Introduction
California’s recent drought (2012–2016) has implications throughout the state for natural resource management and adaptation planning and has generated many discussions about drought characterization and recovery. This study characterizes drought conditions with two indices describing deficits in natural water supply and increases in landscape stress developed on the basis of water balance modeling, at a fine spatial scale to assess the variation in conditions across the entire state, and provides an in-depth evaluation for the Russian River basin in northern California to address local resource management by developing extreme drought scenarios for consideration in planning and adaptation.
Methods
We employed the USGS Basin Characterization Model to characterize drought on the basis of water supply (a measure of recharge plus runoff) and landscape stress (climatic water deficit). These were applied to the state and to the Russian River basin where antecedent soil moisture conditions were evaluated and extreme drought scenarios were developed and run through a water management and reservoir operations model to further explore impacts on water management.
Results
Drought indices indicated that as of the end of water year 2016 when reservoirs were full, additional water supply and landscape replenishment of up to three average years of precipitation in some locations was needed to return to normal conditions. Antecedent soil conditions in the Russian River were determined to contribute to very different water supply results for different years and were necessary to understand to anticipate proper watershed response to climate. Extreme drought scenarios manifested very different kinds of drought and recovery and characterization helps to guide the management response to drought.
Conclusions
These scenarios and indices illustrate how droughts differ with regard to water supply and landscape stress and how long warm droughts recover much more slowly than short very dry droughts due to the depletion of water in the soil and unsaturated zone that require filling before runoff can occur. Recognition of ongoing conditions and likelihood of recovery provides tools and information for a range of resource managers to cope with drought conditions.
Amenity migration describes the movement of peoples to rural landscapes and the transition toward tourism and recreation and away from production-oriented land uses (ranching, timber harvesting). The resulting mosaic of land uses and community structures has important consequences for wildlife and their management. This research note examines amenity-driven changes to social-ecological systems in the Greater Yellowstone Ecosystem, specifically in lower elevations that serve as winter habitat for elk. We present a research agenda informed by a preliminary and exploratory mixed-methods investigation: the creation of a “social-impact” index of land use change on elk winter range and a focus group with wildlife management experts. Our findings suggest that elk are encountering an increasingly diverse landscape with respect to land use, while new ownership patterns increase the complexity of social and community dynamics. These factors, in turn, contribute to increasing difficulty meeting wildlife management objectives. To deal with rising complexity across social and ecological landscapes of the Greater Yellowstone Ecosystem, future research will focus on property life cycle dynamics, as well as systems approaches.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2017.11.002","usgsCitation":"Haggerty, J.H., Epstein, K., Stone, M., and Cross, P.C., 2018, Land use diversification and intensification on elk winter range in Greater Yellowstone: A framework and agenda for social-ecological research: Rangeland Ecology and Management, v. 71, no. 2, p. 171-174, https://doi.org/10.1016/j.rama.2017.11.002.","productDescription":"4 p.","startPage":"171","endPage":"174","ipdsId":"IP-092138","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":469091,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10150/671005","text":"External Repository"},{"id":352521,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"71","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee751e4b0da30c1bfc224","contributors":{"authors":[{"text":"Haggerty, Julia Hobson","contributorId":203309,"corporation":false,"usgs":false,"family":"Haggerty","given":"Julia","email":"","middleInitial":"Hobson","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":731086,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Epstein, Kathleen","contributorId":203310,"corporation":false,"usgs":false,"family":"Epstein","given":"Kathleen","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":731087,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stone, Michael","contributorId":203311,"corporation":false,"usgs":false,"family":"Stone","given":"Michael","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":731088,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cross, Paul C. 0000-0001-8045-5213 pcross@usgs.gov","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":203308,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","email":"pcross@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":731085,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194113,"text":"fs20173080 - 2018 - Gas hydrate in nature","interactions":[],"lastModifiedDate":"2018-01-18T10:30:13","indexId":"fs20173080","displayToPublicDate":"2018-01-17T14:45:00","publicationYear":"2018","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":"2017-3080","title":"Gas hydrate in nature","docAbstract":"Gas hydrate is a naturally occurring, ice-like substance that forms when water and gas combine under high pressure and at moderate temperatures. Methane is the most common gas present in gas hydrate, although other gases may also be included in hydrate structures, particularly in areas close to conventional oil and gas reservoirs. Gas hydrate is widespread in ocean-bottom sediments at water depths greater than 300–500 meters (m; 984–1,640 feet [ft]) and is also present in areas with permanently frozen ground (permafrost). Several countries are evaluating gas hydrate as a possible energy resource in deepwater or permafrost settings. Gas hydrate is also under investigation to determine how environmental change may affect these deposits.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173080","usgsCitation":"Ruppel, C.D., 2018, Gas hydrate in nature: U.S. Geological Survey Fact Sheet 2017–3080, 4 p., https://doi.org/10.3133/fs20173080.","productDescription":"4 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-081102","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":350446,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20173079","text":"Fact Sheet 2017–3079"},{"id":350442,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3080/coverthb.jpg"},{"id":350445,"rank":4,"type":{"id":18,"text":"Project Site"},"url":"https://energy.usgs.gov/OilGas/UnconventionalOilGas/GasHydrates.aspx","text":"U.S. Geological Survey’s Energy Resources Program gas hydrates site"},{"id":350444,"rank":3,"type":{"id":18,"text":"Project Site"},"url":"https://woodshole.er.usgs.gov/project-pages/hydrates/index.html","text":"Overview of the U.S. Geological Survey’s Gas Hydrates Project"},{"id":350443,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3080/fs20173080.pdf","text":"Report","size":"1.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3080"}],"contact":"Coastal and Marine Geology Program Coordinator
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U.S. Geological Survey
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Reston, VA 20192
The Gas Hydrates Project at the U.S. Geological Survey (USGS) focuses on the study of methane hydrates in natural environments. The project is a collaboration between the USGS Energy Resources and the USGS Coastal and Marine Geology Programs and works closely with other U.S. Federal agencies, some State governments, outside research organizations, and international partners. The USGS studies the formation and distribution of gas hydrates in nature, the potential of hydrates as an energy resource, and the interaction between methane hydrates and the environment. The USGS Gas Hydrates Project carries out field programs and participates in drilling expeditions to study marine and terrestrial gas hydrates. USGS scientists also acquire new geophysical data and sample sediments, the water column, and the atmosphere in areas where gas hydrates occur. In addition, project personnel analyze datasets provided by partners and manage unique laboratories that supply state-of-the-art analytical capabilities to advance national and international priorities related to gas hydrates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173079","usgsCitation":"Ruppel, C.D., 2018, The U.S. Geological Survey’s Gas Hydrates Project: U.S. Geological Survey Fact Sheet 2017–3079, 4 p., https://doi.org/10.3133/fs20173079.","productDescription":"4 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-081104","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":350440,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20173080","text":"Fact Sheet 2017–3080","linkHelpText":"- Gas Hydrate in Nature"},{"id":350438,"rank":3,"type":{"id":18,"text":"Project Site"},"url":"https://woodshole.er.usgs.gov/project-pages/hydrates/index.html","text":"Overview of the U.S. Geological Survey’s Gas Hydrates Project: "},{"id":350439,"rank":4,"type":{"id":18,"text":"Project Site"},"url":"https://energy.usgs.gov/OilGas/UnconventionalOilGas/GasHydrates.aspx","text":"U.S. Geological Survey’s Energy Resources Program gas hydrates site:"},{"id":350437,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3079/fs20173079.pdf","text":"Report","size":"547 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3079"},{"id":350436,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3079/coverthb.jpg"}],"contact":"Coastal and Marine Geology Program Coordinator
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Armenia is a landlocked country located in the mountainous Caucasus region between Asia and Europe. It shares borders with the countries of Georgia on the north, Azerbaijan on the east, Iran on the south, and Turkey and Azerbaijan on the west. The Ararat Basin is a transboundary basin in Armenia and Turkey. The Ararat Basin (or Ararat Valley) is an intermountain depression that contains the Aras River and its tributaries, which also form the border between Armenia and Turkey and divide the basin into northern and southern regions. The Ararat Basin also contains Armenia’s largest agricultural and fish farming zone that is supplied by high-quality water from wells completed in the artesian aquifers that underlie the basin. Groundwater constitutes about 40 percent of all water use, and groundwater provides 96 percent of the water used for drinking purposes in Armenia. Since 2000, groundwater withdrawals and consumption in the Ararat Basin of Armenia have increased because of the growth of aquaculture and other uses. Increased groundwater withdrawals caused decreased springflow, reduced well discharges, falling water levels, and a reduction of the number of flowing artesian wells in the southern part of Ararat Basin in Armenia.
In 2016, the U.S. Geological Survey and the U.S. Agency for International Development (USAID) began a cooperative study in Armenia to share science and field techniques to increase the country’s capabilities for groundwater study and modeling. The purpose of this report is to describe the hydrogeologic framework and groundwater conditions of the Ararat Basin in Armenia based on data collected in 2016 and previous hydrogeologic studies. The study area includes the Ararat Basin in Armenia. This report was completed through a partnership with USAID/Armenia in the implementation of its Science, Technology, Innovation, and Partnerships effort through the Advanced Science and Partnerships for Integrated Resource Development program and associated partners, including the Government of Armenia, Armenia’s Hydrogeological Monitoring Center, and the USAID Global Development Lab and its GeoCenter.
