{"pageNumber":"502","pageRowStart":"12525","pageSize":"25","recordCount":40783,"records":[{"id":70156877,"text":"70156877 - 2016 - Mapping extent and change in surface mines within the United States for 2001 to 2006","interactions":[],"lastModifiedDate":"2017-04-06T17:07:18","indexId":"70156877","displayToPublicDate":"2016-02-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2597,"text":"Land Degradation and Development","active":true,"publicationSubtype":{"id":10}},"title":"Mapping extent and change in surface mines within the United States for 2001 to 2006","docAbstract":"A complete, spatially explicit dataset illustrating the 21st century mining footprint for the conterminous United States does not exist. To address this need, we developed a semi-automated procedure to map the country's mining footprint (30-m pixel) and establish a baseline to monitor changes in mine extent over time. The process uses mine seed points derived from the U.S. Energy Information Administration (EIA), U.S. Geological Survey (USGS) Mineral Resources Data System (MRDS), and USGS National Land Cover Dataset (NLCD) and recodes patches of barren land that meet a “distance to seed” requirement and a patch area requirement before mapping a pixel as mining. Seed points derived from EIA coal points, an edited MRDS point file, and 1992 NLCD mine points were used in three separate efforts using different distance and patch area parameters for each. The three products were then merged to create a 2001 map of moderate-to-large mines in the United States, which was subsequently manually edited to reduce omission and commission errors. This process was replicated using NLCD 2006 barren pixels as a base layer to create a 2006 mine map and a 2001–2006 mine change map focusing on areas with surface mine expansion. In 2001, 8,324 km2 of surface mines were mapped. The footprint increased to 9,181 km2 in 2006, representing a 10·3% increase over 5 years. These methods exhibit merit as a timely approach to generate wall-to-wall, spatially explicit maps representing the recent extent of a wide range of surface mining activities across the country.
","language":"English","publisher":"John Wiley and Sons","doi":"10.1002/ldr.2412","usgsCitation":"Soulard, C.E., Acevedo, W., Stehman, S.V., and Parker, O.P., 2016, Mapping extent and change in surface mines within the United States for 2001 to 2006: Land Degradation and Development, v. 27, no. 2, p. 248-257, https://doi.org/10.1002/ldr.2412.","productDescription":"10 p.","startPage":"248","endPage":"257","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-054963","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":324655,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-14","publicationStatus":"PW","scienceBaseUri":"5774f27ce4b07dd077c6a55d","contributors":{"authors":[{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":570924,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Acevedo, William wacevedo@usgs.gov","contributorId":2689,"corporation":false,"usgs":true,"family":"Acevedo","given":"William","email":"wacevedo@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":570925,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stehman, Stephen V.","contributorId":77283,"corporation":false,"usgs":true,"family":"Stehman","given":"Stephen","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":641373,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parker, Owen P.","contributorId":147263,"corporation":false,"usgs":false,"family":"Parker","given":"Owen","email":"","middleInitial":"P.","affiliations":[{"id":6785,"text":"USGS Contractor, Minerals & Environmental Resources Sci Ctr","active":true,"usgs":false}],"preferred":false,"id":570926,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70169274,"text":"70169274 - 2016 - Evaluating geothermal and hydrogeologic controls on regional groundwater temperature distribution","interactions":[],"lastModifiedDate":"2019-07-22T12:38:26","indexId":"70169274","displayToPublicDate":"2016-02-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating geothermal and hydrogeologic controls on regional groundwater temperature distribution","docAbstract":"A one-dimensional (1-D) analytic solution is developed for heat transport through an aquifer system where the vertical temperature profile in the aquifer is nearly uniform. The general anisotropic form of the viscous heat generation term is developed for use in groundwater flow simulations. The 1-D solution is extended to more complex geometries by solving the equation for piece-wise linear or uniform properties and boundary conditions. A moderately complex example, the Eastern Snake River Plain (ESRP), is analyzed to demonstrate the use of the analytic solution for identifying important physical processes. For example, it is shown that viscous heating is variably important and that heat conduction to the land surface is a primary control on the distribution of aquifer and spring temperatures. Use of published values for all aquifer and thermal properties results in a reasonable match between simulated and measured groundwater temperatures over most of the 300 km length of the ESRP, except for geothermal heat flow into the base of the aquifer within 20 km of the Yellowstone hotspot. Previous basal heat flow measurements (∼110 mW/m2) made beneath the ESRP aquifer were collected at distances of >50 km from the Yellowstone Plateau, but a higher basal heat flow of 150 mW/m2 is required to match groundwater temperatures near the Plateau. The ESRP example demonstrates how the new tool can be used during preliminary analysis of a groundwater system, allowing efficient identification of the important physical processes that must be represented during more-complex 2-D and 3-D simulations of combined groundwater and heat flow.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2015WR018204","usgsCitation":"Burns, E.R., Ingebritsen, S.E., Manga, M., and Williams, C.F., 2016, Evaluating geothermal and hydrogeologic controls on regional groundwater temperature distribution: Water Resources Research, v. 52, no. 2, p. 1328-1344, https://doi.org/10.1002/2015WR018204.","productDescription":"17 p.","startPage":"1328","endPage":"1344","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066164","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":471280,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1480710","text":"External Repository"},{"id":319342,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Eastern Snake River Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.44232177734374,\n 42.92224052343343\n ],\n [\n -112.37640380859375,\n 43.068887774169625\n ],\n [\n -112.2637939453125,\n 43.19516498456403\n ],\n [\n -112.1044921875,\n 43.30719248161193\n ],\n [\n -112.00836181640625,\n 43.45890015705449\n ],\n [\n -111.99462890625,\n 43.6102281417864\n ],\n [\n -111.94793701171875,\n 43.70362249839005\n ],\n [\n -111.84906005859375,\n 43.76514352427404\n ],\n [\n -111.78314208984375,\n 43.81471121600004\n ],\n [\n -111.83807373046875,\n 43.91174463910793\n ],\n [\n -111.96441650390625,\n 43.96119063892024\n ],\n [\n -112.13470458984375,\n 43.9473499035071\n ],\n [\n -112.22259521484375,\n 43.91174463910793\n ],\n [\n -112.39562988281249,\n 43.8503744993026\n ],\n [\n -112.576904296875,\n 43.80876526350847\n ],\n [\n -112.78289794921875,\n 43.82461982131735\n ],\n [\n -113.060302734375,\n 43.67581809328344\n ],\n [\n -113.302001953125,\n 43.57840117718351\n ],\n [\n -113.6810302734375,\n 43.367122441110716\n ],\n [\n -113.96942138671875,\n 43.25520534158046\n ],\n [\n -114.12322998046875,\n 43.27320591705845\n ],\n [\n -114.43634033203125,\n 43.279204926082784\n ],\n [\n -114.7137451171875,\n 43.271206115959785\n ],\n [\n -115.07904052734374,\n 43.271206115959785\n ],\n [\n -115.1971435546875,\n 43.22719386727831\n ],\n [\n -115.1531982421875,\n 43.02472955416351\n ],\n [\n -114.88677978515625,\n 42.896088552971065\n ],\n [\n -114.87030029296875,\n 42.72280375732727\n ],\n [\n -114.76867675781249,\n 42.57128708742757\n ],\n [\n -114.32373046875,\n 42.47817430242153\n ],\n [\n -113.873291015625,\n 42.459940352216556\n ],\n [\n -113.51898193359374,\n 42.52069952914966\n ],\n [\n -113.22235107421874,\n 42.60970621339408\n ],\n [\n -113.0218505859375,\n 42.67435857693384\n ],\n [\n -112.74444580078125,\n 42.789354160502775\n ],\n [\n -112.47528076171875,\n 42.88401467044253\n ],\n [\n -112.44232177734374,\n 42.92224052343343\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"52","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-28","publicationStatus":"PW","scienceBaseUri":"56f50fc7e4b0f59b85e1eb4d","contributors":{"authors":[{"text":"Burns, Erick R. 0000-0002-1747-0506 eburns@usgs.gov","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":3094,"corporation":false,"usgs":true,"family":"Burns","given":"Erick","email":"eburns@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":623424,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ingebritsen, Steven E. 0000-0001-6917-9369 seingebr@usgs.gov","orcid":"https://orcid.org/0000-0001-6917-9369","contributorId":818,"corporation":false,"usgs":true,"family":"Ingebritsen","given":"Steven","email":"seingebr@usgs.gov","middleInitial":"E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":623425,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Manga, Michael","contributorId":84679,"corporation":false,"usgs":true,"family":"Manga","given":"Michael","affiliations":[],"preferred":false,"id":623427,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Colin F. 0000-0003-2196-5496 colin@usgs.gov","orcid":"https://orcid.org/0000-0003-2196-5496","contributorId":274,"corporation":false,"usgs":true,"family":"Williams","given":"Colin","email":"colin@usgs.gov","middleInitial":"F.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":623426,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70192535,"text":"70192535 - 2016 - Ungulate reproductive parameters track satellite observations of plant phenology across latitude and climatological regimes","interactions":[],"lastModifiedDate":"2017-10-26T13:15:55","indexId":"70192535","displayToPublicDate":"2016-02-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Ungulate reproductive parameters track satellite observations of plant phenology across latitude and climatological regimes","docAbstract":"The effect of climatically-driven plant phenology on mammalian reproduction is one key to predicting species-specific demographic responses to climate change. Large ungulates face their greatest energetic demands from the later stages of pregnancy through weaning, and so in seasonal environments parturition dates should match periods of high primary productivity. Interannual variation in weather influences the quality and timing of forage availability, which can influence neonatal survival. Here, we evaluated macro-scale patterns in reproductive performance of a widely distributed ungulate (mule deer, Odocoileus hemionus) across contrasting climatological regimes using satellite-derived indices of primary productivity and plant phenology over eight degrees of latitude (890 km) in the American Southwest. The dataset comprised > 180,000 animal observations taken from 54 populations over eight years (2004–2011). Regionally, both the start and peak of growing season (“Start” and “Peak”, respectively) are negatively and significantly correlated with latitude, an unusual pattern stemming from a change in the dominance of spring snowmelt in the north to the influence of the North American Monsoon in the south. Corresponding to the timing and variation in both the Start and Peak, mule deer reproduction was latest, lowest, and most variable at lower latitudes where plant phenology is timed to the onset of monsoonal moisture. Parturition dates closely tracked the growing season across space, lagging behind the Start and preceding the Peak by 27 and 23 days, respectively. Mean juvenile production increased, and variation decreased, with increasing latitude. Temporally, juvenile production was best predicted by primary productivity during summer, which encompassed late pregnancy, parturition, and early lactation. Our findings offer a parsimonious explanation of two key reproductive parameters in ungulate demography, timing of parturition and mean annual production, across latitude and changing climatological regimes. Practically, this demonstrates the potential for broad-scale modeling of couplings between climate, plant phenology, and animal populations using space-borne observations.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0148780","usgsCitation":"Stoner, D., Sexton, J.O., Nagol, J., Bernales, H.H., and Edwards, T., 2016, Ungulate reproductive parameters track satellite observations of plant phenology across latitude and climatological regimes: PLoS ONE, v. 11, no. 2, p. 1-19, https://doi.org/10.1371/journal.pone.0148780.","productDescription":"e0148780; 19 p.","startPage":"1","endPage":"19","ipdsId":"IP-061623","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471281,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0148780","text":"Publisher Index Page"},{"id":347470,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, Utah","otherGeospatial":"Chihuahuan Desert, Colorado Plateau, Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.06005859375,\n 33.63291573870479\n ],\n [\n -108.61083984375,\n 33.63291573870479\n ],\n [\n -108.61083984375,\n 42.65012181368022\n ],\n [\n -114.06005859375,\n 42.65012181368022\n ],\n [\n -114.06005859375,\n 33.63291573870479\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-05","publicationStatus":"PW","scienceBaseUri":"5a07ea6ce4b09af898c8cc86","contributors":{"authors":[{"text":"Stoner, David","contributorId":191912,"corporation":false,"usgs":false,"family":"Stoner","given":"David","affiliations":[],"preferred":false,"id":716338,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sexton, Joseph O.","contributorId":191918,"corporation":false,"usgs":false,"family":"Sexton","given":"Joseph","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":716339,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nagol, Jyoteshwar","contributorId":198512,"corporation":false,"usgs":false,"family":"Nagol","given":"Jyoteshwar","affiliations":[],"preferred":false,"id":716340,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bernales, Heather H.","contributorId":198513,"corporation":false,"usgs":false,"family":"Bernales","given":"Heather","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":716341,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Edwards, Thomas C. Jr. 0000-0002-0773-0909 tce@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-0909","contributorId":191916,"corporation":false,"usgs":true,"family":"Edwards","given":"Thomas C.","suffix":"Jr.","email":"tce@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":716135,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70176956,"text":"70176956 - 2016 - A typology of time-scale mismatches and behavioral interventions to diagnose and solve conservation problems","interactions":[],"lastModifiedDate":"2017-04-27T10:23:50","indexId":"70176956","displayToPublicDate":"2016-02-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"A typology of time-scale mismatches and behavioral interventions to diagnose and solve conservation problems","docAbstract":"Ecological systems often operate on time scales significantly longer or shorter than the time scales typical of human decision making, which causes substantial difficulty for conservation and management in socioecological systems. For example, invasive species may move faster than humans can diagnose problems and initiate solutions, and climate systems may exhibit long-term inertia and short-term fluctuations that obscure learning about the efficacy of management efforts in many ecological systems. We adopted a management-decision framework that distinguishes decision makers within public institutions from individual actors within the social system, calls attention to the ways socioecological systems respond to decision makers’ actions, and notes institutional learning that accrues from observing these responses. We used this framework, along with insights from bedeviling conservation problems, to create a typology that identifies problematic time-scale mismatches occurring between individual decision makers in public institutions and between individual actors in the social or ecological system. We also considered solutions that involve modifying human perception and behavior at the individual level as a means of resolving these problematic mismatches. The potential solutions are derived from the behavioral economics and psychology literature on temporal challenges in decision making, such as the human tendency to discount future outcomes at irrationally high rates. These solutions range from framing environmental decisions to enhance the salience of long-term consequences, to using structured decision processes that make time scales of actions and consequences more explicit, to structural solutions aimed at altering the consequences of short-sighted behavior to make it less appealing. Additional application of these tools and long-term evaluation measures that assess not just behavioral changes but also associated changes in ecological systems are needed.
