The U.S. Geological Survey (USGS) researches Earth science to help address complex issues affecting society and the environment. In 2006, the USGS held the first Scientific Information Management Workshop to bring together staff from across the organization to discuss the data and information management issues affecting the integration and delivery of Earth science research and investigate the use of “communities of practice” as mechanisms to share expertise about these issues. Out of this effort emerged the Council for Data Integration, which was conceived as an official organizational function that would help guide data integration activities and formalize communities of practice into working groups; however, by 2009 it became evident that many members of the Council for Data Integration had an interest in developing data integration solutions and sharing expertise in a less formal, grassroots manner, which transformed the Council into a Community for Data Integration (CDI). As of 2014, the CDI represents a dynamic community of practice focused on advancing science data and information management and integration capabilities across the USGS and the CDI community.
\nThe CDI fosters an environment for collaboration and sharing by bringing together expertise from external partners and representatives across the USGS who are involved in research, data management, and information technology. Membership is voluntary and open to USGS employees and other individuals and organizations willing to contribute to the community (if interested, contact cdi@usgs.gov). The purpose of the CDI is to do the following:
• advance understanding of Earth systems through enhanced use of data and information including associated tools and techniques,
• provide a forum for people doing work with data integration to come together to share ideas and learn new skills and techniques, and
• grow overall USGS capabilities with data and information by increasing visibility of the work of many people throughout the USGS and the CDI community.
To achieve these goals, the CDI operates within four applied areas: monthly forums, annual workshop/webinar series, working groups, and projects. The monthly forums, also known as the Opportunity/Challenge of the Month, provide an open dialogue to share and learn about data integration efforts or to present problems that invite the community to offer solutions, advice, and support. Since 2010, the CDI has also sponsored annual workshops/webinar series to encourage the exchange of ideas, sharing of activities, presentations of current projects, and networking among members. Stemming from common interests, the working groups are focused on efforts to address data management and technical challenges including the development of standards and tools, improving interoperability and information infrastructure, and data preservation within USGS and its partners. The growing support for the activities of the working groups led to the CDI’s first formal request for proposals (RFP) process in 2013 to fund projects that produced tangible products. As of 2014, the CDI continues to hold an annual RFP that creates data management tools and practices, collaboration tools, and training in support of data integration and delivery.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151184","usgsCitation":"Langseth, M.L., Chang, M.Y., Carlino, Jennifer, Birch, D.D., Bradley, Joshua, Bristol, R.S., Conzelmann, Craig, Diehl, R.H., Earle, Paul, Ellison, L.E., Everette, A.L., Fuller, Pam, Gordon, J.M., Govoni, D.L., Guy, M.R., Henkel, H.S., Hutchison, V.B., Kern, Tim, Lightsom, F.L., Long, J.W., Longhenry, Ryan, Preston, T.M., Smith, Stan, Viger, R.J., Wesenberg, Katherine, and Wood, Eric, 2015, Community for Data Integration 2014 annual report: U.S. Geological Survey Open-File Report 2015–1184, 40 p., https://dx.doi.org/10.3133/ofr20151184.","productDescription":"vi, 40 p.","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-066330","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":309412,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1184/ofr20151184.pdf","text":"Report","size":"7.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1184"},{"id":309411,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1184/coverthb.jpg"}],"contact":"Director, Core Science Analytics and Synthesis
U.S. Geological Survey
108 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
http://www.usgs.gov/core_science_systems/
The U.S. Geological Survey (USGS) conducts baseline and storm response photography missions to document and understand the changes in vulnerability of the Nation's coasts to extreme storms (Morgan, 2009). On October 5-6, 2014, the USGS conducted an oblique aerial photographic survey from the Virginia/North Carolina border to Montauk Point, New York, aboard a Cessna 182 at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was flown to collect baseline data to assess incremental changes since the last survey, flown in November 2012, and the data can be used in the assessment of future coastal change.
\nThe images provided in this report are Joint Photographic Experts Group (JPEG) images. ExifTool was used to add the following to the header of each photo: time of collection, Global Positioning System (GPS) latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. The photograph locations are an estimate of the position of the aircraft and do not indicate the location of any feature in the images (see the Navigation Data page). These photographs document the state of the barrier islands and other coastal features at the time of the survey. Pages containing thumbnail images of the photographs, referred to as contact sheets, were created in five-minute segments of flight time. These segments can be found on the Photos and Maps page. Photographs can be opened directly with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet.
\nIn addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML files were created using the photographic navigation files.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds958","usgsCitation":"Morgan, K.L.M., 2015, Baseline coastal oblique aerial photographs collected from the Virginia/North Carolina Border to Montauk Point, New York,St. Petersburg Coastal and Marine Science Center
600 4th Street South
St. Petersburg, FL 33701
(727) 502-8000
http://coastal.er.usgs.gov/
Crop biomass is increasingly being measured with surface reflectance data derived from multispectral broadband (MSBB) and hyperspectral narrowband (HNB) space-borne remotely sensed data to increase the accuracy and efficiency of crop yield models used in a wide array of agricultural applications. However, few studies compare the ability of MSBBs versus HNBs to capture crop biomass variability. Therefore, we used standard data mining techniques to identify a set of MSBB data from the IKONOS, GeoEye-1, Landsat ETM+, MODIS, WorldView-2 sensors and compared their performance with HNB data from the EO-1 Hyperion sensor in explaining crop biomass variability of four important field crops (rice, alfalfa, cotton, maize). The analysis employed two-band (ratio) vegetation indices (TBVIs) and multiband (additive) vegetation indices (MBVIs) derived from Singular Value Decomposition (SVD) and stepwise regression. Results demonstrated that HNB-derived TBVIs and MBVIs performed better than MSBB-derived TBVIs and MBVIs on a per crop basis and for the pooled data: overall, HNB TBVIs explained 5–31% greater variability when compared with various MSBB TBVIs; and HNB MBVIs explained 3–33% greater variability when compared with various MSBB MBVIs. The performance of MSBB MBVIs and TBVIs improved mildly, by combining spectral information across multiple sensors involving IKONOS, GeoEye-1, Landsat ETM+, MODIS, and WorldView-2. A number of HNBs that advance crop biomass modeling were determined. Based on the highest factor loadings on the first component of the SVD, the “red-edge” spectral range (700–740 nm) centered at 722 nm (bandwidth = 10 nm) stood out prominently, while five additional and distinct portions of the recorded spectral range (400–2500 nm) centered at 539 nm, 758 nm, 914 nm, 1130 nm, 1320 nm (bandwidth = 10 nm) were also important. The best HNB vegetation indices for crop biomass estimation involved 549 and 752 nm for rice (R2 = 0.91); 925 and 1104 nm for alfalfa (R2 = 0.81); 722 and 732 nm for cotton (R2 = 0.97); and 529 and 895 nm for maize (R2 = 0.94). The higher spectral resolution of the EO-1 Hyperion hyperspectral sensor and the ability of users to choose distinct HNBs for improved crop biomass estimation outweigh the benefits that come with higher spatial resolution of MSBBs.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.isprsjprs.2015.08.001","usgsCitation":"Marshall, M.T., and Thenkabail, P.S., 2015, Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation: ISPRS Journal of Photogrammetry and Remote Sensing, v. 108, p. 205-218, https://doi.org/10.1016/j.isprsjprs.2015.08.001.","productDescription":"14 p.","startPage":"205","endPage":"218","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060745","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471737,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2015.08.001","text":"Publisher Index Page"},{"id":313981,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.05810546875,\n 40.730608477796636\n ],\n [\n -122.82714843749999,\n 40.3130432088809\n ],\n [\n -122.56347656249999,\n 39.90973623453719\n ],\n [\n -122.62939453125001,\n 39.38526381099774\n ],\n [\n -122.23388671874999,\n 38.496593518947556\n ],\n [\n -121.70654296874999,\n 37.78808138412046\n ],\n [\n -121.17919921875001,\n 37.43997405227057\n ],\n [\n -121.00341796874999,\n 36.96744946416934\n ],\n [\n -120.60791015625,\n 36.4566360115962\n ],\n [\n -120.16845703125,\n 36.01356058518153\n ],\n [\n -119.7509765625,\n 35.35321610123821\n ],\n [\n -119.39941406249999,\n 34.95799531086792\n ],\n [\n -118.93798828125,\n 34.97600151317591\n ],\n [\n -118.63037109375,\n 35.15584570226544\n ],\n [\n -118.63037109375,\n 35.764343479667176\n ],\n [\n -118.89404296875,\n 36.24427318493909\n ],\n [\n -119.39941406249999,\n 36.84446074079564\n ],\n [\n -119.88281249999999,\n 37.23032838760387\n ],\n [\n -120.41015624999999,\n 37.80544394934274\n ],\n [\n -120.84960937499999,\n 38.34165619279595\n ],\n [\n -121.28906250000001,\n 38.89103282648849\n ],\n [\n -121.61865234375,\n 39.52099229357195\n ],\n [\n -121.9921875,\n 39.9434364619742\n ],\n [\n -122.05810546875,\n 40.730608477796636\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"108","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"568e48dee4b0e7a44bc4186b","contributors":{"authors":[{"text":"Marshall, Michael T. mmarshall@usgs.gov","contributorId":5480,"corporation":false,"usgs":true,"family":"Marshall","given":"Michael","email":"mmarshall@usgs.gov","middleInitial":"T.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":581537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":581536,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70156840,"text":"sir20155121 - 2015 - Groundwater-level and storage-volume changes in the Equus Beds aquifer near Wichita, Kansas, predevelopment through January 2015","interactions":[],"lastModifiedDate":"2015-10-01T10:16:44","indexId":"sir20155121","displayToPublicDate":"2015-10-01T10:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5121","title":"Groundwater-level and storage-volume changes in the Equus Beds aquifer near Wichita, Kansas, predevelopment through January 2015","docAbstract":"Development of the Wichita well field began in the 1940s in the Equus Beds aquifer to provide the city of Wichita, Kansas, a new water-supply source. After development of the Wichita well field began, groundwater levels began to decline. Extensive development of irrigation wells that began in the 1970s also contributed to substantial groundwater-level declines. Groundwater-level declines likely enhance movement of brine from past oil and gas production near Burrton, Kansas, and natural saline water from the Arkansas River into the Wichita well field. Groundwater levels reached a historical minimum in 1993 because of drought conditions, irrigation, and the city of Wichita’s withdrawals from the aquifer. In 1993, the city of Wichita adopted the Integrated Local Water Supply Program to ensure that Wichita’s water needs would be met through the year 2050 and beyond as part of its efforts to manage the part of the Equus Beds aquifer Wichita uses. A key component of the Integrated Local Water Supply Program was the Equus Beds Aquifer Storage and Recovery project. The Aquifer Storage and Recovery project’s goal is to store and eventually recover groundwater and help protect the Equus Beds aquifer from oil-field brine water near Burrton, Kansas, and saline water from the Arkansas River. Since 1940, the U.S. Geological Survey has monitored groundwater levels and storage-volume changes in the Equus Beds aquifer to provide data to the city of Wichita in order to better manage its water supply.