The hydrogeologic framework of the Ararat Basin includes several basin-fill stratigraphic units consisting of interbedded dense clays, gravels, sands, volcanic basalts, and andesite deposits. Previously published cross sections and well lithologic logs were used to map nine general hydrogeologic units. Hydrogeologic units were mapped based on lithology and water-bearing potential. Water-level data measured in the water-bearing hydrogeologic units 2, 4, 6, and 8 in 2016 were used to create potentiometric surface maps. In hydrogeologic unit 2, the estimated direction of groundwater flow is from the west to north in the western part of the basin (away from the Aras River) and from north to south (toward the Aras River) in the eastern part of the basin. In hydrogeologic unit 4, the direction of groundwater flow is generally from west to east and north to south (toward the Aras River) except in the western part of the basin where groundwater flow is toward the north or northwest. Hydrogeologic unit 6 has the same general pattern of groundwater flow as unit 4. Hydrogeologic unit 8 is the deepest of the water-bearing units and is confined in the basin. Groundwater flow generally is from the south to north (away from the Aras River) in the western part of the basin and from west to east and north to south (toward the Aras River) elsewhere in the basin.
In addition to water levels, personnel from Armenia’s Hydrogeological Monitoring Center also measured specific conductance at 540 wells and temperature at 2,470 wells in the Ararat Basin using U.S. Geological Survey protocols in 2016. The minimum specific conductance was 377 microsiemens per centimeter (μS/cm), the maximum value was 4,000 μS/cm, and the mean was 998 μS/cm. The maximum water temperature was 24.2 degrees Celsius. An analysis between water temperature and well depth indicated no relation; however, spatially, most wells with cooler water temperatures were within the 2016 pressure boundary or in the western part of the basin. Wells with generally warmer water temperatures were in the eastern part of the basin.
Samples were collected from four groundwater sites and one surface-water site by the U.S. Geological Survey in 2016. The stable-isotope values were similar for all five sites, indicating similar recharge sources for the sampled wells. The Hrazdan River sample was consistent with the groundwater samples, indicating the river could serve as a source of recharge to the Ararat artesian aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175163","usgsCitation":"Valder, J.F., Carter, J.M., Medler, C.J., Thompson, R.F., and Anderson, M.T., 2018, Hydrogeologic framework and groundwater conditions of the Ararat Basin in Armenia: U.S. Geological Survey Scientific Investigations Report 2017–5163, 40 p., https://doi.org/10.3133/sir20175163.","productDescription":"Report: viii, 40 p.; Tables","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-088554","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":350454,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5163/sir20175163_table6.xls","text":"Table 6. Historical water-level and well yield data from various dates ranging from 1981 to 2013 in the Ararat Basin, Armenia","size":"96 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5163 Table 6"},{"id":350430,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5163/coverthb.jpg"},{"id":350452,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5163/sir20175163_table5.xlsx","text":"Table 5. Historical water-level data from 2007 in the Ararat Basin, Armenia, provided to the U.S. Geological Survey by Armenian partners","size":"200 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5163 Table 5"},{"id":350451,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5163/sir20175163_table4.xls","text":"Table 4. Hydrologic data provided to the U.S. Geological Survey from the 2016 well inventory conducted in the Ararat Basin, Armenia, by Armenian partners","size":"808 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5163 Table 4"},{"id":350434,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5163/sir20175163_table1.xlsx","text":"Table 1 Lithologic descriptions, land-surface elevations, geologic layer thicknesses, and hydrogeologic units of the Ararat Basin, Armenia","size":"792 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5163 Table 1"},{"id":350432,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5163/sir20175163.pdf","text":"Report","size":"12.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5163"}],"country":"Armenia","otherGeospatial":"Ararat Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 43.75,\n 39.75\n ],\n [\n 44.8,\n 39.75\n ],\n [\n 44.8,\n 40.25\n ],\n [\n 43.75,\n 40.25\n ],\n [\n 43.75,\n 39.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center, South Dakota Office
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Anthropogenic alteration of the form and concentration of nitrogen (N) in aquatic ecosystems is widespread. Understanding availability and uptake of different N sources at the base of aquatic food webs is critical to establishment of effective nutrient management programs. Stable isotopes of N (14N, 15N) are often used to trace the sources of N fueling aquatic primary production, but effective use of this approach requires obtaining a reliable isotopic ratio for phytoplankton. In this study, we tested the use of flow cytometry to isolate phytoplankton from bulk particulate organic matter (POM) in a portion of the Sacramento River, California, during river-scale nutrient manipulation experiments that involved halting wastewater discharges high in ammonium (NH4+). Field samples were collected using a Lagrangian approach, allowing us to measure changes in phytoplankton N source in the presence and absence of wastewater-derived NH4+. Comparison of δ15N-POM and δ15N-phytoplankton (δ15N-PHY) revealed that their δ15N values followed broadly similar trends. However, after 3 days of downstream travel in the presence of wastewater treatment plant (WWTP) effluent, δ15N-POM and δ15N-PHY in the Sacramento River differed by as much as 7 ‰. Using a stable isotope mixing model approach, we estimated that in the presence of effluent between 40 and 90 % of phytoplankton N was derived from NH4+ after 3 days of downstream transport. An apparent gradual increase over time in the proportion of NH4+ in the phytoplankton N pool suggests that either very low phytoplankton growth rates resulted in an N turnover time that exceeded the travel time sampled during this study, or a portion of the phytoplankton community continued to access nitrate even in the presence of elevated NH4+ concentrations.
","language":"English","publisher":"EGU","doi":"10.5194/bg-15-353-2018","usgsCitation":"Schmidt, C.M., Kraus, T.E., Young, M.B., and Kendall, C., 2018, Use of flow cytometry and stable isotope analysis to determine phytoplankton uptake of wastewater derived ammonium in a nutrient-rich river: Biogeosciences, v. 15, p. 353-367, https://doi.org/10.5194/bg-15-353-2018.","productDescription":"15 p.","startPage":"353","endPage":"367","ipdsId":"IP-085879","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":469092,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/bg-15-353-2018","text":"Publisher Index Page"},{"id":351289,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.74774169921875,\n 38.1151107557172\n ],\n [\n -121.40029907226562,\n 38.1151107557172\n ],\n [\n -121.40029907226562,\n 38.63939998171362\n ],\n [\n -121.74774169921875,\n 38.63939998171362\n ],\n [\n -121.74774169921875,\n 38.1151107557172\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-17","publicationStatus":"PW","scienceBaseUri":"5a7c1e75e4b00f54eb2292ee","contributors":{"authors":[{"text":"Schmidt, Calla M. 0000-0003-2120-9877","orcid":"https://orcid.org/0000-0003-2120-9877","contributorId":201956,"corporation":false,"usgs":false,"family":"Schmidt","given":"Calla","email":"","middleInitial":"M.","affiliations":[{"id":16849,"text":"University of San Francisco","active":true,"usgs":false}],"preferred":false,"id":727266,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kraus, Tamara E. C. 0000-0002-5187-8644 tkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-5187-8644","contributorId":147560,"corporation":false,"usgs":true,"family":"Kraus","given":"Tamara","email":"tkraus@usgs.gov","middleInitial":"E. C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":727265,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Megan B. 0000-0002-0229-4108 mbyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-0229-4108","contributorId":3315,"corporation":false,"usgs":true,"family":"Young","given":"Megan","email":"mbyoung@usgs.gov","middleInitial":"B.