This chapter reviews detection of materials on solid and liquid (lakes and ocean) surfaces in the solar system using ultraviolet to infrared spectroscopy from space, or near space (high altitude aircraft on the Earth), or in the case of remote objects, earth-based and earth-orbiting telescopes. Point spectrometers and imaging spectrometers have been probing the surfaces of our solar system for decades. Spacecraft carrying imaging spectrometers are currently in orbit around Mercury, Venus, Earth, Mars, and Saturn, and systems have recently visited Jupiter, comets, asteroids, and one spectrometer-carrying spacecraft is on its way to Pluto. Together these systems are providing a wealth of data that will enable a better understanding of the composition of condensed matter bodies in the solar system.
Minerals, ices, liquids, and other materials have been detected and mapped on the Earth and all planets and/or their satellites where the surface can be observed from space, with the exception of Venus whose thick atmosphere limits surface observation. Basaltic minerals (e.g., pyroxene and olivine) have been detected with spectroscopy on the Earth, Moon, Mars and some asteroids. The greatest mineralogic diversity seen from space is observed on the Earth and Mars. The Earth, with oceans, active tectonic and hydrologic cycles, and biological processes, displays the greatest material diversity including the detection of amorphous and crystalline inorganic materials, organic compounds, water and water ice.
Water ice is a very common mineral throughout the Solar System and has been unambiguously detected or inferred in every planet and/or their moon(s) where good spectroscopic data has been obtained.
In addition to water ice, other molecular solids have been observed in the solar system using spectroscopic methods. Solid carbon dioxide is found on all systems beyond the Earth except Pluto, although CO2 sometimes appears to be trapped in other solids rather than as an ice on some objects. The largest deposits of carbon dioxide ice are found on Mars. Sulfur dioxide ice is found in the Jupiter system. Nitrogen and methane ices are common beyond the Uranian system.
Saturn’s moon Titan probably has the most complex active extra-terrestrial surface chemistry involving organic compounds. Some of the observed or inferred compounds include ices of benzene (C6H6), cyanoacetylene (HC3N), toluene (C7H8), cyanogen (C2N2), acetonitrile (CH3CN), water (H2O), carbon dioxide (CO2), and ammonia (NH3). Confirming compounds on Titan is hampered by its thick smoggy atmosphere, where in relative terms the atmospheric interferences that hamper surface characterization lie between that of Venus and Earth.
In this chapter we exclude discussion of the planets Jupiter, Saturn, Uranus, and Neptune because their thick atmospheres preclude observing the surface, even if surfaces exist. However, we do discuss spectroscopic observations on a number of the extra-terrestrial satellite bodies. Ammonia was predicted on many icy moons but is notably absent among the definitively detected ices with possible exceptions on Charon and possible trace amounts on some of the Saturnian satellites. Comets, storehouses of many compounds that could exist as ices in their nuclei, have only had small amounts of water ice definitively detected on their surfaces from spectroscopy. Only two asteroids have had a direct detection of surface water ice, although its presence can be inferred in others.
","language":"English","publisher":"Mineralogical Society of America","doi":"10.2138/rmg.2014.78.10","usgsCitation":"Clark, R.N., Swayze, G.A., Carlson, R.R., Grundy, W., and Noll, K., 2014, Spectroscopy from Space: Reviews in Mineralogy and Geochemistry, v. 78, no. 1, p. 399-446, https://doi.org/10.2138/rmg.2014.78.10.","productDescription":"48 p.","startPage":"399","endPage":"446","ipdsId":"IP-036673","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":343176,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"78","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2014-02-27","publicationStatus":"PW","scienceBaseUri":"595611b9e4b0d1f9f0506772","contributors":{"authors":[{"text":"Clark, Roger N. 0000-0002-7021-1220 rclark@usgs.gov","orcid":"https://orcid.org/0000-0002-7021-1220","contributorId":515,"corporation":false,"usgs":true,"family":"Clark","given":"Roger","email":"rclark@usgs.gov","middleInitial":"N.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":702780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Swayze, Gregg A. 0000-0002-1814-7823 gswayze@usgs.gov","orcid":"https://orcid.org/0000-0002-1814-7823","contributorId":518,"corporation":false,"usgs":true,"family":"Swayze","given":"Gregg","email":"gswayze@usgs.gov","middleInitial":"A.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":702779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carlson, Robert R.","contributorId":71944,"corporation":false,"usgs":true,"family":"Carlson","given":"Robert","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":702931,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grundy, Will","contributorId":156333,"corporation":false,"usgs":false,"family":"Grundy","given":"Will","email":"","affiliations":[],"preferred":false,"id":702932,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Noll, Keith","contributorId":193877,"corporation":false,"usgs":false,"family":"Noll","given":"Keith","email":"","affiliations":[],"preferred":false,"id":702933,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70189671,"text":"70189671 - 2014 - Identifying non-point sources of endocrine active compounds and their biological impacts in freshwater lakes","interactions":[],"lastModifiedDate":"2018-09-04T16:40:37","indexId":"70189671","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":887,"text":"Archives of Environmental Contamination and Toxicology","active":true,"publicationSubtype":{"id":10}},"title":"Identifying non-point sources of endocrine active compounds and their biological impacts in freshwater lakes","docAbstract":"Contaminants of emerging concern, particularly endocrine active compounds (EACs), have been identified as a threat to aquatic wildlife. However, little is known about the impact of EACs on lakes through groundwater from onsite wastewater treatment systems (OWTS). This study aims to identify specific contributions of OWTS to Sullivan Lake, Minnesota, USA. Lake hydrology, water chemistry, caged bluegill sunfish (Lepomis macrochirus), and larval fathead minnow (Pimephales promelas) exposures were used to assess whether EACs entered the lake through OWTS inflow and the resultant biological impact on fish. Study areas included two OWTS-influenced near-shore sites with native bluegill spawning habitats and two in-lake control sites without nearby EAC sources. Caged bluegill sunfish were analyzed for plasma vitellogenin concentrations, organosomatic indices, and histological pathologies. Surface and porewater was collected from each site and analyzed for EACs. Porewater was also collected for laboratory exposure of larval fathead minnow, before analysis of predator escape performance and gene expression profiles. Chemical analysis showed EACs present at low concentrations at each study site, whereas discrete variations were reported between sites and between summer and fall samplings. Body condition index and liver vacuolization of sunfish were found to differ among study sites as did gene expression in exposed larval fathead minnows. Interestingly, biological exposure data and water chemistry did not match. Therefore, although results highlight the potential impacts of seepage from OWTS, further investigation of mixture effects and life history factor as well as chemical fate is warranted.
","language":"English","publisher":"Springer","doi":"10.1007/s00244-014-0052-4","usgsCitation":"Baker, B.H., Martinovic-Weigelt, D., Ferrey, M.L., Barber, L.B., Writer, J.H., Rosenberry, D.O., Kiesling, R.L., Lundy, J.R., and Schoenfuss, H.L., 2014, Identifying non-point sources of endocrine active compounds and their biological impacts in freshwater lakes: Archives of Environmental Contamination and Toxicology, v. 67, no. 3, p. 374-388, https://doi.org/10.1007/s00244-014-0052-4.","productDescription":"15 p.","startPage":"374","endPage":"388","ipdsId":"IP-057586","costCenters":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":344078,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","county":"Wright County","city":"Maple Lake Township","otherGeospatial":"Sullivan Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.94999265670776,\n 45.217084825093266\n ],\n [\n -93.93267631530762,\n 45.217084825093266\n ],\n [\n -93.93267631530762,\n 45.22789121544507\n ],\n [\n -93.94999265670776,\n 45.22789121544507\n ],\n [\n -93.94999265670776,\n 45.217084825093266\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"67","issue":"3","noUsgsAuthors":false,"publicationDate":"2014-06-29","publicationStatus":"PW","scienceBaseUri":"59706fbce4b0d1f9f065a905","contributors":{"authors":[{"text":"Baker, Beth H.","contributorId":194915,"corporation":false,"usgs":false,"family":"Baker","given":"Beth","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":705718,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martinovic-Weigelt, Dalma","contributorId":173655,"corporation":false,"usgs":false,"family":"Martinovic-Weigelt","given":"Dalma","affiliations":[{"id":6748,"text":"University of St. Thomas","active":true,"usgs":false}],"preferred":false,"id":705719,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ferrey, Mark L.","contributorId":59912,"corporation":false,"usgs":true,"family":"Ferrey","given":"Mark","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":705720,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barber, Larry B. 0000-0002-0561-0831 lbbarber@usgs.gov","orcid":"https://orcid.org/0000-0002-0561-0831","contributorId":921,"corporation":false,"usgs":true,"family":"Barber","given":"Larry","email":"lbbarber@usgs.gov","middleInitial":"B.","affiliations":[{"id":5044,"text":"National Research Program - 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2014 - Groundwater conditions in Utah, spring of 2014","interactions":[],"lastModifiedDate":"2019-05-22T09:32:52","indexId":"70193118","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":110,"text":"Cooperative Investigations Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"55","title":"Groundwater conditions in Utah, spring of 2014","docAbstract":"This is the fifty-first in a series of annual reports that describe groundwater conditions in Utah. Reports in this series, published cooperatively by the U.S. Geological Survey and the Utah Department of Natural Resources, Division of Water Rights, and the Utah Department of Environmental Quality, Division of Water Quality, provide data to enable interested parties to maintain awareness of changing groundwater conditions.
This report, like the others in the series, contains information on well construction, groundwater withdrawal from wells, water-level changes, precipitation, streamflow, and chemical quality of water. Information on well construction included in this report refers only to wells constructed for new appropriations of groundwater. Supplementary data are included in reports of this series only for those years or areas that are important to a discussion of changing groundwater conditions and for which applicable data are available.
This report includes individual discussions of selected significant areas of groundwater development in the State for calendar year 2013. Most of the reported data were collected by the U.S. Geological Survey in cooperation with the Utah Department of Natural Resources, Division of Water Rights, and the Utah Department of Environmental Quality, Division of Water Quality. This report is also available online at http://www.waterrights.utah.gov/techinfo/ and http://ut.water. usgs.gov/publications/GW2014.pdf. Groundwater conditions in Utah for calendar year 2012 are reported in Burden and others (2013) and are available online at http://ut.water.usgs. gov/publications/GW2013.pdf
","language":"English","publisher":"Utah Department of Natural Resources","usgsCitation":"Burden, C.B., 2014, Groundwater conditions in Utah, spring of 2014: Cooperative Investigations Report 55, x, 118 p.","productDescription":"x, 118 p.","numberOfPages":"132","ipdsId":"IP-056622","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":350085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":364083,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://waterrights.utah.gov/techinfo/wwwpub/GW2014.pdf"}],"country":"United 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\"}}]}","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a6100c9e4b06e28e9c2541d","contributors":{"authors":[{"text":"Burden, Carole B. cburden@usgs.gov","contributorId":852,"corporation":false,"usgs":true,"family":"Burden","given":"Carole","email":"cburden@usgs.gov","middleInitial":"B.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":718032,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70194120,"text":"70194120 - 2014 - Bacterial pathogen gene abundance and relation to recreational water quality at seven Great Lakes beaches","interactions":[],"lastModifiedDate":"2017-11-16T16:52:57","indexId":"70194120","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Bacterial pathogen gene abundance and relation to recreational water quality at seven Great Lakes beaches","docAbstract":"Quantitative assessment of bacterial pathogens, their geographic variability, and distribution in various matrices at Great Lakes beaches are limited. Quantitative PCR (qPCR) was used to test for genes from E. coli O157:H7 (eaeO157), shiga-toxin producing E. coli (stx2), Campylobacter jejuni (mapA), Shigella spp. (ipaH), and a Salmonella enterica-specific (SE) DNA sequence at seven Great Lakes beaches, in algae, water, and sediment. Overall, detection frequencies were mapA>stx2>ipaH>SE>eaeO157. Results were highly variable among beaches and matrices; some correlations with environmental conditions were observed for mapA, stx2, and ipaH detections. Beach seasonal mean mapA abundance in water was correlated with beach seasonal mean log10E. coli concentration. At one beach, stx2 gene abundance was positively correlated with concurrent daily E. coli concentrations. Concentration distributions for stx2, ipaH, and mapA within algae, sediment, and water were statistically different (Non-Detect and Data Analysis in R). Assuming 10, 50, or 100% of gene copies represented viable and presumably infective cells, a quantitative microbial risk assessment tool developed by Michigan State University indicated a moderate probability of illness for Campylobacter jejuni at the study beaches, especially where recreational water quality criteria were exceeded. Pathogen gene quantification may be useful for beach water quality management.