","language":"English","publisher":"Wiley","doi":"10.1111/cobi.12632","usgsCitation":"Wilson, R.S., Hardisty, D.J., Epanchin-Niell, R.S., Runge, M.C., Cottingham, K.L., Urban, D., Maguire, L., Hastings, A., Mumby, P.J., and Peters, D., 2016, A typology of time-scale mismatches and behavioral interventions to diagnose and solve conservation problems: Conservation Biology, v. 30, no. 1, p. 42-49, https://doi.org/10.1111/cobi.12632.","productDescription":"8 p.","startPage":"42","endPage":"49","ipdsId":"IP-064693","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471289,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/2429/58321","text":"External Repository"},{"id":329546,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"1","noUsgsAuthors":false,"publicationDate":"2015-12-18","publicationStatus":"PW","scienceBaseUri":"58009d55e4b0824b2d183b8e","contributors":{"authors":[{"text":"Wilson, Robyn S.","contributorId":175362,"corporation":false,"usgs":false,"family":"Wilson","given":"Robyn","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":650868,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hardisty, David J.","contributorId":175363,"corporation":false,"usgs":false,"family":"Hardisty","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":650869,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Epanchin-Niell, Rebecca S.","contributorId":175364,"corporation":false,"usgs":false,"family":"Epanchin-Niell","given":"Rebecca","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":650870,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":650871,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cottingham, Kathryn L.","contributorId":26425,"corporation":false,"usgs":true,"family":"Cottingham","given":"Kathryn","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":650872,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Urban, Dean L.","contributorId":10674,"corporation":false,"usgs":true,"family":"Urban","given":"Dean L.","affiliations":[],"preferred":false,"id":650873,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Maguire, Lynn A.","contributorId":46861,"corporation":false,"usgs":true,"family":"Maguire","given":"Lynn A.","affiliations":[],"preferred":false,"id":650874,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hastings, Alan","contributorId":175365,"corporation":false,"usgs":false,"family":"Hastings","given":"Alan","email":"","affiliations":[],"preferred":false,"id":650875,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mumby, Peter J.","contributorId":175366,"corporation":false,"usgs":false,"family":"Mumby","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":650876,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Peters, Debra P. C.","contributorId":36903,"corporation":false,"usgs":false,"family":"Peters","given":"Debra P. C.","affiliations":[{"id":25579,"text":"USDA-ARS Jornada Experimental Range, Las Cruces, NM 88003","active":true,"usgs":false}],"preferred":false,"id":650877,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70182721,"text":"70182721 - 2016 - Comparison of measurement- and proxy-based Vs30 values in California","interactions":[],"lastModifiedDate":"2017-02-27T14:42:27","indexId":"70182721","displayToPublicDate":"2016-02-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of measurement- and proxy-based Vs30 values in California","docAbstract":"This study was prompted by the recent availability of a significant amount of openly accessible measured VS30 values and the desire to investigate the trend of using proxy-based models to predict VS30 in the absence of measurements. Comparisons between measured and model-based values were performed. The measured data included 503 VS30 values collected from various projects for 482 seismographic station sites in California. Six proxy-based models—employing geologic mapping, topographic slope, and terrain classification—were also considered. Included was a new terrain class model based on the Yong et al. (2012) approach but recalibrated with updated measured VS30 values. Using the measured VS30 data as the metric for performance, the predictive capabilities of the six models were determined to be statistically indistinguishable. This study also found three models that tend to underpredict VS30 at lower velocities (NEHRP Site Classes D–E) and overpredict at higher velocities (Site Classes B–C).
","language":"English","publisher":"Earthquake Engineering Research Institute ","doi":"10.1193/013114EQS025M","usgsCitation":"Yong, A.K., 2016, Comparison of measurement- and proxy-based Vs30 values in California: Earthquake Spectra, v. 32, no. 1, p. 171-192, https://doi.org/10.1193/013114EQS025M.","productDescription":"22 p. ","startPage":"171","endPage":"192","ipdsId":"IP-056058","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":336289,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-01","publicationStatus":"PW","scienceBaseUri":"58b548c1e4b01ccd54fddfba","contributors":{"authors":[{"text":"Yong, Alan K. 0000-0003-1807-5847 yong@usgs.gov","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":1554,"corporation":false,"usgs":true,"family":"Yong","given":"Alan","email":"yong@usgs.gov","middleInitial":"K.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":673454,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70173661,"text":"70173661 - 2016 - Dynamic occupancy models for explicit colonization processes","interactions":[],"lastModifiedDate":"2016-06-08T10:22:41","indexId":"70173661","displayToPublicDate":"2016-02-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic occupancy models for explicit colonization processes","docAbstract":"The dynamic, multi-season occupancy model framework has become a popular tool for modeling open populations with occupancies that change over time through local colonizations and extinctions. However, few versions of the model relate these probabilities to the occupancies of neighboring sites or patches. We present a modeling framework that incorporates this information and is capable of describing a wide variety of spatiotemporal colonization and extinction processes. A key feature of the model is that it is based on a simple set of small-scale rules describing how the process evolves. The result is a dynamic process that can account for complicated large-scale features. In our model, a site is more likely to be colonized if more of its neighbors were previously occupied and if it provides more appealing environmental characteristics than its neighboring sites. Additionally, a site without occupied neighbors may also become colonized through the inclusion of a long-distance dispersal process. Although similar model specifications have been developed for epidemiological applications, ours formally accounts for detectability using the well-known occupancy modeling framework. After demonstrating the viability and potential of this new form of dynamic occupancy model in a simulation study, we use it to obtain inference for the ongoing Common Myna (Acridotheres tristis) invasion in South Africa. Our results suggest that the Common Myna continues to enlarge its distribution and its spread via short distance movement, rather than long-distance dispersal. Overall, this new modeling framework provides a powerful tool for managers examining the drivers of colonization including short- vs. long-distance dispersal, habitat quality, and distance from source populations.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/15-0416.1","usgsCitation":"Broms, K.M., Hooten, M., Johnson, D., Altwegg, R., and Conquest, L., 2016, Dynamic occupancy models for explicit colonization processes: Ecology, v. 97, no. 1, p. 194-204, https://doi.org/10.1890/15-0416.1.","productDescription":"11 p.","startPage":"194","endPage":"204","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064209","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471292,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1890/15-0416.1","text":"External Repository"},{"id":323254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"97","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-29","publicationStatus":"PW","scienceBaseUri":"575941d6e4b04f417c256803","contributors":{"authors":[{"text":"Broms, Kristin M.","contributorId":171524,"corporation":false,"usgs":false,"family":"Broms","given":"Kristin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":637841,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":637469,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Devin S.","contributorId":47524,"corporation":false,"usgs":true,"family":"Johnson","given":"Devin S.","affiliations":[],"preferred":false,"id":637842,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Altwegg, Res","contributorId":171528,"corporation":false,"usgs":false,"family":"Altwegg","given":"Res","email":"","affiliations":[],"preferred":false,"id":637843,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Conquest, Loveday","contributorId":86624,"corporation":false,"usgs":true,"family":"Conquest","given":"Loveday","email":"","affiliations":[],"preferred":false,"id":637844,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70159446,"text":"70159446 - 2016 - Book review: Mineral resource estimation","interactions":[],"lastModifiedDate":"2016-06-30T14:12:14","indexId":"70159446","displayToPublicDate":"2016-02-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Book review: Mineral resource estimation","docAbstract":"Mineral Resource Estimation is about estimating mineral resources at the scale of an ore deposit and is not to be mistaken with mineral resource assessment, which is undertaken at a significantly broader scale, even if similar data and geospatial/geostatistical methods are used. The book describes geological, statistical, and geostatistical tools and methodologies used in resource estimation and modeling, and presents case studies for illustration. The target audience is the expert, which includes professional mining geologists and engineers, as well as graduate-level and advanced undergraduate students.
\nReview info: Mineral Resource Estimation. By Mario E. Rossi, Clayton V. Deutsch. 2014. ISBN 978-1-4020-5716-8 332 pp.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.2113/econgeo.111.1.272","usgsCitation":"Mihalasky, M.J., 2016, Book review: Mineral resource estimation: Economic Geology, v. 111, no. 1, p. 272-274, https://doi.org/10.2113/econgeo.111.1.272.","productDescription":"3 [.","startPage":"272","endPage":"274","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070293","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":324690,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"111","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-08","publicationStatus":"PW","scienceBaseUri":"577642ade4b07dd077c873ef","contributors":{"authors":[{"text":"Mihalasky, Mark J. 0000-0002-0082-3029 mjm@usgs.gov","orcid":"https://orcid.org/0000-0002-0082-3029","contributorId":3692,"corporation":false,"usgs":true,"family":"Mihalasky","given":"Mark","email":"mjm@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":false,"id":578734,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70188368,"text":"70188368 - 2016 - Lithospheric rheology constrained from twenty-five years of postseismic deformation following the 1989 Mw 6.9 Loma Prieta earthquake","interactions":[],"lastModifiedDate":"2017-06-07T11:21:27","indexId":"70188368","displayToPublicDate":"2016-02-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Lithospheric rheology constrained from twenty-five years of postseismic deformation following the 1989 Mw 6.9 Loma Prieta earthquake","title":"Lithospheric rheology constrained from twenty-five years of postseismic deformation following the 1989 Mw 6.9 Loma Prieta earthquake","docAbstract":"The October 17, 1989 Mw 6.9 Loma Prieta earthquake provides the first opportunity of probing the crustal and upper mantle rheology in the San Francisco Bay Area since the 1906 Mw 7.9 San Francisco earthquake. Here we use geodetic observations including GPS and InSAR to characterize the Loma Prieta earthquake postseismic displacements from 1989 to 2013. Pre-earthquake deformation rates are constrained by nearly 20 yr of USGS trilateration measurements and removed from the postseismic measurements prior to the analysis. We observe GPS horizontal displacements at mean rates of 1–4 mm/yr toward Loma Prieta Mountain until 2000, and ∼2 mm/yr surface subsidence of the northern Santa Cruz Mountains between 1992 and 2002 shown by InSAR, which is not associated with the seasonal and longer-term hydrological deformation in the adjoining Santa Clara Valley. Previous work indicates afterslip dominated in the early (1989–1994) postseismic period, so we focus on modeling the postseismic viscoelastic relaxation constrained by the geodetic observations after 1994. The best fitting model shows an elastic 19-km-thick upper crust above an 11-km-thick viscoelastic lower crust with viscosity of ∼6 × 1018 Pas, underlain by a viscous upper mantle with viscosity between 3 × 1018 and 2 × 1019 Pas. The millimeter-scale postseismic deformation does not resolve the viscosity in the different layers very well, and the lower-crustal relaxation may be localized in a narrow shear zone. However, the inferred lithospheric rheology is consistent with previous estimates based on post-1906 San Francisco earthquake measurements along the San Andreas fault system. The viscoelastic relaxation may also contribute to the enduring increase of aseismic slip and repeating earthquake activity on the San Andreas fault near San Juan Bautista, which continued for at least a decade after the Loma Prieta event.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2015.12.018","usgsCitation":"Huang, M., Burgmann, R., and Pollitz, F., 2016, Lithospheric rheology constrained from twenty-five years of postseismic deformation following the 1989 Mw 6.9 Loma Prieta earthquake: Earth and Planetary Science Letters, v. 435, p. 147-158, https://doi.org/10.1016/j.epsl.2015.12.018.","productDescription":"12 p.","startPage":"147","endPage":"158","ipdsId":"IP-068757","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":471290,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2015.12.018","text":"Publisher Index Page"},{"id":342215,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United states","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.35,\n 37.6\n ],\n [\n -121.25,\n 37.6\n ],\n [\n -121.25,\n 36.8\n ],\n [\n -122.35,\n 36.8\n ],\n [\n -122.35,\n 37.6\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"435","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593910ade4b0764e6c5e8863","contributors":{"authors":[{"text":"Huang, Mong-Han","contributorId":192699,"corporation":false,"usgs":false,"family":"Huang","given":"Mong-Han","email":"","affiliations":[],"preferred":false,"id":697433,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burgmann, Roland","contributorId":192700,"corporation":false,"usgs":false,"family":"Burgmann","given":"Roland","affiliations":[],"preferred":false,"id":697420,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":697418,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70174967,"text":"70174967 - 2016 - Characterization of gas hydrate distribution using conventional 3D seismic data in the Pearl River Mouth Basin, South China Sea","interactions":[],"lastModifiedDate":"2016-07-25T13:03:12","indexId":"70174967","displayToPublicDate":"2016-02-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3906,"text":"Interpretation","active":true,"publicationSubtype":{"id":10}},"title":"Characterization of gas hydrate distribution using conventional 3D seismic data in the Pearl River Mouth Basin, South China Sea","docAbstract":"A new 3D seismic reflection data volume acquired in 2012 has allowed for the detailed mapping and characterization of gas hydrate distribution in the Pearl River Mouth Basin in the South China Sea. Previous studies of core and logging data showed that gas hydrate occurrence at high concentrations is controlled by the presence of relatively coarse-grained sediment and the upward migration of thermogenic gas from the deeper sediment section into the overlying gas hydrate stability zone (BGHSZ); however, the spatial distribution of the gas hydrate remains poorly defined. We used a constrained sparse spike inversion technique to generate acoustic-impedance images of the hydrate-bearing sedimentary section from the newly acquired 3D seismic data volume. High-amplitude reflections just above the bottom-simulating reflectors (BSRs) were interpreted to be associated with the accumulation of gas hydrate with elevated saturations. Enhanced seismic reflections below the BSRs were interpreted to indicate the presence of free gas. The base of the BGHSZ was established using the occurrence of BSRs. In areas absent of well-developed BSRs, the BGHSZ was calculated from a model using the inverted P-wave velocity and subsurface temperature data. Seismic attributes were also extracted along the BGHSZ that indicate variations reservoir properties and inferred hydrocarbon accumulations at each site. Gas hydrate saturations estimated from the inversion of acoustic impedance of conventional 3D seismic data, along with well-log-derived rock-physics models were also used to estimate gas hydrate saturations. Our analysis determined that the gas hydrate petroleum system varies significantly across the Pearl River Mouth Basin and that variability in sedimentary properties as a product of depositional processes and the upward migration of gas from deeper thermogenic sources control the distribution of gas hydrates in this basin.
