{"pageNumber":"622","pageRowStart":"15525","pageSize":"25","recordCount":40825,"records":[{"id":70160883,"text":"70160883 - 2014 - Width and dip of the southern San Andreas Fault at Salt Creek from modeling of geophysical data","interactions":[],"lastModifiedDate":"2017-04-25T10:44:12","indexId":"70160883","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Width and dip of the southern San Andreas Fault at Salt Creek from modeling of geophysical data","docAbstract":"<p><span>We investigate the geometry and width of the southernmost stretch of the San Andreas Fault zone using new gravity and magnetic data along line 7 of the Salton Seismic Imaging Project. In the Salt Creek area of Durmid Hill, the San Andreas Fault coincides with a complex magnetic signature, with high-amplitude, short-wavelength magnetic anomalies superposed on a broader magnetic anomaly that is at least 5 km wide centered 2–3 km northeast of the fault. Marine magnetic data show that high-frequency magnetic anomalies extend more than 1 km west of the mapped trace of the San Andreas Fault. Modeling of magnetic data is consistent with a moderate to steep (&gt; 50 degrees) northeast dip of the San Andreas Fault, but also suggests that the sedimentary sequence is folded west of the fault, causing the short wavelength of the anomalies west of the fault. Gravity anomalies are consistent with the previously modeled seismic velocity structure across the San Andreas Fault. Modeling of gravity data indicates a steep dip for the San Andreas Fault, but does not resolve unequivocally the direction of dip. Gravity data define a deeper basin, bounded by the Powerline and Hot Springs Faults, than imaged by the seismic experiment. This basin extends southeast of Line 7 for nearly 20 km, with linear margins parallel to the San Andreas Fault. These data suggest that the San Andreas Fault zone is wider than indicated by its mapped surface trace.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Not a drop left to drink","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2014 Desert Symposium","conferenceDate":"April 2014","language":"English","publisher":"California State University Desert Studies Center","publisherLocation":"Fullerton, CA","usgsCitation":"Langenheim, V., Athens, N.D., Scheirer, D., Fuis, G.S., Rymer, M.J., and Goldman, M.R., 2014, Width and dip of the southern San Andreas Fault at Salt Creek from modeling of geophysical data, <i>in</i> Not a drop left to drink, April 2014, p. 83-93.","productDescription":"11 p.","startPage":"83","endPage":"93","ipdsId":"IP-054142","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":340114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":313214,"type":{"id":15,"text":"Index Page"},"url":"https://nsm.fullerton.edu/dsc/desert-studies-center-additional-information"}],"country":"United States","state":"California","otherGeospatial":"San Andreas Fault","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58fdbd19e4b0074928294489","contributors":{"editors":[{"text":"Reynolds, Robert E.","contributorId":113220,"corporation":false,"usgs":true,"family":"Reynolds","given":"Robert E.","affiliations":[],"preferred":false,"id":692466,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Langenheim, Victoria E. 0000-0003-2170-5213 zulanger@usgs.gov","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":151042,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria E.","email":"zulanger@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":584145,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Athens, Noah D. nathens@usgs.gov","contributorId":4866,"corporation":false,"usgs":true,"family":"Athens","given":"Noah","email":"nathens@usgs.gov","middleInitial":"D.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":584146,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scheirer, Daniel S. dscheirer@usgs.gov","contributorId":2325,"corporation":false,"usgs":true,"family":"Scheirer","given":"Daniel S.","email":"dscheirer@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":584147,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fuis, Gary S. 0000-0002-3078-1544 fuis@usgs.gov","orcid":"https://orcid.org/0000-0002-3078-1544","contributorId":2639,"corporation":false,"usgs":true,"family":"Fuis","given":"Gary","email":"fuis@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":584148,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rymer, Michael J. mrymer@usgs.gov","contributorId":1522,"corporation":false,"usgs":true,"family":"Rymer","given":"Michael","email":"mrymer@usgs.gov","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":584149,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Goldman, Mark R. 0000-0002-0802-829X goldman@usgs.gov","orcid":"https://orcid.org/0000-0002-0802-829X","contributorId":1521,"corporation":false,"usgs":true,"family":"Goldman","given":"Mark","email":"goldman@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":584150,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70188807,"text":"70188807 - 2014 - 13.3 – Stable Isotope Geochemistry of Mineral Deposits","interactions":[],"lastModifiedDate":"2017-06-26T13:00:54","indexId":"70188807","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"13.3 – Stable Isotope Geochemistry of Mineral Deposits","docAbstract":"<p><span>In this chapter, the intent is to summarize the results of traditional stable isotope studies (mainly H, B, O, C, and S) that have greatly contributed to the understanding of ore-forming processes over the last 60 years and to provide an up-to-date assessment of the application of new nontraditional isotope systems (Fe, Cu, Zn, Se, Mo, Hg, and Tl).</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reference module in earth systems and environmental sciences: Treatise on geochemistry (Second Edition)","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-08-095975-7.01103-7","usgsCitation":"Shanks, W.P., 2014, 13.3 – Stable Isotope Geochemistry of Mineral Deposits, chap. <i>of</i> Reference module in earth systems and environmental sciences: Treatise on geochemistry (Second Edition), p. 59-85, https://doi.org/10.1016/B978-0-08-095975-7.01103-7.","productDescription":"27 p.","startPage":"59","endPage":"85","ipdsId":"IP-038278","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":342890,"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":"59521d26e4b062508e3c36bd","contributors":{"authors":[{"text":"Shanks, W.C. Pat III","contributorId":93949,"corporation":false,"usgs":true,"family":"Shanks","given":"W.C.","suffix":"III","email":"","middleInitial":"Pat","affiliations":[],"preferred":false,"id":700452,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70188048,"text":"70188048 - 2014 - Spatio-temporal patterns and climate variables controlling of biomass carbon stock of global grassland ecosystems from 1982 to 2006","interactions":[],"lastModifiedDate":"2017-05-30T15:15:06","indexId":"70188048","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","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":"Spatio-temporal patterns and climate variables controlling of biomass carbon stock of global grassland ecosystems from 1982 to 2006","docAbstract":"<p><span>Grassland ecosystems play an important role in subsistence agriculture and the global carbon cycle. However, the global spatio-temporal patterns and environmental controls of grassland biomass are not well quantified and understood. The goal of this study was to estimate the spatial and temporal patterns of the global grassland biomass and analyze their driving forces using field measurements, Normalized Difference Vegetation Index (NDVI) time series from satellite data, climate reanalysis data, and a satellite-based statistical model. Results showed that the NDVI-based biomass carbon model developed from this study explained 60% of the variance across 38 sites globally. The global carbon stock in grassland aboveground live biomass was 1.05 Pg·C, averaged from 1982 to 2006, and increased at a rate of 2.43 Tg·C·y</span><sup>−1</sup><span> during this period. Temporal change of the global biomass was significantly and positively correlated with temperature and precipitation. The distribution of biomass carbon density followed the precipitation gradient. The dynamics of regional grassland biomass showed various trends largely determined by regional climate variability, disturbances, and management practices (such as grazing for meat production). The methods and results from this study can be used to monitor the dynamics of grassland aboveground biomass and evaluate grassland susceptibility to climate variability and change, disturbances, and management.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs6031783","usgsCitation":"Xia, J., Liu, S., Liang, S., Chen, Y., Xu, W., and Yuan, W., 2014, Spatio-temporal patterns and climate variables controlling of biomass carbon stock of global grassland ecosystems from 1982 to 2006: Remote Sensing, v. 6, no. 3, p. 1783-1802, https://doi.org/10.3390/rs6031783.","productDescription":"20 p.","startPage":"1783","endPage":"1802","ipdsId":"IP-052038","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":486959,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs6031783","text":"Publisher Index Page"},{"id":341874,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2014-02-26","publicationStatus":"PW","scienceBaseUri":"592e84c7e4b092b266f10dae","contributors":{"authors":[{"text":"Xia, Jiangzhou","contributorId":192427,"corporation":false,"usgs":false,"family":"Xia","given":"Jiangzhou","email":"","affiliations":[],"preferred":false,"id":696484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Shuguang 0000-0002-6027-3479 sliu@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3479","contributorId":147403,"corporation":false,"usgs":true,"family":"Liu","given":"Shuguang","email":"sliu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696320,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liang, Shunlin","contributorId":192428,"corporation":false,"usgs":false,"family":"Liang","given":"Shunlin","email":"","affiliations":[],"preferred":false,"id":696485,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Yang","contributorId":192429,"corporation":false,"usgs":false,"family":"Chen","given":"Yang","email":"","affiliations":[],"preferred":false,"id":696486,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xu, Wenfang","contributorId":192430,"corporation":false,"usgs":false,"family":"Xu","given":"Wenfang","email":"","affiliations":[],"preferred":false,"id":696487,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yuan, Wenping","contributorId":83435,"corporation":false,"usgs":true,"family":"Yuan","given":"Wenping","email":"","affiliations":[],"preferred":false,"id":696488,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70191023,"text":"70191023 - 2014 - Dispersion analysis of passive surface-wave noise generated during hydraulic-fracturing operations","interactions":[],"lastModifiedDate":"2017-09-21T12:06:41","indexId":"70191023","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2165,"text":"Journal of Applied Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Dispersion analysis of passive surface-wave noise generated during hydraulic-fracturing operations","docAbstract":"<p><span>Surface-wave dispersion analysis is useful for estimating near-surface shear-wave velocity models, designing receiver arrays, and suppressing surface waves. Here, we analyze whether passive seismic noise generated during hydraulic-fracturing operations can be used to extract surface-wave dispersion characteristics. Applying seismic interferometry to noise measurements, we extract surface waves by cross-correlating several minutes of passive records; this approach is distinct from previous studies that used hours or days of passive records for cross-correlation. For comparison, we also perform dispersion analysis for an active-source array that has some receivers in common with the passive array. The active and passive data show good agreement in the dispersive character of the fundamental-mode surface-waves. For the higher mode surface waves, however, active and passive data resolve the dispersive properties at different frequency ranges. To demonstrate an application of dispersion analysis, we invert the observed surface-wave dispersion characteristics to determine the near-surface, one-dimensional shear-wave velocity.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jappgeo.2014.09.008","usgsCitation":"Forghani-Arani, F., Willis, M., Snieder, R., Haines, S.S., Behura, J., Batzle, M., and Davidson, M., 2014, Dispersion analysis of passive surface-wave noise generated during hydraulic-fracturing operations: Journal of Applied Geophysics, v. 111, p. 129-134, https://doi.org/10.1016/j.jappgeo.2014.09.008.","productDescription":"6 p.","startPage":"129","endPage":"134","ipdsId":"IP-058038","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":473305,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1556315","text":"Publisher Index Page"},{"id":345987,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"111","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59c4cf97e4b017cf313d3cb8","contributors":{"authors":[{"text":"Forghani-Arani, Farnoush","contributorId":196642,"corporation":false,"usgs":false,"family":"Forghani-Arani","given":"Farnoush","email":"","affiliations":[{"id":34665,"text":"Microseismic Inc.","active":true,"usgs":false}],"preferred":false,"id":710974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willis, Mark","contributorId":196643,"corporation":false,"usgs":false,"family":"Willis","given":"Mark","email":"","affiliations":[{"id":34662,"text":"Halliburton","active":true,"usgs":false}],"preferred":false,"id":710975,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Snieder, Roel","contributorId":196644,"corporation":false,"usgs":false,"family":"Snieder","given":"Roel","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":710976,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haines, Seth S. 0000-0003-2611-8165 shaines@usgs.gov","orcid":"https://orcid.org/0000-0003-2611-8165","contributorId":1344,"corporation":false,"usgs":true,"family":"Haines","given":"Seth","email":"shaines@usgs.gov","middleInitial":"S.