\nGroundwater mostly flowed from west to east in the shallow and deep parts of the Equus Beds aquifer in January 2015. A large area of declines greater than 10 feet in the shallow part of the Equus Beds aquifer from predevelopment (before substantial pumpage began in the area in September 1940) to January 2015 covered most of the central part of the study area, where the city of Wichita well field is located, and extended beyond it. Groundwater-level rises of greater than 10 feet from 1993 (the historical minimum groundwater levels) to January 2015 covered most of the central part of the study area in the shallow and deep parts of the Equus Beds aquifer; rises of greater than 20 feet mostly were within the north-central part of the study area. The 1993 to January 2015 recovery of storage volume previously lost from predevelopment to 1993 was about 46 percent (55,200 acre-feet) for the central part of the study area and the percentage recovery was larger than the 31 percent (59,800 acre-feet) recovery for the entire study area. Groundwater-level rises and the larger percentage recovery of storage volume in the central part of the study area was most likely a result of the city of Wichita adopting the Integrated Local Water Supply Program strategy which reduced Wichita’s pumpage from the Equus Beds aquifer in 2014 to the smallest amount since 1940. January 2015 storage volumes were about 96 percent (3,057,000 acre-feet) and 94 percent (960,000 acre-feet) of total aquifer storage for the study area and the central part of the study area, respectively.
\nGroundwater levels from January 2014 to January 2015 in the central part of the study area rose about 3 feet in some places, probably because Wichita reduced its withdrawals from the aquifer in 2014 by more than 50 percent. Groundwater levels probably recovered less than anticipated because of decreased recharge and net groundwater flow and increased agricultural pumpage. A volumetric water budget for the central part of the study area between 2013 and 2014 showed that the substantial decrease in total pumping (10,412 acre-feet) did not result in an increase in storage volume because it was more than offset by decreased recharge (6,502 acre-feet; artificial and from precipitation) and an even greater decrease in net groundwater flow (11,710 acre-feet).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155121","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Whisnant, J.A., Hansen, C.V., and Eslick, P.J., 2015, Groundwater-level and storage-volume changes in the Equus Beds Aquifer near Wichita, Kansas, predevelopment through January 2015: U.S. Geological Survey Scientific Investigations Report 2015–5121, 27 p., https://dx.doi.org/10.3133/sir20155121.","productDescription":"Report: vi, 27 p.; Appendix","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-067226","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":308476,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5121/sir20155121Apptable1.xlsx","text":"Appendix 1 Table 1–1","size":"97.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"Appendix 1"},{"id":308474,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5121/coverthb.jpg"},{"id":308475,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5121/sir20155121.pdf","text":"Report","size":"8.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5121"}],"country":"United States","state":"Kansas","city":"Wichita","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.72476196289062,\n 38.120512892298976\n ],\n [\n -97.591552734375,\n 38.12591462924157\n ],\n [\n -97.5531005859375,\n 38.06106741381199\n ],\n [\n -97.525634765625,\n 38.01888587738773\n ],\n [\n -97.48306274414062,\n 37.98533963422242\n ],\n [\n -97.44255065917967,\n 37.934991500488344\n ],\n [\n -97.43431091308592,\n 37.91603433975963\n ],\n [\n -97.437744140625,\n 37.90411590881245\n ],\n [\n -97.43019104003906,\n 37.89273742374153\n ],\n [\n -97.415771484375,\n 37.88189913601145\n ],\n [\n -97.40959167480469,\n 37.86347038587407\n ],\n [\n -97.39860534667969,\n 37.84557924966551\n ],\n [\n -97.3828125,\n 37.8271414168374\n ],\n [\n -97.38143920898438,\n 37.81737834565083\n ],\n [\n -97.45834350585936,\n 37.819548028632376\n ],\n [\n -97.47894287109375,\n 37.82822612280363\n ],\n [\n -97.50160217285156,\n 37.82497195707114\n ],\n [\n -97.51739501953125,\n 37.83636090929915\n ],\n [\n -97.52494812011719,\n 37.842868092687425\n ],\n [\n -97.54829406738281,\n 37.846663684549156\n ],\n [\n -97.5592803955078,\n 37.86401247373357\n ],\n [\n -97.5860595703125,\n 37.86292829402713\n ],\n [\n -97.61352539062499,\n 37.87973128703159\n ],\n [\n -97.6409912109375,\n 37.88189913601145\n ],\n [\n -97.66639709472656,\n 37.89219554724437\n ],\n [\n -97.69386291503906,\n 37.899781455245545\n ],\n [\n -97.71720886230469,\n 37.91603433975963\n ],\n [\n -97.72476196289062,\n 38.120512892298976\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
http://ks.water.usgs.gov
The Bisoni-McKay area, a structurally isolated, fault-bounded horst, offset eastward at the south end of the Fish Creek Range, displays a geologic terrane that is previously unrecorded in Nevada, and perhaps elsewhere in North America. This unique terrane is an olistostrome that was shed eastward by listric faulting from the east side of the migrating Antler orogenic forebulge in Late Devonian (early Famennian, ca. 373 Ma) time. Stratigraphic identification of Devonian olistoliths and enclosing matrix that constitute the olistostrome, as well as overlying postemplacement units, is supported by correlation to formations in the main part of the Fish Creek Range and to the northwest in the northern Antelope Range. Precise zonal dating of map units and revised dating of Antler orogenic events are provided by 38 conodont collections recorded in the Devonian/Carboniferous (D/C) Conodont Database and by small collections of conodonts embedded in siltstone and mudstone. Our revision of regional geologic history uses Devonian conodont zones to measure “deep time” to circa millions of years before present.
The upper Upper Devonian (Famennian) tongue of the Woodruff Formation was deposited directly on the olistostrome and is overlain by clastic Mississippian synorogenic deposits. These deposits were shed eastward from the evolving Antler highland and related Roberts Mountains allochthon into the Antler foredeep.