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":727267,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kendall, Carol 0000-0002-0247-3405 ckendall@usgs.gov","orcid":"https://orcid.org/0000-0002-0247-3405","contributorId":1462,"corporation":false,"usgs":true,"family":"Kendall","given":"Carol","email":"ckendall@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":727268,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194839,"text":"70194839 - 2018 - Determining mineralogical variations of aeolian deposits using thermal infrared emissivity and linear deconvolution methods","interactions":[],"lastModifiedDate":"2018-01-17T10:35:29","indexId":"70194839","displayToPublicDate":"2018-01-17T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":666,"text":"Aeolian Research","active":true,"publicationSubtype":{"id":10}},"title":"Determining mineralogical variations of aeolian deposits using thermal infrared emissivity and linear deconvolution methods","docAbstract":"We apply linear deconvolution methods to derive mineral and glass proportions for eight field sample training sites at seven dune fields: (1) Algodones, California; (2) Big Dune, Nevada; (3) Bruneau, Idaho; (4) Great Kobuk Sand Dunes, Alaska; (5) Great Sand Dunes National Park and Preserve, Colorado; (6) Sunset Crater, Arizona; and (7) White Sands National Monument, New Mexico. These dune fields were chosen because they represent a wide range of mineral grain mixtures and allow us to gauge a better understanding of both compositional and sorting effects within terrestrial and extraterrestrial dune systems. We also use actual ASTER TIR emissivity imagery to map the spatial distribution of these minerals throughout the seven dune fields and evaluate the effects of degraded spectral resolution on the accuracy of mineral abundances retrieved. Our results show that hyperspectral data convolutions of our laboratory emissivity spectra outperformed multispectral data convolutions of the same data with respect to the mineral, glass and lithic abundances derived. Both the number and wavelength position of spectral bands greatly impacts the accuracy of linear deconvolution retrieval of feldspar proportions (e.g. K-feldspar vs. plagioclase) especially, as well as the detection of certain mafic and carbonate minerals. In particular, ASTER mapping results show that several of the dune sites display patterns such that less dense minerals typically have higher abundances near the center of the active and most evolved dunes in the field, while more dense minerals and glasses appear to be more abundant along the margins of the active dune fields.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.aeolia.2017.12.001","usgsCitation":"Hubbard, B.E., Hooper, D.M., Solano, F., and Mars, J., 2018, Determining mineralogical variations of aeolian deposits using thermal infrared emissivity and linear deconvolution methods: Aeolian Research, v. 30, p. 54-96, https://doi.org/10.1016/j.aeolia.2017.12.001.","productDescription":"43 p.","startPage":"54","endPage":"96","ipdsId":"IP-080975","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":461075,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.aeolia.2017.12.001","text":"Publisher Index Page"},{"id":438055,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7MS3QWM","text":"USGS data release","linkHelpText":"Visible, Near Infrared, Shortwave Infrared and Thermal Infrared Laboratory Spectra of Samples of Compositionally Variable Dune Fields in the Western United States and Alaska"},{"id":438054,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CC0XTR","text":"USGS data release","linkHelpText":"Linear Deconvolution Mineral Maps of Compositionally Variable Dune Fields in the Western United States and Alaska"},{"id":350459,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Arizona, California, Colorado, Idaho, Nevada, New Mexico","otherGeospatial":"Algodones, Big Dune, Bruneau, Great Kobuk Sand Dunes, Great Sand Dunes National Park and Preserve, Sunset Crater, White Sands National Monument","volume":"30","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60e451e4b06e28e9c14067","contributors":{"authors":[{"text":"Hubbard, Bernard E. 0000-0002-9315-2032 bhubbard@usgs.gov","orcid":"https://orcid.org/0000-0002-9315-2032","contributorId":2342,"corporation":false,"usgs":true,"family":"Hubbard","given":"Bernard","email":"bhubbard@usgs.gov","middleInitial":"E.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":725517,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooper, Donald M.","contributorId":197205,"corporation":false,"usgs":false,"family":"Hooper","given":"Donald","email":"","middleInitial":"M.","affiliations":[{"id":35998,"text":"WEX Foundation","active":true,"usgs":false},{"id":35997,"text":"Southwest Research Institute, San Antonio, TX","active":true,"usgs":false}],"preferred":false,"id":725518,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Solano, Federico 0000-0002-0308-5850 fsolanoc@usgs.gov","orcid":"https://orcid.org/0000-0002-0308-5850","contributorId":4302,"corporation":false,"usgs":true,"family":"Solano","given":"Federico","email":"fsolanoc@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":725519,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mars, John C. jmars@usgs.gov","contributorId":127493,"corporation":false,"usgs":true,"family":"Mars","given":"John C.","email":"jmars@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":725520,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191224,"text":"sir20175117 - 2018 - River meander modeling of the Wabash River near the Interstate 64 Bridge near Grayville, Illinois","interactions":[],"lastModifiedDate":"2018-07-25T12:34:27","indexId":"sir20175117","displayToPublicDate":"2018-01-16T11:30:00","publicationYear":"2018","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":"2017-5117","title":"River meander modeling of the Wabash River near the Interstate 64 Bridge near Grayville, Illinois","docAbstract":"Natural river channels continually evolve and change shape over time. As a result, channel evolution or migration can cause problems for bridge structures that are fixed in the flood plain. A once-stable bridge structure that was uninfluenced by a river’s shape could be encroached upon by a migrating river channel. The potential effect of the actively meandering Wabash River on the Interstate 64 Bridge at the border with Indiana near Grayville, Illinois, was studied using a river migration model called RVR Meander. RVR Meander is a toolbox that can be used to model river channel meander migration with physically based bank erosion methods. This study assesses the Wabash River meandering processes through predictive modeling of natural meandering over the next 100 years, climate change effects through increased river flows, and bank protection measures near the Interstate 64 Bridge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175117","collaboration":"Prepared in cooperation with the Indiana Department of Transportation; Illinois Department of Transportation","usgsCitation":"Lant, J.G., and Boldt, J.A., 2018, River meander modeling of the Wabash River near the Interstate 64 Bridge near Grayville, Illinois: U.S. Geological Survey Scientific Investigations Report 2017–5117, 12 p., https://doi.org/10.3133/sir20175117.","productDescription":"Report: vi, 12 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087393","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":355972,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70G3HWF","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial output data from the RVR Meander model of the Wabash River near the Interstate 64 Bridge near Grayville, Illinois"},{"id":350284,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5117/coverthb.jpg"},{"id":350286,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/sir20175140","text":"Scientific Investigation Report 2017–5140","linkHelpText":"- Development of a Hydraulic Model and Flood-Inundation Maps for the Wabash River near the Interstate 64 Bridge near Grayville, Illinois"},{"id":350285,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5117/sir20175117.pdf","text":"Report","size":"4.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5117"}],"country":"United States","state":"Illinois","city":"Grayville","otherGeospatial":"Wabash River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.01456451416016,\n 38.0833\n ],\n [\n -87.8,\n 38.0833\n ],\n [\n -87.8,\n 38.338694087313534\n ],\n [\n -88.01456451416016,\n 38.338694087313534\n ],\n [\n -88.01456451416016,\n 38.0833\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299
A two-dimensional hydraulic model and digital flood‑inundation maps were developed for a 30-mile reach of the Wabash River near the Interstate 64 Bridge near Grayville, Illinois. The flood-inundation maps, which can be accessed through the U.S. Geological Survey (USGS) Flood Inundation Mapping Science web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Wabash River at Mount Carmel, Ill (USGS station number 03377500). Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at http://waterdata.usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) at http://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site (NWS AHPS site MCRI2). The NWS AHPS forecasts peak stage information that may be used with the maps developed in this study to show predicted areas of flood inundation.
Flood elevations were computed for the Wabash River reach by means of a two-dimensional, finite-volume numerical modeling application for river hydraulics. The hydraulic model was calibrated by using global positioning system measurements of water-surface elevation and the current stage-discharge relation at both USGS streamgage 03377500, Wabash River at Mount Carmel, Ill., and USGS streamgage 03378500, Wabash River at New Harmony, Indiana. The calibrated hydraulic model was then used to compute 27 water-surface elevations for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from less than the action stage (9 ft) to the highest stage (35 ft) of the current stage-discharge rating curve. The simulated water‑surface elevations were then combined with a geographic information system digital elevation model, derived from light detection and ranging data, to delineate the area flooded at each water level.