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":710973,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Behura, Jyoti","contributorId":196645,"corporation":false,"usgs":false,"family":"Behura","given":"Jyoti","email":"","affiliations":[{"id":34663,"text":"Seismic Science LLC","active":true,"usgs":false}],"preferred":false,"id":710977,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Batzle, Mike","contributorId":196646,"corporation":false,"usgs":false,"family":"Batzle","given":"Mike","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":710978,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Davidson, Michael","contributorId":196647,"corporation":false,"usgs":false,"family":"Davidson","given":"Michael","email":"","affiliations":[{"id":17916,"text":"ConocoPhillips","active":true,"usgs":false}],"preferred":false,"id":710979,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70187359,"text":"70187359 - 2014 - The temperature-productivity squeeze: Constraints on brook trout growth along an Appalachian river continuum","interactions":[],"lastModifiedDate":"2017-05-04T12:34:08","indexId":"70187359","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"title":"The temperature-productivity squeeze: Constraints on brook trout growth along an Appalachian river continuum","docAbstract":"<p><span>We tested the hypothesis that brook trout growth rates are controlled by a complex interaction of food availability, water temperature, and competitor density. We quantified trout diet, growth, and consumption in small headwater tributaries characterized as cold with low food and high trout density, larger tributaries characterized as cold with moderate food and moderate trout density, and large main stems characterized as warm with high food and low trout density. Brook trout consumption was highest in the main stem where diets shifted from insects in headwaters to fishes and crayfish in larger streams. Despite high water temperatures, trout growth rates also were consistently highest in the main stem, likely due to competitively dominant trout monopolizing thermal refugia. Temporal changes in trout density had a direct negative effect on brook trout growth rates. Our results suggest that competition for food constrains brook trout growth in small streams, but access to thermal refugia in productive main stem habitats enables dominant trout to supplement growth at a watershed scale. Brook trout conservation in this region should seek to relieve the “temperature-productivity squeeze,” whereby brook trout productivity is constrained by access to habitats that provide both suitable water temperature and sufficient prey.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10750-013-1794-0","usgsCitation":"Petty, J.T., Thorne, D., Huntsman, B.M., and Mazik, P.M., 2014, The temperature-productivity squeeze: Constraints on brook trout growth along an Appalachian river continuum: Hydrobiologia, v. 727, no. 1, p. 151-166, https://doi.org/10.1007/s10750-013-1794-0.","productDescription":"16 p.","startPage":"151","endPage":"166","ipdsId":"IP-042627","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":340823,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Upper Shaver's Fork","volume":"727","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2013-12-31","publicationStatus":"PW","scienceBaseUri":"590c3dcbe4b0e541a038dd2d","contributors":{"authors":[{"text":"Petty, J. Todd","contributorId":166749,"corporation":false,"usgs":false,"family":"Petty","given":"J.","email":"","middleInitial":"Todd","affiliations":[{"id":24497,"text":"West Virginia University, Morgantown, WV","active":true,"usgs":false}],"preferred":false,"id":693608,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thorne, David","contributorId":191765,"corporation":false,"usgs":false,"family":"Thorne","given":"David","email":"","affiliations":[{"id":25281,"text":"West Virginia University, WV","active":true,"usgs":false},{"id":24498,"text":"West Virginia Division of Natural Resources, Point Pleasant, WV","active":true,"usgs":false}],"preferred":false,"id":694167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huntsman, Brock M. 0000-0003-4090-1949","orcid":"https://orcid.org/0000-0003-4090-1949","contributorId":166748,"corporation":false,"usgs":false,"family":"Huntsman","given":"Brock","email":"","middleInitial":"M.","affiliations":[{"id":24497,"text":"West Virginia University, Morgantown, WV","active":true,"usgs":false}],"preferred":false,"id":694168,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mazik, Patricia M. 0000-0002-8046-5929 pmazik@usgs.gov","orcid":"https://orcid.org/0000-0002-8046-5929","contributorId":2318,"corporation":false,"usgs":true,"family":"Mazik","given":"Patricia","email":"pmazik@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":694169,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70188038,"text":"70188038 - 2014 - Evapotranspiration variability and its association with vegetation dynamics in the Nile Basin, 2002–2011","interactions":[],"lastModifiedDate":"2017-05-30T16:22:30","indexId":"70188038","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","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":"Evapotranspiration variability and its association with vegetation dynamics in the Nile Basin, 2002–2011","docAbstract":"<p><span>Evapotranspiration (ET) is a vital component in land-atmosphere interactions. In drylands, over 90% of annual rainfall evaporates. The Nile Basin in Africa is about 42% dryland in a region experiencing rapid population growth and development. The relationship of ET with climate, vegetation and land cover in the basin during 2002–2011 is analyzed using thermal-based Simplified Surface Energy Balance Operational (SSEBop) ET, Normalized Difference Vegetation Index (NDVI)-based MODIS Terrestrial (MOD16) ET, MODIS-derived NDVI as a proxy for vegetation productivity and rainfall from Tropical Rainfall Measuring Mission (TRMM). Interannual variability and trends are analyzed using established statistical methods. Analysis based on thermal-based ET revealed that &gt;50% of the study area exhibited negative ET anomalies for 7 years (2009, driest), while &gt;60% exhibited positive ET anomalies for 3 years (2007, wettest). NDVI-based monthly ET correlated strongly (r &gt; 0.77) with vegetation than thermal-based ET (0.52 &lt; r &lt; 0.73) at </span><i>p</i><span> &lt; 0.001. Climate-zone averaged thermal-based ET anomalies positively correlated (r = 0.6, </span><i>p</i><span> &lt; 0.05) with rainfall in 4 of the 9 investigated climate zones. Thermal-based and NDVI-based ET estimates revealed minor discrepancies over rainfed croplands (60 mm/yr higher for thermal-based ET), but a significant divergence over wetlands (440 mm/yr higher for thermal-based ET). Only 5% of the study area exhibited statistically significant trends in ET.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs6075885","usgsCitation":"Alemu, H., Senay, G., Kaptue, A.T., and Kovalskyy, V., 2014, Evapotranspiration variability and its association with vegetation dynamics in the Nile Basin, 2002–2011: Remote Sensing, v. 6, no. 7, p. 5885-5908, https://doi.org/10.3390/rs6075885.","productDescription":"24 p.","startPage":"5885","endPage":"5908","ipdsId":"IP-057424","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":473457,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs6075885","text":"Publisher Index Page"},{"id":341890,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Nile Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              23.818359375,\n              -3.688855143147035\n            ],\n            [\n              37.6171875,\n              -3.688855143147035\n            ],\n            [\n              37.6171875,\n              31.57853542647338\n            ],\n            [\n              23.818359375,\n              31.57853542647338\n            ],\n            [\n              23.818359375,\n              -3.688855143147035\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"6","issue":"7","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2014-06-25","publicationStatus":"PW","scienceBaseUri":"592e84c7e4b092b266f10db3","contributors":{"authors":[{"text":"Alemu, Henok","contributorId":124527,"corporation":false,"usgs":false,"family":"Alemu","given":"Henok","email":"","affiliations":[{"id":5087,"text":"Geographic Information Science Center of Excellence (GIScCE), South Dakota State University, Brookings, USA","active":true,"usgs":false}],"preferred":false,"id":696572,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":152206,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel B.","email":"senay@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":696291,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kaptue, Armel T.","contributorId":189254,"corporation":false,"usgs":false,"family":"Kaptue","given":"Armel","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":696573,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kovalskyy, Valeriy","contributorId":192062,"corporation":false,"usgs":false,"family":"Kovalskyy","given":"Valeriy","email":"","affiliations":[{"id":26958,"text":"South Dakota State University, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":696574,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70187415,"text":"70187415 - 2014 - Incorporating detection probability into northern Great Plains pronghorn population estimates","interactions":[],"lastModifiedDate":"2017-05-02T13:34:29","indexId":"70187415","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating detection probability into northern Great Plains pronghorn population estimates","docAbstract":"<p><span>Pronghorn (</span><i>Antilocapra americana</i><span>) abundances commonly are estimated using fixed-wing surveys, but these estimates are likely to be negatively biased because of violations of key assumptions underpinning line-transect methodology. Reducing bias and improving precision of abundance estimates through use of detection probability and mark-resight models may allow for more responsive pronghorn management actions. Given their potential application in population estimation, we evaluated detection probability and mark-resight models for use in estimating pronghorn population abundance. We used logistic regression to quantify probabilities that detecting pronghorn might be influenced by group size, animal activity, percent vegetation, cover type, and topography. We estimated pronghorn population size by study area and year using mixed logit-normal mark-resight (MLNM) models. Pronghorn detection probability increased with group size, animal activity, and percent vegetation; overall detection probability was 0.639 (95% CI = 0.612–0.667) with 396 of 620 pronghorn groups detected. Despite model selection uncertainty, the best detection probability models were 44% (range = 8–79%) and 180% (range = 139–217%) greater than traditional pronghorn population estimates. Similarly, the best MLNM models were 28% (range = 3–58%) and 147% (range = 124–180%) greater than traditional population estimates. Detection probability of pronghorn was not constant but depended on both intrinsic and extrinsic factors. When pronghorn detection probability is a function of animal group size, animal activity, landscape complexity, and percent vegetation, traditional aerial survey techniques will result in biased pronghorn abundance estimates. Standardizing survey conditions, increasing resighting occasions, or accounting for variation in individual heterogeneity in mark-resight models will increase the accuracy and precision of pronghorn population estimates.