{"pageNumber":"286","pageRowStart":"7125","pageSize":"25","recordCount":40783,"records":[{"id":70218283,"text":"70218283 - 2020 - A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support","interactions":[],"lastModifiedDate":"2021-02-24T13:11:27.323218","indexId":"70218283","displayToPublicDate":"2020-02-25T06:51:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7599,"text":"Environmental Modeling and Software","active":true,"publicationSubtype":{"id":10}},"title":"A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support","docAbstract":"

Use of physically-motivated numerical models like groundwater flow-and-transport models for probabilistic impact assessments and optimization under uncertainty (OUU) typically incurs such a computational burdensome that these tools cannot be used during decision making. The computational challenges associated with these models can be addressed through emulation. In the land-use/water-quality context, the linear relation between nitrate loading and surface-water/groundwater nitrate concentrations presents an opportunity for employing an efficient model emulator through the application of impulse-response matrices. When paired with first-order second-moment techniques, the emulation strategy gives rise to the “stochastic impulse-response emulator” (SIRE). SIRE is shown to facilitate non-intrusive, near-real time, and risk-based evaluation of nitrate-loading change scenarios, as well as nitrate-loading OUU subject to surface-water/groundwater concentration constraints in high decision variable and parameter dimensions. Two case studies are used to demonstrate SIRE in the nitrate-loading context.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2020.104657","usgsCitation":"White, J., Knowling, M.J., Fienen, M., Feinstein, D.T., McDonald, G.W., and Catherine R. Moore, 2020, A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support: Environmental Modeling and Software, v. 126, 104657, 11 p., https://doi.org/10.1016/j.envsoft.2020.104657.","productDescription":"104657, 11 p.","ipdsId":"IP-114822","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":383585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 175.31982421875,\n -37.65120864327175\n ],\n [\n 175.70159912109375,\n -37.65120864327175\n ],\n [\n 175.70159912109375,\n -37.208456662000174\n ],\n [\n 175.31982421875,\n -37.208456662000174\n ],\n [\n 175.31982421875,\n -37.65120864327175\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, Jeremy T. 0000-0002-4950-1469","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":214251,"corporation":false,"usgs":false,"family":"White","given":"Jeremy T.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":810818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knowling, Matthew J.","contributorId":251909,"corporation":false,"usgs":false,"family":"Knowling","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":810819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810820,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Feinstein, Daniel T. 0000-0003-1151-2530 dtfeinst@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-2530","contributorId":1907,"corporation":false,"usgs":true,"family":"Feinstein","given":"Daniel","email":"dtfeinst@usgs.gov","middleInitial":"T.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810821,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McDonald, Garry W. 0000-0003-3746-4346","orcid":"https://orcid.org/0000-0003-3746-4346","contributorId":251906,"corporation":false,"usgs":false,"family":"McDonald","given":"Garry","email":"","middleInitial":"W.","affiliations":[{"id":50421,"text":"Market Economics","active":true,"usgs":false}],"preferred":false,"id":810822,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Catherine R. Moore","contributorId":251911,"corporation":false,"usgs":false,"family":"Catherine R. Moore","affiliations":[{"id":36909,"text":"CSIRO","active":true,"usgs":false}],"preferred":false,"id":810823,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208691,"text":"70208691 - 2020 - Regional ocean models indicate changing limits to biological invasions in the Bering Sea","interactions":[],"lastModifiedDate":"2020-02-24T19:04:31","indexId":"70208691","displayToPublicDate":"2020-02-24T19:01:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1936,"text":"ICES Journal of Marine Science","active":true,"publicationSubtype":{"id":10}},"title":"Regional ocean models indicate changing limits to biological invasions in the Bering Sea","docAbstract":"Minimal vessel traffic and cold water temperatures are believed to limit non-indigenous species (NIS) in high-latitude ecosystems. We evaluated whether suitable conditions exist in the Bering Sea for the introduction, survival, and reproduction of NIS. We compiled temperature and salinity thresholds of known NIS and compared these to ocean conditions projected during two study periods: current (2003-2012) and mid-century (2030-2039). We also explored patterns of vessel traffic and connectivity for U.S. Bering Sea ports. We found the southeastern Bering Sea had suitable conditions for the year-round survival of 80% of NIS assessed (n=42). However, only 52% of NIS had conditions suitable for reproduction or development (n=25). Conditions north of 58° N that include sub-zero winter water temperatures were unsuitable for the survival and reproduction of most NIS. While mid-century models predicted a northward expansion of suitable conditions, conditions for reproduction remained marginal. Within the highly suitable southeastern Bering Sea is the port of Dutch Harbor, which received the most vessel arrivals and ballast water discharge in the U.S. Bering Sea. Our findings illustrate the potential vulnerability of a commercially important subarctic ecosystem and highlight the need to consider NIS reproductive and developmental life phases when evaluating limits to their establishment.","language":"English","publisher":"Oxford Academic","doi":"10.1093/icesjms/fsaa014","usgsCitation":"Droghini, A., Fischbach, A., Watson, J., and Reimer, J., 2020, Regional ocean models indicate changing limits to biological invasions in the Bering Sea: ICES Journal of Marine Science, fsaa014, 11 p., https://doi.org/10.1093/icesjms/fsaa014.","productDescription":"fsaa014, 11 p.","ipdsId":"IP-106911","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":457617,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/icesjms/fsaa014","text":"Publisher Index Page"},{"id":372591,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Bering Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -175.95703125,\n 52.53627304145948\n ],\n [\n -165.7177734375,\n 54.23955053156177\n ],\n [\n -158.81835937499997,\n 57.633639928856965\n ],\n [\n -162.24609375,\n 58.37867853932655\n ],\n [\n -162.4658203125,\n 60.04290359809164\n ],\n [\n -166.06933593749997,\n 60.780618803458935\n ],\n [\n -165.498046875,\n 62.71446210149774\n ],\n [\n -162.24609375,\n 63.95667333648766\n ],\n [\n -165.234375,\n 64.51064316846676\n ],\n [\n -169.27734375,\n 65.53117097417717\n ],\n [\n -171.5625,\n 63.82128765261384\n ],\n [\n -174.0234375,\n 61.543641475549954\n ],\n [\n -175.95703125,\n 52.53627304145948\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 185.88867187499997,\n 64.11060221954631\n ],\n [\n 183.8671875,\n 64.99793920061401\n ],\n [\n 181.142578125,\n 65.6582745198266\n ],\n [\n 180.791015625,\n 65.23830662451157\n ],\n [\n 179.12109374999997,\n 64.35893097894458\n ],\n [\n 178.8134765625,\n 63.68524808030715\n ],\n [\n 180.3955078125,\n 62.512317938386914\n ],\n [\n 178.505859375,\n 62.14497603754045\n ],\n [\n 176.0888671875,\n 62.04213770379758\n ],\n [\n 172.6171875,\n 61.079544234557304\n ],\n [\n 170.3759765625,\n 59.77852198502987\n ],\n [\n 169.6728515625,\n 59.84481485969105\n ],\n [\n 168.6181640625,\n 60.28340847828243\n ],\n [\n 167.255859375,\n 60.19615576604439\n ],\n [\n 165.05859375,\n 59.17592824927136\n ],\n [\n 163.828125,\n 57.938183012205315\n ],\n [\n 164.70703125,\n 56.218923189166624\n ],\n [\n 168.310546875,\n 54.7246201949245\n ],\n [\n 174.111328125,\n 53.04121304075649\n ],\n [\n 179.0771484375,\n 51.944264879028765\n ],\n [\n 185.625,\n 52.429222277955134\n ],\n [\n 185.88867187499997,\n 64.11060221954631\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Droghini, Amanda 0000-0001-6692-2348","orcid":"https://orcid.org/0000-0001-6692-2348","contributorId":222312,"corporation":false,"usgs":false,"family":"Droghini","given":"Amanda","email":"","affiliations":[{"id":40516,"text":"Alaska Center for Conservation Science University of Alaska Anchorage","active":true,"usgs":false}],"preferred":false,"id":783024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fischbach, Anthony S. 0000-0002-6555-865X afischbach@usgs.gov","orcid":"https://orcid.org/0000-0002-6555-865X","contributorId":200780,"corporation":false,"usgs":true,"family":"Fischbach","given":"Anthony S.","email":"afischbach@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":783023,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watson, Jordan 0000-0002-1686-0377","orcid":"https://orcid.org/0000-0002-1686-0377","contributorId":222313,"corporation":false,"usgs":false,"family":"Watson","given":"Jordan","email":"","affiliations":[{"id":40517,"text":"NOAA Alaska Fisheries Science Center Auke Bay Laboratories","active":true,"usgs":false}],"preferred":false,"id":783026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reimer, Jesika","contributorId":222724,"corporation":false,"usgs":false,"family":"Reimer","given":"Jesika","email":"","affiliations":[{"id":40516,"text":"Alaska Center for Conservation Science University of Alaska Anchorage","active":true,"usgs":false}],"preferred":false,"id":783025,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208409,"text":"sir20205011 - 2020 - Hydrologic and hydraulic analyses of selected streams in Stark County, Ohio","interactions":[],"lastModifiedDate":"2022-04-25T21:37:51.100623","indexId":"sir20205011","displayToPublicDate":"2020-02-24T12:42:30","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5011","displayTitle":"Hydrologic and Hydraulic Analyses of Selected Streams in Stark County, Ohio","title":"Hydrologic and hydraulic analyses of selected streams in Stark County, Ohio","docAbstract":"

To update and expand a part of the Federal Emergency Management Agency Flood Insurance Study, the U.S. Geological Survey, the Muskingum Watershed Conservancy District, and the Stark County Commissioners began a cooperative study. The study consisted of hydrologic and hydraulic analyses for selected reaches of 14 streams in Stark County, Ohio: Broad-Monter Creek, Chatham Ditch, East Branch Nimishillen Creek, Fairhope Ditch, Firestone Ditch, Hayden Ditch, Middle Branch Nimishillen Creek, Middle Branch Nimishillen Creek Tributary Number 1, Nimishillen Creek, Reemsnyder Ditch, Sherrick Run, unnamed stream, West Branch Nimishillen Creek, and Zimber Ditch. The study totaled nearly 50 miles of stream reaches.

Instantaneous peak streamflows for floods with 10-, 4-, 2-, 1-, and 0.2-percent and 1-percent plus annual exceedance probabilities were estimated using historical streamflow data from the streamgages Nimishillen Creek at North Industry, Ohio (U.S. Geological Survey station number 03118500), and Middle Branch Nimishillen Creek at Canton, Ohio (U.S. Geological Survey station number 03118000), regional flood regression equations, and streamflow urbanization techniques.