We propose the following revised dates for important Devonian tectonic events in Nevada: initiation of Antler orogeny, ca. 385 Ma; downwarping of Pilot backbulge basin, ca. 382 Ma; initial uplift of the Antler highland, ca. 373 Ma; third, major pulse of highland uplift, ca. 364 Ma. A summation of regional geologic history indicates that the elapsed time from start of Antler orogeny to start of Roberts Mountains thrusting was ~30 m.y.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Unusual central Nevada geologic terranes produced by Late Devonian antler orogeny and Alamo impact","largerWorkSubtype":{"id":15,"text":"Monograph"},"doi":"10.1130/2015.2517(01)","usgsCitation":"Poole, F.G., and Sandberg, C., 2015, Olistostrome shed eastward from the Antler orogenic forebulge, Bisoni-McKay area, Fish Creek Range, central Nevada, chap. of Unusual central Nevada geologic terranes produced by Late Devonian antler orogeny and Alamo impact, p. 1-38, https://doi.org/10.1130/2015.2517(01).","productDescription":"38 p.","startPage":"1","endPage":"38","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":394761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Poole, Forrest G. 0000-0001-8487-0799 bpoole@usgs.gov","orcid":"https://orcid.org/0000-0001-8487-0799","contributorId":1543,"corporation":false,"usgs":true,"family":"Poole","given":"Forrest","email":"bpoole@usgs.gov","middleInitial":"G.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":831540,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sandberg, Charles sandberg@usgs.gov","contributorId":199124,"corporation":false,"usgs":true,"family":"Sandberg","given":"Charles","email":"sandberg@usgs.gov","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":831541,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188043,"text":"70188043 - 2015 - Characterization of shrubland ecosystem components as continuous fields in the northwest United States","interactions":[],"lastModifiedDate":"2018-03-08T13:04:23","indexId":"70188043","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Characterization of shrubland ecosystem components as continuous fields in the northwest United States","docAbstract":"Accurate and consistent estimates of shrubland ecosystem components are crucial to a better understanding of ecosystem conditions in arid and semiarid lands. An innovative approach was developed by integrating multiple sources of information to quantify shrubland components as continuous field products within the National Land Cover Database (NLCD). The approach consists of several procedures including field sample collections, high-resolution mapping of shrubland components using WorldView-2 imagery and regression tree models, Landsat 8 radiometric balancing and phenological mosaicking, medium resolution estimates of shrubland components following different climate zones using Landsat 8 phenological mosaics and regression tree models, and product validation. Fractional covers of nine shrubland components were estimated: annual herbaceous, bare ground, big sagebrush, herbaceous, litter, sagebrush, shrub, sagebrush height, and shrub height. Our study area included the footprint of six Landsat 8 scenes in the northwestern United States. Results show that most components have relatively significant correlations with validation data, have small normalized root mean square errors, and correspond well with expected ecological gradients. While some uncertainties remain with height estimates, the model formulated in this study provides a cross-validated, unbiased, and cost effective approach to quantify shrubland components at a regional scale and advances knowledge of horizontal and vertical variability of these components.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2015.07.014","usgsCitation":"Xian, G.Z., Homer, C.G., Rigge, M.B., Shi, H., and Meyer, D., 2015, Characterization of shrubland ecosystem components as continuous fields in the northwest United States: Remote Sensing of Environment, v. 168, p. 286-300, https://doi.org/10.1016/j.rse.2015.07.014.","productDescription":"15 p.","startPage":"286","endPage":"300","ipdsId":"IP-061128","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":471744,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2015.07.014","text":"Publisher Index Page"},{"id":341880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122,\n 39\n ],\n [\n -116,\n 39\n ],\n [\n -116,\n 44\n ],\n [\n -122,\n 44\n ],\n [\n -122,\n 39\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"168","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592e84bbe4b092b266f10d3f","contributors":{"authors":[{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":696303,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696304,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696305,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696306,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meyer, Debbie 0000-0002-8841-697X debbie.meyer.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":192028,"corporation":false,"usgs":true,"family":"Meyer","given":"Debbie","email":"debbie.meyer.ctr@usgs.gov","affiliations":[],"preferred":false,"id":696307,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70184225,"text":"70184225 - 2015 - Landsat-8: Status and on-orbit performance","interactions":[],"lastModifiedDate":"2017-05-31T16:19:11","indexId":"70184225","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Landsat-8: Status and on-orbit performance","docAbstract":"Landsat 8 and its two Earth imaging sensors, the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) have been operating on-orbit for 2 ½ years. Landsat 8 has been acquiring substantially more images than initially planned, typically around 700 scenes per day versus a 400 scenes per day requirement, acquiring nearly all land scenes. Both the TIRS and OLI instruments are exceeding their SNR requirements by at least a factor of 2 and are very stable, degrading by at most 1% in responsivity over the mission to date. Both instruments have 100% operable detectors covering their cross track field of view using the redundant detectors as necessary. The geometric performance is excellent, meeting or exceeding all performance requirements. One anomaly occurred with the TIRS Scene Select Mirror (SSM) encoder that affected its operation, though by switching to the side B electronics, this was fully recovered. The one challenge is with the TIRS stray light, which affects the flat fielding and absolute calibration of the TIRS data. The error introduced is smaller in TIRS band 10. Band 11 should not currently be used in science applications.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proc. SPIE 9639, Sensors, Systems, and Next-Generation Satellites XIX","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Sensors, Systems, and Next-Generation Satellites XIX","conferenceDate":"September 21, 2015","conferenceLocation":"Toulouse, France","language":"English","publisher":"SPIE","doi":"10.1117/12.2194905","usgsCitation":"Markham, B.L., Barsi, J.A., Morfitt, R., Choate, M., Montanaro, M., Arvidson, T., and Irons, J.R., 2015, Landsat-8: Status and on-orbit performance, in Proc. SPIE 9639, Sensors, Systems, and Next-Generation Satellites XIX, v. 9639, Toulouse, France, September 21, 2015, 963908, https://doi.org/10.1117/12.2194905.","productDescription":"963908","ipdsId":"IP-068625","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":337714,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9639","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58cba41ce4b0849ce97dc754","contributors":{"authors":[{"text":"Markham, Brian L. 0000-0002-9612-8169","orcid":"https://orcid.org/0000-0002-9612-8169","contributorId":121488,"corporation":false,"usgs":true,"family":"Markham","given":"Brian","email":"","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":680629,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barsi, Julia A.","contributorId":71822,"corporation":false,"usgs":false,"family":"Barsi","given":"Julia","email":"","middleInitial":"A.","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":680630,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morfitt, Ron 0000-0002-4777-4877 rmorfitt@usgs.gov","orcid":"https://orcid.org/0000-0002-4777-4877","contributorId":4097,"corporation":false,"usgs":true,"family":"Morfitt","given":"Ron","email":"rmorfitt@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":680628,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choate, Mike 0000-0002-8101-4994 choate@usgs.gov","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":4618,"corporation":false,"usgs":true,"family":"Choate","given":"Mike","email":"choate@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":684707,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Montanaro, Matthew","contributorId":147004,"corporation":false,"usgs":false,"family":"Montanaro","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":680631,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Arvidson, Terry","contributorId":97801,"corporation":false,"usgs":true,"family":"Arvidson","given":"Terry","email":"","affiliations":[],"preferred":false,"id":684708,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Irons, James R.","contributorId":59284,"corporation":false,"usgs":false,"family":"Irons","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":684709,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70182720,"text":"70182720 - 2015 - Ionospheric current source modeling and global geomagnetic induction using ground geomagnetic observatory data","interactions":[],"lastModifiedDate":"2018-01-19T13:59:31","indexId":"70182720","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Ionospheric current source modeling and global geomagnetic induction using ground geomagnetic observatory data","docAbstract":"Long-period global-scale electromagnetic induction studies of deep Earth conductivity are based almost exclusively on magnetovariational methods and require accurate models of external source spatial structure. We describe approaches to inverting for both the external sources and three-dimensional (3-D) conductivity variations and apply these methods to long-period (T≥1.2 days) geomagnetic observatory data. Our scheme involves three steps: (1) Observatory data from 60 years (only partly overlapping and with many large gaps) are reduced and merged into dominant spatial modes using a scheme based on frequency domain principal components. (2) Resulting modes are inverted for corresponding external source spatial structure, using a simplified conductivity model with radial variations overlain by a two-dimensional thin sheet. The source inversion is regularized using a physically based source covariance, generated through superposition of correlated tilted zonal (quasi-dipole) current loops, representing ionospheric source complexity smoothed by Earth rotation. Free parameters in the source covariance model are tuned by a leave-one-out cross-validation scheme. (3) The estimated data modes are inverted for 3-D Earth conductivity, assuming the source excitation estimated in step 2. Together, these developments constitute key components in a practical scheme for simultaneous inversion of the catalogue of historical and modern observatory data for external source spatial structure and 3-D Earth conductivity.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015JB012063","usgsCitation":"Sun, J., Kelbert, A., and Egbert, G.D., 2015, Ionospheric current source modeling and global geomagnetic induction using ground geomagnetic observatory data: Journal of Geophysical Research, v. 120, no. 10, p. 6771-6796, https://doi.org/10.1002/2015JB012063.","productDescription":"26 p. ","startPage":"6771","endPage":"6796","ipdsId":"IP-068256","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":471750,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jb012063","text":"Publisher Index Page"},{"id":336288,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"120","issue":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-25","publicationStatus":"PW","scienceBaseUri":"58b548c2e4b01ccd54fddfc6","contributors":{"authors":[{"text":"Sun, Jin","contributorId":11084,"corporation":false,"usgs":false,"family":"Sun","given":"Jin","email":"","affiliations":[{"id":32881,"text":"ETH Zurich, Zurich, Switzerland","active":true,"usgs":false},{"id":6702,"text":"College of Earth, Ocean and Atmospheric Sciences, Oregon State University","active":true,"usgs":false}],"preferred":false,"id":673447,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":673448,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Egbert, G. D.","contributorId":184054,"corporation":false,"usgs":false,"family":"Egbert","given":"G.","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":673449,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192770,"text":"70192770 - 2015 - Using time series structural characteristics to analyze grain prices in food insecure countries","interactions":[],"lastModifiedDate":"2017-10-30T15:10:22","indexId":"70192770","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1683,"text":"Food Security","active":true,"publicationSubtype":{"id":10}},"title":"Using time series structural characteristics to analyze grain prices in food insecure countries","docAbstract":"Two components of food security monitoring are accurate forecasts of local grain prices and the ability to identify unusual price behavior. We evaluated a method that can both facilitate forecasts of cross-country grain price data and identify dissimilarities in price behavior across multiple markets. This method, characteristic based clustering (CBC), identifies similarities in multiple time series based on structural characteristics in the data. Here, we conducted a simulation experiment to determine if CBC can be used to improve the accuracy of maize price forecasts. We then compared forecast accuracies among clustered and non-clustered price series over a rolling time horizon. We found that the accuracy of forecasts on clusters of time series were equal to or worse than forecasts based on individual time series. However, in the following experiment we found that CBC was still useful for price analysis. We used the clusters to explore the similarity of price behavior among Kenyan maize markets. We found that price behavior in the isolated markets of Mandera and Marsabit has become increasingly dissimilar from markets in other Kenyan cities, and that these dissimilarities could not be explained solely by geographic distance. The structural isolation of Mandera and Marsabit that we find in this paper is supported by field studies on food security and market integration in Kenya. Our results suggest that a market with a unique price series (as measured by structural characteristics that differ from neighboring markets) may lack market integration and food security.
