n -158.33496093749997,\n 21.210019282760218\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McLean, Jared H.","contributorId":217618,"corporation":false,"usgs":false,"family":"McLean","given":"Jared","email":"","middleInitial":"H.","affiliations":[{"id":37291,"text":"University of Hawaii at Hilo","active":true,"usgs":false}],"preferred":false,"id":767179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rotzoll, Kolja 0000-0002-5910-888X kolja@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-888X","contributorId":3325,"corporation":false,"usgs":true,"family":"Rotzoll","given":"Kolja","email":"kolja@usgs.gov","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":false,"id":767180,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cleaveland, Sean B.","contributorId":217619,"corporation":false,"usgs":false,"family":"Cleaveland","given":"Sean","email":"","middleInitial":"B.","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":767181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Izuka, Scot K. 0000-0002-8758-9414 skizuka@usgs.gov","orcid":"https://orcid.org/0000-0002-8758-9414","contributorId":2645,"corporation":false,"usgs":true,"family":"Izuka","given":"Scot","email":"skizuka@usgs.gov","middleInitial":"K.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":767178,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202422,"text":"70202422 - 2019 - Forecasts of coastal change hazards","interactions":[],"lastModifiedDate":"2019-12-05T08:27:47","indexId":"70202422","displayToPublicDate":"2019-06-30T08:27:37","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Forecasts of coastal change hazards","docAbstract":"Model predictions of severe storm impacts provide coastal residents, emergency managers, and partner organizations valuable predictive information for planning and response to extreme storm events. The foundation of this work is a USGS-developed numerical model to forecast storm-induced coastal water levels and expected coastal change, including dune erosion, overwash, and inundation. The model is operated in three modes: generalized scenarios, real-time storms, and an operational forecast, with each mode requiring slightly different water level inputs. To evaluate and improve the accuracy of the models, we collect data on water levels and coastal change. In particular, observations before, after, and during storm conditions are used to test the different model applications. Forecast validation for Hurricanes Matthew (2016) and Irma (2017) illustrate three cases with demonstrated forecast skill and three cases with poor skill, and reveal elements of the modeling and/or testing approach which require improvement.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Coastal Sediments 2019: Proceedings of the 9th international conference ","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Coastal Sediments 2019","conferenceDate":"May 27-31, 2019","conferenceLocation":"Tampa/St. Petersburg, FL","language":"English","publisher":"World Scientific","doi":"10.1142/9789811204487_0122","usgsCitation":"Doran, K.S., Stockdon, H.F., Joseph Long, and Plant, N.G., 2019, Forecasts of coastal change hazards, in Coastal Sediments 2019: Proceedings of the 9th international conference , Tampa/St. Petersburg, FL, May 27-31, 2019, p. 1400-1409, https://doi.org/10.1142/9789811204487_0122.","productDescription":"10 p.","startPage":"1400","endPage":"1409","ipdsId":"IP-105870","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":369944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Doran, Kara S. 0000-0001-8050-5727 kdoran@usgs.gov","orcid":"https://orcid.org/0000-0001-8050-5727","contributorId":148059,"corporation":false,"usgs":true,"family":"Doran","given":"Kara","email":"kdoran@usgs.gov","middleInitial":"S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":758393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stockdon, Hilary F. 0000-0003-0791-4676 hstockdon@usgs.gov","orcid":"https://orcid.org/0000-0003-0791-4676","contributorId":2153,"corporation":false,"usgs":true,"family":"Stockdon","given":"Hilary","email":"hstockdon@usgs.gov","middleInitial":"F.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":758394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Joseph Long","contributorId":213744,"corporation":false,"usgs":false,"family":"Joseph Long","affiliations":[{"id":38846,"text":"UNC Wilmington","active":true,"usgs":false}],"preferred":false,"id":758395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":758396,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205847,"text":"70205847 - 2019 - Consistency counts: Modeling the effects of a change in protocol on Breeding Bird Survey counts","interactions":[],"lastModifiedDate":"2019-10-21T14:40:37","indexId":"70205847","displayToPublicDate":"2019-06-29T12:59:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Consistency counts: Modeling the effects of a change in protocol on Breeding Bird Survey counts","docAbstract":"Analysis of North American Breeding Bird Survey (BBS) data requires controls for factors that influence detectability of birds along survey routes. Identifying factors that influence the counting process and incorporating them into analyses is a primary means of limiting bias in estimates of population change. Twedt (2015) implemented an alternative counting protocol on operational and non-random BBS survey routes in the southeastern United States. Observers on selected routes employed a time-distance protocol in which they recorded birds in 1-minute intervals and in 2 distance categories. We hypothesized that processing and recording observations using this time-distance protocol could cause observers to count fewer birds relative to observers using the standard protocol. We used a hierarchical log-linear model with a categorical covariate associated with protocol (standard vs time-distance) to assess whether use of the time-distance protocol had a measurable effect on counting birds along BBS routes. We applied this model to BBS data from portions of eight states in which the time-distance protocol was implemented and estimated a protocol effect for 167 bird species. We documented a significant overall effect of the time-distance protocol on observers’ counts of birds. On average, the effect of the time-distance protocol was a 10% decline in count, and 80% of species had lower counts when the time-distance protocol was used on a survey route. However, because the time-distance protocol was only used on a small portion of the operational BBS routes and for a limited time, including the covariate for the time-distance protocol data had insignificant effects on analysis of population change. Although the covariate controlled for the effects of the time-distance protocol in BBS data, the results emphasize the importance of standardization as well as a need to track and, if necessary, control in analyses for changes in counting procedures along BBS routes.","language":"English","publisher":"Oxford Academic","doi":"10.1093/condor/duz009","usgsCitation":"Sauer, J.R., Link, W.A., Ziolkowski, D., Pardieck, K.L., and Twedt, D.J., 2019, Consistency counts: Modeling the effects of a change in protocol on Breeding Bird Survey counts: The Condor, v. 121, no. 2, duz009, 12 p., https://doi.org/10.1093/condor/duz009.","productDescription":"duz009, 12 p.","ipdsId":"IP-081193","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":467495,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/condor/duz009","text":"Publisher Index Page"},{"id":368104,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"121","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Sauer, John R. 0000-0002-4557-3019 jrsauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4557-3019","contributorId":146917,"corporation":false,"usgs":true,"family":"Sauer","given":"John","email":"jrsauer@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":772603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Link, William A. 0000-0002-9913-0256 wlink@usgs.gov","orcid":"https://orcid.org/0000-0002-9913-0256","contributorId":146920,"corporation":false,"usgs":true,"family":"Link","given":"William","email":"wlink@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":773533,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ziolkowski, David 0000-0002-2500-4417 dziolkowski@usgs.gov","orcid":"https://orcid.org/0000-0002-2500-4417","contributorId":195409,"corporation":false,"usgs":true,"family":"Ziolkowski","given":"David","email":"dziolkowski@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":773534,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pardieck, Keith L. 0000-0003-2779-4392 kpardieck@usgs.gov","orcid":"https://orcid.org/0000-0003-2779-4392","contributorId":4104,"corporation":false,"usgs":true,"family":"Pardieck","given":"Keith","email":"kpardieck@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":773535,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Twedt, Daniel J. 0000-0003-1223-5045 dtwedt@usgs.gov","orcid":"https://orcid.org/0000-0003-1223-5045","contributorId":398,"corporation":false,"usgs":true,"family":"Twedt","given":"Daniel","email":"dtwedt@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":773536,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70204354,"text":"70204354 - 2019 - Differentiating anthropogenic and natural sources of uranium by geochemical fingerprinting of groundwater at the Homestake Uranium Mill, Milan, New Mexico, USA","interactions":[],"lastModifiedDate":"2019-12-22T14:35:26","indexId":"70204354","displayToPublicDate":"2019-06-29T07:37:38","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1534,"text":"Environmental Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Differentiating anthropogenic and natural sources of uranium by geochemical fingerprinting of groundwater at the Homestake Uranium Mill, Milan, New Mexico, USA","docAbstract":"A multiparameter geochemical-isotopic fingerprinting approach was used to differentiate natural and anthropogenic signatures of uranium contamination near the Homestake uranium mill site (Site), near Milan, New Mexico, USA. The Site consists of two tailings piles from milling operations and groundwater contamination from these tailings have been noted. The Site lies within the lower San Mateo Creek Basin with multiple regional sources of U contamination from mining and mill operations and is underlain by a heterogeneous alluvial aquifer, which is underlain by basement rock of the Chinle Group and the lowermost San Andres-Glorieta aquifer. To help decipher signatures, several statistical approaches were used including PCA, NMDS, and cluster analysis.