The availability of these maps, along with information on the internet regarding current stage from the USGS streamgage at Mount Carmel, Ill., and forecasted stream stages from the NWS AHPS, provides emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175140","collaboration":"Prepared in cooperation with the Indiana Department of Transportation; Illinois Department of Transportation","usgsCitation":"Boldt, J.A., 2018, Development of a hydraulic model and flood-inundation maps for the Wabash River near the Interstate 64 Bridge near Grayville, Illinois: U.S. Geological Survey Scientific Investigations Report 2017–5140, 13 p., https://doi.org/10.3133/sir20175140.","productDescription":"vi, 13 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087699","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":355963,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78P5ZCD","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets and model for the flood-inundation study of the Wabash River near the Interstate 64 Bridge near Grayville, Illinois"},{"id":350289,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/sir20175117","text":"Scientific Investigations Report 2017–5117","linkHelpText":"- River Meander Modeling of the Wabash River near the Interstate 64 Bridge near Grayville, Illinois"},{"id":350288,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5140/sir20175140.pdf","text":"Report","size":"3.06 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5140"},{"id":350287,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5140/coverthb.jpg"}],"country":"United States","state":"Illinois","city":"Grayville","otherGeospatial":"Wabash River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.01456451416016,\n 38.153727245014004\n ],\n [\n -87.77870178222656,\n 38.153727245014004\n ],\n [\n -87.77870178222656,\n 38.338694087313534\n ],\n [\n -88.01456451416016,\n 38.338694087313534\n ],\n [\n -88.01456451416016,\n 38.153727245014004\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299
Riverscapes are complex, landscape-scale mosaics of connected river and stream habitats embedded in diverse ecological and socioeconomic settings. Social–ecological interactions among stakeholders often complicate natural-resource conservation and management of riverscapes. The management challenges posed by the conservation and restoration of wild salmonid populations in the Columbia River Basin (CRB) of western North America are one such example. Because of their ecological, cultural, and socioeconomic importance, salmonids present a complex management landscape due to interacting environmental factors (eg climate change, invasive species) as well as socioeconomic and political factors (eg dams, hatcheries, land-use change, transboundary agreements). Many of the problems in the CRB can be linked to social–ecological interactions occurring within integrated ecological, human–social, and regional–climatic spheres. Future management and conservation of salmonid populations therefore depends on how well the issues are understood and whether they can be resolved through effective communication and collaboration among ecologists, social scientists, stakeholders, and policy makers.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/fee.1752","usgsCitation":"Hand, B., Flint, C.G., Frissell, C.A., Muhlfeld, C.C., Devlin, S.P., Kennedy, B., Crabtree, R.L., McKee, W.A., Luikart, G., and Stanford, J.A., 2018, A social–ecological perspective for riverscape management in the Columbia River Basin: Frontiers in Ecology and the Environment, v. 16, no. S1, p. S23-S33, https://doi.org/10.1002/fee.1752.","productDescription":"11 p.","startPage":"S23","endPage":"S33","ipdsId":"IP-082252","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":469093,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/fee.1752","text":"Publisher Index Page"},{"id":352016,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Columbia River Basin","volume":"16","issue":"S1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-16","publicationStatus":"PW","scienceBaseUri":"5afee751e4b0da30c1bfc228","contributors":{"authors":[{"text":"Hand, Brian K.","contributorId":139248,"corporation":false,"usgs":false,"family":"Hand","given":"Brian K.","affiliations":[{"id":12707,"text":"Flathead Lake Biological Station, Fish and Wildlife Genomics Group, University of Montana, Polson, MT 59860","active":true,"usgs":false}],"preferred":false,"id":729464,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Courtney G.","contributorId":202755,"corporation":false,"usgs":false,"family":"Flint","given":"Courtney","email":"","middleInitial":"G.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":729465,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frissell, Chris A.","contributorId":202756,"corporation":false,"usgs":false,"family":"Frissell","given":"Chris","email":"","middleInitial":"A.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":729466,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":729463,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Devlin, Shawn P.","contributorId":202757,"corporation":false,"usgs":false,"family":"Devlin","given":"Shawn","email":"","middleInitial":"P.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":729467,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kennedy, Brian P.","contributorId":202785,"corporation":false,"usgs":false,"family":"Kennedy","given":"Brian P.","affiliations":[],"preferred":false,"id":729468,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Crabtree, Robert L.","contributorId":202758,"corporation":false,"usgs":false,"family":"Crabtree","given":"Robert","email":"","middleInitial":"L.","affiliations":[{"id":35162,"text":"Yellowstone Ecological Research Center","active":true,"usgs":false}],"preferred":false,"id":729469,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McKee, W. Arthur","contributorId":202786,"corporation":false,"usgs":false,"family":"McKee","given":"W.","email":"","middleInitial":"Arthur","affiliations":[],"preferred":false,"id":729470,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Luikart, Gordon","contributorId":97409,"corporation":false,"usgs":false,"family":"Luikart","given":"Gordon","affiliations":[{"id":6580,"text":"University of Montana, Flathead Lake Biological Station, Polson, Montana 59860, USA","active":true,"usgs":false}],"preferred":false,"id":729471,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stanford, Jack A.","contributorId":150193,"corporation":false,"usgs":false,"family":"Stanford","given":"Jack","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":729472,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70196689,"text":"70196689 - 2018 - Geoelectric hazard maps for the Mid-Atlantic United States: 100 year extreme values and the 1989 magnetic storm","interactions":[],"lastModifiedDate":"2018-04-24T16:57:19","indexId":"70196689","displayToPublicDate":"2018-01-16T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Geoelectric hazard maps for the Mid-Atlantic United States: 100 year extreme values and the 1989 magnetic storm","docAbstract":"Maps of extreme value geoelectric field amplitude are constructed for the Mid‐Atlantic United States, a region with high population density and critically important power grid infrastructure. Geoelectric field time series for the years 1983–2014 are estimated by convolving Earth surface impedances obtained from 61 magnetotelluric survey sites across the Mid‐Atlantic with historical 1 min (2 min Nyquist) measurements of geomagnetic variation obtained from a nearby observatory. Statistical models are fitted to the maximum geoelectric amplitudes occurring during magnetic storms, and extrapolations made to estimate threshold amplitudes only exceeded, on average, once per century. For the Mid‐Atlantic region, 100 year geoelectric exceedance amplitudes have a range of almost 3 orders of magnitude (from 0.04 V/km at a site in southern Pennsylvania to 24.29 V/km at a site in central Virginia), and they have significant geographic granularity, all of which is due to site‐to‐site differences in magnetotelluric impedance. Maps of these 100 year exceedance amplitudes resemble those of the estimated geoelectric amplitudes attained during the March 1989 magnetic storm, and, in that sense, the March 1989 storm resembles what might be loosely called a “100 year” event. The geoelectric hazard maps reported here stand in stark contrast with the 100 year geoelectric benchmarks developed for the North American Electric Reliability Corporation.
","language":"English","publisher":"AGU","doi":"10.1002/2017GL076042","usgsCitation":"Love, J.J., Lucas, G.M., Kelbert, A., and Bedrosian, P.A., 2018, Geoelectric hazard maps for the Mid-Atlantic United States: 100 year extreme values and the 1989 magnetic storm: Geophysical Research Letters, v. 45, no. 1, p. 5-14, https://doi.org/10.1002/2017GL076042.","productDescription":"10 p.","startPage":"5","endPage":"14","ipdsId":"IP-092653","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":469094,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017gl076042","text":"Publisher Index Page"},{"id":353687,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-04","publicationStatus":"PW","scienceBaseUri":"5afee751e4b0da30c1bfc226","contributors":{"authors":[{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":733974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lucas, Greg M. 0000-0003-1331-1863","orcid":"https://orcid.org/0000-0003-1331-1863","contributorId":202808,"corporation":false,"usgs":true,"family":"Lucas","given":"Greg","email":"","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":733975,"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":733976,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":733977,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70198431,"text":"70198431 - 2018 - Size, age, renewal, and discharge of groundwater carbon","interactions":[],"lastModifiedDate":"2018-08-06T14:30:57","indexId":"70198431","displayToPublicDate":"2018-01-15T14:30:47","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1999,"text":"Inland Waters","active":true,"publicationSubtype":{"id":10}},"title":"Size, age, renewal, and discharge of groundwater carbon","docAbstract":"Groundwater carbon (C) supply to lakes and streams is important to understanding the role of inland waters in global and regional cycles and in the functioning of aquatic ecosystems. We provide new estimates of the size and discharge of the groundwater C pool using data from a broad survey of groundwater C, information on the depth distribution of groundwater, and data on groundwater age. About 0.25 × 106 km3 of the 8 × 106km3 of groundwater resource is within 100 m of the surface and 4.2 × 106 km3 is above 2000 m. Ages show an average groundwater turnover time of 10 yr at 25 m, 350 yr at 100 m, increasing to about 100 000 yr at 600 m. Global groundwater discharge is 16 000 km3 yr−1; >16% of precipitation passes through groundwater. Groundwater dissolved organic C (DOC) can be high in shallow groundwater but stabilizes at ~2–4 mg L−1 at 100 m. Average groundwater dissolved inorganic C (DIC) is ~30–43 mg L−1. Groundwater C content to 2000 m is ~145 Pg, about the same as all marine sediments and about one-sixth that of the surface ocean. Groundwater C discharge to continental waters is 0.68 Pg yr−1, or 3.4 times that estimated from river base-flow and submarine groundwater discharge. This discharge is 68 times previous estimates, implying a total C flux from land of 3.6 Pg yr−1; 80% of discharge occurs from above 40 m and 99% from the upper 100 m.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/20442041.2017.1412918","usgsCitation":"Downing, J.A., and Striegl, R.G., 2018, Size, age, renewal, and discharge of groundwater carbon: Inland Waters, v. 8, no. 1, p. 122-127, https://doi.org/10.1080/20442041.2017.1412918.","productDescription":"6 p.","startPage":"122","endPage":"127","ipdsId":"IP-076067","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":469095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/20442041.2017.1412918","text":"Publisher Index Page"},{"id":356201,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-15","publicationStatus":"PW","scienceBaseUri":"5b6fc4bae4b0f5d57878eac2","contributors":{"authors":[{"text":"Downing, John A.","contributorId":169033,"corporation":false,"usgs":false,"family":"Downing","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":741405,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Striegl, Robert G. 0000-0002-8251-4659 rstriegl@usgs.gov","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":1630,"corporation":false,"usgs":true,"family":"Striegl","given":"Robert","email":"rstriegl@usgs.gov","middleInitial":"G.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":741404,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70195318,"text":"70195318 - 2018 - A guide to calculating habitat-quality metrics to inform conservation of highly mobile species","interactions":[],"lastModifiedDate":"2018-02-08T14:12:45","indexId":"70195318","displayToPublicDate":"2018-01-15T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2827,"text":"Natural Resource Modeling","active":true,"publicationSubtype":{"id":10}},"title":"A guide to calculating habitat-quality metrics to inform conservation of highly mobile species","docAbstract":"Many metrics exist for quantifying the relative value of habitats and pathways used by highly mobile species. Properly selecting and applying such metrics requires substantial background in mathematics and understanding the relevant management arena. To address this multidimensional challenge, we demonstrate and compare three measurements of habitat quality: graph-, occupancy-, and demographic-based metrics. Each metric provides insights into system dynamics, at the expense of increasing amounts and complexity of data and models. Our descriptions and comparisons of diverse habitat-quality metrics provide means for practitioners to overcome the modeling challenges associated with management or conservation of such highly mobile species. Whereas previous guidance for applying habitat-quality metrics has been scattered in diversified tracks of literature, we have brought this information together into an approachable format including accessible descriptions and a modeling case study for a typical example that conservation professionals can adapt for their own decision contexts and focal populations.