","language":"English","publisher":"ACS Publications","doi":"10.1021/es5038657","usgsCitation":"Oster, R.J., Wijesinghe, R.U., Fogarty, L.R., Haack, S.K., Fogarty, L.R., Tucker, T.R., and Riley, S., 2014, Bacterial pathogen gene abundance and relation to recreational water quality at seven Great Lakes beaches: Environmental Science & Technology, v. 48, no. 24, p. 14148-14157, https://doi.org/10.1021/es5038657.","productDescription":"10 p.","startPage":"14148","endPage":"14157","ipdsId":"IP-052094","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":349032,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Great Lakes","volume":"48","issue":"24","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2014-11-25","publicationStatus":"PW","scienceBaseUri":"5a6100c8e4b06e28e9c25411","contributors":{"authors":[{"text":"Oster, Ryan J. roster@usgs.gov","contributorId":5483,"corporation":false,"usgs":true,"family":"Oster","given":"Ryan","email":"roster@usgs.gov","middleInitial":"J.","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":true,"id":722157,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wijesinghe, Rasanthi U. rwijesinghe@usgs.gov","contributorId":5484,"corporation":false,"usgs":true,"family":"Wijesinghe","given":"Rasanthi","email":"rwijesinghe@usgs.gov","middleInitial":"U.","affiliations":[],"preferred":true,"id":722158,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fogarty, Lisa Reynolds 0000-0003-0329-3251 lrfogart@usgs.gov","orcid":"https://orcid.org/0000-0003-0329-3251","contributorId":150958,"corporation":false,"usgs":true,"family":"Fogarty","given":"Lisa","email":"lrfogart@usgs.gov","middleInitial":"Reynolds","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":722159,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haack, Sheridan K. skhaack@usgs.gov","contributorId":1982,"corporation":false,"usgs":true,"family":"Haack","given":"Sheridan","email":"skhaack@usgs.gov","middleInitial":"K.","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":true,"id":722160,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fogarty, Lisa R. 0000-0003-0329-3251 lrfogart@usgs.gov","orcid":"https://orcid.org/0000-0003-0329-3251","contributorId":2053,"corporation":false,"usgs":true,"family":"Fogarty","given":"Lisa","email":"lrfogart@usgs.gov","middleInitial":"R.","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":false,"id":722571,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tucker, Taaja R. 0000-0003-1534-4677 trtucker@usgs.gov","orcid":"https://orcid.org/0000-0003-1534-4677","contributorId":5172,"corporation":false,"usgs":true,"family":"Tucker","given":"Taaja","email":"trtucker@usgs.gov","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":722161,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Riley, Stephen 0000-0002-8968-8416 sriley@usgs.gov","orcid":"https://orcid.org/0000-0002-8968-8416","contributorId":169479,"corporation":false,"usgs":true,"family":"Riley","given":"Stephen","email":"sriley@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":722162,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70189202,"text":"70189202 - 2014 - Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models","interactions":[],"lastModifiedDate":"2017-07-05T16:57:14","indexId":"70189202","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","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":"Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models","docAbstract":"This paper presents a hybrid local-global sensitivity analysis method termed the Distributed Evaluation of Local Sensitivity Analysis (DELSA), which is used here to identify important and unimportant parameters and evaluate how model parameter importance changes as parameter values change. DELSA uses derivative-based “local” methods to obtain the distribution of parameter sensitivity across the parameter space, which promotes consideration of sensitivity analysis results in the context of simulated dynamics. This work presents DELSA, discusses how it relates to existing methods, and uses two hydrologic test cases to compare its performance with the popular global, variance-based Sobol' method. The first test case is a simple nonlinear reservoir model with two parameters. The second test case involves five alternative “bucket-style” hydrologic models with up to 14 parameters applied to a medium-sized catchment (200 km2) in the Belgian Ardennes. Results show that in both examples, Sobol' and DELSA identify similar important and unimportant parameters, with DELSA enabling more detailed insight at much lower computational cost. For example, in the real-world problem the time delay in runoff is the most important parameter in all models, but DELSA shows that for about 20% of parameter sets it is not important at all and alternative mechanisms and parameters dominate. Moreover, the time delay was identified as important in regions producing poor model fits, whereas other parameters were identified as more important in regions of the parameter space producing better model fits. The ability to understand how parameter importance varies through parameter space is critical to inform decisions about, for example, additional data collection and model development. The ability to perform such analyses with modest computational requirements provides exciting opportunities to evaluate complicated models as well as many alternative models.
","language":"English","publisher":"AGU","doi":"10.1002/2013WR014063","usgsCitation":"Rakovec, O., Hill, M.C., Clark, M., Weerts, A.H., Teuling, A.J., and Uijlenhoet, R., 2014, Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models: Water Resources Research, v. 50, no. 1, p. 409-426, https://doi.org/10.1002/2013WR014063.","productDescription":"18 p.","startPage":"409","endPage":"426","ipdsId":"IP-053395","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":487085,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1808/19328","text":"External Repository"},{"id":343373,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"50","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2014-01-17","publicationStatus":"PW","scienceBaseUri":"595dfab7e4b0d1f9f056a7aa","contributors":{"authors":[{"text":"Rakovec, O.","contributorId":194218,"corporation":false,"usgs":false,"family":"Rakovec","given":"O.","email":"","affiliations":[],"preferred":false,"id":703468,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hill, Mary C. mchill@usgs.gov","contributorId":974,"corporation":false,"usgs":true,"family":"Hill","given":"Mary","email":"mchill@usgs.gov","middleInitial":"C.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":703467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clark, M.P.","contributorId":194219,"corporation":false,"usgs":false,"family":"Clark","given":"M.P.","email":"","affiliations":[],"preferred":false,"id":703469,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weerts, A. H.","contributorId":194220,"corporation":false,"usgs":false,"family":"Weerts","given":"A.","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":703470,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Teuling, A. J.","contributorId":138517,"corporation":false,"usgs":false,"family":"Teuling","given":"A.","email":"","middleInitial":"J.","affiliations":[{"id":6920,"text":"Wageningen University, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":703471,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Uijlenhoet, R.","contributorId":138518,"corporation":false,"usgs":false,"family":"Uijlenhoet","given":"R.","email":"","affiliations":[{"id":6920,"text":"Wageningen University, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":703472,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70188392,"text":"70188392 - 2014 - Structure and seismic hazard of the Ventura Avenue anticline and Ventura fault, California: Prospect for large, multisegment ruptures in the Western Transverse Ranges","interactions":[],"lastModifiedDate":"2017-06-07T15:03:07","indexId":"70188392","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Structure and seismic hazard of the Ventura Avenue anticline and Ventura fault, California: Prospect for large, multisegment ruptures in the Western Transverse Ranges","docAbstract":"The Ventura Avenue anticline is one of the fastest uplifting structures in southern California, rising at ∼5 mm/yr. We use well data and seismic reflection profiles to show that the anticline is underlain by the Ventura fault, which extends to seismogenic depth. Fault offset increases with depth, implying that the Ventura Avenue anticline is a fault‐propagation fold. A decrease in the uplift rate since ∼30±10 ka is consistent with the Ventura fault breaking through to the surface at that time and implies that the fault has a recent dip‐slip rate of ∼4.4–6.9 mm/yr.
To the west, the Ventura fault and fold trend continues offshore as the Pitas Point fault and its associated hanging wall anticline. The Ventura–Pitas Point fault appears to flatten at about 7.5 km depth to a detachment, called the Sisar decollement, then step down on a blind thrust fault to the north. Other regional faults, including the San Cayetano and Red Mountain faults, link with this system at depth. We suggest that below 7.5 km, these faults may form a nearly continuous surface, posing the threat of large, multisegment earthquakes.
Holocene marine terraces on the Ventura Avenue anticline suggest that it grows in discrete events with 5–10 m of uplift, with the latest event having occurred ∼800 years ago (Rockwell, 2011). Uplift this large would require large earthquakes (Mw 7.7–8.1) involving the entire Ventura/Pitas Point system and possibly more structures along strike, such as the San Cayetano fault. Because of the local geography and geology, such events would be associated with significant ground shaking amplification and regional tsunamis.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120130125","usgsCitation":"Hubbard, J., Shaw, J.H., Dolan, J.F., Pratt, T.L., McAuliffe, L.J., and Rockwell, T.K., 2014, Structure and seismic hazard of the Ventura Avenue anticline and Ventura fault, California: Prospect for large, multisegment ruptures in the Western Transverse Ranges: Bulletin of the Seismological Society of America, v. 104, no. 3, p. 1070-1087, https://doi.org/10.1785/0120130125.","productDescription":"18 p.","startPage":"1070","endPage":"1087","ipdsId":"IP-052489","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":473277,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10220/20351","text":"External Repository"},{"id":342267,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Ventura Avenue anticline","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.8333,\n 34.6667\n ],\n [\n -118.8333,\n 34.6667\n ],\n [\n -118.8333,\n 34\n ],\n [\n -119.8333,\n 34\n ],\n [\n -119.8333,\n 34.6667\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"104","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2014-05-06","publicationStatus":"PW","scienceBaseUri":"593910b4e4b0764e6c5e88e1","contributors":{"authors":[{"text":"Hubbard, Judith","contributorId":192725,"corporation":false,"usgs":false,"family":"Hubbard","given":"Judith","email":"","affiliations":[{"id":13619,"text":"Department of Earth & Planetary Sciences, Harvard University, Cambridge, MA","active":true,"usgs":false},{"id":5110,"text":"Earth Observatory of Singapore, Nanyang Technological University","active":true,"usgs":false}],"preferred":false,"id":697525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shaw, John H.","contributorId":187766,"corporation":false,"usgs":false,"family":"Shaw","given":"John","email":"","middleInitial":"H.","affiliations":[{"id":13619,"text":"Department of Earth & Planetary Sciences, Harvard University, Cambridge, MA","active":true,"usgs":false}],"preferred":false,"id":697526,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dolan, James F.","contributorId":175461,"corporation":false,"usgs":false,"family":"Dolan","given":"James","email":"","middleInitial":"F.","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":697527,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pratt, Thomas L. 0000-0003-3131-3141 tpratt@usgs.gov","orcid":"https://orcid.org/0000-0003-3131-3141","contributorId":3279,"corporation":false,"usgs":true,"family":"Pratt","given":"Thomas","email":"tpratt@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":697524,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McAuliffe, Lee J.","contributorId":192724,"corporation":false,"usgs":false,"family":"McAuliffe","given":"Lee","email":"","middleInitial":"J.","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":697528,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rockwell, Thomas K.","contributorId":192731,"corporation":false,"usgs":false,"family":"Rockwell","given":"Thomas","email":"","middleInitial":"K.","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":697529,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191616,"text":"70191616 - 2014 - Evaluation and prioritization of stream habitat monitoring in the Lower Columbia Salmon and Steelhead Recovery Domain as related to the habitat monitoring needs of ESA recovery plans","interactions":[],"lastModifiedDate":"2018-03-02T16:29:49","indexId":"70191616","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesNumber":"PNAMP Series 2014-003","title":"Evaluation and prioritization of stream habitat monitoring in the Lower Columbia Salmon and Steelhead Recovery Domain as related to the habitat monitoring needs of ESA recovery plans","docAbstract":"The lower Columbia River and its tributaries once supported abundant runs of salmon and steelhead; however, there are five species currently listed under the federal Endangered Species Act (ESA). The National Marine Fisheries Service has completed, and is proposing for adoption, a comprehensive ESA Recovery Plan for the Lower Columbia Evolutionarily Significant Units (ESUs) based on the recovery plans developed by Oregon and Washington. One of the primary factors attributed to the decline of these species is habitat degradation. There are numerous entities conducting status and/or trends monitoring of instream habitat in the lower Columbia River Basin, but because the programs were developed for agency specific reasons, the existing monitoring efforts are not well coordinated, and often lack the spatial coverage, certainty, or species coverage necessary to answer questions related to status and trends of the ESA listed populations. The Pacific Northwest Aquatic Monitoring Partnership’s Integrated Status and Trends Monitoring (ISTM) project was initiated to improve integration of existing and new monitoring efforts by developing recommendations for sampling frames, protocols, and data sharing. In an effort to meet the ISTM project goals, five objectives were identified: (1) identify and prioritize decisions, questions, and monitoring objectives, (2) evaluate how existing programs align with these management decisions, questions, and objectives, (3) identify the most appropriate monitoring design to inform priority management decisions, questions, and objectives, (4) use trade-off analysis to develop specific recommendations for monitoring based on outcomes of Objectives 1-3 and (5) recommend implementation and reporting mechanisms. This report summarizes the effort to address Objectives 1 and 2, detailing the commonalities among the habitat characteristics that all entities measure and monitor, and how the metrics align with the priorities listed in the comprehensive recovery plan for the Lower Columbia ESUs.