","language":"English","publisher":"Springer","doi":"10.1007/s12571-015-0490-5","usgsCitation":"Davenport, F., and Funk, C., 2015, Using time series structural characteristics to analyze grain prices in food insecure countries: Food Security, v. 7, no. 5, p. 1055-1070, https://doi.org/10.1007/s12571-015-0490-5.","productDescription":"16 p.","startPage":"1055","endPage":"1070","ipdsId":"IP-056081","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":471745,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/10.1007/s12571-015-0490-5","text":"External Repository"},{"id":347732,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"5","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-10","publicationStatus":"PW","scienceBaseUri":"59f83a3ee4b063d5d3098116","contributors":{"authors":[{"text":"Davenport, Frank","contributorId":145816,"corporation":false,"usgs":false,"family":"Davenport","given":"Frank","email":"","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":716872,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":716871,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70187288,"text":"70187288 - 2015 - Dynamics of a recovering Arctic bird population: the importance of climate, density dependence, and site quality","interactions":[],"lastModifiedDate":"2017-04-27T17:03:56","indexId":"70187288","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Dynamics of a recovering Arctic bird population: the importance of climate, density dependence, and site quality","docAbstract":"Intrinsic and extrinsic factors affect vital rates and population-level processes, and understanding these factors is paramount to devising successful management plans for wildlife species. For example, birds time migration in response, in part, to local and broadscale climate fluctuations to initiate breeding upon arrival to nesting territories, and prolonged inclement weather early in the breeding season can inhibit egg-laying and reduce productivity. Also, density-dependent regulation occurs in raptor populations, as territory size is related to resource availability. Arctic Peregrine Falcons (Falco peregrinus tundrius; hereafter Arctic peregrine) have a limited and northern breeding distribution, including the Colville River Special Area (CRSA) in the National Petroleum Reserve–Alaska, USA. We quantified influences of climate, topography, nest productivity, prey habitat, density dependence, and interspecific competition affecting Arctic peregrines in the CRSA by applying the Dail-Madsen model to estimate abundance and vital rates of adults on nesting cliffs from 1981 through 2002. Arctic peregrine abundance increased throughout the 1980s, which spanned the population's recovery from DDT-induced reproductive failure, until exhibiting a stationary trend in the 1990s. Apparent survival rate (i.e., emigration; death) was negatively correlated with the number of adult Arctic peregrines on the cliff the previous year, suggesting effects of density-dependent population regulation. Apparent survival and arrival rates (i.e., immigration; recruitment) were higher during years with earlier snowmelt and milder winters, and apparent survival was positively correlated with nesting season maximum daily temperature. Arrival rate was positively correlated with average Arctic peregrine productivity along a cliff segment from the previous year and initial abundance was positively correlated with cliff height. Higher cliffs with documented higher productivity (presumably indicative of higher-quality habitat), are a priority for continued protection from potential nearby development and disturbance to minimize population-level impacts. Climate change may affect Arctic peregrines in multiple ways, including through access to more snow-free nest sites and a lengthened breeding season that may increase likelihood of nest success. Our work provides insight into factors affecting a population during and after recovery, and demonstrates how the Dail-Madsen model can be used for any unmarked population with multiple years of abundance data collected through repeated surveys.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/14-1591.1","usgsCitation":"Bruggeman, J.E., Swem, T., Andersen, D., Kennedy, P.L., and Nigro, D.A., 2015, Dynamics of a recovering Arctic bird population: the importance of climate, density dependence, and site quality: Ecological Applications, v. 25, no. 7, p. 1932-1943, https://doi.org/10.1890/14-1591.1.","productDescription":"12 p.","startPage":"1932","endPage":"1943","ipdsId":"IP-055304","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":340549,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.5107421875,\n 68.73638345287264\n ],\n [\n -149.94140625,\n 68.73638345287264\n ],\n [\n -149.94140625,\n 70.56149224990756\n ],\n [\n -158.5107421875,\n 70.56149224990756\n ],\n [\n -158.5107421875,\n 68.73638345287264\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59030327e4b0e862d230f735","contributors":{"authors":[{"text":"Bruggeman, Jason E.","contributorId":18983,"corporation":false,"usgs":false,"family":"Bruggeman","given":"Jason","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":693305,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Swem, Ted","contributorId":64463,"corporation":false,"usgs":true,"family":"Swem","given":"Ted","affiliations":[],"preferred":false,"id":693306,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":2168,"corporation":false,"usgs":true,"family":"Andersen","given":"David E.","email":"dea@usgs.gov","affiliations":[{"id":34539,"text":"Minnesota Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":693219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kennedy, Patricia L.","contributorId":172826,"corporation":false,"usgs":false,"family":"Kennedy","given":"Patricia","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":693307,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nigro, Debora A.","contributorId":10628,"corporation":false,"usgs":false,"family":"Nigro","given":"Debora","email":"","middleInitial":"A.","affiliations":[{"id":12934,"text":"Bureau of Land Management, Arctic Field Office","active":true,"usgs":false}],"preferred":false,"id":693308,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70194287,"text":"70194287 - 2015 - Reconstructing turbidity in a glacially influenced lake using the Landsat TM and ETM+ surface reflectance climate data record archive, Lake Clark, Alaska","interactions":[],"lastModifiedDate":"2017-11-21T16:37:41","indexId":"70194287","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Reconstructing turbidity in a glacially influenced lake using the Landsat TM and ETM+ surface reflectance climate data record archive, Lake Clark, Alaska","docAbstract":"Lake Clark is an important nursery lake for sockeye salmon (Oncorhynchus nerka) in the headwaters of Bristol Bay, Alaska, the most productive wild salmon fishery in the world. Reductions in water clarity within Alaska lake systems as a result of increased glacial runoff have been shown to reduce salmon production via reduced abundance of zooplankton and macroinvertebrates. In this study, we reconstruct long-term, lake-wide water clarity for Lake Clark using the Landsat TM and ETM+ surface reflectance products (1985–2014) and in situwater clarity data collected between 2009 and 2013. Analysis of a Landsat scene acquired in 2009, coincident with in situ measurements in the lake, and uncertainty analysis with four scenes acquired within two weeks of field data collection showed that Band 3 surface reflectance was the best indicator of turbidity (r2 = 0.55,RMSE << 0.01). We then processed 151 (98 partial- and 53 whole-lake) Landsat scenes using this relation and detected no significant long-term trend in mean turbidity for Lake Clark between 1991 and 2014. We did, however, detect interannual variation that exhibited a non-significant (r2 = 0.20) but positive correlation (r = 0.20) with regional mean summer air temperature and found the month of May exhibited a significant positive trend (r2 = 0.68, p = 0.02) in turbidity between 2000 and 2014. This study demonstrates the utility of hindcasting turbidity in a glacially influenced lake using the Landsat surface reflectance products. It may also help land and resource managers reconstruct turbidity records for lakes that lack in situ monitoring, and may be useful in predicting future water clarity conditions based on projected climate scenarios.
