Our study objectives were to model the aboveground biomass in a xeric shrub-steppe landscape with airborne light detection and ranging (Lidar) and explore the uncertainty associated with the models we created. We incorporated vegetation vertical structure information obtained from Lidar with ground-measured biomass data, allowing us to scale shrub biomass from small field sites (1 m subplots and 1 ha plots) to a larger landscape. A series of airborne Lidar-derived vegetation metrics were trained and linked with the field-measured biomass in Random Forests (RF) regression models. A Stepwise Multiple Regression (SMR) model was also explored as a comparison. Our results demonstrated that the important predictors from Lidar-derived metrics had a strong correlation with field-measured biomass in the RF regression models with a pseudo R2 of 0.76 and RMSE of 125 g/m2 for shrub biomass and a pseudo R2 of 0.74 and RMSE of 141 g/m2 for total biomass, and a weak correlation with field-measured herbaceous biomass. The SMR results were similar but slightly better than RF, explaining 77–79% of the variance, with RMSE ranging from 120 to 129 g/m2 for shrub and total biomass, respectively. We further explored the computational efficiency and relative accuracies of using point cloud and raster Lidar metrics at different resolutions (1 m to 1 ha). Metrics derived from the Lidar point cloud processing led to improved biomass estimates at nearly all resolutions in comparison to raster-derived Lidar metrics. Only at 1 m were the results from the point cloud and raster products nearly equivalent. The best Lidar prediction models of biomass at the plot-level (1 ha) were achieved when Lidar metrics were derived from an average of fine resolution (1 m) metrics to minimize boundary effects and to smooth variability. Overall, both RF and SMR methods explained more than 74% of the variance in biomass, with the most important Lidar variables being associated with vegetation structure and statistical measures of this structure (e.g., standard deviation of height was a strong predictor of biomass). Using our model results, we developed spatially-explicit Lidar estimates of total and shrub biomass across our study site in the Great Basin, U.S.A., for monitoring and planning in this imperiled ecosystem.
","language":"English","publisher":" MDPI AG","doi":"10.3390/rs9090903","usgsCitation":"Li, A., Dhakal, S., Glenn, N.F., Spaete, L.P., Shinneman, D.J., Pilliod, D.S., Arkle, R., and McIlroy, S., 2017, Lidar aboveground vegetation biomass estimates in shrublands: Prediction, uncertainties and application to coarser scales: Remote Sensing, v. 9, 903; 19 p., https://doi.org/10.3390/rs9090903.","productDescription":"903; 19 p.","ipdsId":"IP-087544","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":469380,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs9090903","text":"Publisher Index Page"},{"id":347848,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Morley Nelson Snake River Birds of Prey National Conservation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.66,\n 42.71473218539458\n ],\n [\n -115,\n 42.71473218539458\n ],\n [\n -115,\n 43.929549935614595\n ],\n [\n -116.66,\n 43.929549935614595\n ],\n [\n -116.66,\n 42.71473218539458\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-31","publicationStatus":"PW","scienceBaseUri":"59f98ba8e4b0531197af9fa0","contributors":{"authors":[{"text":"Li, Aihua","contributorId":169445,"corporation":false,"usgs":false,"family":"Li","given":"Aihua","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":718350,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dhakal, Shital","contributorId":199163,"corporation":false,"usgs":false,"family":"Dhakal","given":"Shital","email":"","affiliations":[],"preferred":false,"id":718352,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Glenn, Nancy F.","contributorId":195241,"corporation":false,"usgs":false,"family":"Glenn","given":"Nancy","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":718351,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spaete, Luke P.","contributorId":199164,"corporation":false,"usgs":false,"family":"Spaete","given":"Luke","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":718353,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":718349,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pilliod, David S. 0000-0003-4207-3518 dpilliod@usgs.gov","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":149254,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","email":"dpilliod@usgs.gov","middleInitial":"S.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":718354,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Arkle, Robert 0000-0003-3021-1389 rarkle@usgs.gov","orcid":"https://orcid.org/0000-0003-3021-1389","contributorId":149893,"corporation":false,"usgs":true,"family":"Arkle","given":"Robert","email":"rarkle@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":718355,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McIlroy, Susan K. 0000-0001-5088-3700 smcilroy@usgs.gov","orcid":"https://orcid.org/0000-0001-5088-3700","contributorId":169446,"corporation":false,"usgs":true,"family":"McIlroy","given":"Susan","email":"smcilroy@usgs.gov","middleInitial":"K.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":718356,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70192201,"text":"fs20173072 - 2017 - FEQinput—An editor for the full equations (FEQ) hydraulic modeling system","interactions":[],"lastModifiedDate":"2017-10-30T13:18:34","indexId":"fs20173072","displayToPublicDate":"2017-10-30T11:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3072","title":"FEQinput—An editor for the full equations (FEQ) hydraulic modeling system","docAbstract":"The Full Equations Model (FEQ) is a computer program that solves the full, dynamic equations of motion for one-dimensional unsteady hydraulic flow in open channels and through control structures. As a result, hydrologists have used FEQ to design and operate flood-control structures, delineate inundation maps, and analyze peak-flow impacts. To aid in fighting floods, hydrologists are using the software to develop a system that uses flood-plain models to simulate real-time streamflow.
Input files for FEQ are composed of text files that contain large amounts of parameters, data, and instructions that are written in a format exclusive to FEQ. Although documentation exists that can aid in the creation and editing of these input files, new users face a steep learning curve in order to understand the specific format and language of the files.
FEQinput provides a set of tools to help a new user overcome the steep learning curve associated with creating and modifying input files for the FEQ hydraulic model and the related utility tool, Full Equations Utilities (FEQUTL).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173072","usgsCitation":"Ancalle, D.S., Ancalle, P.J., and Domanski, M.M., 2017, FEQinput—An editor for the full equations (FEQ) hydraulic modeling system: U.S. Geological Survey Fact Sheet 2017–3072, 4 p., https://doi.org/10.3133/fs20173072.","productDescription":"Report: 4 p.; Project Site","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-082519","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":347141,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3072/fs20173072.pdf","text":"Report","size":"770 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3072"},{"id":347345,"rank":3,"type":{"id":18,"text":"Project Site"},"url":"https://il.water.usgs.gov/proj/feq/software/feqinput/","text":"Software"},{"id":347140,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3072/coverthb2.jpg"}],"contact":"Director, Illinois-Iowa Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, IL 61801
StreamStats version 4, available at https://streamstats.usgs.gov, is a map-based web application that provides an assortment of analytical tools that are useful for water-resources planning and management, and engineering purposes. Developed by the U.S. Geological Survey (USGS), the primary purpose of StreamStats is to provide estimates of streamflow statistics for user-selected ungaged sites on streams and for USGS streamgages, which are locations where streamflow data are collected.
Streamflow statistics, such as the 1-percent flood, the mean flow, and the 7-day 10-year low flow, are used by engineers, land managers, biologists, and many others to help guide decisions in their everyday work. For example, estimates of the 1-percent flood (which is exceeded, on average, once in 100 years and has a 1-percent chance of exceedance in any year) are used to create flood-plain maps that form the basis for setting insurance rates and land-use zoning. This and other streamflow statistics also are used for dam, bridge, and culvert design; water-supply planning and management; permitting of water withdrawals and wastewater and industrial discharges; hydropower facility design and regulation; and setting of minimum allowed streamflows to protect freshwater ecosystems. Streamflow statistics can be computed from available data at USGS streamgages depending on the type of data collected at the stations. Most often, however, streamflow statistics are needed at ungaged sites, where no streamflow data are available to determine the statistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173046","usgsCitation":"Ries, K.G., III, Newson J.K., Smith, M.J., Guthrie, J.D., Steeves, P.A., Haluska, T.L., Kolb, K.R., Thompson, R.F., Santoro, R.D., and Vraga, H.W., 2017, StreamStats, version 4: U.S. Geological Survey Fact 2017–3046, 4 p., https://doi.org/10.3133/fs20173046. [Supersedes USGS Fact SheetStreamStats Coordinator
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
https://water.usgs.gov/osw/streamstats/
The U.S. Geological Survey (USGS) National Water Use Science Project (NWUSP), part of the USGS Water Availability and Use Science Program (WAUSP), has estimated water use in the United States every 5 years since 1950. This report provides an overview of total population, public-supply use, including the population that is served by public-supply systems and the domestic deliveries to those users, and self-supplied domestic water use in the United States for 2015, continuing the task of estimating water use in the United States every 5 years. In this report, estimates for the United States include the 50 States, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands (hereafter referred to as “states” for brevity).
County-level data for total population, public-supply withdrawals and the population served by public-supply systems, and domestic withdrawals for 2015 were published in a data release in an effort to provide data to the public in a timely manner. Data in the current version (1.0) of Dieter and others (2017) contains county-level total withdrawals from groundwater and surface-water sources (both fresh and saline) for public-water supply, the deliveries from those suppliers to domestic users, and the quantities of water from groundwater and surface-water sources for self-supplied domestic users, and total population. Methods used to estimate the various data elements for the public-supply and domestic use categories at the county level are described by Bradley (2017).
This Open-File Report is an interim report summarizing the data published in Dieter and others (2017) at the state and national level. This report includes discussions on the total population, totals for public-supply withdrawals and population served, total domestic withdrawals, and provides comparisons of the 2015 estimates to 2010 estimates (Maupin and others, 2014). Total domestic water use, as described in this report, represents the summation of deliveries from public-water supply to domestic users plus self-supplied domestic withdrawals.