","language":"English","publisher":"Pacific Northwest Aquatic Monitoring Partnership","usgsCitation":"Puls, A.L., Anlauf Dunn, K., and Graham Hudson, B., 2014, Evaluation and prioritization of stream habitat monitoring in the Lower Columbia Salmon and Steelhead Recovery Domain as related to the habitat monitoring needs of ESA recovery plans, 42 p.","productDescription":"42 p.","ipdsId":"IP-050765","costCenters":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"links":[{"id":352198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":352197,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.pnamp.org/sites/default/files/pnamp_2014-003_istm_habitat_report_final.pdf"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afeee10e4b0da30c1bfc757","contributors":{"authors":[{"text":"Puls, Amy L. apuls@usgs.gov","contributorId":3202,"corporation":false,"usgs":true,"family":"Puls","given":"Amy","email":"apuls@usgs.gov","middleInitial":"L.","affiliations":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":712870,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anlauf Dunn, Kara","contributorId":197198,"corporation":false,"usgs":false,"family":"Anlauf Dunn","given":"Kara","email":"","affiliations":[],"preferred":false,"id":712871,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Graham Hudson, Bernadette","contributorId":197199,"corporation":false,"usgs":false,"family":"Graham Hudson","given":"Bernadette","email":"","affiliations":[],"preferred":false,"id":712872,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70191615,"text":"70191615 - 2014 - Using spatial resampling to assess redd count survey length requirements for Pacific Lamprey","interactions":[],"lastModifiedDate":"2017-10-17T14:37:58","indexId":"70191615","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Using spatial resampling to assess redd count survey length requirements for Pacific Lamprey","docAbstract":"Pacific Lamprey Entosphenus tridentatus has declined across its range along the West Coast of North America, and an understanding of all life history phases is needed to address population recovery. Spawning surveys (redd counts) are common tools currently used to monitor returning adult salmonids, but such methodologies are in their infancy for Pacific Lamprey. Our objective was to assess the minimum spawning survey distance required to detect the presence of Pacific Lamprey redds and obtain precise redd density estimates from these data. To do this, we statistically resampled existing spawning locations of Pacific Lamprey collected during spawning surveys in four streams of the Willamette River Basin, Oregon, during spring of 2013. We found that the minimum survey distance for Pacific Lamprey redd detection was inversely related to the observed redd density and was always less than 1.2 km. Survey distance requirements to obtain precise redd counts (±20% of observed redd densities) were also inversely related to redd density and habitat availability, and varied between 1.3 km and 13.7 km. Our results suggest that spawning surveys are a potential tool for monitoring adult Pacific Lamprey abundance, but the specific objectives of the monitoring programs and acknowledgment of unknowns must be considered prior to implementation into recovery plans.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02755947.2014.932867","usgsCitation":"Mayfield, M., Schultz, L.D., Wyss, L.A., Colvin, M., and Schreck, C.B., 2014, Using spatial resampling to assess redd count survey length requirements for Pacific Lamprey: North American Journal of Fisheries Management, v. 34, no. 5, p. 923-931, https://doi.org/10.1080/02755947.2014.932867.","productDescription":"9 p.","startPage":"923","endPage":"931","ipdsId":"IP-055637","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":346708,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.79394531249999,\n 44.257986652122426\n ],\n [\n -122.42340087890624,\n 44.257986652122426\n ],\n [\n -122.42340087890624,\n 44.89090425391711\n ],\n [\n -123.79394531249999,\n 44.89090425391711\n ],\n [\n -123.79394531249999,\n 44.257986652122426\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2014-08-13","publicationStatus":"PW","scienceBaseUri":"59e71694e4b05fe04cd331dc","contributors":{"authors":[{"text":"Mayfield, M.P.","contributorId":195833,"corporation":false,"usgs":false,"family":"Mayfield","given":"M.P.","email":"","affiliations":[],"preferred":false,"id":712883,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schultz, L. D.","contributorId":197200,"corporation":false,"usgs":false,"family":"Schultz","given":"L.","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":712884,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wyss, Lance A.","contributorId":195114,"corporation":false,"usgs":false,"family":"Wyss","given":"Lance","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":712885,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Colvin, M.E.","contributorId":53190,"corporation":false,"usgs":true,"family":"Colvin","given":"M.E.","affiliations":[],"preferred":false,"id":712886,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schreck, Carl B. 0000-0001-8347-1139 carl.schreck@usgs.gov","orcid":"https://orcid.org/0000-0001-8347-1139","contributorId":878,"corporation":false,"usgs":true,"family":"Schreck","given":"Carl","email":"carl.schreck@usgs.gov","middleInitial":"B.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":712869,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70191613,"text":"70191613 - 2014 - Potential fitness benefits of the half-pounder life history in Klamath River steelhead","interactions":[],"lastModifiedDate":"2017-10-17T14:58:08","indexId":"70191613","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Potential fitness benefits of the half-pounder life history in Klamath River steelhead","docAbstract":"Steelhead Oncorhynchus mykiss from several of the world's rivers display the half-pounder life history, a variant characterized by an amphidromous (and, less often, anadromous) return to freshwater in the year of initial ocean entry. We evaluated factors related to expression of the half-pounder life history in wild steelhead from the lower Klamath River basin, California. We also evaluated fitness consequences of the half-pounder phenotype using a simple life history model that was parameterized with our empirical data and outputs from a regional survival equation. The incidence of the half-pounder life history differed among subbasins of origin and smolt ages. Precocious maturation occurred in approximately 8% of half-pounders and was best predicted by individual length in freshwater preceding ocean entry. Adult steelhead of the half-pounder phenotype were smaller and less fecund at age than adult steelhead of the alternative (ocean contingent) phenotype. However, our data suggest that fish of the half-pounder phenotype are more likely to spawn repeatedly than are fish of the ocean contingent phenotype. Models predicted that if lifetime survivorship were equal between phenotypes, the fitness of the half-pounder phenotype would be 17–28% lower than that of the ocean contingent phenotype. To meet the condition of equal fitness between phenotypes would require that first-year ocean survival be 21–40% higher among half-pounders in freshwater than among their cohorts at sea. We concluded that continued expression of the half-pounder phenotype is favored by precocious maturation and increased survival relative to that of the ocean contingent phenotype.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00028487.2014.892536","usgsCitation":"Hodge, B.W., Wilzbach, P., and Duffy, W.G., 2014, Potential fitness benefits of the half-pounder life history in Klamath River steelhead: Transactions of the American Fisheries Society, v. 143, no. 4, p. 864-875, https://doi.org/10.1080/00028487.2014.892536.","productDescription":"12 p.","startPage":"864","endPage":"875","ipdsId":"IP-051296","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":346719,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124,\n 40.5\n ],\n [\n -122,\n 40.5\n ],\n [\n -122,\n 42\n ],\n [\n -124,\n 42\n ],\n [\n -124,\n 40.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"143","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2014-06-16","publicationStatus":"PW","scienceBaseUri":"59e71695e4b05fe04cd331e1","contributors":{"authors":[{"text":"Hodge, Brian W.","contributorId":172966,"corporation":false,"usgs":false,"family":"Hodge","given":"Brian","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":712932,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilzbach, Peggy 0000-0002-3559-3630 paw7002@usgs.gov","orcid":"https://orcid.org/0000-0002-3559-3630","contributorId":3908,"corporation":false,"usgs":true,"family":"Wilzbach","given":"Peggy","email":"paw7002@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":712866,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duffy, Walter G. wgd7001@usgs.gov","contributorId":2491,"corporation":false,"usgs":true,"family":"Duffy","given":"Walter","email":"wgd7001@usgs.gov","middleInitial":"G.","affiliations":[],"preferred":false,"id":712933,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188320,"text":"70188320 - 2014 - Detecting the influence of best management practices on vegetation near ephemeral streams with Landsat data","interactions":[],"lastModifiedDate":"2017-06-06T14:00:55","indexId":"70188320","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3228,"text":"Rangeland Ecology and Management","onlineIssn":"1551-5028","printIssn":"1550-7424","active":true,"publicationSubtype":{"id":10}},"title":"Detecting the influence of best management practices on vegetation near ephemeral streams with Landsat data","docAbstract":"Various best management practices (BMPs) have been implemented on rangelands with the goals of controlling nonpoint source pollution, reducing the impact of livestock in ecologically important riparian areas, and improving grazing distribution. Providing off-stream water sources to livestock in pastures, cross-fencing, and rotational grazing are common rangeland BMPs that have demonstrated success in drawing livestock grazing pressure away from streams. We evaluated the effects of rangeland BMP implementation with six commercial-scale pastures in the northern mixed-grass prairie. Four pastures received a BMP suite consisting of off-stream water, cross-fencing, and deferred-rotation grazing, and two pastures did not receive BMPs. We hypothesized that the BMPs increased the quantity of riparian vegetation cover relative to the conditions in these pastures during the pre-BMP period and to the two pastures that did not receive BMPs. We used a series of 30-m Landsat normalized difference vegetation index (NDVI) images to track the spatial and temporal changes (1984–2010, n = 24) in vegetation cover, to which NDVI has been well correlated. Validation indicated that the remotely sensed signal from in-channel vegetation was representative of ground conditions. The BMP suite was associated with a 15% increase in the in-channel NDVI (0–30 m from stream centerline) and 18% increase in the riparian NDVI (30–180 m from stream center line). Conversely, the in-channel and riparian NDVI of non-BMP pastures declined 30% and 18% over the study period. The majority of change occurred within 2 yr of BMP implementation. The patterns of in-channel NDVI among pastures suggested that BMP implementation likely altered grazing distribution by decreasing the preferential use of riparian and in-channel areas. We demonstrated that satellite imagery time series are useful in retrospectively evaluating the efficacy of conservation practices, providing critical information to guide adaptive management and decision makers.