","language":"English","publisher":"MDPI","doi":"10.3390/rs71013692","usgsCitation":"Baughman, C., Jones, B.M., Bartz, K.K., Young, D.B., and Zimmerman, C.E., 2015, Reconstructing turbidity in a glacially influenced lake using the Landsat TM and ETM+ surface reflectance climate data record archive, Lake Clark, Alaska: Remote Sensing, v. 7, no. 10, p. 13692-13710, https://doi.org/10.3390/rs71013692.","productDescription":"19 p.","startPage":"13692","endPage":"13710","ipdsId":"IP-066580","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":471755,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs71013692","text":"Publisher Index Page"},{"id":349242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Lake Clark","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.84954833984375,\n 60.0113438097352\n ],\n [\n -153.57513427734375,\n 60.0113438097352\n ],\n [\n -153.57513427734375,\n 60.45992621736877\n ],\n [\n -154.84954833984375,\n 60.45992621736877\n ],\n [\n -154.84954833984375,\n 60.0113438097352\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"10","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-20","publicationStatus":"PW","scienceBaseUri":"5a60fe67e4b06e28e9c252f3","contributors":{"authors":[{"text":"Baughman, Carson 0000-0002-9423-9324 cbaughman@usgs.gov","orcid":"https://orcid.org/0000-0002-9423-9324","contributorId":169657,"corporation":false,"usgs":true,"family":"Baughman","given":"Carson","email":"cbaughman@usgs.gov","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":723094,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":723095,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bartz, Krista K.","contributorId":200705,"corporation":false,"usgs":false,"family":"Bartz","given":"Krista","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":723097,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Daniel","contributorId":58468,"corporation":false,"usgs":false,"family":"Young","given":"Daniel","affiliations":[{"id":35763,"text":"National Park Service, Lake Clark National Park and Preserve, Port Alsworth, AK","active":true,"usgs":false}],"preferred":false,"id":723098,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zimmerman, Christian E. 0000-0002-3646-0688 czimmerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3646-0688","contributorId":410,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Christian","email":"czimmerman@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":723096,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70159685,"text":"70159685 - 2015 - Maximum likelihood Bayesian model averaging and its predictive analysis for groundwater reactive transport models","interactions":[],"lastModifiedDate":"2015-11-17T17:00:58","indexId":"70159685","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Maximum likelihood Bayesian model averaging and its predictive analysis for groundwater reactive transport models","docAbstract":"While Bayesian model averaging (BMA) has been widely used in groundwater modeling, it is infrequently applied to groundwater reactive transport modeling because of multiple sources of uncertainty in the coupled hydrogeochemical processes and because of the long execution time of each model run. To resolve these problems, this study analyzed different levels of uncertainty in a hierarchical way, and used the maximum likelihood version of BMA, i.e., MLBMA, to improve the computational efficiency. This study demonstrates the applicability of MLBMA to groundwater reactive transport modeling in a synthetic case in which twenty-seven reactive transport models were designed to predict the reactive transport of hexavalent uranium (U(VI)) based on observations at a former uranium mill site near Naturita, CO. These reactive transport models contain three uncertain model components, i.e., parameterization of hydraulic conductivity, configuration of model boundary, and surface complexation reactions that simulate U(VI) adsorption. These uncertain model components were aggregated into the alternative models by integrating a hierarchical structure into MLBMA. The modeling results of the individual models and MLBMA were analyzed to investigate their predictive performance. The predictive logscore results show that MLBMA generally outperforms the best model, suggesting that using MLBMA is a sound strategy to achieve more robust model predictions relative to a single model. MLBMA works best when the alternative models are structurally distinct and have diverse model predictions. When correlation in model structure exists, two strategies were used to improve predictive performance by retaining structurally distinct models or assigning smaller prior model probabilities to correlated models. Since the synthetic models were designed using data from the Naturita site, the results of this study are expected to provide guidance for real-world modeling. Limitations of applying MLBMA to the synthetic study and future real-world modeling are discussed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2015.07.029","usgsCitation":"Curtis, G.P., Lu, D., and Ye, M., 2015, Maximum likelihood Bayesian model averaging and its predictive analysis for groundwater reactive transport models: Journal of Hydrology: Regional Studies, v. 529, p. 1859-1873, https://doi.org/10.1016/j.jhydrol.2015.07.029.","productDescription":"15 p.","startPage":"1859","endPage":"1873","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064715","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":471754,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1248433","text":"Publisher Index Page"},{"id":311451,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":311449,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S002216941500534X"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.82714843749999,\n 41.902277040963696\n ],\n [\n -121.904296875,\n 38.548165423046584\n ],\n [\n -118.740234375,\n 35.639441068973916\n ],\n [\n -116.3671875,\n 33.284619968887704\n ],\n [\n -116.4111328125,\n 32.62087018318113\n ],\n [\n -117.2900390625,\n 32.54681317351514\n ],\n [\n -118.21289062499999,\n 33.797408767572485\n ],\n [\n -120.14648437499999,\n 34.379712580462204\n ],\n [\n -120.7177734375,\n 34.45221847282654\n ],\n [\n -122.16796875,\n 36.4566360115962\n ],\n [\n -124.0576171875,\n 38.8225909761771\n ],\n [\n -124.71679687499999,\n 40.94671366508002\n ],\n [\n -124.49707031249999,\n 42.032974332441405\n ],\n [\n -122.78320312499999,\n 42.13082130188811\n ],\n [\n -122.82714843749999,\n 41.902277040963696\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"529","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"564c5dd9e4b0ebfbef0d3482","contributors":{"authors":[{"text":"Curtis, Gary P. 0000-0003-3975-8882 gpcurtis@usgs.gov","orcid":"https://orcid.org/0000-0003-3975-8882","contributorId":2346,"corporation":false,"usgs":true,"family":"Curtis","given":"Gary","email":"gpcurtis@usgs.gov","middleInitial":"P.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":580073,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lu, Dan","contributorId":58176,"corporation":false,"usgs":true,"family":"Lu","given":"Dan","affiliations":[],"preferred":false,"id":580074,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ye, Ming","contributorId":78670,"corporation":false,"usgs":true,"family":"Ye","given":"Ming","affiliations":[],"preferred":false,"id":580075,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70180992,"text":"70180992 - 2015 - Impact of wastewater infrastructure upgrades on the urban water cycle: Reduction in halogenated reaction byproducts following conversion from chlorine gas to ultraviolet light disinfection","interactions":[],"lastModifiedDate":"2018-09-12T16:57:07","indexId":"70180992","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Impact of wastewater infrastructure upgrades on the urban water cycle: Reduction in halogenated reaction byproducts following conversion from chlorine gas to ultraviolet light disinfection","docAbstract":"The municipal wastewater treatment facility (WWTF) infrastructure of the United States is being upgraded to expand capacity and improve treatment, which provides opportunities to assess the impact of full-scale operational changes on water quality. Many WWTFs disinfect their effluent prior to discharge using chlorine gas, which reacts with natural and synthetic organic matter to form halogenated disinfection byproducts (HDBPs). Because HDBPs are ubiquitous in chlorine-disinfected drinking water and have adverse human health implications, their concentrations are regulated in potable water supplies. Less is known about the formation and occurrence of HDBPs in disinfected WWTF effluents that are discharged to surface waters and become part of the de facto wastewater reuse cycle. This study investigated HDBPs in the urban water cycle from the stream source of the chlorinated municipal tap water that comprises the WWTF inflow, to the final WWTF effluent disinfection process before discharge back to the stream. The impact of conversion from chlorine-gas to low-pressure ultraviolet light (UV) disinfection at a full-scale (68,000 m3 d−1 design flow) WWTF on HDBP concentrations in the final effluent was assessed, as was transport and attenuation in the receiving stream. Nutrients and trace elements (boron, copper, and uranium) were used to characterize the different urban source waters, and indicated that the pre-upgrade and post-upgrade water chemistry was similar and insensitive to the disinfection process. Chlorinated tap water during the pre-upgrade and post-upgrade samplings contained 11 (mean total concentration = 2.7 μg L−1; n=5) and 10 HDBPs (mean total concentration = 4.5 μg L−1), respectively. Under chlorine-gas disinfection conditions 13 HDBPs (mean total concentration = 1.4 μg L−1) were detected in the WWTF effluent, whereas under UV disinfection conditions, only one HDBP was detected. The chlorinated WWTF effluent had greater relative proportions of nitrogenous, brominated, and iodinated HDBPs than the chlorinated tap water. Conversion of the WWTF to UV disinfection reduced the loading of HDBPs to the receiving stream by >90%.