Values for 2010 are the best available data for 2010 from the USGS Aggregate Water-Use Data System (AWUDS). The 2010 values presented in this report may have been revised from 2010 values published in Maupin and others (2014), and therefore values for 2010 in this report may not exactly match values in Maupin and others (2014).
Withdrawal and population values in this report are rounded to three significant figures. All values are rounded independently, so the sums of individually rounded numbers may not equal the totals. Percent change is calculated on unrounded data and is expressed as an integer. Differences between 2010 and 2015 values are calculated on unrounded data, then the differences are rounded.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171131","usgsCitation":"Dieter, C.A., and Maupin, M.A., 2017, Public supply and domestic water use in the United States, 2015: U.S. Geological Survey Open-File ReportDirector, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228
The Famine Early Warning Systems Network (FEWS NET) team provides food insecurity outlooks for several developing countries in Africa, Central Asia, and Central America. This study describes development of a new global reference evapotranspiration (ETo) seasonal reforecast and skill evaluation with a particular emphasis on the potential use of this dataset by the FEWS NET to support food insecurity early warning. The ETo reforecasts span the 1982-2009 period and are calculated following ASCE’s formulation of Penman-Monteith method driven by seasonal climate forecasts of monthly mean temperature, humidity, wind speed, and solar radiation from NCEP’s CFSv2 and NASA’s GEOS-5 models. The skill evaluation using deterministic and probabilistic scores, focuses on the December-February (DJF), March-May (MAM), June-August (JJA) and September-November (SON) seasons. The results indicate that ETo forecasts are a promising tool for early warning of drought and food insecurity. Globally, the regions where forecasts are most skillful (correlation >0.35 at lead-2) include Western U.S., northern parts of South America, parts of Sahel region and Southern Africa. The FEWS NET regions where forecasts are most skillful (correlation >0.35 at lead-3) include Northern Sub-Saharan Africa (DJF, dry season), Central America (DJF, dry season), parts of East Africa (JJA, wet Season), Southern Africa (JJA, dry season), and Central Asia (MAM, wet season). A case study over parts of East Africa for the JJA season shows that ETo forecasts in combination with the precipitation forecasts could have provided early warning of recent severe drought events (e.g., 2002, 2004, 2009) that contributed to substantial food insecurity in the region.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/jamc-d-17-0104.1","usgsCitation":"Shukla, S., McEvoy, D., Hobbins, M., Husak, G., Huntington, J., Funk, C., Macharia, D., and Verdin, J.P., 2017, Examining the value of global seasonal reference evapotranspiration forecasts to support FEWS NET’s food insecurity outlooks: Journal of Applied Meteorology and Climatology, v. 56, p. 2941-2949, https://doi.org/10.1175/jamc-d-17-0104.1.","productDescription":"9 p.","startPage":"2941","endPage":"2949","ipdsId":"IP-090049","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":461371,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/jamc-d-17-0104.1","text":"Publisher Index Page"},{"id":347733,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f83a2ee4b063d5d3098096","contributors":{"authors":[{"text":"Shukla, Shraddhanand","contributorId":145841,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16255,"text":"Climate Hazards Group University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":716858,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McEvoy, Daniel 0000-0003-3800-718X","orcid":"https://orcid.org/0000-0003-3800-718X","contributorId":198696,"corporation":false,"usgs":false,"family":"McEvoy","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":716859,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hobbins, Michael","contributorId":127605,"corporation":false,"usgs":false,"family":"Hobbins","given":"Michael","email":"","affiliations":[{"id":7075,"text":"National Integrated Drought Information System, Boulder, CO","active":true,"usgs":false}],"preferred":false,"id":716860,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Husak, Gregory","contributorId":145811,"corporation":false,"usgs":false,"family":"Husak","given":"Gregory","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":716861,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huntington, Justin 0000-0002-2699-0108","orcid":"https://orcid.org/0000-0002-2699-0108","contributorId":178785,"corporation":false,"usgs":false,"family":"Huntington","given":"Justin","affiliations":[],"preferred":false,"id":716862,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@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":716857,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Macharia, Denis","contributorId":195985,"corporation":false,"usgs":false,"family":"Macharia","given":"Denis","email":"","affiliations":[],"preferred":false,"id":717864,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Verdin, James P. 0000-0003-0238-9657 verdin@usgs.gov","orcid":"https://orcid.org/0000-0003-0238-9657","contributorId":720,"corporation":false,"usgs":true,"family":"Verdin","given":"James","email":"verdin@usgs.gov","middleInitial":"P.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":717865,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70193051,"text":"ofr20171135 - 2017 - Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California; 2016","interactions":[],"lastModifiedDate":"2023-04-24T21:14:41.275526","indexId":"ofr20171135","displayToPublicDate":"2017-10-30T00:00:00","publicationYear":"2017","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":"2017-1135","title":"Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California; 2016","docAbstract":"Trace-metal concentrations in sediment and in the clam Macoma petalum (formerly reported as Macoma balthica), clam reproductive activity, and benthic macroinvertebrate community structure were investigated in a mudflat 1 kilometer south of the discharge of the Palo Alto Regional Water Quality Control Plant (PARWQCP) in south San Francisco Bay, Calif. This report includes the data collected by U.S. Geological Survey (USGS) scientists for the period January 2014 to December 2016. These append to long-term datasets extending back to 1974. A major focus of the report is an integrated description of the 2016 data within the context of the longer, multi-decadal dataset. This dataset supports the City of Palo Alto’s Near-Field Receiving Water Monitoring Program, initiated in 1994.
Significant reductions in silver and copper concentrations in sediment and M. petalum occurred at the site in the 1980s following the implementation by PARWQCP of advanced wastewater treatment and source control measures. Since the 1990s, concentrations of these elements appear to have stabilized at concentrations somewhat above (silver) or near (copper) regional background concentrations Data for other metals, including chromium (Cr), mercury (Hg), nickel (Ni), selenium (Se), and zinc (Zn), have been collected since 1994. Over this period, concentrations of these elements have remained relatively constant, aside from seasonal variation that is common to all elements. In 2016, concentrations of silver and copper in M. petalum varied seasonally in response to a combination of site-specific metal exposures and annual growth and reproduction, as reported previously. Seasonal patterns for other elements, including Cr, Ni, Zn, Hg, and Se, were generally similar in timing and magnitude as those for Ag and Cu. This record suggests that legacy contamination and regional-scale factors now largely control sedimentary and bioavailable concentrations of silver and copper, as well as other elements of regulatory interest, at the Palo Alto site.
Analyses of the benthic community structure of a mudflat in south San Francisco Bay over a 40-year period show that changes in the community have occurred concurrent with reduced concentrations of metals in the sediment and in the tissues of the biosentinel clam, M. petalum, from the same area. Analysis of M. petalum shows increases in reproductive activity concurrent with the decline in metal concentrations in the tissues of this organism. Reproductive activity is presently stable (2016), with almost all animals initiating reproduction in the fall and spawning the following spring. The entire infaunal community has shifted from being dominated by several opportunistic species to a community where the species are more similar in abundance, a pattern that indicates a more stable community that is subjected to fewer stressors. In addition, two of the opportunistic species (Ampelisca abdita and Streblospio benedicti) that brood their young and live on the surface of the sediment in tubes have shown a continual decline in dominance coincident with the decline in metals; both species had short-lived rebounds in abundance in 2008, 2009, and 2010 and showed signs of increasing abundance in 2016. Heteromastus filiformis (a subsurface polychaete worm that lives in the sediment, consumes sediment and organic particles residing in the sediment, and reproduces by laying its eggs on or in the sediment) showed a concurrent increase in dominance and, in the last several years before 2008, showed a stable population. H. filiformis abundance increased slightly in 2011–2012 and returned to pre-2011 numbers in 2016. An unidentified disturbance occurred on the mudflat in early 2008 that resulted in the loss of the benthic animals, except for deep-dwelling animals like Macoma petalum. However, within two months of this event animals returned to the mudflat. The resilience of the community suggested that the disturbance was not due to a persistent toxin or anoxia. The reproductive mode of most species present in 2016 is reflective of species that were available either as pelagic larvae or as mobile adults. Although oviparous species were lower in number in this group, the authors hypothesize that these species will return slowly as more species move back into the area. The use of functional ecology was highlighted in the 2016 benthic community data, which showed that the animals that have now returned to the mudflat are those that can respond successfully to a physical, nontoxic disturbance. Today, community data show a mix of species that consume the sediment, or filter feed, have pelagic larvae that must survive landing on the sediment, and those that brood their young. USGS scientists view the 2008 disturbance event as a response by the infaunal community to an episodic natural stressor (possibly sediment accretion or a pulse of freshwater), in contrast to the long-term recovery from metal contamination. We will compare this recovery to the long-term recovery observed after the 1970s when the decline in sediment pollutants was the dominating factor.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171135","collaboration":"Prepared in cooperation with the City of Palo Alto, California","usgsCitation":"Cain, D.J., Thompson, J.K., Parchaso, F., Pearson, S., Stewart, R., Turner, M., Barasch, D., and Luoma, S.N., 2017, Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California; 2016: U.S. Geological Survey Open-File Report 2017–1135, 75 p., https://doi.org/10.3133/ofr20171135.","productDescription":"vi, 75 p.","numberOfPages":"82","onlineOnly":"Y","ipdsId":"IP-088104","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":416202,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231017","text":"Open-File Report 2023-1017","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2020"},{"id":416201,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211079","text":"Open-File Report 2021-1079","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2019"},{"id":416200,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20191084","text":"Open-File Report 2019-1084","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2018"},{"id":416199,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181107","text":"Open-File Report 2018-1107","linkHelpText":"- Near-field receiving-water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California—2017"},{"id":416198,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20161118","text":"Open-File Report 2016-1118","linkHelpText":"- Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California; 2015"},{"id":347750,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1135/coverthb_.jpg"},{"id":347751,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1135/ofr.20171135.pdf","text":"Report","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1135"}],"country":"United States","state":"California","city":"Palo Alto","otherGeospatial":"south San Francisco bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.16590881347656,\n 37.398528132728615\n ],\n [\n -121.91184997558595,\n 37.398528132728615\n ],\n [\n -121.91184997558595,\n 37.54566616715801\n ],\n [\n -122.16590881347656,\n 37.54566616715801\n ],\n [\n -122.16590881347656,\n 37.398528132728615\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NRP staff
National Research Program
U.S. Geological Survey
345 Middlefield Road, MS-435
Menlo Park, CA 94025
Bed stability is an important stream habitat attribute because it affects geomorphology and biotic communities. Natural resource managers desire indices of bed stability that can be used under a wide range of geomorphic conditions, are biologically meaningful, and are easily incorporated into sampling protocols. To eliminate potential bias due to presence of instream wood and increase precision of stability values, we modified a stream bed instability index (ISI) to include measurements of bankfull depth (dbf) and median particle diameter (D50) only in riffles and increased the pebble count to decrease variability (i.e., increase precision) in D50.The new riffle-based instability index (RISI) was compared to two established indices: ISI and the riffle stability index (RSI). RISI and ISI were strongly associated with each other but neither was closely associated with RSI. RISI and ISI were closely associated with both a diatom- and two macrovertebrate-based stream health indices, but RSI was only weakly associated with the macroinvertebrate indices. Unexpectedly, precision of D50 did not differ between RISI and ISI. Results suggest that RISI is a viable alternative to both ISI and RSI for evaluating bed stability in multiple stream types. With few data requirements and a simple protocol, RISI may also better conform to riffle-based sampling methods used by some water quality practitioners.