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.634","usgsCitation":"Jacques, C.N., Jenks, J., Grovenburg, T.W., Klaver, R.W., and DePerno, C.S., 2014, Incorporating detection probability into northern Great Plains pronghorn population estimates: Journal of Wildlife Management, v. 78, no. 1, p. 164-174, https://doi.org/10.1002/jwmg.634.","productDescription":"11 p.","startPage":"164","endPage":"174","ipdsId":"IP-042502","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":473276,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/nrem_pubs/231","text":"External Repository"},{"id":340745,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"78","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2013-12-16","publicationStatus":"PW","scienceBaseUri":"59099ab1e4b0fc4e44915810","contributors":{"authors":[{"text":"Jacques, Christopher N.","contributorId":15521,"corporation":false,"usgs":true,"family":"Jacques","given":"Christopher","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":693977,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenks, Jonathan A.","contributorId":51591,"corporation":false,"usgs":true,"family":"Jenks","given":"Jonathan A.","affiliations":[],"preferred":false,"id":693978,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grovenburg, Troy W.","contributorId":57712,"corporation":false,"usgs":true,"family":"Grovenburg","given":"Troy","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":693979,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":693980,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DePerno, Christopher S.","contributorId":10327,"corporation":false,"usgs":true,"family":"DePerno","given":"Christopher","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":693981,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70188054,"text":"70188054 - 2014 - Earth observation based assessment of the water production and water consumption of Nile Basin agro-ecosystems","interactions":[],"lastModifiedDate":"2017-05-31T16:11:56","indexId":"70188054","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","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":"Earth observation based assessment of the water production and water consumption of Nile Basin agro-ecosystems","docAbstract":"<p><span>The increasing competition for water resources requires a better understanding of flows, fluxes, stocks, and the services and benefits related to water consumption. This paper explains how public domain Earth Observation data based on Moderate Resolution Imaging Spectroradiometer (MODIS), Second Generation Meteosat (MSG), Tropical Rainfall Measurement Mission (TRMM) and various altimeter measurements can be used to estimate net water production (rainfall (P) &gt; evapotranspiration (ET)) and net water consumption (ET &gt; P) of Nile Basin agro-ecosystems. Rainfall data from TRMM and the Famine Early Warning System Network (FEWS-NET) RainFall Estimates (RFE) products were used in conjunction with actual evapotranspiration from the Operational Simplified Surface Energy Balance (SSEBop) and ETLook models. Water flows laterally between net water production and net water consumption areas as a result of runoff and withdrawals. This lateral flow between the 15 sub-basins of the Nile was estimated, and partitioned into stream flow and non-stream flow using the discharge data. A series of essential water metrics necessary for successful integrated water management are explained and computed. Net water withdrawal estimates (natural and humanly instigated) were assumed to be the difference between net rainfall (P</span><sub>net</sub><span>) and actual evapotranspiration (ET) and some first estimates of withdrawals—without flow meters—are provided. Groundwater-dependent ecosystems withdraw large volumes of groundwater, which exceed water withdrawals for the irrigation sector. There is a strong need for the development of more open-access Earth Observation databases, especially for information related to actual ET. The fluxes, flows and storage changes presented form the basis for a global framework to describe monthly and annual water accounts in ungauged river basins. </span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs61110306","usgsCitation":"Bastiaanssen, W., Karimi, P., Rebelo, L., Duan, Z., Senay, G., Muthuwatte, L., and Smakhtin, V., 2014, Earth observation based assessment of the water production and water consumption of Nile Basin agro-ecosystems: Remote Sensing, v. 6, no. 11, p. 10306-10334, https://doi.org/10.3390/rs61110306.","productDescription":"29 p.","startPage":"10306","endPage":"10334","ipdsId":"IP-057431","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":473300,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs61110306","text":"Publisher Index Page"},{"id":341872,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Nile Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              23.818359375,\n              -3.688855143147035\n            ],\n            [\n              37.6171875,\n              -3.688855143147035\n            ],\n            [\n              37.6171875,\n              31.57853542647338\n            ],\n            [\n              23.818359375,\n              31.57853542647338\n            ],\n            [\n              23.818359375,\n              -3.688855143147035\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"6","issue":"11","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2014-10-24","publicationStatus":"PW","scienceBaseUri":"592e84c6e4b092b266f10da3","contributors":{"authors":[{"text":"Bastiaanssen, Wim","contributorId":192421,"corporation":false,"usgs":false,"family":"Bastiaanssen","given":"Wim","email":"","affiliations":[],"preferred":false,"id":696478,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karimi, Poolad","contributorId":192422,"corporation":false,"usgs":false,"family":"Karimi","given":"Poolad","email":"","affiliations":[],"preferred":false,"id":696479,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rebelo, Lisa-Maria","contributorId":192423,"corporation":false,"usgs":false,"family":"Rebelo","given":"Lisa-Maria","email":"","affiliations":[],"preferred":false,"id":696480,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duan, Zheng","contributorId":192424,"corporation":false,"usgs":false,"family":"Duan","given":"Zheng","email":"","affiliations":[],"preferred":false,"id":696481,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":166812,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":696333,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Muthuwatte, Lal","contributorId":192425,"corporation":false,"usgs":false,"family":"Muthuwatte","given":"Lal","email":"","affiliations":[],"preferred":false,"id":696482,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smakhtin, Vladimir","contributorId":192426,"corporation":false,"usgs":false,"family":"Smakhtin","given":"Vladimir","email":"","affiliations":[],"preferred":false,"id":696483,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70059572,"text":"70059572 - 2014 - Spatially explicit modeling of 1992-2100 land cover and forest stand age for the conterminous United States","interactions":[],"lastModifiedDate":"2022-03-31T19:37:52.175526","indexId":"70059572","displayToPublicDate":"2013-12-23T09:21:00","publicationYear":"2014","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":"Spatially explicit modeling of 1992-2100 land cover and forest stand age for the conterminous United States","docAbstract":"Information on future land-use and land-cover (LULC) change is needed to analyze the impact of LULC change on ecological processes. The U.S. Geological Survey has produced spatially explicit, thematically detailed LULC projections for the conterminous United States. Four qualitative and quantitative scenarios of LULC change were developed, with characteristics consistent with the Intergovernmental Panel on Climate Change (IPCC) Special Report on 5 Emission Scenarios (SRES). The four quantified scenarios (A1B, A2, B1, and B2) served as input to the Forecasting Scenarios of Land-use Change (FORE-SCE) model. Four spatially explicit datasets consistent with scenario storylines were produced for the conterminous United States, with annual LULC maps from 1992 through 2100. The future projections are characterized by a loss of natural land covers in most scenarios, with corresponding expansion of 10 anthropogenic land uses. Along with the loss of natural land covers, remaining natural land covers experience increased fragmentation under most scenarios, with only the B2 scenario remaining relatively stable in both proportion of remaining natural land covers and basic fragmentation measures. Forest stand age was also modeled. By 2100, scenarios and ecoregions with heavy forest cutting have relatively lower mean stand ages compared to those with less 15 forest cutting. Stand ages differ substantially between unprotected and protected forest lands, as well as between different forest classes. The modeled data were compared to the National Land Cover Database (NLCD) and other data sources to assess model characteristics. The consistent, spatially explicit, and thematically detailed LULC projections and the associated forest stand age data layers have been used to analyze LULC impacts on carbon and greenhouse gas fluxes, 20 biodiversity, climate and weather variability, hydrologic change, and other ecological processes.","language":"English","publisher":"Ecological Society of America","doi":"10.1890/13-1245.1","usgsCitation":"Sohl, T.L., Sayler, K., Bouchard, M., Reker, R.R., Friesz, A.M., Bennett, S.L., Sleeter, B.M., Sleeter, R., Wilson, T., Soulard, C.E., Knuppe, M., and Van Hofwegen, T., 2014, Spatially explicit modeling of 1992-2100 land cover and forest stand age for the conterminous United States: Ecological Applications, v. 24, no. 5, p. 1015-1036, https://doi.org/10.1890/13-1245.1.","productDescription":"22 p. ; Data release","startPage":"1015","endPage":"1036","numberOfPages":"22","ipdsId":"IP-042928","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) 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 \"}}]}","volume":"24","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52b95be3e4b0a747b3e7e7b1","contributors":{"authors":[{"text":"Sohl, Terry L. 0000-0002-9771-4231 sohl@usgs.gov","orcid":"https://orcid.org/0000-0002-9771-4231","contributorId":648,"corporation":false,"usgs":true,"family":"Sohl","given":"Terry","email":"sohl@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":487685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sayler, Kristi L. 0000-0003-2514-242X sayler@usgs.gov","orcid":"https://orcid.org/0000-0003-2514-242X","contributorId":2988,"corporation":false,"usgs":true,"family":"Sayler","given":"Kristi","email":"sayler@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":487687,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bouchard, Michelle 0000-0002-6353-3491 mbouchard@usgs.gov","orcid":"https://orcid.org/0000-0002-6353-3491","contributorId":3765,"corporation":false,"usgs":true,"family":"Bouchard","given":"Michelle","email":"mbouchard@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":487689,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reker, Ryan R. 0000-0001-7524-0082 rreker@usgs.gov","orcid":"https://orcid.org/0000-0001-7524-0082","contributorId":174136,"corporation":false,"usgs":true,"family":"Reker","given":"Ryan","email":"rreker@usgs.gov","middleInitial":"R.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":487693,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Friesz, Aaron M. 0000-0003-4096-3824 afriesz@usgs.gov","orcid":"https://orcid.org/0000-0003-4096-3824","contributorId":5943,"corporation":false,"usgs":true,"family":"Friesz","given":"Aaron","email":"afriesz@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":487691,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bennett, Stacie L.","contributorId":42820,"corporation":false,"usgs":true,"family":"Bennett","given":"Stacie","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":487695,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sleeter, Benjamin M. 0000-0003-2371-9571 bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":487688,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sleeter, Rachel R.","contributorId":7946,"corporation":false,"usgs":true,"family":"Sleeter","given":"Rachel R.","affiliations":[],"preferred":false,"id":487692,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wilson, Tamara 0000-0001-7399-7532 tswilson@usgs.gov","orcid":"https://orcid.org/0000-0001-7399-7532","contributorId":2975,"corporation":false,"usgs":true,"family":"Wilson","given":"Tamara","email":"tswilson@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":487686,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":725409,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Knuppe, Michelle 0000-0002-0374-9477","orcid":"https://orcid.org/0000-0002-0374-9477","contributorId":42125,"corporation":false,"usgs":true,"family":"Knuppe","given":"Michelle","email":"","affiliations":[],"preferred":false,"id":487694,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Van Hofwegen, Travis tvanhofwegen@usgs.gov","contributorId":5529,"corporation":false,"usgs":true,"family":"Van Hofwegen","given":"Travis","email":"tvanhofwegen@usgs.gov","affiliations":[],"preferred":true,"id":487690,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}}