","language":"English","publisher":"Society of Exploration Geophysicists","doi":"10.1190/INT-2015-0020.1","usgsCitation":"Wang, X., Qiang, J., Collett, T.S., Shi, H., Yang, S., Yan, C., Li, Y., Wang, Z., and Chen, D., 2016, Characterization of gas hydrate distribution using conventional 3D seismic data in the Pearl River Mouth Basin, South China Sea: Interpretation, v. 4, no. 1, p. SA25-SA37, https://doi.org/10.1190/INT-2015-0020.1.","productDescription":"13 p.","startPage":"SA25","endPage":"SA37","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062836","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":325592,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"Pearl River Mouth Basin, South China Sea","volume":"4","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5797382ee4b021cadec8ff1b","contributors":{"authors":[{"text":"Wang, Xiujuan","contributorId":87071,"corporation":false,"usgs":true,"family":"Wang","given":"Xiujuan","affiliations":[],"preferred":false,"id":643437,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Qiang, Jin","contributorId":62239,"corporation":false,"usgs":true,"family":"Qiang","given":"Jin","email":"","affiliations":[],"preferred":false,"id":643444,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collett, Timothy S. 0000-0002-7598-4708 tcollett@usgs.gov","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":1698,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","email":"tcollett@usgs.gov","middleInitial":"S.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":643436,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shi, Hesheng","contributorId":173150,"corporation":false,"usgs":false,"family":"Shi","given":"Hesheng","email":"","affiliations":[{"id":27163,"text":"Shenzhen Branch of China National Offshore Oil Corporation Ltd., Shenzhen 518067, China","active":true,"usgs":false}],"preferred":false,"id":643438,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yang, Shengxiong","contributorId":74306,"corporation":false,"usgs":true,"family":"Yang","given":"Shengxiong","affiliations":[],"preferred":false,"id":643439,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yan, Chengzhi","contributorId":173151,"corporation":false,"usgs":false,"family":"Yan","given":"Chengzhi","email":"","affiliations":[{"id":27163,"text":"Shenzhen Branch of China National Offshore Oil Corporation Ltd., Shenzhen 518067, China","active":true,"usgs":false}],"preferred":false,"id":643440,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Li, Yuanping","contributorId":173152,"corporation":false,"usgs":false,"family":"Li","given":"Yuanping","email":"","affiliations":[{"id":27163,"text":"Shenzhen Branch of China National Offshore Oil Corporation Ltd., Shenzhen 518067, China","active":true,"usgs":false}],"preferred":false,"id":643441,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wang, Zhenzhen","contributorId":173153,"corporation":false,"usgs":false,"family":"Wang","given":"Zhenzhen","email":"","affiliations":[{"id":27164,"text":"Zhanjiang Branch of China National Offshore Oil Corporation Ltd., Zhanjiang, 524057, China","active":true,"usgs":false}],"preferred":false,"id":643442,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Chen, Duanxin","contributorId":173154,"corporation":false,"usgs":false,"family":"Chen","given":"Duanxin","email":"","affiliations":[{"id":27165,"text":"Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China","active":true,"usgs":false}],"preferred":false,"id":643443,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70178028,"text":"70178028 - 2016 - Density, distribution, and genetic structure of grizzly bears in the Cabinet-Yaak Ecosystem","interactions":[],"lastModifiedDate":"2021-08-31T15:34:06.340966","indexId":"70178028","displayToPublicDate":"2016-02-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Density, distribution, and genetic structure of grizzly bears in the Cabinet-Yaak Ecosystem","docAbstract":"The conservation status of the 2 threatened grizzly bear (Ursus arctos) populations in the Cabinet-Yaak Ecosystem (CYE) of northern Montana and Idaho had remained unchanged since designation in 1975; however, the current demographic status of these populations was uncertain. No rigorous data on population density and distribution or analysis of recent population genetic structure were available to measure the effectiveness of conservation efforts. We used genetic detection data from hair corral, bear rub, and opportunistic sampling in traditional and spatial capture–recapture models to generate estimates of abundance and density of grizzly bears in the CYE. We calculated mean bear residency on our sampling grid from telemetry data using Huggins and Pledger models to estimate the average number of bears present and to correct our superpopulation estimates for lack of geographic closure. Estimated grizzly bear abundance (all sex and age classes) in the CYE in 2012 was 48–50 bears, approximately half the population recovery goal. Grizzly bear density in the CYE (4.3–4.5 grizzly bears/1,000 km2) was among the lowest of interior North American populations. The sizes of the Cabinet (n = 22–24) and Yaak (n = 18–22) populations were similar. Spatial models produced similar estimates of abundance and density with comparable precision without requiring radio-telemetry data to address assumptions of geographic closure. The 2 populations in the CYE were demographically and reproductively isolated from each other and the Cabinet population was highly inbred. With parentage analysis, we documented natural migrants to the Cabinet and Yaak populations by bears born to parents in the Selkirk and Northern Continental Divide populations. These events supported data from other sources suggesting that the expansion of neighboring populations may eventually help sustain the CYE populations. However, the small size, isolation, and inbreeding documented by this study demonstrate the need for comprehensive management designed to support CYE population growth and increased connectivity and gene flow with other populations.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.1019","usgsCitation":"Kendall, K.C., Macleod, A., Boyd, K.L., Boulanger, J., Royle, J., Kasworm, W.F., Paetkau, D., Proctor, M.F., Graves, T.A., and Annis, K., 2016, Density, distribution, and genetic structure of grizzly bears in the Cabinet-Yaak Ecosystem: Journal of Wildlife Management, v. 80, no. 2, p. 314-331, https://doi.org/10.1002/jwmg.1019.","productDescription":"18 p.","startPage":"314","endPage":"331","ipdsId":"IP-058746","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":330592,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.5045166015625,\n 47.53203824675999\n ],\n [\n -116.5045166015625,\n 49.001843917978526\n ],\n [\n -115.02685546875,\n 49.001843917978526\n ],\n [\n -115.02685546875,\n 47.53203824675999\n ],\n [\n -116.5045166015625,\n 47.53203824675999\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"80","issue":"2","noUsgsAuthors":false,"publicationDate":"2015-11-15","publicationStatus":"PW","scienceBaseUri":"5819a9c4e4b0bb36a4c9102d","chorus":{"doi":"10.1002/jwmg.1019","url":"http://dx.doi.org/10.1002/jwmg.1019","publisher":"Wiley-Blackwell","authors":"Kendall Katherine C., Macleod Amy C., Boyd Kristina L., Boulanger John, Royle J. Andrew, Kasworm Wayne F., Paetkau David, Proctor Michael F., Annis Kim, Graves Tabitha A.","journalName":"The Journal of Wildlife Management","publicationDate":"11/15/2015"},"contributors":{"authors":[{"text":"Kendall, Katherine C. 0000-0002-4831-2287 kkendall@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-2287","contributorId":3081,"corporation":false,"usgs":true,"family":"Kendall","given":"Katherine","email":"kkendall@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":822258,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Macleod, Amy C.","contributorId":65739,"corporation":false,"usgs":true,"family":"Macleod","given":"Amy C.","affiliations":[],"preferred":false,"id":652549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyd, Kristina L.","contributorId":150937,"corporation":false,"usgs":false,"family":"Boyd","given":"Kristina","email":"","middleInitial":"L.","affiliations":[{"id":18146,"text":"Yaak Valley Forest Council","active":true,"usgs":false}],"preferred":false,"id":652550,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boulanger, John","contributorId":176494,"corporation":false,"usgs":false,"family":"Boulanger","given":"John","email":"","affiliations":[],"preferred":false,"id":652551,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":138865,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":652552,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kasworm, Wayne F.","contributorId":150938,"corporation":false,"usgs":false,"family":"Kasworm","given":"Wayne","email":"","middleInitial":"F.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":652553,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Paetkau, David","contributorId":97712,"corporation":false,"usgs":false,"family":"Paetkau","given":"David","email":"","affiliations":[],"preferred":false,"id":652554,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Proctor, Michael F.","contributorId":150939,"corporation":false,"usgs":false,"family":"Proctor","given":"Michael","email":"","middleInitial":"F.","affiliations":[{"id":18147,"text":"Birchdale Ecological","active":true,"usgs":false}],"preferred":false,"id":652555,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Graves, Tabitha A. 0000-0001-5145-2400 tgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":5898,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha","email":"tgraves@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":652557,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Annis, Kim","contributorId":150940,"corporation":false,"usgs":false,"family":"Annis","given":"Kim","email":"","affiliations":[{"id":18148,"text":"MT Fish, Wildlife, and Parks","active":true,"usgs":false}],"preferred":false,"id":652556,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70176519,"text":"70176519 - 2016 - Impacts of climate change on land-use and wetland productivity in the Prairie Pothole Region of North America","interactions":[],"lastModifiedDate":"2018-03-28T11:36:55","indexId":"70176519","displayToPublicDate":"2016-02-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3242,"text":"Regional Environmental Change","active":true,"publicationSubtype":{"id":10}},"title":"Impacts of climate change on land-use and wetland productivity in the Prairie Pothole Region of North America","docAbstract":"Wetland productivity in the Prairie Pothole Region (PPR) of North America is closely linked to climate. A warmer and drier climate, as predicted, will negatively affect the productivity of PPR wetlands and the services they provide. The effect of climate change on wetland productivity, however, will not only depend on natural processes (e.g., evapotranspiration), but also on human responses. Agricultural land use, the predominant use in the PPR, is unlikely to remain static as climate change affects crop yields and prices. Land use in uplands surrounding wetlands will further affect wetland water budgets and hence wetland productivity. The net impact of climate change on wetland productivity will therefore depend on both the direct effects of climate change on wetlands and the indirect effects on upland land use. We examine the effect of climate change and land-use response on semipermanent wetland productivity by combining an economic model of agricultural land-use change with an ecological model of wetland dynamics. Our results suggest that the climate change scenarios evaluated are likely to have profound effects on land use in the North and South Dakota PPR, with wheat displacing other crops and pasture. The combined pressure of land-use and climate change significantly reduces wetland productivity. In a climate scenario with a +4 °C increase in temperature, our model predicts that almost the entire region may lack the wetland productivity necessary to support wetland-dependent species.