","language":"English","publisher":"Springer","doi":"10.1007/s10661-017-6291-x","usgsCitation":"Kusnierz, P., and Holbrook, C., 2017, Relative performance of three stream bed stability indices as indicators of stream health: Environmental Monitoring and Assessment, v. 189, p. 1-10, https://doi.org/10.1007/s10661-017-6291-x.","productDescription":"Article 563; 10 p.","startPage":"1","endPage":"10","ipdsId":"IP-090619","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":347717,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.71923828124999,\n 44.4808302785626\n ],\n [\n -110.61035156249999,\n 44.4808302785626\n ],\n [\n -110.61035156249999,\n 49.001843917978526\n ],\n [\n -114.71923828124999,\n 49.001843917978526\n ],\n [\n -114.71923828124999,\n 44.4808302785626\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"189","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-16","publicationStatus":"PW","scienceBaseUri":"59f83a2ce4b063d5d3098085","contributors":{"authors":[{"text":"Kusnierz, Paul C","contributorId":198849,"corporation":false,"usgs":false,"family":"Kusnierz","given":"Paul C","affiliations":[],"preferred":false,"id":717401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":717400,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70192827,"text":"70192827 - 2017 - Movements and habitat use of White-fronted Geese (Anser albifrons frontalis) during the remigial molt in arctic Alaska, USA","interactions":[],"lastModifiedDate":"2017-10-27T18:47:35","indexId":"70192827","displayToPublicDate":"2017-10-27T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Movements and habitat use of White-fronted Geese (Anser albifrons frontalis) during the remigial molt in arctic Alaska, USA","title":"Movements and habitat use of White-fronted Geese (Anser albifrons frontalis) during the remigial molt in arctic Alaska, USA","docAbstract":"Proposed oil and gas leasing in the National Petroleum Reserve - Alaska has raised questions about possible impacts of development on molting Greater White-fronted Geese (Anser albifrons frontalis) and their habitats. We used GPS transmitters to record fine-scale location data of molting and post-molt White-fronted Geese to assess patterns of movement and resource selection relative to vegetation class, year (2012, 2013), and body mass at capture. Molting White-fronted Geese were located an average of 63.3 ± 4.9 m (SE) from lakeshores. Estimated terrestrial home range size for flightless birds differed between years (2012 = 13.2 ± 2.6 km2; 2013 = 6.5 ± 1.8 km2), but did not vary among habitat strata or with body mass. Molting White-fronted Geese used sedge (Carex aquatilus) dominated low centered polygons and water more frequently than expected given proportional habitat availability, but avoided tussock tundra and wet sedge vegetation classes. Upon regaining flight, individuals tended to remain in the same general area, and the center of their home range only moved an average of 6.9 km. Greater White-fronted Geese that could fly tended to forage further from lakeshores ( = 245 m), and used a larger home range ( = 44.3 ± 9.5 km2) than when flightless.
","language":"English","publisher":"The Waterbird Society","doi":"10.1675/063.040.0308","usgsCitation":"Flint, P.L., and Meixell, B.W., 2017, Movements and habitat use of White-fronted Geese (Anser albifrons frontalis) during the remigial molt in arctic Alaska, USA: Waterbirds, v. 40, no. 3, p. 272-281, https://doi.org/10.1675/063.040.0308.","productDescription":"10 p.","startPage":"272","endPage":"281","ipdsId":"IP-085017","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":461375,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1675/063.040.0308","text":"Publisher Index Page"},{"id":438175,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PR7TG8","text":"USGS data release","linkHelpText":"Greater White-fronted Goose (Anser albifrons) Habitat Use Data, Teshekpuk Lake Special Area, 2012-2013"},{"id":347594,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.22607421875,\n 70.50657489320895\n ],\n [\n -151.50146484375,\n 70.50657489320895\n ],\n [\n -151.50146484375,\n 70.98655968762381\n ],\n [\n -154.22607421875,\n 70.98655968762381\n ],\n [\n -154.22607421875,\n 70.50657489320895\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f44595e4b063d5d306f2ad","contributors":{"authors":[{"text":"Flint, Paul L. 0000-0002-8758-6993 pflint@usgs.gov","orcid":"https://orcid.org/0000-0002-8758-6993","contributorId":3284,"corporation":false,"usgs":true,"family":"Flint","given":"Paul","email":"pflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":717088,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meixell, Brandt W. 0000-0002-6738-0349 bmeixell@usgs.gov","orcid":"https://orcid.org/0000-0002-6738-0349","contributorId":138716,"corporation":false,"usgs":true,"family":"Meixell","given":"Brandt","email":"bmeixell@usgs.gov","middleInitial":"W.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":717089,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70191270,"text":"sir20175112 - 2017 - Hydrogeology and water quality of sand and gravel aquifers in McHenry County, Illinois, 2009–14, and comparison to conditions in 1979","interactions":[],"lastModifiedDate":"2026-04-01T15:55:08.73","indexId":"sir20175112","displayToPublicDate":"2017-10-26T00:00:00","publicationYear":"2017","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":"2017-5112","displayTitle":"Hydrogeology and Water Quality of Sand and Gravel Aquifers in McHenry County, Illinois, 2009–14, and Comparison to Conditions in 1979","title":"Hydrogeology and water quality of sand and gravel aquifers in McHenry County, Illinois, 2009–14, and comparison to conditions in 1979","docAbstract":"Baseline conditions for the sand and gravel aquifers (groundwater) in McHenry County, Illinois, were assessed using data from a countywide network of 44 monitoring wells collecting continuous water-level data from 2009–14. In 2010, water-quality data were collected from 41 of the monitoring wells, along with five additional monitoring wells available from the U.S. Geological Survey National Water Quality Assessment Program. Periodic water-quality data were collected from 2010–14 from selected monitoring wells. The continuous water-level data were used to identify the natural and anthropogenic factors that influenced the water levels at each well. The water-level responses to natural influences such as precipitation, seasonal and annual variations, barometric pressure, and geology, and to anthropogenic influences such as pumping were used to determine (1) likely hydrogeologic setting (degree of aquifer confinement and interconnections) that, in part, are related to lithostratigraphy; and (2) areas of recharge and discharge related to vertical flow directions. Water-level trends generally were determined from the 6 years of data collection (2009–14) to infer effects of weather variability (drought) on recharge.
Precipitation adds an estimated 2.4 inches per year of recharge to the aquifer. Some of this recharge is subsequently discharged to streams and some is discharged to supply wells. A few areas in the eastern half of the county had higher average recharge rates, indicating a need for adequate protection of these recharge areas. Downward vertical flow gradients in upland areas indicate that recharge to the confined aquifer units occurs near upland areas. Upward vertical flow gradients in lowland areas indicate discharge at locations of surface water and groundwater interaction (wetlands, ponds, and streams).
