The biosphere is under unprecedented pressure, reflected in rapid changes in our global ecological, social, technological and economic systems. In many cases, ecological and social systems can adapt to these changes over time, but when a critical threshold is surpassed, a system under stress can undergo catastrophic change and reorganize into a different state. The concept of resilience, introduced more than 40 years ago in the ecological sciences, captures the behaviour of systems that can occur in alternative states. The original definition of resilience forwarded by Holling (1973) is still the most useful. It defines resilience as the amount of disturbance that a system can withstand before it shifts into an alternative stable state. The idea of alternative stable states has clear and profound implications for ecological management. Coral reefs, for example, are high-diversity systems that provide key ecosystem services such as fisheries and coastal protection. Human impacts are causing significant, ongoing reef degradation, and many reefs have shifted from coral- to algal-dominated states in response to anthropogenic pressures such as elevated water temperatures and overfishing. Understanding and differentiating between the factors that help maintain reefs in coral-dominated states vs. those that facilitate a shift to an undesired algal-dominated state is a critical step towards sound management and conservation of these, and other, important social–ecological systems.
\nResilience has gained popularity among both academicians and laypeople, as a term meant to describe a systems’ ability to withstand disturbance. Resilience has become a buzzword in the last decade, as shown by its increasing appearance in calls for research proposals and scientific citation data bases. The term resilience has in many cases lost the clarity of the original definition and in fact is frequently used in a manner in direct opposition to the original definition. Many current uses of the concept are loose and incorrect. The term is becoming increasingly used in a normative sense (Brand & Jax 2007), as if resilience were a desirable quality of systems. However, even systems in highly undesirable states, such as macro-algae dominated reefs, or city cores in poverty traps, may be highly resilient, which is to say they withstand attempts to transform them into different (desirable) states.
\nOperationalizing the concept of resilience for application and management has been difficult. Misuse of the term can have significant negative impacts, because resilience is being used to help guide responses to natural disasters and to assess the sustainability of ecosystems and urban systems and has been driving international research priorities. Resilience has been argued to be a basic emergent property of systems, a process or a rate. We focus on the original concept as described by Holling, which is that of an emergent system property; when a system is in a desirable state and managers wish to enhance resilience, or when the system is in an undesirable state and managers wish to erode resilience and foster a transformation to an alternative state. Fostering or eroding resilience is a process. When a system is perturbed but resilience is not exceeded, then the recovery can be measured as a rate.
\nSeveral frameworks to operationalize resilience have been proposed. A decade ago, a special feature focused on quantifying resilience was published in the journal Ecosystems (Carpenter, Westley & Turner 2005). The approach there was towards identifying surrogates of resilience, but few of the papers proposed quantifiable metrics. Consequently, many ecological resilience frameworks remain vague and difficult to quantify, a problem that this special feature aims to address. However, considerable progress has been made during the last decade (e.g. Pope, Allen & Angeler 2014). Although some argue that resilience is best kept as an unquantifiable, vague concept (Quinlan et al. 2016), to be useful for managers, there must be concrete guidance regarding how and what to manage and how to measure success (Garmestani, Allen & Benson 2013; Spears et al. 2015). Ideas such as ‘resilience thinking’ have utility in helping stakeholders conceptualize their systems, but provide little guidance on how to make resilience useful for ecosystem management, other than suggesting an ambiguous, Goldilocks approach of being just right (e.g. diverse, but not too diverse; connected, but not too connected). Here, we clarify some prominent resilience terms and concepts, introduce and synthesize the papers in this special feature on quantifying resilience and identify core unanswered questions related to resilience.
","language":"English","publisher":"British Ecological Society","publisherLocation":"London, United Kingdom","doi":"10.1111/1365-2664.12649","usgsCitation":"Allen, C.R., and Angeler, D., 2016, Quantifying resilience: Journal of Applied Ecology, v. 53, p. 617-624, https://doi.org/10.1111/1365-2664.12649.","productDescription":"8 p.","startPage":"617","endPage":"624","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071794","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":470999,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.12649","text":"Publisher Index Page"},{"id":324270,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-13","publicationStatus":"PW","scienceBaseUri":"576bb6bae4b07657d1a22941","contributors":{"authors":[{"text":"Allen, Craig R. 0000-0001-8655-8272 allencr@usgs.gov","orcid":"https://orcid.org/0000-0001-8655-8272","contributorId":1979,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"allencr@usgs.gov","middleInitial":"R.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":638379,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Angeler, David G.","contributorId":25027,"corporation":false,"usgs":true,"family":"Angeler","given":"David G.","affiliations":[],"preferred":false,"id":640495,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70170245,"text":"ds992 - 2016 - Long-term trends in naturalized rainbow trout (Oncorhynchus mykiss) populations in the upper Esopus Creek, Ulster County, New York, 2009–15","interactions":[],"lastModifiedDate":"2016-05-13T10:52:04","indexId":"ds992","displayToPublicDate":"2016-05-13T09:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"992","title":"Long-term trends in naturalized rainbow trout (Oncorhynchus mykiss) populations in the upper Esopus Creek, Ulster County, New York, 2009–15","docAbstract":"The U.S. Geological Survey, in cooperation with Cornell Cooperative Extension of Ulster County, New York State Energy Research and Development Authority, the New York State Department of Environmental Conservation, and the New York City Department of Environmental Protection, surveyed fish communities annually on the main stem and tributaries of the upper Esopus Creek, Ulster County, New York, from 2009 to 2015. This report summarizes the density, biomass, and size structure of rainbow trout (Oncorhynchus mykiss) and brown trout (Salmo trutta) populations from the 2015 surveys along with data from the preceding 6 years. The mean density of rainbow trout populations in 2015 was 98 fish per 0.1 hectare, which was the highest value observed since 2010, and the mean biomass of rainbow trout populations in 2015 was 864 grams per 0.1 hectare, which was the highest value observed since 2012.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds992","collaboration":"Prepared in cooperation with Cornell Cooperative Extension of Ulster County, New York State Energy Research and Development Authority, the New York State Department of Environmental Conservation, and the New York City Department of Environmental Protection","usgsCitation":"George, S.D., and Baldigo, B.P., 2016, Long-term trends in naturalized rainbow trout (Oncorhynchus mykiss) populations in the upper Esopus Creek, Ulster County, New York, 2009–15: U.S. Geological Survey Data Series 992, 12 p., https://dx.doi.org/10.3133/ds992.","productDescription":"iv, 12 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070172","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":321177,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0992/coverthb.jpg"},{"id":321178,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0992/ds992.pdf","text":"Report","size":"6.49 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 992"}],"country":"United States","state":"New York","county":"Ulster County","otherGeospatial":"Upper Esopus Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.566667,\n 42.216667\n ],\n [\n -74.566667,\n 41.916667\n ],\n [\n -74.066667,\n 41.916667\n ],\n [\n -74.066667,\n 42.216667\n ],\n [\n -74.566667,\n 42.216667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180-8349
Information requests:
(518) 285-5602
Or visit our Web site at:
http://ny.water.usgs.gov
The software program, QRev computes the discharge from moving-boat acoustic Doppler current profiler measurements using data collected with any of the Teledyne RD Instrument or SonTek bottom tracking acoustic Doppler current profilers. The computation of discharge is independent of the manufacturer of the acoustic Doppler current profiler because QRev applies consistent algorithms independent of the data source. In addition, QRev automates filtering and quality checking of the collected data and provides feedback to the user of potential quality issues with the measurement. Various statistics and characteristics of the measurement, in addition to a simple uncertainty assessment are provided to the user to assist them in properly rating the measurement. QRev saves an extensible markup language file that can be imported into databases or electronic field notes software. The user interacts with QRev through a tablet-friendly graphical user interface. This report is the manual for version 2.8 of QRev.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161052","usgsCitation":"Mueller, D.S., 2016, QRev—Software for computation and quality assurance of acoustic Doppler current profiler moving-boat streamflow measurements—User’s manual for version 2.8: U.S. Geological Survey Open-File Report 2016–1052, 50 p., https://dx.doi.org/10.3133/ofr20161052. ","productDescription":"vii, 50 p.","numberOfPages":"59","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-073112","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":321055,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1052/ofr20161052.pdf","text":"Report","size":"3.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1052"},{"id":321054,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1052/coverthb.jpg"},{"id":324156,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/ofr20161068","text":"Open-File Report 2016–1068 - ","description":"OFR 2016-1052","linkHelpText":"QRev—Software for Computation and Quality Assurance of Acoustic Doppler Current Profiler Moving-Boat Streamflow Measurements—Technical Manual for Version 2.8 "}],"contact":"Chief, USGS Office of Surface Water
415 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
(703) 648-5301
Or visit the Office of Surface Water Web site at: http://water.usgs.gov/osw/
","tableOfContents":"In order to provide surface geodetic measurements with “landslide-wide” spatial coverage, we develop and validate a method for the characterization of 3-D surface deformation using the unique capabilities of the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne repeat-pass radar interferometry system. We apply our method at the well-studied Slumgullion Landslide, which is 3.9 km long and moves persistently at rates up to ∼2 cm/day. A comparison with concurrent GPS measurements validates this method and shows that it provides reliable and accurate 3-D surface deformation measurements. The UAVSAR-derived vector velocity field measurements accurately capture the sharp boundaries defining previously identified kinematic units and geomorphic domains within the landslide. We acquired data across the landslide during spring and summer and identify that the landslide moves more slowly during summer except at its head, presumably in response to spatiotemporal variations in snowmelt infiltration. In order to constrain the mechanics controlling landslide motion from surface velocity measurements, we present an inversion framework for the extraction of slide thickness and basal geometry from dense 3-D surface velocity fields. We find that the average depth of the Slumgullion Landslide is 7.5 m, several meters less than previous depth estimates. We show that by considering a viscoplastic rheology, we can derive tighter theoretical bounds on the rheological parameter relating mean horizontal flow rate to surface velocity. Using inclinometer data for slow-moving, clay-rich landslides across the globe, we find a consistent value for the rheological parameter of 0.85 ± 0.08.