","language":"English","publisher":"Springer","doi":"10.1007/s10113-015-0768-3","usgsCitation":"Rashford, B.S., Adams, R.M., Wu, J., Voldseth, R.A., Guntenspergen, G.R., Werner, B., and Johnson, W., 2016, Impacts of climate change on land-use and wetland productivity in the Prairie Pothole Region of North America: Regional Environmental Change, v. 16, no. 2, p. 515-526, https://doi.org/10.1007/s10113-015-0768-3.","productDescription":"12 p.","startPage":"515","endPage":"526","ipdsId":"IP-061526","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":328758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota, South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.041015625,\n 42.779275360241904\n ],\n [\n -102.041015625,\n 48.980216985374994\n ],\n [\n -96.50390625,\n 48.980216985374994\n ],\n [\n -96.50390625,\n 42.779275360241904\n ],\n [\n -102.041015625,\n 42.779275360241904\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"2","noUsgsAuthors":false,"publicationDate":"2015-02-17","publicationStatus":"PW","scienceBaseUri":"57f7c6cfe4b0bc0bec09cb78","chorus":{"doi":"10.1007/s10113-015-0768-3","url":"http://dx.doi.org/10.1007/s10113-015-0768-3","publisher":"Springer Nature","authors":"Rashford Benjamin S., Adams Richard M., Wu JunJie, Voldseth Richard A., Guntenspergen Glenn R., Werner Brett, Johnson W. Carter","journalName":"Regional Environmental Change","publicationDate":"2/17/2015","auditedOn":"7/29/2016","publiclyAccessibleDate":"2/17/2015"},"contributors":{"authors":[{"text":"Rashford, Benjamin S.","contributorId":174506,"corporation":false,"usgs":false,"family":"Rashford","given":"Benjamin","email":"","middleInitial":"S.","affiliations":[{"id":6656,"text":"University of Wyoming, Renewable Resources","active":true,"usgs":false}],"preferred":false,"id":649078,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, Richard M.","contributorId":174709,"corporation":false,"usgs":false,"family":"Adams","given":"Richard","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":649079,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wu, Jun","contributorId":174710,"corporation":false,"usgs":false,"family":"Wu","given":"Jun","email":"","affiliations":[],"preferred":false,"id":649080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Voldseth, Richard A.","contributorId":98453,"corporation":false,"usgs":true,"family":"Voldseth","given":"Richard","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":649081,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":649082,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Werner, Brett","contributorId":47073,"corporation":false,"usgs":true,"family":"Werner","given":"Brett","affiliations":[],"preferred":false,"id":649083,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johnson, W. Carter","contributorId":17548,"corporation":false,"usgs":true,"family":"Johnson","given":"W. Carter","affiliations":[],"preferred":false,"id":649084,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70177883,"text":"70177883 - 2016 - Monogenetic volcanoes fed by interconnected dikes and sills in the Hopi Buttes volcanic field, Navajo Nation, USA","interactions":[],"lastModifiedDate":"2016-10-25T15:48:32","indexId":"70177883","displayToPublicDate":"2016-02-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Monogenetic volcanoes fed by interconnected dikes and sills in the Hopi Buttes volcanic field, Navajo Nation, USA","docAbstract":"Although monogenetic volcanic fields pose hazards to major cities worldwide, their shallow magma feeders (<500 m depth) are rarely exposed and, therefore, poorly understood. Here, we investigate exposures of dikes and sills in the Hopi Buttes volcanic field, Arizona, to shed light on the nature of its magma feeder system. Shallow exposures reveal a transition zone between intrusion and eruption within 350 m of the syn-eruptive surface. Using a combination of field- and satellite-based observations, we have identified three types of shallow magma systems: (1) dike-dominated, (2) sill-dominated, and (3) interconnected dike-sill networks. Analysis of vent alignments using the pyroclastic massifs and other eruptive centers (e.g., maar-diatremes) shows a NW-SE trend, parallel to that of dikes in the region. We therefore infer that dikes fed many of the eruptions. Dikes are also observed in places transforming to transgressive (ramping) sills. Estimates of the observable volume of dikes (maximum volume of 1.90 × 106 m3) and sills (minimum volume of 8.47 × 105 m3) in this study reveal that sills at Hopi Buttes make up at least 30 % of the shallow intruded volume (∼2.75 × 106 m3 total) within 350 m of the paeosurface. We have also identified saucer-shaped sills, which are not traditionally associated with monogenetic volcanic fields. Our study demonstrates that shallow feeders in monogenetic fields can form geometrically complex networks, particularly those intruding poorly consolidated sedimentary rocks. We conclude that the Hopi Buttes eruptions were primarily fed by NW-SE-striking dikes. However, saucer-shaped sills also played an important role in modulating eruptions by transporting magma toward and away from eruptive conduits. Sill development could have been accompanied by surface uplifts on the order of decimeters. We infer that the characteristic feeder systems described here for the Hopi Buttes may underlie monogenetic fields elsewhere, particularly where magma intersects shallow, and often weak, sedimentary rocks. Results from this study support growing evidence of the important role of shallow sills in active monogenetic fields.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-016-1005-8","usgsCitation":"Muirhead, J.D., Van Eaton, A., Re, G., White, J.D., and Ort, M.H., 2016, Monogenetic volcanoes fed by interconnected dikes and sills in the Hopi Buttes volcanic field, Navajo Nation, USA: Bulletin of Volcanology, v. 78, p. 1-16, https://doi.org/10.1007/s00445-016-1005-8.","productDescription":"Article 11; 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-070246","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":330381,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Hopi Buttes Volcanic Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.35,\n 35.1\n ],\n [\n -110.35,\n 35.3\n ],\n [\n -110,\n 35.3\n ],\n [\n -110,\n 35.1\n ],\n [\n -110.35,\n 35.1\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"78","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-12","publicationStatus":"PW","scienceBaseUri":"58106f98e4b0f497e7961119","contributors":{"authors":[{"text":"Muirhead, James D.","contributorId":176260,"corporation":false,"usgs":false,"family":"Muirhead","given":"James","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":652011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":140076,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa R.","email":"avaneaton@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":652010,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Re, Giuseppe","contributorId":176261,"corporation":false,"usgs":false,"family":"Re","given":"Giuseppe","email":"","affiliations":[],"preferred":false,"id":652012,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, James D. L.","contributorId":176262,"corporation":false,"usgs":false,"family":"White","given":"James","email":"","middleInitial":"D. L.","affiliations":[],"preferred":false,"id":652013,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ort, Michael H.","contributorId":156308,"corporation":false,"usgs":false,"family":"Ort","given":"Michael","email":"","middleInitial":"H.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":true,"id":652014,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70177910,"text":"70177910 - 2016 - Geochemistry of formation waters from the Wolfcamp and “Cline” shales: Insights into brine origin, reservoir connectivity, and fluid flow in the Permian Basin, USA","interactions":[],"lastModifiedDate":"2019-05-24T08:19:21","indexId":"70177910","displayToPublicDate":"2016-01-30T19:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1213,"text":"Chemical Geology","active":true,"publicationSubtype":{"id":10}},"title":"Geochemistry of formation waters from the Wolfcamp and “Cline” shales: Insights into brine origin, reservoir connectivity, and fluid flow in the Permian Basin, USA","docAbstract":"\n
Despite being one of the most important oil producing provinces in the United States, information on basinal hydrogeology and fluid flow in the Permian Basin of Texas and New Mexico is lacking. The source and geochemistry of brines from the basin were investigated (Ordovician- to Guadalupian-age reservoirs) by combining previously published data from conventional reservoirs with geochemical results for 39 new produced water samples, with a focus on those from shales. Salinity of the Ca–Cl-type brines in the basin generally increases with depth reaching a maximum in Devonian (median = 154 g/L) reservoirs, followed by decreases in salinity in the Silurian (median = 77 g/L) and Ordovician (median = 70 g/L) reservoirs. Isotopic data for B, O, H, and Sr and ion chemistry indicate three major types of water. Lower salinity fluids (<70 g/L) of meteoric origin in the middle and upper Permian hydrocarbon reservoirs (1.2–2.5 km depth; Guadalupian and Leonardian age) likely represent meteoric waters that infiltrated through and dissolved halite and anhydrite in the overlying evaporite layer. Saline (>100 g/L), isotopically heavy (O and H) water in Leonardian [Permian] to Pennsylvanian reservoirs (2–3.2 km depth) is evaporated, Late Permian seawater. Water from the Permian Wolfcamp and Pennsylvanian “Cline” shales, which are isotopically similar but lower in salinity and enriched in alkalis, appear to have developed their composition due to post-illitization diffusion into the shales. Samples from the “Cline” shale are further enriched with NH4, Br, I and isotopically light B, sourced from the breakdown of marine kerogen in the unit. Lower salinity waters (<100 g/L) in Devonian and deeper reservoirs (>3 km depth), which plot near the modern local meteoric water line, are distinct from the water in overlying reservoirs. We propose that these deep meteoric waters are part of a newly identified hydrogeologic unit: the Deep Basin Meteoric Aquifer System. Chemical, isotopic, and pressure data suggest that despite over-pressuring in the Wolfcamp shale, there is little potential for vertical fluid migration to the surface environment via natural conduits.
\n
Executive Summary
\nSilver Lake is a 672-acre inland lake located in Oceana County, Michigan, and is a major tourist destination due to its proximity to Lake Michigan and the surrounding outdoor recreational opportunities. In recent years, Silver Lake exhibited patterns of high phosphorus concentrations, elevated chlorophyll a concentrations, and nuisance algal blooms. The U.S. Geological Survey (USGS), in cooperation with the Silver Lake Improvement Board and in collaboration with the Annis Water Resources Institute (AWRI) of Grand Valley State University, designed a study to assess the hydrologic and nutrient inputs to Silver Lake in order to identify the events and conditions that affect the nutrient chemistry and production of algal blooms in the lake. This information can inform water-resource managers in developing various management strategies to prevent or reduce the occurrence of future algal blooms.
\nUSGS and AWRI scientists collected data from November 2012 to December 2014 to provide information for future management decisions for Silver Lake. Silver Lake can be classified as a polymictic lake and has a residence time of approximately 223 days. Based on the mean lake Secchi depth, total phosphorus, and total nitrogen concentrations, Silver Lake is classified as a eutrophic lake. In-situ bioassay results indicate that algal growth in Silver Lake is colimited by both nitrogen and phosphorus. The nutrient budget for Silver Lake was calculated using the BATHTUB model based on 2 years of water-quality data collection. The BATHTUB model, developed by the U.S. Army Corps of Engineers, treats the lake as a well-mixed system with multiple inputs and outlets for both water and dissolved constituents, such as nutrients.
\nBased on results of the BATHTUB model, which were conditioned on observed concentrations and flows, the mean annual input of phosphorus to Silver Lake was approximately 1,342 pounds (lb); the mean annual input of nitrogen to Silver Lake was approximately 51,998 lb. The major measured sources of phosphorus loading to Silver Lake were groundwater and Hunter Creek, whereas the major measured sources of nitrogen to Silver Lake were Hunter Creek, groundwater, and atmospheric deposition. The largest loading of phosphorus and nitrogen to Silver Lake occurred during the spring. Minimal phosphorus deposition (if any) occurred in the lakebed sediment; however, of the nitrogen that entered Silver Lake, approximately 42.2 percent was deposited in the lakebed sediment as simulated by the BATHTUB model.
\nIn addition to measured sources, a septic load model was used to estimate the likely range of septic contribution to groundwater and adjacent surface waters. The likely septic loading scenario estimates that septic systems contribute 47.8 percent of the phosphorus to groundwater and 22.3 percent of phosphorus to Hunter Creek. These results indicate that septic systems are a major source of phosphorus loading to Silver Lake. The likely septic loading scenario indicated that septic systems account for 0.95 percent of the nitrogen load to Hunter Creek and 1.1 percent of the contribution of nitrogen to groundwater.
\nThe BATHTUB model was used to estimate future nutrient loading and eutrophication scenarios based on water-quality data collected from Silver Lake, groundwater, major tributaries, and atmospheric deposition. A separate septic load model was used to estimate the septic contribution to groundwater or directly to surface water, and the nutrient load estimates were modeled using the BATHTUB model to determine subsequent water-quality changes to Silver Lake.
\n\n\n\n- BATHTUB model scenarios based on measured data:
\n
\n\n\n- The first BATHTUB scenario evaluated the condition of Silver Lake and the change to lake water quality (trophic status) as a result of changes in nutrient loading from different sources. Based on BATHTUB model simulations, if groundwater loading of phosphorus and nitrogen only were decreased by 75 percent, and all of the other nutrient inputs stayed the same, the future condition of Silver Lake would most likely remain highly mesotrophic to eutrophic (the current [2014] condition of Silver Lake). If nutrient loading continued to increase in groundwater, the lake would continue to remain eutrophic with more frequent algal blooms. If nutrient loading from Hunter Creek only decreased by 50–75 percent, and all of the other nutrient inputs stayed the same as the baseline dataset, Silver Lake would remain eutrophic to highly mesotrophic. By reducing the input of manageable nutrient sources (Hunter Creek, groundwater, and lawn runoff) by 75 percent, the BATHTUB model simulation indicates that Silver Lake would be classified as mesotrophic, which is indicative of improved water quality, water clarity, and reduced algal bloom frequency.
\n- Simulations also were run using the BATHTUB model to evaluate the number of days Silver Lake could experience algal blooms (algal blooms are defined as modeled chlorophyll a in excess of 10 micrograms per liter [µg/L]) as a result of an increase/decrease in phosphorus and nitrogen loading from groundwater, Hunter Creek, and (or) a combination of sources. If the phosphorus and nitrogen loading from Hunter Creek is decreased (and all other sources are not altered), Silver Lake will continue to experience algal blooms, but less frequently than what is currently experienced. The same scenario holds true if the nutrient loading from groundwater is decreased. Another scenario was simulated using a combination of sources, which includes increases and decreases in phosphorus and nitrogen loading from sources that are the most likely to be managed, and includes groundwater (as a result of conversion of household septic to sewers), Hunter Creek (conversion of household septic to sewers), and lawn runoff. Results of the BATHTUB model indicated that a 50-percent reduction of phosphorus and nitrogen from these sources would result in a considerable decrease in algal bloom frequency (from 231 to 132 days) and severity, and a 75-percent reduction would greatly reduce algal bloom occurrence on Silver Lake (from 231 to 57 days).
\n
\n
\n\n- BATHTUB model scenarios based on septic load model:
\n\n- A scenario also was conducted using the BATHTUB model to simulate the conversion of septic to sewer and included a low, high, and medium (likely) scenario of nutrient loading to Silver Lake. Simulations of the BATHTUB model indicated that, under the likely scenario, the conversion of all onsite septic treatment to sewers would result in an overall change in lake trophic status from eutrophic to mesotrophic, thereby reducing the frequency of algal blooms and algal bloom intensity on Silver Lake (chlorophyll a >10 µg/L, from 231 to 184 days per year, or chlorophyll a >20 µg/L, from 80 to 49 days per year).
