Sedimentary provenance studies have come to be overwhelmingly based upon U–Pb geochronologic measurements performed with detrital zircon while alternative and potentially complementary approaches such as conglomerate clast studies and heavy mineral analysis have faded in importance. Measurement of Pb isotopic compositions in detrital K-feldspar is among the under-utilized approaches available to ascertain sedimentary source regions. While it has been long recognized that common Pb isotope compositions recorded by K-feldspar vary widely and reflect the crustal provinces from which the host basement rocks crystallized, use of the approach has suffered due to a lack of appropriate statistical models and ground truth compositional data from source regions. In this paper, we: (1) present high-throughput LA-ICPMS analysis protocols needed to generate statistically meaningful detrital K-feldspar Pb isotope data sets; (2) develop an interpretative approach based upon 208Pb/206Pb vs. 207Pb/206Pb that incorporate information from the U- and Th-decay systems into one two-dimensional plot that is amenable to analysis using two-dimensional Kolmogorov–Smirnoff statistical tests; (3) generate new Pb isotopic data from basement rocks from southwestern North America to improve knowledge of the Pb isotopic properties of potential source regions; and (4) generate new Pb isotopic data from Lower Eocene to Lower Miocene sedimentary rocks to evaluate changes in drainage patterns that occurred in response to deformation that affected the southern California margin. Through this case study, we demonstrate how our new analytical and interpretative methods could be profitably applied to future geochemical and provenance studies and tectonically driven re-organization of drainage patterns.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2018.11.040","usgsCitation":"Shulaker, D.Z., Grove, M., Hourigan, J.K., Van Buer, N., Sharman, G.R., Howard, K.A., Miller, J., and Barth, A.P., 2019, Detrital K-feldspar Pb isotopic evaluation of extraregional sediment transported through an Eocene tectonic breach of southern California's Cretaceous batholith: Earth and Planetary Science Letters, v. 508, p. 4-17, https://doi.org/10.1016/j.epsl.2018.11.040.","productDescription":"14 p.","startPage":"4","endPage":"17","ipdsId":"IP-103612","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":362078,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"508","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shulaker, Danielle Ziva","contributorId":214181,"corporation":false,"usgs":false,"family":"Shulaker","given":"Danielle","email":"","middleInitial":"Ziva","affiliations":[{"id":38987,"text":"Stanford U.","active":true,"usgs":false}],"preferred":false,"id":759295,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grove, Marty","contributorId":211570,"corporation":false,"usgs":false,"family":"Grove","given":"Marty","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":759296,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hourigan, Jeremy K.","contributorId":99023,"corporation":false,"usgs":true,"family":"Hourigan","given":"Jeremy","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":759297,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Van Buer, Nicholas","contributorId":214183,"corporation":false,"usgs":false,"family":"Van Buer","given":"Nicholas","email":"","affiliations":[{"id":38988,"text":"Cal State Poly Pomona","active":true,"usgs":false}],"preferred":false,"id":759298,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sharman, Glenn R.","contributorId":196537,"corporation":false,"usgs":false,"family":"Sharman","given":"Glenn","email":"","middleInitial":"R.","affiliations":[{"id":34621,"text":"Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA","active":true,"usgs":false}],"preferred":false,"id":759299,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Howard, Keith A. 0000-0002-6462-2947 khoward@usgs.gov","orcid":"https://orcid.org/0000-0002-6462-2947","contributorId":3439,"corporation":false,"usgs":true,"family":"Howard","given":"Keith","email":"khoward@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":759294,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miller, Jonathan","contributorId":214184,"corporation":false,"usgs":false,"family":"Miller","given":"Jonathan","affiliations":[{"id":38989,"text":"San Jose State U.","active":true,"usgs":false}],"preferred":false,"id":759300,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barth, Andrew P.","contributorId":214136,"corporation":false,"usgs":false,"family":"Barth","given":"Andrew","email":"","middleInitial":"P.","affiliations":[{"id":38983,"text":"Indiana University - Purdue University","active":true,"usgs":false}],"preferred":false,"id":759301,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70202630,"text":"70202630 - 2019 - Modeling elk‐to‐livestock transmission risk to predict hotspots of brucellosis spillover","interactions":[],"lastModifiedDate":"2019-06-18T10:54:15","indexId":"70202630","displayToPublicDate":"2019-03-14T16:27:15","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Modeling elk‐to‐livestock transmission risk to predict hotspots of brucellosis spillover","docAbstract":"Wildlife reservoirs of infectious disease are a major source of human‐wildlife conflict because of the risk of potential spillover associated with commingling of wildlife and livestock. In the Greater Yellowstone Ecosystem, the presence of brucellosis (Brucella abortus) in free‐ranging elk (Cervus canadensis) populations is of significant management concern because of the risk of disease transmission from elk to livestock. We identified how spillover risk changes through space and time by developing resource selection functions using telemetry data from 223 female elk to predict the relative probability of female elk occurrence daily during the transmission risk period. We combined these spatiotemporal predictions with elk seroprevalence, demography, and transmission timing data to identify when and where abortions (the primary transmission route of brucellosis) were most likely to occur. Additionally, we integrated our predictions of transmission risk with spatiotemporal data on areas of potential livestock use to estimate the daily risk to livestock. We predicted that approximately half of the transmission risk occurred on areas where livestock may be present (i.e., private property or grazing allotments). Of the transmission risk that occurred in livestock areas, 98% of it was on private ranchlands as opposed to state or federal grazing allotments. Disease prevalence, transmission timing, host abundance, and host distribution were all important factors in determining the potential for spillover risk. Our fine‐resolution (250‐m spatial, 1‐day temporal), large‐scale (17,732 km2) predictions of potential elk‐to‐livestock transmission risk provide wildlife and livestock managers with a useful tool to identify higher risk areas in space and time and proactively focus actions in these areas to separate elk and livestock to reduce spillover risk.