","language":"English","publisher":"Elsevier","doi":"10.2111/REM-D-12-00185.1","usgsCitation":"Rigge, M.B., Smart, A., Wylie, B.K., and de Van Kamp, K., 2014, Detecting the influence of best management practices on vegetation near ephemeral streams with Landsat data: Rangeland Ecology and Management, v. 67, no. 1, p. 1-8, https://doi.org/10.2111/REM-D-12-00185.1.","productDescription":"8 p.","startPage":"1","endPage":"8","ipdsId":"IP-035745","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":342160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"67","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5937bf2fe4b0f6c2d0d9c781","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":697194,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smart, Alexander","contributorId":24262,"corporation":false,"usgs":true,"family":"Smart","given":"Alexander","affiliations":[],"preferred":false,"id":697310,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":697311,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"de Van Kamp, Kendall","contributorId":192662,"corporation":false,"usgs":false,"family":"de Van Kamp","given":"Kendall","email":"","affiliations":[],"preferred":false,"id":697312,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70189207,"text":"70189207 - 2014 - Evaluation of statistically downscaled GCM output as input for hydrological and stream temperature simulation in the Apalachicola–Chattahoochee–Flint River Basin (1961–99)","interactions":[],"lastModifiedDate":"2017-07-05T16:20:39","indexId":"70189207","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1421,"text":"Earth Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of statistically downscaled GCM output as input for hydrological and stream temperature simulation in the Apalachicola–Chattahoochee–Flint River Basin (1961–99)","docAbstract":"The accuracy of statistically downscaled general circulation model (GCM) simulations of daily surface climate for historical conditions (1961–99) and the implications when they are used to drive hydrologic and stream temperature models were assessed for the Apalachicola–Chattahoochee–Flint River basin (ACFB). The ACFB is a 50 000 km2 basin located in the southeastern United States. Three GCMs were statistically downscaled, using an asynchronous regional regression model (ARRM), to ⅛° grids of daily precipitation and minimum and maximum air temperature. These ARRM-based climate datasets were used as input to the Precipitation-Runoff Modeling System (PRMS), a deterministic, distributed-parameter, physical-process watershed model used to simulate and evaluate the effects of various combinations of climate and land use on watershed response. The ACFB was divided into 258 hydrologic response units (HRUs) in which the components of flow (groundwater, subsurface, and surface) are computed in response to climate, land surface, and subsurface characteristics of the basin. Daily simulations of flow components from PRMS were used with the climate to simulate in-stream water temperatures using the Stream Network Temperature (SNTemp) model, a mechanistic, one-dimensional heat transport model for branched stream networks.
The climate, hydrology, and stream temperature for historical conditions were evaluated by comparing model outputs produced from historical climate forcings developed from gridded station data (GSD) versus those produced from the three statistically downscaled GCMs using the ARRM methodology. The PRMS and SNTemp models were forced with the GSD and the outputs produced were treated as “truth.” This allowed for a spatial comparison by HRU of the GSD-based output with ARRM-based output. Distributional similarities between GSD- and ARRM-based model outputs were compared using the two-sample Kolmogorov–Smirnov (KS) test in combination with descriptive metrics such as the mean and variance and an evaluation of rare and sustained events. In general, precipitation and streamflow quantities were negatively biased in the downscaled GCM outputs, and results indicate that the downscaled GCM simulations consistently underestimate the largest precipitation events relative to the GSD. The KS test results indicate that ARRM-based air temperatures are similar to GSD at the daily time step for the majority of the ACFB, with perhaps subweekly averaging for stream temperature. Depending on GCM and spatial location, ARRM-based precipitation and streamflow requires averaging of up to 30 days to become similar to the GSD-based output.
Evaluation of the model skill for historical conditions suggests some guidelines for use of future projections; while it seems correct to place greater confidence in evaluation metrics which perform well historically, this does not necessarily mean those metrics will accurately reflect model outputs for future climatic conditions. Results from this study indicate no “best” overall model, but the breadth of analysis can be used to give the product users an indication of the applicability of the results to address their particular problem. Since results for historical conditions indicate that model outputs can have significant biases associated with them, the range in future projections examined in terms of change relative to historical conditions for each individual GCM may be more appropriate.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/2013EI000554.1","usgsCitation":"Hay, L.E., LaFontaine, J.H., and Markstrom, S.L., 2014, Evaluation of statistically downscaled GCM output as input for hydrological and stream temperature simulation in the Apalachicola–Chattahoochee–Flint River Basin (1961–99): Earth Interactions, v. 18, p. 1-32, https://doi.org/10.1175/2013EI000554.1.","productDescription":"32 p.","startPage":"1","endPage":"32","ipdsId":"IP-052922","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":473306,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/2013ei000554.1","text":"Publisher Index Page"},{"id":343366,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Georgia","otherGeospatial":"Apalachicola–Chattahoochee–Flint River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.60546875,\n 29.6594160549124\n ],\n [\n -83.7158203125,\n 29.6594160549124\n ],\n [\n -83.7158203125,\n 34.470335121217474\n ],\n [\n -85.60546875,\n 34.470335121217474\n ],\n [\n -85.60546875,\n 29.6594160549124\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"18","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2014-04-14","publicationStatus":"PW","scienceBaseUri":"595dfab7e4b0d1f9f056a7a6","contributors":{"authors":[{"text":"Hay, Lauren E. 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":1287,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":703494,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"LaFontaine, Jacob H. 0000-0003-4923-2630 jlafonta@usgs.gov","orcid":"https://orcid.org/0000-0003-4923-2630","contributorId":2258,"corporation":false,"usgs":true,"family":"LaFontaine","given":"Jacob","email":"jlafonta@usgs.gov","middleInitial":"H.","affiliations":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":703495,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Markstrom, Steven L. 0000-0001-7630-9547 markstro@usgs.gov","orcid":"https://orcid.org/0000-0001-7630-9547","contributorId":146553,"corporation":false,"usgs":true,"family":"Markstrom","given":"Steven","email":"markstro@usgs.gov","middleInitial":"L.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":703496,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70189095,"text":"70189095 - 2014 - Multielevation calibration of frequency-domain electromagnetic data","interactions":[],"lastModifiedDate":"2017-06-29T14:58:22","indexId":"70189095","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1808,"text":"Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Multielevation calibration of frequency-domain electromagnetic data","docAbstract":"Systematic calibration errors must be taken into account because they can substantially impact the accuracy of inverted subsurface resistivity models derived from frequency-domain electromagnetic data, resulting in potentially misleading interpretations. We have developed an approach that uses data acquired at multiple elevations over the same location to assess calibration errors. A significant advantage is that this method does not require prior knowledge of subsurface properties from borehole or ground geophysical data (though these can be readily incorporated if available), and is, therefore, well suited to remote areas. The multielevation data were used to solve for calibration parameters and a single subsurface resistivity model that are self consistent over all elevations. The deterministic and Bayesian formulations of the multielevation approach illustrate parameter sensitivity and uncertainty using synthetic- and field-data examples. Multiplicative calibration errors (gain and phase) were found to be better resolved at high frequencies and when data were acquired over a relatively conductive area, whereas additive errors (bias) were reasonably resolved over conductive and resistive areas at all frequencies. The Bayesian approach outperformed the deterministic approach when estimating calibration parameters using multielevation data at a single location; however, joint analysis of multielevation data at multiple locations using the deterministic algorithm yielded the most accurate estimates of calibration parameters. Inversion results using calibration-corrected data revealed marked improvement in misfit, lending added confidence to the interpretation of these models.
The determination of near‐surface (vadose zone and slightly below) fault locations and geometries is important because assessment of ground rupture, strong shaking, geologic slip rates, and rupture histories occurs at shallow depths. However, seismic imaging of fault zones at shallow depths can be difficult due to near‐surface complexities, such as weathering, groundwater saturation, massive (nonlayered) rocks, and vertically layered strata. Combined P‐ and S‐wave seismic‐refraction tomography data can overcome many of the near‐surface, fault‐zone seismic‐imaging problems because of differences in the responses of elastic (bulk and shear) moduli of P and S waves to shallow‐depth, fault‐zone properties. We show that high‐resolution refraction tomography images of P‐ to S‐wave velocity ratios (VP/VS) can reliably identify near‐surface faults. We demonstrate this method using tomography images of the San Andreas fault (SAF) surface‐rupture zone associated with the 18 April 1906 ∼M 7.9 San Francisco earthquake on the San Francisco peninsula in California. There, the SAF cuts through Franciscan mélange, which consists of an incoherent assemblage of greywacke, chert, greenstone, and serpentinite. A near‐vertical zone (∼75° northeast dip) of high P‐wave velocities (up to 3000 m/s), low S‐wave velocities (∼150–600 m/s), high VP/VS ratios (4–8.8), and high Poisson’s ratios (0.44–0.49) characterizes the main surface‐rupture zone to a depth of about 20 m and is consistent with nearby trench observations. We suggest that the combined VP/VSimaging approach can reliably identify most near‐surface fault zones in locations where many other seismic methods cannot be applied.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120130294","usgsCitation":"Catchings, R.D., Rymer, M.J., Goldman, M.R., Sickler, R.R., and Criley, C.J., 2014, A method and example of seismically imaging near‐surface fault zones in geologically complex areas using Vp, Vs, and their ratios: Bulletin of the Seismological Society of America, v. 104, no. 4, p. 1989-2006, https://doi.org/10.1785/0120130294.","productDescription":"18 p.","startPage":"1989","endPage":"2006","ipdsId":"IP-046000","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":333800,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"104","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2014-07-15","publicationStatus":"PW","scienceBaseUri":"588876dbe4b05ccb964baad9","contributors":{"authors":[{"text":"Catchings, Rufus D. 0000-0002-5191-6102 catching@usgs.gov","orcid":"https://orcid.org/0000-0002-5191-6102","contributorId":1519,"corporation":false,"usgs":true,"family":"Catchings","given":"Rufus","email":"catching@usgs.gov","middleInitial":"D.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":660207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rymer, Michael J. mrymer@usgs.gov","contributorId":1522,"corporation":false,"usgs":true,"family":"Rymer","given":"Michael","email":"mrymer@usgs.gov","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":660210,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goldman, Mark R. 0000-0002-0802-829X goldman@usgs.gov","orcid":"https://orcid.org/0000-0002-0802-829X","contributorId":1521,"corporation":false,"usgs":true,"family":"Goldman","given":"Mark","email":"goldman@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":660209,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sickler, Robert R. 0000-0002-9141-625X rsickler@usgs.gov","orcid":"https://orcid.org/0000-0002-9141-625X","contributorId":3235,"corporation":false,"usgs":true,"family":"Sickler","given":"Robert","email":"rsickler@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":660211,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Criley, Coyn J. 0000-0002-0227-0165 ccriley@usgs.gov","orcid":"https://orcid.org/0000-0002-0227-0165","contributorId":3312,"corporation":false,"usgs":true,"family":"Criley","given":"Coyn","email":"ccriley@usgs.gov","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":660208,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70189782,"text":"70189782 - 2014 - CyberShake-derived ground-motion prediction models for the Los Angeles region with application to earthquake early warning","interactions":[],"lastModifiedDate":"2017-07-26T11:02:38","indexId":"70189782","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"CyberShake-derived ground-motion prediction models for the Los Angeles region with application to earthquake early warning","docAbstract":"Real-time applications such as earthquake early warning (EEW) typically use empirical ground-motion prediction equations (GMPEs) along with event magnitude and source-to-site distances to estimate expected shaking levels. In this simplified approach, effects due to finite-fault geometry, directivity and site and basin response are often generalized, which may lead to a significant under- or overestimation of shaking from large earthquakes (M > 6.5) in some locations. For enhanced site-specific ground-motion predictions considering 3-D wave-propagation effects, we develop support vector regression (SVR) models from the SCEC CyberShake low-frequency (<0.5 Hz) and broad-band (0–10 Hz) data sets. CyberShake encompasses 3-D wave-propagation simulations of >415 000 finite-fault rupture scenarios (6.5 ≤ M ≤ 8.5) for southern California defined in UCERF 2.0. We use CyberShake to demonstrate the application of synthetic waveform data to EEW as a ‘proof of concept’, being aware that these simulations are not yet fully validated and might not appropriately sample the range of rupture uncertainty. Our regression models predict the maximum and the temporal evolution of instrumental intensity (MMI) at 71 selected test sites using only the hypocentre, magnitude and rupture ratio, which characterizes uni- and bilateral rupture propagation. Our regression approach is completely data-driven (where here the CyberShake simulations are considered data) and does not enforce pre-defined functional forms or dependencies among input parameters. The models were established from a subset (∼20 per cent) of CyberShake simulations, but can explain MMI values of all >400 k rupture scenarios with a standard deviation of about 0.4 intensity units. We apply our models to determine threshold magnitudes (and warning times) for various active faults in southern California that earthquakes need to exceed to cause at least ‘moderate’, ‘strong’ or ‘very strong’ shaking in the Los Angeles (LA) basin. These thresholds are used to construct a simple and robust EEW algorithm: to declare a warning, the algorithm only needs to locate the earthquake and to verify that the corresponding magnitude threshold is exceeded. The models predict that a relatively moderate M6.5–7 earthquake along the Palos Verdes, Newport-Inglewood/Rose Canyon, Elsinore or San Jacinto faults with a rupture propagating towards LA could cause ‘very strong’ to ‘severe’ shaking in the LA basin; however, warning times for these events could exceed 30 s.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggu198","usgsCitation":"Bose, M., Graves, R., Gill, D., Callaghan, S., and Maechling, P.J., 2014, CyberShake-derived ground-motion prediction models for the Los Angeles region with application to earthquake early warning: Geophysical Journal International, v. 198, no. 3, p. 1438-1457, https://doi.org/10.1093/gji/ggu198.","productDescription":"20 p.","startPage":"1438","endPage":"1457","ipdsId":"IP-054646","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":473293,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1093/gji/ggu198","text":"External Repository"},{"id":344321,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Los Angeles","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119,\n 33\n ],\n [\n -117,\n 33\n ],\n [\n -117,\n 35\n ],\n [\n -119,\n 35\n ],\n [\n -119,\n 33\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"198","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2014-07-03","publicationStatus":"PW","scienceBaseUri":"5979aa58e4b0ec1a488b8c3f","contributors":{"authors":[{"text":"Bose, Maren","contributorId":195135,"corporation":false,"usgs":false,"family":"Bose","given":"Maren","affiliations":[],"preferred":false,"id":706331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graves, Robert 0000-0001-9758-453X rwgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-9758-453X","contributorId":140738,"corporation":false,"usgs":true,"family":"Graves","given":"Robert","email":"rwgraves@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":706330,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gill, David","contributorId":195159,"corporation":false,"usgs":false,"family":"Gill","given":"David","email":"","affiliations":[],"preferred":false,"id":706332,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Callaghan, Scott","contributorId":195136,"corporation":false,"usgs":false,"family":"Callaghan","given":"Scott","email":"","affiliations":[],"preferred":false,"id":706333,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maechling, Phillip J.","contributorId":117072,"corporation":false,"usgs":false,"family":"Maechling","given":"Phillip","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":706334,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70189669,"text":"70189669 - 2014 - Transcriptomic effects-based monitoring for endocrine active chemicals: Assessing relative contribution of treated wastewater to downstream pollution","interactions":[],"lastModifiedDate":"2018-09-14T16:02:33","indexId":"70189669","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Transcriptomic effects-based monitoring for endocrine active chemicals: Assessing relative contribution of treated wastewater to downstream pollution","docAbstract":"The present study investigated whether a combination of targeted analytical chemistry information with unsupervised, data-rich biological methodology (i.e., transcriptomics) could be utilized to evaluate relative contributions of wastewater treatment plant (WWTP) effluents to biological effects. The effects of WWTP effluents on fish exposed to ambient, receiving waters were studied at three locations with distinct WWTP and watershed characteristics. At each location, 4 d exposures of male fathead minnows to the WWTP effluent and upstream and downstream ambient waters were conducted. Transcriptomic analyses were performed on livers using 15 000 feature microarrays, followed by a canonical pathway and gene set enrichment analyses. Enrichment of gene sets indicative of teleost brain–pituitary–gonadal–hepatic (BPGH) axis function indicated that WWTPs serve as an important source of endocrine active chemicals (EACs) that affect the BPGH axis (e.g., cholesterol and steroid metabolism were altered). The results indicated that transcriptomics may even pinpoint pertinent adverse outcomes (i.e., liver vacuolization) and groups of chemicals that preselected chemical analytes may miss. Transcriptomic Effects-Based monitoring was capable of distinguishing sites, and it reflected chemical pollution gradients, thus holding promise for assessment of relative contributions of point sources to pollution and the efficacy of pollution remediation.