The annual exceedance probability streamflows were then used in a Hydrologic Engineering Center-River Analysis System step-backwater model to determine water-surface profiles, flood-inundation boundaries for the 10-, 4-, 2-, 1-, and 0.2-percent and 1-percent plus annual exceedance probability floods, and a regulatory floodway along a selected reach of each stream. Model input included DEM-derived cross sections supplemented with field surveys of open channel cross sections and hydraulic structures, field estimates of roughness values, and annual exceedance probability flood estimates from regional regression equations and historical streamflow data. Flood-inundation boundaries were mapped for the 1- and 0.2-percent annual exceedance probability floods and a regulatory floodway for each stream reach.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205011","collaboration":"Prepared in cooperation with Stark County and the Muskingum Watershed Conservancy District","usgsCitation":"Ostheimer, C.J. and Whitehead, M.T, 2020, Hydrologic and hydraulic analyses of selected streams in Stark County, Ohio: U.S. Geological Survey Scientific Investigations Report 2020–5011, 15 p., https://doi.org/10.3133/sir20205011.","productDescription":"Report: iv, 15 p.; 4 Appendixes; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106471","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":399632,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109726.htm"},{"id":372523,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5011/coverthb.jpg"},{"id":372525,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011_appendix1.pdf","text":"Appendix 1","size":"3.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011 Appendix 1","linkHelpText":"– Technical Support Data Notebook"},{"id":372526,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011_appendix2.pdf","text":"Appendix 2","size":"780 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011 Appendix 2","linkHelpText":"– Floodway data tables"},{"id":372524,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011.pdf","text":"Report","size":"1.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011"},{"id":372527,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011_appendix3.pdf","text":"Appendix 3","size":"1.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011 Appendix 3","linkHelpText":"– Water-surface profiles"},{"id":372528,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011_appendix4.pdf","text":"Appendix 4","size":"8.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011 Appendix 4","linkHelpText":"– Flood-inundation maps"},{"id":372529,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YQJ8B7","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets and hydraulic models for selected streams in Stark County, Ohio"}],"country":"United States","state":"Ohio","county":"Stark County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-81.0864,40.9879],[-81.0865,40.9839],[-81.0866,40.978],[-81.0869,40.9013],[-81.0873,40.728],[-81.0922,40.7285],[-81.1001,40.7281],[-81.1989,40.7292],[-81.1991,40.7224],[-81.2373,40.7237],[-81.241,40.6507],[-81.2755,40.651],[-81.2791,40.6511],[-81.304,40.6518],[-81.3173,40.6519],[-81.4372,40.6529],[-81.4365,40.6584],[-81.4395,40.6625],[-81.4467,40.6657],[-81.4589,40.6654],[-81.4675,40.6555],[-81.6489,40.6346],[-81.6491,40.6681],[-81.6483,40.7371],[-81.648,40.9145],[-81.4201,40.9064],[-81.4164,40.9889],[-81.3932,40.9887],[-81.1059,40.9882],[-81.0925,40.988],[-81.0864,40.9879]]]},\"properties\":{\"name\":\"Stark\",\"state\":\"OH\"}}]}","contact":"

Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard Suite 100
Columbus, OH 43229–1737

","tableOfContents":"","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2020-02-24","noUsgsAuthors":false,"publicationDate":"2020-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Ostheimer, Chad J. 0000-0002-4528-8867","orcid":"https://orcid.org/0000-0002-4528-8867","contributorId":213950,"corporation":false,"usgs":true,"family":"Ostheimer","given":"Chad","email":"","middleInitial":"J.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whitehead, Matthew T. 0000-0002-4888-2597 mtwhiteh@usgs.gov","orcid":"https://orcid.org/0000-0002-4888-2597","contributorId":218036,"corporation":false,"usgs":true,"family":"Whitehead","given":"Matthew T.","email":"mtwhiteh@usgs.gov","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781769,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70208126,"text":"sir20205001 - 2020 - Modeling a 2- and 4-foot drawdown in the Link River to Keno Dam reach of the upper Klamath River, south-central Oregon","interactions":[],"lastModifiedDate":"2022-04-25T20:39:51.337234","indexId":"sir20205001","displayToPublicDate":"2020-02-24T12:23:19","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5001","displayTitle":"Modeling a 2- and 4-Foot Drawdown in the Link River to Keno Dam Reach of the Upper Klamath River, South-Central Oregon","title":"Modeling a 2- and 4-foot drawdown in the Link River to Keno Dam reach of the upper Klamath River, south-central Oregon","docAbstract":"

Executive Summary

The most upstream, pooled reach of the Klamath River in south-central Oregon, from Link River mouth to Keno Dam (Link-Keno), has a water-surface elevation that remains relatively constant throughout the year. Two model scenarios, using an existing two-dimensional hydrodynamic and water-quality model (CE-QUAL-W2), were constructed to examine the effects of lowering the water-surface elevation by 2 and 4 feet (ft) (0.61 and 1.2 meters) throughout an entire calendar year to mimic some of the potential effects of removal or modification of Keno Dam. Model results for these drawdown scenarios were analyzed for changes in velocity, travel time, water temperature, total dissolved solids, inorganic suspended sediment, nutrients, organic matter, chlorophyll a, and dissolved oxygen, compared to the base-case model. The model used in this study had been previously calibrated with the presence of aquatic plants (macrophytes). However, most model analyses were completed for model runs where macrophytes were “turned off” because the species, abundance, and distribution of macrophytes in a lowered-water scenario were all highly uncertain. For comparison, a few model scenario runs were completed with macrophytes enabled within the model. Findings from this study include the following:

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205001","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Sullivan, A.B., and Rounds, S.A., 2020, Modeling a 2- and 4-foot drawdown in the Link River to Keno Dam reach of the upper Klamath River, south-central Oregon: U.S. Geological Survey Scientific Investigations Report 2020–5001, 18 p., https://doi.org/10.3133/sir20205001.","productDescription":"vi, 18 p.","onlineOnly":"Y","ipdsId":"IP-110517","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":372580,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5001/sir20205001.pdf","text":"Report","size":"4.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5001"},{"id":399624,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109725.htm"},{"id":372579,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5001/coverthb.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 -121.80679321289062,\n 42.24935997537791\n ],\n [\n -121.82121276855469,\n 42.23461834757937\n ],\n [\n -121.81159973144531,\n 42.23055108552288\n ],\n [\n -121.79443359375,\n 42.21478796951033\n ],\n [\n -121.79100036621094,\n 42.20614200929954\n ],\n [\n -121.79786682128905,\n 42.18986405028881\n ],\n [\n -121.80816650390625,\n 42.179179360985074\n ],\n [\n -121.81983947753906,\n 42.17765282939429\n ],\n [\n -121.84249877929688,\n 42.17307301351938\n ],\n [\n -121.85760498046875,\n 42.156786537824686\n ],\n [\n -121.86721801757812,\n 42.146096259707384\n ],\n [\n -121.86653137207031,\n 42.134894984239224\n ],\n [\n -121.85897827148438,\n 42.12114525624141\n ],\n [\n -121.86447143554686,\n 42.10943017110108\n ],\n [\n -121.87271118164062,\n 42.09822241118974\n ],\n [\n -121.88095092773439,\n 42.10739254393655\n ],\n [\n -121.89468383789061,\n 42.12267315117256\n ],\n [\n -121.90979003906249,\n 42.132858175814626\n ],\n [\n -121.93313598632812,\n 42.14151415924112\n ],\n [\n -121.94961547851562,\n 42.14660536151124\n ],\n [\n -121.96197509765625,\n 42.14813264235833\n ],\n [\n -121.96540832519531,\n 42.15933157601718\n ],\n [\n -121.99287414550781,\n 42.16238548673798\n ],\n [\n -122.0203399658203,\n 42.156786537824686\n ],\n [\n -122.03887939453125,\n 42.154241397276955\n ],\n [\n -122.05398559570312,\n 42.14151415924112\n ],\n [\n -122.05261230468751,\n 42.12725661490423\n ],\n [\n -122.03819274902344,\n 42.11808935584944\n ],\n [\n -122.02445983886719,\n 42.13947756370087\n ],\n [\n -122.01896667480467,\n 42.14100501649703\n ],\n [\n -122.00042724609374,\n 42.14253243244731\n ],\n [\n -121.98532104492186,\n 42.13845924136697\n ],\n [\n -121.97776794433594,\n 42.12980284036179\n ],\n [\n -121.95716857910155,\n 42.12369172732473\n ],\n [\n -121.94137573242186,\n 42.121654558645524\n ],\n [\n -121.92764282226564,\n 42.111467732769135\n ],\n [\n -121.90910339355467,\n 42.10637370579324\n ],\n [\n -121.89537048339842,\n 42.097203425683055\n ],\n [\n -121.88919067382812,\n 42.081407056615774\n ],\n [\n -121.8610382080078,\n 42.08599350447723\n ],\n [\n -121.8335723876953,\n 42.105864280581365\n ],\n [\n -121.82052612304688,\n 42.13183974703906\n ],\n [\n -121.7889404296875,\n 42.17001961868589\n ],\n [\n -121.77108764648436,\n 42.180705855725115\n ],\n [\n -121.76353454589844,\n 42.20156425714052\n ],\n [\n -121.77040100097655,\n 42.21580506349499\n ],\n [\n -121.78962707519531,\n 42.233093155022765\n ],\n [\n -121.80404663085939,\n 42.242243757492766\n ],\n [\n -121.80679321289062,\n 42.24935997537791\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201

","tableOfContents":"","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2020-02-24","noUsgsAuthors":false,"publicationDate":"2020-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Sullivan, Annett B. 0000-0001-7783-3906 annett@usgs.gov","orcid":"https://orcid.org/0000-0001-7783-3906","contributorId":79821,"corporation":false,"usgs":true,"family":"Sullivan","given":"Annett B.","email":"annett@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":780621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rounds, Stewart A. 0000-0002-8540-2206 sarounds@usgs.gov","orcid":"https://orcid.org/0000-0002-8540-2206","contributorId":905,"corporation":false,"usgs":true,"family":"Rounds","given":"Stewart","email":"sarounds@usgs.gov","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":780622,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70210169,"text":"70210169 - 2020 - Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model","interactions":[],"lastModifiedDate":"2020-05-19T14:20:30.574872","indexId":"70210169","displayToPublicDate":"2020-02-24T09:15:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model","docAbstract":"The spatial variability of snow water equivalent (SWE) can exert a strong influence on the timing and magnitude of snowmelt delivery to a watershed. Therefore, the representation of subgrid or subwatershed snow variability in hydrologic models is important for accurately simulating snowmelt dynamics and runoff response. The U.S. Geological Survey National Hydrologic Model infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS) represents the subgrid variability of SWE with snow depletion curves (SDCs), which relate snow-covered area to watershed-average SWE during the snowmelt period. The main objective of this research was to evaluate the sensitivity of simulated runoff to SDC representation within the NHM-PRMS across the continental United States (CONUS). SDCs for the model experiment were derived assuming a range of SWE coefficient of variation (CV) values and a lognormal probability distribution function. The NHM-PRMS was simulated at a daily time step for each SDC over a 14-year period. Results highlight that increasing the subgrid snow variability (by changing the SDC) resulted in a consistently slower snowmelt rate and longer snowmelt duration when averaged across the hydrologic response unit scale. Simulated runoff was also found to be sensitive to SDC representation, as increases in the subgrid SWE CV by 1.0 resulted in decreases in runoff ratio by as much as 12 percent in snow-dominated regions of the CONUS. Simulated decreases in runoff associated with slower snowmelt rates were approximately inversely proportional to increases in simulated evapotranspiration. High snow persistence and peak SWE:annual precipitation combined with a water limited dryness index were associated with the greatest runoff sensitivity to changing snowmelt. Results from this study highlight the importance of carefully parameterizing SDCs for hydrologic modeling. Furthermore, improving model representation of snowmelt input variability and its relation to runoff generation processes is shown to be an important consideration for future modeling applications.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13735","usgsCitation":"Sexstone, G., Driscoll, J.M., Hay, L., Hammond, J., and Barnhart, T., 2020, Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model: Hydrological Processes, v. 34, no. 11, p. 2365-2380, https://doi.org/10.1002/hyp.13735.","productDescription":"16 p.","startPage":"2365","endPage":"2380","ipdsId":"IP-107421","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":437087,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OEIRJF","text":"USGS data release","linkHelpText":"Data release in support of Runoff sensitivity to snow depletion curve representation within a continental scale 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0000-0001-8913-0546","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":203850,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789388,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Driscoll, Jessica M. 0000-0003-3097-9603 jdriscoll@usgs.gov","orcid":"https://orcid.org/0000-0003-3097-9603","contributorId":167585,"corporation":false,"usgs":true,"family":"Driscoll","given":"Jessica","email":"jdriscoll@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":789389,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hay, Lauren 0000-0003-3763-4595","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":205020,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":789390,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789391,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnhart, Theodore B. 0000-0002-9682-3217","orcid":"https://orcid.org/0000-0002-9682-3217","contributorId":202558,"corporation":false,"usgs":true,"family":"Barnhart","given":"Theodore B.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789392,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70222500,"text":"70222500 - 2020 - A machine learning approach to developing ground motion models from simulated ground motions","interactions":[],"lastModifiedDate":"2021-07-30T12:47:26.178465","indexId":"70222500","displayToPublicDate":"2020-02-24T07:44:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"A machine learning approach to developing ground motion models from simulated ground motions","docAbstract":"