","language":"English","publisher":"AGU","doi":"10.1002/2015JB012559","usgsCitation":"Delbridge, B.G., Burgmann, R., Fielding, E., Hensley, S., and Schulz, W.H., 2016, Three-dimensional surface deformation derived from airborne interferometric UAVSAR: Application to the Slumgullion Landslide: Journal of Geophysical Research B: Solid Earth, v. 121, no. 5, p. 3951-3977, https://doi.org/10.1002/2015JB012559.","productDescription":"27 p.","startPage":"3951","endPage":"3977","ipdsId":"IP-073439","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":471006,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jb012559","text":"Publisher Index Page"},{"id":342559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Slumgullion landslide","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.24355697631836,\n 38.0025881074694\n ],\n [\n -107.24390029907227,\n 38.001911773165546\n ],\n [\n -107.24570274353027,\n 38.000897260013566\n ],\n [\n -107.24990844726561,\n 37.99947691802148\n ],\n [\n -107.25171089172363,\n 37.99792127379998\n ],\n [\n -107.25445747375488,\n 37.99663615151092\n ],\n [\n -107.25643157958984,\n 37.99426876203244\n ],\n [\n -107.25926399230957,\n 37.99311885957278\n ],\n [\n -107.2638988494873,\n 37.99264536508517\n ],\n [\n -107.26776123046875,\n 37.99271300734193\n ],\n [\n -107.27128028869627,\n 37.9919012961438\n ],\n [\n -107.27368354797363,\n 37.990210202299636\n ],\n [\n -107.27428436279297,\n 37.988924944905925\n ],\n [\n -107.27368354797363,\n 37.98703084033719\n ],\n [\n -107.27145195007324,\n 37.9857455272451\n ],\n [\n -107.26638793945312,\n 37.98594847291445\n ],\n [\n -107.26201057434082,\n 37.98689554528256\n ],\n [\n -107.25831985473633,\n 37.988451423348124\n ],\n [\n -107.2540283203125,\n 37.99048077993439\n ],\n [\n -107.25136756896973,\n 37.992510080384456\n ],\n [\n -107.2496509552002,\n 37.994065839378855\n ],\n [\n -107.24733352661133,\n 37.995553925801964\n ],\n [\n -107.24398612976073,\n 37.996365596580716\n ],\n [\n -107.23978042602539,\n 37.996906705443095\n ],\n [\n -107.23703384399414,\n 37.99738017242288\n ],\n [\n -107.23257064819336,\n 37.99778599882999\n ],\n [\n -107.23111152648926,\n 37.998732918380334\n ],\n [\n -107.23008155822754,\n 38.001303066958606\n ],\n [\n -107.22905158996582,\n 38.004211284347456\n ],\n [\n -107.2294807434082,\n 38.00549627389332\n ],\n [\n -107.23145484924316,\n 38.005969685417696\n ],\n [\n -107.23420143127441,\n 38.006713611637316\n ],\n [\n -107.23626136779785,\n 38.00691649929627\n ],\n [\n -107.2390079498291,\n 38.007592787437936\n ],\n [\n -107.24218368530273,\n 38.00752515890449\n ],\n [\n -107.24295616149902,\n 38.005902055387054\n ],\n [\n -107.24355697631836,\n 38.004752335322074\n ],\n [\n -107.24355697631836,\n 38.0025881074694\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"121","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-21","publicationStatus":"PW","scienceBaseUri":"59439c94e4b062508e31a9bd","contributors":{"authors":[{"text":"Delbridge, Brent G. 0000-0003-2808-8772","orcid":"https://orcid.org/0000-0003-2808-8772","contributorId":192986,"corporation":false,"usgs":false,"family":"Delbridge","given":"Brent","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":698379,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burgmann, Roland","contributorId":192700,"corporation":false,"usgs":false,"family":"Burgmann","given":"Roland","affiliations":[],"preferred":false,"id":698380,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fielding, Eric","contributorId":50434,"corporation":false,"usgs":true,"family":"Fielding","given":"Eric","affiliations":[],"preferred":false,"id":698381,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hensley, Scott","contributorId":85313,"corporation":false,"usgs":true,"family":"Hensley","given":"Scott","email":"","affiliations":[],"preferred":false,"id":698382,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schulz, William H. 0000-0001-9980-3580 wschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-9980-3580","contributorId":942,"corporation":false,"usgs":true,"family":"Schulz","given":"William","email":"wschulz@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698343,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70170886,"text":"70170886 - 2016 - Climate regulates alpine lake ice cover phenology and aquatic ecosystem structure","interactions":[],"lastModifiedDate":"2016-06-24T11:29:43","indexId":"70170886","displayToPublicDate":"2016-05-11T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Climate regulates alpine lake ice cover phenology and aquatic ecosystem structure","docAbstract":"High-elevation aquatic ecosystems are highly vulnerable to climate change, yet relatively few records are available to characterize shifts in ecosystem structure or their underlying mechanisms. Using a long-term dataset on seven alpine lakes (3126 to 3620 m) in Colorado, USA, we show that ice-off dates have shifted seven days earlier over the past 33 years and that spring weather conditions – especially snowfall – drive yearly variation in ice-off timing. In the most well-studied lake, earlier ice-off associated with increases in water residence times, thermal stratification, ion concentrations, dissolved nitrogen, pH, and chlorophyll-a. Mechanistically, low spring snowfall and warm temperatures reduce summer stream flow (increasing lake residence times) but enhance melting of glacial and permafrost ice (increasing lake solute inputs). The observed links among hydrological, chemical, and biological responses to climate factors highlight the potential for major shifts in the functioning of alpine lakes due to forecasted climate change.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2016GL069036","usgsCitation":"Preston, D.L., Caine, N., McKnight, D.M., Williams, M.W., Hell, K., Miller, M.P., Hart, S.J., and Johnson, P.T., 2016, Climate regulates alpine lake ice cover phenology and aquatic ecosystem structure: Geophysical Research Letters, v. 43, no. 10, p. 5353-5360, https://doi.org/10.1002/2016GL069036.","productDescription":"8 p.","startPage":"5353","endPage":"5360","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065721","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":471008,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doaj.org/article/74a41412b86246d8b0d27b74c0bce459","text":"Publisher Index Page"},{"id":321123,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","volume":"43","issue":"10","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-28","publicationStatus":"PW","scienceBaseUri":"5734499be4b0dae0d5dd68f4","contributors":{"authors":[{"text":"Preston, Daniel L.","contributorId":58581,"corporation":false,"usgs":true,"family":"Preston","given":"Daniel","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":629149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caine, Nel","contributorId":169277,"corporation":false,"usgs":false,"family":"Caine","given":"Nel","email":"","affiliations":[],"preferred":false,"id":629150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKnight, Diane M.","contributorId":59773,"corporation":false,"usgs":false,"family":"McKnight","given":"Diane","email":"","middleInitial":"M.","affiliations":[{"id":16833,"text":"INSTAAR, University of Colorado","active":true,"usgs":false}],"preferred":false,"id":629151,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Mark W.","contributorId":43046,"corporation":false,"usgs":true,"family":"Williams","given":"Mark","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":629152,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hell, Katherina","contributorId":169278,"corporation":false,"usgs":false,"family":"Hell","given":"Katherina","email":"","affiliations":[],"preferred":false,"id":629153,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miller, Matthew P. 0000-0002-2537-1823 mamiller@usgs.gov","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":3919,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew","email":"mamiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":628924,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hart, Sarah J.","contributorId":169279,"corporation":false,"usgs":false,"family":"Hart","given":"Sarah","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":629154,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, Pieter T.J.","contributorId":28508,"corporation":false,"usgs":true,"family":"Johnson","given":"Pieter","email":"","middleInitial":"T.J.","affiliations":[],"preferred":false,"id":629155,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70169226,"text":"ofr20161051 - 2016 - Streamflow, water quality and constituent loads and yields, Scituate Reservoir drainage area, Rhode Island, water year 2014","interactions":[],"lastModifiedDate":"2016-05-11T10:59:07","indexId":"ofr20161051","displayToPublicDate":"2016-05-11T11:45:00","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":"2016-1051","title":"Streamflow, water quality and constituent loads and yields, Scituate Reservoir drainage area, Rhode Island, water year 2014","docAbstract":"Streamflow and concentrations of sodium and chloride estimated from records of specific conductance were used to calculate loads of sodium and chloride during water year (WY) 2014 (October 1, 2013, through September 30, 2014) for tributaries to the Scituate Reservoir, Rhode Island. Streamflow and water-quality data used in the study were collected by the U.S. Geological Survey and the Providence Water Supply Board in the cooperative study. Streamflow was measured or estimated by the U.S. Geological Survey following standard methods at 23 streamgages; 14 of these streamgages are equipped with instrumentation capable of continuously monitoring water level, specific conductance, and water temperature. Water-quality samples were collected at 37 sampling stations by the Providence Water Supply Board and at 14 continuous-record streamgages by the U.S. Geological Survey during WY 2014 as part of a long-term sampling program; all stations are in the Scituate Reservoir drainage area. Water-quality data collected by the Providence Water Supply Board are summarized by using values of central tendency and are used, in combination with measured (or estimated) streamflows, to calculate loads and yields (loads per unit area) of selected water-quality constituents for WY 2014.