,{"id":70073924,"text":"70073924 - 2014 - Contaminants from Cretaceous black shale: II. Effect of geology, weathering, climate, and land use on salinity and selenium cycling, Mancos Shale landscapes, southwestern United States","interactions":[],"lastModifiedDate":"2020-12-30T16:47:04.54835","indexId":"70073924","displayToPublicDate":"2013-12-20T11:31:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Contaminants from Cretaceous black shale: II. Effect of geology, weathering, climate, and land use on salinity and selenium cycling, Mancos Shale landscapes, southwestern United States","docAbstract":"<p><span>The Cretaceous Mancos Shale (MS) is a known nonpoint source for a significant portion of the salinity and selenium (Se) loads in the Colorado River in the southwestern United States and northwestern corner of Mexico. These two contaminants pose a serious threat to rivers in these arid regions where water supplies are especially critical. Tuttle et al. (companion paper) investigates the cycling of contaminants in a Colorado River tributary watershed (Uncompahgre River, southwestern Colorado) where the MS weathers under natural conditions. This paper builds on those results and uses regional soil data in the same watershed to investigate the impact of MS geology, weathering intensity, land use, and climate on salt and Se storage in and flux from soils on the natural landscape, irrigated agriculture fields, areas undergoing urban development, and wetlands. The size of salinity and Se reservoirs in the MS soils is quantified. Flux calculations show that during modern weathering, natural landscapes cycle salt and Se; however, little of it is released for transport to the Uncompahgre River (10% of the annual salinity and 6% of the annual Se river loads). When irrigated, salinity and Se loads from the MS soil increase (26% and 57% of the river load, respectively), causing the river to be out of compliance with Federal and State Se standards. During 100</span><span>&nbsp;</span><span>years of irrigation, seven times more Se has been removed from agricultural soil than what was lost from natural landscapes during the entire period of pedogenesis. Under more arid conditions, even less salt and Se are expected to be transported from the natural landscape. However, if wetter climates prevail, transport could increase dramatically due to storage of soluble phases in the non-irrigated soil. These results are critical input for water-resource and land-use managers who must decide whether or not the salinity and Se in a watershed can be managed, what sustainable mitigation strategies are possible, and what landscapes should be targeted. The broader implications include providing a reliable approach for quantifying nonpoint-source contamination from MS and other rock units elsewhere that weather under similar conditions and, together with results from our companion paper, address the complex interplay of geology, weathering, climate, and land use on contaminant cycling in the arid Southwest.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2013.12.011","usgsCitation":"Tuttle, M., Fahy, J.W., Elliott, J.G., Grauch, R.I., and Stillings, L., 2014, Contaminants from Cretaceous black shale: II. Effect of geology, weathering, climate, and land use on salinity and selenium cycling, Mancos Shale landscapes, southwestern United States: Applied Geochemistry, v. 46, p. 72-84, https://doi.org/10.1016/j.apgeochem.2013.12.011.","productDescription":"13 p.","startPage":"72","endPage":"84","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":281483,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Colorado River Basin, Mancos Shale","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -107.993508,38.537542 ], [ -107.993508,38.818839 ], [ -107.749497,38.818839 ], [ -107.749497,38.537542 ], [ -107.993508,38.537542 ] ] ] } } ] }","volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd52abe4b0b290850f4aa1","contributors":{"authors":[{"text":"Tuttle, Michele L. mtuttle@usgs.gov","contributorId":1028,"corporation":false,"usgs":true,"family":"Tuttle","given":"Michele L.","email":"mtuttle@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":489231,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fahy, Juli W. jfahy@usgs.gov","contributorId":57362,"corporation":false,"usgs":true,"family":"Fahy","given":"Juli","email":"jfahy@usgs.gov","middleInitial":"W.","affiliations":[],"preferred":false,"id":489234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Elliott, John G. jelliott@usgs.gov","contributorId":832,"corporation":false,"usgs":true,"family":"Elliott","given":"John","email":"jelliott@usgs.gov","middleInitial":"G.","affiliations":[],"preferred":true,"id":489230,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grauch, Richard I. 0000-0002-1763-0813 rgrauch@usgs.gov","orcid":"https://orcid.org/0000-0002-1763-0813","contributorId":1193,"corporation":false,"usgs":true,"family":"Grauch","given":"Richard","email":"rgrauch@usgs.gov","middleInitial":"I.","affiliations":[],"preferred":true,"id":489232,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stillings, Lisa L. 0000-0002-9011-8891 stilling@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-8891","contributorId":3143,"corporation":false,"usgs":true,"family":"Stillings","given":"Lisa L.","email":"stilling@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":489233,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70058881,"text":"70058881 - 2014 - The relation between invertebrate drift and two primary controls, discharge and benthic densities, in a large regulated river","interactions":[],"lastModifiedDate":"2014-01-24T09:32:06","indexId":"70058881","displayToPublicDate":"2013-12-17T10:19:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"The relation between invertebrate drift and two primary controls, discharge and benthic densities, in a large regulated river","docAbstract":"1. Invertebrate drift is a fundamental process in streams and rivers. Studies from laboratory experiments and small streams have identified numerous extrinsic (e.g. discharge, light intensity, water quality) and intrinsic factors (invertebrate life stage, benthic density, behaviour) that govern invertebrate drift concentrations (# m−3), but the factors that govern invertebrate drift in larger rivers remain poorly understood. For example, while large increases or decreases in discharge can lead to large increases in invertebrate drift, the role of smaller, incremental changes in discharge is poorly described. In addition, while we might expect invertebrate drift concentrations to be proportional to benthic densities (# m−2), the benthic–drift relation has not been rigorously evaluated.\n<br>\n2. Here, we develop a framework for modelling invertebrate drift that is derived from sediment transport studies. We use this framework to guide the analysis of high-resolution data sets of benthic density and drift concentration for four important invertebrate taxa from the Colorado River downstream of Glen Canyon Dam (mean daily discharge 325 m3 s−1) that were collected over 18 months and include multiple observations within days. Ramping of regulated flows on this river segment provides an experimental treatment that is repeated daily and allowed us to describe the functional relations between invertebrate drift and two primary controls, discharge and benthic densities.\n<br>\n3. Twofold daily variation in discharge resulted in a >10-fold increase in drift concentrations of benthic invertebrates associated with pools and detritus (i.e. Gammarus lacustris and Potamopyrgus antipodarum). In contrast, drift concentrations of sessile blackfly larvae (Simuliium arcticum), which are associated with high-velocity cobble microhabitats, decreased by over 80% as discharge doubled. Drift concentrations of Chironomidae increased proportional to discharge.\n<br>\n4. Drift of all four taxa was positively related to benthic density. Drift concentrations of Gammarus, Potamopyrgus and Chironomidae were proportional to benthic density. Drift concentrations of Simulium were positively related to benthic density, but the benthic–drift relation was less than proportional (i.e. a doubling of benthic density only led to a 40% increase in drift concentrations).\n<br>\n5. Our study demonstrates that invertebrate drift concentrations in the Colorado River are jointly controlled by discharge and benthic densities, but these controls operate at different timescales. Twofold daily variation in discharge associated with hydropeaking was the primary control on within-day variation in invertebrate drift concentrations. In contrast, benthic density, which varied 10- to 1000-fold among sampling dates, depending on the taxa, was the primary control on invertebrate drift concentrations over longer timescales (weeks to months).","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Freshwater Biology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/fwb.12285","usgsCitation":"Kennedy, T., Yackulic, C.B., Cross, W.F., Grams, P.E., Yard, M., and Copp, A.J., 2014, The relation between invertebrate drift and two primary controls, discharge and benthic densities, in a large regulated river: Freshwater Biology, v. 59, no. 3, p. 557-572, https://doi.org/10.1111/fwb.12285.","productDescription":"16 p.","startPage":"557","endPage":"572","numberOfPages":"16","ipdsId":"IP-045496","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":280362,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280361,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/fwb.12285"}],"country":"United States","state":"Arizona","otherGeospatial":"Glen Canyon Dam","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -111.666667,36.833333 ], [ -111.666667,36.966667 ], [ -111.416667,36.966667 ], [ -111.416667,36.833333 ], [ -111.666667,36.833333 ] ] ] } } ] }","volume":"59","issue":"3","noUsgsAuthors":false,"publicationDate":"2013-12-11","publicationStatus":"PW","scienceBaseUri":"52b172c1e4b0d9b325224604","contributors":{"authors":[{"text":"Kennedy, Theodore A. 0000-0003-3477-3629","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":50227,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":487413,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":487410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cross, Wyatt F.","contributorId":70881,"corporation":false,"usgs":true,"family":"Cross","given":"Wyatt","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":487414,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grams, Paul E. 0000-0002-0873-0708 pgrams@usgs.gov","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":1830,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","email":"pgrams@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":487409,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yard, Michael D. 0000-0002-6580-6027","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":8577,"corporation":false,"usgs":true,"family":"Yard","given":"Michael D.","affiliations":[],"preferred":false,"id":487412,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Copp, Adam J. 0000-0001-7385-0055 acopp@usgs.gov","orcid":"https://orcid.org/0000-0001-7385-0055","contributorId":5194,"corporation":false,"usgs":true,"family":"Copp","given":"Adam","email":"acopp@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":487411,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70058744,"text":"70058744 - 2014 - Climatic variation and tortoise survival: has a desert species met its match?","interactions":[],"lastModifiedDate":"2013-12-13T12:06:21","indexId":"70058744","displayToPublicDate":"2013-12-13T11:57:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Climatic variation and tortoise survival: has a desert species met its match?","docAbstract":"While demographic changes in short-lived species may be observed relatively quickly in response to climate changes, measuring population responses of long-lived species requires long-term studies that are not always available. We analyzed data from a population of threatened Agassiz’s desert tortoises (Gopherus agassizii) at a 2.59 km<sup>2</sup> study plot in the Sonoran Desert ecosystem of Joshua Tree National Park, California, USA from 1978 to 2012 to examine variation in apparent survival and demography in this long-lived species. Transect-based, mark-recapture surveys were conducted in 10 of those years to locate living and dead tortoises. Previous modeling suggested that this area would become unsuitable as tortoise habitat under a warming and drying climate scenario. Estimated adult population size declined greatly from 1996 to 2012. The population appeared to have high apparent survival from 1978 to 1996 but apparent survival decreased from 1997 to 2002, concurrent with persistent drought. The best model relating apparent survivorship of tortoises &ge;18 cm over time was based on a three year moving average of estimated winter precipitation. The postures and positions of a majority of dead tortoises found in 2012 were consistent with death by dehydration and starvation. Some live and many dead tortoises found in 2012 showed signs of predation or scavenging by mammalian carnivores. Coyote (Canis latrans) scats and other evidence from the site confirmed their role as tortoise predators and scavengers. Predation rates may be exacerbated by drought if carnivores switch from preferred mammalian prey to tortoises during dry years. Climate modeling suggests that the region will be subjected to even longer duration droughts in the future and that the plot may become unsuitable for continued tortoise survival. Our results showing wide fluctuations in apparent survival and decreasing tortoise density over time may be early signals of that possible outcome.