The composition of two dune fields, Ogygis Undae and the NE–SW trending dune field in Gale crater (the “Bagnold Dune Field” and “Western Dune Field”), were analyzed using thermal emission spectra from the Mars Global Surveyor (MGS) Thermal Emission Spectrometer (TES) and the Mars Odyssey Thermal Emission Imaging System (THEMIS). The Gale crater dune field was used as a baseline as other orbital compositional analyses have been conducted, and in situ sampling results will soon be available.
Results from unmixing thermal emission spectra showed a spatial variation between feldspar mineral abundances and pyroxene mineral abundances in Ogygis Undae. Other datasets, including nighttime thermal inertia values, also showed variation throughout the dune field. One explanation proposed for this variation is a bimodal distribution of two sand populations. This distribution is seen in some terrestrial dune fields.
The two dune fields varied in both mineral types present and in uniformity of composition. These differences point to different source lithologies and different distances travelled from source material. Examining these differences further will allow for a greater understanding of aeolian processes on Mars.
","language":"English","publisher":"Elsevier","publisherLocation":"New York, NY","doi":"10.1016/j.epsl.2016.10.022","usgsCitation":"Charles, H., Titus, T.N., Hayward, R., Edwards, C., and Ahrens, C., 2016, Comparison of the mineral composition of the sediment found in two Mars dunefields: Ogygis Undae and Gale crater – three distinct endmembers identified: Earth and Planetary Science Letters, v. 458, no. 15, p. 152-160, https://doi.org/10.1016/j.epsl.2016.10.022.","productDescription":"9 p.","startPage":"152","endPage":"160","ipdsId":"IP-071753","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":331096,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"458","issue":"15","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"582ecfece4b04d580bd43528","contributors":{"authors":[{"text":"Charles, Heather hcharles@usgs.gov","contributorId":176924,"corporation":false,"usgs":true,"family":"Charles","given":"Heather","email":"hcharles@usgs.gov","affiliations":[],"preferred":true,"id":653991,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Titus, Timothy N. 0000-0003-0700-4875 ttitus@usgs.gov","orcid":"https://orcid.org/0000-0003-0700-4875","contributorId":146,"corporation":false,"usgs":true,"family":"Titus","given":"Timothy","email":"ttitus@usgs.gov","middleInitial":"N.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":653992,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayward, Rosalyn rhayward@usgs.gov","contributorId":176925,"corporation":false,"usgs":true,"family":"Hayward","given":"Rosalyn","email":"rhayward@usgs.gov","affiliations":[],"preferred":true,"id":653994,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Edwards, Christopher cedwards@usgs.gov","contributorId":147768,"corporation":false,"usgs":true,"family":"Edwards","given":"Christopher","email":"cedwards@usgs.gov","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":653993,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ahrens, Caitlin","contributorId":176926,"corporation":false,"usgs":false,"family":"Ahrens","given":"Caitlin","email":"","affiliations":[],"preferred":false,"id":653995,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178409,"text":"70178409 - 2016 - Wetland shoreline recession in the Mississippi River Delta from petroleum oiling and cyclonic storms","interactions":[],"lastModifiedDate":"2016-12-16T13:08:30","indexId":"70178409","displayToPublicDate":"2016-11-17T00:00:00","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":"Wetland shoreline recession in the Mississippi River Delta from petroleum oiling and cyclonic storms","docAbstract":"We evaluate the relative impact of petroleum spill and storm surge on near-shore wetland loss by quantifying the lateral movement of coastal shores in upper Barataria Bay, Louisiana (USA), between June 2009 and October 2012, a study period that extends from the year prior to the Deepwater Horizon spill to 2.5 years following the spill. We document a distinctly different pattern of shoreline loss in the 2 years following the spill, both from that observed in the year prior to the spill, during which there was no major cyclonic storm, and from change related to Hurricane Isaac, which made landfall in August 2012. Shoreline erosion following oiling was far more spatially extensive and included loss in areas protected from wave-induced erosion. We conclude that petroleum exposure can substantially increase shoreline recession particularly in areas protected from storm-induced degradation and disproportionally alters small oil-exposed barrier islands relative to natural erosion.
","language":"English","publisher":"American Geophysical Union","publisherLocation":"Washington, D.C.","doi":"10.1002/2016GL070624","collaboration":"Jet Propulsion Laboratory, California Institute of Technology","usgsCitation":"Rangoonwala, A., Jones, C.E., and Ramsey, E.W., 2016, Wetland shoreline recession in the Mississippi River Delta from petroleum oiling and cyclonic storms: Geophysical Research Letters, v. 43, no. 22, p. 11,652-11,660, https://doi.org/10.1002/2016GL070624.","productDescription":"9 p.","startPage":"11,652","endPage":"11,660","ipdsId":"IP-074987","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":462037,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gl070624","text":"Publisher Index Page"},{"id":331102,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.966667,\n 29.516667\n ],\n [\n -89.966667,\n 29.408333\n ],\n [\n -89.816667,\n 29.408333\n ],\n [\n -89.816667,\n 29.516667\n ],\n [\n -89.966667,\n 29.516667\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"22","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-17","publicationStatus":"PW","scienceBaseUri":"582ecfebe4b04d580bd43526","contributors":{"authors":[{"text":"Rangoonwala, Amina 0000-0002-0556-0598 rangoonwalaa@usgs.gov","orcid":"https://orcid.org/0000-0002-0556-0598","contributorId":3455,"corporation":false,"usgs":true,"family":"Rangoonwala","given":"Amina","email":"rangoonwalaa@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":654022,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Cathleen E.","contributorId":11890,"corporation":false,"usgs":true,"family":"Jones","given":"Cathleen","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":654023,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramsey, Elijah W. III 0000-0002-4518-5796 ramseye@usgs.gov","orcid":"https://orcid.org/0000-0002-4518-5796","contributorId":2883,"corporation":false,"usgs":true,"family":"Ramsey","given":"Elijah","suffix":"III","email":"ramseye@usgs.gov","middleInitial":"W.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":654024,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178142,"text":"ofr20161191 - 2016 - GIS-based identification of areas that have resource potential for critical minerals in six selected groups of deposit types in Alaska","interactions":[],"lastModifiedDate":"2023-10-11T01:22:47.377543","indexId":"ofr20161191","displayToPublicDate":"2016-11-16T17:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1191","title":"GIS-based identification of areas that have resource potential for critical minerals in six selected groups of deposit types in Alaska","docAbstract":"Alaska has considerable potential for undiscovered mineral resources. This report evaluates potential for undiscovered critical minerals in Alaska. Critical minerals are those for which the United States imports more than half of its total supply and which are largely derived from nations that cannot be considered reliable trading partners. In this report, estimated resource potential and certainty for the state of Alaska are analyzed and mapped for the following six selected mineral deposit groups that may contain one or more critical minerals: (1) rare earth elements-thorium-yttrium-niobium(-uranium-zirconium) [REE-Th-Y-Nb(-U-Zr)] deposits associated with peralkaline to carbonatitic igneous intrusive rocks; (2) placer and paleoplacer gold (Au) deposits that in some places might also produce platinum group elements (PGE), chromium (Cr), tin (Sn), tungsten (W), silver (Ag), or titanium (Ti); (3) platinum group elements(-cobalt-chromium-nickel-titanium-vanadium) [PGE(-Co-Cr-Ni-Ti-V)] deposits associated with mafic to ultramafic intrusive rocks; (4) carbonate-hosted copper(-cobalt-silver-germanium-gallium) [Cu(-Co-Ag-Ge-Ga)] deposits; (5) sandstone-hosted uranium(-vanadium-copper) [U(-V-Cu)] deposits; and (6) tin-tungsten-molybdenum(-tantalum-indium-fluorspar) [Sn-W-Mo(-Ta-In-fluorspar)] deposits associated with specialized granites.
This study used a data-driven, geographic information system (GIS)-implemented method to identify areas that have mineral resource potential in Alaska. This method systematically and simultaneously analyzes geoscience data from multiple geospatially referenced datasets and uses individual subwatersheds (12-digit hydrologic units) as the spatial unit of classification. The final map output uses a red, yellow, green, and gray color scheme to portray estimated relative potential (High, Medium, Low, Unknown) for each of the six groups of mineral deposit types, and it indicates the relative certainty (High, Medium, Low) of that estimate for each 12-digit hydrologic unit through color shading. Accompanying tables describe the data layers employed to score favorability for the presence of each mineral deposit group, the values assigned for specific analysis parameters, and the relative weighting of each data layer that contributes to estimated measures of potential and certainty. Core datasets used include the Alaska Geochemical Database, Version 2.0 (AGDB2); the Alaska Division of Geological & Geophysical Surveys (ADGGS) web-based geochemical database; the digital “Geologic Map of Alaska;” the Alaska Resource Data File (ARDF); and aerial gamma-ray surveys flown as part of the National Uranium Resource Evaluation (NURE) Program by the U.S. Department of Energy.