\n
\n
\n
\n
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155158","collaboration":"Prepared in cooperation with the Silver Lake Improvement Board","usgsCitation":"Brennan, A.K., Hoard, C.J., Duris, J.W., Ogdahl, M.E., and Steinman, A.D., 2015, Water quality and hydrology of Silver Lake, Oceana County, Michigan, with Emphasis on lake response to nutrient loading, 2012–14. U.S. Geological Survey Scientific Investigations Report 2015–5158, 75 p., https://dx.doi.org/10.3133/sir20155158.","productDescription":"xii, 75 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062273","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":315029,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5158/coverthb.jpg"},{"id":315030,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5158/sir20155158.pdf","text":"Report","size":"43.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5158"}],"country":"United States","state":"Michigan","county":"Oceana County","otherGeospatial":"Silver Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.50016784667969,\n 43.70734532390574\n ],\n [\n -86.47733688354492,\n 43.7028779055427\n ],\n [\n -86.46669387817383,\n 43.69977533580068\n ],\n [\n -86.45862579345703,\n 43.693942070030545\n ],\n [\n -86.45021438598633,\n 43.683763524273346\n ],\n [\n -86.44712448120117,\n 43.67730794174066\n ],\n [\n -86.4455795288086,\n 43.6666298770785\n ],\n [\n -86.44918441772461,\n 43.65843379478084\n ],\n [\n -86.45725250244139,\n 43.652969118285434\n ],\n [\n -86.47167205810547,\n 43.646261790183424\n ],\n [\n -86.49003982543945,\n 43.64116868896908\n ],\n [\n -86.50720596313477,\n 43.64005063334694\n ],\n [\n -86.5228271484375,\n 43.639677943516006\n ],\n [\n -86.53329849243164,\n 43.640299091949906\n ],\n [\n -86.5422248840332,\n 43.64390162623238\n ],\n [\n -86.54016494750977,\n 43.65806121899918\n ],\n [\n -86.53123855590819,\n 43.67693548309422\n ],\n [\n -86.52111053466797,\n 43.688853013063195\n ],\n [\n -86.50840759277344,\n 43.70238150517333\n ],\n [\n -86.50016784667969,\n 43.70734532390574\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Michigan Water Science Center
U.S. Geological Survey
6520 Mercantile Way Suite 5
Lansing, MI 48911–5991
http://mi.water.usgs.gov/
","tableOfContents":"\n- Acknowledgments
\n- Executive Summary
\n- Introduction
\n- Study Methods and Sampling Sites
\n- Lake Water-Quality Characteristics
\n- Hydrology: Sources of Water and Nutrients
\n- Nutrient Load Modeling
\n- Summary and Conclusions
\n- References Cited
\n- Appendix 1. Estimation of Streamflow at Silver Lake Dam
\n- References Cited
\n- Appendix 2. Additional Data Collected
\n- References Cited
\n- Appendix 3. Quantification of Groundwater Flow to Silver Lake
\n- References Cited
\n
","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"publishedDate":"2016-01-29","noUsgsAuthors":false,"publicationDate":"2016-01-29","publicationStatus":"PW","scienceBaseUri":"56ac8d2be4b0403299f4d482","contributors":{"authors":[{"text":"Brennan, Angela K. akbrennan@usgs.gov","contributorId":152662,"corporation":false,"usgs":true,"family":"Brennan","given":"Angela K.","email":"akbrennan@usgs.gov","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":false,"id":590083,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoard, Christopher J. 0000-0003-2337-506X cjhoard@usgs.gov","orcid":"https://orcid.org/0000-0003-2337-506X","contributorId":191767,"corporation":false,"usgs":true,"family":"Hoard","given":"Christopher","email":"cjhoard@usgs.gov","middleInitial":"J.","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":false,"id":590084,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duris, Joseph W. 0000-0002-8669-8109 jwduris@usgs.gov","orcid":"https://orcid.org/0000-0002-8669-8109","contributorId":1981,"corporation":false,"usgs":true,"family":"Duris","given":"Joseph","email":"jwduris@usgs.gov","middleInitial":"W.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":false,"id":590085,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ogdahl, Mary E.","contributorId":152664,"corporation":false,"usgs":false,"family":"Ogdahl","given":"Mary","email":"","middleInitial":"E.","affiliations":[{"id":18955,"text":"Annis Water Resources Institute-GVSU","active":true,"usgs":false}],"preferred":false,"id":590087,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Steinman, Alan D.","contributorId":71868,"corporation":false,"usgs":true,"family":"Steinman","given":"Alan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":590086,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70162466,"text":"70162466 - 2016 - The Lassen hydrothermal system","interactions":[],"lastModifiedDate":"2016-01-29T09:32:26","indexId":"70162466","displayToPublicDate":"2016-01-29T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":738,"text":"American Mineralogist","active":true,"publicationSubtype":{"id":10}},"title":"The Lassen hydrothermal system","docAbstract":"The active Lassen hydrothermal system includes a central vapor-dominated zone or zones beneath the Lassen highlands underlain by ~240 °C high-chloride waters that discharge at lower elevations. It is the best-exposed and largest hydrothermal system in the Cascade Range, discharging 41 ± 10 kg/s of steam (~115 MW) and 23 ± 2 kg/s of high-chloride waters (~27 MW). The Lassen system accounts for a full 1/3 of the total high-temperature hydrothermal heat discharge in the U.S. Cascades (140/400 MW). Hydrothermal heat discharge of ~140 MW can be supported by crystallization and cooling of silicic magma at a rate of ~2400 km3/Ma, and the ongoing rates of heat and magmatic CO2 discharge are broadly consistent with a petrologic model for basalt-driven magmatic evolution. The clustering of observed seismicity at ~4–5 km depth may define zones of thermal cracking where the hydrothermal system mines heat from near-plastic rock. If so, the combined areal extent of the primary heat-transfer zones is ~5 km2, the average conductive heat flux over that area is >25 W/m2, and the conductive-boundary length <50 m. Observational records of hydrothermal discharge are likely too short to document long-term transients, whether they are intrinsic to the system or owe to various geologic events such as the eruption of Lassen Peak at 27 ka, deglaciation beginning ~18 ka, the eruptions of Chaos Crags at 1.1 ka, or the minor 1914–1917 eruption at the summit of Lassen Peak. However, there is a rich record of intermittent hydrothermal measurement over the past several decades and more-frequent measurement 2009–present. These data reveal sensitivity to climate and weather conditions, seasonal variability that owes to interaction with the shallow hydrologic system, and a transient 1.5- to twofold increase in high-chloride discharge in response to an earthquake swarm in mid-November 2014.
","language":"English","doi":"10.2138/am-2016-5456","usgsCitation":"Ingebritsen, S.E., Bergfeld, D., Clor, L., and Evans, W.C., 2016, The Lassen hydrothermal system: American Mineralogist, v. 101, p. 343-354, https://doi.org/10.2138/am-2016-5456.","productDescription":"12 p.","startPage":"343","endPage":"354","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065939","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":471298,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2138/am-2016-5456","text":"Publisher Index Page"},{"id":315024,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":315023,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.minsocam.org/MSA/AmMin/TOC/2016/index.html?issue_number=02"}],"country":"United States","state":"California","otherGeospatial":"Lassen Volcanic National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.7,\n 40.3\n ],\n [\n -121.7,\n 40.7\n ],\n [\n -121.2,\n 40.7\n ],\n [\n -121.2,\n 40.3\n ],\n [\n -121.7,\n 40.3\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"101","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-02","publicationStatus":"PW","scienceBaseUri":"56ac8d2ae4b0403299f4d476","contributors":{"authors":[{"text":"Ingebritsen, Steven E. 0000-0001-6917-9369 seingebr@usgs.gov","orcid":"https://orcid.org/0000-0001-6917-9369","contributorId":818,"corporation":false,"usgs":true,"family":"Ingebritsen","given":"Steven","email":"seingebr@usgs.gov","middleInitial":"E.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":589647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bergfeld, Deborah 0000-0003-4570-7627 dbergfel@usgs.gov","orcid":"https://orcid.org/0000-0003-4570-7627","contributorId":152531,"corporation":false,"usgs":true,"family":"Bergfeld","given":"Deborah","email":"dbergfel@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":589648,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clor, Laura 0000-0003-2633-5100 lclor@usgs.gov","orcid":"https://orcid.org/0000-0003-2633-5100","contributorId":150878,"corporation":false,"usgs":false,"family":"Clor","given":"Laura","email":"lclor@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":589649,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Evans, William C. 0000-0001-5942-3102 wcevans@usgs.gov","orcid":"https://orcid.org/0000-0001-5942-3102","contributorId":2353,"corporation":false,"usgs":true,"family":"Evans","given":"William","email":"wcevans@usgs.gov","middleInitial":"C.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":589650,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70162655,"text":"70162655 - 2016 - Wood decay in desert riverine environments","interactions":[],"lastModifiedDate":"2016-02-01T13:41:53","indexId":"70162655","displayToPublicDate":"2016-01-29T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Wood decay in desert riverine environments","docAbstract":"Floodplain forests and the woody debris they produce are major components of riverine ecosystems in many arid and semiarid regions (drylands). We monitored breakdown and nitrogen dynamics in wood and bark from a native riparian tree, Fremont cottonwood (Populus deltoides subsp. wislizeni), along four North American desert streams. We placed locally-obtained, fresh, coarse material [disks or cylinders (∼500–2000 cm3)] along two cold-desert and two warm-desert rivers in the Colorado River Basin. Material was placed in both floodplain and aquatic environments, and left in situ for up to 12 years. We tested the hypothesis that breakdown would be fastest in relatively warm and moist aerobic environments by comparing the time required for 50% loss of initial ash-free dry matter (T50) calculated using exponential decay models incorporating a lag term. In cold-desert sites (Green and Yampa rivers, Colorado), disks of wood with bark attached exposed for up to 12 years in locations rarely inundated lost mass at a slower rate (T50 = 34 yr) than in locations inundated during most spring floods (T50 = 12 yr). At the latter locations, bark alone loss mass at a rate initially similar to whole disks (T50 = 13 yr), but which subsequently slowed. In warm-desert sites monitored for 3 years, cylinders of wood with bark removed lost mass very slowly (T50 = 60 yr) at a location never inundated (Bill Williams River, Arizona), whereas decay rate varied among aquatic locations (T50 = 20 yr in Bill Williams River; T50 = 3 yr in Las Vegas Wash, an effluent-dominated stream warmed by treated wastewater inflows). Invertebrates had a minor role in wood breakdown except at in-stream locations in Las Vegas Wash. The presence and form of change in nitrogen content during exposure varied among riverine environments. Our results suggest woody debris breakdown in desert riverine ecosystems is primarily a microbial process with rates determined by landscape position, local weather, and especially the regional climate through its effect on the flow regime. The increased warmth and aridity expected to accompany climate change in the North American southwest will likely retard the already slow wood decay process on naturally functioning desert river floodplains. Our results have implications for designing environmental flows to manage floodplain forest wood budgets, carbon storage, and nutrient cycling along regulated dryland rivers.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2016.01.023","usgsCitation":"Andersen, D., Stricker, C.A., and Nelson, S.M., 2016, Wood decay in desert riverine environments: Forest Ecology and Management, v. 365, p. 83-95, https://doi.org/10.1016/j.foreco.2016.01.023.","productDescription":"13 p.","startPage":"83","endPage":"95","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070271","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471299,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2016.01.023","text":"Publisher Index Page"},{"id":315016,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, Nevada","otherGeospatial":"Browns Park National Wildlife Refuge, Colorado River, Dinosaur National Monument, Green River, Yampa River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 13.276290893554688,\n 43.683763524273346\n ],\n [\n 13.047637939453123,\n 43.54058479482877\n ],\n [\n 12.946014404296875,\n 43.479331676747684\n ],\n [\n 12.816925048828125,\n 43.47210668475589\n ],\n [\n 12.774696350097654,\n 43.4574048966441\n ],\n [\n 12.775382995605469,\n 43.4374645579086\n ],\n [\n 12.78053283691406,\n 43.41801639874422\n ],\n [\n 12.772293090820312,\n 43.38783445346398\n ],\n [\n 12.77984619140625,\n 43.36312895068202\n ],\n [\n 12.814521789550781,\n 43.29494951091899\n ],\n [\n 12.823104858398438,\n 43.27770522927142\n ],\n [\n 13.016738891601562,\n 43.32967314784173\n ],\n [\n 13.399200439453125,\n 43.487303079190156\n ],\n [\n 13.485031127929688,\n 43.59083558861119\n ],\n [\n 13.486404418945312,\n 43.608736628843445\n ],\n [\n 13.276290893554688,\n 43.683763524273346\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.09423828125,\n 41.04621681452063\n ],\n [\n -106.25976562499999,\n 41.062786068733026\n ],\n [\n -106.0400390625,\n 40.697299008636755\n ],\n [\n -105.99609375,\n 39.53793974517628\n ],\n [\n -106.3037109375,\n 38.59970036588819\n ],\n [\n -107.2705078125,\n 37.735969208590504\n ],\n [\n -108.34716796875,\n 37.37015718405753\n ],\n [\n -109.13818359375,\n 36.87962060502676\n ],\n [\n -110.45654296875,\n 35.47856499535729\n ],\n [\n -111.6650390625,\n 34.161818161230386\n ],\n [\n -114.36767578124999,\n 33.99802726234877\n ],\n [\n -114.14794921875,\n 34.50655662164561\n ],\n [\n -114.5654296875,\n 35.04798673426734\n ],\n [\n -115.07080078125,\n 35.460669951495305\n ],\n [\n -117.35595703124999,\n 37.26530995561875\n ],\n [\n -117.333984375,\n 37.78808138412046\n ],\n [\n -115.72998046875,\n 38.61687046392973\n ],\n [\n -113.79638671875,\n 39.38526381099777\n ],\n [\n -111.357421875,\n 40.195659093364654\n ],\n [\n -109.09423828125,\n 41.04621681452063\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"365","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56ac8d2de4b0403299f4d489","chorus":{"doi":"10.1016/j.foreco.2016.01.023","url":"http://dx.doi.org/10.1016/j.foreco.2016.01.023","publisher":"Elsevier BV","authors":"Andersen Douglas C., Stricker Craig A., Nelson S. Mark","journalName":"Forest Ecology and Management","publicationDate":"4/2016"},"contributors":{"authors":[{"text":"Andersen, Douglas doug_andersen@usgs.gov","contributorId":152661,"corporation":false,"usgs":true,"family":"Andersen","given":"Douglas","email":"doug_andersen@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":590076,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stricker, Craig A. 0000-0002-5031-9437 cstricker@usgs.gov","orcid":"https://orcid.org/0000-0002-5031-9437","contributorId":1097,"corporation":false,"usgs":true,"family":"Stricker","given":"Craig","email":"cstricker@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":590077,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nelson, S. Mark","contributorId":59283,"corporation":false,"usgs":true,"family":"Nelson","given":"S.","email":"","middleInitial":"Mark","affiliations":[],"preferred":false,"id":590078,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70162094,"text":"ofr20161006 - 2016 - The Integrated Landscape Modeling partnership - Current status and future directions","interactions":[],"lastModifiedDate":"2017-10-26T11:02:21","indexId":"ofr20161006","displayToPublicDate":"2016-01-28T17:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1006","title":"The Integrated Landscape Modeling partnership - Current status and future directions","docAbstract":"The Integrated Landscape Modeling (ILM) partnership is an effort by the U.S. Geological Survey (USGS) and U.S. Department of Agriculture (USDA) to identify, evaluate, and develop models to quantify services derived from ecosystems, with a focus on wetland ecosystems and conservation effects. The ILM partnership uses the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) modeling platform to facilitate regional quantifications of ecosystem services under various scenarios of land-cover change that are representative of differing conservation program and practice implementation scenarios. To date, the ILM InVEST partnership has resulted in capabilities to quantify carbon stores, amphibian habitat, plant-community diversity, and pollination services. Work to include waterfowl and grassland bird habitat quality is in progress. Initial InVEST modeling has been focused on the Prairie Pothole Region (PPR) of the United States; future efforts might encompass other regions as data availability and knowledge increase as to how functions affecting ecosystem services differ among regions.