Accurate estimates of demographic parameters are required to infer appropriate ecological relationships and inform management actions. Known-fate data from marked individuals are commonly used to estimate survival rates, whereas N-mixture models use count data from unmarked individuals to estimate multiple demographic parameters. However, a joint approach combining the strengths of both analytical tools has not been developed. Here we develop an integrated model combining known-fate and open N-mixture models, allowing the estimation of detection probability, recruitment, and the joint estimation of survival. We demonstrate our approach through both simulations and an applied example using four years of known-fate and pack count data for wolves (Canis lupus). Simulation results indicated that the integrated model reliably recovered parameters with no evidence of bias, and survival estimates were more precise under the joint model. Results from the applied example indicated that the marked sample of wolves was biased toward individuals with higher apparent survival rates than the unmarked pack mates, suggesting that joint estimates may be more representative of the overall population. Our integrated model is a practical approach for reducing bias while increasing precision and the amount of information gained from mark–resight data sets. We provide implementations in both the BUGS language and an R package.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/15-0385.1","usgsCitation":"Schmidt, J., Johnson, D.S., Lindberg, M.S., and Adams, L., 2015, Estimating demographic parameters using a combination of known-fate and open N-mixture models: Ecology, v. 96, no. 10, p. 2583-2589, https://doi.org/10.1890/15-0385.1.","productDescription":"7 p.","startPage":"2583","endPage":"2589","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063639","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":471741,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1890/15-0385.1","text":"External Repository"},{"id":319338,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Gates of the Arctic National Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.0283203125,\n 67.23806155909902\n ],\n [\n -154.0283203125,\n 68.2042121888185\n ],\n [\n -152.0068359375,\n 68.2042121888185\n ],\n [\n -152.0068359375,\n 67.23806155909902\n ],\n [\n -154.0283203125,\n 67.23806155909902\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"96","issue":"10","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56f50fc6e4b0f59b85e1eb47","contributors":{"authors":[{"text":"Schmidt, Joshua H.","contributorId":167772,"corporation":false,"usgs":false,"family":"Schmidt","given":"Joshua H.","affiliations":[{"id":24828,"text":"Central Alaska Network, National Park Service, Fairbanks, Alaska","active":true,"usgs":false}],"preferred":false,"id":623458,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Devin S.","contributorId":167773,"corporation":false,"usgs":false,"family":"Johnson","given":"Devin","email":"","middleInitial":"S.","affiliations":[{"id":24829,"text":"National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, Seattle, Washington","active":true,"usgs":false}],"preferred":false,"id":623459,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindberg, Mark S.","contributorId":167774,"corporation":false,"usgs":false,"family":"Lindberg","given":"Mark","email":"","middleInitial":"S.","affiliations":[{"id":24830,"text":"Department of Wildlife and Institute of Arctic Biology, University of Alaska, Fairbanks, Alaska","active":true,"usgs":false}],"preferred":false,"id":623460,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Adams, Layne G. 0000-0001-6212-2896 ladams@usgs.gov","orcid":"https://orcid.org/0000-0001-6212-2896","contributorId":2776,"corporation":false,"usgs":true,"family":"Adams","given":"Layne G.","email":"ladams@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":623457,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70173778,"text":"70173778 - 2015 - Effects of climate change on long-term population growth of pronghorn in an arid environment","interactions":[],"lastModifiedDate":"2016-06-22T14:37:09","indexId":"70173778","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Effects of climate change on long-term population growth of pronghorn in an arid environment","docAbstract":"Climate often drives ungulate population dynamics, and as climates change, some areas may become unsuitable for species persistence. Unraveling the relationships between climate and population dynamics, and projecting them across time, advances ecological understanding that informs and steers sustainable conservation for species. Using pronghorn (Antilocapra americana) as an ecological model, we used a Bayesian approach to analyze long-term population, precipitation, and temperature data from 18 populations in the southwestern United States. We determined which long-term (12 and 24 months) or short-term (gestation trimester and lactation period) climatic conditions best predicted annual rate of population growth (λ). We used these predictions to project population trends through 2090. Projections incorporated downscaled climatic data matched to pronghorn range for each population, given a high and a lower atmospheric CO2 concentration scenario. Since the 1990s, 15 of the pronghorn populations declined in abundance. Sixteen populations demonstrated a significant relationship between precipitation and λ, and in 13 of these, temperature was also significant. Precipitation predictors of λ were highly seasonal, with lactation being the most important period, followed by early and late gestation. The influence of temperature on λ was less seasonal than precipitation, and lacked a clear temporal pattern. The climatic projections indicated that all of these pronghorn populations would experience increased temperatures, while the direction and magnitude of precipitation had high population-specific variation. Models predicted that nine populations would be extirpated or approaching extirpation by 2090. Results were consistent across both atmospheric CO2 concentration scenarios, indicating robustness of trends irrespective of climatic severity. In the southwestern United States, the climate underpinning pronghorn populations is shifting, making conditions increasingly inhospitable to pronghorn persistence. This realization informs and steers conservation and management decisions for pronghorn in North America, while exemplifying how similar research can aid ungulates inhabiting arid regions and confronting similar circumstances elsewhere.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/ES15-00266.1","usgsCitation":"Gedir, J.V., Cain, J.W., Harris, G., and Turnbull, T.T., 2015, Effects of climate change on long-term population growth of pronghorn in an arid environment: Ecosphere, v. 6, no. 10, p. 1-20, https://doi.org/10.1890/ES15-00266.1.","productDescription":"20 p.","startPage":"1","endPage":"20","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065177","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":471742,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1890/es15-00266.1","text":"Publisher Index Page"},{"id":438680,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76972HS","text":"USGS data release","linkHelpText":"Impact of Drought on Southwestern Pronghorn Population Trends and Predicted Trajectories in the Southwest in the Face of Climate Change"},{"id":324241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"10","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-22","publicationStatus":"PW","scienceBaseUri":"576bb6b2e4b07657d1a22898","contributors":{"authors":[{"text":"Gedir, Jay V.","contributorId":171735,"corporation":false,"usgs":false,"family":"Gedir","given":"Jay","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":640403,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":638163,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harris, Grant","contributorId":172342,"corporation":false,"usgs":false,"family":"Harris","given":"Grant","affiliations":[],"preferred":false,"id":640404,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Turnbull, Trey T.","contributorId":15909,"corporation":false,"usgs":true,"family":"Turnbull","given":"Trey","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":640405,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70176706,"text":"70176706 - 2015 - Development of the Global Earthquake Model’s neotectonic fault database","interactions":[],"lastModifiedDate":"2016-10-03T14:33:26","indexId":"70176706","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2822,"text":"Natural Hazards","active":true,"publicationSubtype":{"id":10}},"title":"Development of the Global Earthquake Model’s neotectonic fault database","docAbstract":"The Global Earthquake Model (GEM) aims to develop uniform, openly available, standards, datasets and tools for worldwide seismic risk assessment through global collaboration, transparent communication and adapting state-of-the-art science. GEM Faulted Earth (GFE) is one of GEM’s global hazard module projects. This paper describes GFE’s development of a modern neotectonic fault database and a unique graphical interface for the compilation of new fault data. A key design principle is that of an electronic field notebook for capturing observations a geologist would make about a fault. The database is designed to accommodate abundant as well as sparse fault observations. It features two layers, one for capturing neotectonic faults and fold observations, and the other to calculate potential earthquake fault sources from the observations. In order to test the flexibility of the database structure and to start a global compilation, five preexisting databases have been uploaded to the first layer and two to the second. In addition, the GFE project has characterised the world’s approximately 55,000 km of subduction interfaces in a globally consistent manner as a basis for generating earthquake event sets for inclusion in earthquake hazard and risk modelling. Following the subduction interface fault schema and including the trace attributes of the GFE database schema, the 2500-km-long frontal thrust fault system of the Himalaya has also been characterised. We propose the database structure to be used widely, so that neotectonic fault data can make a more complete and beneficial contribution to seismic hazard and risk characterisation globally.
","language":"English","publisher":"Springer","doi":"10.1007/s11069-015-1831-6","usgsCitation":"Christophersen, A., Litchfield, N., Berryman, K., Thomas, R., Basili, R., Wallace, L., Ries, W., Hayes, G.P., Haller, K., Yoshioka, T., Koehler, R., Clark, D., Wolfson-Schwehr, M., Boettcher, M.S., Villamor, P., Horspool, N., Ornthammarath, T., Zuniga, R., Langridge, R.M., Stirling, M.W., Goded, T., Costa, C., and Yeats, R., 2015, Development of the Global Earthquake Model’s neotectonic fault database: Natural Hazards, v. 79, no. 1, p. 111-135, https://doi.org/10.1007/s11069-015-1831-6.","productDescription":"25 p.","startPage":"111","endPage":"135","ipdsId":"IP-065198","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":329243,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"79","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver 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Ocean–atmosphere processes propagate into estuaries from the sea, and atmospheric processes over land propagate into estuaries from watersheds. We examined the effects of these two separate climate-driven processes on pelagic and demersal fish community structure along the salinity gradient in the San Francisco Estuary, California, USA. A 33-year data set (1980–2012) on pelagic and demersal fishes spanning the freshwater to marine regions of the estuary suggested the existence of five estuarine salinity fish guilds: limnetic (salinity = 0–1), oligohaline (salinity = 1–12), mesohaline (salinity = 6–19), polyhaline (salinity = 19–28), and euhaline (salinity = 29–32). Climatic effects propagating from the adjacent Pacific Ocean, indexed by the North Pacific Gyre Oscillation (NPGO), affected demersal and pelagic fish community structure in the euhaline and polyhaline guilds. Climatic effects propagating over land, indexed as freshwater outflow from the watershed (OUT), affected demersal and pelagic fish community structure in the oligohaline, mesohaline, polyhaline, and euhaline guilds. The effects of OUT propagated further down the estuary salinity gradient than the effects of NPGO that propagated up the estuary salinity gradient, exemplifying the role of variable freshwater outflow as an important driver of biotic communities in river-dominated estuaries. These results illustrate how unique sources of climate variability interact to drive biotic communities and, therefore, that climate change is likely to be an important driver in shaping the future trajectory of biotic communities in estuaries and other transitional habitats.