Maps accompanying this report illustrate the scores for estimated mineral resource potential for the six deposit groups for the state of Alaska. Areas that have known potential, as well as new areas that were not previously known to have potential, for the targeted minerals and deposit groups are identified and described. Numerous areas in Alaska, some of them large, have high potential for one or more of the selected groups of deposit types within Alaska.
Matthew Granitto, Timothy S. Hayes, James V. Jones, III, Susan M. Karl, Keith A. Labay, Jeffrey L. Mauk, Jeanine M. Schmidt, Nora B. Shew, Erin Todd, Bronwen Wang, Melanie B. Werdon, and Douglas B. Yager
Alaska Science Center staff
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Alaska Mineral Resources
Alaska Science Center
Digital flood-inundation maps for a 4.9-mile reach of the Yellow River at Plymouth, Indiana (Ind.), were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. 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 05516500, Yellow River at Plymouth, Ind. Current conditions for estimating near-real-time areas of inundation using USGS streamgage information may be obtained on the Internet at http://waterdata.usgs.gov/in/nwis/uv?site_no=05516500. In addition, information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood-warning system (http:/water.weather.gov/ahps/). The NWS AHPS forecasts flood hydrographs at many sites that are often collocated with USGS streamgages, including the Yellow River at Plymouth, Ind. NWS AHPS-forecast peak-stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood and forecasts of flood hydrographs at this site.
For this study, flood profiles were computed for the Yellow River reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the current stage-discharge relations at the Yellow River streamgage, in combination with the flood-insurance study for Marshall County (issued in 2011). The calibrated hydraulic model was then used to determine eight water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to the highest stage of the current stage-discharge rating curve. The 1-percent annual exceedance probability flood profile elevation (flood elevation with recurrence intervals within 100 years) is within the calibrated water-surface elevations for comparison. The simulated water-surface profiles were then used with a geographic information system (GIS) digital elevation model (DEM, derived from Light Detection and Ranging [lidar]) in order to delineate the area flooded at each water level.
The availability of these maps, along with Internet information regarding current stage from the USGS streamgage 05516500, Yellow River at Plymouth, Ind., and forecast 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 postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165117","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Menke, C.D., Bunch, A.R., and Kim, M.H., 2016, Flood-inundation maps for the Yellow River at Plymouth, Indiana: U.S. Geological Survey Scientific Investigations Report 2016–5117, 9 p., https://dx.doi.org/10.3133/sir20165117.","productDescription":"Report: vi, 9 p.; Metadata: 2 files; Read Me; Spatial Data: 2 files","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078607","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":331047,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5117/coverthb.jpg"},{"id":331048,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5117/sir20165117.pdf","text":"Report","size":"2.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5117"},{"id":331049,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2016/5117/sir20165117_dep_grd.metadata","text":"Metadata Depth Grids","size":"16.5 KB"},{"id":331050,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2016/5117/sir20165117_shapefile.metadata","text":"Metadata Shapefiles","size":"16.7 KB"},{"id":331051,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2016/5117/00Readme.txt","text":"Readme","size":"8.18 KB","linkFileType":{"id":2,"text":"txt"}},{"id":331052,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2016/5117/gis_data/depth_grids.zip","text":"Depth Grids","size":"4.37 MB","linkFileType":{"id":6,"text":"zip"}},{"id":331053,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2016/5117/gis_data/shapefile.zip","text":"Shape File","size":"807 KB","linkFileType":{"id":6,"text":"zip"}}],"country":"United States","state":"Indiana","city":"Plymouth","otherGeospatial":"Yellow River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.4,\n 41.3\n ],\n [\n -86.4,\n 41.5\n ],\n [\n -86.2,\n 41.5\n ],\n [\n -86.2,\n 41.3\n ],\n [\n -86.4,\n 41.3 ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278
http://in.water.usgs.gov/
http://ky.water.usgs.gov/
We present two‐dimensional electrical resistivity models of two 40 km magnetotelluric (MT) profiles across the Frome Embayment to the east of the northern Flinders Ranges, South Australia. The lower crust shows low resistivity of 10 Ω m at around 30 km depth. The middle crust is dominated by resistive (>1000 Ω m) basement rocks underlying the Flinders Ranges. Adjacent to the ranges, conductive lower crust is connected to vertical zones of higher conductivity extending to just below the brittle‐ductile transition at ∼10 km depth. The conductive zones narrow in the brittle upper crust and dip at roughly 45° beneath the surface. Zones of enhanced conductivity coincide with higher strain due to topographic loading and sparse seismicity. We propose that fluids are generated through neotectonic metamorphic devolatilization. Low‐resistivity zones display areas of fluid pathways along either preexisting faults or an effect of crustal compression leading to metamorphic fluid generation. The lower crustal conductors are responding to long‐wavelength flexure‐induced strain, while the upper crustal conductors are responding to short wavelength faulting in the brittle regime. MT is a useful tool for imaging crustal strain in response to far‐field stresses in an intraplate setting and provides important constraints for geodynamic modeling and crustal rheology.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016GL071351","usgsCitation":"Thiel, S., Soeffky, P., Krieger, L., Regenauer-Lieb, K., Peacock, J., and Heinson, G., 2016, Conductivity response to intraplate deformation: Evidence for metamorphic devolatilization and crustal‐scale fluid focusing: Geophysical Research Letters, v. 43, no. 21, p. 11,236-11,244, https://doi.org/10.1002/2016GL071351.","productDescription":"9 p.","startPage":"11,236","endPage":"11,244","ipdsId":"IP-081053","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":360475,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia","state":"South Australia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 138.75,\n -30.75\n ],\n [\n 140,\n -30.75\n ],\n [\n 140,\n -29.75\n ],\n [\n 138.75,\n -29.75\n ],\n [\n 138.75,\n -30.75\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"21","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-15","publicationStatus":"PW","scienceBaseUri":"5c1a1534e4b0708288c23544","contributors":{"authors":[{"text":"Thiel, Stephan","contributorId":169326,"corporation":false,"usgs":false,"family":"Thiel","given":"Stephan","email":"","affiliations":[{"id":25477,"text":"Geological Survey of South Australia","active":true,"usgs":false}],"preferred":false,"id":754449,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soeffky, Paul","contributorId":211594,"corporation":false,"usgs":false,"family":"Soeffky","given":"Paul","email":"","affiliations":[{"id":36897,"text":"University of Adelaide","active":true,"usgs":false}],"preferred":false,"id":754450,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krieger, Lars","contributorId":140053,"corporation":false,"usgs":false,"family":"Krieger","given":"Lars","email":"","affiliations":[{"id":13368,"text":"University of Adelaide, Australia","active":true,"usgs":false}],"preferred":false,"id":754451,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Regenauer-Lieb, Klaus","contributorId":211595,"corporation":false,"usgs":false,"family":"Regenauer-Lieb","given":"Klaus","email":"","affiliations":[{"id":27304,"text":"University of New South Wales","active":true,"usgs":false}],"preferred":false,"id":754452,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":754448,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Heinson, Graham","contributorId":211596,"corporation":false,"usgs":false,"family":"Heinson","given":"Graham","email":"","affiliations":[{"id":36897,"text":"University of Adelaide","active":true,"usgs":false}],"preferred":false,"id":754453,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70178392,"text":"70178392 - 2016 - Probability of acoustic transmitter detections by receiver lines in Lake Huron: results of multi-year field tests and simulations","interactions":[],"lastModifiedDate":"2016-11-16T10:28:12","indexId":"70178392","displayToPublicDate":"2016-11-16T11:20:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":773,"text":"Animal Biotelemetry","active":true,"publicationSubtype":{"id":10}},"title":"Probability of acoustic transmitter detections by receiver lines in Lake Huron: results of multi-year field tests and simulations","docAbstract":"Advances in acoustic telemetry technology have led to an improved understanding of the spatial ecology of many freshwater and marine fish species. Understanding the performance of acoustic receivers is necessary to distinguish between tagged fish that may have been present but not detected and from those fish that were absent from the area. In this study, two stationary acoustic transmitters were deployed 250 m apart within each of four acoustic receiver lines each containing at least 10 receivers (i.e., eight acoustic transmitters) located in Saginaw Bay and central Lake Huron for nearly 2 years to determine whether the probability of detecting an acoustic transmission varied as a function of time (i.e., season), location, and distance between acoustic transmitter and receiver. Distances between acoustic transmitters and receivers ranged from 200 m to >10 km in each line. The daily observed probability of detecting an acoustic transmission was used in simulation models to estimate the probability of detecting a moving acoustic transmitter on a line of receivers.