We use a machine learning approach to build a ground motion model (GMM) from a synthetic database of ground motions extracted from the Southern California CyberShake study. An artificial neural network is used to find the optimal weights that best fit the target data (without overfitting), with input parameters chosen to match that of state-of-the-art GMMs. We validate our synthetic-based GMM with empirically based GMMs derived from the globally based Next Generation Attenuation West2 data set, finding near-zero median residuals and similar amplitude and trends (with period) of total variability. Additionally, we find that the artificial neural network GMM has similar bias and variability to empirical GMMs from records of the recent \"urn:x-wiley:grl:media:grl60306:grl60306-math-0001\" Ridgecrest event, which neither GMM has included in its formulation. As simulations continue to better model broadband ground motions, machine learning provides a way to utilize the vast amount of synthetically generated data and guide future parameterization of GMMs.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GL086690","usgsCitation":"Withers, K., Moschetti, M.P., and Thompson, E.M., 2020, A machine learning approach to developing ground motion models from simulated ground motions: Geophysical Research Letters, v. 47, no. 6, e2019GL086690, 9 p., https://doi.org/10.1029/2019GL086690.","productDescription":"e2019GL086690, 9 p.","ipdsId":"IP-116117","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":387574,"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 -119.091796875,\n 33.815666308702774\n ],\n [\n -117.31201171875001,\n 32.34284135639302\n ],\n [\n -114.345703125,\n 32.69486597787505\n ],\n [\n -113.7744140625,\n 34.45221847282654\n ],\n [\n -119.091796875,\n 34.45221847282654\n ],\n [\n -119.091796875,\n 33.815666308702774\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Withers, Kyle 0000-0001-7863-3930","orcid":"https://orcid.org/0000-0001-7863-3930","contributorId":203492,"corporation":false,"usgs":true,"family":"Withers","given":"Kyle","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820322,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208959,"text":"70208959 - 2020 - An experimental study of longitudinal incisional grooves in a mixed bedrock-alluvial channel","interactions":[],"lastModifiedDate":"2020-03-09T12:02:08","indexId":"70208959","displayToPublicDate":"2020-02-23T11:58:44","publicationYear":"2020","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":"An experimental study of longitudinal incisional grooves in a mixed bedrock-alluvial channel","docAbstract":"

Natural bedrock rivers have various bedforms created by erosion. Flow‐parallel incisional grooves formed longitudinally in bedrock are one common example of such bedforms. Although several studies have been conducted regarding these grooves, their formation processes are not well understood. In this study, we conducted a flume experiment to investigate the relationship between the flow structure and longitudinal grooves. The experimental results strongly suggest that longitudinal grooves are formed by moving sediment concentrated in multiple longitudinal pathways by turbulence‐driven secondary flows. The sediment preferentially abrades the bedrock along these flow‐parallel pathways resulting in longitudinal grooves in the bedrock. Measurements of the flow velocity distribution show that the positions of secondary flow cells producing the initial formation of the grooves are altered by the formation of those grooves. Because displaced secondary flows tend to make the sediment collide with the sidewalls of the longitudinal grooves, the grooves grow wider over time and some grooves partially combine with other adjacent grooves. The initial maximum number of longitudinal grooves Nmax strongly depends on the river width‐depth ratio B/D, which defines the number of secondary flow cells, and can be expressed as Nmax = 0.5B/D. However, because some grooves coalesce with other grooves due to the effects of the displacement of secondary flows, the average number of grooves showed a relationship that can be expressed as N = 0.41B/D. Based on this relationship, we inversely estimated the flow discharge of the Abashiri River using the number of longitudinal grooves observed in the river. The result was consistent with the observed annual maximum flow discharge of the river. This suggests that the number of longitudinal grooves can be used as an indicator for estimation of the formative flow discharge in bedrock rivers.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019WR025410","usgsCitation":"Inoue, T., and Nelson, J.M., 2020, An experimental study of longitudinal incisional grooves in a mixed bedrock-alluvial channel: Water Resources Research, v. 56, no. 3, e2019WR025410, 16 p., https://doi.org/10.1029/2019WR025410.","productDescription":"e2019WR025410, 16 p.","ipdsId":"IP-107443","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":487492,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019wr025410","text":"Publisher Index Page"},{"id":373013,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Inoue, Takuya","contributorId":173794,"corporation":false,"usgs":false,"family":"Inoue","given":"Takuya","email":"","affiliations":[{"id":27295,"text":"Civil Engineering Research Institute, Sapporo, Japan","active":true,"usgs":false}],"preferred":false,"id":784200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelson, Jonathan M. 0000-0002-7632-8526 jmn@usgs.gov","orcid":"https://orcid.org/0000-0002-7632-8526","contributorId":2812,"corporation":false,"usgs":true,"family":"Nelson","given":"Jonathan","email":"jmn@usgs.gov","middleInitial":"M.","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":784199,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70209088,"text":"70209088 - 2020 - Long term persistence of aspen in snowdrift-dependent ecosystems","interactions":[],"lastModifiedDate":"2020-03-15T13:48:16","indexId":"70209088","displayToPublicDate":"2020-02-22T13:47:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Long term persistence of aspen in snowdrift-dependent ecosystems","docAbstract":"Quaking aspen (Populus tremuloides) forests throughout the western United States have\nexperienced significant mortality in recent decades, much of which has been influenced by\nclimate variability, especially drought. In the western portion of its range, where most\tprecipitation arrives during winter as snowfall and summers are dry, snowdrifts that persist into\tthe growing season provide soil moisture recharge that sustain many aspen groves that are\timportant locations of biodiversity within the landscape. There is growing concern that reduced\nsnowpack due to climate change may reduce the long-term persistence and productivity of aspen communities in these regions. In this study, we evaluated the potential for climate change and\tdrought to reduce or eliminate isolated aspen communities in southwestern Idaho. We used a landscape simulation model integrated with inputs from an empirically derived biogeochemical\nmodel of growth, and a species distribution model of regeneration to forecast how changes in\nclimate, declining snowpack, and competition with a conifer species is likely to affect aspen\noccupancy over the next 85-years. We found that simulated reductions in snowpack depth (and\nassociated increases in climatic water deficit) caused a reduction in aspen persistence; aspen\noccupancy was reduced under all high emissions climate scenarios. Douglas-fir (Pseudotsuga\nmenziesii) occupancy also declined under all future climates. Aspen regeneration declined over\nthe course of all simulations, with an ensemble ratio of mortality/establishment increasing over\nthe course of both low and high emissions climate scenarios. Climate-induced mortality of aspen\nclones increased in frequency under all climate scenarios and, under the most severe emissions\nscenarios, contributed to a substantial decline of aspen cover. Our research suggests that\nsnowbanks will be an important determinant of long-term persistence of aspen under changing climate in the region.","language":"English","publisher":"Elseiver","doi":"10.1016/j.foreco.2020.118005","usgsCitation":"Kretchun, A.M., Scheller, R., Shinneman, D.J., Soderquist, B., Maguire, K.C., Link, T., and Strand, E.K., 2020, Long term persistence of aspen in snowdrift-dependent ecosystems: Forest Ecology and Management, v. 462, 118005, https://doi.org/10.1016/j.foreco.2020.118005.","productDescription":"118005","ipdsId":"IP-112095","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":457627,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2020.118005","text":"Publisher Index Page"},{"id":373273,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"462","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kretchun, Alec M","contributorId":223372,"corporation":false,"usgs":false,"family":"Kretchun","given":"Alec","email":"","middleInitial":"M","affiliations":[{"id":40703,"text":"Quantum Spatial","active":true,"usgs":false}],"preferred":false,"id":784879,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scheller, Robert M","contributorId":147807,"corporation":false,"usgs":false,"family":"Scheller","given":"Robert M","affiliations":[{"id":16941,"text":"Environmental Science and Management Department, Portland State University","active":true,"usgs":false}],"preferred":false,"id":784880,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":784878,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Soderquist, B","contributorId":223373,"corporation":false,"usgs":false,"family":"Soderquist","given":"B","email":"","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":784882,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maguire, Kaitlin C. 0000-0001-8193-2384","orcid":"https://orcid.org/0000-0001-8193-2384","contributorId":203419,"corporation":false,"usgs":true,"family":"Maguire","given":"Kaitlin","email":"","middleInitial":"C.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":784881,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Link, Timothy E","contributorId":223374,"corporation":false,"usgs":false,"family":"Link","given":"Timothy E","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":784883,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Strand, Eva K.","contributorId":149810,"corporation":false,"usgs":false,"family":"Strand","given":"Eva","email":"","middleInitial":"K.","affiliations":[{"id":17832,"text":"University of Idaho Department of Forest, Rangeland, and Fire Sciences","active":true,"usgs":false}],"preferred":false,"id":784884,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229750,"text":"70229750 - 2020 - Evolving infrasound detections from Bogoslof volcano, Alaska: Insights from atmospheric propagation modeling","interactions":[],"lastModifiedDate":"2022-03-16T14:33:25.456937","indexId":"70229750","displayToPublicDate":"2020-02-22T09:29:22","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Evolving infrasound detections from Bogoslof volcano, Alaska: Insights from atmospheric propagation modeling","docAbstract":"

Bogoslof volcano, a back-arc volcano in Alaska’s Aleutian arc, began an eruptive sequence in mid-December 2016 that ended in late August 2017, with 70 individual eruptive episodes. Because there were no local seismic or infrasound stations on the island, the Alaska Volcano Observatory (AVO) relied on distant geophysical networks and remote sensing techniques to assess activity during the eruption. AVO maintains six infrasound arrays to monitor activity along the Aleutian arc: Adak, the Island of Four Mountains, Okmok, Akutan, Sand Point, and Dillingham. Eruption detection at infrasound arrays is subject to local as well as mesoscale meteorological conditions that vary greatly over both short and long timescales. Infrasound detections from the array nearest to Bogoslof (Okmok), with a latency of about 3 min, played a crucial role in monitoring activity during the eruption. Despite the relative proximity of the Okmok array to Bogoslof (60 km), infrasound detections were not uniformly observed with only about two-thirds of the events successfully detected. The farthest array at Dillingham (816 km) detected approximately half of the explosive events, with all other arrays detecting less than half of the events. We compare observations with infrasound propagation model predictions, using both normal mode and parabolic equation forward models, to interpret the variation in detections of the 70 explosive events across the AVO infrasound network. The forward models utilize the newly created, publicly available AVO-G2S atmospheric reconstruction using numerical weather predictions data for the lower atmosphere, coupled with upper atmosphere empirical models of wind speeds and temperature. We find that long-range detections (> 100 km) of Bogoslof events are largely aligned with seasonal variability in favorable propagation conditions, while regional detections (< 100 km) are less consistent with propagation modeling. Understanding the output of numerical models in comparison to past observations will facilitate their use in future operational settings for AVO and other observatories.