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Biological Conservation","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2013.09.027","usgsCitation":"Lovich, J.E., Yackulic, C.B., Freilich, J., Agha, M., Austin, M., Meyer, K.P., Arundel, T., Hansen, J., Vamstad, M., and Root, S., 2014, Climatic variation and tortoise survival: has a desert species met its match?: Biological Conservation, v. 169, p. 214-224, https://doi.org/10.1016/j.biocon.2013.09.027.","productDescription":"11 p.","startPage":"214","endPage":"224","numberOfPages":"11","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":473324,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2013.09.027","text":"Publisher Index Page"},{"id":280295,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280294,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.biocon.2013.09.027"}],"country":"United States","state":"California","otherGeospatial":"Joshua Tree National Park","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -116.458031,33.670186 ], [ -116.458031,34.129343 ], [ -115.262191,34.129343 ], [ -115.262191,33.670186 ], [ -116.458031,33.670186 ] ] ] } } ] }","volume":"169","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52ac2c62e4b004a77d23c4c9","contributors":{"authors":[{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":487334,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":487335,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freilich, Jerry","contributorId":42518,"corporation":false,"usgs":true,"family":"Freilich","given":"Jerry","email":"","affiliations":[],"preferred":false,"id":487340,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Agha, Mickey","contributorId":22235,"corporation":false,"usgs":false,"family":"Agha","given":"Mickey","email":"","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false},{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":487337,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Austin, Meaghan","contributorId":37244,"corporation":false,"usgs":true,"family":"Austin","given":"Meaghan","affiliations":[],"preferred":false,"id":487339,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meyer, Katherine P.","contributorId":95789,"corporation":false,"usgs":true,"family":"Meyer","given":"Katherine","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":487343,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Arundel, Terence R.","contributorId":11080,"corporation":false,"usgs":true,"family":"Arundel","given":"Terence R.","affiliations":[],"preferred":false,"id":487336,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hansen, Jered","contributorId":34034,"corporation":false,"usgs":true,"family":"Hansen","given":"Jered","affiliations":[],"preferred":false,"id":487338,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Vamstad, Michael S.","contributorId":66590,"corporation":false,"usgs":true,"family":"Vamstad","given":"Michael S.","affiliations":[],"preferred":false,"id":487341,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Root, Stephanie A.","contributorId":87449,"corporation":false,"usgs":true,"family":"Root","given":"Stephanie A.","affiliations":[],"preferred":false,"id":487342,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70058428,"text":"70058428 - 2014 - Fitting statistical distributions to sea duck count data: implications for survey design and abundance estimation","interactions":[],"lastModifiedDate":"2013-12-05T09:39:16","indexId":"70058428","displayToPublicDate":"2013-12-05T09:34:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3475,"text":"Statistical Methodology","active":true,"publicationSubtype":{"id":10}},"title":"Fitting statistical distributions to sea duck count data: implications for survey design and abundance estimation","docAbstract":"Determining appropriate statistical distributions for modeling animal count data is important for accurate estimation of abundance, distribution, and trends. In the case of sea ducks along the U.S. Atlantic coast, managers want to estimate local and regional abundance to detect and track population declines, to define areas of high and low use, and to predict the impact of future habitat change on populations. In this paper, we used a modified marked point process to model survey data that recorded flock sizes of Common eiders, Long-tailed ducks, and Black, Surf, and White-winged scoters. The data come from an experimental aerial survey, conducted by the United States Fish & Wildlife Service (USFWS) Division of Migratory Bird Management, during which east-west transects were flown along the Atlantic Coast from Maine to Florida during the winters of 2009–2011. To model the number of flocks per transect (the points), we compared the fit of four statistical distributions (zero-inflated Poisson, zero-inflated geometric, zero-inflated negative binomial and negative binomial) to data on the number of species-specific sea duck flocks that were recorded for each transect flown. To model the flock sizes (the marks), we compared the fit of flock size data for each species to seven statistical distributions: positive Poisson, positive negative binomial, positive geometric, logarithmic, discretized lognormal, zeta and Yule–Simon. Akaike’s Information Criterion and Vuong’s closeness tests indicated that the negative binomial and discretized lognormal were the best distributions for all species for the points and marks, respectively. These findings have important implications for estimating sea duck abundances as the discretized lognormal is a more skewed distribution than the Poisson and negative binomial, which are frequently used to model avian counts; the lognormal is also less heavy-tailed than the power law distributions (e.g., zeta and Yule–Simon), which are becoming increasingly popular for group size modeling. Choosing appropriate statistical distributions for modeling flock size data is fundamental to accurately estimating population summaries, determining required survey effort, and assessing and propagating uncertainty through decision-making processes.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Statistical Methodology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.stamet.2012.10.002","usgsCitation":"Zipkin, E., Leirness, J.B., Kinlan, B.P., O’Connell, A.F., and Silverman, E.D., 2014, Fitting statistical distributions to sea duck count data: implications for survey design and abundance estimation: Statistical Methodology, v. 17, p. 67-81, https://doi.org/10.1016/j.stamet.2012.10.002.","productDescription":"15 p.","startPage":"67","endPage":"81","numberOfPages":"15","ipdsId":"IP-041848","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":280187,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280186,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.stamet.2012.10.002"}],"volume":"17","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"52a1a089e4b02938ec058835","contributors":{"authors":[{"text":"Zipkin, Elise F.","contributorId":70528,"corporation":false,"usgs":true,"family":"Zipkin","given":"Elise F.","affiliations":[],"preferred":false,"id":487035,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leirness, Jeffery B.","contributorId":24678,"corporation":false,"usgs":true,"family":"Leirness","given":"Jeffery","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":487033,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kinlan, Brian P.","contributorId":24679,"corporation":false,"usgs":true,"family":"Kinlan","given":"Brian","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":487034,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Connell, Allan F. 0000-0001-7032-7023 aoconnell@usgs.gov","orcid":"https://orcid.org/0000-0001-7032-7023","contributorId":471,"corporation":false,"usgs":true,"family":"O’Connell","given":"Allan","email":"aoconnell@usgs.gov","middleInitial":"F.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":487032,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Silverman, Emily D.","contributorId":79220,"corporation":false,"usgs":true,"family":"Silverman","given":"Emily","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":487036,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70048865,"text":"70048865 - 2014 - Hysteresis and uncertainty in soil water-retention curve parameters","interactions":[],"lastModifiedDate":"2018-03-08T15:54:54","indexId":"70048865","displayToPublicDate":"2013-12-01T14:48:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2327,"text":"Journal of Geotechnical and Geoenvironmental Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Hysteresis and uncertainty in soil water-retention curve parameters","docAbstract":"Accurate estimates of soil hydraulic parameters representing wetting and drying paths are required for predicting hydraulic and mechanical responses in a large number of applications. A comprehensive suite of laboratory experiments was conducted to measure hysteretic soil-water characteristic curves (SWCCs) representing a wide range of soil types. Results were used to quantitatively assess differences and uncertainty in three simplifications frequently adopted to estimate wetting-path SWCC parameters from more easily measured drying curves. They are the following: (1) α<sup>w</sup>=2α<sup>d</sup>, (2) n<sup>w</sup>=n<sup>d</sup>, and (3) θ<sup>w</sup><sub>s</sub>=θ<sup>d</sup><sub>s</sub>, where α, n, and θ<sub>s</sub> are fitting parameters entering van Genuchten’s commonly adopted SWCC model, and the superscripts w and d indicate wetting and drying paths, respectively. The average ratio αw/αd for the data set was 2.24±1.25. Nominally cohesive soils had a lower α<sup>w</sup>/α<sup>d</sup> ratio (1.73±0.94) than nominally cohesionless soils (3.14±1.27). The average n<sup>w</sup>/n<sup>d</sup> ratio was 1.01±0.11 with no significant dependency on soil type, thus confirming the n<sup>w</sup>=n<sup>d</sup> simplification for a wider range of soil types than previously available. Water content at zero suction during wetting (θ<sup>w</sup><sub>s</sub>) was consistently less than during drying (θ<sup>d</sup><sub>s</sub>) owing to air entrapment. The θ<sup>w</sup><sub>s</sub>/θ<sup>d</sup>s</sup> ratio averaged 0.85±0.10 and was comparable for nominally cohesive (0.87±0.11) and cohesionless (0.81±0.08) soils. Regression statistics are provided to quantitatively account for uncertainty in estimating hysteretic retention curves. Practical consequences are demonstrated for two case studies.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Geotechnical and Geoenvironmental Engineering","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Society of Civil Engineers","doi":"10.1061/(ASCE)GT.1943-5606.0001071","usgsCitation":"Likos, W.J., Lu, N., and Godt, J.W., 2014, Hysteresis and uncertainty in soil water-retention curve parameters: Journal of Geotechnical and Geoenvironmental Engineering, v. 140, no. 4, 11 p., https://doi.org/10.1061/(ASCE)GT.1943-5606.0001071.","productDescription":"11 p.","numberOfPages":"11","ipdsId":"IP-052321","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":280761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280760,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1061/(ASCE)GT.1943-5606.0001071"}],"volume":"140","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd61dee4b0b290850fdcdf","contributors":{"authors":[{"text":"Likos, William J.","contributorId":14725,"corporation":false,"usgs":true,"family":"Likos","given":"William","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":485767,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lu, Ning","contributorId":191360,"corporation":false,"usgs":false,"family":"Lu","given":"Ning","email":"","affiliations":[{"id":12620,"text":"U.S. Army Corp. of Engineers","active":true,"usgs":false}],"preferred":false,"id":485768,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Godt, Jonathan W. 0000-0002-8737-2493 jgodt@usgs.gov","orcid":"https://orcid.org/0000-0002-8737-2493","contributorId":1166,"corporation":false,"usgs":true,"family":"Godt","given":"Jonathan","email":"jgodt@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":485766,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70098946,"text":"70098946 - 2014 - Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm","interactions":[],"lastModifiedDate":"2017-04-06T16:13:58","indexId":"70098946","displayToPublicDate":"2013-12-01T14:31:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2172,"text":"Journal of Applied Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm","docAbstract":"Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer’s accuracy of 93% and a user’s accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of ≥95% for cultivated croplands and ≥76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season.","language":"English","publisher":"SPIE","doi":"10.1117/1.JRS.8.083685","usgsCitation":"Wu, Z., Thenkabail, P.S., Mueller, R., Zakzeski, A., Melton, F., Johnson, L., Rosevelt, C., Dwyer, J., Jones, J., and Verdin, J.P., 2014, Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm: Journal of Applied Remote Sensing, v. 8, no. 1, Article 083685; 17 p., https://doi.org/10.1117/1.JRS.8.083685.","productDescription":"Article 083685; 17 p.","numberOfPages":"17","ipdsId":"IP-044862","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":473328,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1117/1.jrs.8.083685","text":"Publisher Index Page"},{"id":284264,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":284265,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1117/1.JRS.8.083685"}],"country":"United States","state":"California","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -125.0,33.0 ], [ -125.0,42.0 ], [ -115.0,42.0 ], [ -115.0,33.0 ], [ -125.0,33.0 ] ] ] } } ] }","volume":"8","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd71abe4b0b29085107d2e","contributors":{"authors":[{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":491771,"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":491769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mueller, Rick","contributorId":101182,"corporation":false,"usgs":false,"family":"Mueller","given":"Rick","email":"","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":491778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zakzeski, Audra","contributorId":79796,"corporation":false,"usgs":true,"family":"Zakzeski","given":"Audra","email":"","affiliations":[],"preferred":false,"id":491777,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Melton, F.","contributorId":34039,"corporation":false,"usgs":true,"family":"Melton","given":"F.","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":491774,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Lee","contributorId":60122,"corporation":false,"usgs":true,"family":"Johnson","given":"Lee","email":"","affiliations":[],"preferred":false,"id":491776,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rosevelt, Carolyn","contributorId":25455,"corporation":false,"usgs":true,"family":"Rosevelt","given":"Carolyn","email":"","affiliations":[],"preferred":false,"id":491773,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dwyer, John","contributorId":45042,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","affiliations":[],"preferred":false,"id":491775,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jones, Jeanine","contributorId":6758,"corporation":false,"usgs":true,"family":"Jones","given":"Jeanine","email":"","affiliations":[],"preferred":false,"id":491772,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Verdin, James P. 0000-0003-0238-9657 verdin@usgs.gov","orcid":"https://orcid.org/0000-0003-0238-9657","contributorId":720,"corporation":false,"usgs":true,"family":"Verdin","given":"James","email":"verdin@usgs.gov","middleInitial":"P.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":491770,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70049728,"text":"70049728 - 2014 - Agricultural disturbance response models for invertebrate and algal metrics from streams at two spatial scales within the U.S.","interactions":[],"lastModifiedDate":"2014-01-24T09:47:59","indexId":"70049728","displayToPublicDate":"2013-12-01T11:21:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"title":"Agricultural disturbance response models for invertebrate and algal metrics from streams at two spatial scales within the U.S.","docAbstract":"As part of the USGS study of nutrient enrichment of streams in agricultural regions throughout the United States, about 30 sites within each of eight study areas were selected to capture a gradient of nutrient conditions. The objective was to develop watershed disturbance predictive models for macroinvertebrate and algal metrics at national and three regional landscape scales to obtain a better understanding of important explanatory variables. Explanatory variables in models were generated from landscape data, habitat, and chemistry. Instream nutrient concentration and variables assessing the amount of disturbance to the riparian zone (e.g., percent row crops or percent agriculture) were selected as most important explanatory variable in almost all boosted regression tree models regardless of landscape scale or assemblage. Frequently, TN and TP concentration and riparian agricultural land use variables showed a threshold type response at relatively low values to biotic metrics modeled. Some measure of habitat condition was also commonly selected in the final invertebrate models, though the variable(s) varied across regions. Results suggest national models tended to account for more general landscape/climate differences, while regional models incorporated both broad landscape scale and more specific local-scale variables.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Hydrobiologia","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s10750-013-1774-4","usgsCitation":"Waite, I.R., 2014, Agricultural disturbance response models for invertebrate and algal metrics from streams at two spatial scales within the U.S.: Hydrobiologia, v. 726, no. 1, p. 285-303, https://doi.org/10.1007/s10750-013-1774-4.","productDescription":"19 p.","startPage":"285","endPage":"303","numberOfPages":"19","ipdsId":"IP-038732","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":280866,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280865,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10750-013-1774-4"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.8,24.5 ], [ -124.8,49.383333 ], [ -66.95,49.383333 ], [ -66.95,24.5 ], [ -124.8,24.5 ] ] ] } } ] }","volume":"726","issue":"1","noUsgsAuthors":false,"publicationDate":"2013-12-18","publicationStatus":"PW","scienceBaseUri":"53cd4c08e4b0b290850f0b8b","contributors":{"authors":[{"text":"Waite, Ian R. 0000-0003-1681-6955 iwaite@usgs.gov","orcid":"https://orcid.org/0000-0003-1681-6955","contributorId":616,"corporation":false,"usgs":true,"family":"Waite","given":"Ian","email":"iwaite@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":486107,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70046853,"text":"70046853 - 2014 - Deep-ocean ferromanganese crusts and nodules","interactions":[],"lastModifiedDate":"2017-02-03T12:38:01","indexId":"70046853","displayToPublicDate":"2013-12-01T10:52:50","publicationYear":"2014","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Deep-ocean ferromanganese crusts and nodules","docAbstract":"Ferromanganese crusts and nodules may provide a future resource for a large variety of metals, including many that are essential for emerging high- and green-technology applications. A brief review of nodules and crusts provides a setting for a discussion on the latest (past 10 years) research related to the geochemistry of sequestration of metals from seawater. Special attention is given to cobalt, nickel, titanium, rare earth elements and yttrium, bismuth, platinum, tungsten, tantalum, hafnium, tellurium, molybdenum, niobium, zirconium, and lithium. Sequestration from seawater by sorption, surface oxidation, substitution, and precipitation of discrete phases is discussed. Mechanisms of metal enrichment reflect modes of formation of the crusts and nodules, such as hydrogenetic (from seawater), diagenetic (from porewaters), and mixed diagenetic–hydrogenetic processes.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Treatise on geochemistry","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-08-095975-7.01111-6","usgsCitation":"Hein, J.R., and Koschinsky, A., 2014, Deep-ocean ferromanganese crusts and nodules, chap. <i>of</i> Treatise on geochemistry, v. 13, p. 273-291, https://doi.org/10.1016/B978-0-08-095975-7.01111-6.","productDescription":"19 p.","startPage":"273","endPage":"291","ipdsId":"IP-030576","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":284154,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":284153,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/B978-0-08-095975-7.01111-6"}],"volume":"13","edition":"Second","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd53f0e4b0b290850f574a","contributors":{"authors":[{"text":"Hein, James R. 0000-0002-5321-899X jhein@usgs.gov","orcid":"https://orcid.org/0000-0002-5321-899X","contributorId":2828,"corporation":false,"usgs":true,"family":"Hein","given":"James","email":"jhein@usgs.gov","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":480471,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koschinsky, Andrea","contributorId":83813,"corporation":false,"usgs":true,"family":"Koschinsky","given":"Andrea","affiliations":[],"preferred":false,"id":480472,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70096238,"text":"70096238 - 2014 - Near-bottom circulation and dispersion of sediment containing <i>Alexandrium fundyense</i> cysts in the Gulf of Maine during 2010-2011","interactions":[],"lastModifiedDate":"2014-05-29T14:45:26","indexId":"70096238","displayToPublicDate":"2013-12-01T10:38:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1371,"text":"Deep-Sea Research Part II: Topical Studies in Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Near-bottom circulation and dispersion of sediment containing <i>Alexandrium fundyense</i> cysts in the Gulf of Maine during 2010-2011","docAbstract":"The life cycle of <i>Alexandrium fundyense</i> in the Gulf of Maine includes a dormant cyst stage that spends the winter predominantly in the bottom sediment. Wave-current bottom stress caused by storms and tides induces resuspension of cyst-containing sediment during winter and spring. Resuspended sediment could be transported by water flow to different locations in the Gulf and the redistribution of sediment containing <i>A. fundyense</i> cysts could alter the spatial and temporal manifestation of its spring bloom. The present study evaluates model near-bottom flow during storms, when sediment resuspension and redistribution are most likely to occur, between October and May when <i>A. fundyense</i> cells are predominantly in cyst form. Simulated water column sediment (mud) concentrations from representative locations of the Gulf are used to initialize particle tracking simulations for the period October 2010–May 2011. Particles are tracked in full three-dimensional model solutions including a sinking velocity characteristic of cyst and aggregated mud settling (0.1 mm s<sup>−</sup>1). Although most of the material was redeposited near the source areas, small percentages of total resuspended sediment from some locations in the western (~4%) and eastern (2%) Maine shelf and the Bay of Fundy (1%) traveled distances longer than 100 km before resettling. The redistribution changed seasonally and was sensitive to the prescribed sinking rate. Estimates of the amount of cysts redistributed with the sediment were small compared to the inventory of cysts in the upper few centimeters of sediment but could potentially have more relevance immediately after deposition.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Deep-Sea Research Part II: Topical Studies in Oceanography","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr2.2013.11.003","usgsCitation":"Aretxabaleta, A., Butman, B., Signell, R.P., Dalyander, P., Sherwood, C.R., Sheremet, V.A., and McGillicuddy, D.J., 2014, Near-bottom circulation and dispersion of sediment containing <i>Alexandrium fundyense</i> cysts in the Gulf of Maine during 2010-2011: Deep-Sea Research Part II: Topical Studies in Oceanography, v. 103, p. 96-111, https://doi.org/10.1016/j.dsr2.2013.11.003.","productDescription":"16 p.","startPage":"96","endPage":"111","numberOfPages":"16","ipdsId":"IP-051764","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":473329,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dsr2.2013.11.003","text":"Publisher Index Page"},{"id":283874,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":283873,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.dsr2.2013.11.003"}],"country":"United States","state":"Maine","otherGeospatial":"Bay Of Fundy;Gulf Of Maine","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -71.0,42.5 ], [ -71.0,45.5 ], [ -65.0,45.5 ], [ -65.0,42.5 ], [ -71.0,42.5 ] ] ] } } ] }","volume":"103","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53885706e4b0318b93124adf","contributors":{"authors":[{"text":"Aretxabaleta, Alfredo L.","contributorId":41311,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo L.","affiliations":[],"preferred":false,"id":491497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Butman, Bradford 0000-0002-4174-2073 bbutman@usgs.gov","orcid":"https://orcid.org/0000-0002-4174-2073","contributorId":943,"corporation":false,"usgs":true,"family":"Butman","given":"Bradford","email":"bbutman@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":491493,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Signell, Richard P. rsignell@usgs.gov","contributorId":1435,"corporation":false,"usgs":true,"family":"Signell","given":"Richard","email":"rsignell@usgs.gov","middleInitial":"P.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":491494,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dalyander, P. Soupy 0000-0001-9583-0872","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":65177,"corporation":false,"usgs":true,"family":"Dalyander","given":"P. Soupy","affiliations":[],"preferred":false,"id":491499,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":491495,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sheremet, Vitalii A.","contributorId":52886,"corporation":false,"usgs":true,"family":"Sheremet","given":"Vitalii","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":491498,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McGillicuddy, Dennis J. Jr.","contributorId":13541,"corporation":false,"usgs":true,"family":"McGillicuddy","given":"Dennis","suffix":"Jr.