The probability of detecting an acoustic transmitter on a receiver 1000 m away differed by month for different receiver lines in Lake Huron and Saginaw Bay but was similar for paired acoustic transmitters deployed 250 m apart within the same line. Mean probability of detecting an acoustic transmitter at 1000 m calculated over the study period varied among acoustic transmitters 250 m apart within a line and differed among receiver lines in Lake Huron and Saginaw Bay. The simulated probability of detecting a moving acoustic transmitter on a receiver line was characterized by short periods of time with decreased detection. Although increased receiver spacing and higher fish movement rates decreased simulated detection probability, the location of the simulated receiver line in Lake Huron had the strongest effect on simulated detection probability.
Performance of receiver lines in Lake Huron varied across a range of spatiotemporal scales and was inconsistent among receiver lines. Our simulations indicated that if 69 kHz acoustic transmitters operating at 158 dB in 10–30 m of freshwater were being used, then receivers should be placed 1000 m apart to ensure that all fish moving at 1 m s−1 or less will be detected 90% of days over a 2-year period. Whereas these results can be used as general guidelines for designing new studies, the irregular variation in acoustic transmitter detection probabilities we observed among receiver line locations in Lake Huron makes designing receiver lines in similar systems challenging and emphasizes the need to conduct post hoc analyses of acoustic transmitter detection probabilities.
The Potomac River is a large source of N to Chesapeake Bay, where reducing nutrient loads is a focus of efforts to improve trophic status. Better understanding of NO3– loss, reflected in part by diel variation in NO3– concentrations, may refine model predictions of N loads to the Bay. We analyzed 2 y of high-frequency NO3– sensor data in the Potomac to quantify seasonal variation in the magnitude and timing of diel NO3– loss. Diel patterns were evident, especially during low flow, despite broad seasonal and flow-driven variation in NO3– concentrations. Diel variation was ~0.01 mg N/L in winter and 0.02 to 0.03 mg N/L in summer with intermediate values in spring and autumn, equivalent to <1% of the daily mean NO3– concentration in winter and ~2 to 4% in summer. Maximum diel NO3– values generally occurred in mid- to late morning, with more repeatable patterns in summer and wider variation in autumn and winter. Diel NO3– loss reduced loads by 0.7% in winter and 3% in summer. These losses were less than estimates of total in-stream NO3– load loss across the basin that averaged 33% of the annual groundwater contribution to the river. Water temperature and discharge had stronger relationships to the daily magnitude of diel NO3– variation than did photosynthetically active radiation. Estimated diel areal NO3– loss rates were generally >1000 mg N m–2 d–1, greater than most published values because measurements in this large river integrate over a greater depth/unit stream bottom area than do those from smaller rivers. These diel NO3– patterns are consistent with the influence of photoautotrophic uptake and related denitrification, but we cannot attribute these patterns to assimilation alone because the magnitude and timing of diel dynamics were affected to an unknown extent by processes, such as evapotranspiration, transient storage, and hydrodynamic dispersion. Improvements to diel loss estimates will require additional high-frequency measures, such as dissolved O2, dissolved organic N, and NH4+, and deployment of 2 measurement stations.
","language":"English","publisher":"The University of Chicago Press","doi":"10.1086/688777","usgsCitation":"Burns, D.A., Miller, M.P., Pellerin, B., and Capel, P.D., 2016, Patterns of diel variation in nitrate concentrations in the Potomac River: Freshwater Science, v. 35, no. 4, p. 1117-1132, https://doi.org/10.1086/688777.","productDescription":"16 p.","startPage":"1117","endPage":"1132","ipdsId":"IP-070616","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":438507,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7HT2MD4","text":"USGS data release","linkHelpText":"Water Quality and Hydrologic Data (2011-13) for Freshwater Science Paper titled, "Patterns of Diel Variation in Nitrate Concentrations in the Potomac River""},{"id":331066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Potomac River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.6893310546875,\n 38.052416771864834\n ],\n [\n -79.6893310546875,\n 39.8928799002948\n ],\n [\n -77.080078125,\n 39.8928799002948\n ],\n [\n -77.080078125,\n 38.052416771864834\n ],\n [\n -79.6893310546875,\n 38.052416771864834\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"4","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"582dd8e8e4b04d580bd3fa85","contributors":{"authors":[{"text":"Burns, Douglas A. 0000-0001-6516-2869 daburns@usgs.gov","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":1237,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas","email":"daburns@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653932,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":653933,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pellerin, Brian A. 0000-0003-3712-7884 bpeller@usgs.gov","orcid":"https://orcid.org/0000-0003-3712-7884","contributorId":147077,"corporation":false,"usgs":true,"family":"Pellerin","given":"Brian","email":"bpeller@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":653934,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Capel, Paul D. 0000-0003-1620-5185 capel@usgs.gov","orcid":"https://orcid.org/0000-0003-1620-5185","contributorId":1002,"corporation":false,"usgs":true,"family":"Capel","given":"Paul","email":"capel@usgs.gov","middleInitial":"D.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":653935,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70178387,"text":"70178387 - 2016 - Interannual water-level fluctuations and the vegetation of prairie potholes: Potential impacts of climate change","interactions":[],"lastModifiedDate":"2017-01-03T16:05:22","indexId":"70178387","displayToPublicDate":"2016-11-16T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Interannual water-level fluctuations and the vegetation of prairie potholes: Potential impacts of climate change","docAbstract":"Mean water depth and range of interannual water-level fluctuations over wet-dry cycles in precipitation are major drivers of vegetation zone formation in North American prairie potholes. We used harmonic hydrological models, which require only mean interannual water depth and amplitude of water-level fluctuations over a wet–dry cycle, to examine how the vegetation zones in a pothole would respond to small changes in water depth and/or amplitude of water-level fluctuations. Field data from wetlands in Saskatchewan, North Dakota, and South Dakota were used to parameterize harmonic models for four pothole classes. Six scenarios in which small negative or positive changes in either mean water depth, amplitude of interannual fluctuations, or both, were modeled to predict if they would affect the number of zones in each wetland class. The results indicated that, in some cases, even small changes in mean water depth when coupled with a small change in amplitude of water-level fluctuations can shift a prairie pothole wetland from one class to another. Our results suggest that climate change could alter the relative proportion of different wetland classes in the prairie pothole region.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-016-0850-8","usgsCitation":"van der Valk, A., and Mushet, D.M., 2016, Interannual water-level fluctuations and the vegetation of prairie potholes: Potential impacts of climate change: Wetlands, v. 36, no. 2, p. 397-406, https://doi.org/10.1007/s13157-016-0850-8.","productDescription":"10 p.","startPage":"397","endPage":"406","ipdsId":"IP-072077","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470416,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1303&context=eeob_ag_pubs","text":"External Repository"},{"id":331061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-14","publicationStatus":"PW","scienceBaseUri":"582dd8e9e4b04d580bd3fa87","contributors":{"authors":[{"text":"van der Valk, Arnold","contributorId":145612,"corporation":false,"usgs":false,"family":"van der Valk","given":"Arnold","affiliations":[{"id":15296,"text":"Iowa State University, Ames, IA, USA","active":true,"usgs":false}],"preferred":false,"id":653912,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":653911,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178380,"text":"70178380 - 2016 - Bioenergy production and forest landscape change in the southeastern United States","interactions":[],"lastModifiedDate":"2018-12-20T11:53:27","indexId":"70178380","displayToPublicDate":"2016-11-16T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1718,"text":"GCB Bioenergy","active":true,"publicationSubtype":{"id":10}},"title":"Bioenergy production and forest landscape change in the southeastern United States","docAbstract":"Production of woody biomass for bioenergy, whether wood pellets or liquid biofuels, has the potential to cause substantial landscape change and concomitant effects on forest ecosystems, but the landscape effects of alternative production scenarios have not been fully assessed. We simulated landscape change from 2010 to 2050 under five scenarios of woody biomass production for wood pellets and liquid biofuels in North Carolina, in the southeastern United States, a region that is a substantial producer of wood biomass for bioenergy and contains high biodiversity. Modeled scenarios varied biomass feedstocks, incorporating harvest of ‘conventional’ forests, which include naturally regenerating as well as planted forests that exist on the landscape even without bioenergy production, as well as purpose-grown woody crops grown on marginal lands. Results reveal trade-offs among scenarios in terms of overall forest area and the characteristics of the remaining forest in 2050. Meeting demand for biomass from conventional forests resulted in more total forest land compared with a baseline, business-as-usual scenario. However, the remaining forest was composed of more intensively managed forest and less of the bottomland hardwood and longleaf pine habitats that support biodiversity. Converting marginal forest to purpose-grown crops reduced forest area, but the remaining forest contained more of the critical habitats for biodiversity. Conversion of marginal agricultural lands to purpose-grown crops resulted in smaller differences from the baseline scenario in terms of forest area and the characteristics of remaining forest habitats. Each scenario affected the dominant type of land-use change in some regions, especially in the coastal plain that harbors high levels of biodiversity. Our results demonstrate the complex landscape effects of alternative bioenergy scenarios, highlight that the regions most likely to be affected by bioenergy production are also critical for biodiversity, and point to the challenges associated with evaluating bioenergy sustainability.