","language":"English","publisher":"Springer","doi":"10.1007/s00445-020-1360-3","usgsCitation":"Schwaiger, H., Lyons, J.J., Iezzi, A., Fee, D., and Haney, M.M., 2020, Evolving infrasound detections from Bogoslof volcano, Alaska: Insights from atmospheric propagation modeling: Bulletin of Volcanology, v. 82, 27, 14 p., https://doi.org/10.1007/s00445-020-1360-3.","productDescription":"27, 14 p.","ipdsId":"IP-108990","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":397149,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bogoslof Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -179.5166015625,\n 48.40003249610685\n ],\n [\n -157.5,\n 48.40003249610685\n ],\n [\n -157.5,\n 58.768200159239576\n ],\n [\n -179.5166015625,\n 58.768200159239576\n ],\n [\n -179.5166015625,\n 48.40003249610685\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"82","noUsgsAuthors":false,"publicationDate":"2020-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Schwaiger, Hans 0000-0001-7397-8833","orcid":"https://orcid.org/0000-0001-7397-8833","contributorId":214983,"corporation":false,"usgs":true,"family":"Schwaiger","given":"Hans","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":838173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":838174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iezzi, Alexandra M. 0000-0002-6782-7681","orcid":"https://orcid.org/0000-0002-6782-7681","contributorId":196436,"corporation":false,"usgs":false,"family":"Iezzi","given":"Alexandra M.","affiliations":[],"preferred":false,"id":838175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fee, David 0000-0002-0936-9977","orcid":"https://orcid.org/0000-0002-0936-9977","contributorId":267231,"corporation":false,"usgs":false,"family":"Fee","given":"David","affiliations":[{"id":13097,"text":"Geophysical Institute, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":838176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":838177,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208644,"text":"70208644 - 2020 - Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony","interactions":[],"lastModifiedDate":"2022-03-31T18:52:42.924432","indexId":"70208644","displayToPublicDate":"2020-02-22T06:42:15","publicationYear":"2020","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":"Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony","docAbstract":"Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid response initiatives. However, few studies have leveraged remote sensing technologies and computing frameworks capable of providing rangeland managers with maps of exotic annual grass cover at relatively high spatiotemporal resolutions and near real-time latencies. Here, we developed a system for automated mapping of invasive annual grass (%) cover using in situ observations, harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables, and machine learning techniques. A robust and automated cloud, cloud shadow, water, and snow/ice masking procedure (mean overall accuracy >81%) was implemented using time-series outlier detection and data mining techniques prior to spatiotemporal interpolation of HLS data via regression tree models (r = 0.94; mean absolute error (MAE) = 0.02). Weekly, cloud-free normalized difference vegetation index (NDVI) image composites (2016–2018) were used to construct a suite of spectral and phenological metrics (e.g., start and end of season dates), consistent with information derived from Moderate Resolution Image Spectroradiometer (MODIS) data. These metrics were incorporated into a data mining framework that accurately (r = 0.83; MAE = 11) modeled and mapped exotic annual grass (%) cover throughout dryland ecosystems in the western United States at a native, 30-m spatial resolution. Our results show that inclusion of weekly HLS time-series data and derived indicators improves our ability to map exotic annual grass cover, as compared to distribution models that use MODIS products or monthly, seasonal, or annual HLS composites as primary inputs. This research fills a critical gap in our ability to effectively assess, manage, and monitor drylands by providing a framework that allows for an accurate and timely depiction of land surface phenology and exotic annual grass cover at spatial and temporal resolutions that are meaningful to local resource managers.","language":"English","publisher":"MDPI","doi":"10.3390/rs12040725","usgsCitation":"Pastick, N., Dahal, D., Wylie, B.K., Parajuli, S., Boyte, S.P., and Wu, Z., 2020, Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony: Remote Sensing, v. 12, no. 4, 725, 17 p.; Data release, https://doi.org/10.3390/rs12040725.","productDescription":"725, 17 p.; Data release","ipdsId":"IP-114798","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457631,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12040725","text":"Publisher Index Page"},{"id":437093,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91NJ2PD","text":"USGS data release","linkHelpText":"Near real time estimation of annual exotic herbaceous fractional cover in the sagebrush ecosystem 30m, USA, July 2020"},{"id":437092,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KKPT07","text":"USGS data release","linkHelpText":"Weekly cloud free Harmonized Landsat Sentinel-2 (HLS) Normalized Difference Vegetation Index (NDVI) data for western United States (2016 &amp;amp;ndash; 2019)."},{"id":437091,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XT1BV2","text":"USGS data release","linkHelpText":"Fractional estimates of exotic annual grass cover in dryland ecosystems of western United States (2016 - 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Each year these warnings guide life-saving assistance that reaches millions of people. A new NASA multi-model, remote sensing-based hydrological forecasting and analysis system, NHyFAS, has been developed to support such efforts by improving the FEWS NET’s current early warning capabilities. NHyFAS derives its skill from two sources: (i) accurate initial conditions, as produced by an offline land modeling system through the application and/or assimilation of various satellite data (precipitation, soil moisture, and terrestrial water storage); and (ii) meteorological forcing data during the forecast period as produced by a state-of-the-art ocean-land-atmosphere forecast system. The land modeling framework used is the Land Information System (LIS), which employs a suite of land surface models, allowing multi-model ensembles and multiple data assimilation strategies to better estimate land surface conditions. An evaluation of NHyFAS shows that its one-to-five month forecasts successfully capture known historic drought events. The system also benefits from strong collaboration with end-user partners in Africa and the Middle East, who provide insights on strategies to formulate and communicate early warning indicators to water and food security communities. The additional lead time provided by this system will increase the speed, accuracy and efficacy of humanitarian disaster relief, helping to save lives and livelihoods.","language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-18-0264.1","usgsCitation":"Arsenault, K., Shukla, S., Hazra, A., Getirana, A., McNally, A., Kumar, S., Koster, R., Peters-Lidard, C., Zaitchik, B., Badr, H., Jung, H.C., Narapusetty, B., , N., Wang, S., Mocko, D.M., Funk, C., Harrison, L., Husak, G.J., Adoum, A., Galu, G., Magadzire, T., Roningen, J., Shaw, M.J., Eylander, J., Bergaoui, K., McDonnell, R.A., and Verdin, J., 2020, The NASA hydrological forecast system for food and water security applications: Bulletin of the American Meteorological Society, v. 101, no. 7, p. 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Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal subsurface thaw dynamics of an aufeis feature inferred from geophysical methods","docAbstract":"Aufeis are sheets of ice unique to cold regions that originate from repeated flooding and freezing events during the winter. They have hydrological importance associated with summer flows and possibly winter insulation, but little is known about the seasonal dynamics of the unfrozen sediment layer beneath them. This layer may support perennial groundwater flow in regions with otherwise continuous permafrost. For this study, ground penetrating radar (GPR) were collected in September 2016 (maximum thaw) and April 2017 (maximum frozen) at the Kuparuk aufeis field on the North Slope of Alaska. Supporting surface nuclear magnetic resonance (NMR) data were collected during the maximum frozen campaign. These point-in-time geophysical data sets were augmented by continuous subsurface temperature data and periodic Structure-from-Motion digital elevation models (DEM) collected seasonally. GPR and difference DEM data showed maximum ice thicknesses of up to 6 m over the sediment surface. Below the ice, GPR and NMR identified regions of permafrost and regions of seasonally frozen sediment (i.e., the active layer) underlain by a substantial perennially unfrozen zone or “lateral talik” that ranged from 0 m to over 13 m thick. The seasonally frozen cobble layer above the talik was typically 3 to 5 m thick, with freezing apparently enabled by relatively high thermal diffusivity of the overlying ice and rock cobbles. The large talik beneath the aufeis and active layer suggests that year-round groundwater flow and coupled heat transport occurs beneath much of the feature. Highly permeable alluvial material and discrete zones of apparent groundwater upwelling indicated by geophysical and ground temperature data allows direct connection between the aufeis and the talik below.","language":"English","publisher":"Wiley","doi":"10.1029/2019JF005345","usgsCitation":"Terry, N., Grunewald, E., Briggs, M.A., Gooseff, M., Huryn, A.D., Kass, M.A., Tape, K., Hendrickson, P., and Lane, J., 2020, Seasonal subsurface thaw dynamics of an aufeis feature inferred from geophysical methods: Journal of Geophysical Research: Earth Surface, v. 125, no. 3, e2019JF005345, 18 p., https://doi.org/10.1029/2019JF005345.","productDescription":"e2019JF005345, 18 p.","ipdsId":"IP-106506","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":372655,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","county":"Kuparuk River drainage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -149.37011718749997,\n 69.79413569863112\n ],\n [\n -148.55712890625,\n 70.44415495538642\n ],\n [\n -149.30419921875,\n 70.49557354093136\n ],\n [\n -149.85351562499997,\n 69.80172356231073\n ],\n [\n -149.37011718749997,\n 69.79413569863112\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Terry, Neil 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":783169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grunewald, Elliot","contributorId":193963,"corporation":false,"usgs":false,"family":"Grunewald","given":"Elliot","email":"","affiliations":[],"preferred":false,"id":783170,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":783171,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gooseff, Michael","contributorId":181942,"corporation":false,"usgs":false,"family":"Gooseff","given":"Michael","affiliations":[],"preferred":false,"id":783172,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huryn, Alexander D. 0000-0002-1365-2361","orcid":"https://orcid.org/0000-0002-1365-2361","contributorId":20164,"corporation":false,"usgs":false,"family":"Huryn","given":"Alexander","email":"","middleInitial":"D.","affiliations":[{"id":28219,"text":"The University of Alabama, Department of Biological Sciences, Tuscaloosa, AL 35487","active":true,"usgs":false}],"preferred":false,"id":783173,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kass, M. Andy","contributorId":103593,"corporation":false,"usgs":true,"family":"Kass","given":"M.","email":"","middleInitial":"Andy","affiliations":[],"preferred":false,"id":783174,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tape, Ken 0000-0002-1039-6868","orcid":"https://orcid.org/0000-0002-1039-6868","contributorId":214222,"corporation":false,"usgs":false,"family":"Tape","given":"Ken","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":783175,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hendrickson, Patrick","contributorId":214223,"corporation":false,"usgs":false,"family":"Hendrickson","given":"Patrick","email":"","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":783176,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lane, John W. Jr. 0000-0002-3558-243X","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":210076,"corporation":false,"usgs":true,"family":"Lane","given":"John W.","suffix":"Jr.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":783177,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70208590,"text":"sir20195142 - 2020 - Assessment of soil and water resources in the Organ Mountains-Desert Peaks National Monument, New Mexico","interactions":[],"lastModifiedDate":"2022-04-25T20:20:35.352401","indexId":"sir20195142","displayToPublicDate":"2020-02-21T13:52:10","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5142","displayTitle":"Assessment of Soil and Water Resources in the Organ Mountains-Desert Peaks National Monument, New Mexico","title":"Assessment of soil and water resources in the Organ Mountains-Desert Peaks National Monument, New Mexico","docAbstract":"

The Organ Mountains-Desert Peaks National Monument (Monument) in southern New Mexico was established in 2014. Given anticipated future demands in the Monument for recreation, livestock grazing, and maintenance of rights-of-way (for example, pipelines and powerlines), the Bureau of Land Management (BLM) needs a better understanding of the current soil and water resources and how infrastructure improvements could affect these resources and the watershed. Specifically, the BLM is concerned with infiltration and erosion and their relations to existing or planned infrastructure, such as roads, campgrounds, location of livestock grazing, and rights-of-way. Alternatives to the current land-use conditions, land-management practices, and infrastructure will be assessed by BLM to best protect Monument resources. The U.S. Geological Survey, in cooperation with the BLM, conducted a study to assess the soil and water resources within the Monument to provide an inventory and compilation of natural-resource information needed by resource managers for the BLM’s land-use planning process for this new national monument. The overall objectives of this study were to (1) compile and interpret existing soil- and water-resource data for the Monument and (2) provide a basic assessment of the surface hydrological effects of selected alternatives to current land use and infrastructure. Data were compiled by using geographic information system software and evaluated for hydrologic and landscape properties that influence infiltration, runoff, and erosion. The effects of changing vegetation were simulated by using different scenarios in the Rangeland Hydrology and Erosion Model. Results of this model indicate areas where soil loss or runoff may occur.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195142","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Blake, J.M., Mitchell, A.C., Shephard, Z., Ball, G., Chavarria, S., and Douglas-Mankin, K.R., 2020, Assessment of soil and water resources in the Organ Mountains-Desert Peaks National Monument, New Mexico: U.S. Geological Survey Scientific Investigations Report 2019–5142, 64 p., https://doi.org/10.3133/sir20195142.","productDescription":"Report: x, 64 p.; Data Release","numberOfPages":"78","onlineOnly":"Y","ipdsId":"IP-098054","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":372464,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5142/sir20195142.pdf","text":"Report","size":"87.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5142"},{"id":399617,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109724.htm"},{"id":372465,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JVHA4Z","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Database associated with the assessment of soil and water resources in the Organ Mountains-Desert Peaks National Monument"},{"id":372463,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5142/coverthb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Organ Mountains-Desert Peaks National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.3,\n 31.8511\n ],\n [\n -106.4639,\n 31.8511\n ],\n [\n -106.4639,\n 32.6628\n ],\n [\n -107.3,\n 32.6628\n ],\n [\n -107.3,\n 31.8511\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, New Mexico 87113