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":491496,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70048647,"text":"70048647 - 2014 - The Mw 5.8 Mineral, Virginia, earthquake of August 2011 and aftershock sequence: constraints on earthquake source parameters and fault geometry","interactions":[],"lastModifiedDate":"2014-02-24T11:04:12","indexId":"70048647","displayToPublicDate":"2013-12-01T10:09:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"The Mw 5.8 Mineral, Virginia, earthquake of August 2011 and aftershock sequence: constraints on earthquake source parameters and fault geometry","docAbstract":"<p>The M<sub>w</sub> 5.8 earthquake of 23 August 2011 (17:51:04 UTC) (moment, M0 5.7×10<sup>17</sup>  N·m) occurred near Mineral, Virginia, within the central Virginia seismic zone and was felt by more people than any other earthquake in United States history. The U.S. Geological Survey (USGS) received 148,638 felt reports from 31 states and 4 Canadian provinces. The USGS PAGER system estimates as many as 120,000 people were exposed to shaking intensity levels of IV and greater, with approximately 10,000 exposed to shaking as high as intensity VIII. Both regional and teleseismic moment tensor solutions characterize the earthquake as a northeast‐striking reverse fault that nucleated at a depth of approximately 7±2  km. The distribution of reported macroseismic intensities is roughly ten times the area of a similarly sized earthquake in the western United States (Horton and Williams, 2012). Near‐source and far‐field damage reports, which extend as far away as Washington, D.C., (135 km away) and Baltimore, Maryland, (200 km away) are consistent with an earthquake of this size and depth in the eastern United States (EUS).</p>\n<br/>\n<p>Within the first few days following the earthquake, several government and academic institutions installed 36 portable seismograph stations in the epicentral region, making this among the best‐recorded aftershock sequences in the EUS. Based on modeling of these data, we provide a detailed description of the source parameters of the mainshock and analysis of the subsequent aftershock sequence for defining the fault geometry, area of rupture, and observations of the aftershock sequence magnitude–frequency and temporal distribution. The observed slope of the magnitude–frequency curve or b‐value for the aftershock sequence is consistent with previous EUS studies (b=0.75), suggesting that most of the accumulated strain was released by the mainshock. The aftershocks define a rupture that extends between approximately 2–8 km in depth and 8–10 km along the strike of the fault plane. Best‐fit modeling of the geometry of the aftershock sequence defines a rupture plane that strikes N36°E and dips to the east‐southeast at 49.5°. Moment tensor solutions of the mainshock and larger aftershocks are consistent with the distribution of aftershock locations, both indicating reverse slip along a northeast–southwest striking southeast‐dipping fault plane.</p>","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Bulletin of the Seismological Society of America","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120130058","usgsCitation":"McNamara, D.E., Benz, H., Herrmann, R., Bergman, E.A., Earle, P., Meltzer, A., Withers, M., and Chapman, M., 2014, The Mw 5.8 Mineral, Virginia, earthquake of August 2011 and aftershock sequence: constraints on earthquake source parameters and fault geometry: Bulletin of the Seismological Society of America, v. 104, no. 1, p. 40-54, https://doi.org/10.1785/0120130058.","productDescription":"15 p.","startPage":"40","endPage":"54","numberOfPages":"15","ipdsId":"IP-051290","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":280971,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280970,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1785/0120130058"}],"country":"Canada;United States","state":"Virginia","city":"Mineral","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -88.86,30.86 ], [ -88.86,46.86 ], [ -66.8,46.86 ], [ -66.8,30.86 ], [ -88.86,30.86 ] ] ] } } ] }","volume":"104","issue":"1","noUsgsAuthors":false,"publicationDate":"2013-12-24","publicationStatus":"PW","scienceBaseUri":"53cd7714e4b0b2908510b519","contributors":{"authors":[{"text":"McNamara, Daniel E. 0000-0001-6860-0350 mcnamara@usgs.gov","orcid":"https://orcid.org/0000-0001-6860-0350","contributorId":402,"corporation":false,"usgs":true,"family":"McNamara","given":"Daniel","email":"mcnamara@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":485272,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benz, H.M.","contributorId":21594,"corporation":false,"usgs":true,"family":"Benz","given":"H.M.","email":"","affiliations":[],"preferred":false,"id":485274,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herrmann, Robert B.","contributorId":80255,"corporation":false,"usgs":false,"family":"Herrmann","given":"Robert B.","affiliations":[],"preferred":false,"id":485278,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bergman, Eric A. 0000-0002-7069-8286","orcid":"https://orcid.org/0000-0002-7069-8286","contributorId":84513,"corporation":false,"usgs":false,"family":"Bergman","given":"Eric","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":485279,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Earle, Paul","contributorId":13536,"corporation":false,"usgs":true,"family":"Earle","given":"Paul","affiliations":[],"preferred":false,"id":485273,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meltzer, Anne","contributorId":64559,"corporation":false,"usgs":true,"family":"Meltzer","given":"Anne","affiliations":[],"preferred":false,"id":485277,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Withers, Mitch","contributorId":24684,"corporation":false,"usgs":true,"family":"Withers","given":"Mitch","email":"","affiliations":[],"preferred":false,"id":485275,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chapman, Martin","contributorId":45622,"corporation":false,"usgs":true,"family":"Chapman","given":"Martin","affiliations":[],"preferred":false,"id":485276,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70093432,"text":"70093432 - 2014 - An evaluation of temporal changes in sediment accumulation and impacts on carbon burial in Mobile Bay, Alabama, USA","interactions":[],"lastModifiedDate":"2025-05-13T17:02:45.609004","indexId":"70093432","displayToPublicDate":"2013-12-01T09:56:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"An evaluation of temporal changes in sediment accumulation and impacts on carbon burial in Mobile Bay, Alabama, USA","docAbstract":"The estuarine environment can serve as either a source or sink of carbon relative to the coastal ocean carbon budget. A variety of time-dependent processes such as sedimentation, carbon supply, and productivity dictate how estuarine systems operate, and Mobile Bay is a system that has experienced both natural and anthropogenic perturbations that influenced depositional processes and carbon cycling. Sediments from eight box cores provide a record of change in bulk sediment accumulation and carbon burial over the past 110 years. Accumulation rates in the central part of the basin (0.09 g cm<sup>−2</sup>) were 60–80 % less than those observed at the head (0.361 g cm<sup>−2</sup>) and mouth (0.564 g cm<sup>−2</sup>) of the bay. Sediment accumulation in the central bay decreased during the past 90 years in response to both anthropogenic (causeway construction) and natural (tropical cyclones) perturbations. Sediment accumulation inevitably increased the residence time of organic carbon in the oxic zone, as observed in modeled remineralization rates, and reduced the overall carbon burial. Such observations highlight the critical balance among sediment accumulation, carbon remineralization, and carbon burial in dynamic coastal environments. Time-series analysis based solely on short-term observation would not capture the long-term effects of changes in sedimentation on carbon cycling. Identifying these relationships over longer timescales (multi-annual to decadal) will provide a far better evaluation of coastal ocean carbon budgets.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Estuaries and Coasts","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s12237-013-9731-z","usgsCitation":"Smith, C.G., and Osterman, L.E., 2014, An evaluation of temporal changes in sediment accumulation and impacts on carbon burial in Mobile Bay, Alabama, USA: Estuaries and Coasts, v. 37, no. 5, p. 1092-1106, https://doi.org/10.1007/s12237-013-9731-z.","productDescription":"15 p.","startPage":"1092","endPage":"1106","numberOfPages":"15","ipdsId":"IP-045967","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":282102,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":282101,"rank":1,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s12237-013-9731-z"}],"country":"United States","state":"Alabama","otherGeospatial":"Mobile Bay","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -88.25,30.25 ], [ -88.25,30.833333 ], [ -87.583333,30.833333 ], [ -87.583333,30.25 ], [ -88.25,30.25 ] ] ] } } ] }","volume":"37","issue":"5","noUsgsAuthors":false,"publicationDate":"2013-12-04","publicationStatus":"PW","scienceBaseUri":"53cd4c87e4b0b290850f107a","contributors":{"authors":[{"text":"Smith, Christopher G. 0000-0002-8075-4763 cgsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-8075-4763","contributorId":3410,"corporation":false,"usgs":true,"family":"Smith","given":"Christopher","email":"cgsmith@usgs.gov","middleInitial":"G.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":490008,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Osterman, Lisa E. osterman@usgs.gov","contributorId":3058,"corporation":false,"usgs":true,"family":"Osterman","given":"Lisa","email":"osterman@usgs.gov","middleInitial":"E.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":490007,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70059149,"text":"70059149 - 2014 - Improving groundwater predictions utilizing seasonal precipitation forecasts from general circulation models forced with sea surface temperature forecasts","interactions":[],"lastModifiedDate":"2013-12-19T09:49:32","indexId":"70059149","displayToPublicDate":"2013-12-01T09:45:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2341,"text":"Journal of Hydrologic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Improving groundwater predictions utilizing seasonal precipitation forecasts from general circulation models forced with sea surface temperature forecasts","docAbstract":"Recent studies have found a significant association between climatic variability and basin hydroclimatology, particularly groundwater levels, over the southeast United States. The research reported in this paper evaluates the potential in developing 6-month-ahead groundwater-level forecasts based on the precipitation forecasts from ECHAM 4.5 General Circulation Model Forced with Sea Surface Temperature forecasts. Ten groundwater wells and nine streamgauges from the USGS Groundwater Climate Response Network and Hydro-Climatic Data Network were selected to represent groundwater and surface water flows, respectively, having minimal anthropogenic influences within the Flint River Basin in Georgia, United States. The writers employ two low-dimensional models [principle component regression (PCR) and canonical correlation analysis (CCA)] for predicting groundwater and streamflow at both seasonal and monthly timescales. Three modeling schemes are considered at the beginning of January to predict winter (January, February, and March) and spring (April, May, and June) streamflow and groundwater for the selected sites within the Flint River Basin. The first scheme (model 1) is a null model and is developed using PCR for every streamflow and groundwater site using previous 3-month observations (October, November, and December) available at that particular site as predictors. Modeling schemes 2 and 3 are developed using PCR and CCA, respectively, to evaluate the role of precipitation forecasts in improving monthly and seasonal groundwater predictions. Modeling scheme 3, which employs a CCA approach, is developed for each site by considering observed groundwater levels from nearby sites as predictands. The performance of these three schemes is evaluated using two metrics (correlation coefficient and relative RMS error) by developing groundwater-level forecasts based on leave-five-out cross-validation. Results from the research reported in this paper show that using precipitation forecasts in climate models improves the ability to predict the interannual variability of winter and spring streamflow and groundwater levels over the basin. However, significant conditional bias exists in all the three modeling schemes, which indicates the need to consider improved modeling schemes as well as the availability of longer time-series of observed hydroclimatic information over the basin.