The largest tributary to the reservoir (the Ponaganset River, which was monitored by the U.S. Geological Survey) contributed a mean streamflow of 23 cubic feet per second to the reservoir during WY 2014. For the same time period, annual mean streamflows measured (or estimated) for the other monitoring stations in this study ranged from about 0.35 to about 14 cubic feet per second. Together, tributaries (equipped with instrumentation capable of continuously monitoring specific conductance) transported about 1,200,000 kilograms of sodium and 2,100,000 kilograms of chloride to the Scituate Reservoir during WY 2014; sodium and chloride yields for the tributaries ranged from 7,700 to 45,000 kilograms per year per square mile and from 12,000 to 75,000 kilograms per year per square mile, respectively.
At the stations where water-quality samples were collected by the Providence Water Supply Board, the median of the median chloride concentrations was 24 milligrams per liter, median nitrite concentration was 0.002 milligrams per liter as nitrogen (N), median nitrate concentration was 0.01 milligrams per liter as N, median orthophosphate concentration was 0.07 milligrams per liter as phosphate, and median concentrations of total coliform bacteria and Escherichia coli were 320 and 20 colony forming units per 100 milliliters, respectively. The medians of the median daily loads (and yields) of chloride, nitrite, nitrate, orthophosphate, and total coliform and Escherichia coli bacteria were 62 kilograms per day (42 kilograms per day per square mile), 19 grams per day (6.1 grams per day per square mile), 79 grams per day (36 grams per day per square mile), 380 grams per day (150 grams per day per square mile), 13,000 million colony forming units per day (8,300 million colony forming units per day per square mile), and 1,000 million colony forming units per day (470 million colony forming units per day per square mile), respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161051","collaboration":"Prepared in cooperation with the Providence Water Supply Board","usgsCitation":"Smith, K.P., 2016, Streamflow, water quality, and constituent loads and yields, Scituate Reservoir drainage area, Rhode Island, water year 2014: U.S. Geological Survey Open-File Report 2016–1051, 31 p., https://dx.doi.org/10.3133/ofr20161051.","productDescription":"Report: v, 31 p.; Appendix 1","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069938","costCenters":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"links":[{"id":320747,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1051/ofr20161051.pdf","text":"Report","size":"11.1 (MB)","linkFileType":{"id":1,"text":"pdf"},"description":"OF 2016-1051"},{"id":320748,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1051/ofr20161051_appendix1.xlsx","text":"Appendix 1","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix 1","linkHelpText":"Appendix 1. Water-quality data collected by the Providence Water Supply Board at 37 monitoring stations in the Scituate Reservoir drainage area, Rhode Island, water year 2014. Excel (30 KB)"},{"id":320746,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1051/coverthb.jpg"}],"country":"United States","state":"Rhode Island","otherGeospatial":"Scituate Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.7572021484375,\n 41.738784653087464\n ],\n [\n -71.7572021484375,\n 41.90304362629451\n ],\n [\n -71.55567169189453,\n 41.90304362629451\n ],\n [\n -71.55567169189453,\n 41.738784653087464\n ],\n [\n -71.7572021484375,\n 41.738784653087464\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
or visit our Web site at:
http://newengland.water.usgs.gov
The U.S. Corn Belt is one of the most intensive agricultural regions of the world and is drained by the Upper Mississippi River (UMR), which forms one of the largest drainage basins in the U.S. While the effects of agricultural nitrate (NO3-) on water quality in the UMR have been well documented, its impact on the production of nitrous oxide (N2O) has not been reported. Using a novel equilibration technique, we present the largest data set of freshwater dissolved N2O concentrations (0.7 to 6 times saturation) and examine the controls on its variability over a 350 km reach of the UMR. Driven by a supersaturated water column, the UMR was an important atmospheric N2O source (+68 mg N2ONm-2 yr-1) that varies nonlinearly with the NO3-concentration. Our analyses indicated that a projected doubling of the NO3-concentration by 2050 would cause dissolved N2O concentrations and emissions to increase by about 40%.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2016GL068710","usgsCitation":"Turner, P., Griffis, T., Baker, J., Lee, X., Crawford, J.T., Loken, L., and Venterea, R., 2016, Regional-scale controls on dissolved nitrous oxide in the Upper Mississippi River: Geophysical Research Letters, v. 43, no. 9, p. 4400-4407, https://doi.org/10.1002/2016GL068710.","productDescription":"8 p.","startPage":"4400","endPage":"4407","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071258","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":471012,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gl068710","text":"Publisher Index Page"},{"id":321112,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Upper Mississippi River","volume":"43","issue":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-06","publicationStatus":"PW","scienceBaseUri":"5734499ce4b0dae0d5dd6903","contributors":{"authors":[{"text":"Turner, P.A.","contributorId":169214,"corporation":false,"usgs":false,"family":"Turner","given":"P.A.","email":"","affiliations":[{"id":25441,"text":"University of Minnesota, Department of Soil, Water and Climate","active":true,"usgs":false}],"preferred":false,"id":628997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Griffis, T.J.","contributorId":169215,"corporation":false,"usgs":false,"family":"Griffis","given":"T.J.","email":"","affiliations":[{"id":25442,"text":"U.S. Department of Agriculture - Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":628998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baker, J.M.","contributorId":169216,"corporation":false,"usgs":false,"family":"Baker","given":"J.M.","email":"","affiliations":[{"id":25443,"text":"Yale University, School of Forestry and Environmental Studies","active":true,"usgs":false}],"preferred":false,"id":628999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lee, X.","contributorId":169217,"corporation":false,"usgs":false,"family":"Lee","given":"X.","email":"","affiliations":[{"id":25444,"text":"Yale-Nanjing University of Information, Science and Technology","active":true,"usgs":false}],"preferred":false,"id":629000,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crawford, John T. 0000-0003-4440-6945 jtcrawford@usgs.gov","orcid":"https://orcid.org/0000-0003-4440-6945","contributorId":4081,"corporation":false,"usgs":true,"family":"Crawford","given":"John","email":"jtcrawford@usgs.gov","middleInitial":"T.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":628996,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loken, Luke C. lloken@usgs.gov","contributorId":169218,"corporation":false,"usgs":true,"family":"Loken","given":"Luke C.","email":"lloken@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":629001,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Venterea, R.T.","contributorId":53994,"corporation":false,"usgs":true,"family":"Venterea","given":"R.T.","email":"","affiliations":[],"preferred":false,"id":629002,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70170902,"text":"70170902 - 2016 - The ecology of methane in streams and rivers: Patterns, controls, and global significance","interactions":[],"lastModifiedDate":"2016-05-11T10:34:05","indexId":"70170902","displayToPublicDate":"2016-05-11T11:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1459,"text":"Ecological Monographs","active":true,"publicationSubtype":{"id":10}},"title":"The ecology of methane in streams and rivers: Patterns, controls, and global significance","docAbstract":"Streams and rivers can substantially modify organic carbon (OC) inputs from terrestrial landscapes, and much of this processing is the result of microbial respiration. While carbon dioxide (CO2) is the major end-product of ecosystem respiration, methane (CH4) is also present in many fluvial environments even though methanogenesis typically requires anoxic conditions that may be scarce in these systems. Given recent recognition of the pervasiveness of this greenhouse gas in streams and rivers, we synthesized existing research and data to identify patterns and drivers of CH4, knowledge gaps, and research opportunities. This included examining the history of lotic CH4 research, creating a database of concentrations and fluxes (MethDB) to generate a global-scale estimate of fluvial CH4 efflux, and developing a conceptual framework and using this framework to consider how