Regional land-use models must address several foundational elements, including understanding geographic setting, establishing regional land-use histories, modeling process and representing drivers of change, representing local land-use patterns, managing issues of scale and complexity, and development of scenarios. Key difficulties include managing an array of biophysical and socioeconomic processes across multiple spatial and temporal scales, and acquiring and utilizing empirical data to support the analysis of those processes. The Southeastern and Pacific Northwest regions of the United States, two heavily forested regions with significant forest industries, are examined in the context of these foundational elements. Geographic setting fundamentally affects both the primary land cover (forest) in the two regions, and the structure and form of land use (forestry). Land-use histories of the regions can be used to parameterize land-use models, validate model performance, and explore land-use scenarios. Drivers of change in the two regions are many and varied, with issues of scale and complexity posing significant challenges. Careful scenario development can be used to simplify process-based land-use models, and can improve our ability to address specific research questions. The successful modeling of land-use change in these two areas requires integration of both top-down and bottom-up drivers of change, using scenario frameworks to both guide and simplify the modeling process. Modular approaches, with utilization and integration of existing process models, allow regional land-use modelers the opportunity to better represent primary drivers of land-use change. However, availability of data to represent driving forces remains a primary obstacle.
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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":782445,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barnes, Christopher 0000-0002-4608-4364 christopher.barnes.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-4608-4364","contributorId":198908,"corporation":false,"usgs":true,"family":"Barnes","given":"Christopher","email":"christopher.barnes.ctr@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":782446,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70118928,"text":"70118928 - 2010 - Field evaluation of a two-dimensional hydrodynamic model near boulders for habitat calculation","interactions":[],"lastModifiedDate":"2017-01-11T16:08:27","indexId":"70118928","displayToPublicDate":"2009-06-24T11:24:35","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Field evaluation of a two-dimensional hydrodynamic model near boulders for habitat calculation","docAbstract":"Two-dimensional hydrodynamic models are now widely used in aquatic habitat studies. To test the sensitivity of calculated habitat outcomes to limitations of such a model and of typical field data, bathmetry, depth and velocity data were collected for three discharges in the vicinity of two large boulders in the South Platte River (Colorado) and used in the River2D model. Simulated depth and velocity were compared with observed values at 204 locations and the differences in habitat numbers produced by observed and simulated conditions were calculated. The bulk of the differences between simulated and observed depth and velocity values were found to lie within the likely error of measurement. However, the effect of flow simulation outliers on potential habitat outcomes must be considered when using 2D models for habitat simulation. Furthermore, the shape of the habitat suitability relation can influence the effects of simulation errors. Habitat relations with steep slopes in the velocity ranges found in similar study areas are expected to be sensitive to the magnitude of error found here. Comparison of habitat values derived from simulated and observed depth and velocity revealed a small tendency to under-predict habitat values.","language":"English","publisher":"Wiley","doi":"10.1002/rra.1278","usgsCitation":"Waddle, T., 2010, Field evaluation of a two-dimensional hydrodynamic model near boulders for habitat calculation: River Research and Applications, v. 26, no. 6, p. 730-741, https://doi.org/10.1002/rra.1278.","productDescription":"12 p.","startPage":"730","endPage":"741","numberOfPages":"12","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":475960,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.1278","text":"Publisher Index Page"},{"id":291486,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"6","noUsgsAuthors":false,"publicationDate":"2009-06-24","publicationStatus":"PW","scienceBaseUri":"53db5843e4b0fba533fa357a","contributors":{"authors":[{"text":"Waddle, Terry","contributorId":47848,"corporation":false,"usgs":true,"family":"Waddle","given":"Terry","affiliations":[],"preferred":false,"id":497511,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":97513,"text":"ds402 - 2010 - A Compilation of Spatial Datasets and Surface-Water and Ground-Water Data from the U.S. Geological Survey and Other Federal and Oklahoma State Agencies for the Kickapoo Tribe of Oklahoma","interactions":[],"lastModifiedDate":"2012-02-02T00:14:32","indexId":"ds402","displayToPublicDate":"2009-05-19T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"402","title":"A Compilation of Spatial Datasets and Surface-Water and Ground-Water Data from the U.S. Geological Survey and Other Federal and Oklahoma State Agencies for the Kickapoo Tribe of Oklahoma","docAbstract":"This report contains spatial datasets of natural and anthropogenic features and spatial datasets detailing surface-water, ground-water, and other types of environmental information collected in and surrounding Kickapoo Tribal Lands. Spatial datasets were compiled from Federal and Oklahoma State agencies. Surface-water, ground-water, and other types of environmental information of natural and anthropogenic features were compiled from USGS National Water Information System database, Oklahoma Department of Environmental Quality online Geographic Information System data viewer, Oklahoma Water Resources Board online Water Information Mapping System, and U.S. Environmental Protection Agency online Modernized STORET database.\r\n\r\nThese spatial datasets were compiled from many different sources with varying quality. Because of the different sources, features common to multiple layers may not overlay exactly. Users should check the metadata to determine proper use of these data. These data were not checked for accuracy or completeness. Should a question of accuracy or completeness arise, the user should contact the originator cited in the metadata. \r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ds402","collaboration":"Prepared by the U.S. Geological Survey in cooperation with the Kickapoo Tribe of Oklahoma Department of Environmental Programs","usgsCitation":"Mashburn, S., 2010, A Compilation of Spatial Datasets and Surface-Water and Ground-Water Data from the U.S. Geological Survey and Other Federal and Oklahoma State Agencies for the Kickapoo Tribe of Oklahoma: U.S. Geological Survey Data Series 402, 1 DVD; Downloads Directory, https://doi.org/10.3133/ds402.","productDescription":"1 DVD; Downloads Directory","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":126275,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_402.jpg"},{"id":13470,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/402/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd4950e4b0b290850ef0b9","contributors":{"authors":[{"text":"Mashburn, Shana Lichelle","contributorId":51403,"corporation":false,"usgs":true,"family":"Mashburn","given":"Shana Lichelle","affiliations":[],"preferred":false,"id":302357,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70208545,"text":"70208545 - 2010 - Automated masking of cloud and cloud shadow for forest change analysis using Landsat images","interactions":[],"lastModifiedDate":"2020-02-20T10:09:11","indexId":"70208545","displayToPublicDate":"2008-04-06T12:56:07","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Automated masking of cloud and cloud shadow for forest change analysis using Landsat images","docAbstract":"Accurate masking of cloud and cloud shadow is a prerequisite for reliable mapping of land surface attributes. Cloud contamination is particularly a problem for land cover change analysis, because unflagged clouds may be mapped as false changes, and the level of such false changes can be comparable to or many times more than that of actual changes, even for images with small percentages of cloud cover. Here we develop an algorithm for automatically flagging clouds and their shadows in Landsat images. This algorithm uses clear view forest pixels as a reference to define cloud boundaries for separating cloud from clear view surfaces in a spectral-temperature space. Shadow locations are predicted according to cloud height estimates and sun illumination geometry, and actual shadow pixels are identified by searching the darkest pixels surrounding the predicted shadow locations. This algorithm produced omission errors of around 1% for the cloud class, although the errors were higher for an image that had very low cloud cover and one acquired in a semiarid environment. While higher values were reported for other error measures, most of the errors were found around the edges of detected clouds and shadows, and many were due to difficulties in flagging thin clouds and the shadow cast by them, both by the developed algorithm and by the image analyst in deriving the reference data. We concluded that this algorithm is especially suitable for forest change analysis, because the commission and omission errors of the derived masks are not likely to significantly bias change analysis results.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431160903369642","usgsCitation":"Huang, C., Thomas, N., Goward, S.N., Masek, J.G., Zhu, Z., Townshend, J., and Vogelmann, J., 2010, Automated masking of cloud and cloud shadow for forest change analysis using Landsat images: International Journal of Remote Sensing, v. 31, no. 20, p. 5449-5464, https://doi.org/10.1080/01431160903369642.","productDescription":"16 p.","startPage":"5449","endPage":"5464","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":372349,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"20","noUsgsAuthors":false,"publicationDate":"2010-10-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Huang, Chengquan 0000-0003-0055-9798","orcid":"https://orcid.org/0000-0003-0055-9798","contributorId":198972,"corporation":false,"usgs":false,"family":"Huang","given":"Chengquan","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":782380,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thomas, Nancy","contributorId":7657,"corporation":false,"usgs":true,"family":"Thomas","given":"Nancy","affiliations":[],"preferred":false,"id":782381,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goward, Samuel N.","contributorId":44459,"corporation":false,"usgs":true,"family":"Goward","given":"Samuel","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":782382,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Masek, Jeffery G.","contributorId":87438,"corporation":false,"usgs":true,"family":"Masek","given":"Jeffery","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":782383,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true}],"preferred":true,"id":782384,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Townshend, J.R.G.","contributorId":15321,"corporation":false,"usgs":true,"family":"Townshend","given":"J.R.G.","email":"","affiliations":[],"preferred":false,"id":782385,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Vogelmann, James 