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2020-02-21","noUsgsAuthors":false,"publicationDate":"2020-02-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Blake, Johanna M. 0000-0003-4667-0096","orcid":"https://orcid.org/0000-0003-4667-0096","contributorId":211907,"corporation":false,"usgs":true,"family":"Blake","given":"Johanna M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782632,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mitchell, Aurelia C. 0000-0003-3302-4546","orcid":"https://orcid.org/0000-0003-3302-4546","contributorId":222580,"corporation":false,"usgs":true,"family":"Mitchell","given":"Aurelia C.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782635,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shephard, Zachary M. 0000-0003-2994-3355","orcid":"https://orcid.org/0000-0003-2994-3355","contributorId":222581,"corporation":false,"usgs":true,"family":"Shephard","given":"Zachary","email":"","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782636,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ball, Grady 0000-0003-3030-055X","orcid":"https://orcid.org/0000-0003-3030-055X","contributorId":222582,"corporation":false,"usgs":true,"family":"Ball","given":"Grady","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782637,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chavarria, Shaleene 0000-0001-8792-1010","orcid":"https://orcid.org/0000-0001-8792-1010","contributorId":222578,"corporation":false,"usgs":true,"family":"Chavarria","given":"Shaleene","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782633,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":222579,"corporation":false,"usgs":false,"family":"Douglas-Mankin","given":"Kyle R.","affiliations":[{"id":40563,"text":"Former NMWSC","active":true,"usgs":false}],"preferred":false,"id":782634,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208833,"text":"70208833 - 2020 - Broad-scale impacts of an invasive native predator on a sensitive native prey species within the shifting avian community of the North American Great Basin","interactions":[],"lastModifiedDate":"2020-03-03T08:16:45","indexId":"70208833","displayToPublicDate":"2020-02-21T08:12:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Broad-scale impacts of an invasive native predator on a sensitive native prey species within the shifting avian community of the North American Great Basin","docAbstract":"Human enterprise has modified ecosystem processes through direct and indirect alteration of native predators’ distribution and abundance. For example, human activities subsidize food, water, and shelter availability to generalist predators whose subsequent increased abundance impacts lower trophic-level prey species. The common raven (Corvus corax; hereafter, raven) is an avian predator, native to the northern hemisphere, that can become invasive when subsidized. Raven populations are increasing at unprecedented rates in many regions globally. Information regarding scale of impact and potential ecological thresholds is needed to guide conservation actions aimed at reducing adverse effects on sensitive prey. We conducted a multi-part analysis to investigate broad-scale variation in raven densities and impacts on nesting greater sage-grouse (Centrocercus urophasianus), an indicator species for sagebrush ecosystems in western North America. We estimated raven densities using 16,000 point surveys over 10 years within the Great Basin, USA, and examined associations with anthropogenic and environmental covariates. Average density was 0.54 ravens km-2 (95% CI: 0.42–0.70), with higher densities at lower relative elevations comprising increased agriculture and development. We then used a reduced dataset to estimate the effect of raven density on sage-grouse nest survival (nests = 737). We identified negative impacts to nesting sage-grouse, especially where raven density exceeded ~ 0.40 km-2, a potential ecological threshold. We mapped regions where elevated raven densities were predicted to depress sage-grouse population growth in the absence of compensatory demographic responses from other sage-grouse life-history stages, and found ~ 64% of sage-grouse breeding areas were adversely impacted by high raven density.","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2020.108409","usgsCitation":"Coates, P.S., O'Neil, S., Brussee, B.E., Ricca, M.A., Jackson, P.J., Dinkins, J.B., Howe, K., Moser, A.M., Foster, L.J., and Delahunty, D.J., 2020, Broad-scale impacts of an invasive native predator on a sensitive native prey species within the shifting avian community of the North American Great Basin: Biological Conservation, v. 243, 108409, 10 p., https://doi.org/10.1016/j.biocon.2020.108409.","productDescription":"108409, 10 p.","ipdsId":"IP-112833","costCenters":[{"id":651,"text":"Western Ecological Research 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34.994003757575776\n ],\n [\n -113.9501953125,\n 36.27970720524017\n ],\n [\n -113.4228515625,\n 37.54457732085582\n ],\n [\n -111.3134765625,\n 39.470125122358176\n ],\n [\n -112.1044921875,\n 43.51668853502906\n ],\n [\n -113.2470703125,\n 45.182036837015886\n ],\n [\n -114.82910156249999,\n 44.24519901522129\n ],\n [\n -116.76269531249999,\n 44.213709909702054\n ],\n [\n -118.69628906249999,\n 44.402391829093915\n ],\n [\n -120.76171875,\n 44.24519901522129\n ],\n [\n -121.9921875,\n 41.86956082699455\n ],\n [\n -120.41015624999999,\n 39.470125122358176\n ],\n [\n -119.267578125,\n 36.66841891894786\n ],\n [\n -118.91601562499999,\n 35.67514743608467\n ],\n [\n -116.76269531249999,\n 34.34343606848294\n ],\n [\n -115.26855468749999,\n 34.59704151614417\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"243","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coates, Peter S. 0000-0003-2672-9994 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Wildlife","active":true,"usgs":false}],"preferred":false,"id":783556,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Delahunty, David J","contributorId":221820,"corporation":false,"usgs":false,"family":"Delahunty","given":"David","email":"","middleInitial":"J","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":783557,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70208018,"text":"sir20205004 - 2020 - Stormwater quality of infrastructure elements in Rapid City, South Dakota, 2016–18","interactions":[],"lastModifiedDate":"2022-04-25T20:51:46.467441","indexId":"sir20205004","displayToPublicDate":"2020-02-20T12:18:20","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations 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As runoff flows over the land or impervious surfaces (paved streets, parking lots, and building roofs), it accumulates debris, chemicals, sediment, and other contaminants that can adversely affect water quality if the runoff discharge remains untreated. Pathogens, commonly measured using fecal indicator bacteria such as Escherichia coli, enterococci, or fecal coliform, are the most-frequent cause of water-quality impairment in rivers and streams in the United States. Rapid Creek originates in the western Black Hills area and flows east through Rapid City, South Dakota, to its mouth at the Cheyenne River. The water quality of Rapid Creek is important because the reach that flows through Rapid City is a valuable spawning area for a self-sustaining trout fishery, is actively used for recreation, and is a seasonal municipal water supply for the City of Rapid City. These uses (fishery, recreation, and water supply) are considered beneficial uses by the South Dakota Department of Environment and Natural Resources. Numerical criteria have been established for total suspended solids and Escherichia coli concentrations, among other water-quality constituents, for these beneficial uses. The objectives of this study were to improve the method by which fecal indicator bacteria and total suspended solids are quantified in the urban drainages within Rapid City and to provide information that helps identify origins of fecal indicator bacteria and total suspended solids. This information can be used in hydrologic models to estimate fecal indicator bacteria and total suspended solid loading from certain infrastructure elements in urban environments.

Stormwater samples analyzed for Escherichia coli, total suspended solids, specific conductance, and pH were collected in three drainage basin flowpaths within Rapid City: Jackson, Wildwood, and the Eco Prayer Park. Data-collection activities for this study focused on upgradient urban flowpath elements during rainfall events. This approach builds upon previous stormwater assessments that characterized the water quality in urban basin outlets near the downstream end of the stormwater flowpaths. Within each flowpath group, 4–6 sites were selected to represent the various infrastructure elements of the runoff process. These elements included roof downspouts, parking lots, street curbs and gutters, open channels, underground storm sewers, and stormwater ponds or best-management practice facilities.

In general, the concentrations of Escherichia coli and total suspended solids increased in the downstream direction for all flowpath sites. The wash-off process after the first flush is evident for total suspended solids and specific conductance; however, Escherichia coli concentrations did not necessarily follow the same pattern. Escherichia coli concentrations in the latter part of the runoff period were similar to or greater than the initial concentrations of the first set of samples. Stormwater-quality data were summarized by infrastructure type (roof downspout, parking lot, street curb, and channel/storm sewer) to provide information about approximate water-quality concentrations originating at the upper end of urban flowpaths. Escherichia coli and total suspended solid concentrations were lowest in samples collected from locations most isolated from human influence (roof downspouts); the median concentrations at these sites were 4 most probable number per 100 milliliters and 15 milligrams per liter, respectively. The delivery potential of fecal indicator bacteria and sediment from parking lots and street curbs was similar; median concentrations of Escherichia coli and total suspended solids were around 150–220 most probable number per 100 milliliters and 56–86 milligrams per liter, respectively. The downstream receiving channels and storm sewers where stormwater was aggregated typically contained the highest Escherichia coli concentrations (median was 1,800 most probable number per 100 milliliters), but the total suspended solid concentrations were similar to upstream elements in the flowpath (median was 69 milligrams per liter). The data collected from this study demonstrate that stormwater is contaminated with fecal indicator bacteria upon initial contact with impervious surfaces and highlight the importance of controlling the volume of stormwater discharges into receiving waterbodies via storage structures and pervious elements. Diluting stormwater with high concentrations of Escherichia coli with the receiving water’s (Rapid Creek) lower concentration of Escherichia coli is likely the primary mechanism for meeting the beneficial-use criterion threshold of 235 most probable number per 100 milliliters. Although total suspended solid concentrations in the upper parts of the basin (parking lots and street curbs) also begin at concentrations (56 to 86 milligrams per liter) above the beneficial-use criterion for Rapid Creek (53 milligrams per liter), current stormwater-control practices (storage ponds, swales, and wetlands) may be able to reduce suspended-sediment concentrations to meet this threshold.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205004","collaboration":"Prepared in cooperation with the City of Rapid City","usgsCitation":"Hoogestraat, G.K., 2020, Stormwater quality of infrastructure elements in Rapid City, South Dakota, 2016–18: U.S. Geological Survey Scientific Investigations Report 2020–5004, 24 p., https://doi.org/10.3133/sir20205004.","productDescription":"Report: vii, 24 p.; Appendix; Dataset","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-108184","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":399627,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109723.htm"},{"id":372437,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System database","linkHelpText":"– USGS water data for the Nation"},{"id":372436,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5004/sir20205004_appendix1.csv","text":"Appendix 1","size":"12.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5004 Appendix 1"},{"id":372434,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5004/coverthb.jpg"},{"id":372435,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5004/sir20205004.pdf","text":"Report","size":"3.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5004"}],"country":"United States","state":"South Dakota","city":"Rapid City","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.32,\n 44.0111\n ],\n [\n -103.1364,\n 44.0111\n ],\n [\n -103.1364,\n 44.125\n ],\n [\n -103.32,\n 44.125\n ],\n [\n -103.32,\n 44.0111\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-02-20","noUsgsAuthors":false,"publicationDate":"2020-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Hoogestraat, Galen K. 0000-0001-5360-3903 ghoogest@usgs.gov","orcid":"https://orcid.org/0000-0001-5360-3903","contributorId":167614,"corporation":false,"usgs":true,"family":"Hoogestraat","given":"Galen","email":"ghoogest@usgs.gov","middleInitial":"K.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":780163,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70208495,"text":"sir20195145 - 2020 - Hydrogeology and interactions of groundwater and surface water near Mill Creek and the Herring River, Wellfleet, Massachusetts, 2017–18","interactions":[],"lastModifiedDate":"2022-04-25T20:25:23.43755","indexId":"sir20195145","displayToPublicDate":"2020-02-20T12:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5145","displayTitle":"Hydrogeology and Interactions of Groundwater and Surface Water Near Mill Creek and the Herring River, Wellfleet, Massachusetts, 2017–18","title":"Hydrogeology and interactions of groundwater and surface water near Mill Creek and the Herring River, Wellfleet, Massachusetts, 2017–18","docAbstract":"

Groundwater levels and stream stage were monitored by the U.S. Geological Survey, in cooperation with the Friends of Herring River, at 19 sites in the Mill Creek Basin, a tributary of the Herring River in Wellfleet, Massachusetts, on outer Cape Cod, to provide baseline data prior to a proposed restoration of tidal flow to the Herring River estuary at the Cape Cod National Seashore. Tidal flow in the Herring River has been restricted by a tide-control structure since 1909. Baseline data are necessary to understand current conditions and provide information on water levels for comparison to future water levels under the proposed Herring River restoration, which includes restoration of salt marshes by enhancing tidal flow to the Herring River and construction of a tide-control structure on Mill Creek to prevent the flooding of upstream private properties, including a golf course.