\nTrilinear piper diagrams indicate two end member water types at the Site, sulfate-Na-K and sulfate-Ca. Natural alluvial aquifer groundwater in this area, relatively unaffected by mining or milling, appears to be more dominated by bicarbonate than sulfate and the deeper San Andres-Glorieta aquifer that has a mixture of sulfate and bicarbonate. Uranium concentrations from the Site fall into three broad categories, less than the drinking water standard of 30 µg/L (n=3), from 30 to 100 µg/L (n=9) and greater than 100 µg/L (n=8). Component loadings in a principal component analysis are highest for uranium isotopes, 228Ra, gross alpha-beta, molybdenum, chloride, uranium, and sodium, which affect the similarities or differences among wells sampled. Results suggest that several alluvial wells upgradient from the Site have anthropogenic fingerprints from regional sources related to upgradient mining. Wells with higher uranium concentrations have uranium activity ratios close to 1, which is indicative of mining or milling signatures. These same wells have elevated radon activities. This information can be used to inform Site managers on the source of water related to uranium at the Site and provide an approach for geochemical fingerprinting.","language":"English","publisher":"Springer","doi":"10.1007/s12665-019-8385-y","usgsCitation":"Blake, J., Harte, P., and Becher, K., 2019, Differentiating anthropogenic and natural sources of uranium by geochemical fingerprinting of groundwater at the Homestake Uranium Mill, Milan, New Mexico, USA: Environmental Earth Sciences, v. 78, no. 384, https://doi.org/10.1007/s12665-019-8385-y.","ipdsId":"IP-085089","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":365731,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","city":"Milan","otherGeospatial":"San Mateo Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.19061279296875,\n 35.007502842952896\n ],\n [\n -107.47650146484374,\n 35.007502842952896\n ],\n [\n -107.47650146484374,\n 35.39800594715108\n ],\n [\n -108.19061279296875,\n 35.39800594715108\n ],\n [\n -108.19061279296875,\n 35.007502842952896\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"78","issue":"384","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Blake, Johanna 0000-0003-4667-0096","orcid":"https://orcid.org/0000-0003-4667-0096","contributorId":217272,"corporation":false,"usgs":true,"family":"Blake","given":"Johanna","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harte, Philip T. 0000-0002-7718-1204","orcid":"https://orcid.org/0000-0002-7718-1204","contributorId":217273,"corporation":false,"usgs":true,"family":"Harte","given":"Philip","middleInitial":"T.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766489,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Becher, Kent 0000-0002-3947-0793","orcid":"https://orcid.org/0000-0002-3947-0793","contributorId":217274,"corporation":false,"usgs":true,"family":"Becher","given":"Kent","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766490,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70203473,"text":"ofr20191056 - 2019 - Optimization of salt marsh management at the Chincoteague National Wildlife Refuge, Virginia, through use of structured decision making","interactions":[],"lastModifiedDate":"2024-03-04T18:44:10.106398","indexId":"ofr20191056","displayToPublicDate":"2019-06-28T13:45:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1056","displayTitle":"Optimization of Salt Marsh Management at the Chincoteague National Wildlife Refuge, Virginia, Through Use of Structured Decision Making","title":"Optimization of salt marsh management at the Chincoteague National Wildlife Refuge, Virginia, through use of structured decision making","docAbstract":"Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing salt marsh management decisions at the Chincoteague National Wildlife Refuge in Virginia. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of 12 salt marsh management units within the refuge and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that would be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per salt marsh management unit, that would maximize total management benefits at different cost constraints at the refuge scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to approximately $2.5 million, but that further expenditures may yield diminishing return on investment. For multiple salt marsh management units, a scenario incorporating managing grazing practices within the marsh was selected to maximize benefits while constraining total costs for the refuge at less than $2.5 million. Thin-layer deposition was predicted to increase the total management benefit substantially, but at considerable total costs ($2.5 million to $83 million). The prototype presented here provides a framework for decision making at the Chincoteague National Wildlife Refuge that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191056","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., and Holcomb, K.S., 2019, Optimization of salt marsh management at the Chincoteague National Wildlife Refuge, Virginia, through use of structured decision making: U.S. Geological Survey Open-File Report 2019–1056, 29 p., https://doi.org/10.3133/ofr20191056.","productDescription":"vi, 29 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101219","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":364633,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1056/coverthb.jpg"},{"id":364634,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1056/ofr20191056.pdf","text":"Report","size":"2.60 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1056"}],"country":"United States","state":"Virginia","otherGeospatial":"Chincoteague Island, Chincoteague National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.38440704345703,\n 37.91278405007035\n ],\n [\n -75.3830337524414,\n 37.916169765380076\n ],\n [\n -75.37445068359375,\n 37.917117737741826\n ],\n [\n -75.37273406982422,\n 37.916576040745376\n ],\n [\n -75.36895751953125,\n 37.91468006984207\n ],\n [\n -75.36449432373047,\n 37.91562806140234\n ],\n [\n -75.35608291625977,\n 37.91562806140234\n ],\n [\n -75.35076141357422,\n 37.91684688974227\n ],\n [\n -75.34818649291992,\n 37.920367835943516\n ],\n [\n -75.34629821777344,\n 37.924565666890146\n ],\n [\n -75.34406661987303,\n 37.9276800317787\n ],\n [\n -75.34046173095703,\n 37.936074619194514\n ],\n [\n -75.33823013305664,\n 37.93945926228315\n ],\n [\n -75.34475326538086,\n 37.94094845586459\n ],\n [\n -75.34612655639648,\n 37.947040297194114\n ],\n [\n -75.34114837646484,\n 37.953673061162604\n ],\n [\n -75.33102035522461,\n 37.958681077955525\n ],\n [\n -75.32638549804688,\n 37.958004338883114\n ],\n [\n -75.32655715942383,\n 37.96720745598712\n ],\n [\n -75.31213760375975,\n 37.98141588591056\n ],\n [\n -75.31848907470703,\n 37.983310135428795\n ],\n [\n -75.3219223022461,\n 37.98019812825676\n ],\n [\n -75.32432556152342,\n 37.98182180063798\n ],\n [\n -75.32827377319335,\n 37.982904228934125\n ],\n [\n -75.33102035522461,\n 37.98141588591056\n ],\n [\n -75.33187866210936,\n 37.97153793552019\n ],\n [\n -75.33308029174805,\n 37.968966744104264\n ],\n [\n -75.33565521240234,\n 37.96829009981737\n ],\n [\n -75.34097671508789,\n 37.9630120603577\n ],\n [\n -75.34664154052734,\n 37.959493156611366\n ],\n [\n -75.35642623901367,\n 37.953266990781884\n ],\n [\n -75.36518096923828,\n 37.94568659832042\n ],\n [\n -75.36792755126953,\n 37.942166864531906\n ],\n [\n -75.36672592163085,\n 37.94798787156644\n ],\n [\n -75.3691291809082,\n 37.94988298364895\n ],\n [\n -75.37359237670898,\n 37.94825860485678\n ],\n [\n -75.37445068359375,\n 37.944874367025136\n ],\n [\n -75.37273406982422,\n 37.94162535206254\n ],\n [\n -75.37256240844727,\n 37.93851157792925\n ],\n [\n -75.377197265625,\n 37.935262281661146\n ],\n [\n -75.38389205932617,\n 37.932554425046405\n ],\n [\n -75.39127349853516,\n 37.92402402474885\n ],\n [\n -75.39505004882812,\n 37.916305190751174\n ],\n [\n -75.40895462036133,\n 37.90492859043573\n ],\n [\n -75.40672302246094,\n 37.89937508713545\n ],\n [\n -75.40157318115234,\n 37.898020510564386\n ],\n [\n -75.39573669433594,\n 37.89896871678171\n ],\n [\n -75.39281845092773,\n 37.899781455245545\n ],\n [\n -75.38818359375,\n 37.89910417381559\n ],\n [\n -75.38320541381836,\n 37.90072963877702\n ],\n [\n -75.38114547729492,\n 37.90398046100267\n ],\n [\n -75.3823471069336,\n 37.90858554667319\n ],\n [\n -75.38440704345703,\n 37.91115885137137\n ],\n [\n -75.38440704345703,\n 37.91278405007035\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708
Dissolved selenium is a contaminant of concern in the lower Gunnison River Basin, Colorado. Selenium is naturally present in the Cretaceous Mancos Shale and is leached to groundwater and surface water by irrigation. The groundwater on the east side of the Uncompahgre River in Delta and Montrose Counties is one of the primary sources of selenium concentration and load to surface water in the lower Gunnison River Basin. Although little information about the contribution of groundwater to surface water has been historically available, groundwater has often been implicated as an appreciable source of selenium to surface water. From 2012 to 2016, the U.S. Geological Survey, in cooperation with the Bureau of Reclamation, the Colorado Water Conservation Board, and the Gunnison Basin Selenium Management Program, established a 30-well groundwater-monitoring network on irrigated land to characterize the hydrology and groundwater quality of the shallow groundwater system on the east side of the Uncompahgre River in the lower Gunnison River Basin. The installation of the 30-well network and the data collected allowed for the development of a conceptual model of selenium mobilization and transport in the shallow groundwater system. Monitoring wells were completed in surficial deposits and in weathered Mancos Shale, which generally exhibited unconfined and confined conditions, respectively. Groundwater-quality monitoring provides information on the distribution of selenium and the geochemical processes controlling selenium concentrations in shallow groundwater. Monitoring wells were sampled between August 2013 and March 2015 to understand groundwater quality, seasonality, sources of recharge, and groundwater age. Concentrations of dissolved selenium ranged from below the limit of detection to 4,100 micrograms per liter (µg/L), with a median concentration of 14 µg/L. Concentrations showed a high degree of spatial variability and no seasonal difference. Similarly, no seasonal pattern was observed in specific conductance values of groundwater despite the considerably lower specific conductance value of irrigation water.