0000-0002-0804-5823 vogel@usgs.gov","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":192352,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James","email":"vogel@usgs.gov","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":782386,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70171013,"text":"70171013 - 2010 - Monitoring and characterizing natural hazards with satellite InSAR imagery","interactions":[],"lastModifiedDate":"2021-01-08T16:39:36.991136","indexId":"70171013","displayToPublicDate":"2008-01-01T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5089,"text":"Annals of GIS","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring and characterizing natural hazards with satellite InSAR imagery","docAbstract":"Interferometric synthetic aperture radar (InSAR) provides an all-weather imaging capability for measuring ground-surface deformation and inferring changes in land surface characteristics. InSAR enables scientists to monitor and characterize hazards posed by volcanic, seismic, and hydrogeologic processes, by landslides and wildfires, and by human activities such as mining and fluid extraction or injection. Measuring how a volcano's surface deforms before, during, and after eruptions provides essential information about magma dynamics and a basis for mitigating volcanic hazards. Measuring spatial and temporal patterns of surface deformation in seismically active regions is extraordinarily useful for understanding rupture dynamics and estimating seismic risks. Measuring how landslides develop and activate is a prerequisite to minimizing associated hazards. Mapping surface subsidence or uplift related to extraction or injection of fluids during exploitation of groundwater aquifers or petroleum reservoirs provides fundamental data on aquifer or reservoir properties and improves our ability to mitigate undesired consequences. Monitoring dynamic water-level changes in wetlands improves hydrological modeling predictions and the assessment of future flood impacts. In addition, InSAR imagery can provide near-real-time estimates of fire scar extents and fire severity for wildfire management and control. All-weather satellite radar imagery is critical for studying various natural processes and is playing an increasingly important role in understanding and forecasting natural hazards.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/19475681003700914","usgsCitation":"Lu, Z., Zhang, J., Zhang, Y., and Dzurisin, D., 2010, Monitoring and characterizing natural hazards with satellite InSAR imagery: Annals of GIS, v. 16, no. 1, p. 55-66, https://doi.org/10.1080/19475681003700914.","productDescription":"12 p.","startPage":"55","endPage":"66","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"links":[{"id":488987,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/19475681003700914","text":"Publisher Index Page"},{"id":382027,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"576913dae4b07657d19ff1b6","contributors":{"authors":[{"text":"Lu, Zhong 0000-0001-9181-1818 lu@usgs.gov","orcid":"https://orcid.org/0000-0001-9181-1818","contributorId":901,"corporation":false,"usgs":true,"family":"Lu","given":"Zhong","email":"lu@usgs.gov","affiliations":[],"preferred":true,"id":629537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, Jixian","contributorId":36396,"corporation":false,"usgs":true,"family":"Zhang","given":"Jixian","affiliations":[],"preferred":false,"id":629538,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Yonghong","contributorId":82563,"corporation":false,"usgs":true,"family":"Zhang","given":"Yonghong","email":"","affiliations":[],"preferred":false,"id":629539,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dzurisin, Daniel 0000-0002-0138-5067 dzurisin@usgs.gov","orcid":"https://orcid.org/0000-0002-0138-5067","contributorId":538,"corporation":false,"usgs":true,"family":"Dzurisin","given":"Daniel","email":"dzurisin@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":629540,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":79157,"text":"sim2899 - 2010 - Geologic map of Lassen Volcanic National Park and vicinity, California","interactions":[],"lastModifiedDate":"2022-04-14T19:09:33.427847","indexId":"sim2899","displayToPublicDate":"2006-09-20T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2899","title":"Geologic map of Lassen Volcanic National Park and vicinity, California","docAbstract":"The geologic map of Lassen Volcanic National Park (LVNP) and vicinity encompasses 1,905 km2 at the south end of the Cascade Range in Shasta, Lassen, Tehama, and Plumas Counties, northeastern California (fig. 1, sheet 3). The park includes 430 km2 of scenic volcanic features, glacially sculpted terrain, and the most spectacular array of thermal features in the Cascade Range. Interest in preserving the scenic wonders of the Lassen area as a national park arose in the early 1900s to protect it from commercial development and led to the establishment in 1907 of two small national monuments centered on Lassen Peak and Cinder Cone. The eruptions of Lassen Peak in 1914-15 were the first in the Cascade Range since widespread settling of the West in the late 1800s. Through the printed media, the eruptions aroused considerable public interest and inspired renewed efforts, which had languished since 1907, to establish a national park. In 1916, Lassen Volcanic National Park was established by combining the areas of the previously established national monuments and adjacent lands. The southernmost Cascade Range is bounded on the west by the Sacramento Valley and the Klamath Mountains, on the south by the Sierra Nevada, and on the east by the Basin and Range geologic provinces. Most of the map area is underlain by middle to late Pleistocene volcanic rocks; Holocene, early Pleistocene, and late Pliocene volcanic rocks (<3.5 m.y.) are less common. Paleozoic and Mesozoic rocks are inferred to underlie the volcanic deposits (Jachens and Saltus, 1983), but the nearest exposures of pre-Tertiary rocks are 15 km to the south, 9 km to the southwest, and 12 km to the west. Diller (1895) recognized the young volcanic geology and produced the first geologic map of the Lassen area. The map (sheet 1) builds on and extends geologic mapping by Williams (1932), Macdonald (1963, 1964, 1965), and Wilson (1961). The Lassen Peak area mapped by Christiansen and others (2002) and published in greater detail (1:24,000) was modified for inclusion here. Figure 2 (sheet 3) shows the mapping credit for previous work; figure 3 (sheet 3) shows locations discussed throughout the text. A CD-ROM entitled Database for the Geologic Map of Lassen Volcanic National Park and Vicinity, California accompanies the printed map (Muffler and others, 2010). The CD-ROM contains ESRI compatible geographic information system data files used to create the 1:50,000-scale geologic map, both geologic and topographic data and their associated metadata files, and printable versions of the geologic map and pamphlet as PDF formatted files. The 1:50,000-scale geologic map was compiled from 1:24,000-scale geologic maps of individual quadrangles that are also included in the CD-ROM. It also contains ancillary data that support the map including locations of rock samples selected for chemical analysis (Clynne and others, 2008) and radiometric dating, photographs of geologic features, and links to related data or web sites. Data contained in the CD-ROM are also available on this Web site. The southernmost Cascade Range consists of a regional platform of basalt and basaltic andesite, with subordinate andesite and sparse dacite. Nested within these regional rocks are 'volcanic centers', defined as large, long-lived, composite, calc-alkaline edifices erupting the full range of compositions from basalt to rhyolite, but dominated by andesite and dacite. Volcanic centers are produced by the focusing of basaltic flux from the mantle and resultant enhanced interaction of mafic magma with the crust. Collectively, volcanic centers mark the axis of the southernmost Cascade Range. The map area includes the entire Lassen Volcanic Center, parts of three older volcanic centers (Maidu, Dittmar, and Latour), and the products of regional volcanism (fig. 4, sheet 3). Terminology used for subdivision of the Lassen Volcanic Center has been modified from Clynne (1984, 1990).","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/sim2899","usgsCitation":"Clynne, M.A., and Muffler, L.P., 2010, Geologic map of Lassen Volcanic National Park and vicinity, California: U.S. Geological Survey Scientific Investigations Map 2899, Report: iii, 95 p.; 3 Sheets: 58.00 × 42.00 inches or smaller; Database, https://doi.org/10.3133/sim2899.","productDescription":"Report: iii, 95 p.; 3 Sheets: 58.00 × 42.00 inches or smaller; Database","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":438843,"rank":101,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N23XJ6","text":"USGS data release","linkHelpText":"Database for the geologic map of Lassen Volcanic National Park and vicinity, California"},{"id":115898,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim_2899.gif"},{"id":398747,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_94720.htm"},{"id":14411,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/2899/","linkFileType":{"id":5,"text":"html"}}],"scale":"50000","projection":"Lambert Conformal Conic projection","country":"United States","state":"California","otherGeospatial":"Lassen Volcanic National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.75,\n 40.3333\n ],\n [\n -121.125,\n 40.3333\n ],\n [\n -121.125,\n 40.6667\n ],\n [\n -121.75,\n 40.6667\n ],\n [\n -121.75,\n 40.3333\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b1ae4b07f02db6a867b","contributors":{"authors":[{"text":"Clynne, Michael A. 0000-0002-4220-2968 mclynne@usgs.gov","orcid":"https://orcid.org/0000-0002-4220-2968","contributorId":2032,"corporation":false,"usgs":true,"family":"Clynne","given":"Michael","email":"mclynne@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":289245,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muffler, L.J. Patrick","contributorId":72739,"corporation":false,"usgs":false,"family":"Muffler","given":"L.J.","email":"","middleInitial":"Patrick","affiliations":[],"preferred":false,"id":289246,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70209185,"text":"70209185 - 2009 - Recording rotational and translational ground motions of two TAIGER explosions in northeastern Taiwan on 4 March 2008 ","interactions":[],"lastModifiedDate":"2020-03-23T07:48:59","indexId":"70209185","displayToPublicDate":"2020-05-01T07:44:52","publicationYear":"2009","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":"Recording rotational and translational ground motions of two TAIGER explosions in northeastern Taiwan on 4 March 2008 ","docAbstract":"Two explosions were set off on 4 March 2008 at the N3 explosion site in northeastern Taiwan. The code name for the first shot with 3000 kg explosives is N3P and that for the second shot with 750 kg explosives is N3. To record these two explosions, 8 triaxial rotational sensors, 13 triaxial accelerometers, and 12 six-channel, 24 bit dataloggers with Global Positioning System receivers were deployed to continuously record several hours before and after the explosions. These instruments were installed at about 250 m (1 station), 500 m (11 stations), and 600 m (1 station) from the explosions. The 11 stations form a center array with station spacing of about 5 m.