The ILM partnership is also developing the capability for field-scale process-based modeling of depressional wetland ecosystems using the Agricultural Policy/Environmental Extender (APEX) model. Progress was made towards the development of techniques to use the APEX model for closed-basin depressional wetlands of the PPR, in addition to the open systems that the model was originally designed to simulate. The ILM partnership has matured to the stage where effects of conservation programs and practices on multiple ecosystem services can now be simulated in selected areas. Future work might include the continued development of modeling capabilities, as well as development and evaluation of differing conservation program and practice scenarios of interest to partner agencies including the USDA’s Farm Service Agency (FSA) and Natural Resources Conservation Service (NRCS). When combined, the ecosystem services modeling capabilities of InVEST and the process-based abilities of the APEX model should provide complementary information needed to meet USDA and the Department of the Interior information needs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161006","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture Natural Resources Conservation Service and Farm Service Agency","usgsCitation":"Mushet, D.M., and Scherff, E.J., 2016, The integrated landscape modeling partnership—Current status and future directions (ver. 1.1, December 2016): U.S. Geological Survey Open-File Report 2016–1006, 59 p., https://dx.doi.org/10.3133/ofr20161006.","productDescription":"72 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070297","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":314982,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1006/coverthb1.1.jpg"},{"id":332701,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2016/1006/version_history.txt"},{"id":314983,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1006/ofr20161006.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1006"}],"country":"United States","state":"Iowa, Minnesota, Nebraska, North Dakota, South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.82080078125,\n 48.99463598353408\n ],\n [\n -105.13916015625,\n 48.90805939965008\n ],\n [\n -104.83154296875,\n 48.44377831058805\n ],\n [\n -104.4140625,\n 47.945786463687185\n ],\n [\n -103.18359375,\n 47.87214396888731\n ],\n [\n -102.39257812499999,\n 47.502358951968596\n ],\n [\n -101.29394531249999,\n 47.010225655683485\n ],\n [\n -101.0302734375,\n 46.66451741754235\n ],\n [\n -100.96435546875,\n 45.87471224890479\n ],\n [\n -100.70068359374999,\n 45.27488643704894\n ],\n [\n -100.8544921875,\n 44.4808302785626\n ],\n [\n -100.30517578125,\n 43.929549935614595\n ],\n [\n -98.89892578125,\n 43.03677585761058\n ],\n [\n -97.22900390625,\n 42.84375132629021\n ],\n [\n -95.07568359375,\n 42.04929263868686\n ],\n [\n -93.955078125,\n 41.590796851056005\n ],\n [\n -93.05419921875,\n 41.57436130598913\n ],\n [\n -92.4169921875,\n 41.77131167976407\n ],\n [\n -92.35107421874999,\n 42.391008609205045\n ],\n [\n -92.74658203125,\n 43.34116005412307\n ],\n [\n -93.31787109374999,\n 43.929549935614595\n ],\n [\n -93.88916015625,\n 44.2294565683017\n ],\n [\n -94.68017578125,\n 45.413876460821086\n ],\n [\n -94.9658203125,\n 46.84516443029279\n ],\n [\n -96.6357421875,\n 48.472921272487824\n ],\n [\n -98.0859375,\n 48.951366470947725\n ],\n [\n -103.82080078125,\n 48.99463598353408\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted January 28, 2016; Version 1.1: December 30, 2016","contact":"Director, USGS Northern Prairie Wildlife Research Center
8711 37th Street Southeast
Jamestown, North Dakota 58401
\nhttp://www.npwrc.usgs.gov/
","tableOfContents":"\n- Acknowledgments
\n- Abstract
\n- Introduction
\n- Background
\n- InVEST Modeling
\n- APEX Modeling
\n- Other Related Modeling
\n- Summary
\n- References Cited
\n- Appendixes 1-8
\n
","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2016-01-28","revisedDate":"2016-12-30","noUsgsAuthors":false,"publicationDate":"2016-01-28","publicationStatus":"PW","scienceBaseUri":"56ab3bb2e4b07ca61bfe3bf0","contributors":{"authors":[{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":588487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scherff, Eric J. escherff@usgs.gov","contributorId":4390,"corporation":false,"usgs":true,"family":"Scherff","given":"Eric","email":"escherff@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":657125,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70161817,"text":"ofr20151241 - 2016 - A multidimensional representation model of geographic features","interactions":[],"lastModifiedDate":"2016-01-29T08:25:49","indexId":"ofr20151241","displayToPublicDate":"2016-01-28T16:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1241","title":"A multidimensional representation model of geographic features","docAbstract":"A multidimensional model of geographic features has been developed and implemented with data from The National Map of the U.S. Geological Survey. The model, programmed in C++ and implemented as a feature library, was tested with data from the National Hydrography Dataset demonstrating the capability to handle changes in feature attributes, such as increases in chlorine concentration in a stream, and feature geometry, such as the changing shoreline of barrier islands over time. Data can be entered directly, from a comma separated file, or features with attributes and relationships can be automatically populated in the model from data in the Spatial Data Transfer Standard format.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151241","usgsCitation":"Usery, E.L., Timson, George, and Coletti, Mark, 2015, A multidimensional representation model of geographic features: U.S. Geological Survey Open-File Report 2015–1241, 10 p., https://dx.doi.org/10.3133/ofr20151241.","productDescription":"iii, 10 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-059943","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":314951,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1241/ofr20151241.pdf","text":"Report","size":"406 kb","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1241"},{"id":314950,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1241/coverthb.jpg"}],"contact":"Director, Center of Excellence for Geospatial Information Science (CEGIS)
1400 Independence Road
Rolla, MO 65401
http://cegis.usgs.gov
","tableOfContents":"- Abstract
- Introduction
- Theory of Geographic Feature Representation
- System Design and Implementation
- Populating the Feature Library
- Conclusions
- References Cited
","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2016-01-28","noUsgsAuthors":false,"publicationDate":"2016-01-28","publicationStatus":"PW","scienceBaseUri":"56ab3ba7e4b07ca61bfe3bcf","contributors":{"authors":[{"text":"Usery, E. Lynn 0000-0002-2766-2173 usery@usgs.gov","orcid":"https://orcid.org/0000-0002-2766-2173","contributorId":231,"corporation":false,"usgs":true,"family":"Usery","given":"E.","email":"usery@usgs.gov","middleInitial":"Lynn","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":587848,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Timson, George timson@usgs.gov","contributorId":5206,"corporation":false,"usgs":true,"family":"Timson","given":"George","email":"timson@usgs.gov","affiliations":[],"preferred":false,"id":590014,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coletti, Mark","contributorId":152660,"corporation":false,"usgs":false,"family":"Coletti","given":"Mark","email":"","affiliations":[],"preferred":false,"id":590075,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70175245,"text":"70175245 - 2016 - Spatial and temporal variation in positioning probability of acoustic telemetry arrays: Fine-scale variability and complex interactions","interactions":[],"lastModifiedDate":"2016-08-03T12:37:15","indexId":"70175245","displayToPublicDate":"2016-01-28T13:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":773,"text":"Animal Biotelemetry","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal variation in positioning probability of acoustic telemetry arrays: Fine-scale variability and complex interactions","docAbstract":"\n
Background
\n
As popularity of positional acoustic telemetry systems increases, so does the need to better understand how they perform in real-world applications, where variation in performance can bias study conclusions. Studies assessing variability in positional telemetry system performance have focused primarily on position accuracy, or comparing performance inside and outside the array. Here, we explored spatial and temporal variation in positioning probability within a 140-receiver Vemco Positioning System (VPS) array used to monitor lake trout,Salvelinus namaycush, spawning behavior over 23 km2 in Lake Huron, North America.
\n
\n
Methods
\n
Variability in VPS positioning probability was assessed between August and November from 2012 to 2014 using 43 stationary transmitters distributed throughout the array. Various analyses were used to relate positioning probability to number of fish transmitters in the array, wave height, and thermal stratification. We also assessed the prevalence of ‘close proximity detection interference’ (CPDI) in our array by analyzing detection probability of 35 transmitters on collocated receivers.
\n
\n
Results
\n
Positioning probability of the VPS array varied greatly over time and space. Number of fish transmitters present in the array was a significant driver of reduced positioning probability, especially during lake trout spawning period when the fish were aggregated. Relationships between positioning probability and environmental variables were complex and varied over small spatial and temporal scales. One possible confounding variable was the large range of water depth over which receivers were deployed. Another confounding factor was the high prevalence of CPDI, which decreased exponentially with water depth and was less evident when wave heights were higher than normal.
\n
\n
Conclusions
\n
Some variables that negatively influenced positioning can be minimized through careful planning (e.g., number of tagged fish released, transmitter power level). However, results suggested that the acoustic environment was highly variable over small spatial and temporal scales in response to complex interactions between many variables. Therefore, models that predict positioning or detection efficiencies as a function of environmental variables may not be attainable in most systems. The best defense against biased study conclusions is incorporation of in situ measures of system performance that allow for retrospective analysis of array performance after a study is completed.