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Hydrologic Engineering","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Society of Civil Engineers","doi":"10.1061/(ASCE)HE.1943-5584.0000776","usgsCitation":"Almanaseer, N., Sankarasubramanian, A., and Bales, J., 2014, Improving groundwater predictions utilizing seasonal precipitation forecasts from general circulation models forced with sea surface temperature forecasts: Journal of Hydrologic Engineering, v. 19, no. 1, p. 87-98, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000776.","productDescription":"12 p.","startPage":"87","endPage":"98","numberOfPages":"12","ipdsId":"IP-042885","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":280427,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":280411,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000776"}],"country":"United States","state":"Georgia","otherGeospatial":"Flint River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -85.0,31.0 ], [ -85.0,33.5 ], [ -83.5,33.5 ], [ -83.5,31.0 ], [ -85.0,31.0 ] ] ] } } ] }","volume":"19","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd6230e4b0b290850fe033","contributors":{"authors":[{"text":"Almanaseer, Naser","contributorId":13732,"corporation":false,"usgs":true,"family":"Almanaseer","given":"Naser","email":"","affiliations":[],"preferred":false,"id":487497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankarasubramanian, A.","contributorId":23062,"corporation":false,"usgs":true,"family":"Sankarasubramanian","given":"A.","affiliations":[],"preferred":false,"id":487498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bales, Jerad","contributorId":47390,"corporation":false,"usgs":true,"family":"Bales","given":"Jerad","affiliations":[],"preferred":false,"id":487499,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70073409,"text":"70073409 - 2014 - HiRISE observations of new impact craters exposing Martian ground ice","interactions":[],"lastModifiedDate":"2018-11-02T10:56:31","indexId":"70073409","displayToPublicDate":"2013-12-01T09:33:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2317,"text":"Journal of Geophysical Research E: Planets","active":true,"publicationSubtype":{"id":10}},"title":"HiRISE observations of new impact craters exposing Martian ground ice","docAbstract":"Twenty small new impact craters or clusters have been observed to excavate bright material inferred to be ice at mid and high latitudes on Mars. In the northern hemisphere, the craters are widely distributed geographically and occur at latitudes as low as 39°N. Stability modeling suggests that this ice distribution requires a long-term average atmospheric water vapor content around 25 precipitable microns, more than double the present value, which is consistent with the expected effect of recent orbital variations. Alternatively, near-surface humidity could be higher than expected for current column abundances if water vapor is not well-mixed with atmospheric CO<sub>2</sub>, or the vapor pressure at the ice table could be lower due to salts. Ice in and around the craters remains visibly bright for months to years, indicating that it is clean ice rather than ice-cemented regolith. Although some clean ice may be produced by the impact process, it is likely that the original ground ice was excess ice (exceeding dry soil pore space) in many cases. Observations of the craters suggest small-scale heterogeneities in this excess ice. The origin of such ice is uncertain. Ice lens formation by migration of thin films of liquid is most consistent with local heterogeneity in ice content and common surface boulders, but in some cases nearby thermokarst landforms suggest large amounts of excess ice that may be best explained by a degraded ice sheet.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Geophysical Research E: Planets","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1002/2013JE004482","usgsCitation":"Dundas, C.M., Byrne, S., McEwen, A.S., Mellon, M.T., Kennedy, M.R., Daubar, I., and Saper, L., 2014, HiRISE observations of new impact craters exposing Martian ground ice: Journal of Geophysical Research E: Planets, v. 119, no. 1, p. 109-127, https://doi.org/10.1002/2013JE004482.","productDescription":"19 p.","startPage":"109","endPage":"127","numberOfPages":"19","ipdsId":"IP-049885","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":281220,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/2013JE004482"},{"id":281230,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"119","issue":"1","noUsgsAuthors":false,"publicationDate":"2014-01-27","publicationStatus":"PW","scienceBaseUri":"53cd6076e4b0b290850fcf0f","contributors":{"authors":[{"text":"Dundas, Colin M. 0000-0003-2343-7224 cdundas@usgs.gov","orcid":"https://orcid.org/0000-0003-2343-7224","contributorId":2937,"corporation":false,"usgs":true,"family":"Dundas","given":"Colin","email":"cdundas@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":488709,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byrne, Shane","contributorId":53513,"corporation":false,"usgs":false,"family":"Byrne","given":"Shane","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":488714,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McEwen, Alfred S.","contributorId":61657,"corporation":false,"usgs":false,"family":"McEwen","given":"Alfred","email":"","middleInitial":"S.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":488715,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mellon, Michael T.","contributorId":8603,"corporation":false,"usgs":false,"family":"Mellon","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":7037,"text":"Southwest Research Institute, Boulder, Colorado","active":true,"usgs":false}],"preferred":false,"id":488710,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kennedy, Megan R.","contributorId":19474,"corporation":false,"usgs":true,"family":"Kennedy","given":"Megan","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":488711,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Daubar, Ingrid J.","contributorId":34431,"corporation":false,"usgs":true,"family":"Daubar","given":"Ingrid J.","affiliations":[],"preferred":false,"id":488713,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Saper, Lee","contributorId":32085,"corporation":false,"usgs":true,"family":"Saper","given":"Lee","email":"","affiliations":[],"preferred":false,"id":488712,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70111687,"text":"70111687 - 2014 - Virtual Beach 3: user's guide","interactions":[],"lastModifiedDate":"2014-07-08T08:27:34","indexId":"70111687","displayToPublicDate":"2013-12-01T08:52:19","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesNumber":"EPA/600/R-13/311","title":"Virtual Beach 3: user's guide","docAbstract":"<p>Virtual Beach version 3 (VB<sub>3</sub>) is a decision support tool that constructs site-specific statistical models to predict fecal indicator bacteria (FIB) concentrations at recreational beaches.  VB<sub>3</sub> is primarily designed for beach managers responsible for making decisions regarding beach closures or the issuance of swimming advisories due to pathogen contamination.  However, researchers, scientists, engineers, and students interested in studying relationships between water quality indicators and ambient environmental conditions will find VB<sub>3</sub> useful.  VB<sub>3</sub> reads input data from a text file or Excel document, assists the user in preparing the data for analysis, enables automated model selection using a wide array of possible model evaluation criteria, and provides predictions using a chosen model parameterized with new data.  With an integrated mapping component to determine the geographic orientation of the beach, the software can automatically decompose wind/current/wave speed and magnitude information into along-shore and onshore/offshore components for use in subsequent analyses.  Data can be examined using simple scatter plots to evaluate relationships between the response and independent variables (IVs).  VB<sub>3</sub> can produce interaction terms between the primary IVs, and it can also test an array of transformations to maximize the linearity of the relationship The software includes search routines for finding the \"best\" models from an array of possible choices.  Automated censoring of statistical models with highly correlated IVs occurs during the selection process.  Models can be constructed either using previously collected data or forecasted environmental information.  VB<sub>3</sub> has residual diagnostics for regression models, including automated outlier identification and removal using DFFITs or Cook's Distances.</p>","language":"English","publisher":"US EPA Office of Research and Development Ecosystems Research Division","publisherLocation":"Athens, GA","usgsCitation":"Cyterski, M., Brooks, W., Galvin, M., Wolfe, K., Carvin, R., Roddick, T., Fienen, M., and Corsi, S., 2014, Virtual Beach 3: user's guide, 86 p.","productDescription":"86 p.","numberOfPages":"88","ipdsId":"IP-053145","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":289444,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":289501,"type":{"id":15,"text":"Index Page"},"url":"https://www2.epa.gov/exposure-assessment-models/virtual-beach-v-30-user-guide"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53bbc188e4b084059e8bff0c","contributors":{"authors":[{"text":"Cyterski, Mike","contributorId":64161,"corporation":false,"usgs":true,"family":"Cyterski","given":"Mike","affiliations":[],"preferred":false,"id":494434,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brooks, Wesley","contributorId":29738,"corporation":false,"usgs":true,"family":"Brooks","given":"Wesley","affiliations":[],"preferred":false,"id":494431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Galvin, Mike","contributorId":26972,"corporation":false,"usgs":true,"family":"Galvin","given":"Mike","email":"","affiliations":[],"preferred":false,"id":494430,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wolfe, Kurt","contributorId":50825,"corporation":false,"usgs":true,"family":"Wolfe","given":"Kurt","email":"","affiliations":[],"preferred":false,"id":494433,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carvin, Rebecca","contributorId":97820,"corporation":false,"usgs":true,"family":"Carvin","given":"Rebecca","affiliations":[],"preferred":false,"id":494437,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roddick, Tonia","contributorId":40129,"corporation":false,"usgs":true,"family":"Roddick","given":"Tonia","email":"","affiliations":[],"preferred":false,"id":494432,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fienen, Mike 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":85507,"corporation":false,"usgs":true,"family":"Fienen","given":"Mike","email":"","affiliations":[],"preferred":false,"id":494436,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Corsi, Steve","contributorId":68652,"corporation":false,"usgs":true,"family":"Corsi","given":"Steve","email":"","affiliations":[],"preferred":false,"id":494435,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70132431,"text":"70132431 - 2014 - Actual evapotranspiration (water use) assessment of the Colorado River Basin at the Landsat resolution using the operational simplified surface energy balance model","interactions":[],"lastModifiedDate":"2017-01-18T11:37:09","indexId":"70132431","displayToPublicDate":"2013-12-01T00:00:00","publicationYear":"2014","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":"Actual evapotranspiration (water use) assessment of the Colorado River Basin at the Landsat resolution using the operational simplified surface energy balance model","docAbstract":"<p>Accurately estimating consumptive water use in the Colorado River Basin (CRB) is important for assessing and managing limited water resources in the basin. Increasing water demand from various sectors may threaten long-term sustainability of the water supply in the arid southwestern United States. We have developed a first-ever basin-wide actual evapotranspiration (ET<sub>a</sub>) map of the CRB at the Landsat scale for water use assessment at the field level. We used the operational Simplified Surface Energy Balance (SSEBop) model for estimating ET<sub>a</sub> using 328 cloud-free Landsat images acquired during 2010. Our results show that cropland had the highest ET<sub>a</sub> among all land cover classes except for water. Validation using eddy covariance measured ET<sub>a</sub> showed that the SSEBop model nicely captured the variability in annual ET<sub>a</sub> with an overall R<sup>2</sup> of 0.78 and a mean bias error of about 10%. Comparison with water balance-based ETa showed good agreement (R<sup>2 </sup>= 0.85) at the sub-basin level. Though there was good correlation (R<sup>2</sup> = 0.79) between Moderate Resolution Imaging Spectroradiometer (MODIS)-based ETa (1 km spatial resolution) and Landsat-based ET<sub>a</sub> (30 m spatial resolution), the spatial distribution of MODIS-based ET<sub>a</sub> was not suitable for water use assessment at the field level. In contrast, Landsat-based ET<sub>a</sub> has good potential to be used at the field level for water management. With further validation using multiple years and sites, our methodology can be applied for regular production of ET<sub>a</sub> maps of larger areas such as the conterminous United States.</p>","language":"English","publisher":"MDPI","doi":"10.3390/rs6010233","usgsCitation":"Singh, R.K., Senay, G.B., Velpuri, N.M., Bohms, S., Russell L, S., and Verdin, J.P., 2014, Actual evapotranspiration (water use) assessment of the Colorado River Basin at the Landsat resolution using the operational simplified surface energy balance model: Remote Sensing, v. 6, no. 1, p. 233-256, https://doi.org/10.3390/rs6010233.","productDescription":"24 p.","startPage":"233","endPage":"256","numberOfPages":"24","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-049231","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":473330,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index 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