Except for one rotational sensor, onscale records were obtained. Although the N3P shot used four times larger amounts of explosives than those used for the N3 shot, the peak ground translational acceleration and rotational velocity at the 13 station sites from the N3P shot are only about 1.5 times larger than those for the N3 shot. We also observed large variations (by tens of percent) of translational accelerations and rotational velocities at the center array with station spacing of about 5 m. The largest peak rotational velocity was observed for the x component: 2.74 and 1.75 mrad/sec at a distance of 254 m from the N3P and N3 shots, respectively.
The main purpose of this article is to document our recordings of rotational and translation motions from two explosions in Taiwan and to release the data online for open access. The translational acceleration data from this experiment have been analyzed by Langston et al. (2009), and we plan to submit an article with analysis of the rotational velocity data in the future.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120080176","usgsCitation":"Lin, C., Liu, C., and Lee, W., 2009, Recording rotational and translational ground motions of two TAIGER explosions in northeastern Taiwan on 4 March 2008 : Bulletin of the Seismological Society of America, v. 99, no. 2B, p. 1237-1250, https://doi.org/10.1785/0120080176.","productDescription":"14 p.","startPage":"1237","endPage":"1250","costCenters":[],"links":[{"id":373430,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Taiwan","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[121.77782,24.39427],[121.17563,22.79086],[120.74708,21.97057],[120.22008,22.81486],[120.10619,23.55626],[120.69468,24.53845],[121.49504,25.29546],[121.95124,24.9976],[121.77782,24.39427]]]},\"properties\":{\"name\":\"Taiwan\"}}]}","volume":"99","issue":"2B","noUsgsAuthors":false,"publicationDate":"2009-05-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Lin, Chin-Jen","contributorId":199136,"corporation":false,"usgs":false,"family":"Lin","given":"Chin-Jen","email":"","affiliations":[],"preferred":false,"id":785285,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Chun-Chi","contributorId":75240,"corporation":false,"usgs":true,"family":"Liu","given":"Chun-Chi","email":"","affiliations":[],"preferred":false,"id":785286,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lee, W.H.K.","contributorId":35303,"corporation":false,"usgs":true,"family":"Lee","given":"W.H.K.","affiliations":[],"preferred":false,"id":785287,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":97537,"text":"ds424 - 2009 - Database of the geologic map of North America— Adapted from the map by J.C. Reed, Jr. and others (2005)","interactions":[],"lastModifiedDate":"2021-09-17T20:58:34.429635","indexId":"ds424","displayToPublicDate":"2020-01-10T11:15:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"424","displayTitle":"Database of the Geologic Map of North America: Adapted from the Map by J.C. Reed, Jr. and others (2005)","title":"Database of the geologic map of North America— Adapted from the map by J.C. Reed, Jr. and others (2005)","docAbstract":"The Geological Society of America's (GSA) Geologic Map of North America (Reed and others, 2005a; 1:5,000,000) shows the geology of a significantly large area of the Earth, centered on North and Central America and including the submarine geology of parts of the Atlantic and Pacific Oceans. This map is now converted to a Geographic Information System (GIS) database that contains all geologic and base-map information shown on the two printed map sheets and the accompanying explanation sheet. We anticipate this map database will be revised at some unspecified time in the future, likely through the actions of a steering committee managed by the GSA and staffed by scientists from agencies including those responsible for the original map compilation.
Regarding the use of this product, as noted by the map's compilers:
“The Geologic Map of North America is an essential educational tool for teaching the geology of North America to university students and for the continuing education of professional geologists in North America and elsewhere. In addition, simplified maps derived from the Geologic Map of North America are useful for enlightening younger students and the general public about the geology of the continent.”
With publication of this database, the preparation of any type of simplified map is made significantly easier. More important perhaps, the database provides a more accessible means to explore the map information and to compare and analyze it in conjunction with other types of information (for example, land use, soils, biology) to better understand the complex interrelations among factors that affect Earth resources, hazards, ecosystems, and climate.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds424","collaboration":"Prepared in cooperation with the Geological Society of America","usgsCitation":"Garrity, C.P., and Soller, D.R., 2009, Database of the Geologic Map of North America; adapted from the map by J.C. Reed, Jr. and others (2005): U.S. Geological Survey Data Series 424 [https://pubs.usgs.gov/ds/424/].","productDescription":"Report: iii, 7 p.;Database; Metadata; ReadMe","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":173,"text":"Central Region Earth Surface Processes","active":false,"usgs":true}],"links":[{"id":371142,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/424/ds424_map_units.pdf","text":"Explanation of Map Units","size":"7.33 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":371141,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/ds/424/USGS_DS_424.zip","text":"GIS Data Bundle","size":"71.5 MB","linkFileType":{"id":6,"text":"zip"}},{"id":371140,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/424/metadata.txt","size":"70.8 KB","linkFileType":{"id":2,"text":"txt"}},{"id":371139,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/424/ds424_readme.pdf","size":"34.0 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":371138,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/ds/424/version_history.txt","size":"494 B","linkFileType":{"id":2,"text":"txt"}},{"id":371137,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/424/ds424_text.pdf","text":"Report","size":"93.1 KB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 424"},{"id":389454,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_86688.htm"},{"id":371136,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/424/coverthb.jpg"}],"otherGeospatial":"North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.6953125,\n 6.839169626342808\n ],\n [\n -77.16796875,\n 9.102096738726456\n ],\n [\n -75.05859375,\n 13.752724664396988\n ],\n [\n -62.57812500000001,\n 16.804541076383455\n ],\n [\n -58.18359375,\n 44.84029065139799\n ],\n [\n -51.85546874999999,\n 47.15984001304432\n ],\n [\n -36.38671875,\n 65.07213008560697\n ],\n [\n -19.51171875,\n 70.72897946208789\n ],\n [\n -10.546875,\n 81.79875708305217\n ],\n [\n -27.0703125,\n 83.7539108491127\n ],\n [\n -42.01171875,\n 83.65755395878921\n ],\n [\n -58.71093750000001,\n 82.58610635020881\n ],\n [\n -73.30078125,\n 83.31873282163234\n ],\n [\n -85.78125,\n 82.76537263027352\n ],\n [\n -102.3046875,\n 80.26825877186884\n ],\n [\n -123.22265625000001,\n 76.84081641443098\n ],\n [\n -129.7265625,\n 72.0739114882038\n ],\n [\n -135.52734375,\n 70.08056215839737\n ],\n [\n -137.63671875,\n 69.47296854140573\n ],\n [\n -155.21484375,\n 72.01972876525514\n ],\n [\n -163.65234374999997,\n 70.55417853776078\n ],\n [\n -167.87109375,\n 68.13885164925573\n ],\n [\n -172.44140625,\n 63.074865690586634\n ],\n [\n -170.5078125,\n 58.99531118795094\n ],\n [\n -165.234375,\n 57.89149735271034\n ],\n [\n -171.03515625,\n 53.54030739150022\n ],\n [\n -169.27734375,\n 50.62507306341435\n ],\n [\n -148.88671874999997,\n 58.07787626787517\n ],\n [\n -137.98828125,\n 54.87660665410869\n ],\n [\n -131.1328125,\n 50.51342652633956\n ],\n [\n -127.44140625,\n 41.902277040963696\n ],\n [\n -123.57421875,\n 34.88593094075317\n ],\n [\n -107.40234375,\n 16.804541076383455\n ],\n [\n -85.95703125,\n 8.581021215641854\n ],\n [\n -79.8046875,\n 5.965753671065536\n ],\n [\n -77.6953125,\n 6.839169626342808\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0","contact":"National Geologic Map Database
Resources Page
Our purpose is to annually update our creep-data archive on San Francisco Bay region active faults for use by the scientific research community. Earlier data (1979-2001) were reported in Galehouse (2002) and were analyzed and described in detail in a summary report (Galehouse and Lienkaemper, 2003). A complete analysis of our earlier results obtained on the Hayward Fault was presented in Lienkaemper, Galehouse and Simpson (2001) and updated in Lienkaemper and others (2012). Lienkaemper and others (2014) provide a new overview and analysis of fault creep along all sections of the northern San Andreas Fault system, from which they estimate by how much fault creep reduces the seismic hazard for each fault section.