human activities may modify fluvial CH4 dynamics. Current understanding of CH4 in streams and rivers has been strongly influenced by goals of understanding OC processing and quantifying the contribution of CH4 to ecosystem C fluxes. Less effort has been directed towards investigating processes that dictate in situ CH4 production and loss. CH4 makes a meager contribution to watershed or landscape C budgets, but streams and rivers are often significant CH4 sources to the atmosphere across these same spatial extents. Most fluvial systems are supersaturated with CH4 and we estimate an annual global emission of 26.8 Tg CH4, equivalent to ~15-40% of wetland and lake effluxes, respectively. Less clear is the role of CH4 oxidation, methanogenesis, and total anaerobic respiration to whole ecosystem production and respiration. Controls on CH4 generation and persistence can be viewed in terms of proximate controls that influence methanogenesis (organic matter, temperature, alternative electron acceptors, nutrients) and distal geomorphic and hydrologic drivers. Multiple controls combined with its extreme redox status and low solubility result in high spatial and temporal variance of CH4 in fluvial environments, which presents a substantial challenge for understanding its larger-scale dynamics. Further understanding of CH4 production and consumption, anaerobic metabolism, and ecosystem energetics in streams and rivers can be achieved through more directed studies and comparison with knowledge from terrestrial, wetland, and aquatic disciplines.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/15-1027.1","usgsCitation":"Stanley, E.H., Casson, N.J., Christel, S.T., Crawford, J.T., Loken, L., and Oliver, S., 2016, The ecology of methane in streams and rivers: Patterns, controls, and global significance: Ecological Monographs, v. 86, no. 2, p. 146-171, https://doi.org/10.1890/15-1027.1.","productDescription":"16 p.","startPage":"146","endPage":"171","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066395","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":471013,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10680/1574","text":"External Repository"},{"id":321111,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"86","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-07","publicationStatus":"PW","scienceBaseUri":"5734499de4b0dae0d5dd690d","contributors":{"authors":[{"text":"Stanley, Emily H.","contributorId":55725,"corporation":false,"usgs":false,"family":"Stanley","given":"Emily","email":"","middleInitial":"H.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":629004,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Casson, Nora J.","contributorId":169271,"corporation":false,"usgs":false,"family":"Casson","given":"Nora","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":629005,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Christel, Samuel T.","contributorId":169272,"corporation":false,"usgs":false,"family":"Christel","given":"Samuel","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":629006,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crawford, John T. 0000-0003-4440-6945 jtcrawford@usgs.gov","orcid":"https://orcid.org/0000-0003-4440-6945","contributorId":4081,"corporation":false,"usgs":true,"family":"Crawford","given":"John","email":"jtcrawford@usgs.gov","middleInitial":"T.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":629003,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loken, Luke C. lloken@usgs.gov","contributorId":169218,"corporation":false,"usgs":true,"family":"Loken","given":"Luke C.","email":"lloken@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":629007,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oliver, Samantha K.","contributorId":169273,"corporation":false,"usgs":false,"family":"Oliver","given":"Samantha K.","affiliations":[],"preferred":false,"id":629008,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70188601,"text":"70188601 - 2016 - A study of the 2015 Mw 8.3 Illapel earthquake and tsunami: Numerical and analytical approaches","interactions":[],"lastModifiedDate":"2017-06-16T12:23:37","indexId":"70188601","displayToPublicDate":"2016-05-11T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3208,"text":"Pure and Applied Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"A study of the 2015 Mw 8.3 Illapel earthquake and tsunami: Numerical and analytical approaches","docAbstract":"The September 16, 2015 Illapel, Chile earthquake\ntriggered a large tsunami, causing both economic losses and\nfatalities. To study the coastal effects of this earthquake, and to\nunderstand how such hazards might be accurately modeled in the\nfuture, different finite fault models of the Illapel rupture are used to\ndefine the initial condition for tsunami simulation. The numerical\ncode Non-hydrostatic Evolution of Ocean WAVEs (NEOWAVE)\nis employed to model the tsunami evolution through the Pacific\nOcean. Because only a short time is available for emergency\nresponse, and since the earthquake and tsunami sources are close to\nthe coast, gaining a rapid understanding of the near-field run-up\nbehavior is highly relevant to Chile. Therefore, an analytical\nsolution of the 2 ? 1 D shallow water wave equations is considered.\nWith this solution, we show that we can quickly estimate the\nrun-up distribution along the coastline, to first order. After the\nearthquake and tsunami, field observations were measured in the\nsurrounded coastal region, where the tsunami resulted in significant\nrun-up. First, we compare the analytical and numerical solutions to\ntest the accuracy of the analytical approach and the field observations,\nimplying the analytic approach can accurately model tsunami\nrun-up after an earthquake, without sacrificing the time necessary\nfor a full numerical inversion. Then, we compare both with field\nrun-up measurements. We observe the consistency between the two\napproaches. To complete the analysis, a tsunami source inversion is\nperformed using run-up field measurements only. These inversion\nresults are compared with seismic models, and are shown to capture\nthe broad-scale details of those models, without the necessity of the\ndetailed data sets they invert.","language":"English","publisher":"SpringerLink","doi":"10.1007/s00024-016-1305-0","usgsCitation":"Fuentes, M., Riquelme, S., Hayes, G.P., Medina, M., Melgar, D., Vargas, G., Gonzalez, J., and Villalobos, A., 2016, A study of the 2015 Mw 8.3 Illapel earthquake and tsunami: Numerical and analytical approaches: Pure and Applied Geophysics, v. 173, p. 1847-1858, https://doi.org/10.1007/s00024-016-1305-0.","productDescription":"12 p. 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2016 - Persistent and novel threats to the biodiversity of Kazakhstan’s steppes and semi-deserts","interactions":[],"lastModifiedDate":"2017-11-22T17:29:34","indexId":"70170904","displayToPublicDate":"2016-05-10T13:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1006,"text":"Biodiversity and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Persistent and novel threats to the biodiversity of Kazakhstan’s steppes and semi-deserts","docAbstract":"Temperate grasslands have suffered disproportionally from conversion to cropland, degradation and fragmentation. A large proportion of the world’s remaining near-natural grassland is situated in Kazakhstan. We aimed to assess current and emerging threats to steppe and semi-desert biodiversity in Kazakhstan and evaluate conservation research priorities. We conducted a horizon-scanning exercise among conservationists from academia and practice. We first compiled a list of 45 potential threats. These were then ranked by the survey participants according to their perceived severity, the need for research on them, and their novelty. The highest-ranked threats were related to changes in land use (leading to habitat loss and deterioration), direct persecution of wildlife, and rapid infrastructure development due to economic and population growth. Research needs were identified largely in the same areas, and the mean scores of threat severity and research need were highly correlated. Novel threats comprised habitat loss by photovoltaic and wind power stations, climate change and changes in agriculture such as the introduction of biofuels. However, novelty was not correlated with threat severity or research priority, suggesting that the most severe threats are the established ones. Important goals towards more effective steppe and semi-desert conservation in Kazakhstan include more cross-sector collaboration (e.g. by involving stakeholders in conservation and agriculture), greater allocation of funds to under-staffed areas (e.g. protected area management), better representativeness and complementarity in the protected area system and enhanced data collection for wildlife monitoring and threat assessments (including the use of citizen-science databases).