","language":"English","publisher":"ACS","doi":"10.1021/es404027n","usgsCitation":"Martinovic-Weigelt, D., Mehinto, A.C., Ankley, G., Denslow, N., Barber, L.B., Lee, K., King, R.J., Schoenfuss, H.L., Schroeder, A.L., and Villeneuve, D.L., 2014, Transcriptomic effects-based monitoring for endocrine active chemicals: Assessing relative contribution of treated wastewater to downstream pollution: Environmental Science & Technology, v. 48, no. 4, p. 2385-2394, https://doi.org/10.1021/es404027n.","productDescription":"10 p.","startPage":"2385","endPage":"2394","ipdsId":"IP-053126","costCenters":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":344075,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","issue":"4","noUsgsAuthors":false,"publicationDate":"2014-01-10","publicationStatus":"PW","scienceBaseUri":"59706fbce4b0d1f9f065a911","contributors":{"authors":[{"text":"Martinovic-Weigelt, Dalma","contributorId":173655,"corporation":false,"usgs":false,"family":"Martinovic-Weigelt","given":"Dalma","affiliations":[{"id":6748,"text":"University of St. Thomas","active":true,"usgs":false}],"preferred":false,"id":705708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mehinto, Alvine C.","contributorId":104387,"corporation":false,"usgs":true,"family":"Mehinto","given":"Alvine","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":705709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ankley, Gerald T.","contributorId":177970,"corporation":false,"usgs":false,"family":"Ankley","given":"Gerald T.","affiliations":[{"id":13485,"text":"U.S. Environmental Protection Agency, Duluth, MN","active":true,"usgs":false}],"preferred":false,"id":705710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Denslow, Nancy D.","contributorId":72831,"corporation":false,"usgs":true,"family":"Denslow","given":"Nancy D.","affiliations":[],"preferred":false,"id":705711,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barber, Larry B. 0000-0002-0561-0831 lbbarber@usgs.gov","orcid":"https://orcid.org/0000-0002-0561-0831","contributorId":921,"corporation":false,"usgs":true,"family":"Barber","given":"Larry","email":"lbbarber@usgs.gov","middleInitial":"B.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":705712,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lee, Kathy 0000-0002-7683-1367 klee@usgs.gov","orcid":"https://orcid.org/0000-0002-7683-1367","contributorId":2538,"corporation":false,"usgs":true,"family":"Lee","given":"Kathy","email":"klee@usgs.gov","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":705713,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"King, Ryan J.","contributorId":194914,"corporation":false,"usgs":false,"family":"King","given":"Ryan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":705714,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schoenfuss, Heiko L.","contributorId":76409,"corporation":false,"usgs":false,"family":"Schoenfuss","given":"Heiko","email":"","middleInitial":"L.","affiliations":[{"id":13317,"text":"Saint Cloud State University","active":true,"usgs":false}],"preferred":false,"id":705715,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schroeder, Anthony L.","contributorId":173596,"corporation":false,"usgs":false,"family":"Schroeder","given":"Anthony","email":"","middleInitial":"L.","affiliations":[{"id":12503,"text":"University of Minnesota - Saint Paul","active":true,"usgs":false},{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":705716,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Villeneuve, Daniel L.","contributorId":32091,"corporation":false,"usgs":false,"family":"Villeneuve","given":"Daniel","email":"","middleInitial":"L.","affiliations":[{"id":13485,"text":"U.S. Environmental Protection Agency, Duluth, MN","active":true,"usgs":false}],"preferred":false,"id":705717,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70191008,"text":"70191008 - 2014 - Integrating research tools to support the management of social-ecological systems under climate change","interactions":[],"lastModifiedDate":"2017-09-20T14:52:32","indexId":"70191008","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1468,"text":"Ecology and Society","active":true,"publicationSubtype":{"id":10}},"title":"Integrating research tools to support the management of social-ecological systems under climate change","docAbstract":"Developing resource management strategies in the face of climate change is complicated by the considerable uncertainty associated with projections of climate and its impacts and by the complex interactions between social and ecological variables. The broad, interconnected nature of this challenge has resulted in calls for analytical frameworks that integrate research tools and can support natural resource management decision making in the face of uncertainty and complex interactions. We respond to this call by first reviewing three methods that have proven useful for climate change research, but whose application and development have been largely isolated: species distribution modeling, scenario planning, and simulation modeling. Species distribution models provide data-driven estimates of the future distributions of species of interest, but they face several limitations and their output alone is not sufficient to guide complex decisions for how best to manage resources given social and economic considerations along with dynamic and uncertain future conditions. Researchers and managers are increasingly exploring potential futures of social-ecological systems through scenario planning, but this process often lacks quantitative response modeling and validation procedures. Simulation models are well placed to provide added rigor to scenario planning because of their ability to reproduce complex system dynamics, but the scenarios and management options explored in simulations are often not developed by stakeholders, and there is not a clear consensus on how to include climate model outputs. We see these strengths and weaknesses as complementarities and offer an analytical framework for integrating these three tools. We then describe the ways in which this framework can help shift climate change research from useful to usable.
","language":"English","publisher":"Ecology and Society","doi":"10.5751/ES-06813-190341","usgsCitation":"Miller, B.W., and Morisette, J.T., 2014, Integrating research tools to support the management of social-ecological systems under climate change: Ecology and Society, v. 19, no. 3, p. 1-12, https://doi.org/10.5751/ES-06813-190341.","productDescription":"Article 41; 12 p.","startPage":"1","endPage":"12","ipdsId":"IP-056771","costCenters":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"links":[{"id":473287,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/es-06813-190341","text":"Publisher Index Page"},{"id":345942,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59c37e3be4b091459a631706","contributors":{"authors":[{"text":"Miller, Brian W. 0000-0003-1716-1161","orcid":"https://orcid.org/0000-0003-1716-1161","contributorId":196603,"corporation":false,"usgs":true,"family":"Miller","given":"Brian","email":"","middleInitial":"W.","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":710905,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morisette, Jeffrey T. 0000-0002-0483-0082 morisettej@usgs.gov","orcid":"https://orcid.org/0000-0002-0483-0082","contributorId":307,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","email":"morisettej@usgs.gov","middleInitial":"T.","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":710904,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70191023,"text":"70191023 - 2014 - Dispersion analysis of passive surface-wave noise generated during hydraulic-fracturing operations","interactions":[],"lastModifiedDate":"2017-09-21T12:06:41","indexId":"70191023","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2165,"text":"Journal of Applied Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Dispersion analysis of passive surface-wave noise generated during hydraulic-fracturing operations","docAbstract":"Surface-wave dispersion analysis is useful for estimating near-surface shear-wave velocity models, designing receiver arrays, and suppressing surface waves. Here, we analyze whether passive seismic noise generated during hydraulic-fracturing operations can be used to extract surface-wave dispersion characteristics. Applying seismic interferometry to noise measurements, we extract surface waves by cross-correlating several minutes of passive records; this approach is distinct from previous studies that used hours or days of passive records for cross-correlation. For comparison, we also perform dispersion analysis for an active-source array that has some receivers in common with the passive array. The active and passive data show good agreement in the dispersive character of the fundamental-mode surface-waves. For the higher mode surface waves, however, active and passive data resolve the dispersive properties at different frequency ranges. To demonstrate an application of dispersion analysis, we invert the observed surface-wave dispersion characteristics to determine the near-surface, one-dimensional shear-wave velocity.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jappgeo.2014.09.008","usgsCitation":"Forghani-Arani, F., Willis, M., Snieder, R., Haines, S.S., Behura, J., Batzle, M., and Davidson, M., 2014, Dispersion analysis of passive surface-wave noise generated during hydraulic-fracturing operations: Journal of Applied Geophysics, v. 111, p. 129-134, https://doi.org/10.1016/j.jappgeo.2014.09.008.","productDescription":"6 p.","startPage":"129","endPage":"134","ipdsId":"IP-058038","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":473305,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1556315","text":"Publisher Index Page"},{"id":345987,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"111","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59c4cf97e4b017cf313d3cb8","contributors":{"authors":[{"text":"Forghani-Arani, Farnoush","contributorId":196642,"corporation":false,"usgs":false,"family":"Forghani-Arani","given":"Farnoush","email":"","affiliations":[{"id":34665,"text":"Microseismic Inc.","active":true,"usgs":false}],"preferred":false,"id":710974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willis, Mark","contributorId":196643,"corporation":false,"usgs":false,"family":"Willis","given":"Mark","email":"","affiliations":[{"id":34662,"text":"Halliburton","active":true,"usgs":false}],"preferred":false,"id":710975,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Snieder, Roel","contributorId":196644,"corporation":false,"usgs":false,"family":"Snieder","given":"Roel","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":710976,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haines, Seth S. 0000-0003-2611-8165 shaines@usgs.gov","orcid":"https://orcid.org/0000-0003-2611-8165","contributorId":1344,"corporation":false,"usgs":true,"family":"Haines","given":"Seth","email":"shaines@usgs.gov","middleInitial":"S.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":710973,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Behura, Jyoti","contributorId":196645,"corporation":false,"usgs":false,"family":"Behura","given":"Jyoti","email":"","affiliations":[{"id":34663,"text":"Seismic Science LLC","active":true,"usgs":false}],"preferred":false,"id":710977,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Batzle, Mike","contributorId":196646,"corporation":false,"usgs":false,"family":"Batzle","given":"Mike","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":710978,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Davidson, Michael","contributorId":196647,"corporation":false,"usgs":false,"family":"Davidson","given":"Michael","email":"","affiliations":[{"id":17916,"text":"ConocoPhillips","active":true,"usgs":false}],"preferred":false,"id":710979,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70188057,"text":"70188057 - 2014 - Land cover characterization and mapping of South America for the year 2010 using Landsat 30 m satellite data","interactions":[],"lastModifiedDate":"2017-05-30T13:33:33","indexId":"70188057","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Land cover characterization and mapping of South America for the year 2010 using Landsat 30 m satellite data","docAbstract":"Detailed and accurate land cover and land cover change information is needed for South America because the continent is in constant flux, experiencing some of the highest rates of land cover change and forest loss in the world. The land cover data available for the entire continent are too coarse (250 m to 1 km) for resource managers, government and non-government organizations, and Earth scientists to develop conservation strategies, formulate resource management options, and monitor land cover dynamics. We used Landsat 30 m satellite data of 2010 and prepared the land cover database of South America using state-of-the-science remote sensing techniques. We produced regionally consistent and locally relevant land cover information by processing a large volume of data covering the entire continent. Our analysis revealed that in 2010, 50% of South America was covered by forests, 2.5% was covered by water, and 0.02% was covered by snow and ice. The percent forest area of South America varies from 9.5% in Uruguay to 96.5% in French Guiana. We used very high resolution (<5 m) satellite data to validate the land cover product. The overall accuracy of the 2010 South American 30-m land cover map is 89% with a Kappa coefficient of 79%. Accuracy of barren areas needs to improve possibly using multi-temporal Landsat data. An update of land cover and change database of South America with additional land cover classes is needed. The results from this study are useful for developing resource management strategies, formulating biodiversity conservation strategies, and regular land cover monitoring and forecasting.