","language":"English","publisher":"Wiley","doi":"10.1111/gcbb.12386","usgsCitation":"Costanza, J.K., Abt, R.C., McKerrow, A., and Collazo, J., 2016, Bioenergy production and forest landscape change in the southeastern United States: GCB Bioenergy, v. 9, no. 5, p. 924-939, https://doi.org/10.1111/gcbb.12386.","productDescription":"16 p.","startPage":"924","endPage":"939","ipdsId":"IP-075800","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true},{"id":38315,"text":"GAP Analysis Project","active":true,"usgs":true}],"links":[{"id":470417,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcbb.12386","text":"Publisher Index Page"},{"id":331063,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"582dd8e9e4b04d580bd3fa89","chorus":{"doi":"10.1111/gcbb.12386","url":"http://dx.doi.org/10.1111/gcbb.12386","publisher":"Wiley-Blackwell","authors":"Costanza Jennifer K., Abt Robert C., McKerrow Alexa J., Collazo Jaime A.","journalName":"GCB Bioenergy","publicationDate":"8/1/2016","publiclyAccessibleDate":"8/1/2016"},"contributors":{"authors":[{"text":"Costanza, Jennifer K.","contributorId":176907,"corporation":false,"usgs":false,"family":"Costanza","given":"Jennifer","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":653929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abt, Robert C.","contributorId":174475,"corporation":false,"usgs":false,"family":"Abt","given":"Robert","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":653930,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKerrow, Alexa 0000-0002-8312-2905 amckerrow@usgs.gov","orcid":"https://orcid.org/0000-0002-8312-2905","contributorId":127753,"corporation":false,"usgs":true,"family":"McKerrow","given":"Alexa","email":"amckerrow@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":653931,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collazo, Jaime A. 0000-0002-1816-7744 jaime_collazo@usgs.gov","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":173448,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime A.","email":"jaime_collazo@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":653877,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193968,"text":"70193968 - 2016 - River rating complexity","interactions":[],"lastModifiedDate":"2025-01-29T15:54:06.16656","indexId":"70193968","displayToPublicDate":"2016-11-16T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"River rating complexity","docAbstract":"Accuracy of streamflow data depends on the veracity of the rating model used to derive a continuous time series of discharge from the surrogate variables that can readily be collected autonomously at a streamgage. Ratings are typically represented as a simple monotonic increasing function (simple rating), meaning the discharge is a function of stage alone, however this is never truly the case unless the flow is completely uniform at all stages and in transitions from one stage to the next. For example, at some streamflow-monitoring sites the discharge on the rising limb of the hydrograph is discernably larger than the discharge at the same stage on the falling limb of the hydrograph. This is the so-called “loop rating curve” (loop rating). In many cases, these loops are quite small and variation between rising- and falling-limb discharge measurements made at the same stage are well within the accuracy of the measurements. However, certain hydraulic conditions can produce a loop that is large enough to preclude use of a monotonic rating. A detailed data campaign for the Mississippi River at St. Louis, Missouri during a multi-peaked flood over a 56-day period in 2015 demonstrates the rating complexity at this location. The shifting-control method used to deal with complexity at this site matched all measurements within 8%.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"River flow 2016","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Proceedings of the International Conference on Fluvial Hydraulics (River flow 2016)","conferenceDate":"July 11-14, 2016","conferenceLocation":"St. Louis, MO","language":"English","publisher":"CRC Press","usgsCitation":"Holmes, R.R., 2016, River rating complexity, in River flow 2016, St. Louis, MO, July 11-14, 2016, p. 679-686.","productDescription":"8 p.","startPage":"679","endPage":"686","ipdsId":"IP-071265","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":348967,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":348966,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.crcpress.com/River-Flow-2016-Iowa-City-USA-July-11-14-2016/Constantinescu-Garcia-Hanes/p/book/9781138029132","linkFileType":{"id":5,"text":"html"}},{"id":350997,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ja/70193968/70193968.pdf","text":"USGS open-access version of article","size":"507 kB","linkFileType":{"id":1,"text":"pdf"},"description":"USGS open-access version of article"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fc9be4b06e28e9c24040","contributors":{"authors":[{"text":"Holmes, Robert R. Jr. 0000-0002-5060-3999 bholmes@usgs.gov","orcid":"https://orcid.org/0000-0002-5060-3999","contributorId":156293,"corporation":false,"usgs":true,"family":"Holmes","given":"Robert","suffix":"Jr.","email":"bholmes@usgs.gov","middleInitial":"R.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":false,"id":721769,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70178266,"text":"sir20165133 - 2016 - Quantifying seepage using heat as a tracer in selected irrigation canals, Walker River Basin, Nevada, 2012 and 2013","interactions":[],"lastModifiedDate":"2025-05-14T18:37:27.795967","indexId":"sir20165133","displayToPublicDate":"2016-11-16T00:00:00","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":"2016-5133","title":"Quantifying seepage using heat as a tracer in selected irrigation canals, Walker River Basin, Nevada, 2012 and 2013","docAbstract":"The Walker River is an important source of water for western Nevada. The river provides water for agriculture and recharge to local aquifers used by several communities. Farmers began diverting water from the Walker River in the 1860s to support growing agricultural development. Over time, the reduced inflows into Walker Lake from upstream reservoirs and diversions have resulted in 170 feet of lake level decline and increased dissolved-solids concentrations to levels that threaten aquatic ecosystems, including survival of Lahonton cutthroat trout, a native species listed in the Endangered Species Act. Investigations of the water-budget components in the Walker River Basin have revealed uncertainty in the recharge to aquifers from irrigation canals. To address this need, the U.S. Geological Survey conducted an extensive field study from March 2012 through October 2013 to quantify seepage losses in selected canals in the Smith Valley, Mason Valley, and Walker Lake Valley irrigation areas.
The seepage rates estimated for the 2012 and 2013 irrigation seasons in the Smith Valley transect sites (Saroni and Plymouth canals) ranged between 0.01 to 2.5 feet per day (ft/d) (0.01 to 0.68 cubic feet per second per mile [ft3/s-mi]). From 2012 to 2013, the average number of days the canals had flowing water decreased from 190 to 125 due to drier climate and lack of water available for diversion from the Walker River. The nearly 50-percent reductions in volumetric loss rates between 2012 and 2013 were associated with less than average diversions into canals from the Walker River and reductions in infiltration rates following routine canal maintenance.
Models developed for the Saroni canal in 2012 were recalibrated in 2013 to evaluate changes in seepage as a result of siltation. Just prior to the 2012 irrigation season, nearly the entire length of the canal was cleared of vegetation and debris to improve flow conveyance. In 2013, following the first year of maintenance, a 90-percent reduction in seepage was observed at one of the transect sites. The removal of sediment-clogged layers during canal maintenance may have more profound effects on seepage rates beyond what was observed at the transect sites. The seepage rates for the Saroni canal in 2012 ranged from 0.02 to 1.6 ft/d (0.03 to 0.4 ft3/s-mi). The total seepage loss in the Saroni canal for the 2012 and 2013 irrigation seasons was estimated to be 1,100 and 590 acre-feet (acre-ft), respectively.
Seepage rates on the Plymouth canal in Smith Valley in 2012 were among the lowest, ranging from 0.01 to 0.2 ft/d (0.01 to 0.1 ft3/s-mi). In 2013, the seepage rate on the Plymouth canal was similar to 2012; however, the volumetric loss was reduced by 50 percent due to the 50-percent reduction in number of canal flow days. Lower rates of seepage on the Plymouth canal for the 2012 and 2013 irrigation seasons were estimated to be 210 and 130 acre-ft, respectively.
The seepage rates estimated for the 2012 and 2013 irrigation seasons in the Mason Valley transect sites (Fox, Mickey, and Campbell ditches) ranged from 0.1 to 3.3 ft/d (0.2 to 1.3 ft3/s-mi). The influence of water-table declines on seepage was observed at the Mickey and Campbell ditches. In 2012, the estimated seepage on the Mickey ditch was 1.6 ft/d during a period when the water-table altitude was at or above the canal altitude. Following extensive declines in the water table, the hydraulic gradient increased between the canal and the shallow aquifer, thereby increasing the seepage rates to 3.2 ft/d in 2013. During the period of hydraulic disconnection, seepage rates increased to 9.5 ft/d during intermittent periods of canal flow. For the Mickey ditch, the seepage loss in 2013 was 1.5 times the rate estimated in 2012 despite the canal having 45 days less flow. Similarly, the Campbell ditch seepage loss increased slightly from 660 to 700 acre-ft, a factor of 1.1, with 49 days less flow. The seepage loss for the Fox ditch did not exhibit significant year to year variability. The annual seepage loss estimated for 2012 and 2013 in the Fox ditch was 2,100 and 2,200 acre-ft, respectively.