Monitoring wells were sampled for major and minor ions, metals, and nutrients and a subset of wells was sampled for trace elements, dissolved gases, pesticides, and volatile organic compounds. The results were compared to health‑based and aesthetically based standards, which include the U.S. Environmental Protection Agency Maximum Contaminant Level (EPA MCL), and EPA Secondary Maximum Contaminant Levels (SMCL), as well as EPA Health-based Standards Drinking Water Advisories. Health‑based standards were exceeded for arsenic in 22 percent, sodium in 20 percent, and nitrates in 2 percent of the monitoring wells sampled. Aesthetically based standards were exceeded for total dissolved solids in 33 percent, chloride in 11 percent, iron in 85 percent, and manganese in 30 percent of the wells sampled. Many of these same constituents, such as arsenic, iron, and manganese, are naturally occurring but become elevated in areas that have anoxic, mixed, and suboxic conditions. Some areas of potential vulnerability to anthropogenic-sourced constituents in the sand and gravel aquifers were evidenced by trace amounts of volatile organic compounds and pesticides detected in water-quality samples from shallow wells (total depth less of than 46 feet below land surface) near urban settings, and by the detection of elevated major ions (chloride, sodium, magnesium, and calcium) associated, in part, with road-salt applications. Source analysis for chloride indicates mixtures of road salt, water softeners, and sewage.
Continuously measured specific conductance values were used as a surrogate for continuously measured chloride concentrations in the groundwater. The estimated chloride concentrations generally were highest in spring and lowest in summer, and occasionally peak during spring melt. Overall, the range of concentrations varied depending on the local thickness and hydraulic conductivity of the aquifer.
Water levels and water quality from the countywide groundwater monitoring network were compared to water levels and water-quality results in 1979 from a previous U.S. Geological Survey study. Potentiometric surface maps show areas with inferred decreases of water levels near the southern and southeastern areas of McHenry County. Significant increases were noted for total dissolved solids and specific conductance. Chloride concentrations increased as much as 521 percent in three of six wells resampled in 2015 from the previous study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175112","collaboration":"Prepared in cooperation with McHenry County, Illinois","usgsCitation":"Gahala, A.M., 2017, Hydrogeology and water quality of sand and gravel aquifers in McHenry County, Illinois, 2009–14, and comparison to conditions in 1979 (ver. 1.1, August 2022): U.S. Geological Survey Scientific Investigations Report 2017–5112, 91 p., https://doi.org/10.3133/sir20175112.","productDescription":"ix, 91 p.","numberOfPages":"106","onlineOnly":"Y","ipdsId":"IP-067438","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":404906,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2017/5112/versionHist.txt","text":"Version History","size":"1.36 kB","linkFileType":{"id":2,"text":"txt"}},{"id":404904,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5112/coverthb2.jpg"},{"id":347422,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5112/sir20175112.pdf","text":"Report","size":"6.67 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5112"},{"id":501947,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_106395.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Illinois","county":"McHenry County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-88.3016,42.4979],[-88.1971,42.4981],[-88.1979,42.4562],[-88.1974,42.4167],[-88.1966,42.3286],[-88.1994,42.2432],[-88.1992,42.1555],[-88.2382,42.155],[-88.3539,42.1547],[-88.4703,42.1552],[-88.5891,42.1556],[-88.7061,42.1564],[-88.7057,42.2418],[-88.7041,42.329],[-88.705,42.4167],[-88.7059,42.4972],[-88.6737,42.4977],[-88.6288,42.4985],[-88.5047,42.4981],[-88.4099,42.4977],[-88.3016,42.4979]]]},\"properties\":{\"name\":\"McHenry\",\"state\":\"IL\"}}]}","edition":"Version 1.0: October 26, 2017; Version 1.1: August 17, 2022","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 N Goodwin
Urbana, IL 61801
The National Land Cover Database (NLCD) provides thematic land cover and land cover change data at 30-meter spatial resolution for the United States. Although the NLCD is considered to be the leading thematic land cover/land use product and overall classification accuracy across the NLCD is high, performance and consistency in the vast shrub and grasslands of the Western United States is lower than desired. To address these issues and fulfill the needs of stakeholders requiring more accurate rangeland data, the USGS has developed a method to quantify these areas in terms of the continuous cover of several cover components. These components include the cover of shrub, sagebrush (Artemisia spp), big sagebrush (Artemisia tridentata spp.), herbaceous, annual herbaceous, litter, and bare ground, and shrub and sagebrush height. To produce maps of component cover, we collected field data that were then associated with spectral values in WorldView-2 and Landsat imagery using regression tree models. The current report outlines the procedures and results of converting these continuous cover components to three thematic NLCD classes: barren, shrubland, and grassland. To accomplish this, we developed a series of indices and conditional models using continuous cover of shrub, bare ground, herbaceous, and litter as inputs. The continuous cover data are currently available for two large regions in the Western United States. Accuracy of the “cross-walked” product was assessed relative to that of NLCD 2011 at independent validation points (n=787) across these two regions. Overall thematic accuracy of the “cross-walked” product was 0.70, compared to 0.63 for NLCD 2011. The kappa value was considerably higher for the “cross-walked” product at 0.41 compared to 0.28 for NLCD 2011. Accuracy was also evaluated relative to the values of training points (n=75,000) used in the development of the continuous cover components. Again, the “cross-walked” product outperformed NLCD 2011, with an overall accuracy of 0.81, compared to 0.66 for NLCD 2011. These results demonstrated that our continuous cover predictions and models were successful in increasing thematic classification accuracy in Western United States shrublands. We plan to directly use the “cross-walked” product, where available, in the NLCD 2016 product.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171119","usgsCitation":"Rigge, M.B., Gass, Leila, Homer, C.G., and Xian, G.Z., 2017, Methods for converting continuous shrubland ecosystem component values to thematic National Land Cover Database classes: U.S. Geological Survey Open-File Report 2017–1119, 10 p., https://doi.org/10.3133/ofr20171119.","productDescription":"iv, 10 p.","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-089077","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":347404,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1119/coverthb.jpg"},{"id":347405,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1119/ofr20171119.pdf","text":"Report","size":"2.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1119"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Estimates of population abundance are important to wildlife management and conservation. However, it can be difficult to characterize the numbers of broadly distributed, low-density, and elusive bird species. Although Golden Eagles (Aquila chrysaetos) are rare, difficult to detect, and broadly distributed, they are concentrated during their autumn migration at monitoring sites in eastern North America. We used hawk-count data collected by citizen scientists in a virtual mark–recapture modeling analysis to estimate the numbers of Golden Eagles that migrate in autumn along Kittatinny Ridge, an Important Bird Area in Pennsylvania, USA. In order to evaluate the sensitivity of our abundance estimates to variation in eagle capture histories, we applied candidate models to 8 different sets of capture histories, constructed with or without age-class information and using known mean flight speeds 6 1, 2, 4, or 6 SE for eagles to travel between hawk-count sites. Although some abundance estimates were produced by models that poorly fitted the data (ĉ > 3.0), 2 sets of population estimates were produced by acceptably performing models (cˆ less than or equal to 3.0). Application of these models to count data from November, 2002–2011, suggested a mean population abundance of 1,354 6 117 SE (range: 873–1,938). We found that Golden Eagles left the ridgeline at different rates and in different places along the route, and that typically ,50% of individuals were detected at the hawk-count sites. Our study demonstrates a useful technique for estimating population abundance that may be applicable to other migrant species that are repeatedly detected at multiple monitoring sites along a topographic diversion or leading line.
","language":"English","publisher":"BioOne","doi":"10.1650/CONDOR-16-166.1","usgsCitation":"Dennhardt, A.J., Duerr, A.E., Brandes, D., and Katzner, T., 2017, Applying citizen-science data and mark-recapture models to estimate numbers of migrant golden eagles in an important bird area in eastern North America: The Condor, v. 119, no. 4, p. 817-831, https://doi.org/10.1650/CONDOR-16-166.1.","productDescription":"15 p.","startPage":"817","endPage":"831","ipdsId":"IP-074201","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":469394,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-16-166.1","text":"Publisher Index Page"},{"id":347305,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennslyvania","otherGeospatial":"Kittatinny Ridge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.123046875,\n 39.257778150283364\n ],\n [\n -73.916015625,\n 39.257778150283364\n ],\n [\n -73.916015625,\n 42.78733853171998\n ],\n [\n -81.123046875,\n 42.78733853171998\n ],\n [\n -81.123046875,\n 39.257778150283364\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"119","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f1a29ee4b0220bbd9d9eea","contributors":{"authors":[{"text":"Dennhardt, Andrew J.","contributorId":198247,"corporation":false,"usgs":false,"family":"Dennhardt","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":715500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duerr, Adam E.","contributorId":190590,"corporation":false,"usgs":false,"family":"Duerr","given":"Adam","email":"","middleInitial":"E.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":715501,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandes, David","contributorId":138917,"corporation":false,"usgs":false,"family":"Brandes","given":"David","email":"","affiliations":[{"id":35653,"text":"Lafayette College, Easton, PA","active":true,"usgs":false}],"preferred":false,"id":715502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":715499,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192302,"text":"70192302 - 2017 - Buried shallow fault slip from the South Napa earthquake revealed by near-field geodesy","interactions":[],"lastModifiedDate":"2017-10-26T09:32:58","indexId":"70192302","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Buried shallow fault slip from the South Napa earthquake revealed by near-field geodesy","docAbstract":"Earthquake-related fault slip in the upper hundreds of meters of Earth’s surface has remained largely unstudied because of challenges measuring deformation in the near field of a fault rupture. We analyze centimeter-scale accuracy mobile laser scanning (MLS) data of deformed vine rows within ±300 m of the principal surface expression of the M (magnitude) 6.0 2014 South Napa earthquake. Rather than assuming surface displacement equivalence to fault slip, we invert the near-field data with a model that allows for, but does not require, the fault to be buried below the surface. The inversion maps the position on a preexisting fault plane of a slip front that terminates ~3 to 25 m below the surface coseismically and within a few hours postseismically. The lack of surface-breaching fault slip is verified by two trenches. We estimate near-surface slip ranging from ~0.5 to 1.25 m. Surface displacement can underestimate fault slip by as much as 30%. This implies that similar biases could be present in short-term geologic slip rates used in seismic hazard analyses. Along strike and downdip, we find deficits in slip: The along-strike deficit is erased after ~1 month by afterslip. We find no evidence of off-fault deformation and conclude that the downdip shallow slip deficit for this event is likely an artifact. As near-field geodetic data rapidly proliferate and will become commonplace, we suggest that analyses of near-surface fault rupture should also use more sophisticated mechanical models and subsurface geomechanical tests.