","language":"English","publisher":"MDPI","doi":"10.3390/rs6109494","usgsCitation":"Giri, C., and Long, J., 2014, Land cover characterization and mapping of South America for the year 2010 using Landsat 30 m satellite data: Remote Sensing, v. 6, no. 10, p. 9494-9510, https://doi.org/10.3390/rs6109494.","productDescription":"17 p.","startPage":"9494","endPage":"9510","ipdsId":"IP-059806","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":473301,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs6109494","text":"Publisher Index Page"},{"id":341862,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"South America","volume":"6","issue":"10","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2014-10-08","publicationStatus":"PW","scienceBaseUri":"592e84c6e4b092b266f10d9f","contributors":{"authors":[{"text":"Giri, Chandra cgiri@usgs.gov","contributorId":189128,"corporation":false,"usgs":true,"family":"Giri","given":"Chandra","email":"cgiri@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696340,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, Jordan 0000-0002-4814-464X jlong@usgs.gov","orcid":"https://orcid.org/0000-0002-4814-464X","contributorId":3609,"corporation":false,"usgs":true,"family":"Long","given":"Jordan","email":"jlong@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696341,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70193262,"text":"70193262 - 2014 - Trends in the capture fisheries in Cuyo East Pass, Philippines","interactions":[],"lastModifiedDate":"2017-11-15T14:41:45","indexId":"70193262","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5553,"text":"International Journal of Fisheries and Aquatic Studies","active":true,"publicationSubtype":{"id":10}},"title":"Trends in the capture fisheries in Cuyo East Pass, Philippines","docAbstract":"Findings are presented of a comprehensive analysis of time series catch and effort data from 2000 to 2006 collected from a multi-species, multi-gear and two-sector (municipal and commercial) capture fisheries in Cuyo East Pass, Philippines. Multivariate techniques were used to determine temporal variation in species composition and gear selectivity that corresponded with annual trends in catch and effort. Distinct annual variation in species composition was found for five fisheries classified according to sector-gear combination, corresponding decline in catch diversity, noted shifts in gears used, and an erratic CPUE trend as a result of catch variation. These patterns and trends illustrate the occurrence of ecosystem overfishing for Cuyo East Pass. Our approach provided a holistic representation of the fishing situation, condition of the fisheries and corresponding implications to the ecosystem, fitting well within the context of the ecosystem approach to fisheries management.
","language":"English","publisher":"International Journal of Fisheries and Aquatic Studies","usgsCitation":"San Diego, T.A., and Fisher, W.L., 2014, Trends in the capture fisheries in Cuyo East Pass, Philippines: International Journal of Fisheries and Aquatic Studies, v. 1, no. 3, p. 57-72.","productDescription":"16 p.","startPage":"57","endPage":"72","ipdsId":"IP-014204","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":347854,"type":{"id":15,"text":"Index Page"},"url":"https://www.fisheriesjournal.com/vol1issue3/14.1.html"}],"country":"Philippines","otherGeospatial":"Cuyo East Pass","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 120.80017089843749,\n 10.055402736564236\n ],\n [\n 122.10205078125,\n 10.055402736564236\n ],\n [\n 122.10205078125,\n 11.689893557325728\n ],\n [\n 120.80017089843749,\n 11.689893557325728\n ],\n [\n 120.80017089843749,\n 10.055402736564236\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"1","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a6100c8e4b06e28e9c25419","contributors":{"authors":[{"text":"San Diego, Tee-Jay A.","contributorId":200421,"corporation":false,"usgs":false,"family":"San Diego","given":"Tee-Jay","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":722259,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fisher, William L. wfisher@usgs.gov","contributorId":1229,"corporation":false,"usgs":true,"family":"Fisher","given":"William","email":"wfisher@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":true,"id":718467,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70188048,"text":"70188048 - 2014 - Spatio-temporal patterns and climate variables controlling of biomass carbon stock of global grassland ecosystems from 1982 to 2006","interactions":[],"lastModifiedDate":"2017-05-30T15:15:06","indexId":"70188048","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Spatio-temporal patterns and climate variables controlling of biomass carbon stock of global grassland ecosystems from 1982 to 2006","docAbstract":"Grassland ecosystems play an important role in subsistence agriculture and the global carbon cycle. However, the global spatio-temporal patterns and environmental controls of grassland biomass are not well quantified and understood. The goal of this study was to estimate the spatial and temporal patterns of the global grassland biomass and analyze their driving forces using field measurements, Normalized Difference Vegetation Index (NDVI) time series from satellite data, climate reanalysis data, and a satellite-based statistical model. Results showed that the NDVI-based biomass carbon model developed from this study explained 60% of the variance across 38 sites globally. The global carbon stock in grassland aboveground live biomass was 1.05 Pg·C, averaged from 1982 to 2006, and increased at a rate of 2.43 Tg·C·y−1 during this period. Temporal change of the global biomass was significantly and positively correlated with temperature and precipitation. The distribution of biomass carbon density followed the precipitation gradient. The dynamics of regional grassland biomass showed various trends largely determined by regional climate variability, disturbances, and management practices (such as grazing for meat production). The methods and results from this study can be used to monitor the dynamics of grassland aboveground biomass and evaluate grassland susceptibility to climate variability and change, disturbances, and management.
","language":"English","publisher":"MDPI","doi":"10.3390/rs6031783","usgsCitation":"Xia, J., Liu, S., Liang, S., Chen, Y., Xu, W., and Yuan, W., 2014, Spatio-temporal patterns and climate variables controlling of biomass carbon stock of global grassland ecosystems from 1982 to 2006: Remote Sensing, v. 6, no. 3, p. 1783-1802, https://doi.org/10.3390/rs6031783.","productDescription":"20 p.","startPage":"1783","endPage":"1802","ipdsId":"IP-052038","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":486959,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs6031783","text":"Publisher Index Page"},{"id":341874,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2014-02-26","publicationStatus":"PW","scienceBaseUri":"592e84c7e4b092b266f10dae","contributors":{"authors":[{"text":"Xia, Jiangzhou","contributorId":192427,"corporation":false,"usgs":false,"family":"Xia","given":"Jiangzhou","email":"","affiliations":[],"preferred":false,"id":696484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Shuguang 0000-0002-6027-3479 sliu@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3479","contributorId":147403,"corporation":false,"usgs":true,"family":"Liu","given":"Shuguang","email":"sliu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696320,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liang, Shunlin","contributorId":192428,"corporation":false,"usgs":false,"family":"Liang","given":"Shunlin","email":"","affiliations":[],"preferred":false,"id":696485,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Yang","contributorId":192429,"corporation":false,"usgs":false,"family":"Chen","given":"Yang","email":"","affiliations":[],"preferred":false,"id":696486,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xu, Wenfang","contributorId":192430,"corporation":false,"usgs":false,"family":"Xu","given":"Wenfang","email":"","affiliations":[],"preferred":false,"id":696487,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yuan, Wenping","contributorId":83435,"corporation":false,"usgs":true,"family":"Yuan","given":"Wenping","email":"","affiliations":[],"preferred":false,"id":696488,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70187708,"text":"70187708 - 2014 - Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)","interactions":[],"lastModifiedDate":"2017-05-31T16:12:20","indexId":"70187708","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)","docAbstract":"Forest cover loss and bare ground gain from 2006 to 2010 for the conterminous United States (CONUS) were quantified at a 30 m spatial resolution using Web-Enabled Landsat Data available from the USGS Center for Earth Resources Observation and Science (EROS) (http://landsat.usgs.gov/WELD.php). The approach related multi-temporal WELD metrics and expert-derived training data for forest cover loss and bare ground gain through a decision tree classification algorithm. Forest cover loss was reported at state and ecoregional scales, and the identification of core forests' absent of change was made and verified using LiDAR data from the GLAS (Geoscience Laser Altimetry System) instrument. Bare ground gain correlated with population change for large metropolitan statistical areas (MSAs) outside of desert or semi-desert environments. GoogleEarth™ time-series images were used to validate the products. Mapped forest cover loss totaled 53,084 km2 and was found to be depicted conservatively, with a user's accuracy of 78% and a producer's accuracy of 68%. Excluding errors of adjacency, user's and producer's accuracies rose to 93% and 89%, respectively. Mapped bare ground gain equaled 5974 km2 and nearly matched the estimated area from the reference (GoogleEarth™) classification; however, user's (42%) and producer's (49%) accuracies were much less than those of the forest cover loss product. Excluding errors of adjacency, user's and producer's accuracies rose to 62% and 75%, respectively. Compared to recent 2001–2006 USGS National Land Cover Database validation data for forest loss (82% and 30% for respective user's and producer's accuracies) and urban gain (72% and 18% for respective user's and producer's accuracies), results using a single CONUS-scale model with WELD data are promising and point to the potential for national-scale operational mapping of key land cover transitions. However, validation results highlighted limitations, some of which can be addressed by improving training data, creating a more robust image feature space, adding contemporaneous Landsat 5 data to the inputs, and modifying definition sets to account for differences in temporal and spatial observational scales. The presented land cover extent and change data are available via the official WELD website (ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2013.08.014","usgsCitation":"Hansen, M., Egorov, A., Potapov, P., Stehman, S., Tyukavina, A., Turubanova, S., Roy, D.P., Goetz, S., Loveland, T., Ju, J., Kommareddy, A., Kovalskyy, V., Forsyth, C., and Bents, T., 2014, Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD): Remote Sensing of Environment, v. 140, p. 466-484, https://doi.org/10.1016/j.rse.2013.08.014.","productDescription":"19 p.","startPage":"466","endPage":"484","ipdsId":"IP-046262","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"140","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"591c0fcce4b0a7fdb43ddf00","contributors":{"authors":[{"text":"Hansen, M.C.","contributorId":69690,"corporation":false,"usgs":false,"family":"Hansen","given":"M.C.","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":695302,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Egorov, Alexey","contributorId":81719,"corporation":false,"usgs":false,"family":"Egorov","given":"Alexey","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":695303,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Potapov, P.V.","contributorId":19677,"corporation":false,"usgs":false,"family":"Potapov","given":"P.V.","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":695304,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stehman, S.V.","contributorId":91974,"corporation":false,"usgs":false,"family":"Stehman","given":"S.V.","email":"","affiliations":[{"id":27852,"text":"State University of New York, Syracuse","active":true,"usgs":false}],"preferred":false,"id":695306,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tyukavina, A.","contributorId":19872,"corporation":false,"usgs":false,"family":"Tyukavina","given":"A.","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":695307,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Turubanova, S.A.","contributorId":108388,"corporation":false,"usgs":false,"family":"Turubanova","given":"S.A.","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":695308,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Roy, David P.","contributorId":54761,"corporation":false,"usgs":false,"family":"Roy","given":"David","email":"","middleInitial":"P.","affiliations":[{"id":26958,"text":"South Dakota State University, Brookings, SD","active":true,"usgs":false},{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false},{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":695309,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Goetz, S.J.","contributorId":55186,"corporation":false,"usgs":false,"family":"Goetz","given":"S.J.","email":"","affiliations":[{"id":25456,"text":"Woods Hole Research Center, Falmouth, MA, United States","active":true,"usgs":false}],"preferred":false,"id":695310,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Loveland, Thomas R. 0000-0003-3114-6646","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":106125,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas R.