The seepage rates estimated for the 2013 irrigation season in the Walker Lake Valley transect sites (Schurz Lateral Canals 1A and 2A, and Canal 2) ranged from 0.7 to 0.9 ft/d (0.4 to 1.3 ft3/s-mi). In Walker Lake Valley, diversions into Lateral Canals 1A and 2A during the 2013 irrigation season were highly intermittent, a characteristic common of lateral diversions. The annual estimated seepage loss in Walker Lake Valley ranged between 50 and 725 acre-ft among the transect sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165133","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Naranjo, R.C., and Smith, D.W., 2016, Quantifying seepage using heat as a tracer in selected irrigation canals, Walker River Basin, Nevada, 2012 and 2013: U.S. Geological Survey Scientific Investigations Report 2016-5133, 169 p.,\nhttps://dx.doi.org/10.3133/sir20165133.","productDescription":"Report: viii, 169 p.; 2 Appendixes","onlineOnly":"Y","ipdsId":"IP-066495","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":331031,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5133/sir20165133_appendix_6a.xlsx","text":"Appendix 6A","size":"16.4 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5133 Appendix 6A"},{"id":331030,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5133/sir20165133.pdf","text":"Report","size":"11.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5133"},{"id":331032,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5133/sir20165133_appendix_6b.xlsx","text":"Appendix 6B","size":"13.9 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5133 Appendix 6B"},{"id":331029,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5133/coverthb.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Walker River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.75,\n 38\n ],\n [\n -119.75,\n 39.25\n ],\n [\n -118.25,\n 39.25\n ],\n [\n -118.25,\n 38\n ],\n [\n -119.75,\n 38\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Rd.
Carson City, NV 89701
http://nv.water.usgs.gov/
The City of San Antonio and the surrounding municipalities in Bexar County, Texas, are among the fastest growing cities in the Nation. Increases in residential and commercial development are changing runoff patterns and likely will increase chemical loads into streams. The U.S. Geological Survey, in cooperation with the San Antonio River Authority, evaluated the concentrations and distributional patterns of selected “compounds of emerging concern” (CECs) by collecting and analyzing water-quality samples from 20 sites in the San Antonio River Basin, Tex., during 2011–12. On the basis of their chemical composition or similar uses, the CECs discussed in this fact sheet are wastewater compounds, pharmaceutical compounds (hereinafter referred to as “pharmaceuticals”), and steroidal hormone and sterol compounds (hereinafter referred to as “steroidal hormones and sterols”). Three synoptic sampling events were completed during 2011–12 to analyze for CECs in the San Antonio River Basin. Samples were analyzed for 54 wastewater compounds, 13 pharmaceuticals, 17 steroidal hormones, and 4 sterols. Overall, the concentrations of all CECs analyzed for during this study were low, generally close to or less than the laboratory reporting level.
Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
The multitude of wetlands in the Prairie Pothole Region of North America forms one of Earth’s largest wetland complexes. The midcontinent location exposes this ecologically and economically important wetland system to a highly variable climate, markedly influencing ponded-water levels, hydroperiods, chemical characteristics, and biota of individual basins. Given their dominance on the landscape and recognized value, great interest in how projected future changes in climate will affect prairie-pothole wetlands has developed and spawned much scientific research. On June 2, 2015, a special symposium, “Midcontinent Prairie-Pothole Wetlands: Influence of a Changed Climate,” was held at the annual meeting of the Society of Wetland Scientists in Providence, Rhode Island, USA. The symposium’s twelve presenters covered a wide range of relevant topics delivered to a standing-room-only audience. Following the symposium, the presenters recognized the need to publish their presented papers as a combined product to facilitate widespread distribution. The need for additional papers to more fully cover the topic of prairie-pothole wetlands and climate change was also identified. This supplemental issue of Wetlands is the realization of that vision.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-016-0852-6","usgsCitation":"Mushet, D.M., 2016, Midcontinent Prairie-Pothole wetlands and climate change: An Introduction to the Supplemental Issue: Wetlands, v. 36, no. s2, p. 223-228, https://doi.org/10.1007/s13157-016-0852-6.","productDescription":"6 p.","startPage":"223","endPage":"228","ipdsId":"IP-076703","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":488540,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s13157-016-0852-6","text":"Publisher Index Page"},{"id":331060,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Prairie Pothole Region","volume":"36","issue":"s2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-09","publicationStatus":"PW","scienceBaseUri":"582dd8e9e4b04d580bd3fa8b","contributors":{"authors":[{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":653918,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70175480,"text":"ofr20161122 - 2016 - Groundwater quality from private domestic water-supply wells in the vicinity of petroleum production in Southwestern Indiana","interactions":[],"lastModifiedDate":"2016-11-23T13:09:40","indexId":"ofr20161122","displayToPublicDate":"2016-11-16T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1122","title":"Groundwater quality from private domestic water-supply wells in the vicinity of petroleum production in Southwestern Indiana","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20161122","collaboration":"Agency for Toxic Substances and Disease Registry","usgsCitation":"Risch, M.R., and Silcox, C.A., 2016, Groundwater quality from private domestic water-supply wells in the vicinity of petroleum production in Southwestern Indiana: U.S. Geological Survey Open-File Report 2016-1122, 29 p., https://doi.org/10.3133/ofr20161122.","productDescription":"29 p.","startPage":"1","endPage":"29","numberOfPages":"29","ipdsId":"IP-077952","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":331221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5836b8dfe4b0d9329c801c5f","contributors":{"authors":[{"text":"Risch, Martin R. 0000-0002-7908-7887 mrrisch@usgs.gov","orcid":"https://orcid.org/0000-0002-7908-7887","contributorId":2118,"corporation":false,"usgs":true,"family":"Risch","given":"Martin","email":"mrrisch@usgs.gov","middleInitial":"R.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":645401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Silcox, Cheryl A. casilcox@usgs.gov","contributorId":5080,"corporation":false,"usgs":true,"family":"Silcox","given":"Cheryl","email":"casilcox@usgs.gov","middleInitial":"A.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":645402,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70184236,"text":"70184236 - 2016 - Tradeoffs between physical captures and PIT tag antenna array detections: A case study for the Lower Colorado River Basin population of humpback chub (Gila cypha)","interactions":[],"lastModifiedDate":"2017-03-06T10:38:54","indexId":"70184236","displayToPublicDate":"2016-11-16T00:00:00","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":"Tradeoffs between physical captures and PIT tag antenna array detections: A case study for the Lower Colorado River Basin population of humpback chub (Gila cypha)","docAbstract":"A key component of many monitoring programs for special status species involves capture and handling of individuals as part of capture-recapture efforts for tracking population health and demography. Minimizing negative impacts from sampling, such as through reduced handling, aids prevention of negative impacts on species from monitoring efforts. Using simulation analyses, we found that long-term population monitoring techniques, requiring physical capture (i.e. hoop-net sampling), can be reduced and supplemented with passive detections (i.e. PIT tag antenna array detections) without negatively affecting estimates of adult humpback chub (HBC; Gila cypha) survival (S) and skipped spawning probabilities (γ' = spawner transitions to a skipped spawner, γ′ = skipped spawner remains a skipped spawner). Based on our findings of the array’s in situ detection efficiency (0.42), estimability of such demographic parameters would improve over hoop-netting alone. In addition, the array provides insight into HBC population dynamics and movement patterns outside of traditional sampling periods. However, given current timing of sampling efforts, spawner abundance estimates were negatively biased when hoop-netting was reduced, suggesting not all spawning HBC are present during the current sampling events. Despite this, our findings demonstrate that PIT tag antenna arrays, even with moderate potential detectability, may allow for reduced handling of special status species while also offering potentially more efficient monitoring strategies, especially if ideal timing of sampling can be determined.