Considerations for Resource Managers
Despite much research and investment into understanding and managing nutrients across agricultural landscapes, nutrient runoff to freshwater ecosystems is still a major concern. We argue there is currently a disconnect between the management of watershed surfaces (agricultural landscape) and river networks (riverine landscape). These landscapes are commonly managed separately, but there is limited cohesiveness between agricultural landscape-focused research and river science, despite similar end goals. Interdisciplinary research into stream networks that drain agricultural landscapes is expanding but is fraught with problems. Conceptual frameworks are useful tools to order phenomena, reveal patterns and processes, and in interdisciplinary river science, enable the joining of multiple areas of understanding into a single conceptual–empirical structure. We present a framework for the interdisciplinary study and management of agricultural and riverine landscapes. The framework includes components of an ecosystems approach to the study of catchment–stream networks, resilience thinking, and strategic adaptive management. Application of the framework is illustrated through a study of the Fox Basin in Wisconsin, USA. To fully realize the goal of nutrient reduction in the basin, we suggest that greater emphasis is needed on where best management practices (BMPs) are used within the spatial context of the combined watershed–stream network system, including BMPs within the river channel. Targeted placement of BMPs throughout the riverine landscape would increase the overall buffering capacity of the system to nutrient runoff and thus its resilience to current and future disturbances.
","language":"English","publisher":"American Society of Agronomy, Crop Science Society of America, & Soil Science Society of America","doi":"10.2134/jeq2017.08.0319","usgsCitation":"Kreiling, R.M., Thoms, M.C., and Richardson, W.B., 2018, Beyond the edge: Linking agricultural landscapes, stream networks, and best management practices: Journal of Environmental Quality, v. 47, p. 42-53, https://doi.org/10.2134/jeq2017.08.0319.","productDescription":"12 p.","startPage":"42","endPage":"53","ipdsId":"IP-088966","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":351379,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a7d6ffde4b00f54eb2441a3","contributors":{"authors":[{"text":"Kreiling, Rebecca M. 0000-0002-9295-4156 rkreiling@usgs.gov","orcid":"https://orcid.org/0000-0002-9295-4156","contributorId":4234,"corporation":false,"usgs":true,"family":"Kreiling","given":"Rebecca","email":"rkreiling@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":false,"id":727805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thoms, Martin C. 0000-0002-8074-0476","orcid":"https://orcid.org/0000-0002-8074-0476","contributorId":145710,"corporation":false,"usgs":false,"family":"Thoms","given":"Martin","email":"","middleInitial":"C.","affiliations":[{"id":16205,"text":"Riverine Landscapes Research Laboratory, University of New England, NSW, Australia","active":true,"usgs":false}],"preferred":false,"id":727806,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richardson, William B. 0000-0002-7471-4394 wrichardson@usgs.gov","orcid":"https://orcid.org/0000-0002-7471-4394","contributorId":3277,"corporation":false,"usgs":true,"family":"Richardson","given":"William","email":"wrichardson@usgs.gov","middleInitial":"B.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":727807,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70195392,"text":"70195392 - 2018 - Estimating restorable wetland water storage at landscape scales","interactions":[],"lastModifiedDate":"2020-09-01T14:25:35.772943","indexId":"70195392","displayToPublicDate":"2018-01-15T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Estimating restorable wetland water storage at landscape scales","docAbstract":"Globally, hydrologic modifications such as ditching and subsurface drainage have significantly reduced wetland water storage capacity (i.e., volume of surface water a wetland can retain) and consequent wetland functions. While wetland area has been well documented across many landscapes and used to guide restoration efforts, few studies have directly quantified the associated wetland storage capacity. Here, we present a novel raster-based approach to quantify both contemporary and potential (i.e., restorable) storage capacities of individual depressional basins across landscapes. We demonstrate the utility of this method by applying it to the Delmarva Peninsula, a region punctuated by both depressional wetlands and drainage ditches. Across the entire peninsula, we estimated that restoration (i.e., plugging ditches) could increase storage capacity by 80%. Focusing on an individual watershed, we found that over 59% of restorable storage capacity occurs within 20 m of the drainage network, and that 93% occurs within 1 m elevation of the drainage network. Our demonstration highlights widespread ditching in this landscape, spatial patterns of both contemporary and potential storage capacities, and clear opportunities for hydrologic restoration. In Delmarva and more broadly, our novel approach can inform targeted landscape-scale conservation and restoration efforts to optimize hydrologically mediated wetland functions.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.11405","usgsCitation":"Jones, C.N., Evenson, G.R., McLaughlin, D.L., Vanderhoof, M.K., Lang, M.W., McCarty, G.W., Golden, H.E., Lane, C., and Alexander, L., 2018, Estimating restorable wetland water storage at landscape scales: Hydrological Processes, v. 32, no. 2, p. 305-313, https://doi.org/10.1002/hyp.11405.","productDescription":"9 p.","startPage":"305","endPage":"313","ipdsId":"IP-088286","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":461079,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5907502","text":"External Repository"},{"id":351516,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-13","publicationStatus":"PW","scienceBaseUri":"5afee751e4b0da30c1bfc22a","contributors":{"authors":[{"text":"Jones, Charles Nathan","contributorId":202421,"corporation":false,"usgs":false,"family":"Jones","given":"Charles","email":"","middleInitial":"Nathan","affiliations":[{"id":36428,"text":"The National Socio-Environmental Synthesis Center, University of Maryland","active":true,"usgs":false}],"preferred":false,"id":728374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evenson, Grey R.","contributorId":202422,"corporation":false,"usgs":false,"family":"Evenson","given":"Grey","email":"","middleInitial":"R.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":728375,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McLaughlin, Daniel L.","contributorId":156435,"corporation":false,"usgs":false,"family":"McLaughlin","given":"Daniel","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":728376,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":728373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lang, Megan W.","contributorId":196284,"corporation":false,"usgs":false,"family":"Lang","given":"Megan","email":"","middleInitial":"W.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":728377,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McCarty, Greg W.","contributorId":143675,"corporation":false,"usgs":false,"family":"McCarty","given":"Greg","email":"","middleInitial":"W.","affiliations":[{"id":15298,"text":"USDA-ARS Hydrology and Remote Sensing Laboratory, Bldg 007, BARC-W, 10300 Baltimore Avenue, Beltsville, Maryland 20705, United States","active":true,"usgs":false}],"preferred":false,"id":728378,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Golden, Heather E.","contributorId":202423,"corporation":false,"usgs":false,"family":"Golden","given":"Heather","email":"","middleInitial":"E.","affiliations":[{"id":36429,"text":"USEPA ORD","active":true,"usgs":false}],"preferred":false,"id":728379,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lane, Charles R.","contributorId":138991,"corporation":false,"usgs":false,"family":"Lane","given":"Charles R.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":728380,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Alexander, Laurie C.","contributorId":138989,"corporation":false,"usgs":false,"family":"Alexander","given":"Laurie C.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":728381,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70198089,"text":"70198089 - 2018 - Infrared heater system for warming tropical forest understory plants and soils","interactions":[],"lastModifiedDate":"2018-07-13T10:22:15","indexId":"70198089","displayToPublicDate":"2018-01-15T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Infrared heater system for warming tropical forest understory plants and soils","docAbstract":"The response of tropical forests to global warming is one of the largest uncertainties\nin predicting the future carbon balance of Earth. To determine the likely effects of elevated\ntemperatures on tropical forest understory plants and soils, as well as other\necosystems, an infrared (IR) heater system was developed to provide in situ warming\nfor the Tropical Responses to Altered Climate Experiment (TRACE) in the Luquillo\nExperimental Forest in Puerto Rico. Three replicate heated 4-m-\ndiameter\nplots were\nwarmed to maintain a 4°C increase in understory vegetation compared to three unheated\ncontrol plots, as sensed by IR thermometers. The equipment was larger than\nany used previously and was subjected to challenges different from those of many\ntemperate ecosystem warming systems, including frequent power surges and outages,\nhigh humidity, heavy rains, hurricanes, saturated clayey soils, and steep slopes. The\nsystem was able to maintain the target 4.0°C increase in hourly average vegetation\ntemperatures to within ± 0.1°C. The vegetation was heterogeneous and on a 21°\nslope, which decreased uniformity of the warming treatment on the plots; yet, the\ngreen leaves were fairly uniformly warmed, and there was little difference among\n0–10 cm depth soil temperatures at the plot centers, edges, and midway between. Soil\ntemperatures at the 40–50 cm depth increased about 3°C compared to the controls\nafter a month of warming. As expected, the soil in the heated plots dried faster than\nthat of the control plots, but the average soil moisture remained adequate for the\nplants. The TRACE heating system produced an adequately uniform warming precisely\ncontrolled down to at least 50-cm\nsoil depth, thereby creating a treatment that allows\nfor assessing mechanistic responses of tropical plants and soil to warming, with applicability\nto other ecosystems. No physical obstacles to scaling the approach to taller\nvegetation (i.e., trees) and larger plots were observed.","language":"English","publisher":"Wiley","doi":"10.1002/ece3.3780","usgsCitation":"Kimball, B.A., Alonso-Rodriguez, A.M., Cavaleri, M.A., Reed, S.C., Gonzalez, G., and Wood, T.E., 2018, Infrared heater system for warming tropical forest understory plants and soils: Ecology and Evolution, v. 8, no. 4, p. 1932-1944, https://doi.org/10.1002/ece3.3780.","productDescription":"13 p.","startPage":"1932","endPage":"1944","ipdsId":"IP-092002","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":469096,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.3780","text":"Publisher Index Page"},{"id":355668,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -65.7478094101,18.3093884924 ], [ -65.7478094101,18.3233015696 ], [ -65.7264590263,18.3233015696 ], [ -65.7264590263,18.3093884924 ], [ -65.7478094101,18.3093884924 ] ] ] } } ] }","volume":"8","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-15","publicationStatus":"PW","scienceBaseUri":"5b6fc4bbe4b0f5d57878eac4","contributors":{"authors":[{"text":"Kimball, Bruce A.","contributorId":206280,"corporation":false,"usgs":false,"family":"Kimball","given":"Bruce","email":"","middleInitial":"A.","affiliations":[{"id":37299,"text":"The Greenleaf Group","active":true,"usgs":false}],"preferred":false,"id":739963,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alonso-Rodriguez, Aura M.","contributorId":206281,"corporation":false,"usgs":false,"family":"Alonso-Rodriguez","given":"Aura","email":"","middleInitial":"M.","affiliations":[{"id":37300,"text":"International Institute of Tropical Forestry, USDA Forest Service, Sabana Field Research Station, Luquillo, Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":739964,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cavaleri, Molly A.","contributorId":206282,"corporation":false,"usgs":false,"family":"Cavaleri","given":"Molly","email":"","middleInitial":"A.","affiliations":[{"id":34284,"text":"School of Forest Resources and Environmental Science, Michigan Technological University","active":true,"usgs":false}],"preferred":false,"id":739965,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":739962,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gonzalez, Grizelle","contributorId":191117,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Grizelle","email":"","affiliations":[],"preferred":false,"id":739966,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wood, Tana E.","contributorId":202372,"corporation":false,"usgs":false,"family":"Wood","given":"Tana","email":"","middleInitial":"E.","affiliations":[{"id":36399,"text":"International Institute of Tropical Forestry, USDA Forest Service, Rio Piedras, PR","active":true,"usgs":false}],"preferred":false,"id":739967,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70194647,"text":"ofr20171159 - 2018 - Processing of next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data for the DuPage County streamflow simulation system","interactions":[],"lastModifiedDate":"2018-01-25T15:19:19","indexId":"ofr20171159","displayToPublicDate":"2018-01-12T13:45:00","publicationYear":"2018","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":"2017-1159","title":"Processing of next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data for the DuPage County streamflow simulation system","docAbstract":"The U.S. Geological Survey, in cooperation with DuPage County Stormwater Management Department, is testing a near real-time streamflow simulation system that assists in the management and operation of reservoirs and other flood-control structures in the Salt Creek and West Branch DuPage River drainage basins in DuPage County, Illinois. As part of this effort, the U.S. Geological Survey maintains a database of hourly meteorological and hydrologic data for use in this near real-time streamflow simulation system. Among these data are next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data, which are retrieved from the North Central River Forecasting Center of the National Weather Service. The DuPage County streamflow simulation system uses these quantitative precipitation forecast data to create streamflow predictions for the two simulated drainage basins. This report discusses in detail how these data are processed for inclusion in the Watershed Data Management files used in the streamflow simulation system for the Salt Creek and West Branch DuPage River drainage basins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171159","collaboration":"Prepared in cooperation with the DuPage County Stormwater Management Department","usgsCitation":"Bera, Maitreyee, and Ortel, T.W., 2018, Processing of next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data for the DuPage County streamflow simulation system: \nU.S. Geological Survey Open-File Report 2017–1159, 16 p., https://doi.org/10.3133/ofr20171159.","productDescription":"iv, 15 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087229","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":350409,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1159/coverthb.jpg"},{"id":350410,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1159/ofr20171159.pdf","text":"Report","size":"3.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1159"}],"country":"United States","state":"Illinois","county":"DuPage County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-88.2634,41.9876],[-88.1473,41.9883],[-88.0342,41.9925],[-87.9175,41.9938],[-87.9188,41.9076],[-87.9178,41.8185],[-87.9142,41.7318],[-87.9139,41.7172],[-87.9438,41.7017],[-87.9482,41.694],[-87.9674,41.6879],[-87.9883,41.6877],[-88.0013,41.6874],[-88.0308,41.6868],[-88.0317,41.7295],[-88.1499,41.7272],[-88.2625,41.7251],[-88.2628,41.811],[-88.2632,41.8623],[-88.2631,41.9],[-88.2634,41.9876]]]},\"properties\":{\"name\":\"Dupage\",\"state\":\"IL\"}}]}","contact":"Director, Illinois-Iowa Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, IL 61801
Snake fungal disease (SFD) results from a skin infection that has been documented only in snakes. Historically, reports of snakes with skin infections of unknown origin have been sporadic. Recently, the number of reported cases of skin infections in snakes has increased substantially. This emerging infectious disease, confirmed in numerous species of snakes, is caused by the fungus Ophidiomyces ophiodiicola. As of August 2017, O. ophiodiicola has been detected in at least 23 States and one Canadian Province. However, researchers suspect that SFD may be more widely distributed than these documented cases suggest, because efforts to monitor the health of many snake populations are limited. Snake fungal disease may also be underreported in populations where it affects snakes infrequently or in species that develop less severe illness. Signs of SFD include crusted or ulcerated scales, nodules (that is, abnormal bumps) under the skin, and facial disfiguration that can be quite severe, leading to emaciation and death. Many snake populations are already in decline due to habitat loss and dwindling prey populations, and the recent emergence of SFD may accelerate this decline, causing certain species to disappear entirely from some locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173064","usgsCitation":"Thompson, N.E., Lankau, E.W., and Rogall, G.M., 2018, Snake fungal disease in North America—U.S. Geological Survey updates: U.S. Geological Survey Fact Sheet 2017–3064, 4 p., https://doi.org/10.3133/fs20173064.","productDescription":"4 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-086524","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":350374,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3064/coverthb1.jpg"},{"id":350375,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3064/fs20173064.pdf","text":"Report","size":"9.57 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director, National Wildlife Health Center
U.S. Geological Survey
6006 Schroeder Road
Madison, WI 53711
Remote camera trapping is an efficient noninvasive technique for monitoring rare and elusive species, such as cheetahs. The unique pelage pattern of cheetahs allows for identification of individuals from photographs, providing detection histories that are naturally suited for abundance estimation using capture–recapture methods. Furthermore, the spatial location of photographic detections allows for the use of spatial capture–recapture models, which provide estimates of density. In this chapter, we describe aspects of cheetah ecology that should be considered when designing camera trapping surveys (e.g., social structure, natural densities, and home range size) to estimate cheetah density and provide guidance for future camera trap sampling and analysis.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Cheetahs: Biology and Conservation","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-804088-1.00029-0","usgsCitation":"Ezequiel Fabiano, Boast, L., Fuller, A.K., and Chris Sutherland, 2018, The use of remote camera trapping to study cheetahs, chap. 29 of Cheetahs: Biology and Conservation, p. 415-425, https://doi.org/10.1016/B978-0-12-804088-1.00029-0.","productDescription":"11 p.","startPage":"415","endPage":"425","ipdsId":"IP-080279","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":367608,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ezequiel Fabiano","contributorId":217268,"corporation":false,"usgs":false,"family":"Ezequiel Fabiano","affiliations":[{"id":39588,"text":"University of Namibia","active":true,"usgs":false}],"preferred":false,"id":766480,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boast, Lorraine","contributorId":217269,"corporation":false,"usgs":false,"family":"Boast","given":"Lorraine","email":"","affiliations":[{"id":39589,"text":"Cheetah Conservation Botswana","active":true,"usgs":false}],"preferred":false,"id":766481,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":766479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chris Sutherland","contributorId":196873,"corporation":false,"usgs":false,"family":"Chris Sutherland","affiliations":[],"preferred":false,"id":766482,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70190434,"text":"sir20175094 - 2018 - Nutrient and metal loads estimated by using discrete, automated, and continuous water-quality monitoring techniques for the Blackstone River at the Massachusetts-Rhode Island State line, water years 2013–14","interactions":[],"lastModifiedDate":"2018-01-10T16:40:29","indexId":"sir20175094","displayToPublicDate":"2018-01-10T17:20:00","publicationYear":"2018","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":"2017-5094","title":"Nutrient and metal loads estimated by using discrete, automated, and continuous water-quality monitoring techniques for the Blackstone River at the Massachusetts-Rhode Island State line, water years 2013–14","docAbstract":"Flow-proportional composite water samples were collected in water years 2013 and 2014 by the U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, from the Blackstone River at Millville, Massachusetts (U.S. Geological Survey station 01111230), about 0.5 mile from the border with Rhode Island. Samples were collected in order to better understand the dynamics of selected nutrient and metal constituents, assist with planning, guide activities to meet water-quality goals, and provide real-time water-quality information to the public. An automated system collected the samples at 14-day intervals to determine total and dissolved nitrogen and phosphorus concentrations, to provide accurate monthly nutrient concentration data, and to calculate monthly load estimates. Concentrations of dissolved trace metals and total aluminum were determined from 4-day composite water samples that were collected twice monthly by the automated system. Results from 4-day composites provide stakeholders with information to evaluate trace metals on the basis of chronic 4-day exposure criteria for aquatic life, and the potential to use the biotic ligand model to evaluate copper concentrations. Nutrient, trace metal, suspended sediment, dissolved organic carbon, and chlorophyll a concentrations were determined from discrete samples collected at the Millville station and from across the stream transect at the upstream railroad bridge, and these concentrations served as a means to evaluate the representativeness of the Millville point location.