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":695311,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ju, J.","contributorId":85801,"corporation":false,"usgs":false,"family":"Ju","given":"J.","email":"","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":695312,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kommareddy, A.","contributorId":105638,"corporation":false,"usgs":false,"family":"Kommareddy","given":"A.","email":"","affiliations":[{"id":26958,"text":"South Dakota State University, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":695317,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kovalskyy, Valeriy","contributorId":192062,"corporation":false,"usgs":false,"family":"Kovalskyy","given":"Valeriy","email":"","affiliations":[{"id":26958,"text":"South Dakota State University, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":695318,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Forsyth, C.","contributorId":192034,"corporation":false,"usgs":false,"family":"Forsyth","given":"C.","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":695319,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Bents, T.","contributorId":139577,"corporation":false,"usgs":false,"family":"Bents","given":"T.","email":"","affiliations":[{"id":33302,"text":"University of Kansas, Lawrence","active":true,"usgs":false}],"preferred":false,"id":695320,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70193091,"text":"70193091 - 2014 - Thermochronology of Cretaceous batholithic rocks in the northern Peninsular Ranges batholith, southern California: Implications for the Late Cretaceous tectonic evolution of southern California","interactions":[],"lastModifiedDate":"2017-12-20T17:16:33","indexId":"70193091","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"title":"Thermochronology of Cretaceous batholithic rocks in the northern Peninsular Ranges batholith, southern California: Implications for the Late Cretaceous tectonic evolution of southern California","docAbstract":"The thermochronology for several suites of Mesozoic metamorphic and plutonic rocks collected throughout the northern Peninsular Ranges batholith (PRB) was studied as part of a collaborative isotopic study to further our understanding of the magmatic and tectonic history of southern California. These sample suites include: a traverse through the plutonic rocks across the northern PRB (N = 29), a traverse across a central structural and metamorphic transition zone of mainly metasedimentary rocks at Searl ridge (N = 20), plutonic samples from several drill cores (N = 7) and surface samples (N = 2) from the Los Angeles Basin, a traverse across the Eastern Peninsular Ranges mylonite zone (N = 6), and a suite of plutonic samples collected across the northern PRB (N = 13) from which only biotite 40Ar/39Ar ages were obtained. These geochronologic data help to characterize five major petrologic, geochemical, and isotopic zonations of the PRB (western zone, WZ; western transition zone, WTZ; eastern transition zone, ETZ; eastern zone, EZ; and upper-plate zone, UPZ).
Apparent cooling rates were calculated using U-Pb zircon (zr) and titanite (sphene) ages; 40Ar/39Ar ages from hornblende (hbl), biotite (bi), and K-feldspar (Kf); and apatite fission-track (AFT) ages from the same samples. The apparent cooling rates across the northern PRB vary from relatively rapid in the west (zr-hbl ~210 °C/m.y.; zr-bio ~160 °C/m.y.; zr-Kf ~80 °C/m.y.) to less rapid in the central (zr-hb ~280 °C/m.y.; zr-bio ~90 °C/m.y.; zr-Kf ~60 °C/m.y.) and eastern (zr-hbl ~185 °C/m.y.; zr-bio ~180 °C/m.y.; zr-Kf ~60 °C/m.y.) zones. An exception in the eastern zone, the massive San Jacinto pluton, appears to have cooled very rapidly (zr-bio ~385 °C/m.y.). Apparent cooling rates for the UPZ samples are consistently slower in comparison (~25–45 °C/m.y.), regardless of which geochronometers are used.
Notable characteristics of the various ages from different dating methods include: (1) Zircon ages indicate a progressive younging of magmatic activity from west to east between ca. 125 and 90 Ma. (2) Various geochronometers were apparently affected by emplacement of the voluminous (ETZ and EZ) La Posta–type plutons emplaced between 99 and 91 Ma. Those minerals affected include K-feldspar in the western zone rocks, biotite and K-feldspar in the WTZ rocks, and white mica and K-feldspar in rocks from Searl ridge. (3) The AFT ages record the time the rocks cooled through the AFT closure temperature (~100 °C in these rocks), likely due to exhumation. Throughout most of the northern traverse, the apatite data indicate the rocks cooled relatively quickly through the apatite partial annealing zone (PAZ; from ~110 °C to 60 °C) and remained at temperatures less than 60 °C as continued exhumation cooled them to present-day surface temperatures. The ages indicate that the western “arc” terrane of the WZ was being uplifted and cooled at ca. 91 Ma, during or shortly after intrusion of the 99–91 Ma La Posta–type plutons to the east. Uplift and cooling occurred later, between ca. 70 Ma and ca. 55 Ma, in the central WTZ, ETZ, and EZ rocks, possibly as upwarping in response to events in the UPZ. The UPZ experienced differential exhumation at ca. 50–35 Ma: Cooling on the western edge was taking place at about the same time or shortly after cooling in the younger samples in the ETZ and EZ, whereas on the east side of the UPZ, the rocks cooled later (ca. 35 Ma) and spent a prolonged time in the apatite PAZ compared to most northern traverse samples.
Apparent cooling rates from Los Angeles Basin drill core samples of plutonic rocks show that four are similar to the WTZ thermal histories, and two are similar to the WTZ histories, indicating that the eastern part of the Los Angeles Basin area is underlain by mainly western zone PRB rocks.
Thermal histories revealed by samples from Searl ridge indicate that the WTZ magmatism intruded the metasedimentary rocks prior to their deformation and metamorphism at ca. 97 Ma. Both low-grade schists and metasandstones of the western side of the ridge and high-grade gneisses of the eastern side of the ridge have thermal histories consistent with eastern zone rocks—suggesting a temporal/thermal relationship between the western transition zone and the eastern zones.
Limited ages from six samples across the Eastern Peninsular Ranges mylonite zone (EPRMZ) indicate that this zone underwent cooling after emplacement of the youngest UPZ rocks at 85 Ma, suggesting that thrusting along the EPRMZ was either coeval with emplacement of the UPZ plutonic rocks or occurred shortly afterwards (~10–15 m.y.). Alternatively, the EPRMZ thrusting may have occurred at temperatures under ~180 °C at yet a later date.
The geochronology presented here differs slightly from previous studies for similar rocks exposed across the middle and southern portions of the PRB, in that our data define a relatively smooth progression of magmatism from west to east, and the transition from western, oceanic-arc plutonism to eastern, continental arc plutonism is interpreted to have occurred at ca. 99–97 Ma and not at ca. 105 Ma.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Peninsular ranges Batholith, Baja California and southern California: Geological Society of America Memoir 211","language":"English","publisher":"Geological Society of America","doi":"10.1130/2014.1211(06)","usgsCitation":"Miggins, D.P., Premo, W.R., Snee, L.W., Yeoman, R., Naeaer, N.D., Naeser, C.W., and Morton, D., 2014, Thermochronology of Cretaceous batholithic rocks in the northern Peninsular Ranges batholith, southern California: Implications for the Late Cretaceous tectonic evolution of southern California, chap. of Peninsular ranges Batholith, Baja California and southern California: Geological Society of America Memoir 211, p. 199-261, https://doi.org/10.1130/2014.1211(06).","productDescription":"63 p.","startPage":"199","endPage":"261","ipdsId":"IP-039597","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":350159,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a6100c9e4b06e28e9c2541f","contributors":{"authors":[{"text":"Miggins, Daniel P.","contributorId":199027,"corporation":false,"usgs":false,"family":"Miggins","given":"Daniel","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":717946,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Premo, Wayne R. 0000-0001-9904-4801 wpremo@usgs.gov","orcid":"https://orcid.org/0000-0001-9904-4801","contributorId":1697,"corporation":false,"usgs":true,"family":"Premo","given":"Wayne","email":"wpremo@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":717950,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Snee, Lawrence W.","contributorId":199028,"corporation":false,"usgs":false,"family":"Snee","given":"Lawrence","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":717947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yeoman, Ross","contributorId":199030,"corporation":false,"usgs":false,"family":"Yeoman","given":"Ross","affiliations":[],"preferred":false,"id":717951,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Naeaer, Nancy D.","contributorId":199029,"corporation":false,"usgs":false,"family":"Naeaer","given":"Nancy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":717948,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Naeser, Charles W.","contributorId":199026,"corporation":false,"usgs":false,"family":"Naeser","given":"Charles","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":717945,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morton, Douglas M.","contributorId":199010,"corporation":false,"usgs":false,"family":"Morton","given":"Douglas M.","affiliations":[],"preferred":false,"id":717949,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70188032,"text":"70188032 - 2014 - Detecting emergence, growth, and senescence of wetland vegetation with polarimetric synthetic aperture radar (SAR) data","interactions":[],"lastModifiedDate":"2017-05-31T15:19:27","indexId":"70188032","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Detecting emergence, growth, and senescence of wetland vegetation with polarimetric synthetic aperture radar (SAR) data","docAbstract":"Wetlands provide ecosystem goods and services vitally important to humans. Land managers and policymakers working to conserve wetlands require regularly updated information on the statuses of wetlands across the landscape. However, wetlands are challenging to map remotely with high accuracy and consistency. We investigated the use of multitemporal polarimetric synthetic aperture radar (SAR) data acquired with Canada’s Radarsat-2 system to track within-season changes in wetland vegetation and surface water. We speculated, a priori, how temporal and morphological traits of different types of wetland vegetation should respond over a growing season with respect to four energy-scattering mechanisms. We used ground-based monitoring data and other ancillary information to assess the limits and consistency of the SAR data for tracking seasonal changes in wetlands. We found the traits of different types of vertical emergent wetland vegetation were detected well with the SAR data and corresponded with our anticipated backscatter responses. We also found using data from Landsat’s optical/infrared sensors in conjunction with SAR data helped remove confusion of wetland features with upland grasslands. These results suggest SAR data can provide useful monitoring information on the statuses of wetlands over time.