\n
\n
Keywords
\nVemco Positioning System Positional telemetry Performance Detection probability Close proximity detection interference Thermal stratification Wave height Signal code collision","language":"English","publisher":"Biomed Central","publisherLocation":"London","doi":"10.1186/s40317-016-0097-4","usgsCitation":"Binder, T., Holbrook, C., Hayden, T.A., and Krueger, C., 2016, Spatial and temporal variation in positioning probability of acoustic telemetry arrays: Fine-scale variability and complex interactions: Animal Biotelemetry, v. 4, no. 4, https://doi.org/10.1186/s40317-016-0097-4.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072050","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":471300,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-016-0097-4","text":"Publisher Index Page"},{"id":326038,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"4","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-28","publicationStatus":"PW","scienceBaseUri":"57a315d1e4b006cb45558ba6","contributors":{"authors":[{"text":"Binder, Thomas 0000-0001-9266-9120 tbinder@usgs.gov","orcid":"https://orcid.org/0000-0001-9266-9120","contributorId":4958,"corporation":false,"usgs":true,"family":"Binder","given":"Thomas","email":"tbinder@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":644515,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":644516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayden, Todd A. 0000-0002-0451-0425 thayden@usgs.gov","orcid":"https://orcid.org/0000-0002-0451-0425","contributorId":5987,"corporation":false,"usgs":true,"family":"Hayden","given":"Todd","email":"thayden@usgs.gov","middleInitial":"A.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":644517,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krueger, Charles C.","contributorId":73131,"corporation":false,"usgs":true,"family":"Krueger","given":"Charles C.","affiliations":[],"preferred":false,"id":644518,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70162539,"text":"70162539 - 2016 - Hyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation)","interactions":[],"lastModifiedDate":"2016-01-28T09:53:21","indexId":"70162539","displayToPublicDate":"2016-01-28T10:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"title":"Hyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation)","docAbstract":"Evapotranspiration (ET) is an important component of micro- and macro-scale climatic processes. In agriculture, estimates of ET are frequently used to monitor droughts, schedule irrigation, and assess crop water productivity over large areas. Currently, in situ measurements of ET are difficult to scale up for regional applications, so remote sensing technology has been increasingly used to estimate crop ET. Ratio-based vegetation indices retrieved from optical remote sensing, like the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index, and Enhanced Vegetation Index are critical components of these models, particularly for the partitioning of ET into transpiration and soil evaporation. These indices have their limitations, however, and can induce large model bias and error. In this study, micrometeorological and spectroradiometric data collected over two growing seasons in cotton, maize, and rice fields in the Central Valley of California were used to identify spectral wavelengths from 428 to 2295 nm that produced the highest correlation to and lowest error with ET, transpiration, and soil evaporation. The analysis was performed with hyperspectral narrowbands (HNBs) at 10 nm intervals and multispectral broadbands (MSBBs) commonly retrieved by Earth observation platforms. The study revealed that (1) HNB indices consistently explained more variability in ET (ΔR2 = 0.12), transpiration (ΔR2 = 0.17), and soil evaporation (ΔR2 = 0.14) than MSBB indices; (2) the relationship between transpiration using the ratio-based index most commonly used for ET modeling, NDVI, was strong (R2 = 0.51), but the hyperspectral equivalent was superior (R2 = 0.68); and (3) soil evaporation was not estimated well using ratio-based indices from the literature (highest R2 = 0.37), but could be after further evaluation, using ratio-based indices centered on 743 and 953 nm (R2 = 0.72) or 428 and 1518 nm (R2 = 0.69).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2015.12.025","usgsCitation":"Marshall, M.T., Thenkabail, P.S., Biggs, T., and Post, K., 2016, Hyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation): Agricultural and Forest Meteorology, v. 218-219, p. 122-134, https://doi.org/10.1016/j.agrformet.2015.12.025.","productDescription":"13 p.","startPage":"122","endPage":"134","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065032","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471301,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agrformet.2015.12.025","text":"Publisher Index Page"},{"id":314939,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.73925781250001,\n 40.07807142745009\n ],\n [\n -121.904296875,\n 39.977120098439634\n ],\n [\n -121.201171875,\n 38.856820134743636\n ],\n [\n -120.84960937499999,\n 37.996162679728116\n ],\n [\n -120.5419921875,\n 37.474858084971046\n ],\n [\n -119.53125,\n 36.63316209558658\n ],\n [\n -119.00390625,\n 36.491973470593685\n ],\n [\n -118.125,\n 35.460669951495305\n ],\n [\n -118.828125,\n 35.10193405724606\n ],\n [\n -119.83886718750001,\n 35.17380831799959\n ],\n [\n -120.7177734375,\n 35.67514743608467\n ],\n [\n -121.06933593749999,\n 36.70365959719456\n ],\n [\n -121.37695312499999,\n 37.37015718405753\n ],\n [\n -121.9482421875,\n 37.96152331396616\n ],\n [\n -122.3876953125,\n 38.41055825094609\n ],\n [\n -122.56347656249999,\n 39.36827914916014\n ],\n [\n -122.73925781250001,\n 40.07807142745009\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"218-219","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56ab3bade4b07ca61bfe3bdb","chorus":{"doi":"10.1016/j.agrformet.2015.12.025","url":"http://dx.doi.org/10.1016/j.agrformet.2015.12.025","publisher":"Elsevier BV","authors":"Marshall Michael, Thenkabail Prasad, Biggs Trent, Post Kirk","journalName":"Agricultural and Forest Meteorology","publicationDate":"3/2016","publiclyAccessibleDate":"12/8/2015"},"contributors":{"authors":[{"text":"Marshall, Michael T. mmarshall@usgs.gov","contributorId":5480,"corporation":false,"usgs":true,"family":"Marshall","given":"Michael","email":"mmarshall@usgs.gov","middleInitial":"T.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":589798,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":589797,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Biggs, Trent","contributorId":152640,"corporation":false,"usgs":false,"family":"Biggs","given":"Trent","affiliations":[],"preferred":false,"id":589996,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Post, Kirk","contributorId":152641,"corporation":false,"usgs":false,"family":"Post","given":"Kirk","email":"","affiliations":[],"preferred":false,"id":589997,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70160085,"text":"sir20155171 - 2016 - Surface-water quality and suspended-sediment quantity and quality within the Big River Basin, southeastern Missouri, 2011-13","interactions":[],"lastModifiedDate":"2016-08-10T11:13:05","indexId":"sir20155171","displayToPublicDate":"2016-01-28T09:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5171","title":"Surface-water quality and suspended-sediment quantity and quality within the Big River Basin, southeastern Missouri, 2011-13","docAbstract":"Missouri was the leading producer of lead in the United States—as well as the world—for more than a century. One of the lead sources is known as the Old Lead Belt, located in southeast Missouri. The primary ore mineral in the region is galena, which can be found both in surface deposits and underground as deep as 200 feet. More than 8.5 million tons of lead were produced from the Old Lead Belt before operations ceased in 1972. Although active lead mining has ended, the effects of mining activities still remain in the form of large mine waste piles on the landscape typically near tributaries and the main stem of the Big River, which drains the Old Lead Belt. Six large mine waste piles encompassing more than 2,800 acres, exist within the Big River Basin. These six mine waste piles have been an available source of trace element-rich suspended sediments transported by natural erosional processes downstream into the Big River.
\nA study was performed by the U.S. Geological Survey in cooperation with U.S. Environmental Protection Agency, Region 7, to calculate and characterize suspended-sediment quantity and quality within the Big River basin after reclamation of the mine waste piles ended in 2012. Streamflow and suspended sediments were quantified and sampled at two locations along a 68-mile reach of the Big River between Bonne Terre and Byrnes Mill, Missouri. The results will help regulatory agencies, such as the U.S. Environmental Protection Agency and U.S. Fish and Wildlife Service, determine impaired reaches and ecosystems for remedial and restoration efforts.
\nContinuous stream stage, water temperature, and turbidity, and discrete suspended-sediment concentration data were collected at the two sites between October 2011 and September 2013. Suspended-sediment samples were collected during various hydrologic conditions to develop a regression model between discrete suspended-sediment concentration and continuous turbidity. Suspended sediments collected during stormflow events were analyzed for concentrations of trace elements such as barium, cadmium, lead, and zinc within two sediment size fractions. Event loads and annual loads of suspended sediment and select trace elements in suspended sediments also were calculated.
\nSuspended-sediment loads computed by the regression model increased downstream from about 201,000 tons at the upstream site to about 355,000 tons at the downstream site during the study period. Stormflow-event-based (hereinafter referred to as “event-based”) suspended-sediment loads ranged from 180 to 32,000 tons at the upstream sampling site and 390 to 53,000 tons at the downstream site along the Big River. Although only seven stormflow events at the upstream site and six at the downstream site were sampled, the event-based suspended-sediment loads accounted for nearly 30 percent of the total suspended-sediment loads computed at both sites, indicating most of the suspended sediment transported through the Big River occurs during higher streamflows.
\nSediment quality guidelines, known as the threshold effect concentration and the probable effect concentration, used to assess toxicity of trace-element concentrations in sediments were compared to the cadmium, lead, and zinc concentrations in suspended sediment samples collected during stormflow events. All concentrations of cadmium, lead, and zinc in event-based suspended sediment samples exceeded the threshold and probable effect concentrations. Lead and zinc concentrations in the sediment size fraction less than 0.063 millimeters also exceeded the toxic effect threshold, above which sediment is considered to be heavily polluted causing adverse effects on sediment-dwelling organisms. Concentrations of cadmium and zinc in event-based suspended sediment samples were notably higher in samples from the upstream site than samples from the downstream site, indicating the sources of sediments enriched in these trace elements decrease in the downstream area of the watershed. The reduction in concentration of cadmium and zinc could be from dissolution of the constituents during transport or possibly a decrease in downstream source material. The lead concentration exceedance of the probable effects concentration as well as the threshold effects concentration indicates that lead-rich suspended sediments in the fraction less than 0.063 millimeters are readily available within the Big River Basin for transport. These sediments remain in the system from historical mining, and as the reclamation of mine waste piles in the upstream area of the watershed reduce additional sediment loadings, these fine sediments may be continually released as the river scours the streambed and erodes stream banks causing the lead-rich suspended sediment to remain in a state of equilibrium.
\nBarium concentrations in suspended-sediments were nearly twice as high in stormflow event samples collected at the downstream site as compared to samples from the upstream site. The source of barium in the Big River could be from Mineral Fork and Mill Creek, which flow through the historical barite (barium sulfate, also known as tiff) mining district in Washington County, and discharge into the Big River between the two study sites.
\nTotal trace-element loads and yields in suspended sediments were computed from the sampled events for each year in the study. The total barium loads in suspended sediments were higher for sampled events collected at the downstream site than the upstream site during both study years. Cadmium and zinc loads in suspended sediments were lower at the downstream site than the upstream site, although the decrease in total load was not substantial during the study period. Lead loads in suspended sediments were lower at the downstream site during the first study year, with a slightly higher load downstream in the second year though the increase from upstream to downstream was small. Event-based yields were higher at the upstream site, indicating that readily available sediment sources are closer to the upstream site where more mining affected areas are located. The estimates determined during large precipitation events indicate that large sources of suspended sediments with large concentrations of trace elements are still available for transport within the Big River.
\n
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155171","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency, Region 7","usgsCitation":"Barr, M.N., 2016, Surface-water quality and suspended-sediment quantity and quality within the Big River Basin, southeastern Missouri, 2011–13: U.S. Geological Survey Scientific Investigations Report 2015–5171, 39 p., https://dx.doi.org/10.3133/sir20155171.","productDescription":"vi, 39 p.","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2011-01-01","ipdsId":"IP-065903","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":314930,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5171/coverthb.jpg"},{"id":314931,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5171/sir20155171.pdf","text":"Report","size":"2.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5171"}],"country":"United States","state":"Missouri","otherGeospatial":"Big River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.80749511718749,\n 38.586820096127674\n ],\n [\n -90.6427001953125,\n 38.45789034424927\n ],\n [\n -90.582275390625,\n 38.371808917147554\n ],\n [\n -90.5767822265625,\n 38.285624966683756\n ],\n [\n -90.5877685546875,\n 38.16479533621134\n ],\n [\n -90.4779052734375,\n 38.06539235133249\n ],\n [\n -90.362548828125,\n 38.013476231041935\n ],\n [\n -90.23071289062499,\n 37.931200459333716\n ],\n [\n -90.1318359375,\n 37.84883250647402\n ],\n [\n -90.0384521484375,\n 37.814123701604466\n ],\n [\n -89.901123046875,\n 37.714244967649265\n ],\n [\n -90.0164794921875,\n 37.57505900514994\n ],\n [\n -90.208740234375,\n 37.58376576718623\n ],\n [\n -90.3900146484375,\n 37.58376576718623\n ],\n [\n -90.538330078125,\n 37.53150992479082\n ],\n [\n -90.703125,\n 37.413800350662875\n ],\n [\n -90.7635498046875,\n 37.28279464911045\n ],\n [\n -90.8404541015625,\n 37.17344871200958\n ],\n [\n -91.07666015625,\n 37.18657859524883\n ],\n [\n -91.23596191406249,\n 37.23470197166817\n ],\n [\n -91.23046875,\n 37.43997405227057\n ],\n [\n -91.109619140625,\n 37.58376576718623\n ],\n [\n -91.1920166015625,\n 37.688167468408025\n ],\n [\n -91.3238525390625,\n 37.76637243960176\n ],\n [\n -91.263427734375,\n 37.84015683604134\n ],\n [\n -91.131591796875,\n 37.883524980871336\n ],\n [\n -91.04919433593749,\n 38.03078569382294\n ],\n [\n -91.0711669921875,\n 38.12591462924157\n ],\n [\n -90.9173583984375,\n 38.16479533621134\n ],\n [\n -90.9228515625,\n 38.25974980039479\n ],\n [\n -90.9613037109375,\n 38.298559092254344\n ],\n [\n -90.999755859375,\n 38.38472766885085\n ],\n [\n -90.94482421875,\n 38.46219172306828\n ],\n [\n -90.8843994140625,\n 38.5008925889646\n ],\n [\n -90.80749511718749,\n 38.60828592850559\n ],\n [\n -90.80749511718749,\n 38.586820096127674\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road, MS-100
Rolla, MO 65401
http://mo.water.usgs.gov
","tableOfContents":"- Abstract
- Introduction
- Methods
- Surface-Water Quality
- Suspended-Sediment Quantity
- Suspended-Sediment Quality
- Summary
- References Cited
","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2016-01-28","noUsgsAuthors":false,"publicationDate":"2016-01-28","publicationStatus":"PW","scienceBaseUri":"56ab3bb0e4b07ca61bfe3be3","contributors":{"authors":[{"text":"Barr, Miya N. 0000-0002-9961-9190 mnbarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9961-9190","contributorId":3686,"corporation":false,"usgs":true,"family":"Barr","given":"Miya","email":"mnbarr@usgs.gov","middleInitial":"N.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":581818,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70168586,"text":"70168586 - 2016 - A submarine landslide source for the devastating 1964 Chenega tsunami, southern Alaska","interactions":[],"lastModifiedDate":"2017-06-07T16:47:11","indexId":"70168586","displayToPublicDate":"2016-01-28T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"A submarine landslide source for the devastating 1964 Chenega tsunami, southern Alaska","docAbstract":"\n
\n
\n
During the 1964 Great Alaska earthquake (Mw 9.2), several fjords, straits, and bays throughout southern Alaska experienced significant tsunami runup of localized, but unexplained origin. Dangerous Passage is a glacimarine fjord in western Prince William Sound, which experienced a tsunami that devastated the village of Chenega where 23 of 75 inhabitants were lost – the highest relative loss of any community during the earthquake. Previous studies suggested the source of the devastating tsunami was either from a local submarine landslide of unknown origin or from coseismic tectonic displacement. Here we present new observations from high-resolution multibeam bathymetry and seismic reflection surveys conducted in the waters adjacent to the village of Chenega. The seabed morphology and substrate architecture reveal a large submarine landslide complex in water depths of 120–360 m. Analysis of bathymetric change between 1957 and 2014 indicates the upper 20–50 m (∼0.7 km3) of glacimarine sediment was destabilized and evacuated from the steep face of a submerged moraine and an adjacent ∼21 km2 perched sedimentary basin. Once mobilized, landslide debris poured over the steep, 130 m-high face of a deeper moraine and then blanketed the terminal basin (∼465 m water depth) in 11 ± 5 m of sediment. These results, combined with inverse tsunami travel-time modeling, suggest that earthquake- triggered submarine landslides generated the tsunami that struck the village of Chenega roughly 4 min after shaking began. Unlike other tsunamigenic landslides observed in and around Prince William Sound in 1964, the failures in Dangerous Passage are not linked to an active submarine delta. The requisite environmental conditions needed to generate large submarine landslides in glacimarine fjords around the world may be more common than previously thought.