Reduction-oxidation processes are important controls on selenium mobility. Nitrate derived from geologic material was a primary control on reduction-oxidation conditions in groundwater and inhibited selenium reduction to less mobile forms. Nitrate was reduced by denitrification in groundwater, but it was not reduced to the extent necessary to allow for selenium reduction. Groundwater ages were determined for groundwater samples from eight wells and ranged from 6 to 20 years old. Isotopic data indicate groundwater was recharged by irrigation water; no information collected supported an older, deeper source of recharge to the shallow groundwater system. Data on water level in all wells showed response to irrigation practices, but the response was delayed in some wells, which may be an indication of distance from recharge source.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20195029","collaboration":"Prepared in cooperation with the Bureau of Reclamation, the Colorado Water Conservation Board, and the Gunnison Basin Selenium Management Program","usgsCitation":"Thomas, J.C., McMahon, P.B., and Arnold, L.R., 2019, Groundwater quality and hydrology with emphasis on selenium mobilization and transport in the lower Gunnison River Basin, Colorado, 2012–16: U.S. Geological Survey Scientific Investigations Report 2019–5029, 69 p., https://doi.org/10.3133/sir20195029.","productDescription":"viii, 69 p.","onlineOnly":"Y","ipdsId":"IP-084069","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":365132,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5029/coverthb.jpg"},{"id":365133,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5029/sir20195029.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5029"}],"country":"United States","state":"Colorado","otherGeospatial":"Lower Gunnison River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.80584716796875,\n 39.01064750994083\n ],\n [\n -109.11895751953125,\n 38.8782049970615\n ],\n [\n -108.6328125,\n 38.10214399750345\n ],\n [\n -108.69598388671875,\n 37.77288579232439\n ],\n [\n -107.87750244140625,\n 37.309014074275915\n ],\n [\n -107.4462890625,\n 37.31338308990806\n ],\n [\n -107.1441650390625,\n 37.727280276860036\n ],\n [\n -107.18536376953125,\n 38.07620357665235\n ],\n [\n -107.26776123046875,\n 38.50304202775689\n ],\n [\n -107.50671386718749,\n 38.9380483825641\n ],\n [\n -107.6495361328125,\n 39.115144700901475\n ],\n [\n -108.80584716796875,\n 39.01064750994083\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
Infections from antibiotic resistant microorganisms are considered to be one of the greatest global public health challenges that result in huge annual economic losses. While genes that impart resistance to antibiotics (AbR) existed long before the discovery and use of antibiotics, anthropogenic uses of antibiotics in agriculture, domesticated animals, and humans are known to influence the prevalence of these genes in pathogenic microorganisms. It is critical to understand the role that natural and anthropogenic processes have on the occurrence and distribution of antibiotic resistance in microbial populations to minimize health risks associated with exposures. As part of this research, 15 antibiotic resistance genes were analyzed in coastal sediments and soils along the eastern seaboard of the USA using presence/absence quantitative and digital polymerase chain reaction assays. Samples (53 soil and 192 sediment samples including 54 replicates) were collected from a variety of coastal settings where human and wildlife exposure is likely. At least one of the antibiotic resistance genes was detected in 76.4% of the samples. Samples that contained at least five or more antibiotic resistance genes (5.7%) where typically hydrologically down gradient of watersheds influenced by combined sewer outfalls (CSO). The most frequently detected antibiotic resistance target genes were found in 33.2%, 34.4%, and 42.2% of samples (target genes blaSHV, tetO, and aadA2, respectively). These data provide unique insight into potential exposure of AbR genes over a large geographical region of the eastern seaboard of the USA.
","language":"English","publisher":"Springer International Publishing","doi":"10.1007/s10661-019-7426-z","usgsCitation":"Griffin, D.W., Benzel, W., Fisher, S.C., Focazio, M.J., Iwanowicz, L.R., Loftin, K., Reilly, T.J., and Jones, D.K., 2019, The presence of antibiotic resistance genes in coastal soil and sediment samples from the eastern seaboard of the USA: Environmental Monitoring and Assessment, v. 19, no. 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Northern Spotted Owls in Washington and Oregon—2018 Progress Report","interactions":[],"lastModifiedDate":"2019-07-01T09:36:40","indexId":"ofr20191074","displayToPublicDate":"2019-06-28T10:34:46","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1074","displayTitle":"Effects of Barred Owl (Strix varia) Removal on Population Demography of Northern Spotted Owls (Strix occidentalis caurina) in Washington and Oregon, 2015–18","title":"Effects of experimental removal of Barred Owls on population Demography of Northern Spotted Owls in Washington and Oregon—2018 Progress Report","docAbstract":"Populations of Northern Spotted Owls (Strix occidentalis caurina; herineafter referred to as Spotted Owl) have declined throughout the subspecies’ geographic range. Evidence indicates that competition with invading Barred Owls (S. varia) has contributed significantly to those declines. A pilot study in California showed that removal of Barred Owls coupled with conservation of suitable habitat conditions can slow or even reverse population declines of Spotted Owls. It is unknown, however, whether similar results can be obtained in areas with different forest conditions, greater densities of Barred Owls, and fewer remaining Spotted Owls. We initiated a before-after-control-impact (BACI) experiment at three study areas in Oregon and Washington to determine if removal of Barred Owls can improve population trends of Spotted Owls. This report describes research accomplishments and initial results from the first 3.5 years of the study (March 2015–August 2018).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191074","usgsCitation":"Wiens, J.D., Dugger, K.M., Lesmeister, D.B., Dilione, K.E., and Simon, D.C., 2019, Effects of Barred Owl (Strix varia) removal on population demography of Northern Spotted Owls (Strix occidentalis caurina) in Washington and Oregon, 2015–18: U.S. Geological Survey Open-File Report 2019-1074, 17 p., https://doi.org/10.3133/ofr20191074.","productDescription":"vi, 17 p.","onlineOnly":"Y","ipdsId":"IP-108218","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":365174,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1074/coverthb.jpg"},{"id":365175,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1074/ofr20191074.pdf","text":"Report","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1074"}],"country":"United States","state":"Oregon, Washington","city":"Cle Elum","otherGeospatial":"Oregon Coast Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.9093017578125,\n 43.79885402720353\n ],\n [\n -122.3382568359375,\n 43.79885402720353\n ],\n [\n -122.3382568359375,\n 45.41773242370463\n ],\n [\n -123.9093017578125,\n 45.41773242370463\n ],\n [\n -123.9093017578125,\n 43.79885402720353\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.991943359375,\n 42.532844281713125\n ],\n [\n -120.86608886718749,\n 42.532844281713125\n ],\n [\n -120.86608886718749,\n 43.55252937447483\n ],\n [\n -122.991943359375,\n 43.55252937447483\n ],\n [\n -122.991943359375,\n 42.532844281713125\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.37695312499999,\n 46.619261036171515\n ],\n [\n -120.14648437499999,\n 46.619261036171515\n ],\n [\n -120.14648437499999,\n 47.73932336136857\n ],\n [\n -121.37695312499999,\n 47.73932336136857\n ],\n [\n -121.37695312499999,\n 46.619261036171515\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
The carbon dioxide (CO2) storage capacity of saline formations may be constrained by reservoir pressure limitations. Brine extraction could be necessary to increase the CO2 storage capacity of a given formation, manage the extent of the underground CO2 plume and induced pressure front, and control the migration direction. To estimate the additional CO2 storage capacity of a saline formation that can be made accessible by extraction of in-situ brines, a three-dimensional (3D) generic cubic cell containing one CO2 injector in the middle surrounded by four brine extractors at each corner of the cell was assumed. A series of Tough2-ECO2N reservoir simulations were constructed with varying reservoir properties and run. Based on a series of scenarios, a mechanism was developed and demonstrated that resulted in derivation of a function to provide estimates of the ratio of total CO2 injection over the brine extraction rate for a given scenario. We selected multiple saline formations in U.S. basins and evaluated the potential to increase the combined dynamic CO2 storage capacity of the selected saline formations to over 1000 million metric tonnes per year (Mt/yr) of CO2 for 100 years by means of brine extraction. Such storage capacities may be adequate to accommodate the CO2 injection rates suggested for the United States under a “beyond two-degree Celsius scenario” (B2DS) that has been proposed to maintain global temperature rise to less than 2°C above pre-industrial reported levels. The results suggest that B2DS goals could be achieved with a volume ratio of brine extraction to CO2injection as low as 1:4, which is far lower than the ratios that have been commonly assumed in the literature.