","language":"English","publisher":"SpringerLink","doi":"10.1007/s10531-016-1083-0","usgsCitation":"Kamp, J., Koshkin, M.A., Bragina, T.M., Katzner, T., Milner-Gulland, E., Schreiber, D., Sheldon, R., Shmalenko, A., Smelansky, I., Terraube, J., and Urazaliev, R., 2016, Persistent and novel threats to the biodiversity of Kazakhstan’s steppes and semi-deserts: Biodiversity and Conservation, v. 25, no. 12, p. 2521-2541, https://doi.org/10.1007/s10531-016-1083-0.","productDescription":"22 p.","startPage":"2521","endPage":"2541","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072885","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":471015,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://ora.ox.ac.uk/objects/uuid:52eb1886-2ef5-4c70-bf17-b0a616f99a04","text":"External Repository"},{"id":321092,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Kazakhstan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 47.02148437499999,\n 40.88029480552824\n ],\n [\n 47.02148437499999,\n 55.25407706707272\n ],\n [\n 85.7373046875,\n 55.25407706707272\n ],\n [\n 85.7373046875,\n 40.88029480552824\n ],\n [\n 47.02148437499999,\n 40.88029480552824\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"12","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-19","publicationStatus":"PW","scienceBaseUri":"5732f820e4b0dae0d5dc6443","contributors":{"authors":[{"text":"Kamp, Johannes","contributorId":169223,"corporation":false,"usgs":false,"family":"Kamp","given":"Johannes","email":"","affiliations":[{"id":25445,"text":"University of Münster","active":true,"usgs":false}],"preferred":false,"id":629012,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koshkin, Maxim A","contributorId":169224,"corporation":false,"usgs":false,"family":"Koshkin","given":"Maxim","email":"","middleInitial":"A","affiliations":[{"id":16617,"text":"University of East Anglia","active":true,"usgs":false}],"preferred":false,"id":629013,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bragina, Tatyana M","contributorId":169225,"corporation":false,"usgs":false,"family":"Bragina","given":"Tatyana","email":"","middleInitial":"M","affiliations":[{"id":25446,"text":"Kostanai State University","active":true,"usgs":false}],"preferred":false,"id":629014,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":5979,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":629011,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Milner-Gulland, E. 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This innovative database, named POLARIS, is constructed using available high-resolution geospatial environmental data and a state-of-the-art machine learning algorithm (DSMART-HPC) to remap the Soil Survey Geographic (SSURGO) database. This 9 billion grid cell database is possible using available high performance computing resources. POLARIS provides a spatially continuous, internally consistent, quantitative prediction of soil series. It offers potential solutions to the primary weaknesses in SSURGO: 1) unmapped areas are gap-filled using survey data from the surrounding regions, 2) the artificial discontinuities at political boundaries are removed, and 3) the use of high resolution environmental covariate data leads to a spatial disaggregation of the coarse polygons. The geospatial environmental covariates that have the largest role in assembling POLARIS over the contiguous United States (CONUS) are fine-scale (30 m) elevation data and coarse-scale (~ 2 km) estimates of the geographic distribution of uranium, thorium, and potassium. A preliminary validation of POLARIS using the NRCS National Soil Information System (NASIS) database shows variable performance over CONUS. In general, the best performance is obtained at grid cells where DSMART-HPC is most able to reduce the chance of misclassification. The important role of environmental covariates in limiting prediction uncertainty suggests including additional covariates is pivotal to improving POLARIS' accuracy. This database has the potential to improve the modeling of biogeochemical, water, and energy cycles in environmental models; enhance availability of data for precision agriculture; and assist hydrologic monitoring and forecasting to ensure food and water security.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geoderma.2016.03.025","usgsCitation":"Chaney, N.W., Wood, E.F., McBratney, A., Hempel, J.W., Nauman, T.W., Brungard, C.W., and Odgers, N.P., 2016, POLARIS: A 30-meter probabilistic soil series map of the contiguous United States: Geoderma, v. 274, p. 54-67, https://doi.org/10.1016/j.geoderma.2016.03.025.","productDescription":"14 p.","startPage":"54","endPage":"67","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069596","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":471014,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geoderma.2016.03.025","text":"Publisher Index Page"},{"id":321091,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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USA","active":true,"usgs":false}],"preferred":false,"id":629053,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McBratney, Alexander B","contributorId":169245,"corporation":false,"usgs":false,"family":"McBratney","given":"Alexander B","affiliations":[{"id":25455,"text":"Department of Environmental Sciences, Faculty of Agriculture and Environment, The University of Sydney, Sydney, Australia","active":true,"usgs":false}],"preferred":false,"id":629055,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hempel, Jonathan W","contributorId":169244,"corporation":false,"usgs":false,"family":"Hempel","given":"Jonathan","email":"","middleInitial":"W","affiliations":[{"id":25454,"text":"National Soil Survey Center, NRCS, Lincoln, Nebraska, USA","active":true,"usgs":false}],"preferred":false,"id":629054,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nauman, Travis W. 0000-0001-8004-0608 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,{"id":70171495,"text":"70171495 - 2016 - Recruitment synchrony of yellow perch (Perca flavescens, Percidae) in the Great Lakes region, 1966–2008","interactions":[],"lastModifiedDate":"2016-06-01T15:53:04","indexId":"70171495","displayToPublicDate":"2016-05-09T01:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Recruitment synchrony of yellow perch (Perca flavescens, Percidae) in the Great Lakes region, 1966–2008","docAbstract":"Population-level reproductive success (recruitment) of many fish populations is characterized by high inter-annual variation and related to annual variation in key environmental factors (e.g., climate). When such environmental factors are annually correlated across broad spatial scales, spatially separated populations may display recruitment synchrony (i.e., the Moran effect). We investigated inter-annual (1966–2008) variation in yellow perch (Perca flavescens, Percidae) recruitment using 16 datasets describing populations located in four of the five Laurentian Great Lakes (Erie, Huron, Michigan, and Ontario) and Lake St. Clair. We indexed relative year class strength using catch-curve residuals for each year-class across 2–4 years and compared relative year-class strength among sampling locations. Results indicate that perch recruitment is positively synchronized across the region. In addition, the spatial scale of this synchrony appears to be broader than previous estimates for both yellow perch and freshwater fish in general. To investigate potential factors influencing relative year-class strength, we related year-class strength to regional indices of annual climatic conditions (spring-summer air temperature, winter air temperature, and spring precipitation) using data from 14 weather stations across the Great Lakes region. We found that mean spring-summer temperature is significantly positively related to recruitment success among Great Lakes yellow perch populations.
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University","active":true,"usgs":false}],"preferred":false,"id":631315,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70169357,"text":"sir20165036 - 2016 - Flood-inundation maps for the East Fork White River at Shoals, Indiana","interactions":[],"lastModifiedDate":"2016-05-18T09:55:41","indexId":"sir20165036","displayToPublicDate":"2016-05-06T14:00:00","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":"2016-5036","title":"Flood-inundation maps for the East Fork White River at Shoals, Indiana","docAbstract":"Digital flood-inundation maps for a 5.9-mile reach of the East Fork White River at Shoals, Indiana (Ind.), were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana 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The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/ depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the East Fork White River at Shoals, Ind. (USGS station number 03373500). Near-real-time stages at this streamgage may be obtained on the Internet from the USGS National Water Information System at http://waterdata.usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) at http://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site (NWS AHPS site SHLI3). NWS AHPS forecast peak stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation.
Flood profiles were computed for the East Fork White River reach by means of a one-dimensional, step-backwater model developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated by using the current stage-discharge relation (USGS rating no. 43.0) at USGS streamgage 03373500, East Fork White River at Shoals, Ind. The calibrated hydraulic model was then used to compute 26 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from approximately bankfull (10 ft) to the highest stage of the current stage-discharge rating curve (35 ft). The simulated water-surface profiles were then combined with a geographic information system (GIS) digital elevation model (DEM), derived from light detection and ranging (lidar) data, to delineate the area flooded at each water level. The areal extent of the 24-ft flood-inundation map was verified with photographs from a flood event on July 20, 2015.
The availability of these maps, along with information on the Internet regarding current stage from the USGS streamgage at East Fork White River at Shoals, Ind., and forecasted stream stages from the NWS AHPS, provides emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.
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U.S. Geological Survey
5957 Lakeside Blvd
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http://in.water.usgs.gov/
Hydrologic and hydraulic analyses were done for selected reaches of five streams in and near Shelby, Richland County, Ohio. The U.S. Geological Survey (USGS), in cooperation with the Muskingum Watershed Conservancy District, conducted these analyses on the Black Fork Mohican River and four tributaries: Seltzer Park Creek, Seltzer Park Tributary, Tuby Run, and West Branch. Drainage areas of the four stream reaches studied range from 0.51 to 60.3 square miles. The analyses included estimation of the 10-, 2-, 1-, and 0.2-percent annual-exceedance probability (AEP) flood-peak discharges using the USGS Ohio StreamStats application. Peak discharge estimates, along with cross-sectional and hydraulic structure geometries, and estimates of channel roughness coefficients were used as input to step-backwater models. The step-backwater water models were used to determine water-surface elevation profiles of four flood-peak discharges and a regulatory floodway. This study involved the installation of, and data collection at, a streamflow-gaging station (Black Fork Mohican River at Shelby, Ohio, 03129197), precipitation gage (Rain gage at Reservoir Number Two at Shelby, Ohio, 405209082393200), and seven submersible pressure transducers on six selected river reaches. Two precipitation-runoff models, one for the winter events and one for nonwinter events for the headwaters of the Black Fork Mohican River, were developed and calibrated using the data collected. With the exception of the runoff curve numbers, all other parameters used in the two precipitation-runoff models were identical. The Nash-Sutcliffe model efficiency coefficients were 0.737, 0.899, and 0.544 for the nonwinter events and 0.850 and 0.671 for the winter events. Both of the precipitation-runoff models underestimated the total volume of water, with residual runoff ranging from -0.27 inches to -1.53 inches. The results of this study can be used to assess possible mitigation options and define flood hazard areas that will contribute to the protection of life and property. This study could also assist emergency managers, community officials, and residents in determining when flooding may occur and planning evacuation routes during a flood.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155187","collaboration":"Prepared in cooperation with the Muskingum Watershed Conservancy District","usgsCitation":"Huitger, C.A, Ostheimer, C.J., and Koltun, G.F., 2016, Hydrologic and hydraulic analyses for the Black Fork Mohican River Basin in and near Shelby, Ohio: U.S. Geological Survey Scientific Investigations Report 2015–5187, 39 p., 2 appendixes, https://dx.doi.org/10.3133/sir20155187.","productDescription":"Report: vi, 39 p.; 5 Appendixes","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-060945","costCenters":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"links":[{"id":320916,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5187/appendix/sir20155187_appendix1-table1-1.csv","text":"Appendix 1 - Table 1-1","size":"84.1 KB csv","description":"SIR 2015-5187"},{"id":320915,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5187/sir20155187.pdf","text":"Report","size":"1.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5187"},{"id":320917,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5187/appendix/sir20155187_appendix1-table1-2.csv","text":"Appendix 1 - Table 1-2","size":"62 KB csv","description":"SIR 2015-5187"},{"id":320914,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5187/coverthb.jpg"},{"id":320919,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5187/appendix/sir20155187_appendix1-table1-4.csv","text":"Appendix 1 - Table 1-4","size":"50 KB csv","description":"SIR 2015-5187"},{"id":320918,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5187/appendix/sir20155187_appendix1-table1-3.csv","text":"Appendix 1 - Table 1-3","size":"19 KB csv","description":"SIR 2015-5187"},{"id":320920,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5187/appendix/sir20155187_appendix1-table1-5.csv","text":"Appendix 1 - Table 1-5","size":"22 KB csv","description":"SIR 2015-5187"}],"country":"United States","state":"Ohio","city":"Shelby","otherGeospatial":"Black Fork Mohican River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.70902633666992,\n 40.83563216247778\n ],\n [\n -82.70902633666992,\n 40.91934991356069\n ],\n [\n -82.61959075927734,\n 40.91934991356069\n ],\n [\n -82.61959075927734,\n 40.83563216247778\n ],\n [\n -82.70902633666992,\n 40.83563216247778\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio Water Science Center
6480 Doubletree Ave
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The U.S. Geological Survey and Idaho Department of Environmental Quality Idaho National Laboratory (INL) Oversight Program in cooperation with the U.S. Department of Energy determined background concentrations of selected chemical and radiochemical constituents in the eastern Snake River Plain aquifer to aid with ongoing cleanup efforts at the INL. Chemical and radiochemical constituents including calcium, magnesium, sodium, potassium, silica, chloride, sulfate, fluoride, bicarbonate, chromium, nitrate, tritium, strontium-90, chlorine-36, iodine-129, plutonium-238, plutonium-239, -240 (undivided), americium-241, technetium-99, uranium-234, uranium-235, and uranium-238 were selected for the background study because they were either not analyzed in earlier studies or new data became available to give a more recent determination of background concentrations. Samples of water collected from wells and springs at and near the INL that were not believed to be influenced by wastewater disposal were used to identify background concentrations. Groundwater in the eastern Snake River Plain aquifer at and near the INL was divided into two major water types (western tributary and eastern regional) based on concentrations of lithium less than and greater than 5 micrograms per liter (μg/L). Median concentrations for each constituent were used to define the upper limit of background.