From 1979 until his retirement from the project in 2001, Jon Galehouse of San Francisco State University (SFSU) and many student research assistants measured creep (aseismic slip) rates on these faults. The creep measurement project, which was initiated by Galehouse, continued through the Geosciences Department at SFSU from 2001-2006 under the direction of Karen Grove and John Caskey (Grove and Caskey, 2005) and since 2006 under Caskey (2007). Forrest McFarland has managed most of the technical and logistical project operations, as well as data processing and compilation since 2001. Data from 2001-2007 are found in McFarland and others (2007). From 2009 onward, we have released the raw data annually using this report (OF2009-1119) as a permanent publication link, while publishing more detailed analyses of these data in the scientific literature, such as Lienkaemper and others (2014).
We maintain a project Web site (http://funnel.sfsu.edu/creep/) that includes the following information: project description, project personnel, creep characteristics and measurement, map of creep-measurement sites, creep-measurement site information, and links to data plots for each measurement site. Our most current, annually updated results are, therefore, accessible to the scientific community and to the general public. Information about the project can currently be requested by the public by an email link (fltcreep@sfsu.edu) found on our project Web site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20091119","usgsCitation":"McFarland, F., Lienkaemper, J.J., and Caskey, S.J., 2009, Data from theodolite measurements of creep rates on San Francisco Bay region faults, California (Version 1.0: July 8, 2009; Version 1.1: April 19, 2010; Version 1.2: May 5, 2011; Version 1.3: April 19, 2012; Version 1.4: March 20, 2013; Version 1.5: February 3, 2014; Version 1.6: March 16, 2015; Version 1.8: March 29, 2016): U.S. Geological Survey Open-File Report 2009-1119, Report: 21 p.; Data Files, https://doi.org/10.3133/ofr20091119.","productDescription":"Report: 21 p.; Data Files","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"1979-01-01","temporalEnd":"2012-12-31","costCenters":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":369532,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/of/2009/1119/data","text":"Data folder"},{"id":269859,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2009/1119/ofr20091119.pdf","text":"Report","size":"2.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2009-1119"},{"id":394202,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_86844.htm"},{"id":369531,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2009/1119/versionhistory.txt","size":"1.86 KB"},{"id":118504,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2009/1119/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.8,\n 36.8\n ],\n [\n -121.0722,\n 36.8\n ],\n [\n -121.0722,\n 39.8\n ],\n [\n -123.8,\n 39.8\n ],\n [\n -123.8,\n 36.8\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: July 8, 2009; Version 1.1: April 19, 2010; Version 1.2: May 5, 2011; Version 1.3: April 19, 2012; Version 1.4: March 20, 2013; Version 1.5: February 3, 2014; Version 1.6: March 16, 2015; Version 1.8: March 29, 2016","contact":"Earthquake Science Center in Menlo Park, California
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
Habitat modeling is an important tool used to simulate the potential distribution of a species for a variety of basic and applied questions. The desert tortoise (Gopherus agassizii) is a federally listed threatened species in the Mojave Desert and parts of the Sonoran Desert of California, Nevada, Utah, and Arizona. Land managers in this region require reliable information about the potential distribution of desert tortoise habitat to plan conservation efforts, guide monitoring activities, monitor changes in the amount and quality of habitat available, minimize and mitigate disturbances, and ultimately to assess the status of the tortoise and its habitat toward recovery of the species. By applying information from the literature and our knowledge or assumptions of environmental variables that could potentially explain variability in the quality of desert tortoise habitat, we developed a quantitative habitat model for the desert tortoise using an extensive set of field-collected presence data. Sixteen environmental data layers were converted into a grid covering the study area and merged with the desert tortoise presence data that we gathered for input into the Maxent habitat-modeling algorithm. This model provides output of the statistical probability of habitat potential that can be used to map potential areas of desert tortoise habitat. This type of analysis, while robust in its predictions of habitat, does not account for anthropogenic changes that may have altered habitat with relatively high potential into areas with lower potential.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20091102","collaboration":"Prepared as a part of the Department of the Interior on the Landscape - Mojave Project for the Western Region, of the U.S. Geological Survey ","usgsCitation":"Nussear, K.E., Esque, T.C., Inman, R.D., Gass, Leila, Thomas, K.A., Wallace, C.S.A., Blainey, J.B., Miller, D.M., and Webb, R.H., 2009, Modeling habitat of the desert tortoise (Gopherus agassizii) in the Mojave and parts of the Sonoran Deserts of California, Nevada, Utah, and Arizona: U.S. Geological Survey Open-File Report 2009-1102, 18 p.","productDescription":"Report: iv, 18 p.; 2 Companion Files","numberOfPages":"18","additionalOnlineFiles":"Y","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":367969,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2009/1102/ofr20091102_dt_Habitat_Model.zip","size":"162 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":" - Habitat Model"},{"id":195896,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2009/1102/coverthb.gif"},{"id":367970,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2009/1102/ofr20091102_Environmental_Layers.zip","size":"27.4 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":" - Environmental Layers"},{"id":367968,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2009/1102/ofr20091102.pdf","text":"Report","size":"1.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2009-1102"}],"country":"United States","state":"California, Nevada, Utah, Arizona","otherGeospatial":"Mojave Desert, Sonoran Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.30029296875,\n 32.76880048488168\n ],\n [\n -109.2919921875,\n 32.76880048488168\n ],\n [\n -109.2919921875,\n 39.2492708462234\n ],\n [\n -120.30029296875,\n 39.2492708462234\n ],\n [\n -120.30029296875,\n 32.76880048488168\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Modoc Hall, Room 3006
Sacramento, CA 95819
Fishery-independent (FI) surveys provide critical information used for the sustainable management and conservation of fish populations. Because fisheries management often requires the effects of management actions to be evaluated and detected within a relatively short time frame, it is important that research be directed toward FI survey evaluation, especially with respect to the ability to detect temporal trends. Using annual FI gill-net survey data for Lake Erie walleyes Sander vitreus collected from 1978 to 2006 as a case study, our goals were to (1) highlight the usefulness of hierarchical models for estimating spatial and temporal sources of variation in catch per effort (CPE); (2) demonstrate how the resulting variance estimates can be used to examine the statistical power to detect temporal trends in CPE in relation to sample size, duration of sampling, and decisions regarding what data are most appropriate for analysis; and (3) discuss recommendations for evaluating FI surveys and analyzing the resulting data to support fisheries management. This case study illustrated that the statistical power to detect temporal trends was low over relatively short sampling periods (e.g., 5–10 years) unless the annual decline in CPE reached 10–20%. For example, if 50 sites were sampled each year, a 10% annual decline in CPE would not be detected with more than 0.80 power until 15 years of sampling, and a 5% annual decline would not be detected with more than 0.8 power for approximately 22 years. Because the evaluation of FI surveys is essential for ensuring that trends in fish populations can be detected over management-relevant time periods, we suggest using a meta-analysis–type approach across systems to quantify sources of spatial and temporal variation. This approach can be used to evaluate and identify sampling designs that increase the ability of managers to make inferences about trends in fish stocks.
","language":"English","publisher":"Taylor & Francis","doi":"10.1577/M08-197.1","usgsCitation":"Wagner, T., Vandergoot, C.S., and Tyson, J., 2009, Evaluating the power to detect temporal trends in fishery independent surveys: A case study based on Gillnets Set in the Ohio waters of Lake Erie for walleye: North American Journal of Fisheries Management, v. 29, no. 3, p. 805-816, https://doi.org/10.1577/M08-197.1.","productDescription":"11 p.","startPage":"805","endPage":"816","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-008027","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":323907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.5352783203125,\n 41.97174336327968\n ],\n [\n -80.540771484375,\n 42.32606244456202\n ],\n [\n -81.287841796875,\n 42.200038266046754\n ],\n [\n -82.40295410156249,\n 41.672911819602085\n ],\n [\n -82.6885986328125,\n 41.672911819602085\n ],\n [\n -83.067626953125,\n 41.86137915587359\n ],\n [\n -83.111572265625,\n 41.95131994679697\n ],\n [\n -83.4356689453125,\n 41.701627343789184\n ],\n [\n -82.9852294921875,\n 41.56203190200195\n ],\n [\n -82.99072265625,\n 41.46742831254425\n ],\n [\n -82.913818359375,\n 41.40153558289846\n ],\n [\n -82.7490234375,\n 41.422134246213616\n ],\n [\n -82.6611328125,\n 41.44684402008925\n ],\n [\n -82.45788574218749,\n 41.347948493443546\n ],\n [\n -82.0458984375,\n 41.492120839687786\n ],\n [\n -81.88110351562499,\n 41.44272637767212\n ],\n [\n -81.6888427734375,\n 41.44684402008925\n ],\n [\n -81.3262939453125,\n 41.7180304600481\n ],\n [\n -81.1285400390625,\n 41.79179268262892\n ],\n [\n -80.804443359375,\n 41.87774145109676\n ],\n [\n -80.5352783203125,\n 41.97174336327968\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2009-06-01","publicationStatus":"PW","scienceBaseUri":"57651f33e4b07657d19c7898","contributors":{"authors":[{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":637167,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vandergoot, Christopher S.","contributorId":71849,"corporation":false,"usgs":false,"family":"Vandergoot","given":"Christopher","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":639602,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tyson, Jeff","contributorId":147298,"corporation":false,"usgs":false,"family":"Tyson","given":"Jeff","affiliations":[],"preferred":false,"id":639603,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70007521,"text":"70007521 - 2009 - Contributions of nitrogen to the Barnegat Bay-Little Egg Harbor Estuary: Updated loading estimates","interactions":[],"lastModifiedDate":"2016-04-25T14:32:31","indexId":"70007521","displayToPublicDate":"2015-07-14T13:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Contributions of nitrogen to the Barnegat Bay-Little Egg Harbor Estuary: Updated loading estimates","docAbstract":"Based on the most recent and most accurate data available through 2008, the total load of nitrogen to the Barnegat Bay‐Little Egg Harbor (BB‐LEH) estuary from the most substantial sources (surface water, including surface‐water discharge and direct storm runoff; ground‐water discharge; and atmospheric deposition) is estimated to be 650,000 kilograms of nitrogen per year (kg N/yr). Surface water contributes 66 percent (431,000 kg N/yr), direct ground‐ water discharge accounts for 12 percent (78,000 kg N/yr), and atmospheric deposition accounts for 22 percent (141,000 kg N/yr). This new loading estimate was compared to a previously published estimate produced by using similar methodology but less current data through 1997. Findings of the present study include a substantially lower estimate of atmospheric deposition of nitrogen to the estuary compared to the previous estimate. The study results also offer further support of the relation between land use and nitrogen levels, and indicate that the Toms and Metedeconk River basins account for more than 60 percent of the nitrogen load to the estuary from surface‐water discharge. Differences between the two estimates can be attributed to both the use of more accurate and more recent data in the revised estimate, and actual changes in the magnitude of nitrogen loads from various sources. Gaps in available water‐quality and hydrologic data are documented, and additional analysis and monitoring that may improve the reliability of future nitrogen loading estimates are presented.