","language":"English","publisherLocation":"Elsevier","doi":"10.1016/j.ijggc.2019.06.009","usgsCitation":"Jahediesfanjani, H., Anderson, S.T., and Warwick, P., 2019, Improving pressure-limited CO2 storage capacity in saline formations by means of brine extraction: International Journal of Greenhouse Gas Control, v. 88, p. 299-310, https://doi.org/10.1016/j.ijggc.2019.06.009.","productDescription":"12 p.","startPage":"299","endPage":"310","ipdsId":"IP-104245","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":467496,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijggc.2019.06.009","text":"Publisher Index Page"},{"id":365243,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"88","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jahediesfanjani, Hossein 0000-0001-6281-5166","orcid":"https://orcid.org/0000-0001-6281-5166","contributorId":201000,"corporation":false,"usgs":false,"family":"Jahediesfanjani","given":"Hossein","affiliations":[],"preferred":false,"id":765279,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Steven T. 0000-0003-3481-3424 sanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-3481-3424","contributorId":2532,"corporation":false,"usgs":true,"family":"Anderson","given":"Steven","email":"sanderson@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":765280,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warwick, Peter D. 0000-0002-3152-7783","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":205928,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":765278,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227763,"text":"70227763 - 2019 - Nonlethal detection of Asian fish tapeworm in the federally-endangered Humpback Chub using a molecular screening tool","interactions":[],"lastModifiedDate":"2022-01-28T13:04:18.386073","indexId":"70227763","displayToPublicDate":"2019-06-28T07:02:11","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Nonlethal detection of Asian fish tapeworm in the federally-endangered Humpback Chub using a molecular screening tool","docAbstract":"Optimal spawning habitat of federally endangered Humpback Chub Gila cypha exists within the Little Colorado River; however, temperatures in the Little Colorado River are also ideal for proliferation of the invasive pathogenic Asian fish tapeworm Schyzocotyle acheilognathi. The current standard for positive identification of the parasite is necropsy and visual examination of the gut via microscopy, a methodology undesirable for assessing infection in endangered fishes. A swab taken at the rectum of the fish and analyzed using a novel DNA primer targeting the cestode cytochrome oxidase I subunit mtDNA gene region offers a convenient nonlethal sampling tool. To validate the nonlethal methodology, primer sensitivity (51.9%) and specificity (79.5%) were calculated by exposing captive fish to the parasite and comparing results using the lethal standard method (i.e., visual examination). Subsequently, we utilized this nonlethal methodology to address prevalence of infection and infection frequency across a range of size-classes in a wild population of Humpback Chub and addressed possible patterns and mechanisms of infection in the Little Colorado River. When wild Humpback Chub were screened for infection prevalence in spring 2015 (21.4%, n = 140) and fall 2015 (6.6%, n = 258), the relative frequency of infection was highest in juveniles and subadult fish (200–300 mm). Elevated levels of infection near the major spawning grounds of Humpback Chub in the Little Colorado River promote parasitic infection, which may continue to persist without treatment or actions to control infections. We demonstrated that rectal swabs, in conjunction with PCR and primer-specific identification, offer a time-efficient nonlethal method to detect infection of Asian fish tapeworm in an endangered fish.
In February 2017, the Grand River Dam Authority filed to relicense the Pensacola Hydroelectric Project with the Federal Energy Regulatory Commission. The predominant feature of the Pensacola Hydroelectric Project is Pensacola Dam, which impounds Grand Lake O’ the Cherokees (locally called Grand Lake) in northeastern Oklahoma. Identification of information gaps and assessment of project effects on stakeholders are central aspects of the Federal Energy Regulatory Commission relicensing process. Due to the natural changes to the reservoir over time, new capacity and area tables are needed periodically. The most recent complete capacity and area table was produced in 1940. Capacity and area tables identify the relations between the elevation of the water surface and the volume of water that can be impounded at each water surface elevation. This report (1) presents an updated capacity and area table for Grand Lake O’ the Cherokees for 2009, (2) describes the methods used to calculate the updated capacity and area values presented in the table, and (3) compares the updated capacity table to historical capacity tables produced from a survey in 1940 and from a hydrographic survey of the lake by the Oklahoma Water Resources Board in 2009.
The new capacity values computed for Grand Lake O’ the Cherokees indicate that capacity at conservation pool elevation has decreased about 157,000 acre-feet or 10 percent since 1940 and capacity at top of dam elevation has decreased about 200,000 acre-feet or 8 percent since 1940. This difference in the capacities could be attributed to the advancements of technologies; the techniques used for surveying lakes have changed from the 1940 survey to the 2009 survey. Another possible reason for loss in capacity could be as time progresses, lakes like Grand Lake O’ the Cherokees slowly impound sediment carried by the rivers that feed into the lakes, thus diminishing the amount of water that the lake holds. The most recent survey used measured water depths and Global Position System collected electronically, but the methods used to collect data in 1940 are unknown. Due to the advancement of technology, the 2009 survey is likely more precise than the 1940 survey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195040","collaboration":"Prepared in cooperation with the Grand River Dam Authority","usgsCitation":"Hunter, S.L., and Labriola, L.G., 2019, Capacity and area of Grand Lake O’ the Cherokees, northeastern Oklahoma, 2009: U.S. Geological Survey Scientific Investigations Report 2019–5040, 18 p., https://doi.org/10.3133/sir20195040.","productDescription":"Report: vi, 18 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-104220","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":365105,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VDBFWJ","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"Data release for capacity and area of Grand Lake O’ the Cherokees, northeastern Oklahoma, 2009"},{"id":365103,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5040/coverthb.jpg"},{"id":365104,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5040/sir20195040.pdf","text":"Report","size":"12.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5040"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Grand Lake O’ the Cherokees","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.79141235351561,\n 36.832370801556834\n ],\n [\n -94.81475830078124,\n 36.771892444961026\n ],\n [\n -94.80926513671875,\n 36.705861603381145\n ],\n [\n -94.86007690429688,\n 36.66952043455806\n ],\n [\n -94.910888671875,\n 36.691547435472636\n ],\n [\n -95.08941650390625,\n 36.485348924361425\n ],\n [\n -95.0592041015625,\n 36.45000844447082\n ],\n [\n -94.94522094726562,\n 36.48093224547937\n ],\n [\n -94.89715576171875,\n 36.45000844447082\n ],\n [\n -94.85733032226562,\n 36.465471886798134\n ],\n [\n -94.89715576171875,\n 36.518465989675875\n ],\n [\n -94.87518310546875,\n 36.53612263184686\n ],\n [\n -94.85183715820312,\n 36.516258626036624\n ],\n [\n -94.79141235351561,\n 36.52839834681223\n ],\n [\n -94.72686767578125,\n 36.54163950596125\n ],\n [\n -94.7515869140625,\n 36.59127365634205\n ],\n [\n -94.73648071289061,\n 36.62103883480288\n ],\n [\n -94.63485717773438,\n 36.64638529597495\n ],\n [\n -94.69253540039062,\n 36.76639204454785\n ],\n [\n -94.70489501953125,\n 36.82247761166621\n ],\n [\n -94.79141235351561,\n 36.832370801556834\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma Water Science Center
U.S. Geological Survey
202 NW 66th Street, Building 7
Oklahoma City, OK 73116
The Stochastic Empirical Loading and Dilution Model (SELDM) was developed by the U.S. Geological Survey (USGS) in cooperation with the Federal Highway Administration to simulate stormwater quality. To assess the effects of runoff, SELDM uses a stochastic mass-balance approach to estimate combinations of pre-storm streamflow, stormflow, highway runoff, event mean concentrations (EMCs) and stormwater constituent loads from a site of interest. In addition, SELDM can be used to assess the effects of stormwater Best Management Practices (BMPs), which are designed to mitigate the adverse effects of runoff into a waterbody.
Adverse effects of stormwater on receiving waters are one of the greatest unsolved water-quality problems Nationwide. State DOTs, municipalities, Federal facilities, and private property owners who manage impervious surfaces need information about the potential magnitude of their contributions and the potential effectiveness of methods to mitigate the adverse effects of runoff. Because the efficacy of at-site controls are limited, information about the potential effectiveness of alternative strategies is needed.