Analytical results from samples collected with the automated flow-proportional sampling system provided the means to calculate monthly and annual loading data. Total nitrogen and total phosphorus loads in water year (WY) 2013 were about 447,000 and 36,000 kilograms (kg), respectively. In WY 2014, annual loads of total nitrogen and total phosphorus were about 342,000 and 21,000 kg, respectively. Total nitrogen and total phosphorus loads from WYs 2013 and 2014 were about 56 and 65 percent lower than those reported for WYs 2008 and 2009. The higher loads in 2008 and 2009 may be explained by the higher than average flows in WY 2009 and by facility upgrades made by wastewater treatment facilities in the basin.
Median loads were determined from composite samples collected with the automated system between October 2012 and October 2014. Median dissolved cadmium and chromium 4-day loads were 0.55 and 0.84 kg, respectively. Dissolved copper and total lead median 4-day loads were 8.02 and 1.42 kg, respectively. The dissolved nickel median 4-day load was 5.45 kg, and the dissolved zinc median 4-day load was 36 kg. Median total aluminum 4-day loads were about 197 kg.
Spearman’s rank correlation analyses were used with discrete sample concentrations and continuous records of temperature, specific conductance, turbidity, and chlorophyll a to identify correlations between variables that could be used to develop regression equations for estimating real-time concentrations of constituents. Correlation coefficients were generated for flow, precipitation, antecedent precipitation, physical parameters, and chemical constituents. A 95-percent confidence limit for each value of Spearman’s rho was calculated, and multiple linear regression analysis using ordinary least squares regression techniques was used to develop regression equations for concentrations of total phosphorus, total nitrogen, suspended sediment concentration, total copper, and total aluminum. Although the correlations are based on the limited amount of data collected as part of this study, the potential to monitor water-quality changes in real time may be of value to resource managers and decision makers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175094","isbn":"ISBN 978-1-4113-4181-4","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Sorenson, J.R., Granato, G.E., and Smith, K.P., 2018, Nutrient and metal loads estimated by using discrete, automated, and continuous water-quality monitoring techniques for the Blackstone River at the Massachusetts-Rhode Island State line, water years 2013–14: U.S. Geological Survey Scientific Investigations Report 2017–5094, 41 p., https://doi.org/10.3133/sir20175094.","productDescription":"Report: ix, 41 p.; 4 Tables","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-079789","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":350359,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5094/sir20175094.pdf","text":"Report","size":"4.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5094"},{"id":350366,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5094/tables/sir20175094_table6.csv","text":"Table 6","size":"19.4 csv","linkHelpText":"- Concentrations of nutrients, trace metals, and suspended sediment in manually collected samples from the upstream railroad bridge and from the collection point at the Blackstone River at Millville, Massachusetts, station (01111230) during water years 2013 and 2014."},{"id":350368,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5094/tables/sir20175094_table7.csv","text":"Table 7","size":"13.1 KB csv","linkHelpText":"- Loads of nutrients based on 14-day nutrient composite samples, and loads of dissolved trace metals and total aluminum based on 4-day metal composite samples collected by the automated sampling system from the point location at the Blackstone River at Millville, Massachusetts, station (01111230) during water years 2013 and 2014."},{"id":350361,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5094/tables/sir20175094_table4.xlsx","text":"Table 4 (Microsoft Excel)","size":"48.5 KB"},{"id":350358,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5094/coverthb.jpg"},{"id":350360,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5094/tables/sir20175094_table4.csv","text":"Table 4","size":"15 KB csv","linkHelpText":"- Concentrations of nutrients, trace metals, and suspended sediment in sample pairs collected from the upstream railroad bridge and from the point location at the Blackstone River at Millville, Massachusetts, station (01111230)."},{"id":350367,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5094/tables/sir20175094_table7.xlsx","text":"Table 7 (Microsoft Excel)","size":"44 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":350364,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5094/tables/sir20175094_table5.csv","text":"Table 5","size":"13.7 KB csv","linkHelpText":"- Concentrations of nutrients, total aluminum, and dissolved trace metals in 14-day nutrient composite samples and 4-day metal composite samples collected by using the automated sampling system from the point location at the Blackstone River at Millville, Massachusetts, station (01111230) during water years 2013 and 2014."},{"id":350365,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5094/tables/sir20175094_table6.xlsx","text":"Table 6 (Microsoft Excel)","size":"46.7 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":350363,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5094/tables/sir20175094_table5.xlsx","text":"Table 5 (Microsoft Excel)","size":"39.6 KB","linkFileType":{"id":3,"text":"xlsx"}}],"country":"United States","state":"Massachusetts, Rhode Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.9167,\n 41.8333\n ],\n [\n -71.3333,\n 41.8333\n ],\n [\n -71.3333,\n 42.3333\n ],\n [\n -71.9167,\n 42.3333\n ],\n [\n -71.9167,\n 41.8333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
","tableOfContents":"
The Geospatial Semantic Web (GSW) is an emerging technology that uses the Internet for more effective knowledge engineering and information extraction. Among the aims of the GSW are to structure the semantic specifications of data to reduce ambiguity and to link those data more efficiently. The data are stored as triples, the basic data unit in graph databases, which are similar to the vector data model of geographic information systems (GIS); that is, a node-edge-node model that forms a graph of semantically related information. The GSW is supported by emerging technologies such as linked geospatial data, described below, that enable it to store and manage geographical data that require new cartographic methods for visualization. This report describes a map that can interact with linked geospatial data using a simulation of a data query approach called the browsable graph to find information that is semantically related to a subject of interest, visualized using the Data Driven Documents (D3) library. Such a semantically enabled map functions as a map knowledge base (MKB) (Varanka and Usery, 2017).
A MKB differs from a database in an important way. The central element of a triple, alternatively called the edge or property, is composed of a logic formalization that structures the relation between the first and third parts, the nodes or objects. Node-edge-node represents the graphic form of the triple, and the subject-property-object terms represent the data structure. Object classes connect to build a federated graph, similar to a network in visual form. Because the triple property is a logical statement (a predicate), the data graph represents logical propositions or assertions accepted to be true about the subject matter. These logical formalizations can be manipulated to calculate new triples, representing inferred logical assertions, from the existing data.
To demonstrate a MKB system, a technical proof-of-concept is developed that uses geographically attributed Resource Description Framework (RDF) serializations of linked data for mapping. The proof-of-concept focuses on accessing triple data from visual elements of a geographic map as the interface to the MKB. The map interface is embedded with other essential functions such as SPARQL Protocol and RDF Query Language (SPARQL) data query endpoint services and reasoning capabilities of Apache Marmotta (Apache Software Foundation, 2017). An RDF database of the Geographic Names Information System (GNIS), which contains official names of domestic feature in the United States, was linked to a county data layer from The National Map of the U.S. Geological Survey. The county data are part of a broader Government Units theme offered to the public as Esri shapefiles. The shapefile used to draw the map itself was converted to a geographic-oriented JavaScript Object Notation (JSON) (GeoJSON) format and linked through various properties with a linked geodata version of the GNIS database called “GNIS–LD” (Butler and others, 2016; B. Regalia and others, University of California-Santa Barbara, written commun., 2017). The GNIS–LD files originated in Terse RDF Triple Language (Turtle) format but were converted to a JSON format specialized in linked data, “JSON–LD” (Beckett and Berners-Lee, 2011; Sorny and others, 2014). The GNIS–LD database is composed of roughly three predominant triple data graphs: Features, Names, and History. The graphs include a set of namespace prefixes used by each of the attributes. Predefining the prefixes made the conversion to the JSON–LD format simple to complete because Turtle and JSON–LD are variant specifications of the basic RDF concept.