","language":"English","publisher":"Elsevier","publisherLocation":"New York","doi":"10.1016/j.fishres.2016.06.014","usgsCitation":"Pearson, K.N., Kendall, W., Winkelman, D.L., and Persons, W.R., 2016, Tradeoffs between physical captures and PIT tag antenna array detections: A case study for the Lower Colorado River Basin population of humpback chub (Gila cypha): Fisheries Research, v. 183, p. 263-274, https://doi.org/10.1016/j.fishres.2016.06.014.","productDescription":"12 p.","startPage":"263","endPage":"274","ipdsId":"IP-062594","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":336852,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Little Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.825,\n 36.23\n ],\n [\n -111.71,\n 36.23\n ],\n [\n -111.71,\n 36.13\n ],\n [\n -111.825,\n 36.13\n ],\n [\n -111.825,\n 36.23\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"183","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58be8338e4b014cc3a3a99dd","contributors":{"authors":[{"text":"Pearson, Kristen Nicole","contributorId":171538,"corporation":false,"usgs":false,"family":"Pearson","given":"Kristen","email":"","middleInitial":"Nicole","affiliations":[],"preferred":false,"id":680765,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, William L. 0000-0003-0084-9891 wkendall@usgs.gov","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":166709,"corporation":false,"usgs":true,"family":"Kendall","given":"William L.","email":"wkendall@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":680686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Winkelman, Dana L. 0000-0002-5247-0114 danaw@usgs.gov","orcid":"https://orcid.org/0000-0002-5247-0114","contributorId":4141,"corporation":false,"usgs":true,"family":"Winkelman","given":"Dana","email":"danaw@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":680766,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Persons, William R. wpersons@usgs.gov","contributorId":4028,"corporation":false,"usgs":true,"family":"Persons","given":"William","email":"wpersons@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":680767,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70190038,"text":"70190038 - 2016 - A rare moderate‐sized (Mw 4.9) earthquake in Kansas: Rupture process of the Milan, Kansas, earthquake of 12 November 2014 and its relationship to fluid injection","interactions":[],"lastModifiedDate":"2017-08-06T16:19:26","indexId":"70190038","displayToPublicDate":"2016-11-16T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A rare moderate‐sized (Mw 4.9) earthquake in Kansas: Rupture process of the Milan, Kansas, earthquake of 12 November 2014 and its relationship to fluid injection","title":"A rare moderate‐sized (Mw 4.9) earthquake in Kansas: Rupture process of the Milan, Kansas, earthquake of 12 November 2014 and its relationship to fluid injection","docAbstract":"The largest recorded earthquake in Kansas occurred northeast of Milan on 12 November 2014 (Mw 4.9) in a region previously devoid of significant seismic activity. Applying multistation processing to data from local stations, we are able to detail the rupture process and rupture geometry of the mainshock, identify the causative fault plane, and delineate the expansion and extent of the subsequent seismic activity. The earthquake followed rapid increases of fluid injection by multiple wastewater injection wells in the vicinity of the fault. The source parameters and behavior of the Milan earthquake and foreshock–aftershock sequence are similar to characteristics of other earthquakes induced by wastewater injection into permeable formations overlying crystalline basement. This earthquake also provides an opportunity to test the empirical relation that uses felt area to estimate moment magnitude for historical earthquakes for Kansas.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220160100","usgsCitation":"Choy, G., Rubinstein, J.L., Yeck, W.L., McNamara, D.E., Mueller, C., and Boyd, O.S., 2016, A rare moderate‐sized (Mw 4.9) earthquake in Kansas: Rupture process of the Milan, Kansas, earthquake of 12 November 2014 and its relationship to fluid injection: Seismological Research Letters, v. 87, no. 6, p. 1433-1441, https://doi.org/10.1785/0220160100.","productDescription":"9 p.","startPage":"1433","endPage":"1441","ipdsId":"IP-076956","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":344606,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"87","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-14","publicationStatus":"PW","scienceBaseUri":"59882a94e4b05ba66e9ffdd8","contributors":{"authors":[{"text":"Choy, George choy@usgs.gov","contributorId":2161,"corporation":false,"usgs":true,"family":"Choy","given":"George","email":"choy@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":707276,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rubinstein, Justin L. 0000-0003-1274-6785 jrubinstein@usgs.gov","orcid":"https://orcid.org/0000-0003-1274-6785","contributorId":2404,"corporation":false,"usgs":true,"family":"Rubinstein","given":"Justin","email":"jrubinstein@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":707278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":707277,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McNamara, Daniel E. 0000-0001-6860-0350 mcnamara@usgs.gov","orcid":"https://orcid.org/0000-0001-6860-0350","contributorId":402,"corporation":false,"usgs":true,"family":"McNamara","given":"Daniel","email":"mcnamara@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":707279,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":707280,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boyd, Oliver S. 0000-0001-9457-0407 olboyd@usgs.gov","orcid":"https://orcid.org/0000-0001-9457-0407","contributorId":140739,"corporation":false,"usgs":true,"family":"Boyd","given":"Oliver","email":"olboyd@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":707281,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70176543,"text":"ofr20161167 - 2016 - Potash—A vital agricultural nutrient sourced from geologic deposits","interactions":[],"lastModifiedDate":"2017-01-10T10:36:20","indexId":"ofr20161167","displayToPublicDate":"2016-11-15T11:45:00","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":"2016-1167","title":"Potash—A vital agricultural nutrient sourced from geologic deposits","docAbstract":"This report summarizes the primary sources of potash in the United States. Potash is an essential nutrient that, along with phosphorus and nitrogen, is used as fertilizer for growing crops. Plants require sufficient potash to activate enzymes, which in turn catalyze chemical reactions important for water uptake and photosynthesis. When potassium is available in quantities necessary for healthy plant growth, disease resistance and physical quality are improved and crop yield and shelf life are increased. Potash is a water-soluble compound of potassium formed by geologic and hydrologic processes. The principal potash sources discussed are the large, stratiform deposits that formed during retreat and evaporation of intracontinental seas. The Paradox, Delaware, Holbrook, Michigan, and Williston sedimentary basins in the United States are examples where extensive potash beds were deposited. Ancient marine-type potash deposits that are close to the surface can be mined using conventional underground mining methods. In situ solution mining can be used where beds are too deep, making underground mining cost-prohibitive, or where underground mines are converted to in situ solution mines. Quaternary brine is another source of potash that is recovered by solar evaporation in manmade ponds. Groundwater from Pleistocene Lake Bonneville (Wendover, Utah) and the present-day Great Salt Lake in Utah are sources of potashbearing brine. Brine from these sources pumped to solar ponds is evaporated and potash concentrated for harvesting, processing, and refinement. Although there is sufficient potash to meet near-term demand, the large marine-type deposits are either geographically restricted to a few areas or are too deep to easily mine. Other regions lack sources of potash brine from groundwater or surface water. Thus, some areas of the world rely heavily on potash imports. Political, economic, and global population pressures may limit the ability of some countries from securing potash resources in the future. In this context, a historical perspective on U.S. potash production in a global framework is discussed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161167","isbn":"978-1-4113-4101-2","usgsCitation":"Yager, D.B., 2016, Potash—A vital agricultural nutrient sourced from geologic deposits: U.S. Geological Survey Open-File Report 2016-1167, 28 p., https://dx.doi.org/10.3133/ofr20161167.","productDescription":"v, 28 p.","numberOfPages":"38","onlineOnly":"N","ipdsId":"IP-067057","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":330757,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1167/coverthb.jpg"},{"id":330758,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1167/ofr20161167.pdf","text":"Report","size":"4.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1176"}],"contact":"Center Director
USGS Central Mineral and Environmental Resources Science Center
U.S. Geological Survey
Box 25046, MS 973
Denver, CO 80225
Using a geology-based assessment methodology, the U.S. Geological Survey assessed technically recoverable mean resources of 20 billion barrels of oil and 16 trillion cubic feet of gas in the Wolfcamp shale in the Midland Basin part of the Permian Basin Province, Texas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163092","usgsCitation":"Gaswirth, S.B., Marra, K.R., Lillis, P.G., Mercier, T.J., Leathers-Miller, H.M., Schenk, C.J., Klett, T.R., Le, P.A., Tennyson, M.E., Hawkins, S.J., Brownfield, M.E., Pitman, J.K., and Finn, T.M., 2016, Assessment of undiscovered continuous oil resources in the Wolfcamp shale of the Midland Basin, Permian Basin Province, Texas, 2016: U.S. Geological Survey Fact Sheet 2016–3092, 4 p., https://doi.org/10.3133/fs20163092.","productDescription":"4 p.","onlineOnly":"N","ipdsId":"IP-080336","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":438509,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B1VZNJ","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project - Permian Basin Province, Midland Basin, Wolfcamp Shale Assessment Unit Boundaries and Assessment Input Data Forms, Version 2.0"},{"id":438508,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72B8W5Q","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project-Permian Basin Province, Midland Basin, Wolfcamp Shale Assessment Units"},{"id":330961,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3092/coverthb.jpg"},{"id":330962,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3092/fs20163092.pdf","text":"Report","size":"804 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3092"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103,\n 30.5\n ],\n [\n -103,\n 33.8\n ],\n [\n -100,\n 33.8\n ],\n [\n -100,\n 30.5\n ],\n [\n -103,\n 30.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver Federal Center
Denver, CO 80225-0046
Many debris flows increase in volume as they travel downstream, enhancing their mobility and hazard. Volumetric growth can result from diverse physical processes, such as channel sediment entrainment, stream bank collapse, adjacent landsliding, hillslope erosion and rilling, and coalescence of multiple debris flows; incorporating these varied phenomena into physics-based debris-flow models is challenging. As an alternative, we embedded effects of debris-flow growth into an empirical/statistical approach to forecast potential inundation areas within digital landscapes in a GIS framework. Our approach used an empirical debris-growth function to account for the effects of growth phenomena. We applied this methodology to a debris-flow-prone area in the Oregon Coast Range, USA, where detailed mapping revealed areas of erosion and deposition along paths of debris flows that occurred during a large storm in 1996. Erosion was predominant in stream channels with slopes > 5°. Using pre- and post-event aerial photography, we derived upslope contributing area and channel-length growth factors. Our method reproduced the observed inundation patterns produced by individual debris flows; it also generated reproducible, objective potential inundation maps for entire drainage networks. These maps better matched observations than those using previous methods that focus on proximal or distal regions of a drainage network.