The USGS, in cooperation with the Oregon Department of Transportation (ODOT), conducted a study to research methods in which SELDM can be used to enhance the efficiency of ODOT’s stormwater program, support the development of a stormwater banking program, and meet environmental goals. Results can be used to develop a strategic, systems-level approach to stormwater management by considering entire watersheds instead of individual road crossings. Two watersheds, Bear Creek and Mill Creek, in western Oregon were selected for analysis. Within each watershed, seven road crossings were selected for demonstrating the utility of SELDM in nested basins.
Precipitation statistics, pre-storm streamflow, runoff coefficients, and hydrograph recession factors were calculated for each location and used in SELDM to simulate flow, water-quality concentrations, and constituent loads in the upstream basin, from the highway (or developed area), and downstream from the road crossing. Three water-quality constituents were selected for modeling: suspended-sediment concentration (SSC), total phosphorus (TP), and total copper (TCu). Using water-quality transport curves, the relations between streamflow and SSC and between streamflow and TP were simulated. Concentrations of TCu were simulated by configuring a linear relation between SSC and TCu. A generic BMP was simulated using the median treatment statistics for flow reductions, hydrograph extensions, concentration reductions, and minimum irreducible concentrations from nine BMP categories with data from the 2012 International BMP database.
Five simulation scenarios were modeled for demonstrative purposes. These simulations were used to evaluate potential effects of different watershed properties, water-quality inputs, and stormwater mitigation measures. Instream EMCs were compared to hypothetical water-quality criteria for suspended sediment, total phosphorus, and total copper to demonstrate the concept of water-quality risk analysis. For all five scenarios, it was assumed that highway runoff concentrations were independent of location or average annual daily traffic. These five scenarios are as follows:
• Simulation Scenario 1—Natural Conditions (hereafter Simulation Scenario 1) represents conditions in an undeveloped watershed. This scenario demonstrates that the strategic placement of a hypothetical road crossing within a watershed could be used to avoid exceeding water-quality standards of TP and SSC, but that no location choice results in meeting TCu standards. Implementation of BMP had the most pronounced effects on downstream water-quality constituent EMCs at road crossings with the highest ratio of highway catchment area to upstream drainage area, but the largest effect of BMP treatment on mean annual load is based on highway catchment area alone.
• Simulation Scenario 2—Current Conditions (hereafter Simulation Scenario 2) represents current watershed conditions, where all developed area upstream from the road crossing was modeled as a highway and combined with the undeveloped part of the upstream drainage area (scenario 2A) and where the output from scenario 2A is used for the upstream area (developed area and the undeveloped area), and where the road crossing is added as usual (scenario 2B). Scenario 2 results indicate that attaining water-quality standards is more difficult with upstream developed areas. Specific road-crossing sites can be selected to achieve the fewest water-quality exceedances per year, but water-quality targets are not met without BMP implementation, and in some instances are not achievable even with BMP implementation. Results from this scenario also serve to quantify the upper limit of constituent reduction if funding were available to implement BMPs to large areas of development, and to quantify how much area would need BMP implementation to achieve water-quality targets.
• Simulation Scenario 3—Alternative Road Layouts (hereafter Simulation Scenario 3) was designed to assess the sensitivity of SELDM to various road layouts. In this scenario, different highway configurations were superimposed at one road crossing. Results indicate that downstream waterquality constituent EMCs did not exhibit much variation, but annual water-quality constituent loads varied considerably.
• Simulation Scenario 4—Varying Road Width (hereafter Simulation Scenario 4) was designed to assess the sensitivity of SELDM to road width. Similar to scenario 3, the results indicate little variation in downstream water-quality constituent EMCs, but annual water-quality constituent loads increased in proportion to road width.
• Simulation scenario 5—Changes to Impervious Area (hereafter Simulation Scenario 5) was designed to investigate the effects of changing amounts of imperviousness upstream from the road crossing. Results indicate that the downstream water-quality constituent EMCs are highly correlated with the percentage of impervious area upstream.
Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
The Sahara is the largest warm desert in the world, but its age has been controversial, with estimates ranging from Miocene to Holocene. Mineralogical and geochemical data show that paleosols of Pliocene to mid-Pleistocene age on Fuerteventura and Gran Canaria in the Canary Islands have developed in part from inputs of dust from Africa. These paleosols contain quartz and mica, minerals that are abundant in African dust but are rare in the basaltic rocks that dominate the Canary Islands. Trace elements with minimal mobility, Sc, Cr, Hf, Th, and Ta as well as the rare earth elements, show that paleosols have compositions that are intermediate between those of local rocks and African-derived dust. Thus, results reported here and in a recently published study by others indicate that 9 paleosols record delivery of African dust to the Canary Islands between ~4.8–2.8 Ma, ~3.0–2.9 Ma, ~2.3–1.46 Ma, and ~0.4 Ma. A long-term paleosol record of African dust input agrees with deep-sea records off the coast of western Africa that imply increased dust fluxes to the eastern Atlantic Ocean at ~4.6 Ma. It is concluded that the Sahara Desert has been in existence as an arid-region dust source, at least intermittently, for much of the Pliocene and continuing into the Pleistocene.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.palaeo.2019.109245","usgsCitation":"Muhs, D., Meco, J., Budahn, J.R., Skipp, G.L., Betancort, J.F., and Lomoschitz, A., 2019, The antiquity of the Sahara Desert: New evidence from the mineralogy and geochemistry of Pliocene paleosols on the Canary Islands, Spain: Palaeogeography, Palaeoclimatology, Palaeoecology, v. 533, 109245, 24 p., https://doi.org/10.1016/j.palaeo.2019.109245.","productDescription":"109245, 24 p.","ipdsId":"IP-101486","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":467497,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.palaeo.2019.109245","text":"Publisher Index Page"},{"id":379393,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Spain","otherGeospatial":"Canary Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -14.0625,\n 27.907058371121995\n ],\n [\n -13.524169921874998,\n 28.526622418648127\n ],\n [\n -13.271484375,\n 29.248063243796576\n ],\n [\n -13.403320312499998,\n 29.420460341013133\n ],\n [\n -13.897705078125,\n 29.267232865200878\n ],\n [\n -14.798583984375002,\n 28.43971381702788\n ],\n [\n -15.633544921875,\n 28.468691297348148\n ],\n [\n -16.28173828125,\n 28.680949728554964\n ],\n [\n -17.86376953125,\n 28.98892237190413\n ],\n [\n -18.10546875,\n 28.545925723233477\n ],\n [\n -17.962646484375,\n 28.352733760237818\n ],\n [\n -18.270263671875,\n 27.644606381943326\n ],\n [\n -17.73193359375,\n 27.45953933271788\n ],\n [\n -16.962890625,\n 27.829360859789794\n ],\n [\n -16.54541015625,\n 27.829360859789794\n ],\n [\n -15.5126953125,\n 27.6251403350933\n ],\n [\n -14.0625,\n 27.907058371121995\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"533","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Muhs, Daniel R. 0000-0001-7449-251X dmuhs@usgs.gov","orcid":"https://orcid.org/0000-0001-7449-251X","contributorId":168575,"corporation":false,"usgs":true,"family":"Muhs","given":"Daniel R.","email":"dmuhs@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":801429,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meco, Joaquin","contributorId":243048,"corporation":false,"usgs":false,"family":"Meco","given":"Joaquin","affiliations":[{"id":48625,"text":"University of Las Palmas","active":true,"usgs":false}],"preferred":false,"id":801430,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Budahn, James R. 0000-0001-9794-8882","orcid":"https://orcid.org/0000-0001-9794-8882","contributorId":177797,"corporation":false,"usgs":false,"family":"Budahn","given":"James","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":801431,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skipp, Gary L. 0000-0002-9404-0980","orcid":"https://orcid.org/0000-0002-9404-0980","contributorId":201777,"corporation":false,"usgs":true,"family":"Skipp","given":"Gary","email":"","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":801432,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Betancort, Juan F.","contributorId":243049,"corporation":false,"usgs":false,"family":"Betancort","given":"Juan","email":"","middleInitial":"F.","affiliations":[{"id":48625,"text":"University of Las Palmas","active":true,"usgs":false}],"preferred":false,"id":801433,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lomoschitz, Alejandro","contributorId":243050,"corporation":false,"usgs":false,"family":"Lomoschitz","given":"Alejandro","email":"","affiliations":[{"id":48625,"text":"University of Las Palmas","active":true,"usgs":false}],"preferred":false,"id":801434,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70203897,"text":"sir20195061 - 2019 - Sand Creek characterization study for Oncorhynchus clarkii virginalis (Rio Grande Cutthroat Trout), Great Sand Dunes National Park and Preserve, Colorado","interactions":[],"lastModifiedDate":"2019-07-05T14:56:42","indexId":"sir20195061","displayToPublicDate":"2019-06-27T15:40:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5061","displayTitle":"Sand Creek Characterization Study for Oncorhynchus clarkii virginalis (Rio Grande Cutthroat Trout), Great Sand Dunes National Park and Preserve, Colorado","title":"Sand Creek characterization study for Oncorhynchus clarkii virginalis (Rio Grande Cutthroat Trout), Great Sand Dunes National Park and Preserve, Colorado","docAbstract":"The Oncorhynchus clarkii virginalis (Rio Grande cutthroat trout, RGCT) has undergone extensive declines in distribution and population. The RGCT is the southernmost distributed subspecies of cutthroat trout. Native to the Rio Grande Basin in Colorado and New Mexico, the subspecies is also found in the headwaters of the Pecos River and Canadian River basins in New Mexico. Currently, RGCT populations represent approximately 12 percent of the historic distribution. There are many factors that have contributed to the decline of the RGCT including small population sizes; hybridization with non-native salmonids; competition with non-native salmonids; angling; and loss of habitat resulting from wildfire, stream drying, disease, increased water temperatures; and poor land management.