","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.1700525","usgsCitation":"Brooks, B.A., Minson, S.E., Glennie, C.L., Nevitt, J., Dawson, T.E., Rubin, R.S., Ericksen, T., Lockner, D.A., Hudnut, K.W., Langenheim, V., Lutz, A., Murray, J.R., Schwartz, D.P., and Zaccone, D., 2017, Buried shallow fault slip from the South Napa earthquake revealed by near-field geodesy: Science Advances, v. 3, no. 7, e1700525; 12 p., https://doi.org/10.1126/sciadv.1700525.","productDescription":"e1700525; 12 p.","ipdsId":"IP-088981","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":469391,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.1700525","text":"Publisher Index Page"},{"id":347346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123,\n 37.5\n ],\n [\n -122,\n 37.5\n ],\n [\n -122,\n 39\n ],\n [\n -123,\n 39\n ],\n [\n -123,\n 37.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f1a2a1e4b0220bbd9d9f16","contributors":{"authors":[{"text":"Brooks, Benjamin A. 0000-0001-7954-6281 bbrooks@usgs.gov","orcid":"https://orcid.org/0000-0001-7954-6281","contributorId":5237,"corporation":false,"usgs":true,"family":"Brooks","given":"Benjamin","email":"bbrooks@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715192,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Glennie, Craig L.","contributorId":198143,"corporation":false,"usgs":false,"family":"Glennie","given":"Craig","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":715193,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nevitt, Johanna 0000-0003-3819-1773 jnevitt@usgs.gov","orcid":"https://orcid.org/0000-0003-3819-1773","contributorId":198144,"corporation":false,"usgs":true,"family":"Nevitt","given":"Johanna","email":"jnevitt@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715194,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dawson, Timothy E.","contributorId":24429,"corporation":false,"usgs":false,"family":"Dawson","given":"Timothy","email":"","middleInitial":"E.","affiliations":[{"id":7099,"text":"Calif. Geol. Survey","active":true,"usgs":false}],"preferred":false,"id":715195,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rubin, Ron S.","contributorId":127696,"corporation":false,"usgs":false,"family":"Rubin","given":"Ron","email":"","middleInitial":"S.","affiliations":[{"id":7099,"text":"Calif. Geol. Survey","active":true,"usgs":false}],"preferred":false,"id":715196,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ericksen, Todd 0000-0001-9340-575X tericksen@usgs.gov","orcid":"https://orcid.org/0000-0001-9340-575X","contributorId":198145,"corporation":false,"usgs":true,"family":"Ericksen","given":"Todd","email":"tericksen@usgs.gov","affiliations":[],"preferred":true,"id":715197,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lockner, David A. 0000-0001-8630-6833 dlockner@usgs.gov","orcid":"https://orcid.org/0000-0001-8630-6833","contributorId":567,"corporation":false,"usgs":true,"family":"Lockner","given":"David","email":"dlockner@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":715198,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hudnut, Kenneth W. 0000-0002-3168-4797 hudnut@usgs.gov","orcid":"https://orcid.org/0000-0002-3168-4797","contributorId":2550,"corporation":false,"usgs":true,"family":"Hudnut","given":"Kenneth","email":"hudnut@usgs.gov","middleInitial":"W.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715199,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Langenheim, Victoria E. 0000-0003-2170-5213 zulanger@usgs.gov","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":151042,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria E.","email":"zulanger@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":715200,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lutz, Andrew","contributorId":198146,"corporation":false,"usgs":false,"family":"Lutz","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":715201,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Murray, Jessica R. 0000-0002-6144-1681 jrmurray@usgs.gov","orcid":"https://orcid.org/0000-0002-6144-1681","contributorId":2759,"corporation":false,"usgs":true,"family":"Murray","given":"Jessica","email":"jrmurray@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715203,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Schwartz, David P. 0000-0001-5193-9200 dschwartz@usgs.gov","orcid":"https://orcid.org/0000-0001-5193-9200","contributorId":1940,"corporation":false,"usgs":true,"family":"Schwartz","given":"David","email":"dschwartz@usgs.gov","middleInitial":"P.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715204,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Zaccone, Dana","contributorId":198147,"corporation":false,"usgs":false,"family":"Zaccone","given":"Dana","email":"","affiliations":[],"preferred":false,"id":715205,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70192308,"text":"70192308 - 2017 - Shear-wave velocity model from Rayleigh wave group velocities centered on the Sacramento/San Joaquin Delta","interactions":[],"lastModifiedDate":"2017-10-25T11:34:59","indexId":"70192308","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3208,"text":"Pure and Applied Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Shear-wave velocity model from Rayleigh wave group velocities centered on the Sacramento/San Joaquin Delta","docAbstract":"Rayleigh wave group velocities obtained from ambient noise tomography are inverted for an upper crustal model of the Central Valley, California, centered on the Sacramento/San Joaquin Delta. Two methods were tried; the first uses SURF96, a least-squares routine. It provides a good fit to the data, but convergence is dependent on the starting model. The second uses a genetic algorithm, whose starting model is random. This method was tried at several nodes in the model and compared to the output from SURF96. The genetic code is run five times and the variance of the output of all five models can be used to obtain an estimate of error. SURF96 produces a more regular solution mostly because it is typically run with a smoothing constraint. Models from the genetic code are generally consistent with the SURF96 code sometimes producing lower velocities at depth. The full model, calculated using SURF96, employed a 2-pass strategy, which used a variable damping scheme in the first pass. The resulting model shows low velocities near the surface in the Central Valley with a broad asymmetrical sedimentary basin located close to the western edge of the Central Valley near 122°W longitude. At shallow depths the Rio Vista Basin is found nestled between the Pittsburgh/Kirby Hills and Midland faults, but a significant basin also seems to exist to the west of the Kirby Hills fault. There are other possible correlations between fast and slow velocities in the Central Valley and geologic features such as the Stockton Arch, oil or gas producing regions and the fault-controlled western boundary of the Central Valley.","language":"English","publisher":"Springer","doi":"10.1007/s00024-017-1587-x","usgsCitation":"Fletcher, J.P., and Erdem, J., 2017, Shear-wave velocity model from Rayleigh wave group velocities centered on the Sacramento/San Joaquin Delta: Pure and Applied Geophysics, v. 174, no. 10, p. 3825-3839, https://doi.org/10.1007/s00024-017-1587-x.","productDescription":"15 p.","startPage":"3825","endPage":"3839","ipdsId":"IP-081360","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":461377,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00024-017-1587-x","text":"Publisher Index Page"},{"id":347340,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento/San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.48358154296874,\n 37.23470197166817\n ],\n [\n -121.45111083984375,\n 37.23470197166817\n ],\n [\n -121.45111083984375,\n 38.57393751557591\n ],\n [\n -123.48358154296874,\n 38.57393751557591\n ],\n [\n -123.48358154296874,\n 37.23470197166817\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"174","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-21","publicationStatus":"PW","scienceBaseUri":"59f1a2a0e4b0220bbd9d9f0d","contributors":{"authors":[{"text":"Fletcher, Jon Peter B. 0000-0001-8885-6177 jfletcher@usgs.gov","orcid":"https://orcid.org/0000-0001-8885-6177","contributorId":1216,"corporation":false,"usgs":true,"family":"Fletcher","given":"Jon","email":"jfletcher@usgs.gov","middleInitial":"Peter B.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715226,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erdem, Jemile 0000-0003-2353-9431 jerdem@usgs.gov","orcid":"https://orcid.org/0000-0003-2353-9431","contributorId":127700,"corporation":false,"usgs":true,"family":"Erdem","given":"Jemile","email":"jerdem@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715227,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70192315,"text":"70192315 - 2017 - Projected warming portends seasonal shifts of stream temperatures in the Crown of the Continent Ecosystem, USA and Canada","interactions":[],"lastModifiedDate":"2017-10-26T09:31:34","indexId":"70192315","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Projected warming portends seasonal shifts of stream temperatures in the Crown of the Continent Ecosystem, USA and Canada","docAbstract":"Climate warming is expected to increase stream temperatures in mountainous regions of western North America, yet the degree to which future climate change may influence seasonal patterns of stream temperature is uncertain. In this study, a spatially explicit statistical model framework was integrated with empirical stream temperature data (approximately four million bi-hourly recordings) and high-resolution climate and land surface data to estimate monthly stream temperatures and potential change under future climate scenarios in the Crown of the Continent Ecosystem, USA and Canada (72,000 km2). Moderate and extreme warming scenarios forecast increasing stream temperatures during spring, summer, and fall, with the largest increases predicted during summer (July, August, and September). Additionally, thermal regimes characteristic of current August temperatures, the warmest month of the year, may be exceeded during July and September, suggesting an earlier and extended duration of warm summer stream temperatures. Models estimate that the largest magnitude of temperature warming relative to current conditions may be observed during the shoulder months of winter (April and November). Summer stream temperature warming is likely to be most pronounced in glacial-fed streams where models predict the largest magnitude (> 50%) of change due to the loss of alpine glaciers. We provide the first broad-scale analysis of seasonal climate effects on spatiotemporal patterns of stream temperature in the Crown of the Continent Ecosystem for better understanding climate change impacts on freshwater habitats and guiding conservation and climate adaptation strategies.","language":"English","publisher":"Springer","doi":"10.1007/s10584-017-2060-7","usgsCitation":"Jones, L.A., Muhlfeld, C.C., and Marshall, L.A., 2017, Projected warming portends seasonal shifts of stream temperatures in the Crown of the Continent Ecosystem, USA and Canada: Climatic Change, v. 144, no. 4, p. 641-655, https://doi.org/10.1007/s10584-017-2060-7.","productDescription":"15 p.","startPage":"641","endPage":"655","ipdsId":"IP-081391","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":347330,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.27929687499999,\n 43.54854811091286\n ],\n [\n -113.37890625,\n 43.13306116240612\n ],\n [\n -111.884765625,\n 43.99281450048989\n ],\n [\n -111.357421875,\n 45.058001435398275\n ],\n [\n -110.1708984375,\n 46.437856895024204\n ],\n [\n -110.302734375,\n 47.040182144806664\n ],\n [\n -112.54394531249999,\n 49.66762782262194\n ],\n [\n -114.08203125,\n 50.708634400828224\n ],\n [\n -116.76269531249999,\n 53.35710874569601\n ],\n [\n -119.267578125,\n 54.92714186454645\n ],\n [\n -121.728515625,\n 55.70235509327093\n ],\n [\n -122.9150390625,\n 