","language":"English","publisher":"Elsevier ","doi":"10.1016/j.geomorph.2016.07.039","usgsCitation":"Reid, M.E., Coe, J.A., and Brien, D., 2016, Forecasting inundation from debris flows that grow during travel, with application to the Oregon Coast Range, USA: Geomorphology, v. 273, p. 396-411, https://doi.org/10.1016/j.geomorph.2016.07.039.","productDescription":"16 p. ","startPage":"396","endPage":"411","ipdsId":"IP-071225","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":470422,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2016.07.039","text":"Publisher Index Page"},{"id":336725,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"273","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b7eba5e4b01ccd5500baf1","contributors":{"authors":[{"text":"Reid, Mark E. 0000-0002-5595-1503 mreid@usgs.gov","orcid":"https://orcid.org/0000-0002-5595-1503","contributorId":1167,"corporation":false,"usgs":true,"family":"Reid","given":"Mark","email":"mreid@usgs.gov","middleInitial":"E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":673853,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":673854,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brien, Dianne dbrien@usgs.gov","contributorId":176271,"corporation":false,"usgs":true,"family":"Brien","given":"Dianne","email":"dbrien@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":673855,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178356,"text":"70178356 - 2016 - An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data","interactions":[],"lastModifiedDate":"2017-01-17T19:03:37","indexId":"70178356","displayToPublicDate":"2016-11-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data","docAbstract":"Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that will improve its accuracy and robustness. Landsat 8 data and Moderate-Resolution Imaging Spectroradiometer-scaled Normalized Difference Vegetation Index (NDVI) were used to develop regression tree models. A Python procedure was designed to generate random replications of model parameter options across a range of model development data sizes and rule number constraints. The mean absolute difference (MAD) between the predicted and actual NDVI (scaled NDVI, value from 0–200) and its variability across the different randomized replications were calculated to assess the accuracy and stability of the models. In our case study, a six-rule regression tree model developed from 80% of the sample data had the lowest MAD (MADtraining = 2.5 and MADtesting = 2.4), which was suggested as the optimal model. This study demonstrates how the training data and rule number selections impact model accuracy and provides important guidance for future remote-sensing-based ecosystem modeling.
","language":"English","publisher":"MDPI","doi":"10.3390/rs8110943","usgsCitation":"Gu, Y., Wylie, B.K., Boyte, S.P., Picotte, J.J., Howard, D., Smith, K., and Nelson, K., 2016, An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data: Remote Sensing, v. 8, p. 1-13, https://doi.org/10.3390/rs8110943.","productDescription":"Article 943; 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-079805","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470423,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8110943","text":"Publisher Index Page"},{"id":331008,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-11","publicationStatus":"PW","scienceBaseUri":"582c2ce3e4b0c253be072bfa","contributors":{"authors":[{"text":"Gu, Yingxin 0000-0002-3544-1856 ygu@usgs.gov","orcid":"https://orcid.org/0000-0002-3544-1856","contributorId":139586,"corporation":false,"usgs":true,"family":"Gu","given":"Yingxin","email":"ygu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":653754,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":653755,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyte, Stephen P. 0000-0002-5462-3225 sboyte@usgs.gov","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":139238,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen","email":"sboyte@usgs.gov","middleInitial":"P.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":653756,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Picotte, Joshua J. 0000-0002-4021-4623 jpicotte@usgs.gov","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":4626,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua","email":"jpicotte@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":653757,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Howard, Danny 0000-0002-7563-7538 danny.howard.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":176610,"corporation":false,"usgs":true,"family":"Howard","given":"Danny","email":"danny.howard.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":653758,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Kelcy 0000-0001-6811-1485 kelcy.smith.ctr@usgs.gov","orcid":"https://orcid.org/0000-0001-6811-1485","contributorId":176844,"corporation":false,"usgs":true,"family":"Smith","given":"Kelcy","email":"kelcy.smith.ctr@usgs.gov","affiliations":[],"preferred":false,"id":653760,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nelson, Kurtis 0000-0003-4911-4511 knelson@usgs.gov","orcid":"https://orcid.org/0000-0003-4911-4511","contributorId":3602,"corporation":false,"usgs":true,"family":"Nelson","given":"Kurtis","email":"knelson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":653759,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70178355,"text":"70178355 - 2016 - Role of riparian shade on the fish assemblage of a reservoir littoral","interactions":[],"lastModifiedDate":"2016-11-15T12:00:05","indexId":"70178355","displayToPublicDate":"2016-11-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Role of riparian shade on the fish assemblage of a reservoir littoral","docAbstract":"Research into the effects of shade on reservoir fish assemblages is lacking, with most investigations focused on streams. Unlike many streams, the canopy in a reservoir shades only a narrow fringe of water adjacent to the shoreline, and may not have the influential effect on the aquatic environment reported in streams. We compared fish assemblages between shaded and unshaded sites in a shallow reservoir. Overall species richness (gamma diversity) was higher in shaded sites, and fish assemblage composition differed between shaded and unshaded sites. Average light intensity was 66 % lower in shaded sites, and differences in average temperature and dissolved oxygen were small. Unlike streams where shade can have large effects on water physicochemistry, in reservoirs shade-related differences in fish assemblages seemed to be linked principally to differences in light intensity. Diversity in light intensity in shaded and unshaded sites in reservoirs can create various mosaics of light-based habitats that enable diversity of species assemblages. Managing to promote the habitat diversity provided by shade may require coping with the artificial nature of reservoir riparian zones and water level fluctuations.
","language":"English","publisher":"Springer","doi":"10.1007/s10641-016-0519-4","usgsCitation":"Raines, C.D., and Miranda, L.E., 2016, Role of riparian shade on the fish assemblage of a reservoir littoral: Environmental Biology of Fishes, v. 99, no. 10, p. 753-760, https://doi.org/10.1007/s10641-016-0519-4.","productDescription":"8 p.","startPage":"753","endPage":"760","ipdsId":"IP-076309","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":331009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","issue":"10","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-19","publicationStatus":"PW","scienceBaseUri":"582c2ce3e4b0c253be072bfc","contributors":{"authors":[{"text":"Raines, C. D.","contributorId":176859,"corporation":false,"usgs":false,"family":"Raines","given":"C.","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":653819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":653753,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178375,"text":"70178375 - 2016 - Critical considerations for the application of environmental DNA methods to detect aquatic species","interactions":[],"lastModifiedDate":"2017-11-27T10:25:57","indexId":"70178375","displayToPublicDate":"2016-11-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Critical considerations for the application of environmental DNA methods to detect aquatic species","docAbstract":"The integrity of wetlands is of global concern. A common approach to evaluating ecological integrity involves bioassessment procedures that quantify the degree to which communities deviate from historical norms. While helpful, bioassessment provides little information about how altered conditions connect to community response. More detailed information is needed for conservation and restoration. We have illustrated an approach to addressing this challenge using structural equation modeling (SEM) and long-term monitoring data from Rocky Mountain National Park (RMNP). Wetlands in RMNP are threatened by a complex history of anthropogenic disturbance including direct alteration of hydrologic regimes; elimination of elk, wolves, and grizzly bears; reintroduction of elk (absent their primary predators); and the extirpation of beaver. More recently, nonnative moose were introduced to the region and have expanded into the park. Bioassessment suggests that up to half of the park's wetlands are not in reference condition. We developed and evaluated a general hypothesis about how human alterations influence wetland integrity and then develop a specific model using RMNP wetlands. Bioassessment revealed three bioindicators that appear to be highly sensitive to human disturbance (HD): (1) conservatism, (2) degree of invasion, and (3) cover of native forbs. SEM analyses suggest several ways human activities have impacted wetland integrity and the landscape of RMNP. First, degradation is highest where the combined effects of all types of direct HD have been the greatest (i.e., there is a general, overall effect). Second, specific HDs appear to create a “mixed-bag” of complex indirect effects, including reduced invasion and increased conservatism, but also reduced native forb cover. Some of these effects are associated with alterations to hydrologic regimes, while others are associated with altered shrub production. Third, landscape features created by historical beaver activity continue to influence wetland integrity years after beavers have abandoned sites via persistent landforms and reduced biomass of tall shrubs. Our model provides a system-level perspective on wetland integrity and provides a context for future evaluations and investigations. It also suggests scientifically supported natural resource management strategies that can assist in the National Park Service mission of maintaining or, when indicated, restoring ecological integrity “unimpaired for future generations.”