","language":"English","publisher":"MDPI","doi":"10.3390/w6030694","usgsCitation":"Gallant, A.L., Kaya, S.G., White, L., Brisco, B., Roth, M.F., Sadinski, W.J., and Rover, J., 2014, Detecting emergence, growth, and senescence of wetland vegetation with polarimetric synthetic aperture radar (SAR) data: Water, v. 6, no. 3, p. 694-722, https://doi.org/10.3390/w6030694.","productDescription":"29 p.","startPage":"694","endPage":"722","ipdsId":"IP-053361","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":473304,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w6030694","text":"Publisher Index Page"},{"id":341958,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2014-03-24","publicationStatus":"PW","scienceBaseUri":"592fd640e4b0e9bd0ea8970a","contributors":{"authors":[{"text":"Gallant, Alisa L. 0000-0002-3029-6637 gallant@usgs.gov","orcid":"https://orcid.org/0000-0002-3029-6637","contributorId":2940,"corporation":false,"usgs":true,"family":"Gallant","given":"Alisa","email":"gallant@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kaya, Shannon G.","contributorId":192330,"corporation":false,"usgs":false,"family":"Kaya","given":"Shannon","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":696253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Lori","contributorId":192557,"corporation":false,"usgs":false,"family":"White","given":"Lori","email":"","affiliations":[],"preferred":false,"id":696254,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brisco, Brian","contributorId":37665,"corporation":false,"usgs":true,"family":"Brisco","given":"Brian","email":"","affiliations":[],"preferred":false,"id":696255,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roth, Mark F. 0000-0001-5095-1865 mroth@usgs.gov","orcid":"https://orcid.org/0000-0001-5095-1865","contributorId":3286,"corporation":false,"usgs":true,"family":"Roth","given":"Mark","email":"mroth@usgs.gov","middleInitial":"F.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":696256,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sadinski, Walter J. wsadinski@usgs.gov","contributorId":3287,"corporation":false,"usgs":true,"family":"Sadinski","given":"Walter","email":"wsadinski@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":696257,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rover, Jennifer 0000-0002-3437-4030 jrover@usgs.gov","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":192333,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"jrover@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":696258,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70188031,"text":"70188031 - 2014 - Development of a generic auto-calibration package for regional ecological modeling and application in the Central Plains of the United States","interactions":[],"lastModifiedDate":"2017-05-31T15:23:13","indexId":"70188031","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1457,"text":"Ecological Informatics","active":true,"publicationSubtype":{"id":10}},"title":"Development of a generic auto-calibration package for regional ecological modeling and application in the Central Plains of the United States","docAbstract":"Process-oriented ecological models are frequently used for predicting potential impacts of global changes such as climate and land-cover changes, which can be useful for policy making. It is critical but challenging to automatically derive optimal parameter values at different scales, especially at regional scale, and validate the model performance. In this study, we developed an automatic calibration (auto-calibration) function for a well-established biogeochemical model—the General Ensemble Biogeochemical Modeling System (GEMS)-Erosion Deposition Carbon Model (EDCM)—using data assimilation technique: the Shuffled Complex Evolution algorithm and a model-inversion R package—Flexible Modeling Environment (FME). The new functionality can support multi-parameter and multi-objective auto-calibration of EDCM at the both pixel and regional levels. We also developed a post-processing procedure for GEMS to provide options to save the pixel-based or aggregated county-land cover specific parameter values for subsequent simulations. In our case study, we successfully applied the updated model (EDCM-Auto) for a single crop pixel with a corn–wheat rotation and a large ecological region (Level II)—Central USA Plains. The evaluation results indicate that EDCM-Auto is applicable at multiple scales and is capable to handle land cover changes (e.g., crop rotations). The model also performs well in capturing the spatial pattern of grain yield production for crops and net primary production (NPP) for other ecosystems across the region, which is a good example for implementing calibration and validation of ecological models with readily available survey data (grain yield) and remote sensing data (NPP) at regional and national levels. The developed platform for auto-calibration can be readily expanded to incorporate other model inversion algorithms and potential R packages, and also be applied to other ecological models.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoinf.2013.11.008","usgsCitation":"Wu, Y., Liu, S., Li, Z., Dahal, D., Young, C.J., Schmidt, G.L., Liu, J., Davis, B., Sohl, T.L., Werner, J.M., and Oeding, J., 2014, Development of a generic auto-calibration package for regional ecological modeling and application in the Central Plains of the United States: Ecological Informatics, v. 19, p. 35-46, https://doi.org/10.1016/j.ecoinf.2013.11.008.","productDescription":"12 p.","startPage":"35","endPage":"46","ipdsId":"IP-052570","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341959,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592fd641e4b0e9bd0ea8970f","contributors":{"authors":[{"text":"Wu, Yiping ywu@usgs.gov","contributorId":987,"corporation":false,"usgs":true,"family":"Wu","given":"Yiping","email":"ywu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696251,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Shuguang 0000-0002-6027-3479 sliu@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3479","contributorId":147403,"corporation":false,"usgs":true,"family":"Liu","given":"Shuguang","email":"sliu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696822,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Zhengpeng","contributorId":80812,"corporation":false,"usgs":true,"family":"Li","given":"Zhengpeng","affiliations":[],"preferred":false,"id":696823,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696824,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Young, Claudia J. 0000-0002-0859-7206 cyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-0859-7206","contributorId":2770,"corporation":false,"usgs":true,"family":"Young","given":"Claudia","email":"cyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":696825,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmidt, Gail L. 0000-0002-9684-8158 gschmidt@usgs.gov","orcid":"https://orcid.org/0000-0002-9684-8158","contributorId":3475,"corporation":false,"usgs":true,"family":"Schmidt","given":"Gail","email":"gschmidt@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696826,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Liu, Jinxun 0000-0003-0561-8988 jxliu@usgs.gov","orcid":"https://orcid.org/0000-0003-0561-8988","contributorId":3414,"corporation":false,"usgs":true,"family":"Liu","given":"Jinxun","email":"jxliu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":696827,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Davis, Brian","contributorId":57142,"corporation":false,"usgs":true,"family":"Davis","given":"Brian","affiliations":[],"preferred":false,"id":696828,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sohl, Terry L. 0000-0002-9771-4231 sohl@usgs.gov","orcid":"https://orcid.org/0000-0002-9771-4231","contributorId":648,"corporation":false,"usgs":true,"family":"Sohl","given":"Terry","email":"sohl@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":696829,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Werner, Jeremy M.","contributorId":192558,"corporation":false,"usgs":false,"family":"Werner","given":"Jeremy","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":696830,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Oeding, Jennifer joeding@usgs.gov","contributorId":4070,"corporation":false,"usgs":true,"family":"Oeding","given":"Jennifer","email":"joeding@usgs.gov","affiliations":[],"preferred":true,"id":696831,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70193082,"text":"70193082 - 2014 - Pb-Sr-Nd-O isotopic characterization of Mesozoic rocks throughout the northern end of the Peninsular Ranges batholith: Isotopic evidence for the magmatic evolution of oceanic arc–continental margin accretion during the Late Cretaceous of southern California","interactions":[],"lastModifiedDate":"2017-12-20T16:59:26","indexId":"70193082","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"title":"Pb-Sr-Nd-O isotopic characterization of Mesozoic rocks throughout the northern end of the Peninsular Ranges batholith: Isotopic evidence for the magmatic evolution of oceanic arc–continental margin accretion during the Late Cretaceous of southern California","docAbstract":"Within the duration of the U.S. Geological Survey (USGS)–based Southern California Areal Mapping Project (SCAMP), many samples from the northern Peninsular Ranges batholith were studied for their whole-rock radioisotopic systematics (rubidium-strontium [Rb-Sr], uranium-thorium-lead [U-Th-Pb], and samarium-neodymium [Sm-Nd]), as well as oxygen (O), a stable isotope. The results of three main studies are presented separately, but here we combine them (>400 analyses) to produce a very complete Pb-Sr-Nd-O isotopic profile of an arc-continent collisional zone—perhaps the most complete in the world. In addition, because many of these samples have U-Pb zircon as well as argon mineral age determinations, we have good control of the timing for Pb-Sr-Nd-O isotopic variations.
The ages and isotopic variations help to delineate at least four zones across the batholith from west to east—an older western zone (126–108 Ma), a transitional zone (111–93 Ma), an eastern zone (94–91 Ma), and a much younger allochthonous thrust sheet (ca. 84 Ma), which is the upper plate of the Eastern Peninsular Ranges mylonite zone. Average initial 87Sr/86 Sr (Sri), initial 206Pb/204Pb (206 Pbi), initial 208Pb/204Pb (average 208Pbi), initial epsilon Nd (average εNdi), and δ18O signatures range from 0.704, 18.787, 38.445, +3.1, and 4.0‰–9.0‰, respectively, in the westernmost zone, to 0.7071, 19.199, 38.777, −5, and 9‰–12‰, respectively, in the easternmost zone. The older western zone is therefore the more chemically and isotopically juvenile, characterized mostly by values that are slightly displaced from a mantle array at ca. 115 Ma, and similar to some modern island-arc signatures. In contrast, the isotopic signatures in the eastern zones indicate significant amounts of crustal involvement in the magmatic plumbing of those plutons. These isotopic signatures confirm previously published results that interpreted the Peninsular Ranges batholith as a progressively contaminated magmatic arc. The Peninsular Ranges batholith magmatic arc was initially an oceanic arc built on Panthalassan lithosphere that eventually evolved into a continental margin magmatic arc collision zone, eventually overriding North American cratonic lithosphere. Our Pb-Sr-Nd data further suggest that the western arc rocks represent a nearshore or inboard oceanic arc, as they exhibit isotopic signatures that are more enriched than typical mid-ocean-ridge basalt (MORB). Isotopic signatures from the central zone are transitional and indicate that enriched crustal magma sources were becoming involved in the northern Peninsular Ranges batholith magmatic plumbing. As the oceanic arc–continental margin collision progressed, a mixture of oceanic mantle and continental magmatic sources transpired. Magmatic production in the northern Peninsular Ranges batholith moved eastward and continued to tap enriched crustal magmatic sources. Similar modeling has been previously proposed for two other western margin magmatic arcs, the Sierra Nevada batholith of central California and the Idaho batholith.
Calculated initial Nd signatures at ca. 100 Ma for Permian–Jurassic and Proterozoic basement rocks from the nearby San Gabriel Mountains and possible source areas along the southwestern Laurentian margin of southern California, southwestern Arizona, and northern Sonora strongly suggest their involvement with deep crustal magma mixing beneath the eastern zones of the Peninsular Ranges batholith, as well as farther east in continental lithospheric zones.
Last, several samples from the allochthonous, easternmost upper-plate zone, which are considerably younger (ca. 84 Ma) than any of the rocks from the northern Peninsular Ranges batholith proper, have even more enriched average Sri, 206Pbi, 208Pbi, and εNdisignatures of 0.7079, 19.344, 38.881, and −6.6, respectively, indicative of the most-evolved magma sources in the northern Peninsular Ranges batholith and similar to radioisotopic values for rocks from the nearby Transverse Ranges, suggesting a genetic connection between the two.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Peninsular ranges Batholith, Baja California and southern California: Geological Society of America Memoir 211","language":"English","publisher":"Geological Society of America","doi":"10.1130/2014.1211(07)","usgsCitation":"Kistler, R.W., Wooden, J.L., Premo, W.R., and Morton, D., 2014, Pb-Sr-Nd-O isotopic characterization of Mesozoic rocks throughout the northern end of the Peninsular Ranges batholith: Isotopic evidence for the magmatic evolution of oceanic arc–continental margin accretion during the Late Cretaceous of southern California, chap. of Peninsular ranges Batholith, Baja California and southern California: Geological Society of America Memoir 211, p. 263-316, https://doi.org/10.1130/2014.1211(07).","productDescription":"54 p.","startPage":"263","endPage":"316","ipdsId":"IP-037893","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":350156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a6100c9e4b06e28e9c25425","contributors":{"authors":[{"text":"Kistler, Ronald W.","contributorId":199009,"corporation":false,"usgs":false,"family":"Kistler","given":"Ronald","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":717899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wooden, Joseph L.","contributorId":193587,"corporation":false,"usgs":false,"family":"Wooden","given":"Joseph","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":717898,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Premo, Wayne R. 0000-0001-9904-4801 wpremo@usgs.gov","orcid":"https://orcid.org/0000-0001-9904-4801","contributorId":1697,"corporation":false,"usgs":true,"family":"Premo","given":"Wayne","email":"wpremo@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":717896,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morton, Douglas M.","contributorId":199010,"corporation":false,"usgs":false,"family":"Morton","given":"Douglas M.","affiliations":[],"preferred":false,"id":717897,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}