","largerWorkTitle":"Barnegat Bay Partnership State of the Bay Technical Report","language":"English","publisher":"U.S. Geological Survey","collaboration":"Prepared in cooperation with the Barnegat Bay National Estuary Program","usgsCitation":"Wieben, C.M., and Baker, R.J., 2009, Contributions of nitrogen to the Barnegat Bay-Little Egg Harbor Estuary: Updated loading estimates, 25 p.","productDescription":"25 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-017449","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":320532,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":320531,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://bbp.ocean.edu/pages/184.asp"}],"country":"United States","state":"New Jersey","otherGeospatial":"Barnegat Bay‐Little Egg Harbor estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.014892578125,\n 40.14318974292438\n ],\n [\n -74.04510498046875,\n 40.03392360399664\n ],\n [\n -74.07257080078125,\n 39.87812720644829\n ],\n [\n -74.0863037109375,\n 39.74521015328692\n ],\n [\n -74.15496826171874,\n 39.65011210186371\n ],\n [\n -74.22088623046875,\n 39.5633531658293\n ],\n [\n -74.2840576171875,\n 39.50615988027491\n ],\n [\n -74.3609619140625,\n 39.5146359327835\n ],\n [\n -74.41864013671875,\n 39.53370327008705\n ],\n [\n -74.47906494140625,\n 39.552765371831015\n ],\n [\n -74.49554443359375,\n 39.592990390285024\n ],\n [\n -74.4873046875,\n 39.66491373749128\n ],\n [\n -74.4488525390625,\n 39.707186656826565\n ],\n [\n -74.36370849609375,\n 39.7958755252971\n ],\n [\n -74.33624267578125,\n 39.838068180000015\n ],\n [\n -74.35821533203125,\n 39.871803651624425\n ],\n [\n -74.42962646484375,\n 39.90762941952987\n ],\n [\n -74.46258544921875,\n 39.95817509460007\n ],\n [\n -74.5037841796875,\n 40.03182061333687\n ],\n [\n -74.5037841796875,\n 40.157885249506506\n ],\n [\n -74.4708251953125,\n 40.24179856487036\n ],\n [\n -74.4158935546875,\n 40.25228042623783\n ],\n [\n -74.29504394531249,\n 40.250184183819854\n ],\n [\n -74.19891357421875,\n 40.2313150803688\n ],\n [\n -74.14672851562499,\n 40.21873275657031\n ],\n [\n -74.1192626953125,\n 40.20195268954057\n ],\n [\n -74.0643310546875,\n 40.176774799905445\n ],\n [\n -74.014892578125,\n 40.14318974292438\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"571f3fb3e4b071321fe56a10","contributors":{"authors":[{"text":"Wieben, Christine M. 0000-0001-5825-5119 cwieben@usgs.gov","orcid":"https://orcid.org/0000-0001-5825-5119","contributorId":4270,"corporation":false,"usgs":true,"family":"Wieben","given":"Christine","email":"cwieben@usgs.gov","middleInitial":"M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":626208,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baker, Ronald J. rbaker@usgs.gov","contributorId":1436,"corporation":false,"usgs":true,"family":"Baker","given":"Ronald","email":"rbaker@usgs.gov","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":626209,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70041533,"text":"70041533 - 2009 - The observed relationship between wave conditions and beach response, Ocean Beach, San Francisco, CA","interactions":[],"lastModifiedDate":"2015-10-29T14:24:25","indexId":"70041533","displayToPublicDate":"2015-07-06T08:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"The observed relationship between wave conditions and beach response, Ocean Beach, San Francisco, CA","docAbstract":"Understanding how sandy beaches respond to storms is critical for effective sediment management and developing successful erosion mitigation efforts. However, only limited progress has been made in relating observed beach changes to wave conditions, with one of the major limiting factors being the lack of temporally dense beach topography and nearshore wave data in most studies. This study uses temporally dense beach topographic and offshore wave data to directly link beach response and wave forcing with generally good results. Ocean Beach is an open coast high-energy sandy beach located in San Francisco, CA, USA. From April 2004 through the end of 2008, 60 three-dimensional topographic beach surveys were conducted on approximately a monthly basis, with more frequent “short-term surveys during the winters of 2005-06 and 2006-07. Shoreline position data from the short-term surveys show good correlation with offshore wave height, period, and direction averaged over several days prior to the survey (mean R*=0.54 for entire beach). There is, however, considerable alongshore variation in model performance, with R- values ranging from 0.81 to 0.19 for individual sections of the beach. After wave height, the direction of wave approach was the most important factor in determining the response of the shoreline, followed by wave period. Our results indicate that an empirical predictive model of beach response to wave conditions at Ocean Beach is possible with frequent beach mapping and wave data, and that such a model could be useful to coastal managers.