55.25407706707272\n ],\n [\n -123.04687499999999,\n 54.54657953840501\n ],\n [\n -122.2119140625,\n 52.5897007687178\n ],\n [\n -120.76171875,\n 50.764259357116465\n ],\n [\n -119.66308593749999,\n 48.83579746243093\n ],\n [\n -118.037109375,\n 47.30903424774781\n ],\n [\n -117.6416015625,\n 45.706179285330855\n ],\n [\n -117.24609374999999,\n 44.5278427984555\n ],\n [\n -116.27929687499999,\n 43.54854811091286\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"144","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-05","publicationStatus":"PW","scienceBaseUri":"59f1a2a0e4b0220bbd9d9f0a","contributors":{"authors":[{"text":"Jones, Leslie A. 0000-0002-4953-7189 lajones@usgs.gov","orcid":"https://orcid.org/0000-0002-4953-7189","contributorId":4599,"corporation":false,"usgs":true,"family":"Jones","given":"Leslie","email":"lajones@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":715260,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muhlfeld, Clint C. 0000-0002-4599-4059 cmuhlfeld@usgs.gov","orcid":"https://orcid.org/0000-0002-4599-4059","contributorId":924,"corporation":false,"usgs":true,"family":"Muhlfeld","given":"Clint","email":"cmuhlfeld@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":715258,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marshall, Lucy A. 0000-0003-0450-4292","orcid":"https://orcid.org/0000-0003-0450-4292","contributorId":198080,"corporation":false,"usgs":false,"family":"Marshall","given":"Lucy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":715259,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192058,"text":"70192058 - 2017 - Characterizing sources of uncertainty from global climate models and downscaling techniques","interactions":[],"lastModifiedDate":"2018-01-05T14:23:27","indexId":"70192058","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5202,"text":"Journal of Applied Meteorology and Climatology","onlineIssn":"1558-8432","printIssn":"1558-8424","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing sources of uncertainty from global climate models and downscaling techniques","docAbstract":"In recent years climate model experiments have been increasingly oriented towards providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here we present a method, based on a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. We apply the method to the Southeast U.S. using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios are typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast U.S. for precipitation and ~30% for extreme heat days (> 35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a sub-sample of all models are available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. We conclude with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JAMC-D-17-0087.1","usgsCitation":"Wootten, A., Terando, A., Reich, B.J., Boyles, R.P., and Semazzi, F., 2017, Characterizing sources of uncertainty from global climate models and downscaling techniques: Journal of Applied Meteorology and Climatology, v. 56, p. 3245-3262, https://doi.org/10.1175/JAMC-D-17-0087.1.","productDescription":"18 p.","startPage":"3245","endPage":"3262","ipdsId":"IP-088255","costCenters":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"links":[{"id":469388,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/jamc-d-17-0087.1","text":"Publisher Index Page"},{"id":347350,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f1a2a1e4b0220bbd9d9f1c","contributors":{"authors":[{"text":"Wootten, Adrienne","contributorId":197529,"corporation":false,"usgs":false,"family":"Wootten","given":"Adrienne","affiliations":[],"preferred":false,"id":714033,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terando, Adam 0000-0002-9280-043X aterando@usgs.gov","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":197511,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","email":"aterando@usgs.gov","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":714032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reich, Brian J.","contributorId":150871,"corporation":false,"usgs":false,"family":"Reich","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":714034,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyles, Ryan P. 0000-0001-9272-867X rboyles@usgs.gov","orcid":"https://orcid.org/0000-0001-9272-867X","contributorId":197670,"corporation":false,"usgs":true,"family":"Boyles","given":"Ryan","email":"rboyles@usgs.gov","middleInitial":"P.","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":714035,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Semazzi, Fred","contributorId":197671,"corporation":false,"usgs":false,"family":"Semazzi","given":"Fred","affiliations":[],"preferred":false,"id":714036,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70191727,"text":"70191727 - 2017 - Fractional crystallization-induced variations in sulfides from the Noril’sk-Talnakh mining district (polar Siberia, Russia)","interactions":[],"lastModifiedDate":"2019-12-21T08:38:31","indexId":"70191727","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2954,"text":"Ore Geology Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Fractional crystallization-induced variations in sulfides from the Noril’sk-Talnakh mining district (polar Siberia, Russia)","docAbstract":"The distribution of platinum-group elements (PGE) within zoned magmatic ore bodies has been extensively studied and appears to be controlled by the partitioning behavior of the PGE during fractional crystallization of magmatic sulfide liquids. However, other chalcophile elements, especially TABS (Te, As, Bi, Sb, and Sn) have been neglected despite their critical role in forming platinum-group minerals (PGM). TABS are volatile trace elements that are considered to be mobile so investigating their primary distribution may be challenging in magmatic ore bodies that have been somewhat altered. Magmatic sulfide ore bodies from the Noril’sk-Talnakh mining district (polar Siberia, Russia) offer an exceptional opportunity to investigate the behavior of TABS during fractional crystallization of sulfide liquids and PGM formation as the primary features of the ore bodies have been relatively well preserved. In this study, new petrographic (2D and 3D) and whole-rock geochemical data from Cu-poor to Cu-rich sulfide ores of the Noril’sk-Talnakh mining district are integrated with published data to consider the role of fractional crystallization in generating mineralogical and geochemical variations across the different ore types (disseminated to massive). Despite textural variations in Cu-rich massive sulfides (lenses, veins, and breccias), these sulfides have similar chemical compositions, which suggests that Cu-rich veins and breccias formed from fractionated sulfide liquids that were injected into the surrounding rocks. Numerical modeling using the median disseminated sulfide composition as the initial sulfide liquid composition and recent DMSS/liq and DISS/liq predicts the compositional variations observed in the massive sulfides, especially in terms of Pt, Pd, and TABS. Therefore, distribution of these elements in the massive sulfides was likely controlled by their partitioning behavior during sulfide liquid fractional crystallization, prior to PGM formation. Our observations indicate that in the Cu-poor massive sulfides the PGM formed as the result of exsolution from sulfide minerals whereas in the Cu-rich massive sulfides the PGM formed by crystallization from late-stage fractionated sulfide liquids. We suggest that the significant amount of Sn-bearing PGM may be related to crustal contamination from granodiorite, whereas As, Bi, Te, and Sb were likely added to the magma along with S from sedimentary rocks. Large PGM that are scarce and randomly distributed may account for most of the whole-rock Pt budget. Based on our results, we propose a holistic genetic model for the formation of the magmatic sulfide ore bodies of the Noril’sk-Talnakh mining district.","language":"English","publisher":"Elsevier","doi":"10.1016/j.oregeorev.2017.05.016","usgsCitation":"Duran, C., Barnes, S., Plese, P., Prasek, M.K., Zientek, M.L., and Page, P., 2017, Fractional crystallization-induced variations in sulfides from the Noril’sk-Talnakh mining district (polar Siberia, Russia): Ore Geology Reviews, v. 90, p. 326-351, https://doi.org/10.1016/j.oregeorev.2017.05.016.","productDescription":"26 p.","startPage":"326","endPage":"351","ipdsId":"IP-084455","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":469395,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.oregeorev.2017.05.016","text":"Publisher Index Page"},{"id":347374,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia","state":"Siberia","otherGeospatial":"Noril’sk-Talnakh mining district","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 99.49218749999999,\n 60.58696734225869\n ],\n [\n 131.484375,\n 60.58696734225869\n ],\n [\n 131.484375,\n 71.96538769913127\n ],\n [\n 99.49218749999999,\n 71.96538769913127\n ],\n [\n 99.49218749999999,\n 60.58696734225869\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"90","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f1a2a3e4b0220bbd9d9f2b","contributors":{"authors":[{"text":"Duran, C.J.","contributorId":197322,"corporation":false,"usgs":false,"family":"Duran","given":"C.J.","email":"","affiliations":[],"preferred":false,"id":713193,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnes, S-J.","contributorId":197321,"corporation":false,"usgs":false,"family":"Barnes","given":"S-J.","affiliations":[],"preferred":false,"id":713192,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plese, P.","contributorId":197323,"corporation":false,"usgs":false,"family":"Plese","given":"P.","email":"","affiliations":[],"preferred":false,"id":713194,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prasek, M. Kudrna","contributorId":197324,"corporation":false,"usgs":false,"family":"Prasek","given":"M.","email":"","middleInitial":"Kudrna","affiliations":[],"preferred":false,"id":713195,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zientek, Michael L. 0000-0002-8522-9626 mzientek@usgs.gov","orcid":"https://orcid.org/0000-0002-8522-9626","contributorId":2420,"corporation":false,"usgs":true,"family":"Zientek","given":"Michael","email":"mzientek@usgs.gov","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":713191,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Page, P.","contributorId":197325,"corporation":false,"usgs":false,"family":"Page","given":"P.","email":"","affiliations":[],"preferred":false,"id":713196,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70192331,"text":"70192331 - 2017 - Partial polygon pruning of hydrographic features in automated generalization","interactions":[],"lastModifiedDate":"2017-10-25T10:06:11","indexId":"70192331","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3618,"text":"Transactions in GIS","active":true,"publicationSubtype":{"id":10}},"title":"Partial polygon pruning of hydrographic features in automated generalization","docAbstract":"This paper demonstrates a working method to automatically detect and prune portions of waterbody polygons to support creation of a multi-scale hydrographic database. Water features are known to be sensitive to scale change; and thus multiple representations are required to maintain visual and geographic logic at smaller scales. Partial pruning of polygonal features—such as long and sinuous reservoir arms, stream channels that are too narrow at the target scale, and islands that begin to coalesce—entails concurrent management of the length and width of polygonal features as well as integrating pruned polygons with other generalized point and linear hydrographic features to maintain stream network connectivity. The implementation follows data representation standards developed by the U.S. Geological Survey (USGS) for the National Hydrography Dataset (NHD). Portions of polygonal rivers, streams, and canals are automatically characterized for width, length, and connectivity. This paper describes an algorithm for automatic detection and subsequent processing, and shows results for a sample of NHD subbasins in different landscape conditions in the United States.","language":"English","publisher":"John Wiley & Sons, Ltd.","doi":"10.1111/tgis.12270","usgsCitation":"Stum, A.K., Buttenfield, B.P., and Stanislawski, L.V., 2017, Partial polygon pruning of hydrographic features in automated generalization: Transactions in GIS, v. 21, no. 5, p. 1061-1078, https://doi.org/10.1111/tgis.12270.","productDescription":"18 p.","startPage":"1061","endPage":"1078","ipdsId":"IP-078637","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":347314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","issue":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-08","publicationStatus":"PW","scienceBaseUri":"59f1a29ee4b0220bbd9d9ef8","contributors":{"authors":[{"text":"Stum, Alexander K.","contributorId":198209,"corporation":false,"usgs":false,"family":"Stum","given":"Alexander","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":715372,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buttenfield, Barbara P.","contributorId":184069,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":715373,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanislawski, Larry V. 0000-0002-9437-0576 lstan@usgs.gov","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":3386,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","email":"lstan@usgs.gov","middleInitial":"V.