The eastern side of Colorado’s Rio Grande Basin is also home to Great Sand Dunes National Park and Preserve and the Sand Creek watershed. This study was designed to (1) characterize current physical and biological conditions of waterbodies within the Sand Creek watershed, from headwaters to lower terminus near the dune field; (2) characterize the spatial extent of existing fisheries within the Sand Creek watershed to inform the scope of potential future reclamation efforts; and (3) evaluate key limiting factors for a future native RGCT reintroduction.
Bathymetric profiles were completed for two lakes within the upper Sand Creek drainage to characterize the physical geometry of each lake and to estimate the total lake volume required for future piscicide treatment and (or) fish removal efforts. Physical and biological conditions evaluated included stream water temperature and intermittency, discharge, and the genetics and existing fish community distribution and composition within the Sand Creek watershed were key components of this study. A baseline established the geographic extent and biological constraints factored into future piscicide treatment planning and native trout reintroduction efforts.
As a result of this work, the Sand Creek watershed can be broken up into several distinct categories: Lakes that are good candidates for reclamation and reintroduction of RGCT, lakes that are poor candidates for reclamation, streams that currently have fish and are good candidates for reclamation and reintroduction, streams that currently lack fish and may be good candidates for introduction of RGCT, and streams that currently lack fish and are not good candidates for introduction of RGCT. This characterization study report is intended to inform State and Federal managers of the likelihood that the Sand Creek watershed can support a sustainable population of RGCT should they be reintroduced.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195061","collaboration":"Prepared in Cooperation with the National Park Service","usgsCitation":"McGee, B.N., Todd, A.S., and Terry, K.A., 2019, Sand Creek characterization study for Oncorhynchus clarkii virginalis (Rio Grande cutthroat trout), Great Sand Dunes National Park and Preserve, Colorado: U.S. Geological Survey Scientific Investigations Report 2019–5061, 38 p., https://doi.org/10.3133/sir20195061.","productDescription":"38 p.","onlineOnly":"Y","ipdsId":"IP-101143","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":365287,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5061/versionHist.txt","text":"Version History","size":"1 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2019–5061 version history"},{"id":365110,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5061/sir20195061.pdf","text":"Report","size":"17.4 MB ","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5061"},{"id":365109,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5061/coverthb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Great Sand Dunes National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.9686279296875,\n 37.57505900514996\n ],\n [\n -105.2764892578125,\n 37.57505900514996\n ],\n [\n -105.2764892578125,\n 38.048091067457236\n ],\n [\n -105.9686279296875,\n 38.048091067457236\n ],\n [\n -105.9686279296875,\n 37.57505900514996\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geology, Geophysics and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS-973
Denver, CO 80225-0046
Water samples from 50 domestic wells located <1 km (proximal) and >1 km (distal) from shale-gas wells in upland areas of the Marcellus Shale region were analyzed for chemical, isotopic, and groundwater-age tracers. Uplands were targeted because natural mixing with brine and hydrocarbons from deep formations is less common in those areas compared to valleys. CH4-isotope, predrill CH4-concentration, and other data indicate that one proximal sample (5% of proximal samples) contains thermogenic CH4 (2.6 mg/L) from a relatively shallow source (Catskill/Lock Haven Formations) that appears to have been mobilized by shale-gas production activities. Another proximal sample contains five other volatile hydrocarbons (0.03–0.4 μg/L), including benzene, more hydrocarbons than in any other sample. Modeled groundwater-age distributions, calibrated to 3H, SF6, and 14C concentrations, indicate that water in that sample recharged prior to shale-gas development, suggesting that land-surface releases associated with shale-gas production were not the source of those hydrocarbons, although subsurface leakage from a nearby gas well directly into the groundwater cannot be ruled out. Age distributions in the samples span ∼20 to >10000 years and have implications for relating occurrences of hydrocarbons in groundwater to land-surface releases associated with recent shale-gas production and for the time required to flush contaminants from the system.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.9b01440","usgsCitation":"McMahon, P.B., Lindsey, B.D., Conlon, M.D., Hunt, A.G., Belitz, K., Jurgens, B., and Varela, B.A., 2019, Hydrocarbons in upland groundwater, Marcellus Shale Region, Northeastern Pennsylvania and Southern New York, USA: Environmental Science & Technology, v. 53, no. 14, p. 8027-8035, https://doi.org/10.1021/acs.est.9b01440.","productDescription":"9 p.","startPage":"8027","endPage":"8035","ipdsId":"IP-104959","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"links":[{"id":437401,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93M7JCD","text":"USGS data release","linkHelpText":"Data Release for Hydrocarbons in Upland Groundwater, Marcellus Shale Region, Northeastern Pennsylvania and Southern New York, USA"},{"id":365631,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York, Pennsylvania","volume":" 53","issue":"14","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"McMahon, Peter B. 0000-0001-7452-2379 pmcmahon@usgs.gov","orcid":"https://orcid.org/0000-0001-7452-2379","contributorId":724,"corporation":false,"usgs":true,"family":"McMahon","given":"Peter","email":"pmcmahon@usgs.gov","middleInitial":"B.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766203,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lindsey, Bruce D. 0000-0002-7180-4319 blindsey@usgs.gov","orcid":"https://orcid.org/0000-0002-7180-4319","contributorId":175346,"corporation":false,"usgs":true,"family":"Lindsey","given":"Bruce","email":"blindsey@usgs.gov","middleInitial":"D.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - 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Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":766207,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":203409,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766208,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Varela, Brian A. 0000-0001-9849-6742 bvarela@usgs.gov","orcid":"https://orcid.org/0000-0001-9849-6742","contributorId":178091,"corporation":false,"usgs":true,"family":"Varela","given":"Brian","email":"bvarela@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":766209,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70204037,"text":"70204037 - 2019 - Morphology and genesis of giant seafloor depressions on the southeasterncontinental shelf of the Korean Peninsula","interactions":[],"lastModifiedDate":"2019-06-28T09:25:09","indexId":"70204037","displayToPublicDate":"2019-06-27T14:31:58","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2667,"text":"Marine Geology","active":true,"publicationSubtype":{"id":10}},"title":"Morphology and genesis of giant seafloor depressions on the southeasterncontinental shelf of the Korean Peninsula","docAbstract":"We identify and describe five giant seafloor depressions from the southeastern continental shelf of the Korean Peninsula using multibeam bathymetry, sub-bottom profiler, and multi-channel seismic reflection data, supplemented by piston cores. Multibeam bathymetry data from the shelf show four crescent-shaped depressions (SD1 to SD4) and one near-circular depression (SD5) within a group of NW-SE trending depressions, the largest covering an area of about 7 km2 on the seafloor. The depressions reach up to ~4.5 km in width and ~2 km in length and have asymmetric cross-sections. Some have depths as large as 40 m below the surrounding seafloor with walls as steep as 45°. The depressions are confined to water depths between 130 and 170 m and bounded on the north by a large submarine