\nThe upper limit of background concentrations for inorganic chemicals for western tributary water was 40.7 milligrams per liter (mg/L) for calcium, 15.3 mg/L for magnesium, 8.30 mg/L for sodium, 2.32 mg/L for potassium, 23.1 mg/L for silica, 11.8 mg/L for chloride, 21.4 mg/L for sulfate, 0.20 mg/L for fluoride, 176 mg/L for bicarbonate, 4.00 μg/L for chromium, and 0.655 mg/L for nitrate.
\nThe upper limit of background concentrations for inorganic chemicals for eastern regional water was 34.05 mg/L for calcium, 13.85 mg/L for magnesium, 14.85 mg/L for sodium, 3.22 mg/L for potassium, 31.0 mg/L for silica, 14.15 mg/L for chloride, 20.2 mg/L for sulfate, 0.4675 mg/L for fluoride, 165 mg/L for bicarbonate, 3.00 μg/L for chromium, and 0.995 mg/L for nitrate.
\nThe upper limit of background concentrations for radiochemical constituents for western tributary water was 34.15 ±2.35 picocuries per liter (pCi/L) for tritium, 0.00098 ±0.00006 pCi/L for chlorine-36, 0.000011 ±0.000005 pCi/L for iodine-129, <0.0000054 pCi/L for technetium-99, 0 pCi/L for strontium-90, plutonium-238, plutonium-239, -240 (undivided), and americium-241, 1.36 pCi/L with undetermined uncertainty for uranium-234, 0.025 ±0.001 pCi/L for uranium-235, and 0.541 ±0.001 pCi/L for uranium-238.
\nThe upper limit of background concentrations for radiochemical constituents for eastern regional water was 5.43 ±0.574 pCi/L for tritium, 0.0002048 ±0.0000054 pCi/L for chlorine-36, 0.000000865 ±0.000000015 pCi/L for iodine-129, <0.0000054 pCi/L for technetium-99, 0 pCi/L for strontium-90, plutonium-238, plutonium-239, -240 (undivided), and americium-241, 1.32 ±0.77 pCi/L for uranium-234, 0.016 ±0.012 pCi/L for uranium-235, and 0.477 ±0.044 pCi/L for uranium-238.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165056","collaboration":"Prepared in cooperation with the U.S. Department of Energy","usgsCitation":"Bartholomay, R.C., and Hall, L.F., 2016, Evaluation of background concentrations of selected chemical and radiochemical constituents in water from the eastern Snake River Plain aquifer at and near the Idaho National Laboratory, Idaho: U.S. Geological Survey Scientific Investigations Report 2016–5056, (DOE/ID-22237), 19 p.,\nhttps://dx.doi.org/10.3133/sir20165056.","productDescription":"Report: v, 19 p.; Appendixes A-C","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-065188","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":321010,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5056/sir20165056.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5056 Report PDF"},{"id":321011,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5056/sir20165056_appendixa.xlsx","text":"Appendix A","size":"36 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5056 Appendix A"},{"id":321009,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5056/coverthb.jpg"},{"id":321012,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5056/sir20165056_appendixb.xlsx","text":"Appendix B","size":"75 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5056 Appendix B"},{"id":321013,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5056/sir20165056_appendixc.xlsx","text":"Appendix C","size":"81 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5056 Appendix C"}],"country":"United States","state":"Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.75,\n 44.25\n ],\n [\n -113.75,\n 43.30\n ],\n [\n -112.25,\n 43.30\n ],\n [\n -112.25,\n 44.25\n ],\n [\n -113.75,\n 44.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
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The 20th century climate for the Southeastern United States and surrounding areas as simulated by global climate models used in the Coupled Model Intercomparison Project Phase 5 (CMIP5) was evaluated. A suite of statistics that characterize various aspects of the regional climate was calculated from both model simulations and observation-based datasets. CMIP5 global climate models were ranked by their ability to reproduce the observed climate. Differences in the performance of the models between regions of the United States (the Southeastern and Northwestern United States) warrant a regional-scale assessment of CMIP5 models.
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U.S. Department of the Interior
North Carolina State University, Campus Box 7617
Raleigh, NC 27695
(919) 515-2229
https://www.doi.gov/csc/southeast/
Knowledge about vegetation and fire history of the mountains of Northern Sicily is scanty. We analysed five sites to fill this gap and used terrestrial plant macrofossils to establish robust radiocarbon chronologies. Palynological records from Gorgo Tondo, Gorgo Lungo, Marcato Cixé, Urgo Pietra Giordano and Gorgo Pollicino show that under natural or near natural conditions, deciduous forests (Quercus pubescens, Q. cerris, Fraxinus ornus, Ulmus), that included a substantial portion of evergreen broadleaved species (Q. suber, Q. ilex, Hedera helix), prevailed in the upper meso-mediterranean belt. Mesophilous deciduous and evergreen broadleaved trees (Fagus sylvatica, Ilex aquifolium) dominated in the natural or quasi-natural forests of the oro-mediterranean belt. Forests were repeatedly opened for agricultural purposes. Fire activity was closely associated with farming, providing evidence that burning was a primary land use tool since Neolithic times. Land use and fire activity intensified during the Early Neolithic at 5000 bc, at the onset of the Bronze Age at 2500 bc and at the onset of the Iron Age at 800 bc. Our data and previous studies suggest that the large majority of open land communities in Sicily, from the coastal lowlands to the mountain areas below the thorny-cushion Astragalus belt (ca. 1,800 m a.s.l.), would rapidly develop into forests if land use ceased. Mesophilous Fagus-Ilex forests developed under warm mid Holocene conditions and were resilient to the combined impacts of humans and climate. The past ecology suggests a resilience of these summer-drought adapted communities to climate warming of about 2 °C. Hence, they may be particularly suited to provide heat and drought-adaptedFagus sylvatica ecotypes for maintaining drought-sensitive Central European beech forests under global warming conditions.