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":715371,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192397,"text":"70192397 - 2017 - Climate change and alpine stream biology: progress, challenges, and opportunities for the future","interactions":[],"lastModifiedDate":"2017-10-26T09:22:57","indexId":"70192397","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1023,"text":"Biological Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Climate change and alpine stream biology: progress, challenges, and opportunities for the future","docAbstract":"In alpine regions worldwide, climate change is dramatically altering ecosystems and affecting biodiversity in many ways. For streams, receding alpine glaciers and snowfields, paired with altered precipitation regimes, are driving shifts in hydrology, species distributions, basal resources, and threatening the very existence of some habitats and biota. Alpine streams harbour substantial species and genetic diversity due to significant habitat insularity and environmental heterogeneity. Climate change is expected to affect alpine stream biodiversity across many levels of biological resolution from micro- to macroscopic organisms and genes to communities. Herein, we describe the current state of alpine stream biology from an organism-focused perspective. We begin by reviewing seven standard and emerging approaches that combine to form the current state of the discipline. We follow with a call for increased synthesis across existing approaches to improve understanding of how these imperiled ecosystems are responding to rapid environmental change. We then take a forward-looking viewpoint on how alpine stream biologists can make better use of existing data sets through temporal comparisons, integrate remote sensing and geographic information system (GIS) technologies, and apply genomic tools to refine knowledge of underlying evolutionary processes. We conclude with comments about the future of biodiversity conservation in alpine streams to confront the daunting challenge of mitigating the effects of rapid environmental change in these sentinel ecosystems.
","language":"English","publisher":"Wiley","doi":"10.1111/brv.12319","usgsCitation":"Hotaling, S., Finn, D.S., Giersch, J., Weisrock, D.W., and Jacobsen, D., 2017, Climate change and alpine stream biology: progress, challenges, and opportunities for the future: Biological Reviews, v. 92, no. 4, p. 2024-2045, https://doi.org/10.1111/brv.12319.","productDescription":"22 p.","startPage":"2024","endPage":"2045","ipdsId":"IP-079039","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":469397,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/brv.12319","text":"External Repository"},{"id":347387,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"92","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-20","publicationStatus":"PW","scienceBaseUri":"59f1a29be4b0220bbd9d9ed4","contributors":{"authors":[{"text":"Hotaling, Scott 0000-0002-5965-0986","orcid":"https://orcid.org/0000-0002-5965-0986","contributorId":176860,"corporation":false,"usgs":false,"family":"Hotaling","given":"Scott","email":"","affiliations":[],"preferred":false,"id":715676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finn, Debra S.","contributorId":198312,"corporation":false,"usgs":false,"family":"Finn","given":"Debra","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":715677,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Giersch, J. Joseph 0000-0001-7818-3941 jgiersch@usgs.gov","orcid":"https://orcid.org/0000-0001-7818-3941","contributorId":4022,"corporation":false,"usgs":true,"family":"Giersch","given":"J. Joseph","email":"jgiersch@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":false,"id":715675,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weisrock, David W.","contributorId":198313,"corporation":false,"usgs":false,"family":"Weisrock","given":"David","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":715678,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jacobsen, Dean 0000-0001-5137-297X","orcid":"https://orcid.org/0000-0001-5137-297X","contributorId":198314,"corporation":false,"usgs":false,"family":"Jacobsen","given":"Dean","email":"","affiliations":[],"preferred":false,"id":715679,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70192372,"text":"70192372 - 2017 - Assessing models of arsenic occurrence in drinking water from bedrock aquifers in New Hampshire","interactions":[],"lastModifiedDate":"2017-10-25T09:37:46","indexId":"70192372","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2234,"text":"Journal of Contemporary Water Research and Education","active":true,"publicationSubtype":{"id":10}},"title":"Assessing models of arsenic occurrence in drinking water from bedrock aquifers in New Hampshire","docAbstract":"Three existing multivariate logistic regression models were assessed using new data to evaluate the capacity of the models to correctly predict the probability of groundwater arsenic concentrations exceeding the threshold values of 1, 5, and 10 micrograms per liter (µg/L) in New Hampshire, USA. A recently released testing dataset includes arsenic concentrations from groundwater samples collected in 2004–2005 from a mix of 367 public-supply and private domestic wells. The use of this dataset to test three existing logistic regression models demonstrated enhanced overall predictive accuracy for the 5 and 10 μg/L models. Overall accuracies of 54.8, 76.3, and 86.4 percent were reported for the 1, 5, and 10 μg/L models, respectively. The state was divided by counties into northwest and southeast regions. Regional differences in accuracy were identified; models had an average accuracy of 83.1 percent for the counties in the northwest and 63.7 percent in the southeast. This is most likely due to high model specificity in the northwest and regional differences in arsenic occurrence. Though these models have limitations, they allow for arsenic hazard assessment across the region. The introduction of well-type (public or private), well depth, and casing length as explanatory variables may be appropriate measures to improve model performance. Our findings indicate that the original models generalize to the testing dataset, and should continue to serve as an important vehicle of preventative public health that may be applied to other groundwater contaminants in New Hampshire.","language":"English","publisher":"Wiley","doi":"10.1111/j.1936-704X.2017.03238.x","usgsCitation":"Andy, C., Fahnestock, M.F., Lombard, M.A., Hayes, L., Bryce, J., and Ayotte, J.D., 2017, Assessing models of arsenic occurrence in drinking water from bedrock aquifers in New Hampshire: Journal of Contemporary Water Research and Education, v. 160, no. 1, p. 25-41, https://doi.org/10.1111/j.1936-704X.2017.03238.x.","productDescription":"17 p.","startPage":"25","endPage":"41","ipdsId":"IP-078863","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":469389,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/j.1936-704x.2017.03238.x","text":"Publisher Index 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2017","interactions":[],"lastModifiedDate":"2017-10-25T10:13:23","indexId":"ofr20171117","displayToPublicDate":"2017-10-24T18:15:00","publicationYear":"2017","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":"2017-1117","title":"U.S. Geological Survey input-data forms for the assessment of the Spraberry Formation of the Midland Basin, Permian Basin Province, Texas, 2017","docAbstract":"In 2017, the U.S. Geological Survey (USGS) completed an updated assessment of undiscovered, technically recoverable oil and gas resources in the Spraberry Formation of the Midland Basin (Permian Basin Province) in southwestern Texas (Marra and others, 2017). The Spraberry Formation was assessed using both the standard continuous (unconventional) and conventional methodologies established by the USGS for three assessment units (AUs): (1) Lower Spraberry Continuous Oil Trend AU, (2) Middle Spraberry Continuous Oil Trend AU, and (3) Northern Spraberry Conventional Oil AU. The revised assessment resulted in total estimated mean resources of 4,245 million barrels of oil, 3,112 billion cubic feet of gas, and 311 million barrels of natural gas liquids. The purpose of this report is to provide supplemental documentation of the input parameters used in the USGS 2017 Spraberry Formation assessment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171117","usgsCitation":"Marra, K.R., 2017, U.S. Geological Survey input-data forms for the assessment of the Spraberry Formation of the Midland Basin, Permian Basin Province, Texas, 2017: U.S. Geological Survey Open-File Report 2017–1117, 46 p., https://doi.org/10.3133/ofr20171117.","productDescription":"iii, 46 p.","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-089773","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":347263,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20173029","text":"Fact Sheet 2017–3029:","linkHelpText":"Assessment of Undiscovered Oil and Gas Resources in the Spraberry Formation of the Midland Basin, Permian Basin Province, Texas, 2017"},{"id":347257,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1117/coverthb.jpg"},{"id":347258,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1117/ofr20171117.pdf","text":"Report","size":"372 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1117"}],"country":"United States","state":"Texas","otherGeospatial":"Spraberry Formation of the Midland Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.75,\n 30.3333\n ],\n [\n -99.25,\n 30.3333\n ],\n [\n -99.25,\n 34\n ],\n [\n -103.75,\n 34\n ],\n [\n -103.75,\n 30.3333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Central Energy Resources Science Center
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The U.S. Geological Survey, in cooperation with the Iowa Department of Natural Resources, constructed Precipitation-Runoff Modeling System models to estimate daily streamflow for 12 river basins in western Iowa that drain into the Missouri River. The Precipitation-Runoff Modeling System is a deterministic, distributed-parameter, physical-process-based modeling system developed to evaluate the response of streamflow and general drainage basin hydrology to various combinations of climate and land use. Calibration periods for each basin varied depending on the period of record available for daily mean streamflow measurements at U.S. Geological Survey streamflow-gaging stations.