\n
\n
\n
The origin and antiquity of the subdued topography of the Yukon–Tanana Upland (YTU), the physiographic province between the Denali and Tintina faults, are unresolved questions in the geologic history of interior Alaska and adjacent Yukon. We present apatite fission-track (AFT) results for 33 samples from the 2300 km2 western Fortymile district in the YTU in Alaska and propose an exhumation model that is consistent with preservation of volcanic rocks in valleys that requires base level stability of several drainages since latest Cretaceous–Paleocene time. AFT thermochronology indicates widespread cooling below ∼110 °C at ∼56–47 Ma (early Eocene) and ∼44–36 Ma (middle Eocene). Samples with ∼33–27, ∼19, and ∼10 Ma AFT ages, obtained near a major northeast-trending fault zone, apparently reflect hydrothermal fluid flow. Uplift and erosion following ∼107 Ma magmatism exposed plutonic rocks to different extents in various crustal blocks by latest Cretaceous time. We interpret the Eocene AFT ages to suggest that higher elevations were eroded during the Paleogene subtropical climate of the subarctic, while base level remained essentially stable. Tertiary basins outboard of the YTU contain sediment that may account for the required >2 km of removed overburden that was not carried to the sea by the ancestral Yukon River system. We consider a climate driven explanation for the Eocene AFT ages to be most consistent with geologic constraints in concert with block faulting related to translation on the Denali and Tintina faults resulting from oblique subduction along the southern margin of Alaska.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Canadian Journal of Earth Sciences","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"National Research Council Canada","publisherLocation":"Ottawa","doi":"10.1139/cjes-2015-0138","usgsCitation":"Dusel-Bacon, C., Bacon, C.R., O'Sullivan, P., and Day, W.C., 2016, Apatite fission-track evidence for regional exhumation in the subtropical Eocene, block faulting, and localized fluid flow in east-central Alaska: Canadian Journal of Earth Sciences, v. 53, no. 3, p. 260-280, https://doi.org/10.1139/cjes-2015-0138.","productDescription":"21 p.","startPage":"260","endPage":"280","numberOfPages":"21","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063505","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":471306,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.nrcresearchpress.com/doi/abs/10.1139/cjes-2015-0138","text":"External Repository"},{"id":318650,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon–Tanana Upland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -141.0205078125,\n 62.30879369102805\n ],\n [\n -141.15234374999997,\n 68.65655498475735\n ],\n [\n -142.8662109375,\n 68.41535220938925\n ],\n [\n -144.580078125,\n 67.92514047803861\n ],\n [\n -146.8212890625,\n 67.49175133394853\n ],\n [\n -149.4140625,\n 67.06743335108298\n ],\n [\n -150.7763671875,\n 66.75724984139227\n ],\n [\n -152.9736328125,\n 66.72254132270653\n ],\n [\n -154.9951171875,\n 66.67038675925365\n ],\n [\n -157.10449218749997,\n 66.07154649351564\n ],\n [\n -158.115234375,\n 65.23830662451157\n ],\n [\n -158.33496093749997,\n 64.51064316846676\n ],\n [\n -157.67578125,\n 63.470144746565445\n ],\n [\n -157.1923828125,\n 62.83508901142283\n ],\n [\n -156.13769531249997,\n 62.24746627771428\n ],\n [\n -154.16015625,\n 62.492027730426905\n ],\n [\n -152.666015625,\n 63.03503931552975\n ],\n [\n -150.908203125,\n 63.450509218001095\n ],\n [\n -150.029296875,\n 63.724183392123315\n ],\n [\n -149.326171875,\n 64.05297838071347\n ],\n [\n -148.7109375,\n 64.24459476798192\n ],\n [\n -146.1181640625,\n 64.20637724320852\n ],\n [\n -144.7998046875,\n 63.82128765261384\n ],\n [\n -143.56933593749997,\n 63.52897054110277\n ],\n [\n -142.9541015625,\n 62.935234870604695\n ],\n [\n -141.0205078125,\n 62.30879369102805\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"53","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56deb43ce4b015c306fb89a5","contributors":{"authors":[{"text":"Dusel-Bacon, Cynthia 0000-0001-8481-739X cdusel@usgs.gov","orcid":"https://orcid.org/0000-0001-8481-739X","contributorId":2797,"corporation":false,"usgs":true,"family":"Dusel-Bacon","given":"Cynthia","email":"cdusel@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":621995,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bacon, Charles R. 0000-0002-2165-5618 cbacon@usgs.gov","orcid":"https://orcid.org/0000-0002-2165-5618","contributorId":2909,"corporation":false,"usgs":true,"family":"Bacon","given":"Charles","email":"cbacon@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":621997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Sullivan, Paul B.","contributorId":36627,"corporation":false,"usgs":true,"family":"O'Sullivan","given":"Paul B.","affiliations":[],"preferred":false,"id":621996,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Day, Warren C. 0000-0002-9278-2120 wday@usgs.gov","orcid":"https://orcid.org/0000-0002-9278-2120","contributorId":1308,"corporation":false,"usgs":true,"family":"Day","given":"Warren","email":"wday@usgs.gov","middleInitial":"C.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":621998,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70159478,"text":"70159478 - 2016 - Determining generic velocity and density models for crustal amplification calculations, with an update of the Boore and Joyner (1997) Generic Site Amplification for Graphic Site Amplification","interactions":[],"lastModifiedDate":"2016-02-01T13:39:08","indexId":"70159478","displayToPublicDate":"2016-01-26T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Determining generic velocity and density models for crustal amplification calculations, with an update of the Boore and Joyner (1997) Generic Site Amplification for Graphic Site Amplification","docAbstract":"This short note contains two contributions related to deriving depth‐dependent velocity and density models for use in computing generic crustal amplifications. The first contribution is a method for interpolating two velocity profiles to obtain a third profile with a time‐averaged velocity ![\"Graphic\"](\"http://www.bssaonline.org/content/early/2015/12/29/0120150229/embed/inline-graphic-2.gif\")
to depth Z that is equal to a specified value (e.g., for shear‐wave velocity VS, ![\"Graphic\"](\"http://www.bssaonline.org/content/early/2015/12/29/0120150229/embed/inline-graphic-3.gif\")
for Z=30 m, in which the subscript S has been added to indicate that the average is for shear‐wave velocities). The second contribution is a procedure for obtaining densities from VS. The first contribution is used to extend and revise the Boore and Joyner (1997) generic rock VS model, for which ![\"Graphic\"](\"http://www.bssaonline.org/content/early/2015/12/29/0120150229/embed/inline-graphic-4.gif\")
, to a model with the more common ![\"Graphic\"](\"http://www.bssaonline.org/content/early/2015/12/29/0120150229/embed/inline-graphic-5.gif\")
. This new model is then used with the densities from the second contribution to compute crustal amplifications for a generic site with ![\"Graphic\"](\"http://www.bssaonline.org/content/early/2015/12/29/0120150229/embed/inline-graphic-6.gif\")
.
","language":"English","publisher":"Seismological Society of America","publisherLocation":"Stanford, CA","doi":"10.1785/0120150229","usgsCitation":"Boore, D., 2016, Determining generic velocity and density models for crustal amplification calculations, with an update of the Boore and Joyner (1997) Generic Site Amplification for Graphic Site Amplification: Bulletin of the Seismological Society of America, v. 106, no. 1, p. 316-320, https://doi.org/10.1785/0120150229.","productDescription":"5 p.","startPage":"316","endPage":"320","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068561","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":314868,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"106","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-29","publicationStatus":"PW","scienceBaseUri":"56a898aee4b0b28f1184dbc9","contributors":{"authors":[{"text":"Boore, David 0000-0002-8605-9673 boore@usgs.gov","orcid":"https://orcid.org/0000-0002-8605-9673","contributorId":140502,"corporation":false,"usgs":true,"family":"Boore","given":"David","email":"boore@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":579138,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70176649,"text":"70176649 - 2016 - Predicting thermally stressful events in rivers with a strategy to evaluate management alternatives","interactions":[],"lastModifiedDate":"2017-07-21T14:34:53","indexId":"70176649","displayToPublicDate":"2016-01-26T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Predicting thermally stressful events in rivers with a strategy to evaluate management alternatives","docAbstract":"Water temperature is an important factor in river ecology. Numerous models have been developed to predict river temperature. However, many were not designed to predict thermally stressful periods. Because such events are rare, traditionally applied analyses are inappropriate. Here, we developed two logistic regression models to predict thermally stressful events in the Delaware River at the US Geological Survey gage near Lordville, New York. One model predicted the probability of an event >20.0 °C, and a second predicted an event >22.2 °C. Both models were strong (independent test data sensitivity 0.94 and 1.00, specificity 0.96 and 0.96) predicting 63 of 67 events in the >20.0 °C model and all 15 events in the >22.2 °C model. Both showed negative relationships with released volume from the upstream Cannonsville Reservoir and positive relationships with difference between air temperature and previous day's water temperature at Lordville. We further predicted how increasing release volumes from Cannonsville Reservoir affected the probabilities of correctly predicted events. For the >20.0 °C model, an increase of 0.5 to a proportionally adjusted release (that accounts for other sources) resulted in 35.9% of events in the training data falling below cutoffs; increasing this adjustment by 1.0 resulted in 81.7% falling below cutoffs. For the >22.2 °C these adjustments resulted in 71.1% and 100.0% of events falling below cutoffs. Results from these analyses can help managers make informed decisions on alternative release scenarios.","language":"English","publisher":"John Wiley & Sons, Ltd.","doi":"10.1002/rra.2998","usgsCitation":"Maloney, K., Cole, J.C., and Schmid, M., 2016, Predicting thermally stressful events in rivers with a strategy to evaluate management alternatives: River Research and Applications, no. 32, p. 1428-1437, https://doi.org/10.1002/rra.2998.","productDescription":"9 p. ","startPage":"1428","endPage":"1437","ipdsId":"IP-065139","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":328919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Delaware River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.4815673828125,\n 39.70296052957233\n ],\n [\n -74.498291015625,\n 39.8465036024177\n ],\n [\n -74.4927978515625,\n 40.26695230509781\n ],\n [\n -74.970703125,\n 40.75974059207392\n ],\n [\n -74.6685791015625,\n 40.979898069620155\n ],\n [\n -74.5806884765625,\n 41.335575973123895\n ],\n [\n -74.11376953125,\n 42.13082130188811\n ],\n [\n -74.9432373046875,\n 42.44372793752476\n ],\n [\n -75.574951171875,\n 42.00848901572399\n ],\n [\n -75.8880615234375,\n 41.244772343082104\n ],\n [\n -76.343994140625,\n 40.329795743702064\n ],\n [\n -76.04736328125,\n 39.73253798438173\n ],\n [\n -75.4815673828125,\n 39.70296052957233\n ]\n ]\n ]\n }\n }\n ]\n}","issue":"32","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-26","publicationStatus":"PW","scienceBaseUri":"57f7c6cfe4b0bc0bec09cb7a","contributors":{"authors":[{"text":"Maloney, K.O. 0000-0003-2304-0745","orcid":"https://orcid.org/0000-0003-2304-0745","contributorId":105414,"corporation":false,"usgs":true,"family":"Maloney","given":"K.O.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":649493,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cole, J. C.","contributorId":51292,"corporation":false,"usgs":true,"family":"Cole","given":"J.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":649494,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmid, M.","contributorId":96000,"corporation":false,"usgs":true,"family":"Schmid","given":"M.","email":"","affiliations":[],"preferred":false,"id":649495,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
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