Analysis of data collected during monitoring-well installation at eight locations on or near the golf course and Mill Creek, along with analysis of existing information, determined that parts of the study area are underlain by salt marsh deposits up to 18 feet (ft) thick. These marsh deposits are directly underlain by estuarine sediments, and adjacent upland areas are underlain by medium to very coarse sand. The freshwater lens on the golf course is 70 ft thick or more.

Groundwater levels at individual wells in the study area fluctuated by 1.3 to 2.6 ft during the study period (June 1, 2017, to June 14, 2018). Total precipitation during this period was 60.8 inches, about 10 inches greater than the long-term (2000–17) annual average (50.3 inches). Groundwater levels on Cape Cod generally were normal to above normal during the study owing to the higher than normal precipitation. Tidal amplitudes of groundwater levels caused by daily fluctuations at nearby tidal waterbodies (M2 tidal harmonic) were as large as 0.12 ft at a well 105 ft from the tidally restricted Herring River and as large as 0.06 ft at a well 575 ft from Wellfleet Harbor. Tidal fluctuations in groundwater levels were generally limited to areas about 1,500 ft from the nearest tidal waterbody. Under the initial proposed restoration, where mean tides would be maintained similar to current conditions, tidal fluctuations would be restored to parts of Mill Creek, and subsequent tidal fluctuations in groundwater levels could increase at some of the areas closest to the proposed tide-control structure, but the fluctuations would be less than about 0.06 ft in magnitude.

Regression models were used to describe the variability of daily mean tidally filtered groundwater levels and daily maximum stream stage in Mill Creek. Significant independent variables for the groundwater-level model included daily tidally filtered Wellfleet Harbor stage with a lag time of zero to 2 days, 7-day precipitation, the growing degree days (50 degrees Fahrenheit), and the quartile of groundwater levels relative to a long period of record at a nearby observation well.

Significant independent variables to predict the Mill Creek stage included daily mean groundwater levels in nearby wells, 7-day precipitation, growing degree days (50 degrees Fahrenheit), and a binary indicator of either a flooded or nonflooded condition on the golf course near Mill Creek. Flooding in Mill Creek occurred primarily when groundwater levels at nearby wells reached certain thresholds, when the precipitation in the preceding 7 days was at least 0.92–1.04 inches, and during the nongrowing season.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195145","collaboration":"Prepared in cooperation with the Friends of Herring River","usgsCitation":"Mullaney, J.R., Barclay, J.R., Laabs, K.L., and Lavallee, K.D., 2020, Hydrogeology and interactions of groundwater and surface water near Mill Creek and the Herring River, Wellfleet, Massachusetts, 2017–18: U.S. Geological Survey Scientific Investigations Report 2019–5145, 60 p., https://doi.org/10.3133/sir20195145.","productDescription":"Report: viii, 60 p.; Data Release; Project Site","numberOfPages":"72","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103306","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":437103,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P903HI9K","text":"USGS data release","linkHelpText":"Data on Models to Describe Groundwater Levels and Stream Stage near the Herring River, Wellfleet, Cape Cod, Massachusetts, 2017-2022"},{"id":399619,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109683.htm"},{"id":372270,"rank":4,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/centers/new-england-water/science/groundwater-and-surface-water-monitoring-mill-creek-watershed","text":"Project site","linkHelpText":"- Groundwater and Surface-Water Monitoring in the Mill Creek Watershed, Wellfleet and Truro, Massachusetts"},{"id":372269,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T167II","text":"USGS data release","linkHelpText":"Data on Tidally Filtered Groundwater and Estuary Water Levels, and Climatological Data Near Mill Creek and the Herring River, Cape Cod, Wellfleet, Massachusetts, 2017–2018"},{"id":372451,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5145/sir20195145.pdf","text":"Report","size":"6.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5145"},{"id":372267,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5145/coverthb2.jpg"}],"country":"United States","state":"Massachusetts","county":"Barnstable County","city":"Wellfleet","otherGeospatial":"Mill Creek, Herring River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.06719589233398,\n 41.92412111618309\n ],\n [\n -70.04968643188475,\n 41.92412111618309\n ],\n [\n -70.04968643188475,\n 41.9377858285046\n ],\n [\n -70.06719589233398,\n 41.9377858285046\n ],\n [\n -70.06719589233398,\n 41.92412111618309\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, New Hampshire 03275