A geographic information system tool was used to delineate each basin and estimate initial values for model parameters based on basin physical and geographical features. A U.S. Geological Survey automatic calibration tool that uses a shuffled complex evolution algorithm was used for initial calibration, and then manual modifications were made to parameter values to complete the calibration of each basin model. The main objective of the calibration was to match daily discharge values of simulated streamflow to measured daily discharge values. The Precipitation-Runoff Modeling System model was calibrated at 42 sites located in the 12 river basins in western Iowa.
The accuracy of the simulated daily streamflow values at the 42 calibration sites varied by river and by site. The models were satisfactory at 36 of the sites based on statistical results. Unsatisfactory performance at the six other sites can be attributed to several factors: (1) low flow, no flow, and flashy flow conditions in headwater subbasins having a small drainage area; (2) poor representation of the groundwater and storage components of flow within a basin; (3) lack of accounting for basin withdrawals and water use; and (4) limited availability and accuracy of meteorological input data. The Precipitation-Runoff Modeling System models of 12 river basins in western Iowa will provide water-resource managers with a consistent and documented method for estimating streamflow at ungaged sites and aid in environmental studies, hydraulic design, water management, and water-quality projects.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175091","collaboration":"Prepared in cooperation with the Iowa Department of Natural Resources","usgsCitation":"Christiansen, D.E., Haj, A.E., and Risely, J.C., 2017, Simulation of daily streamflow for 12 river basins in western Iowa using the Precipitation-Runoff Modeling System: U.S. Geological Survey Scientific Investigations Report 2017–5091, 27 p., https://doi.org/10.3133/sir20175091. 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Iowa Water Science Center
U.S. Geological Survey
P.O. Box 1230
Iowa City, IA 52240
At the 3DEM-5 workshop in 2013, we presented a paper entitled \"ModEM: developing 3D EM inversion for the masses\", outlining our then recent development of a modular system for inversion of EM geophysical data, called ModEM. As promised in that presentation, we made a version of the code that is suitable for 3D modeling and inversion of magnetotelluric data freely available for academic use shortly thereafter. There are now over 250 registered users, of ModEM3DMT from around the globe. To date at least 50 publications cite use of ModEM for 3D inversion of real MT datasets to address diverse problems in applied and basic Earth Science research at a range of scales. Here we present an overview of some of these results, focusing on studies that the authors have been involved in, and are thus most familiar to us.
","conferenceTitle":"6th International Symposium on Three-Dimensional Electromagnetics","conferenceDate":"March 28-30, 2017","conferenceLocation":"Berkeley, CA","language":"English","publisher":"Australian Society of Exploration Geophysicists","usgsCitation":"Egbert, G.D., Meqbel, N., and Kelbert, A., 2017, Some results from ModEM3DMT, the freely available OSU 3D MT inversion code, 6th International Symposium on Three-Dimensional Electromagnetics, Berkeley, CA, March 28-30, 2017, 4 p.","productDescription":"4 p.","ipdsId":"IP-084666","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":358781,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c10aaeee4b034bf6a7e5e41","contributors":{"authors":[{"text":"Egbert, Gary D.","contributorId":187462,"corporation":false,"usgs":false,"family":"Egbert","given":"Gary","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":680511,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meqbel, Naser","contributorId":187463,"corporation":false,"usgs":false,"family":"Meqbel","given":"Naser","email":"","affiliations":[],"preferred":false,"id":680512,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelbert, Anna 0000-0003-4395-398X akelbert@usgs.gov","orcid":"https://orcid.org/0000-0003-4395-398X","contributorId":184053,"corporation":false,"usgs":true,"family":"Kelbert","given":"Anna","email":"akelbert@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":680513,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192233,"text":"70192233 - 2017 - Remote measurement of river discharge using thermal particle image velocimetry (PIV) and various sources of bathymetric information","interactions":[],"lastModifiedDate":"2017-10-24T12:21:45","indexId":"70192233","displayToPublicDate":"2017-10-24T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Remote measurement of river discharge using thermal particle image velocimetry (PIV) and various sources of bathymetric information","docAbstract":"Although river discharge is a fundamental hydrologic quantity, conventional methods of streamgaging are impractical, expensive, and potentially dangerous in remote locations. This study evaluated the potential for measuring discharge via various forms of remote sensing, primarily thermal imaging of flow velocities but also spectrally-based depth retrieval from passive optical image data. We acquired thermal image time series from bridges spanning five streams in Alaska and observed strong agreement between velocities measured in situ and those inferred by Particle Image Velocimetry (PIV), which quantified advection of thermal features by the flow. The resulting surface velocities were converted to depth-averaged velocities by applying site-specific, calibrated velocity indices. Field spectra from three clear-flowing streams provided strong relationships between depth and reflectance, suggesting that, under favorable conditions, spectrally-based bathymetric mapping could complement thermal PIV in a hybrid approach to remote sensing of river discharge; this strategy would not be applicable to larger, more turbid rivers, however. A more flexible and efficient alternative might involve inferring depth from thermal data based on relationships between depth and integral length scales of turbulent fluctuations in temperature, captured as variations in image brightness. We observed moderately strong correlations for a site-aggregated data set that reduced station-to-station variability but encompassed a broad range of depths. Discharges calculated using thermal PIV-derived velocities were within 15% of in situ measurements when combined with depths measured directly in the field or estimated from field spectra and within 40% when the depth information also was derived from thermal images. The results of this initial, proof-of-concept investigation suggest that remote sensing techniques could facilitate measurement of river discharge.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2017.09.004","usgsCitation":"Legleiter, C.J., Kinzel, P.J., and Nelson, J.M., 2017, Remote measurement of river discharge using thermal particle image velocimetry (PIV) and various sources of bathymetric information: Journal of Hydrology, v. 554, p. 490-506, https://doi.org/10.1016/j.jhydrol.2017.09.004.","productDescription":"17 p.","startPage":"490","endPage":"506","ipdsId":"IP-084918","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":469406,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2017.09.004","text":"Publisher Index Page"},{"id":438181,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ST7N0J","text":"USGS data release","linkHelpText":"Thermal image time series from rivers in Alaska, September 18-20, 2016"},{"id":438180,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J964K7","text":"USGS data release","linkHelpText":"ADCP data from rivers in Alaska, September 18-20, 2016"},{"id":438179,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7M906TJ","text":"USGS data release","linkHelpText":"Field spectra from rivers in Alaska, September 19-21, 2016"},{"id":347224,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"554","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f0511ee4b0220bbd9a1d64","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":714904,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":714905,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nelson, Jonathan M. 0000-0002-7632-8526 jmn@usgs.gov","orcid":"https://orcid.org/0000-0002-7632-8526","contributorId":2812,"corporation":false,"usgs":true,"family":"Nelson","given":"Jonathan","email":"jmn@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":714906,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}