channel that was plausibly formed by fluvial or tidal processes during the Last Glacial Maximum (LGM) sea-level lowstand. Multi-channel seismic and sub-bottom profiler data reveal truncated depression walls and the presence of sediment drift deposits within the depressions, indicating that both erosion and deposition are active processes. Flaser and lenticular bedding in the cored drift deposits along with variable grain size (ranging between ~2.6 phi and ~4.3 phi) are diagnostic features of the bottom currents influenced by tidal forces. Depressions SD1 to SD4 lack evidence of fluid or gas escape. In contrast, many features of depression SD5 are characteristic of gas escapes and blowouts, including acoustic anomalies, a 20-m-high carbonate mound or carbonate-encrusted mound, and mud dikes and mud patches in cores. Based on the SD5 example, we think it is likely that the other crescent-shaped seafloor depressions formed originally as pockmarks by gas/fluid venting, and have since become inactive. The pockmarks represent zones of weakened sediment that were eroded, expanded, and merged by bottom currents to form larger seafloor depressions. Modern currents are strong enough to transport shelf sediments, and these currents were probably much stronger at lower sea levels when the Korea Strait was a more restricted passage between the East China Sea and East Sea.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.margeo.2019.105966","usgsCitation":"Cukur, D., Kong, G., Chun, J., Kang, M., Um, I., Kwon, T., Jordan, S.E., and Kim, K., 2019, Morphology and genesis of giant seafloor depressions on the southeasterncontinental shelf of the Korean Peninsula: Marine Geology, v. 415, 105966, 13 p., https://doi.org/10.1016/j.margeo.2019.105966.","productDescription":"105966, 13 p.","ipdsId":"IP-106382","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":365123,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"North Korea, South Korea","otherGeospatial":"Korea Strait","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 127.869873046875,\n 33.284619968887675\n ],\n [\n 130.97900390625,\n 33.284619968887675\n ],\n [\n 130.97900390625,\n 37.18657859524883\n ],\n [\n 127.869873046875,\n 37.18657859524883\n ],\n [\n 127.869873046875,\n 33.284619968887675\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"415","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cukur, Deniz","contributorId":216636,"corporation":false,"usgs":false,"family":"Cukur","given":"Deniz","email":"","affiliations":[{"id":39491,"text":"Korea Institute of Geoscience and Mineral Resources (KIGAM","active":true,"usgs":false}],"preferred":false,"id":765221,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kong, Gee-Soo","contributorId":216637,"corporation":false,"usgs":false,"family":"Kong","given":"Gee-Soo","email":"","affiliations":[{"id":39491,"text":"Korea Institute of Geoscience and Mineral Resources (KIGAM","active":true,"usgs":false}],"preferred":false,"id":765254,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chun, Jong-Hwa","contributorId":216638,"corporation":false,"usgs":false,"family":"Chun","given":"Jong-Hwa","email":"","affiliations":[{"id":39491,"text":"Korea Institute of Geoscience and Mineral Resources (KIGAM","active":true,"usgs":false}],"preferred":false,"id":765223,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kang, Moo-Hee","contributorId":216639,"corporation":false,"usgs":false,"family":"Kang","given":"Moo-Hee","email":"","affiliations":[{"id":39491,"text":"Korea Institute of Geoscience and Mineral Resources (KIGAM","active":true,"usgs":false}],"preferred":false,"id":765224,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Um, In-Kwon","contributorId":216640,"corporation":false,"usgs":false,"family":"Um","given":"In-Kwon","email":"","affiliations":[{"id":39491,"text":"Korea Institute of Geoscience and Mineral Resources (KIGAM","active":true,"usgs":false}],"preferred":false,"id":765225,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kwon, Taekhyun","contributorId":216641,"corporation":false,"usgs":false,"family":"Kwon","given":"Taekhyun","email":"","affiliations":[{"id":39491,"text":"Korea Institute of Geoscience and Mineral Resources (KIGAM","active":true,"usgs":false}],"preferred":false,"id":765226,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jordan, Samuel E. 0000-0001-6074-3330","orcid":"https://orcid.org/0000-0001-6074-3330","contributorId":216635,"corporation":false,"usgs":true,"family":"Jordan","given":"Samuel","email":"","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":765220,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kim, Kyong-O","contributorId":216642,"corporation":false,"usgs":false,"family":"Kim","given":"Kyong-O","email":"","affiliations":[{"id":39491,"text":"Korea Institute of Geoscience and Mineral Resources (KIGAM","active":true,"usgs":false}],"preferred":false,"id":765227,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70204084,"text":"70204084 - 2019 - Principles and history of luminescence dating","interactions":[],"lastModifiedDate":"2019-09-24T08:41:11","indexId":"70204084","displayToPublicDate":"2019-06-27T13:41:54","publicationYear":"2019","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"1","title":"Principles and history of luminescence dating","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Handbook of Luminescence Dating","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Whittles Publishing","isbn":"9781849953955","usgsCitation":"Mahan, S.A., and DeWitt, R., 2019, Principles and history of luminescence dating, chap. 1 of Handbook of Luminescence Dating.","ipdsId":"IP-091196","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":367614,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":367613,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.whittlespublishing.com/Handbook_of_Luminescence_Dating"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":765418,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeWitt, Regina 0000-0003-2876-5489","orcid":"https://orcid.org/0000-0003-2876-5489","contributorId":216736,"corporation":false,"usgs":false,"family":"DeWitt","given":"Regina","email":"","affiliations":[{"id":39507,"text":"East Carolina University (in North Carolina)","active":true,"usgs":false}],"preferred":false,"id":765419,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70263727,"text":"70263727 - 2019 - Evaluating tradeoffs in the response of Sora (Porzana carolina) and waterfowl to the timing of early autumn wetland inundation","interactions":[],"lastModifiedDate":"2025-02-20T19:12:23.90001","indexId":"70263727","displayToPublicDate":"2019-06-27T13:04:35","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Evaluating tradeoffs in the response of Sora (Porzana carolina) and waterfowl to the timing of early autumn wetland inundation","title":"Evaluating tradeoffs in the response of Sora (Porzana carolina) and waterfowl to the timing of early autumn wetland inundation","docAbstract":"Wetland loss has increased the importance of multi-species management in remaining wetlands, which provide habitat for a multitude of wetland-dependent species. Many public wetlands across the mid-latitude United States are managed as moist soil impoundments with emphasis on migratory waterfowl. However, how the timing of these water management decisions affects rails is still uncertain. Wetland managers identified this as an area of uncertainty regarding timing of alternative water management strategies to benefit waterfowl and rails, which was addressed through a 3-year management experiment. Sora (Porzana carolina) and waterfowl were surveyed on 10 public wetland properties in Missouri, USA from 2014-2016, and their responses to early autumn inundation of managed palustrine wetlands were compared. A total of 558 Sora surveys detected 5,755 birds (20.6 birds/survey ± 30.8 SD), and 1,304 waterfowl surveys detected 1,411,779 birds (15,686.4 birds/survey ± 23,933.9 SD). Sora responded positively (birds/ha) to inundation of moist soil impoundments earlier in autumn migration (August). The top model for Sora included treatment, year and region of Missouri. There was no difference in waterfowl abundance between early or late inundation. Inundating wetlands earlier in autumn migration can provide habitat for migrating Sora without negative effects on waterfowl use of those wetlands, and wetland managers can incorporate this into their decision-making framework.