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2020-02-12","noUsgsAuthors":false,"publicationDate":"2020-02-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Mullaney, John R. 0000-0003-4936-5046 jmullane@usgs.gov","orcid":"https://orcid.org/0000-0003-4936-5046","contributorId":1957,"corporation":false,"usgs":true,"family":"Mullaney","given":"John","email":"jmullane@usgs.gov","middleInitial":"R.","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782150,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barclay, Janet R. 0000-0003-1643-6901 jbarclay@usgs.gov","orcid":"https://orcid.org/0000-0003-1643-6901","contributorId":222437,"corporation":false,"usgs":true,"family":"Barclay","given":"Janet","email":"jbarclay@usgs.gov","middleInitial":"R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782151,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Laabs, Kaitlin L. 0000-0002-7798-3485 klaabs@usgs.gov","orcid":"https://orcid.org/0000-0002-7798-3485","contributorId":222438,"corporation":false,"usgs":true,"family":"Laabs","given":"Kaitlin","email":"klaabs@usgs.gov","middleInitial":"L.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782152,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lavallee, Katherine D. 0000-0003-0747-9344","orcid":"https://orcid.org/0000-0003-0747-9344","contributorId":222439,"corporation":false,"usgs":false,"family":"Lavallee","given":"Katherine","email":"","middleInitial":"D.","affiliations":[],"preferred":true,"id":782153,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70209599,"text":"70209599 - 2020 - Training data selection for annual land cover classification for the LCMAP initiative","interactions":[],"lastModifiedDate":"2020-04-15T11:55:08.292617","indexId":"70209599","displayToPublicDate":"2020-02-20T06:53:08","publicationYear":"2020","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":"Training data selection for annual land cover classification for the LCMAP initiative","docAbstract":"The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative characterizes changes in land cover, use, and condition with the goal of producing land change information that improves understanding of the earth system and provides insight into the impacts of land change on society. For LCMAP, all available high-quality data from the Landsat archive is used in a time series approach to detect land surface change. Annual thematic land cover maps are produced by classifying time series models. In this paper, we describe optimization of the classification method used to derive the thematic land cover product. We investigated the influences of auxiliary data, sample size, and training from different sources such as the U.S. Geological Survey’s Land Cover Trends project and National Land Cover Database (NLCD 2001 and NLCD 2011). Results were evaluated and validated based on independent data from the training dataset. We found that refining auxiliary data effectively reduced artifacts in the thematic land cover map that are related to data availability (i.e., SLC-off). The classification accuracy and stability were improved considerably by using a total of 20 million training pixels with a minimum of 600,000 and a maximum of 8 million training pixels per class. Finally, the NLCD 2001 training data delivered the best classification accuracy. Comparing to the original LCMAP classification strategy (Trends training data, 20,000 samples), the optimized classification strategy considerably improved the annual land cover map accuracy.","language":"English","publisher":"MDPI","doi":"10.3390/rs12040699","collaboration":"","usgsCitation":"Zhou, Q., Tollerud, H.J., Barber, C., Smith, K., and Zelenak, D.J., 2020, Training data selection for annual land cover classification for the LCMAP initiative: Remote Sensing, v. 12, no. 4, 699, 16 p., https://doi.org/10.3390/rs12040699.","productDescription":"699, 16 p.","ipdsId":"IP-114747","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457658,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12040699","text":"Publisher Index Page"},{"id":374001,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":223103,"corporation":false,"usgs":true,"family":"Zhou","given":"Qiang","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":787081,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tollerud, Heather J. 0000-0001-9507-4456","orcid":"https://orcid.org/0000-0001-9507-4456","contributorId":210820,"corporation":false,"usgs":true,"family":"Tollerud","given":"Heather","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":787082,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barber, Christopher P. 0000-0003-0570-1140","orcid":"https://orcid.org/0000-0003-0570-1140","contributorId":223102,"corporation":false,"usgs":true,"family":"Barber","given":"Christopher","middleInitial":"P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":787083,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Kelcy 0000-0001-6811-1485 kelcy.smith.ctr@usgs.gov","orcid":"https://orcid.org/0000-0001-6811-1485","contributorId":176844,"corporation":false,"usgs":true,"family":"Smith","given":"Kelcy","email":"kelcy.smith.ctr@usgs.gov","affiliations":[],"preferred":false,"id":787084,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zelenak, Daniel J. 0000-0003-3457-0960","orcid":"https://orcid.org/0000-0003-3457-0960","contributorId":224118,"corporation":false,"usgs":true,"family":"Zelenak","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":787085,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208797,"text":"70208797 - 2020 - Monitoring chemical contaminants in the Gulf of Maine, using sediments and mussels (Mytilus edulis): An evaluation","interactions":[],"lastModifiedDate":"2020-03-02T06:45:08","indexId":"70208797","displayToPublicDate":"2020-02-20T06:43:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2676,"text":"Marine Pollution Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring chemical contaminants in the Gulf of Maine, using sediments and mussels (Mytilus edulis): An evaluation","docAbstract":"The objective of this paper is to determine whether contaminant data on mussels and sediments can be used interchangeably, or not, when assessing the degree of anthropogenic contamination of a water body. To obtain adequate coverage of the entire Gulf of Maine, Bay of Fundy sediment samples were collected, analyzed and combined with similar data from four coastal monitoring programs. This required careful interpretation but provided robust results consistent with published literature. A strong correspondence was found between sediment\nand mussel concentrations for polycyclic aromatic hydrocarbons, moderate to weak correspondence for polychlorinated biphenyls, and except for mercury and zinc, little to no correspondence was found for metals. We conclude that mussel contaminant data are likely sufficient for providing information on the spatial and temporal distribution of chemical contaminants, in coastal waters, under a broad range of environmental conditions\nand contaminant levels, and unlike sediments, provide direct information on contaminant bioavailability.","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpolbul.2020.110956","usgsCitation":"Elskus, A., LeBlanc, L., Latimer, J.S., Page, D., Harding, G., and Wells, P.G., 2020, Monitoring chemical contaminants in the Gulf of Maine, using sediments and mussels (Mytilus edulis): An evaluation: Marine Pollution Bulletin, v. 153, 110956, 9 p., https://doi.org/10.1016/j.marpolbul.2020.110956.","productDescription":"110956, 9 p.","ipdsId":"IP-110220","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":457663,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10775826","text":"External Repository"},{"id":372756,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Gulf of Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -65.390625,\n 43.34116005412307\n ],\n [\n -65.830078125,\n 43.99281450048989\n ],\n [\n -64.51171875,\n 44.94924926661153\n ],\n [\n -63.984375,\n 45.460130637921004\n ],\n [\n -64.86328125,\n 45.79816953017265\n ],\n [\n -67.87353515625,\n 45.506346901083425\n ],\n [\n -70.24658203125,\n 44.22945656830167\n ],\n [\n -71.4111328125,\n 42.90816007196054\n ],\n [\n -71.34521484375,\n 41.902277040963696\n ],\n [\n -70.46630859375,\n 41.45919537950706\n ],\n [\n -68.115234375,\n 42.4234565179383\n ],\n [\n -65.390625,\n 43.34116005412307\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"153","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Elskus, Adria 0000-0003-1192-5124 aelskus@usgs.gov","orcid":"https://orcid.org/0000-0003-1192-5124","contributorId":130,"corporation":false,"usgs":true,"family":"Elskus","given":"Adria","email":"aelskus@usgs.gov","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":783422,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"LeBlanc, Lawrence A","contributorId":222882,"corporation":false,"usgs":false,"family":"LeBlanc","given":"Lawrence A","affiliations":[{"id":40617,"text":"Lawrence LeBlanc Consulting","active":true,"usgs":false}],"preferred":false,"id":783423,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Latimer, James S","contributorId":222883,"corporation":false,"usgs":false,"family":"Latimer","given":"James","email":"","middleInitial":"S","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":783424,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Page, David","contributorId":222884,"corporation":false,"usgs":false,"family":"Page","given":"David","email":"","affiliations":[{"id":33315,"text":"Bowdoin College","active":true,"usgs":false}],"preferred":false,"id":783425,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harding, Gareth","contributorId":222885,"corporation":false,"usgs":false,"family":"Harding","given":"Gareth","email":"","affiliations":[{"id":40618,"text":"Fisheries & Oceans, Bedford Institute of Oceanography","active":true,"usgs":false}],"preferred":false,"id":783426,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wells, Peter G","contributorId":222886,"corporation":false,"usgs":false,"family":"Wells","given":"Peter","email":"","middleInitial":"G","affiliations":[{"id":40619,"text":"International Ocean Institute Canada, Dalhousie University","active":true,"usgs":false}],"preferred":false,"id":783427,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208767,"text":"70208767 - 2020 - Black oystercatcher (Haematopus bachmani) population size, use of marine reserve complexes, and spatial distribution in Oregon","interactions":[],"lastModifiedDate":"2020-02-28T08:44:29","indexId":"70208767","displayToPublicDate":"2020-02-19T08:44:22","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2901,"text":"Northwestern Naturalist","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Black oystercatcher (Haematopus bachmani) population size, use of marine reserve complexes, and spatial distribution in Oregon","title":"Black oystercatcher (Haematopus bachmani) population size, use of marine reserve complexes, and spatial distribution in Oregon","docAbstract":"The Black Oystercatcher is a large shorebird found along the west coast of North America. Because of their small global population size, low reproductive rate, and dependence on rocky intertidal habitats, they are considered a “species of high conservation concern” and may act as an indicator of intertidal ecosystem health. In 2015, the Audubon Society of Portland initiated a 3-year shore-based population survey in Oregon building upon long-term monitoring previously conducted by the U.S. Geological Survey (USGS) and others. The objectives were to 1) Estimate the current minimum population of breeding Black Oystercatchers in Oregon and to compare that to previous estimates to better understand the population trend; 2) Document oystercatcher abundance adjacent to the Oregon’s system of Marine Reserves and Marine Protected Areas; and 3) Describe spatial distribution of breeding oystercatchers along the coast. We targeted all rocky shoreline habitats along Oregon’s coastline to perform abundance surveys each spring. A total of 75 survey routes were sampled using a standardized protocol. Trained volunteer community scientists conducted the majority of the surveys. We used N-mixture statistical models to estimate oystercatcher population size and probability of detection. Population estimates from the best fitting models were consistent, with estimates ranging from 506 oystercatchers in 2016 (95% credible interval, 463-560) to 629 (548-743) in 2015. These estimates indicated a small but stable population. Probability of detection remained consistent across years (ranging from 0.51 to 0.53). The effect of geographic region corresponded with greater bird density in the southern region of Oregon. Oystercatcher abundance adjacent to MR/MPAs accounted for between 12.4-18.3% of the total population estimate which was lower than expected (~25%). We recommend that subsequent conservation efforts directed on Black Oystercatchers in Oregon balance limiting human disturbance, particularly on the north and central coasts, with ensuring protection of core habitats on the south coast where much of the population resides.","language":"English","publisher":"BioONE","doi":"10.1898/1051-1733-101.1.14","usgsCitation":"Liebezeit, J., O’Connor, A., Lyons, J., Shannon, C., Stephensen, S., and Elliott-Smith, E., 2020, Black oystercatcher (Haematopus bachmani) population size, use of marine reserve complexes, and spatial distribution in Oregon: Northwestern Naturalist, v. 101, no. 1, p. 14-26, https://doi.org/10.1898/1051-1733-101.1.14.","productDescription":"13 p.","startPage":"14","endPage":"26","ipdsId":"IP-107204","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":372725,"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 -124.8046875,\n 41.95949009892467\n ],\n [\n -123.04687499999999,\n 41.95949009892467\n ],\n [\n -123.04687499999999,\n 46.20264638061019\n ],\n [\n -124.8046875,\n 46.20264638061019\n ],\n [\n -124.8046875,\n 41.95949009892467\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"101","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liebezeit, Joe","contributorId":216263,"corporation":false,"usgs":false,"family":"Liebezeit","given":"Joe","email":"","affiliations":[{"id":36680,"text":"Audubon Society of Portland","active":true,"usgs":false}],"preferred":false,"id":783328,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Connor, Amelia","contributorId":222845,"corporation":false,"usgs":false,"family":"O’Connor","given":"Amelia","email":"","affiliations":[{"id":40610,"text":"Otter Rock, OR","active":true,"usgs":false}],"preferred":false,"id":783329,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":210574,"corporation":false,"usgs":true,"family":"Lyons","given":"James E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":783327,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shannon, Courtney","contributorId":222846,"corporation":false,"usgs":false,"family":"Shannon","given":"Courtney","email":"","affiliations":[{"id":36680,"text":"Audubon Society of Portland","active":true,"usgs":false}],"preferred":false,"id":783330,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stephensen, Shawn","contributorId":222847,"corporation":false,"usgs":false,"family":"Stephensen","given":"Shawn","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":783331,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Elliott-Smith, Elise 0000-0003-1399-0093 eelliott-smith@usgs.gov","orcid":"https://orcid.org/0000-0003-1399-0093","contributorId":222848,"corporation":false,"usgs":true,"family":"Elliott-Smith","given":"Elise","email":"eelliott-smith@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":783332,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209452,"text":"70209452 - 2020 - An important biogeochemical link between organic and inorganic carbon cycling: Effects of organic alkalinity on carbonate chemistry in coastal waters influenced by intertidal salt marshes","interactions":[],"lastModifiedDate":"2020-04-08T12:09:23.162203","indexId":"70209452","displayToPublicDate":"2020-02-19T07:04:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"An important biogeochemical link between organic and inorganic carbon cycling: Effects of organic alkalinity on carbonate chemistry in coastal waters influenced by intertidal salt marshes","docAbstract":"Organic acid charge groups in dissolved organic carbon (DOC) contribute to total alkalinity (TA), i.e. organic alkalinity (OrgAlk). Its effect is often ignored or treated as a calculation uncertainty in many aquatic CO2 studies. This study evaluated the variability, sources, and characteristics of OrgAlk in estuarine waters exchanged tidally with a groundwater-influenced salt marsh in the northeast USA. Importantly, OrgAlk was found to serve as a biogeochemical medium linking organic and inorganic carbon cycling through its effects on pH, CO2 system speciation, and buffering capacity (H = -(∂pH/∂[H+])-1). Both the concentrations and characteristics of the identified organic acid charge groups, as well as water pH, influenced the magnitude and sign of the OrgAlk effects. The two main charge groups identified include carboxylic and phenolic or amine groups, with concentrations and pK values varying across tides and seasons. OrgAlk and DOC in the tidal creek were highly variable over tidal and seasonal cycles, and may be sourced from both terrestrial groundwater and in situ production in salt marsh sediments. OrgAlk seems to be more preserved over DOC in groundwater, although DOC and OrgAlk largely covaried in marsh tidal water, but with variable OrgAlk:DOC ratios. This highlights the insufficiency of using a fixed proportion of DOC to account for organic acid charge groups. OrgAlk was found to affect H+ concentrations by ~ 1 – 40 nmol kg-1 (equivalent to a pH change of ~ 0.03 – 0.26), pCO2 by ~ 30 – 1590 atm and buffering capacity by ~ 0.00 – 0.14 mmol kg-1 at relative OrgAlk contributions of 0.9 – 4.3% of TA observed in the marsh-influenced tidal water. Thus OrgAlk may have a significant influence on coastal inorganic carbon cycling. Further theoretical calculations confirm that these concentrations of OrgAlk would have sizable impacts on both carbonate speciation and, ultimately, air-sea CO2 fluxes in different coastal environments, ranging from estuarine to shelf waters. A new conceptual model linking organic and inorganic carbon cycling for coastal waters is proposed to highlight the sources and sinks of organic acid charge groups, as well as their biogeochemical behaviors and mechanistic control on the CO2 system.","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2020.02.013","collaboration":"","usgsCitation":"Song, S., Wang, Z., Gonneea Eagle, M., Kroeger, K.D., Chu, S.N., Li, D., and Liang, H., 2020, An important biogeochemical link between organic and inorganic carbon cycling: Effects of organic alkalinity on carbonate chemistry in coastal waters influenced by intertidal salt marshes: Geochimica et Cosmochimica Acta, v. 275, p. 123-139, https://doi.org/10.1016/j.gca.2020.02.013.","productDescription":"17 p.","startPage":"123","endPage":"139","ipdsId":"IP-111625","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":457676,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gca.2020.02.013","text":"Publisher Index Page"},{"id":373830,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"275","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Song, Shuzhen","contributorId":223876,"corporation":false,"usgs":false,"family":"Song","given":"Shuzhen","email":"","affiliations":[{"id":40785,"text":"State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China","active":true,"usgs":false}],"preferred":false,"id":786528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wang, Zhaohui Aleck","contributorId":174589,"corporation":false,"usgs":false,"family":"Wang","given":"Zhaohui Aleck","affiliations":[{"id":13627,"text":"Woods Hole Oceanographic Institution, Woods Hole, MA","active":true,"usgs":false}],"preferred":false,"id":786529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gonneea Eagle, Meagan 0000-0001-5072-2755 mgonneea@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":174590,"corporation":false,"usgs":true,"family":"Gonneea Eagle","given":"Meagan","email":"mgonneea@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":786530,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":786531,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chu, Sophie N.","contributorId":174603,"corporation":false,"usgs":false,"family":"Chu","given":"Sophie","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":786532,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Li, Daoji","contributorId":223877,"corporation":false,"usgs":false,"family":"Li","given":"Daoji","email":"","affiliations":[{"id":40785,"text":"State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China","active":true,"usgs":false}],"preferred":false,"id":786533,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Liang, Haorui","contributorId":223878,"corporation":false,"usgs":false,"family":"Liang","given":"Haorui","email":"","affiliations":[{"id":40786,"text":"College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao, Shandong 266100, China","active":true,"usgs":false}],"preferred":false,"id":786534,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70249282,"text":"70249282 - 2020 - Local earthquake Vp and Vs tomography in the Mount St. Helens region with the iMUSH broadband array","interactions":[],"lastModifiedDate":"2023-10-03T12:03:57.128024","indexId":"70249282","displayToPublicDate":"2020-02-19T06:59:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Local earthquake Vp and Vs tomography in the Mount St. Helens region with the iMUSH broadband array","docAbstract":"