","language":"English","publisher":"Coastal Education & Research Foundation","usgsCitation":"Hansen, J., and Barnard, P., 2009, The observed relationship between wave conditions and beach response, Ocean Beach, San Francisco, CA: Journal of Coastal Research, no. Special Issue 56, p. 1771-1775.","productDescription":"5 p.","startPage":"1771","endPage":"1775","numberOfPages":"5","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-011160","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":310776,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Francisco","otherGeospatial":"Ocean Beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.60604858398438,\n 37.505368263398104\n ],\n [\n -122.60604858398438,\n 37.804358908571395\n ],\n [\n -122.43301391601562,\n 37.804358908571395\n ],\n [\n -122.43301391601562,\n 37.505368263398104\n ],\n [\n -122.60604858398438,\n 37.505368263398104\n ]\n ]\n ]\n }\n }\n ]\n}","issue":"Special Issue 56","publicComments":"Proceedings of the 10th International Coastal Symposium","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56334344e4b048076347eeed","contributors":{"authors":[{"text":"Hansen, J.E.","contributorId":11855,"corporation":false,"usgs":true,"family":"Hansen","given":"J.E.","email":"","affiliations":[],"preferred":false,"id":578725,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnard, P.L.","contributorId":20527,"corporation":false,"usgs":true,"family":"Barnard","given":"P.L.","email":"","affiliations":[],"preferred":false,"id":578726,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70004127,"text":"70004127 - 2009 - Responses of stream nitrate and dissolved organic carbon loadings to hydrological forcing and climate change in an upland forest of the northeast USA","interactions":[],"lastModifiedDate":"2015-11-16T14:43:54","indexId":"70004127","displayToPublicDate":"2015-06-08T09:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Responses of stream nitrate and dissolved organic carbon loadings to hydrological forcing and climate change in an upland forest of the northeast USA","docAbstract":"[1] In coming decades, higher annual temperatures, increased growing season length, and increased dormant season precipitation are expected across the northeastern United States in response to anthropogenic forcing of global climate. We synthesized long-term stream hydrochemical data from the Sleepers River Research Watershed in Vermont, United States, to explore the relationship of catchment wetness to stream nitrate and DOC loadings. We modeled changes in growing season length and precipitation patterns to simulate future climate scenarios and to assess how stream nutrient loadings respond to climate change. Model results for the 2070–2099 time period suggest that stream nutrient loadings during both the dormant and growing seasons will respond to climate change. During a warmer climate, growing season stream fluxes (runoff +20%, nitrate +57%, and DOC +58%) increase as more precipitation (+28%) and quick flow (+39%) occur during a longer growing season (+43 days). During the dormant season, stream water and nutrient loadings decrease. Net annual stream runoff (+8%) and DOC loading (+9%) increases are commensurate with the magnitude of the average increase of net annual precipitation (+7%). Net annual stream water and DOC loadings are primarily affected by increased dormant season precipitation. In contrast, decreased annual loading of stream nitrate (−2%) reflects a larger effect of growing season controls on stream nitrate and the effects of lengthened growing seasons in a warmer climate. Our findings suggest that leaching of nitrate and DOC from catchment soils will be affected by anthropogenic climate forcing, thereby affecting the timing and magnitude of annual stream loadings in the northeastern United States.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2008JG000778","usgsCitation":"Sebestyen, S.D., Boyer, E.W., and Shanley, J.B., 2009, Responses of stream nitrate and dissolved organic carbon loadings to hydrological forcing and climate change in an upland forest of the northeast USA: Journal of Geophysical Research, v. 114, no. G2, 11 p., https://doi.org/10.1029/2008JG000778.","productDescription":"11 p.","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-006854","costCenters":[],"links":[{"id":475963,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2008jg000778","text":"Publisher Index Page"},{"id":311383,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":311382,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/10.1029/2008JG000778/abstract"}],"country":"United States","state":"Vermont","otherGeospatial":"Sleepers River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.8890380859375,\n 44.10139306449849\n ],\n [\n -71.8890380859375,\n 44.896741421341964\n ],\n [\n -71.0101318359375,\n 44.896741421341964\n ],\n [\n -71.0101318359375,\n 44.10139306449849\n ],\n [\n -71.8890380859375,\n 44.10139306449849\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"114","issue":"G2","noUsgsAuthors":false,"publicationDate":"2009-04-07","publicationStatus":"PW","scienceBaseUri":"564b0c5be4b0ebfbef0d3183","contributors":{"authors":[{"text":"Sebestyen, Stephen D.","contributorId":107562,"corporation":false,"usgs":true,"family":"Sebestyen","given":"Stephen","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":579889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boyer, Elizabeth W.","contributorId":44659,"corporation":false,"usgs":false,"family":"Boyer","given":"Elizabeth","email":"","middleInitial":"W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":579890,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shanley, James B. 0000-0002-4234-3437 jshanley@usgs.gov","orcid":"https://orcid.org/0000-0002-4234-3437","contributorId":1953,"corporation":false,"usgs":true,"family":"Shanley","given":"James","email":"jshanley@usgs.gov","middleInitial":"B.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":579891,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192959,"text":"70192959 - 2009 - Characterization of rock samples and mineralogical controls on leachates","interactions":[],"lastModifiedDate":"2017-12-21T10:35:51","indexId":"70192959","displayToPublicDate":"2015-06-02T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"Characterization of rock samples and mineralogical controls on leachates","docAbstract":"Rocks associated with coal beds typically include shale, sandstone, and (or) limestone. In addition to common rock-forming minerals, all of these rock types may contain sulfide and sulfate minerals, various carbonate minerals, and organic material. These different minerals have inherently different solubility characteristics, as well as different acid-generating or acid-neutralizing potentials. The abundance and composition of sulfur- and carbonate-bearing minerals are of particular interest in interpreting the leaching column data because (1) pyrite and carbonate minerals are the primary controls on the acid-base account of a sample, (2) these minerals incorporate trace metals that can be released during weathering, and (3) these minerals readily react during weathering due to mineral dissolution and oxidation of iron.
Rock samples were collected by the Pennsylvania Department of Environmental Protection (PaDEP) from five different sites to assess the draft standardized leaching column method (ADTI-WP2) for the prediction of weathering rates and water quality at coal mines. Samples were sent to USGS laboratories for mineralogical characterization and to ActLabs for chemical analysis. The samples represent a variety of rock types (shales, sandstones, and coal refuse) that are typical of coal overburden in the eastern United States. These particular samples were chosen for testing the weathering protocols because they represent a range of geochemical and lithologic characteristics, sulfur contents, and acid-base accounting characteristics (Hornberger et al., 2003). The rocks contain variable amounts of pyrite and carbonate minerals and vary in texture.
This chapter includes bulk rock chemical data and detailed mineralogical and textural data for unweathered starting materials used in the interlaboratory validation study, and for two samples used in the early phases of leaching column tests (Wadesville Sandstone, Leechburg Coal Refuse). We also characterize some of the post-weathering rock samples, report trace-element content in leachate, and discuss mineralogical controls on leachate quality based on data from one of the participating laboratories. Table 5.1 lists the samples described in this chapter, the sample numbers, and comments on the characteristics of each lithology. Sample locations are plotted in Figure 5.1. Chapters 2 and 3 describe the sample locations, sample preparation protocols, ABA characteristics, and rationale for selection of rock samples for testing. Microprobe data for pyrite and carbonate minerals are tabulated in Appendix 5.1. Leachate data, along with a series of graphs showing concentration and cumulative transport trends, for the laboratory data discussed in this chapter are included as Excel spreadsheets in Appendices 5.2 and 5.3. Leach column data for the interlaboratory study are evaluated and interpreted in Chapters 7 -11.
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EPA Method 1627","language":"English","publisher":"Office of Surface Mining Reclamation and Enforcement","usgsCitation":"Hammarstrom, J.M., Cravotta, C., Galeone, D.G., Jackson, J.C., and Dulong, F.T., 2009, Characterization of rock samples and mineralogical controls on leachates, 51 p.","productDescription":"51 p.","startPage":"61","endPage":"111","ipdsId":"IP-011226","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":350149,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":347650,"type":{"id":15,"text":"Index Page"},"url":"https://aciddrainage.com/publications/"}],"country":"United States","state":"Arkansas, Florida, Georgia, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Michigan, Mississippi, Missouri, Nebraska, Ohio, Oklahoma, Pennsylvania, Tennessee, Texas, Virginia, West Virginia","geographicExtents":"{\n \"type\": 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III 0000-0003-3116-4684 cravotta@usgs.gov","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":196993,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","email":"cravotta@usgs.gov","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":false,"id":717439,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Galeone, Daniel G. 0000-0002-8007-9278 dgaleone@usgs.gov","orcid":"https://orcid.org/0000-0002-8007-9278","contributorId":2301,"corporation":false,"usgs":true,"family":"Galeone","given":"Daniel","email":"dgaleone@usgs.gov","middleInitial":"G.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":717440,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, John C. jjackson@usgs.gov","contributorId":2652,"corporation":false,"usgs":true,"family":"Jackson","given":"John","email":"jjackson@usgs.gov","middleInitial":"C.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":717443,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dulong, Frank T. 0000-0001-7388-647X fdulong@usgs.gov","orcid":"https://orcid.org/0000-0001-7388-647X","contributorId":650,"corporation":false,"usgs":true,"family":"Dulong","given":"Frank","email":"fdulong@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":717441,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70157388,"text":"70157388 - 2009 - Estimating phosphorus concentrations following alum treatment using apparent settling velocity","interactions":[],"lastModifiedDate":"2018-02-06T12:36:14","indexId":"70157388","displayToPublicDate":"2015-06-01T05:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2592,"text":"Lake and Reservoir Management","active":true,"publicationSubtype":{"id":10}},"title":"Estimating phosphorus concentrations following alum treatment using apparent settling velocity","docAbstract":"he apparent settling velocity (Vs) is a term used in empirical, steady-state, mass-balance lake models to represent the net phosphorus flux from the water column. The Vollenweider (1969) mixed-reactor lake model was rearranged and used to calculate Vs values for total phosphorus (TP) for three lakes treated with alum to reduce the internal flux of P to the water column (Delavan Lake, Wisconsin; Lake Morey, Vermont; and West Twin Lake, Ohio). An analysis of Vs values was conducted using data from these three lakes for both the pre- and post-alum treated conditions. Analysis of Vs values for both the pre- and post-alum conditions in Lake Morey and West Twin Lake resulted in a post-treatment mean Vs value of 7 ± 2.0 m·yr−1. The effect of the alum treatment, although short-lived in Delavan Lake, resulted in a mean post-treatment Vs value of 3.4 ± 0.3 m·yr−1. The consistency in the post-treatment Vs values in Lake Morey and West Twin Lake is used to demonstrate a predictive analysis method for water column TP concentrations in lakes following a successful treatment of the anoxic sediment area with alum. Additional pre- and post-alum in-lake and watershed loading data are needed to advance this concept into a management model.