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1548","usgsCitation":"Schweiger, E.W., Grace, J.B., Cooper, D., Bobowski, B., and Britten, M., 2016, Using structural equation modeling to link human activities to wetland ecological integrity: Ecosphere, v. 7, no. 11, p. 1-30, https://doi.org/10.1002/ecs2.1548.","productDescription":"e01548; 30 p.","startPage":"1","endPage":"30","ipdsId":"IP-074267","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":470418,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1548","text":"Publisher Index Page"},{"id":331006,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountain National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.93292236328124,\n 40.15998434802335\n ],\n [\n -105.93292236328124,\n 40.69625781921317\n ],\n [\n -105.4302978515625,\n 40.69625781921317\n ],\n [\n -105.4302978515625,\n 40.15998434802335\n ],\n [\n -105.93292236328124,\n 40.15998434802335\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"11","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-10","publicationStatus":"PW","scienceBaseUri":"582c2ce3e4b0c253be072bf8","chorus":{"doi":"10.1002/ecs2.1548","url":"http://dx.doi.org/10.1002/ecs2.1548","publisher":"Wiley-Blackwell","authors":"Schweiger E. William, Grace James B., Cooper David, Bobowski Ben, Britten Mike","journalName":"Ecosphere","publicationDate":"11/2016"},"contributors":{"authors":[{"text":"Schweiger, E. William","contributorId":53635,"corporation":false,"usgs":true,"family":"Schweiger","given":"E.","email":"","middleInitial":"William","affiliations":[],"preferred":false,"id":653814,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grace, James B. 0000-0001-6374-4726 gracej@usgs.gov","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":884,"corporation":false,"usgs":true,"family":"Grace","given":"James","email":"gracej@usgs.gov","middleInitial":"B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":653815,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cooper, David","contributorId":176856,"corporation":false,"usgs":false,"family":"Cooper","given":"David","affiliations":[],"preferred":false,"id":653816,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bobowski, Ben","contributorId":176857,"corporation":false,"usgs":false,"family":"Bobowski","given":"Ben","email":"","affiliations":[],"preferred":false,"id":653817,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Britten, Mike","contributorId":176858,"corporation":false,"usgs":false,"family":"Britten","given":"Mike","email":"","affiliations":[],"preferred":false,"id":653818,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178337,"text":"70178337 - 2016 - Effects of climate and water balance across grasslands of varying C3 and C4 grass cover","interactions":[],"lastModifiedDate":"2016-11-14T12:30:15","indexId":"70178337","displayToPublicDate":"2016-11-14T13:20:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Effects of climate and water balance across grasslands of varying C3 and C4 grass cover","docAbstract":"Climate change in grassland ecosystems may lead to divergent shifts in the abundance and distribution of C3 and C4 grasses. Many studies relate mean climate conditions over relatively long time periods to plant cover, but there is still much uncertainty about how the balance of C3and C4 species will be affected by climate at a finer temporal scale than season (individual events to months). We monitored cover at five grassland sites with co-dominant C3 and C4 grass species or only dominant C3 grass species for 6 yr in national parks across the Colorado Plateau region to assess the influence of specific months of climate and water balance on changes in grass cover. C4 grass cover increased and decreased to a larger degree than C3 grass cover with extremely dry and wet consecutive years, but this response varied by ecological site. Climate and water balance explained 10–49% of the inter-annual variability of cover of C3 and C4 grasses at all sites. High precipitation in the spring and in previous year monsoon storms influenced changes in cover of C4 grasses, with measures of water balance in the same months explaining additional variability. C3 grasses in grasslands where they were dominant were influenced primarily by longer periods of climate, while C3 grasses in grasslands where they were co-dominant with C4 grasses were influenced little by climate anomalies at either short or long periods of time. Our results suggest that future changes in spring and summer climate and water balance are likely to affect cover of both C3 and C4 grasses, but cover of C4 grasses may be affected more strongly, and the degree of change will depend on soils and topography where they are growing and the timing of the growing season.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1577","usgsCitation":"Witwicki, D.L., Munson, S.M., and Thoma, D.P., 2016, Effects of climate and water balance across grasslands of varying C3 and C4 grass cover: Ecosphere, v. 7, no. 11, e01577; 19 p., https://doi.org/10.1002/ecs2.1577.","productDescription":"e01577; 19 p.","ipdsId":"IP-074409","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":470424,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1577","text":"Publisher Index Page"},{"id":330974,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-08","publicationStatus":"PW","scienceBaseUri":"582adb41e4b0c253bdfff08c","chorus":{"doi":"10.1002/ecs2.1577","url":"http://dx.doi.org/10.1002/ecs2.1577","publisher":"Wiley-Blackwell","authors":"Witwicki Dana L., Munson Seth M., Thoma David P.","journalName":"Ecosphere","publicationDate":"11/2016","auditedOn":"11/15/2016"},"contributors":{"authors":[{"text":"Witwicki, Dana L.","contributorId":72473,"corporation":false,"usgs":true,"family":"Witwicki","given":"Dana","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":653649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":653650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thoma, David P.","contributorId":45975,"corporation":false,"usgs":true,"family":"Thoma","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":653651,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178339,"text":"70178339 - 2016 - Aquatic-macroinvertebrate communities of Prairie-Pothole wetlands and lakes under a changed climate","interactions":[],"lastModifiedDate":"2017-01-03T16:07:07","indexId":"70178339","displayToPublicDate":"2016-11-14T13:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Aquatic-macroinvertebrate communities of Prairie-Pothole wetlands and lakes under a changed climate","docAbstract":"Understanding how aquatic-macroinvertebrate communities respond to changes in climate is important for biodiversity conservation in the Prairie Pothole Region and other wetland-rich landscapes. We sampled macroinvertebrate communities of 162 wetlands and lakes previously sampled from 1966 to 1976, a much drier period compared to our 2012–2013 sampling timeframe. To identify possible influences of a changed climate and predation pressures on macroinvertebrates, we compared two predictors of aquatic-macroinvertebrate communities: ponded-water dissolved-ion concentration and vertebrate-predator presence/abundance. Further, we make inferences of how macroinvertebrate communities were structured during the drier period when the range of dissolved-ion concentrations was much greater and fish occurrence in aquatic habitats was rare. We found that aquatic-macroinvertebrate community structure was influenced by dissolved-ion concentrations through a complex combination of direct and indirect relationships. Ion concentrations also influenced predator occurrence and abundance, which indirectly affected macroinvertebrate communities. It is important to consider both abiotic and biotic gradients when predicting how invertebrate communities will respond to climate change. Generally, in the wetlands and lakes we studied, freshening of ponded water resulted in more homogenous communities than occurred during a much drier period when salinity range among sites was greater.
","language":"English","publisher":"Wetlands","doi":"10.1007/s13157-016-0848-2","usgsCitation":"McLean, K.I., Mushet, D.M., Renton, D., and Stockwell, C., 2016, Aquatic-macroinvertebrate communities of Prairie-Pothole wetlands and lakes under a changed climate: Wetlands, v. 36, no. s2, p. 423-435, https://doi.org/10.1007/s13157-016-0848-2.","productDescription":"13 p.","startPage":"423","endPage":"435","ipdsId":"IP-071577","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":330972,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"s2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-11","publicationStatus":"PW","scienceBaseUri":"582adb43e4b0c253bdfff094","contributors":{"authors":[{"text":"McLean, Kyle I. kmclean@usgs.gov","contributorId":147397,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","middleInitial":"I.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":653645,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":653646,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Renton, David A. drenton@usgs.gov","contributorId":138600,"corporation":false,"usgs":true,"family":"Renton","given":"David A.","email":"drenton@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":653647,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stockwell, Craig A.","contributorId":55257,"corporation":false,"usgs":true,"family":"Stockwell","given":"Craig A.","affiliations":[],"preferred":false,"id":653648,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}