","language":"English","publisher":"BioOne","doi":"10.1675/063.042.0203","usgsCitation":"Fournier, A., Mengel, D., Gbur, E., Raedeke, A., and Krementz, D.G., 2019, Evaluating tradeoffs in the response of Sora (Porzana carolina) and waterfowl to the timing of early autumn wetland inundation: Waterbirds, v. 42, no. 2, p. 168-178, https://doi.org/10.1675/063.042.0203.","productDescription":"11 p.","startPage":"168","endPage":"178","ipdsId":"IP-093134","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":489951,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1675/063.042.0203","text":"Publisher Index Page"},{"id":482298,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"42","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fournier, Ariel M.","contributorId":351134,"corporation":false,"usgs":false,"family":"Fournier","given":"Ariel M.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":927964,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mengel, Doreen C.","contributorId":351135,"corporation":false,"usgs":false,"family":"Mengel","given":"Doreen C.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":927965,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gbur, Edward","contributorId":351136,"corporation":false,"usgs":false,"family":"Gbur","given":"Edward","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":927966,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Raedeke, Andy","contributorId":341916,"corporation":false,"usgs":false,"family":"Raedeke","given":"Andy","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":927967,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Krementz, David G. 0000-0002-5661-4541 dkrementz@usgs.gov","orcid":"https://orcid.org/0000-0002-5661-4541","contributorId":2827,"corporation":false,"usgs":true,"family":"Krementz","given":"David","email":"dkrementz@usgs.gov","middleInitial":"G.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":927968,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70204078,"text":"70204078 - 2019 - Spatial conservation planning under uncertainty: Adapting to climate change risks using modern portfolio theory","interactions":[],"lastModifiedDate":"2020-12-08T18:02:17.95215","indexId":"70204078","displayToPublicDate":"2019-06-27T12:47:12","publicationYear":"2019","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":"Spatial conservation planning under uncertainty: Adapting to climate change risks using modern portfolio theory","docAbstract":"Climate change and urban growth impact habitats, species, and ecosystem services. To buffer against global change, an established adaptation strategy is designing protected areas to increase representation and complementarity of biodiversity features. Uncertainty regarding the scale and magnitude of landscape change complicates reserve planning and exposes decision makers to risk of failing to meet conservation goals. Conservation planning tends to treat risk as an absolute measure, ignoring the context of the management problem and risk preferences of stakeholders. Application to conservation of risk management theory emphasizes diversification of portfolio of assets, with the goal of reducing the impact of system volatility on investment return. We use principles of Modern Portfolio Theory (MPT), which quantifies risk as the variance and correlation among assets, to formalize diversification as an explicit strategy for managing risk in climate‐driven reserve design. We extend MPT to specify a framework that evaluates multiple conservation objectives, allows decision makers to balance management benefits and risk when preferences are contested or unknown, and includes additional decision options such as parcel divestment when evaluating candidate reserve designs. We apply an efficient search algorithm that optimizes portfolio design for large conservation problems and a game theoretic approach to evaluate portfolio tradeoffs that satisfy decision makers with divergent benefit and risk tolerances, or when a single decision maker cannot resolve their own preferences. Evaluating several risk profiles for a case study in South Carolina, our results suggest that a reserve design may be somewhat robust to differences in risk attitude but that budgets will likely be important determinants of conservation planning strategies, particularly when divestment is considered a viable alternative. We identify a possible fiscal threshold where adequate resources allow protecting a sufficiently diverse portfolio of habitats such that the risk of failing to achieve conservation objectives is considerably lower. For a range of sea‐level rise projections, conversion of habitat to open water (14‐180%) and wetland loss (1‐7%) are unable to be compensated under the current protected network. In contrast, optimal reserve design outcomes are predicted to ameliorate expected losses relative to current and future habitat protected under the existing conservation estate.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1962","usgsCitation":"Eaton, M., Yurek, S., Haider, Z., Martin, J., Johnson, F., Udell, B., Charkhgard, H., and Kwon, C., 2019, Spatial conservation planning under uncertainty: Adapting to climate change risks using modern portfolio theory: Ecological Applications, v. 29, no. 2, e01962, 19 p., https://doi.org/10.1002/eap.1962.","productDescription":"e01962, 19 p.","ipdsId":"IP-103774","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":365283,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","otherGeospatial":"Cape Romain National Wildlife Refuge, Frances Marion National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": 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Between 1996 and 2016, we necropsied 127 apparently healthy pelagic olive ridley turtles (Lepidochelys olivacea) that died from drowning bycatch in fisheries and 44 live or freshly dead stranded turtles from the west coast of North and Central America and Hawaii. Seven % (9/127) of pelagic and 47% (21/44) of stranded turtles had renal granulomas associated with S. Typhimurium. Stranded animals were 12 times more likely than pelagic animals to have Salmonella-induced nephritis suggesting that Salmonella may have been a contributing cause of stranding. S. Typhimurium was the only Salmonella serovar detected in L. olivacea, and phylogenetic analysis from whole genome sequencing showed that the isolates from L. olivacea formed a single clade distinct from other S. Typhimurium. Molecular clock analysis revealed that this novel clade may have originated as recently as a few decades ago. The phylogenetic lineage leading to this group is enriched for non-synonymous changes within the genomic area of Salmonella pathogenicity island 1 suggesting that these genes are important for host adaptation.
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The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage 05451770 on the Iowa River at County Highway E49 near Tama, Iowa. Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at https://waterdata.usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site.
Flood profiles were computed for the stream reach by means of a calibrated one-dimensional and two-dimensional step-backwater hydraulic model. The model was calibrated by using the current stage-discharge relation at the USGS streamgage 05451770 on the Iowa River at County Highway E49 near Tama, Iowa, and stage and discharge data from historic flooding events that were recorded at the streamgage.
The hydraulic model was then used to compute eight water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from the NWS “action stage” of 11 feet (ft) to 18 ft, the stage exceeding the estimated 0.2-percent annual exceedance probability (500-year recurrence interval) flood, as determined at the USGS streamgage 05451770. The simulated water-surface profiles were then combined with a geographic information system digital elevation model to delineate the area flooded at each flood stage (water level).
In addition, potential modifications to hydraulic structures within the flood plain were modeled so any effects from the potential modifications could be evaluated. Four comparison points, which were along the flood plain, showed little to no change (less than 0.1 ft) in flood elevation from the existing conditions within the flood plain for the 11- to 16-ft stages as referenced to the USGS streamgage 05451770. There were greater changes (more than 0.1 ft) in flood elevation for the 2 comparison points that were closest to the modified hydraulic structure for the 2 highest modeled stages of 17 and 18 ft.
The availability of these maps, along with internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195050","collaboration":"Prepared in cooperation with the Sac and Fox Tribe of the Mississippi in Iowa","usgsCitation":"Cigrand, C.V., 2019, Flood-inundation maps for the Iowa River at the Meskwaki Settlement in Iowa, 2019: U.S. Geological Survey Scientific Investigations Report 2019–5050, 12 p., https://doi.org/10.3133/sir20195050.","productDescription":"12 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-103795","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":365048,"rank":3,"type":{"id":30,"text":"Data Release"},"url":" https://doi.org/10.5066/P912FO3L ","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial datasets for the flood-inundation study for the Iowa River at the Meskwaki Settlement in Iowa, 2019"},{"id":365046,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5050/coverthb.jpg"},{"id":365047,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5050/sir20195050.pdf","text":"Report","size":"26.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5050"}],"country":"United States","state":"Iowa","otherGeospatial":"Meskwaki Settlement","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -92.7175,41.916666666666664 ], [ -92.7175,42.034166666666664 ], [ -92.55,42.034166666666664 ], [ -92.55,41.916666666666664 ], [ -92.7175,41.916666666666664 ] ] ] } } ] }","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
We present a petrologic and mineral physics database as part of the U.S. Geological Survey National Crustal Model (NCM). Each of 209 geologic units, 134 of which are currently part of the geologic framework within the NCM, was assigned a mineralogical composition according to generalized classifications with some refinement for specific geologic formations. This report is concerned with the petrology and mineral physics of each geologic unit within the NCM, which control the physical behavior of the solid mineral matrix within the rock.
This mineral physics database builds on the work of Abers and Hacker to include 13 minerals specific to continental rock types. We explored the effect of this database on zero-porosity anharmonic P- and S-wave rock velocities and density relative to a well-used empirical study of relations between wavespeeds and density by Brocher. We found that empirical relations between P-wave velocity and S-wave velocity or density do well on average but can differ from mineral physics calculations by up to 15 percent in S-wave velocity and almost 40 percent in density. This is consistent with Brocher’s study where he obtained similar results for in situ measurements and laboratory rock specimens.
Additionally, the substantial presence of quartz in many rocks plays a major role in crustal seismic velocities and density due to quartz’s α–β phase transition, which can interfere with these empirical relationships. With increasing depth, quartz P-wave velocity can suddenly jump by 15 percent accompanied by little change in S-wave velocity and a modest decrease in density. Empirical relations based on observed P-wave velocity where P-wave velocity is positively correlated with S-wave velocity and density would then significantly overestimate both S-wave velocity and density.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191035","usgsCitation":"Sowers, T., and Boyd, O.S., 2019, Petrologic and mineral physics database for use with the U.S. Geological Survey National Crustal Model: U.S. Geological Survey Open-File Report 2019–1035, 17 p.,https://doi.org/10.3133/ofr20191035.","productDescription":"17 p.","onlineOnly":"Y","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":437403,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FK25WM","text":"USGS data release","linkHelpText":"MinVel"},{"id":364257,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HN170G","text":"USGS data release","linkHelpText":"Petrologic and Mineral Physics Database for use with the USGS National Crustal Model - Data Release"},{"id":365082,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://github.com/usgs/MinVel","text":"MinVel ","linkHelpText":"software that supports this report is available in the GitHub repository."},{"id":364255,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1035/coverthb.jpg"},{"id":364256,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1035/ofr20191035.pdf","text":"Report","size":"1.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1035"}],"contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046
Information concerning the availability, use, and quality of water in East Carroll Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, 39.63 million gallons per day (Mgal/d) of water were withdrawn in East Carroll Parish: 32.43 Mgal/d from groundwater sources and 7.20 Mgal/d from surface-water sources. Withdrawals for agricultural use—composed of general irrigation, rice irrigation, and livestock—accounted for 97 percent (38.55 Mgal/d) of the total water withdrawn. Other categories of use included public supply and rural domestic. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 1980 at 47.96 Mgal/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193014","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., 2019, Water resources of East Carroll Parish, Louisiana: U.S. Geological Survey Fact Sheet 2019–3014, 6 p., https://doi.org/10.3133/fs20193014.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-081702","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":365084,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3014/fs20193014.pdf","text":"Report","size":"929 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3014"},{"id":365083,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3014/coverthb.jpg"},{"id":365085,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"}],"country":"United States","state":"Louisiana","county":"East Carroll 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Carroll\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816