","language":"English","publisher":"North American Lake Management Society","doi":"10.1080/07438149909353949","usgsCitation":"Panuska, J., and Robertson, D.M., 2009, Estimating phosphorus concentrations following alum treatment using apparent settling velocity: Lake and Reservoir Management, v. 15, no. 1, p. 28-38, https://doi.org/10.1080/07438149909353949.","productDescription":"11 p.","startPage":"28","endPage":"38","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[],"links":[{"id":475965,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/07438149909353949","text":"Publisher Index Page"},{"id":308376,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio, Vermont, Wisconsin","otherGeospatial":"Lake Delevan, Lake Morey, West Twin 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\"}}]}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56027bbee4b03bc34f54482c","contributors":{"authors":[{"text":"Panuska, John","contributorId":31025,"corporation":false,"usgs":false,"family":"Panuska","given":"John","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":572948,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robertson, Dale M. 0000-0001-6799-0596 dzrobert@usgs.gov","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":150760,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale","email":"dzrobert@usgs.gov","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":572949,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70041614,"text":"70041614 - 2009 - Diffusion-equation representations of landform evolution in the simplest circumstances: Appendix C","interactions":[],"lastModifiedDate":"2015-10-29T10:03:39","indexId":"70041614","displayToPublicDate":"2014-12-08T08:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":13,"text":"Handbook"},"title":"Diffusion-equation representations of landform evolution in the simplest circumstances: Appendix C","docAbstract":"The diffusion equation is one of the three great partial differential equations of classical physics. It describes the flow or diffusion of heat in the presence of temperature gradients, fluid flow in porous media in the presence of pressure gradients, and the diffusion of molecules in the presence of chemical gradients. [The other two equations are the wave equation, which describes the propagation of electromagnetic waves (including light), acoustic (sound) waves, and elastic (seismic) waves radiated from earthquakes; and LaPlace’s equation, which describes the behavior of electric, gravitational, and fluid potentials, all part of potential field theory. The diffusion equation reduces to LaPlace’s equation at steady state, when the field of interest does not depend on t. Poisson’s equation is LaPlace’s equation with a source term.]
\nJoseph Fourier developed the diffusion equation for heat conduction in 1807, and it has significant associations with probability theory (Narasimhan, 2009), as we will see shortly. In a novel and fascinating application, Gene Humphreys has employed solutions of the diffusion equation to describe the density of desert tortoises in the presence of population gradients caused by new dirt roads cut in the Mojave Desert. These new dirt roads induce an immediate line sink for unsuspecting tortoises. As of this writing in early September, I am not sure whether Gene has published this work.
\nMost of us here know that the diffusion equation has also been used to describe the evolution through time of scarp-like landforms, including fault scarps, shoreline scarps, or a set of marine terraces. The methods, models, and data employed in such studies have been described in the literature many times over the past 25 years. For most situations, everything you will ever need (or want) to know can be found in Hanks et al. (1984) and Hanks (2000), the latter being a review of numerous studies of the 1980s and 1990s and a summary of available estimates of the mass diffusivity κ. The geometric parameterization of scarp-like landforms is shown in Figure 1.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Friends of the Pleistocene 2009 Pacific Cell Field Trip: Paleoseismic, geomorphic, and geodetic studies across the Central Great Basin: Exploring active deformation along the eastern edge of the Pacific/North American plate boundary.","largerWorkSubtype":{"id":13,"text":"Handbook"},"language":"English","usgsCitation":"Hanks, T.C., 2009, Diffusion-equation representations of landform evolution in the simplest circumstances: Appendix C, 6 p.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-016448","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":310751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56334338e4b048076347eebf","contributors":{"authors":[{"text":"Hanks, Thomas C. 0000-0003-0928-0056 thanks@usgs.gov","orcid":"https://orcid.org/0000-0003-0928-0056","contributorId":3065,"corporation":false,"usgs":true,"family":"Hanks","given":"Thomas","email":"thanks@usgs.gov","middleInitial":"C.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":578665,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70120865,"text":"70120865 - 2009 - Online interactive U.S. Reservoir Sedimentation Survey Database","interactions":[],"lastModifiedDate":"2014-08-18T10:27:35","indexId":"70120865","displayToPublicDate":"2013-08-18T10:22:00","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1578,"text":"Eos, Transactions, American Geophysical Union","onlineIssn":"2324-9250","printIssn":"0096-394","active":true,"publicationSubtype":{"id":10}},"title":"Online interactive U.S. Reservoir Sedimentation Survey Database","docAbstract":"In April 2009, the U.S. Geological Survey and the Natural Resources Conservation Service (prior to 1994, the Soil Conservation Service) created the Reservoir Sedimentation Survey Database (RESSED) and Web site, the most comprehensive compilation of data from reservoir bathymetric and dry basin surveys in the United States. RESSED data can be useful for a number of purposes, including calculating changes in reservoir storage characteristics, quantifying rates of sediment delivery to reservoirs, and estimating erosion rates in a reservoir's watershed.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Eos, Transactions American Geophysical Union","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1029/2009EO230003","usgsCitation":"Gray, J., Bernard, J., Schwarz, G., Stewart, D.W., and Ray, K., 2009, Online interactive U.S. Reservoir Sedimentation Survey Database: Eos, Transactions, American Geophysical Union, v. 90, no. 23, https://doi.org/10.1029/2009EO230003.","productDescription":"1 p.","startPage":"199","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":475967,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2009eo230003","text":"Publisher Index Page"},{"id":292389,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":292387,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1029/2009EO230003"}],"country":"Puerto Rico;United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 144.616667,12.233333 ], [ 144.616667,71.833333 ], [ -64.566667,71.833333 ], [ -64.566667,12.233333 ], [ 144.616667,12.233333 ] ] ] } } ] }","volume":"90","issue":"23","noUsgsAuthors":false,"publicationDate":"2011-06-03","publicationStatus":"PW","scienceBaseUri":"53f25fe9e4b033341871893b","contributors":{"authors":[{"text":"Gray, J.B.","contributorId":73119,"corporation":false,"usgs":true,"family":"Gray","given":"J.B.","email":"","affiliations":[],"preferred":false,"id":498503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bernard, J.M.","contributorId":43999,"corporation":false,"usgs":true,"family":"Bernard","given":"J.M.","email":"","affiliations":[],"preferred":false,"id":498502,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwarz, G. E. 0000-0002-9239-4566","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":14852,"corporation":false,"usgs":true,"family":"Schwarz","given":"G. E.","affiliations":[],"preferred":false,"id":498501,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stewart, D. W.","contributorId":86194,"corporation":false,"usgs":true,"family":"Stewart","given":"D.","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":498505,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ray, K.T.","contributorId":77758,"corporation":false,"usgs":true,"family":"Ray","given":"K.T.","email":"","affiliations":[],"preferred":false,"id":498504,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70047288,"text":"70047288 - 2009 - Converting nonstandard fish sampling data to standardized data","interactions":[],"lastModifiedDate":"2021-06-04T16:44:48.968639","indexId":"70047288","displayToPublicDate":"2013-01-01T21:25:04","publicationYear":"2009","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"12","title":"Converting nonstandard fish sampling data to standardized data","docAbstract":"Fishery biologists spend considerable effort over multiple years collecting data on fish population and community status using a particular sampling method or set of methods. However, new (and often more effective) sampling methods and technologies are continuously being developed. To incorporate these new sampling techniques, fishery biologists need a means for converting sample data collected using old methods so they can be compared with data collected using new methods. Similarly, fishery biologists often need a means to compare fish sample data collected using the same method over time (e.g., from year to year) and space (e.g., between sample sites). If fish abundance, species presence, or richness are estimated using an unbiased statistical estimator, the estimates can be validly compared, even if the fish sample data were collected with different methods. However, if unbiased statistical estimators were not used, biologists need methods for adjusting fish sampling data collected using different methods or using the same method collected under different sampling conditions. In this chapter, we describe and provide examples of statistical techniques for converting nonstandard fish sampling data to standardized data and for making comparisons of fish sampling data collected at different times or at different locations. We define standard fish sampling data as data collected using the standardized fish sampling methods described throughout this book. Any other sampling methods and associated data are thus defined as nonstandard. Before delving into the details of the statistical modeling techniques, we describe the nature of fish sample data, their uses, and their limitations.
Catch-effort measures, such as relative abundance and catch per unit effort (CPUE), are more formally described as indices. Here, we define an index as any measure or count of a species or community (e.g., species richness) based on direct observation without an estimate of the ability to count individuals or species. Indices have some very desirable characteristics for use in fisheries research and management. In general (but not always), indices require less effort to collect and are usually more precise than unbiased population estimators (e.g., CPUE versus capture–recapture estimates of abundance). The proper use of indices for assessment of fish populations or communities, however, requires that the relationship between an index and the true value (e.g., fish density, species richness) is relatively constant (1) across the observable range of true values, (2) through time when evaluating trends at a single location, and (3) across space when making comparisons among locations.