","language":"English","publisher":"International Work Group for Palaeoethnobotany","publisherLocation":"Berlin","doi":"10.1007/s00334-016-0569-8","usgsCitation":"Tinner, W., Vescovi, E., Van Leeuwen, J., Colombaroli, D., Henne, P., Kaltenrieder, P., Morales-Molino, C., Beffa, G., Gnaegi, B., Van der Knaap, P.W., La Mantia, T., and Pasta, S., 2016, Holocene vegetation and fire history of the mountains of northern Sicily (Italy): Vegetation History and Archaeobotany, v. 25, no. 5, p. 499-519, https://doi.org/10.1007/s00334-016-0569-8.","productDescription":"21 p.","startPage":"499","endPage":"519","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071709","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":471018,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://boris.unibe.ch/82033/","text":"External Repository"},{"id":323973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","state":"Sicily","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 12.359619140624998,\n 36.56260003738548\n ],\n [\n 12.359619140624998,\n 38.53097889440026\n ],\n [\n 15.677490234374998,\n 38.53097889440026\n ],\n [\n 15.677490234374998,\n 36.56260003738548\n ],\n [\n 12.359619140624998,\n 36.56260003738548\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-28","publicationStatus":"PW","scienceBaseUri":"576913cae4b07657d19ff101","contributors":{"authors":[{"text":"Tinner, Willy 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Knaap","given":"Pim","email":"","middleInitial":"W O","affiliations":[{"id":25430,"text":"University of Bern","active":true,"usgs":false}],"preferred":false,"id":628766,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"La Mantia, Tommaso","contributorId":169175,"corporation":false,"usgs":false,"family":"La Mantia","given":"Tommaso","email":"","affiliations":[{"id":25431,"text":"University of Palermo","active":true,"usgs":false}],"preferred":false,"id":628767,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pasta, Salvatore","contributorId":169176,"corporation":false,"usgs":false,"family":"Pasta","given":"Salvatore","email":"","affiliations":[{"id":25432,"text":"National Council of Research, Palermo, Italy","active":true,"usgs":false}],"preferred":false,"id":628768,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70170861,"text":"70170861 - 2016 - Modeling suitable habitat of invasive red lionfish Pterois volitans (Linnaeus, 1758) in North and South America’s coastal waters","interactions":[],"lastModifiedDate":"2016-07-07T10:09:23","indexId":"70170861","displayToPublicDate":"2016-05-05T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":868,"text":"Aquatic Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Modeling suitable habitat of invasive red lionfish Pterois volitans (Linnaeus, 1758) in North and South America’s coastal waters","docAbstract":"We used two common correlative species-distribution models to predict suitable habitat of invasive red lionfish Pterois volitans (Linnaeus, 1758) in the western Atlantic and eastern Pacific Oceans. The Generalized Linear Model (GLM) and the Maximum Entropy (Maxent) model were applied using the Software for Assisted Habitat Modeling. We compared models developed using native occurrences, using non-native occurrences, and using both native and non-native occurrences. Models were trained using occurrence data collected before 2010 and evaluated with occurrence data collected from the invaded range during or after 2010. We considered a total of 22 marine environmental variables. Models built with non-native only or both native and non-native occurrence data outperformed those that used only native occurrences. Evaluation metrics based on the independent test data were highest for models that used both native and non-native occurrences. Bathymetry was the strongest environmental predictor for all models and showed increasing suitability as ocean floor depth decreased, with salinity ranking the second strongest predictor for models that used native and both native and non-native occurrences, indicating low habitat suitability for salinities <30. Our model results also suggest that red lionfish could continue to invade southern latitudes in the western Atlantic Ocean and may establish localized populations in the eastern Pacific Ocean. We reiterate the importance in the choice of the training data source (native, non-native, or native/non-native) used to develop correlative species distribution models for invasive species.
\nLikelihood testing of induced earthquakes in Oklahoma and Kansas has identified the parameters that optimize the forecasting ability of smoothed seismicity models and quantified the recent temporal stability of the spatial seismicity patterns. Use of the most recent 1-year period of earthquake data and use of 10–20-km smoothing distances produced the greatest likelihood. The likelihood that the locations of January–June 2015 earthquakes were consistent with optimized forecasts decayed with increasing elapsed time between the catalogs used for model development and testing. Likelihood tests with two additional sets of earthquakes from 2014 exhibit a strong sensitivity of the rate of decay to the smoothing distance. Marked reductions in likelihood are caused by the nonstationarity of the induced earthquake locations. Our results indicate a multiple-fold benefit from smoothed seismicity models in developing short-term earthquake rate forecasts for induced earthquakes in Oklahoma and Kansas, relative to the use of seismic source zones.
","language":"English","publisher":"American Geophysical Union","publisherLocation":"Washington, D.C.","doi":"10.1002/2016GL068948","usgsCitation":"Moschetti, M.P., Hoover, S.M., and Mueller, C., 2016, Likelihood testing of seismicity-based rate forecasts of induced earthquakes in Oklahoma and Kansas: Geophysical Research Letters, v. 43, no. 10, p. 4913-4921, https://doi.org/10.1002/2016GL068948.","productDescription":"9 p.","startPage":"4913","endPage":"4921","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-075465","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":471020,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gl068948","text":"Publisher Index Page"},{"id":321014,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.537109375,\n 33.58716733904656\n ],\n [\n -97.679443359375,\n 33.61461929233378\n ],\n [\n -99.38232421875,\n 36.53612263184686\n ],\n [\n -99.51416015625,\n 37.00255267215955\n ],\n [\n -98.41552734375,\n 37.413800350662875\n ],\n [\n -97.3828125,\n 37.54457732085582\n ],\n [\n -95.592041015625,\n 35.16482750605027\n ],\n [\n -95.537109375,\n 33.58716733904656\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-18","publicationStatus":"PW","scienceBaseUri":"572dc04ee4b0dae0d5d8f207","contributors":{"authors":[{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":628863,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoover, Susan M. 0000-0002-8682-6668 shoover@usgs.gov","orcid":"https://orcid.org/0000-0002-8682-6668","contributorId":5715,"corporation":false,"usgs":true,"family":"Hoover","given":"Susan","email":"shoover@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":628864,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mueller, Charles 0000-0002-1868-9710 cmueller@usgs.gov","orcid":"https://orcid.org/0000-0002-1868-9710","contributorId":140380,"corporation":false,"usgs":true,"family":"Mueller","given":"Charles","email":"cmueller@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":628865,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70176578,"text":"70176578 - 2016 - Integrating early detection with DNA barcoding: species identification of a non-native monitor lizard (Squamata: Varanidae) carcass in Mississippi, U.S.A. ","interactions":[],"lastModifiedDate":"2016-09-21T16:15:13","indexId":"70176578","displayToPublicDate":"2016-05-05T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Integrating early detection with DNA barcoding: species identification of a non-native monitor lizard (Squamata: Varanidae) carcass in Mississippi, U.S.A. ","docAbstract":"The Storm 3 is a browser-based data logger manufactured by WaterLOG/Xylem Incorporated that operates over a temperature range of −40 to 60 degrees Celsius (°C). A Storm logger with no built-in telemetry (Storm3-00) and a logger with built-in cellular modem (Storm3-03) were evaluated by the U.S. Geological Survey (USGS) Hydrologic Instrumentation Facility (HIF) for conformance to the manufacturer’s specifications with bench tests, for recording data over the device’s operating temperature range with temperature chamber tests, and for field performance with an outdoor deployment test.
\nThe procedures followed and the results obtained from the testing are described in this publication. The device met most of the manufacturer’s stated specifications. An exception was power consumption, which was about 10 percent above the manufacturer’s specifications. It was also observed that enabling WiFi doubles the Storm 3’s power consumption. In addition, several logging errors were made by two units during deployment testing, but it could not be determined whether these errors were the fault of the Storm or of an attached sensor.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161054","usgsCitation":"Kunkle, G.A., 2016, Evaluation of the Storm 3 data logger manufactured by Waterlog/Xylem Incorporated—Results of Bench, Temperature, and Field Deployment Testing: U.S. Geological Survey Open-File Report 2016–1054, 9 p., https://dx.doi.org/10.3133/ofr20161054.","productDescription":"iii, 9 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-069059","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":320970,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1054/ofr20161054.pdf","text":"Report","size":"373 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1054"},{"id":320969,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1054/coverthb.jpg","description":"OFR 2016-1054"}],"contact":"Chief, Hydrologic Instrumentation Facility
U.S. Geological Survey
Building 2101
Stennis Space Center, MS 39529
http://water.usgs.gov/hif/
Estuarine navigation channels have long been recognized as conduits for saltwater intrusion into coastal wetlands. Salt flux decomposition and time series measurements of velocity and salinity were used to examine salt flux components and drivers of baroclinic and barotropic exchange in the Houma Navigation Channel, an estuarine channel located in the Mississippi River delta plain that receives substantial freshwater inputs from the Mississippi-Atchafalaya River system at its inland extent. Two modes of vertical current structure were identified from the time series data. The first mode, accounting for 90% of the total flow field variability, strongly resembled a barotropic current structure and was coherent with alongshelf wind stress over the coastal Gulf of Mexico. The second mode was indicative of gravitational circulation and was linked to variability in tidal stirring and the horizontal salinity gradient along the channel’s length. Tidal oscillatory salt flux was more important than gravitational circulation in transporting salt upestuary, except over equatorial phases of the fortnightly tidal cycle during times when river inflows were minimal. During all tidal cycles sampled, the advective flux, driven by a combination of freshwater discharge and wind-driven changes in storage, was the dominant transport term, and net flux of salt was always out of the estuary. These findings indicate that although human-made channels can effectively facilitate inland intrusion of saline water, this intrusion can be minimized or even reversed when they are subject to significant freshwater inputs.