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The significance of palaeo-ice sheets is further underlined by the broad range of disciplines concerned with reconstructing their behaviour, many of which have undergone a rapid expansion since the 1980s. In particular, there has been a major increase in the size and qualitative diversity of empirical data used to reconstruct and date ice sheets, and major improvements in our ability to simulate their dynamics in numerical ice sheet models. These developments have made it increasingly necessary to forge interdisciplinary links between sub-disciplines and to link numerical modelling with observations and dating of proxy records. The aim of this paper is to evaluate recent developments in the methods used to reconstruct ice sheets and outline some key challenges that remain, with an emphasis on how future work might integrate terrestrial and marine evidence together with numerical modelling. Our focus is on pan-ice sheet reconstructions of the last deglaciation, but regional case studies are used to illustrate methodological achievements, challenges and opportunities. Whilst various disciplines have made important progress in our understanding of ice-sheet dynamics, it is clear that data-model integration remains under-used, and that uncertainties remain poorly quantified in both empirically-based and numerical ice-sheet reconstructions. The representation of past climate will continue to be the largest source of uncertainty for numerical modelling. As such, palaeo-observations are critical to constrain and validate modelling. State-of-the-art numerical models will continue to improve both in model resolution and in the breadth of inclusion of relevant processes, thereby enabling more accurate and more direct comparison with the increasing range of palaeo-observations. Thus, the capability is developing to use all relevant palaeo-records to more strongly constrain deglacial (and to a lesser extent pre-LGM) ice sheet evolution. In working towards that goal, the accurate representation of uncertainties is required for both constraint data and model outputs. Close cooperation between modelling and data-gathering communities is essential to ensure this capability is realised and continues to progress.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2015.07.016","usgsCitation":"Stokes, C.R., Tarasov, L., Blomdin, R., Cronin, T.M., Fisher, T.G., Gyllencreutz, R., Hattestrand, C., Heyman, J., Hindmarsh, R.C., Hughes, A.L., Jakobsson, M., Kirchner, N., Livingstone, S.J., Margold, M., Murton, J.B., Noormets, R., Peltier, W.R., Peteet, D.M., Piper, D.J., Preusser, F., Renssen, H., Roberts, D.H., Roche, D.M., Saint-Ange, F., Stroeven, A.P., and Teller, J.T., 2015, On the reconstruction of palaeo-ice sheets: Recent advances and future challenges: Quaternary Science Reviews, v. 125, p. 15-49, https://doi.org/10.1016/j.quascirev.2015.07.016.","productDescription":"35 p.","startPage":"15","endPage":"49","ipdsId":"IP-066534","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":471758,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research.vu.nl/en/publications/a75d46e2-1f29-499f-b305-fec5b88ae18b","text":"External Repository"},{"id":335192,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"125","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58a00057e4b099f50d3e0469","contributors":{"authors":[{"text":"Stokes, Chris R.","contributorId":179153,"corporation":false,"usgs":false,"family":"Stokes","given":"Chris","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":663003,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tarasov, Lev","contributorId":179154,"corporation":false,"usgs":false,"family":"Tarasov","given":"Lev","email":"","affiliations":[],"preferred":false,"id":663004,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blomdin, Robin","contributorId":179155,"corporation":false,"usgs":false,"family":"Blomdin","given":"Robin","email":"","affiliations":[],"preferred":false,"id":663005,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cronin, Thomas M. 0000-0002-2643-0979 tcronin@usgs.gov","orcid":"https://orcid.org/0000-0002-2643-0979","contributorId":2579,"corporation":false,"usgs":true,"family":"Cronin","given":"Thomas","email":"tcronin@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":663002,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fisher, Timothy G.","contributorId":179156,"corporation":false,"usgs":false,"family":"Fisher","given":"Timothy","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":663006,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gyllencreutz, Richard","contributorId":179157,"corporation":false,"usgs":false,"family":"Gyllencreutz","given":"Richard","email":"","affiliations":[],"preferred":false,"id":663007,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hattestrand, Clas","contributorId":179158,"corporation":false,"usgs":false,"family":"Hattestrand","given":"Clas","email":"","affiliations":[],"preferred":false,"id":663008,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Heyman, Jakob","contributorId":179159,"corporation":false,"usgs":false,"family":"Heyman","given":"Jakob","email":"","affiliations":[],"preferred":false,"id":663009,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hindmarsh, Richard C. 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Motivated by previous studies at the site that indicated riverbed clogging plays an important role in the performance of the riverbank filtration system, we investigated the spatiotemporal variability and nature of the clogging. In particular, we investigated whether the clogging was due to abiotic or biotic mechanisms. A secondary aspect of the study was the testing of different methods to monitor riverbed clogging and related processes, such as seepage. Monitoring was conducted using both point-based approaches and spatially extensive geophysical approaches, including: grain-size analysis, temperature sensing, electrical resistivity tomography, seepage meters, microbial analysis, and cryocoring, along two transects. The point monitoring measurements suggested a substantial increase in riverbed biomass (2 orders of magnitude) after the dam was raised compared to the small increase (∼2%) in fine-grained sediment. These changes were concomitant with decreased seepage. The decreased seepage eventually led to the development of an unsaturated zone beneath the riverbed, which further decreased infiltration capacity. Comparison of our time-lapse grain-size and biomass datasets suggested that biotic processes played a greater role in clogging than did abiotic processes. Cryocoring and autonomous temperature loggers were most useful for locally monitoring clogging agents, while electrical resistivity data were useful for interpreting the spatial extent of a pumping-induced unsaturated zone that developed beneath the riverbed after riverbed clogging was initiated. The improved understanding of spatiotemporally variable riverbed clogging and monitoring approaches is expected to be useful for optimizing the riverbank filtration system operations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2015.08.012","usgsCitation":"Ulrich, C., Hubbard, S.S., Florsheim, J., Rosenberry, D.O., Borglin, S., Trotta, M., and Seymour, D., 2015, Riverbed clogging associated with a California riverbank filtration system: An assessment of mechanisms and monitoring approaches: Journal of Hydrology, v. 529, no. 3, p. 1740-1753, https://doi.org/10.1016/j.jhydrol.2015.08.012.","productDescription":"14 p.","startPage":"1740","endPage":"1753","ipdsId":"IP-068292","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":471757,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1501369","text":"Publisher Index Page"},{"id":329458,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"529","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57fe679ee4b0824b2d143717","contributors":{"authors":[{"text":"Ulrich, Craig","contributorId":175248,"corporation":false,"usgs":false,"family":"Ulrich","given":"Craig","email":"","affiliations":[],"preferred":false,"id":650550,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hubbard, Susan S.","contributorId":175249,"corporation":false,"usgs":false,"family":"Hubbard","given":"Susan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":650551,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Florsheim, Joan","contributorId":115633,"corporation":false,"usgs":true,"family":"Florsheim","given":"Joan","email":"","affiliations":[],"preferred":false,"id":650552,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rosenberry, Donald O. 0000-0003-0681-5641 rosenber@usgs.gov","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":1312,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald","email":"rosenber@usgs.gov","middleInitial":"O.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":650549,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Borglin, Sharon","contributorId":175251,"corporation":false,"usgs":false,"family":"Borglin","given":"Sharon","email":"","affiliations":[],"preferred":false,"id":650553,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Trotta, Marcus","contributorId":175252,"corporation":false,"usgs":false,"family":"Trotta","given":"Marcus","email":"","affiliations":[{"id":17863,"text":"Sonoma County Water Agency","active":true,"usgs":false}],"preferred":false,"id":650554,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Seymour, Donald","contributorId":175253,"corporation":false,"usgs":false,"family":"Seymour","given":"Donald","email":"","affiliations":[{"id":17863,"text":"Sonoma County Water Agency","active":true,"usgs":false}],"preferred":false,"id":650579,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70171529,"text":"70171529 - 2015 - Regional and temporal differences in nitrate trends discerned from long-term water quality monitoring data","interactions":[],"lastModifiedDate":"2016-06-03T16:38:41","indexId":"70171529","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Regional and temporal differences in nitrate trends discerned from long-term water quality monitoring data","docAbstract":"Riverine nitrate (NO3) is a well-documented driver of eutrophication and hypoxia in coastal areas. The development of the elevated river NO3 concentration is linked to anthropogenic inputs from municipal, agricultural, and atmospheric sources. The intensity of these sources has varied regionally, through time, and in response to multiple causes such as economic drivers and policy responses. This study uses long-term water quality, land use, and other ancillary data to further describe the evolution of river NO3 concentrations at 22 monitoring stations in the United States (U.S.). The stations were selected for long-term data availability and to represent a range of climate and land-use conditions. We examined NO3 at the monitoring stations, using a flow-weighting scheme meant to account for interannual flow variability allowing greater focus on river chemical conditions. River NO3 concentration increased strongly during 1945-1980 at most of the stations and have remained elevated, but stopped increasing during 1981-2008. NO3 increased to a greater extent at monitoring stations in the Midwest U.S. and less so at those in the Eastern and Western U.S. We discuss 20th Century agricultural development in the U.S. and demonstrate that regional differences in NO3 concentration patterns were strongly related to an agricultural index developed using principal components analysis. This unique century-scale dataset adds to our understanding of long-term NO3 patterns in the U.S.