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The summary provides data on exploration budgets by region and mineral commodity, identifies significant mineral discoveries and areas of mineral exploration, discusses government programs affecting the mineral exploration industry, and presents analyses of exploration activities by the mineral industry based upon these data.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Mining Engineering","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"SME","usgsCitation":"Wilburn, D., 2009, Exploration review: Mining Engineering, v. 61, no. 5, p. 35-49.","productDescription":"15 p.","startPage":"35","endPage":"49","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":271590,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"61","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"517f9669e4b0e41721f7a358","contributors":{"authors":[{"text":"Wilburn, D.R.","contributorId":98911,"corporation":false,"usgs":true,"family":"Wilburn","given":"D.R.","email":"","affiliations":[],"preferred":false,"id":478009,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70043458,"text":"70043458 - 2009 - Genetic structure in the Anaxyrus boreas species group (anura, Bufonidae): an evaluation of the Southern Rocky Mountain population","interactions":[],"lastModifiedDate":"2013-05-29T09:54:17","indexId":"70043458","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":251,"text":"Final Report","active":false,"publicationSubtype":{"id":4}},"title":"Genetic structure in the Anaxyrus boreas species group (anura, Bufonidae): an evaluation of the Southern Rocky Mountain population","docAbstract":"The Anaxyrus boreas species group is comprised of four species endemic to the western United States: A. boreas, A. canorus, A. exsul, and A. nelsoni. Disjunct populations of the widespread western toad Anaxyrus boreas from Colorado and southern Wyoming, the southern rocky mountain population (SRMP), were previously candidates for listing under the United States Endangered Species Act (ESA) as a distinct population segment (DPS), but were removed due to a lack of significant genetic differentiation in preliminary studies. The purpose of this study was to conduct phylogeographic and population genetic analyses of A. boreas and three related species using mitochondrial DNA sequence data and nuclear microsatellite genotype data. 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They reference unnamed sources for pre-1997 horseshoe crab harvest to conclude that recent harvest exceeds historic harvest. In fact, reported landings from New Jersey, Delaware, Maryland, and Virginia in 2006 (352 metric tons [mt]) were between landings in 1989 (365 mt) and 1990 (232 mt) (www.st.nmfs.noaa.gov/st1/commercial/inaex.html), despite nonmandatory reporting coastwide before 1998 (Kreamer and Michels 2009). They present egg densities from New Jersey beaches only. Of the 11 Delaware beaches sampled, eggs in the top 5 centimeters exceeded their monitoring target of 50,000 per square meter at 5 in 2006 and at 6 in 2007 (Kalasz et al. 2008). They rely on the Delaware trawl survey for historic trends. Nine fishery-independent surveys have been used to assess trends in the Delaware Bay region, and several began before 1990 (Smith et al. 2009a).","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"BioScience","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Institute of Biological Sciences","doi":"10.1525/bio.2009.59.7.20","usgsCitation":"Smith, D., Hallerman, E.M., Millard, M.J., Sweka, J.A., and Weber, R.G., 2009, An incomplete analysis: BioScience, v. 59, no. 7, p. 541-541, https://doi.org/10.1525/bio.2009.59.7.20.","productDescription":"1 p.","startPage":"541","endPage":"541","ipdsId":"IP-012363","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":488167,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.bioone.org/doi/10.1525/bio.2009.59.7.20","text":"External Repository"},{"id":273999,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":273998,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1525/bio.2009.59.7.20"}],"volume":"59","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51c2d2dfe4b08857aac4238b","contributors":{"authors":[{"text":"Smith, David 0000-0001-6074-9257","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":1989,"corporation":false,"usgs":false,"family":"Smith","given":"David","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":473587,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hallerman, Eric M.","contributorId":40501,"corporation":false,"usgs":true,"family":"Hallerman","given":"Eric","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":473589,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Millard, Michael J.","contributorId":23411,"corporation":false,"usgs":false,"family":"Millard","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":473588,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sweka, John A.","contributorId":80945,"corporation":false,"usgs":true,"family":"Sweka","given":"John","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":473591,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Weber, Richard G.","contributorId":66995,"corporation":false,"usgs":true,"family":"Weber","given":"Richard","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":473590,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70043516,"text":"70043516 - 2009 - Disaster response and the international charter program","interactions":[],"lastModifiedDate":"2013-04-25T15:04:20","indexId":"70043516","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Disaster response and the international charter program","docAbstract":"In a meeting held in Vienna, Austria in 1999, a small group of space agencies conceived and approved a program to provide emergency response satellite data to those affected by disasters anywhere in the world. The purpose of this group, which came to be known as the “International Charter - Space and Major Disasters”, is to promote cooperation among space agencies in the use of satellite data to manage crises during and after disasters. When tropical storms, floods, oil spills, earthquakes, landslides, volcanoes or fires endanger human life, the Charter member agencies provide valuable information about these events’ extent and impact.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Photogrammetric Engineering and Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"ASPRS","usgsCitation":"Stryker, T., and Jones, B., 2009, Disaster response and the international charter program: Photogrammetric Engineering and Remote Sensing, v. 2009, no. 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These archival products supplement direct access to current and historical water data provided by NWISWeb. Beginning with Water Year 2006, annual water data reports are available as individual electronic Site Data Sheets for the entire Nation for retrieval, download, and localized printing on demand. National distribution includes tabular and map interfaces for search, query, display and download of data. From 1962 until 2005, reports were published by State as paper documents, although most reports since the mid-1990s are also available in electronic form through this web page. Reports prior to 1962 were published in occasional USGS Water-Supply Papers and other reports.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/wdr2009","usgsCitation":"Water Resources Division, U.S. Geological Survey, 2009, Water-resources data for the United States: water year 2009: U.S. Geological Survey Water Data Report 2009, HTML Document, https://doi.org/10.3133/wdr2009.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":269338,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/wdr2009.jpg"},{"id":269336,"type":{"id":15,"text":"Index Page"},"url":"https://wdr.water.usgs.gov/wy2009/search.jsp"},{"id":269337,"type":{"id":15,"text":"Index Page"},"url":"https://wdr.water.usgs.gov/"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 172.5,18.9 ], [ 172.5,71.4 ], [ -66.9,71.4 ], [ -66.9,18.9 ], [ 172.5,18.9 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5142f18be4b073a963ff6621","contributors":{"authors":[{"text":"Water Resources Division, U.S. Geological Survey","contributorId":128075,"corporation":true,"usgs":false,"organization":"Water Resources Division, U.S. Geological Survey","id":535453,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70038123,"text":"70038123 - 2009 - Technological advances in suspended‐sediment surrogate monitoring","interactions":[],"lastModifiedDate":"2018-04-02T17:15:18","indexId":"70038123","displayToPublicDate":"2012-06-18T12:08:00","publicationYear":"2009","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Technological advances in suspended‐sediment surrogate monitoring","docAbstract":"Surrogate technologies to continuously monitor suspended sediment show promise toward supplanting traditional data collection methods requiring routine collection and analysis of water samples. Commercially available instruments operating on bulk optic (turbidity), laser optic, pressure difference, and acoustic backscatter principles are evaluated based on cost, reliability, robustness, accuracy, sample volume, susceptibility to biological fouling, and suitable range of mass concentration and particle size distribution. In situ turbidimeters are widely used. They provide reliable data where the point measurements can be reliably correlated to the river's mean cross section concentration value, effects of biological fouling can be minimized, and concentrations remain below the sensor's upper measurement limit. In situ laser diffraction instruments have similar limitations and can cost 6 times the approximate $5000 purchase price of a turbidimeter. However, laser diffraction instruments provide volumetric‐concentration data in 32 size classes. Pressure differential instruments measure mass density in a water column, thus integrating substantially more streamflow than a point measurement. They are designed for monitoring medium‐to‐large concentrations, are generally unaffected by biological fouling, and cost about the same as a turbidimeter. However, their performance has been marginal in field applications. Acoustic Doppler profilers use acoustic backscatter to measure suspended sediment concentrations in orders of magnitude more streamflow than do instruments that rely on point measurements. The technology is relatively robust and generally immune to effects of biological fouling. Cost of a single‐frequency device is about double that of a turbidimeter. Multifrequency arrays also provide the potential to resolve concentrations by clay silt versus sand size fractions. Multifrequency hydroacoustics shows the most promise for revolutionizing collection of continuous suspended sediment data by instruments that require only periodic calibration for correlation to mean concentrations in river cross sections. Broad application of proven suspended sediment surrogate technologies has the potential to revolutionize fluvial sediment monitoring. Once applied, benefits could be enormous, providing for safer, more frequent and consistent, arguably more accurate, and ultimately less expensive sediment data for managing the world's sedimentary resources.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2008WR007063","usgsCitation":"Gray, J.R., and Gartner, J.W., 2009, Technological advances in suspended‐sediment surrogate monitoring: Water Resources Research, v. 45, no. 4, Article W00D29; 20 p., https://doi.org/10.1029/2008WR007063.","productDescription":"Article W00D29; 20 p.","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":475975,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2008wr007063","text":"Publisher Index Page"},{"id":257928,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"4","noUsgsAuthors":false,"publicationDate":"2009-03-06","publicationStatus":"PW","scienceBaseUri":"505ba43de4b08c986b3201d2","contributors":{"authors":[{"text":"Gray, John R. 0000-0002-8817-3701 jrgray@usgs.gov","orcid":"https://orcid.org/0000-0002-8817-3701","contributorId":1158,"corporation":false,"usgs":true,"family":"Gray","given":"John","email":"jrgray@usgs.gov","middleInitial":"R.","affiliations":[{"id":5058,"text":"Office of the Chief Scientist for Water","active":true,"usgs":true}],"preferred":true,"id":463463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gartner, Jeffrey W.","contributorId":77524,"corporation":false,"usgs":true,"family":"Gartner","given":"Jeffrey","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":463464,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}