","language":"English","publisher":"MDPI","doi":"10.3390/w8050184","usgsCitation":"Snedden, G., 2016, Drivers of barotropic and baroclinic exchange through an estuarine navigation channel in the Mississippi River Delta Plain: Water, v. 8, no. 5, Article 184: 15 p., https://doi.org/10.3390/w8050184.","productDescription":"Article 184: 15 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069649","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":471023,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w8050184","text":"Publisher Index Page"},{"id":320948,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Houma Navigation Canal","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.74844360351562,\n 29.216904948184734\n ],\n [\n -90.74844360351562,\n 29.58898286696141\n ],\n [\n -90.604248046875,\n 29.58898286696141\n ],\n [\n -90.604248046875,\n 29.216904948184734\n ],\n [\n -90.74844360351562,\n 29.216904948184734\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"5","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-30","publicationStatus":"PW","scienceBaseUri":"572b0f19e4b0b13d391a83ec","contributors":{"authors":[{"text":"Snedden, Gregg 0000-0001-7821-3709 sneddeng@usgs.gov","orcid":"https://orcid.org/0000-0001-7821-3709","contributorId":140235,"corporation":false,"usgs":true,"family":"Snedden","given":"Gregg","email":"sneddeng@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":628526,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70170139,"text":"sir20165046 - 2016 - Simulation of deep ventilation in Crater Lake, Oregon, 1951–2099","interactions":[],"lastModifiedDate":"2021-10-12T17:00:16.258141","indexId":"sir20165046","displayToPublicDate":"2016-05-04T10:00:00","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":"2016-5046","title":"Simulation of deep ventilation in Crater Lake, Oregon, 1951–2099","docAbstract":"The frequency of deep ventilation events in Crater Lake, a caldera lake in the Oregon Cascade Mountains, was simulated in six future climate scenarios, using a 1-dimensional deep ventilation model (1DDV) that was developed to simulate the ventilation of deep water initiated by reverse stratification and subsequent thermobaric instability. The model was calibrated and validated with lake temperature data collected from 1994 to 2011. Wind and air temperature data from three general circulation models and two representative concentration pathways were used to simulate the change in lake temperature and the frequency of deep ventilation events in possible future climates. The lumped model air2water was used to project lake surface temperature, a required boundary condition for the lake model, based on air temperature in the future climates.
The 1DDV model was used to simulate daily water temperature profiles through 2099. All future climate scenarios projected increased water temperature throughout the water column and a substantive reduction in the frequency of deep ventilation events. The least extreme scenario projected the frequency of deep ventilation events to decrease from about 1 in 2 years in current conditions to about 1 in 3 years by 2100. The most extreme scenario considered projected the frequency of deep ventilation events to be about 1 in 7.7 years by 2100. All scenarios predicted that the temperature of the entire water column will be greater than 4 °C for increasing lengths of time in the future and that the conditions required for thermobaric instability induced mixing will become rare or non-existent.
The disruption of deep ventilation by itself does not provide a complete picture of the potential ecological and water quality consequences of warming climate to Crater Lake. Estimating the effect of warming climate on deep water oxygen depletion and water clarity will require careful modeling studies to combine the physical mixing processes affected by the atmosphere with the multitude of factors affecting the growth of algae and corresponding water clarity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165046","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Wood, T.M., Wherry, S.A., Piccolroaz, S., and Girdner, S.F., 2016, Simulation of deep ventilation in Crater Lake, Oregon, 1951–2099: U.S. Geological Survey Scientific Investigations Report 2016–5046, 43 p. https://doi.org/10.3133/sir20165046","productDescription":"vii, 43 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-066051","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":320860,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5046/sir20165046.pdf","text":"Report","size":"3.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-5046 Report PDF"},{"id":320859,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5046/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Crater Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.18616485595703,\n 42.892567248047285\n ],\n [\n -122.18616485595703,\n 42.986065036562955\n ],\n [\n -122.03922271728514,\n 42.986065036562955\n ],\n [\n -122.03922271728514,\n 42.892567248047285\n ],\n [\n -122.18616485595703,\n 42.892567248047285\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: February 2020; Version 1.0: October 2016","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
http://or.water.usgs.gov
During July 2015, water samples were collected from 18 wetlands on the Lake Traverse Indian Reservation in northeastern South Dakota and southeastern North Dakota and analyzed for physical properties and 54 pesticides. This study by the U.S. Geological Survey in cooperation with the Sisseton-Wahpeton Oyate was designed to provide an update on pesticide concentrations of the same 18 wetlands that were sampled for a reconnaissance-level assessment during July 2006. The purpose of this report is to present the results of the assessment of pesticide concentrations in selected Lake Traverse Indian Reservation wetlands during July 2015 and provide a comparison of pesticide concentrations between 2006 and 2015.
Of the 54 pesticides that were analyzed for in the samples collected during July 2015, 47 pesticides were not detected in any samples. Seven pesticides—2-chloro-4-isopropylamino-6-amino-s-triazine (CIAT); 2,4–D; acetachlor; atrazine; glyphosate; metolachlor; and prometon—were detected in the 2015 samples with estimated concentrations or concentrations greater than the laboratory reporting level, and most pesticides were detected at low concentrations in only a few samples. Samples from all wetlands contained at least one detected pesticide. The maximum number of pesticides detected in a wetland sample was six, and the median number of pesticides detected was three.
The most commonly detected pesticides in the 2015 samples were atrazine and the atrazine degradate CIAT (also known as deethylatrazine), which were detected in 14 and 13 of the wetlands sampled, respectively. Glyphosate was detected in samples from 11 wetlands, and metolachlor was detected in samples from 10 wetlands. The other detected pesticides were 2,4–D (4 wetlands), acetochlor (3 wetlands), and prometon (1 wetland).
The same pesticides that were detected in the 2006 samples were detected in the 2015 samples, with the exception of simazine, which was detected only in one sample in 2006. Atrazine and CIAT were the most commonly detected pesticides in both sampling years; however, atrazine and CIAT were detected in fewer wetlands in 2015 (14 and 13 wetlands, respectively) than in 2006 (17 wetlands for both pesticides). The pesticides 2,4–D and prometon also were detected in fewer wetlands in 2015 than 2006, and simazine was only detected in 2006. In contrast, acetochlor, glyphosate, and metolachlor were detected in samples from more wetlands in 2015 than in 2006. In samples from individual wetlands, the number of pesticides detected was similar between 2006 and 2015. At least one pesticide was detected in all wetlands in 2015, and all but one wetland had pesticide detections in 2006.
Concentrations of pesticides detected in samples from wetlands were compared to selected water-quality (human-health and aquatic-life) benchmarks. None of the concentrations in either 2006 or 2015 were greater than water-quality benchmarks, with the exception of atrazine. All detections of atrazine in the 2006 and 2015 samples were greater than the acute benchmark of 0.001 microgram per liter (μg/L) for vascular plants. In addition, some concentrations of 2,4–D and atrazine were within an order of magnitude of a water-quality benchmark. The 2,4–D concentrations in the 2015 samples from three wetlands were within an order of magnitude of the U.S. Environmental Protection Agency’s Maximum Contaminant Level of 70 μg/L (that is, sample concentrations were greater than 7.0 μg/L). The maximum dissolved atrazine concentration of 0.185 μg/L in the 2015 samples along with the concentrations in 2006 samples from two wetlands were within an order of magnitude of the acute benchmark of less than 1 μg/L for nonvascular plants (that is, concentrations were greater than 0.1 μg/L).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds984","collaboration":"Prepared in cooperation with the Sisseton-Wahpeton Oyate","usgsCitation":"Carter, J.M., and Thompson, R.F., 2016, Pesticide concentrations in wetlands on the Lake Traverse Indian Reservation, South and North Dakota, July 2015: U.S. Geological Survey Data Series Report 984, 32 p., https://dx.doi.org/10.3133/ds984.","productDescription":"vi, 32 p.","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-072207","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":320926,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0984/coverthb.jpg"},{"id":320927,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0984/ds984.pdf","text":"Report","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 984"}],"country":"United States","state":"North Dakota, South Dakota","otherGeospatial":"Lake Traverse Indian Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.56021118164062,\n 45.93778073466329\n ],\n [\n -97.5146484375,\n 46.02462129598765\n ],\n [\n -97.18505859374999,\n 44.97645666320777\n ],\n [\n -96.85272216796875,\n 45.60058738537025\n ],\n [\n -96.85684204101562,\n 45.622682153628226\n ],\n [\n -96.84173583984374,\n 45.64188792039229\n ],\n [\n -96.78543090820312,\n 45.68123916702059\n ],\n [\n -96.70989990234374,\n 45.71864517367924\n ],\n [\n -96.66320800781249,\n 45.74261022090537\n ],\n [\n -96.61102294921875,\n 45.79625461321962\n ],\n [\n -96.5753173828125,\n 45.84602106744846\n ],\n [\n -96.56021118164062,\n 45.93778073466329\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Dakota Water Science Center
U.S. Geological Survey
1608 Mountain View Road
Rapid City, South Dakota 57702