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vjkelly@usgs.gov","contributorId":4161,"corporation":false,"usgs":true,"family":"Kelly","given":"Valerie","email":"vjkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":631607,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crawford, Charles G. 0000-0003-1653-7841 cgcrawfo@usgs.gov","orcid":"https://orcid.org/0000-0003-1653-7841","contributorId":1064,"corporation":false,"usgs":true,"family":"Crawford","given":"Charles","email":"cgcrawfo@usgs.gov","middleInitial":"G.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":631608,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70171526,"text":"70171526 - 2015 - Understanding natural capital","interactions":[],"lastModifiedDate":"2021-04-09T16:10:54.414424","indexId":"70171526","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Understanding natural capital","docAbstract":"This chapter serves to introduce the geophysics of Neotropical steeplands. Topics are covered in a general manner with hyperlinks to active research and monitoring sites (such as the National Hurricane Center and US Geological Survey publication). Topics covered include ‘tropical climate and weather,’ ‘climate variations and trends,’ Neotropical ‘geology, and soils,’ ‘hillslopes and erosion,’ ‘lakes and reservoirs,’ and ‘effects of land cover on water quality and quantity.’ Obviously, this is a lot of information to cover in a short chapter, hence the use of hyperlinks. The last theme ‘effects of land cover on water quality and quantity’ is covered by case studies, in all of which I have been centrally involved. These studies were chosen because they are among the few studies with sufficient data of high enough quality to reach definitive conclusions.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Managing watersheds for ecosystem services in the steepland neotropics","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Inter-American Development Bank","usgsCitation":"Stallard, R.F., 2015, Understanding natural capital, chap. of Managing watersheds for ecosystem services in the steepland neotropics, p. 17-47.","productDescription":"31 p.","startPage":"17","endPage":"47","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065661","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":328251,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57cfe8c0e4b04836416a0e58","contributors":{"editors":[{"text":"Hall, Jefferson S.","contributorId":169939,"corporation":false,"usgs":false,"family":"Hall","given":"Jefferson","email":"","middleInitial":"S.","affiliations":[{"id":25632,"text":"Smithsonian Tropical Research Institute, Balboa, Panama","active":true,"usgs":false}],"preferred":false,"id":640029,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Kirn, Vanessa","contributorId":169940,"corporation":false,"usgs":false,"family":"Kirn","given":"Vanessa","email":"","affiliations":[{"id":25632,"text":"Smithsonian Tropical Research Institute, Balboa, Panama","active":true,"usgs":false}],"preferred":false,"id":640030,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Yanguas-Fernandez, Estrella","contributorId":172253,"corporation":false,"usgs":false,"family":"Yanguas-Fernandez","given":"Estrella","email":"","affiliations":[],"preferred":false,"id":640031,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Stallard, Robert F. 0000-0001-8209-7608 stallard@usgs.gov","orcid":"https://orcid.org/0000-0001-8209-7608","contributorId":1924,"corporation":false,"usgs":true,"family":"Stallard","given":"Robert","email":"stallard@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":631599,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70157061,"text":"sir20155124 - 2015 - Discharge, suspended sediment, bedload, and water quality in Clear Creek, western Nevada, water years 2010-12","interactions":[],"lastModifiedDate":"2015-10-01T09:04:14","indexId":"sir20155124","displayToPublicDate":"2015-09-30T17:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5124","title":"Discharge, suspended sediment, bedload, and water quality in Clear Creek, western Nevada, water years 2010-12","docAbstract":"Clear Creek is a small stream that drains the eastern Sierra Nevada near Lake Tahoe, flows roughly parallel to the U.S. Highway 50 corridor, and discharges to the Carson River near Carson City, Nevada. Historical and ongoing development in the drainage basin is thought to be affecting Clear Creek and its sediment-transport characteristics. A baseline study from water years 2004–07 collected and evaluated data at three Clear Creek sampling sites. These data included discharge, selected water-quality parameters, and suspended-sediment concentrations, loads, and yields. This study builds on what was learned from the baseline study in water years 2004–07 and serves as a continuation of the data collection and analyses of the Clear Creek discharge regime and associated water-quality and sediment concentrations and loads during water years 2010–12.
\nDuring this study, total annual sediment loads ranged from 355 tons per year in 2010 to 1,768 tons per year in 2011 and were significantly lower than the previous study (water years 2004–07). Bedload represented between 29 and 38 percent of total sediment load in water years 2010–12, and between 72 and 90 percent of the total sediment load in water years 2004–07, which indicates a decrease in bedload between study periods. Annual suspended-sediment loads in water years 2010–12 indicated no significant change from water years 2004–07. Mean daily discharge was significantly lower in water years 2010–12 than in waters years 2004–07 and may be the reason for the decrease in bedload that resulted in a lower total sediment load.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155124","collaboration":"Prepared in cooperation with the Nevada Department of Transportation","usgsCitation":"Huntington, J.M., and Savard, C.S., 2015, Discharge, suspended sediment, bedload, and water quality in Clear Creek, western Nevada, water years 2010–12: U.S. Geological Survey Scientific Investigations Report 2015-5124, 39 p., https://dx.doi.org/10.3133/sir20155124.","productDescription":"vi, 39 p.","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2009-10-01","temporalEnd":"2010-09-30","ipdsId":"IP-040257","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":309378,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5124/coverthb.jpg"},{"id":309379,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5124/sir20155124.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5124 PDF"}],"country":"United States","state":"Nevada","otherGeospatial":"Clear Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.91920471191406,\n 39.02665200282546\n ],\n [\n -119.91920471191406,\n 39.188360332930166\n ],\n [\n -119.72333908081055,\n 39.188360332930166\n ],\n [\n -119.72333908081055,\n 39.02665200282546\n ],\n [\n -119.91920471191406,\n 39.02665200282546\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Rd.
Carson City, NV 89701
http://nevada.usgs.gov/water/
Methods to estimate irrigation withdrawal using nationally available datasets and techniques that are transferable to other agricultural regions were evaluated by the U.S. Geological Survey as part of the Apalachicola-Chattahoochee-Flint (ACF) River Basin focus area study of the National Water Census (ACF–FAS). These methods investigated the spatial, temporal, and quantitative distributions of water withdrawal for irrigation in the southwestern Georgia region of the ACF–FAS, filling a vital need to inform science-based decisions regarding resource management and conservation. The crop– demand method assumed that only enough water is pumped onto a crop to satisfy the deficit between evapotranspiration and precipitation. A second method applied a geostatistical regimen of variography and conditional simulation to monthly metered irrigation withdrawal to estimate irrigation withdrawal where data do not exist. A third method analyzed Landsat satellite imagery using an automated approach to generate monthly estimates of irrigated lands. These methods were evaluated independently and compared collectively with measured water withdrawal information available in the Georgia part of the ACF–FAS, principally in the Chattahoochee-Flint River Basin. An assessment of each method’s contribution to the National Water Census program was also made to identify transfer value of the methods to the national program and other water census studies. None of the three methods evaluated represent a turnkey process to estimate irrigation withdrawal on any spatial (local or regional) or temporal (monthly or annual) extent. Each method requires additional information on agricultural practices during the growing season to complete the withdrawal estimation process. Spatial and temporal limitations inherent in identifying irrigated acres during the growing season, and in designing spatially and temporally representative monitor (meter) networks, can belie the ability of the methods to produce accurate irrigation-withdrawal estimates that can be used to produce dependable and consistent assessments of water availability and use for the National Water Census. Emerging satellite-data products and techniques for data analysis can generate high spatial-resolution estimates of irrigated-acres distributions with near-term temporal frequencies compatible with the needs of the ACF–FAS and the National Water Census.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155118","collaboration":"Prepared in cooperation with the National Water Census Program","usgsCitation":"Painter, J.A., Torak, L.J., and Jones, J.W., 2015, Evaluation and comparison of methods to estimate irrigation withdrawal for the National Water Census Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin in southwestern Georgia, U.S. Geological Survey Scientific Investigations ReportDirector, South Atlantic Water Science Center
North Carolina–South Carolina–Georgia
720 Gracern Road, Suite 129
Columbia, SC 29210
Phone: (803) 750-6100
http://ga.water.usgs.gov/