To convert a shapefile into GeoJSON format to capture the geospatial coordinate geometry objects, an online converter, Mapshaper, was used (Bloch, 2013). To convert the Turtle files, a custom converter written in Java reconstructs the files by parsing each grouping of attributes belonging to one subject and pasting the data into a new file that follows the syntax of JSON–LD. Additionally, the Features file contained its own set of geometries, which was exported into a separate JSON–LD file along with its elevation value to form a fourth file, named “features-geo.json.” Extracted data from external files can be represented in HyperText Markup Language (HTML) path objects. The goal was to import multiple JSON–LD files using this approach.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171150","usgsCitation":"Powell, L.J., and Varanka, D.E., 2018, A linked GeoData map for enabling information access: U.S. Geological Survey Open–File Report 2017–1150, 6 p, https://doi.org/10.3133/ofr20171150.","productDescription":"iv, 6 p.","numberOfPages":"14","onlineOnly":"Y","ipdsId":"IP-090452","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":350413,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1150/coverthb.jpg"},{"id":350414,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1150/ofr20171150.pdf","text":"Report","size":"376 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1150"}],"contact":"Director, National Geospatial Technical Operations Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Agricultural activities can affect water quality and the health of aquatic ecosystems; many water-quality issues originate with the movement of water, agricultural chemicals, and eroded soil from agricultural areas to streams and groundwater. Most agricultural activities are designed to sustain or increase crop production, while some are designed to protect soil and water resources. Numerous soil- and water-protection practices are designed to reduce the volume and velocity of runoff and increase infiltration. This report presents a conceptual framework that combines generalized concepts on the movement of water, the environmental behavior of chemicals and eroded soil, and the designed functions of various agricultural activities, as they relate to hydrology, to create attainable expectations for the protection of—with the goal of improving—water quality through changes in an agricultural activity.
The framework presented uses two types of decision trees to guide decision making toward attainable expectations regarding the effectiveness of changing agricultural activities to protect and improve water quality in streams. One decision tree organizes decision making by considering the hydrologic setting and chemical behaviors, largely at the field scale. This decision tree can help determine which agricultural activities could effectively protect and improve water quality in a stream from the movement of chemicals, or sediment, from a field. The second decision tree is a chemical fate accounting tree. This decision tree helps set attainable expectations for the permanent removal of sediment, elements, and organic chemicals—such as herbicides and insecticides—through trapping or conservation tillage practices. Collectively, this conceptual framework consolidates diverse hydrologic settings, chemicals, and agricultural activities into a single, broad context that can be used to set attainable expectations for agricultural activities. This framework also enables better decision making for future agricultural activities as a means to reduce current, and prevent new, water-quality issues.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175095","usgsCitation":"Capel, P.D., Wolock, D.M., Coupe, R.H., and Roth, J.L., 2018, A conceptual framework for effectively anticipating water-quality changes resulting from changes in agricultural activities: U.S. Geological Survey Scientific Investigations Report 2017–5095, 35 p., https://doi.org/10.3133/sir20175095.","productDescription":"Report: viii, 35 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-071052","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":350408,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75T3HN9","text":"USGS data release","description":"USGS data release","linkHelpText":"Data set used to develop a conceptual framework for effectively anticipating water-quality changes resulting from changes in agricultural activities"},{"id":349840,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5095/coverthb.jpg"},{"id":349841,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5095/sir20175095.pdf","text":"Report","size":"2.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5095"}],"contact":"National Water-Quality Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
PDEPTH is an interactive, graphical computer program used to construct interpreted geological source models for observed potential-field geophysical profile data. The current version of PDEPTH has been adapted to the Windows platform from an earlier DOS-based version. The input total-field magnetic anomaly and vertical gravity anomaly profiles can be filtered to produce derivative products such as reduced-to-pole magnetic profiles, pseudogravity profiles, pseudomagnetic profiles, and upward-or-downward-continued profiles. A variety of source-location methods can be applied to the original and filtered profiles to estimate (and display on a cross section) the locations and physical properties of contacts, sheet edges, horizontal line sources, point sources, and interface surfaces. Two-and-a-half-dimensional source bodies having polygonal cross sections can be constructed using a mouse and keyboard. These bodies can then be adjusted until the calculated gravity and magnetic fields of the source bodies are close to the observed profiles. Auxiliary information such as the topographic surface, bathymetric surface, seismic basement, and geologic contact locations can be displayed on the cross section using optional input files. Test data files, used to demonstrate the source location methods in the report, and several utility programs are included.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C: Computer programs in Book 7: Automated data processing and computations","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C17","collaboration":"Prepared in cooperation with the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office under Interagency Agreement number IAG DE-AI52-12NA30865/DE-NA0001654. The USGS does not provide technical support for the software associated with this publication.","usgsCitation":"Phillips, J.D., 2018, PDEPTH—A computer program for the geophysical interpretation of magnetic and gravity profiles through Fourier filtering, source-depth analysis, and forward modeling: U.S. Geological Survey Techniques and Methods, book 7, chap. C17, 23 p., https://doi.org/10.3133/tm7C17.","productDescription":"viii, 23 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-032003","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":425658,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/tm/07/c17/readme.txt","text":"Read Me","size":"4.00 KB","linkFileType":{"id":2,"text":"txt"}},{"id":425657,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c17/PDepthDataFiles.zip","size":"2.21 MB","linkFileType":{"id":6,"text":"zip"}},{"id":350415,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c17/coverthb.jpg"},{"id":350416,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c17/tm7c17.pdf","text":"Report","size":"776 kB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 7-C17"}],"publicComments":"This report is Chapter 17 of Section C: Computer programs in Book 7: Automated data processing and computations.","contact":"Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS 973
Denver, CO 80225
Impact crater populations crucially help us to understand solar system dynamics, planetary surface histories, and surface modification processes. A single previous effort to standardize how crater data are displayed in graphs, tables, and archives, was in a 1978 NASA report by the Crater Analysis Techniques Working Group, published in 1979 in Icarus. The report had a significant lasting effect, but later decades brought major advances in statistical and computer sciences while the crater field has remained fairly stagnant. In this new work, we revisit the fundamental techniques for displaying and analyzing crater population data and demonstrate better statistical methods that can be used. Specifically, we address (1) how crater size-frequency distributions (SFDs) are constructed, (2) how error bars are assigned to SFDs, and (3) how SFDs are fit to power laws and other models. We show how the new methods yield results similar to those of previous techniques in that the SFDs have familiar shapes but better account for multiple sources of uncertainty. We also recommend graphic, display, and archiving methods that reflect computers' capabilities and fulfill NASA's current requirements for Data Management Plans.
","language":"English","publisher":"The Meteoritical Society","doi":"10.1111/maps.12990","usgsCitation":"Robbins, S.J., Riggs, J.D., Weaver, B.P., Bierhaus, E.B., Chapman, C.R., Kirchoff, M.R., Singer, K.N., and Gaddis, L., 2018, Revised recommended methods for analyzing crater size-frequency distributions: Meteoritics and Planetary Science, v. 53, no. 4, p. 891-931, https://doi.org/10.1111/maps.12990.","productDescription":"41 p.","startPage":"891","endPage":"931","ipdsId":"IP-080708","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":469097,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/maps.12990","text":"Publisher Index Page"},{"id":355669,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-10","publicationStatus":"PW","scienceBaseUri":"5b6fc4bbe4b0f5d57878eac6","contributors":{"authors":[{"text":"Robbins, Stuart J.","contributorId":204229,"corporation":false,"usgs":false,"family":"Robbins","given":"Stuart","email":"","middleInitial":"J.","affiliations":[{"id":36712,"text":"Southwest Research Institute","active":true,"usgs":false}],"preferred":false,"id":739951,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Riggs, Jamie D.","contributorId":204236,"corporation":false,"usgs":false,"family":"Riggs","given":"Jamie","email":"","middleInitial":"D.","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":739952,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weaver, Brian P.","contributorId":204237,"corporation":false,"usgs":false,"family":"Weaver","given":"Brian","email":"","middleInitial":"P.","affiliations":[{"id":36891,"text":"Statistical Sciences, CCS-6, Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":739953,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bierhaus, Edward B.","contributorId":206277,"corporation":false,"usgs":false,"family":"Bierhaus","given":"Edward","email":"","middleInitial":"B.","affiliations":[{"id":37297,"text":"Lockheed Martin Space Systems Company","active":true,"usgs":false}],"preferred":false,"id":739954,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chapman, Clark R.","contributorId":206278,"corporation":false,"usgs":false,"family":"Chapman","given":"Clark","email":"","middleInitial":"R.","affiliations":[{"id":37298,"text":"Southwest Research Institute, Boulder, CO","active":true,"usgs":false}],"preferred":false,"id":739955,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kirchoff, Michelle R.","contributorId":206279,"corporation":false,"usgs":false,"family":"Kirchoff","given":"Michelle","email":"","middleInitial":"R.","affiliations":[{"id":37298,"text":"Southwest Research Institute, Boulder, CO","active":true,"usgs":false}],"preferred":false,"id":739956,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Singer, Kelsi N.","contributorId":196151,"corporation":false,"usgs":false,"family":"Singer","given":"Kelsi","email":"","middleInitial":"N.","affiliations":[{"id":7037,"text":"Southwest Research Institute, Boulder, Colorado","active":true,"usgs":false}],"preferred":false,"id":739957,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gaddis, Lisa 0000-0001-9953-5483 lgaddis@usgs.gov","orcid":"https://orcid.org/0000-0001-9953-5483","contributorId":206276,"corporation":false,"usgs":true,"family":"Gaddis","given":"Lisa","email":"lgaddis@usgs.gov","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":739950,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ]}