We present new 3-D P wave and S wave velocity models of the upper 20 km of the Mount St. Helens (MSH) region. These were obtained using local-source arrival time tomography from earthquakes and explosions recorded at 70 broadband stations deployed as part of the imaging Magma Under St. Helens (iMUSH) project and augmented by several data sets. Principal features of our models include (1) low P wave and S wave velocities along the St. Helens seismic zone to depths of at least 20 km corresponding to high conductivity imaged by iMUSH magnetotelluric studies. This delineates a zone of weakness that magma can exploit at the location of MSH; (2) a 5- to 7-km diameter, 6–15 km deep, 3–6% negative P wave and S wave velocity anomaly beneath MSH, consistent with previous estimates of the source region for recent eruptions. We interpret this as a magma storage region containing up to 15–20 km3 of partial melt, which is about 5 times more than the largest documented eruption at MSH; (3) a broad region of low P wave velocity below 10-km depth extending between Mount Adams and Mount Rainier along and to the east of the main Cascade arc, which is likely due to high-temperature arc crust and possible presence of fluids or melt; (4) several anomalies associated with surface-mapped features, including high-velocity igneous units such as the Spud Mountain and Spirit Lake plutons and low velocities in the Chehalis sedimentary basin and the Indian Heaven volcanic field. Our results place further constraints on the geometry of these features at depth.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GC008888","usgsCitation":"Ulberg, C.W., Creager, K.C., Moran, S.C., Abers, G.A., Thelen, W., Levander, A., Kiser, E., Schmandt, B., Hansen, S.M., and Crosson, R., 2020, Local earthquake Vp and Vs tomography in the Mount St. Helens region with the iMUSH broadband array: Geochemistry, Geophysics, Geosystems, v. 21, no. 3, e2019GC008888, 19 p., https://doi.org/10.1029/2019GC008888.","productDescription":"e2019GC008888, 19 p.","ipdsId":"IP-109540","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":499956,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/10e92d8761c546c0a2933c5b382ad0b0","text":"External Repository"},{"id":421529,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount St. Helens","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.44905655105634,\n 46.36626254146168\n ],\n [\n -122.44905655105634,\n 46.044679655934544\n ],\n [\n -121.915601723516,\n 46.044679655934544\n ],\n [\n -121.915601723516,\n 46.36626254146168\n ],\n [\n -122.44905655105634,\n 46.36626254146168\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"21","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Ulberg, Carl W 0000-0001-6198-809X","orcid":"https://orcid.org/0000-0001-6198-809X","contributorId":221909,"corporation":false,"usgs":false,"family":"Ulberg","given":"Carl","email":"","middleInitial":"W","affiliations":[{"id":6934,"text":"University of 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Eric","contributorId":140928,"corporation":false,"usgs":false,"family":"Kiser","given":"Eric","email":"","affiliations":[{"id":13619,"text":"Department of Earth & Planetary Sciences, Harvard University, Cambridge, MA","active":true,"usgs":false}],"preferred":false,"id":884986,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schmandt, Brandon","contributorId":202750,"corporation":false,"usgs":false,"family":"Schmandt","given":"Brandon","email":"","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":884987,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hansen, Steven M.","contributorId":202751,"corporation":false,"usgs":false,"family":"Hansen","given":"Steven","email":"","middleInitial":"M.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":884988,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Crosson, Robert 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equilibrium of a high-SiO2, andesite at fO2 = RRO: Implications for Augustine volcano and other high-fO2 arc andesites","docAbstract":"

Understanding the impact of magmatic plumbing systems on explosive volcanic activity is important for hazard management. This study describes phase equilibria experiments using a high-silica andesite (HSA; SiO2 = 62.5 wt%) from the 2006 eruption of Augustine Volcano, Alaska. Experiments were conducted under H2O saturated conditions, f02\">f02f02 = RRO (Re–ReO2 or ~ Ni–NiO + 2), at pressures of 50–200 MPa (PTotal = PH2O), and at temperatures of 800–1060 °C. Run durations varied from 23 to 539 h, inversely scaled with temperature. The natural Augustine HSA phase assemblage (plagioclase, two pyroxenes, Fe–Ti oxides, magnesio-hornblende) was reproduced at 860–880 °C and 120–200 MPa. Comparing experimental and natural glass and plagioclase compositions further refined those conditions to ~ 870 °C and 120–170 MPa. Crystallization of euhedral quartz was accompanied by biotite and small amounts of cummingtonite at T ≤ 850 °C. The relatively high temperature appearance of these typically low-T phases indicates that higher f02\">f02f02 could enhance their stability. The storage conditions estimated from our experiments compare well with previous magma plumbing system models for Augustine from geophysical and petrological data. The refined experimental pressure range suggests a storage depth of 4.6–6.6 km, assuming a crustal density of 2650 kg/m3. The strong petrological and geochemical similarities between the products of the 2006, 1986, and 1976 eruptions suggest that the Augustine magmatic system had generally consistent crystallization conditions for the HSA lithology during that > 30-year time interval. The experimental results broad implications for understanding higher f02\">f02 magmas at andesitic arc volcanoes.

","language":"English","publisher":"Springer","doi":"10.1007/s00410-020-1663-6","usgsCitation":"De Angelis, S.H., Larsen, J.F., Coombs, M.L., Utley, J.E., and Dunn, A.P., 2020, Phase equilibrium of a high-SiO2, andesite at fO2 = RRO: Implications for Augustine volcano and other high-fO2 arc andesites: Contributions to Mineralogy and Petrology, v. 175, 24, 20 p., https://doi.org/10.1007/s00410-020-1663-6.","productDescription":"24, 20 p.","ipdsId":"IP-081173","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":379543,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Augustine Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.445556640625,\n 58.50517468678928\n ],\n [\n -150.919189453125,\n 58.50517468678928\n ],\n [\n -150.919189453125,\n 61.70549883819642\n ],\n [\n -155.445556640625,\n 61.70549883819642\n ],\n [\n -155.445556640625,\n 58.50517468678928\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"175","noUsgsAuthors":false,"publicationDate":"2020-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"De Angelis, Sarah H.","contributorId":243409,"corporation":false,"usgs":false,"family":"De Angelis","given":"Sarah","email":"","middleInitial":"H.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":802207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larsen, Jessica F.","contributorId":200930,"corporation":false,"usgs":false,"family":"Larsen","given":"Jessica","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":802208,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coombs, Michelle L. 0000-0002-6002-6806 mcoombs@usgs.gov","orcid":"https://orcid.org/0000-0002-6002-6806","contributorId":2809,"corporation":false,"usgs":true,"family":"Coombs","given":"Michelle","email":"mcoombs@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":802209,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Utley, James E. P.","contributorId":243410,"corporation":false,"usgs":false,"family":"Utley","given":"James","email":"","middleInitial":"E. P.","affiliations":[{"id":16977,"text":"University of Liverpool","active":true,"usgs":false}],"preferred":false,"id":802210,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dunn, Andrew P.","contributorId":238780,"corporation":false,"usgs":false,"family":"Dunn","given":"Andrew","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":802211,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209858,"text":"70209858 - 2020 - Structural control on megathrust rupture and slip behavior: Insights from the 2016 Mw 7.8 Pedernales Ecuador earthquake","interactions":[],"lastModifiedDate":"2020-05-01T12:23:04.416756","indexId":"70209858","displayToPublicDate":"2020-02-17T07:17:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Structural control on megathrust rupture and slip behavior: Insights from the 2016 Mw 7.8 Pedernales Ecuador earthquake","docAbstract":"The heterogeneous seafloor topography of the Nazca Plate as it enters the Ecuador subduction zone provides an opportunity to document the influence of seafloor roughness on slip behavior and megathrust rupture. The 2016 Mw 7.8 Pedernales Ecuador earthquake was followed by a rich and active postseismic sequence. An internationally coordinated rapid response effort installed a temporary seismic network to densify coastal stations of the permanent Ecuadorian national seismic network. A combination of 82 onshore short and intermediate period and broadband seismic stations and six ocean bottom seismometers recorded the postseismic Pedernales sequence for over a year after the mainshock. A robust earthquake catalog combined with calibrated relocations for a subset of magnitude ≥4 earthquakes shows pronounced spatial and temporal clustering. A range of slip behavior accommodates postseismic deformation including earthquakes, slow slip events, and earthquake swarms. Models of plate coupling and the consistency of earthquake clustering and slip behavior through multiple seismic cycles reveal a segmented subduction zone primarily controlled by subducted seafloor topography, accreted terranes, and inherited structure. The 2016 Pedernales mainshock triggered moderate to strong earthquakes (5 ≤ M ≤ 7) and earthquake swarms north of the mainshock rupture close to the epicenter of the 1906 Mw 8.8 earthquake and in the segment of the subduction zone that ruptured in 1958 in a Mw 7.7 earthquake.","language":"English","publisher":"AGU","doi":"10.1029/2019JB018001","collaboration":"","usgsCitation":"Soto-Cordero, L., Meltzer, A., Bergman, E.A., Hoskins, M., Stachnik, J.C., Agurto-Detzel, H., Alvarado, A., Beck, S.L., Charvis, P., Font, Y., Hayes, G.P., Hernandez, S., Leon-Rios, S., Lynner, C., Nocquet, J., Regnier, M., Rietbrock, A., Rolandone, F., and Ruiz, M., 2020, Structural control on megathrust rupture and slip behavior: Insights from the 2016 Mw 7.8 Pedernales Ecuador earthquake: Journal of Geophysical Research: Earth Surface, v. 125, no. 2, e2019JB018001, 28 p., https://doi.org/10.1029/2019JB018001.","productDescription":"e2019JB018001, 28 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novel method to capture rails","docAbstract":"

Avian research may require investigators to capture birds for many reasons, including to collect measurements and attach leg bands or transmitters. The effectiveness and efficiency of capture techniques varies by species, habitat, and time of year. Rails are particularly challenging to capture because of their secretive behavior and the dense vegetation they inhabit. As such, basic natural history questions for many rail species remain unanswered. We paired audio lures with modified noose carpets to capture and study 69 Yuma Ridgway's rails (Rallus obsoletus yumanensis) in the southwestern United States during 2016–2018. We compared results with other more commonly used capture methods, and our results show that noose carpets paired with audio lures can be an effective tool to capture rails, thereby facilitating studies of their ecology and life history. Noose carpets are easy to use, cheap to build and maintain, and effective over a wide range of conditions. This method could be used to capture rails other than the Yuma Ridgway's rail by adjusting the noose size, noose line weight, and audio lures to match the target species. Published 2020. This article is a U.S. Government work and is in the public domain in the USA.

","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1068","usgsCitation":"Harrity, E.J., and Conway, C.J., 2020, Noose carpets: A novel method to capture rails: Wildlife Society Bulletin, v. 44, no. 1, p. 15-22, https://doi.org/10.1002/wsb.1068.","productDescription":"8 p.","startPage":"15","endPage":"22","ipdsId":"IP-106237","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":499994,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/a613d83b56e84915afbeb5e5fa63c7cf","text":"External Repository"},{"id":392429,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.6640625,\n 32.713355353177555\n ],\n [\n -113.37890625,\n 32.713355353177555\n ],\n [\n -113.37890625,\n 35.10193405724606\n ],\n [\n -115.6640625,\n 35.10193405724606\n ],\n [\n -115.6640625,\n 32.713355353177555\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Harrity, Eamon J.","contributorId":264532,"corporation":false,"usgs":false,"family":"Harrity","given":"Eamon","email":"","middleInitial":"J.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":827640,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":827639,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}