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21645","usgsCitation":"Rayl, N., Proffitt, K., Almberg, E.S., Jones, J.D., Merkle, J., Gude, J., and Cross, P.C., 2019, Modeling elk‐to‐livestock transmission risk to predict hotspots of brucellosis spillover: Journal of Wildlife Management, v. 83, no. 4, p. 817-829, https://doi.org/10.1002/jwmg.21645.","productDescription":"13 p.","startPage":"817","endPage":"829","ipdsId":"IP-100336","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":467814,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.21645","text":"Publisher Index Page"},{"id":362077,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","volume":"83","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Rayl, Nathaniel D.","contributorId":199082,"corporation":false,"usgs":false,"family":"Rayl","given":"Nathaniel D.","affiliations":[],"preferred":false,"id":759303,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Proffitt, Kelly 0000-0001-5528-3309","orcid":"https://orcid.org/0000-0001-5528-3309","contributorId":210093,"corporation":false,"usgs":false,"family":"Proffitt","given":"Kelly","email":"","affiliations":[{"id":38065,"text":"Montana Fish, Wildlife and Parks, Bozeman, Montana","active":true,"usgs":false}],"preferred":false,"id":759305,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Almberg, Emily S.","contributorId":207014,"corporation":false,"usgs":false,"family":"Almberg","given":"Emily","email":"","middleInitial":"S.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":759304,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Jennifer D.","contributorId":145754,"corporation":false,"usgs":false,"family":"Jones","given":"Jennifer","email":"","middleInitial":"D.","affiliations":[{"id":16227,"text":"Institute on Ecosystems,Montana State University MT, 59715 USA","active":true,"usgs":false}],"preferred":false,"id":759308,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Merkle, Jerod","contributorId":172972,"corporation":false,"usgs":false,"family":"Merkle","given":"Jerod","affiliations":[{"id":35288,"text":"Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":759306,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gude, Justin A.","contributorId":210094,"corporation":false,"usgs":false,"family":"Gude","given":"Justin A.","affiliations":[{"id":38066,"text":"Montana Fish, Wildlife and Parks,","active":true,"usgs":false}],"preferred":false,"id":759307,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cross, Paul C. 0000-0001-8045-5213 pcross@usgs.gov","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":2709,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","email":"pcross@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":759302,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202298,"text":"sir20195003 - 2019 - Climate, streamflow, and lake-level trends in the Great Lakes Basin of the United States and Canada, water years 1960–2015","interactions":[],"lastModifiedDate":"2019-03-15T16:14:59","indexId":"sir20195003","displayToPublicDate":"2019-03-14T16:15:18","publicationYear":"2019","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":"2019-5003","displayTitle":"Climate, Streamflow, and Lake-Level Trends in the Great Lakes Basin of the United States and Canada, Water Years 1960–2015","title":"Climate, streamflow, and lake-level trends in the Great Lakes Basin of the United States and Canada, water years 1960–2015","docAbstract":"Water levels in the Great Lakes fluctuate substantially because of complex interactions among inputs (precipitation and streamflow), outputs (evaporation and outflow), and other factors. This report by the U.S. Geological Survey in cooperation with the Great Lakes Restoration Initiative was completed to describe trends in climate, streamflow, lake levels, and major water-budget components within the Great Lakes Basin for water years (WYs) 1960–2015 (study period). Resulting trends are applicable only to the study period and should not be considered indicative of longer-term trends.
Analyses of climate trends used monthly data from the Parameter-elevation Regressions on Independent Slopes Model, which are available only for the United States. Trend tests were completed for annual and seasonal time series of monthly means for total precipitation, daily minimum air temperature (Tmin), and daily maximum air temperature (Tmax). Statistical significance for all time-trend tests (climate, streamflow, and lake levels) was determined using the Mann‑Kendall test for probability values less than or equal to 0.10. Trend analyses were completed without adjustments for serial correlation; however, a modified Mann-Kendall test was subsequently used to examine potential effects of short-term persistence in time-series data. Effects of short-term persistence were considered inconsequential for climate data and minor for streamflow data; however, the presence of short-term persistence in water-budget components had more substantial effects on trend analyses.
Spatial distributions of trends in climatic data for WYs 1960–2015 for the U.S. part of the Great Lakes Basin (land only) indicate (1) generally ubiquitous upward trends in Tmin and (2) a sharp transition from neutral or downward trends in precipitation northwest of Lake Michigan to generally upward trends east of Lake Michigan. Trends in Tmax were not statistically significant. Analyses of annual climatic data aggregated for the U.S. land part of the Great Lakes Basin indicated statistically significant upward trends for precipitation and Tmin, and similar statistically significant trends existed for all the individual lake subbasins except Lake Superior.
Of 103 U.S. Geological Survey streamgages analyzed for streamflow trends, 71 had significant annual trends (54 upward and 17 downward). Downward trends in annual streamflow are concentrated northwest of Lake Michigan (16 streamgages), and upward trends are concentrated east of Lake Michigan (53 streamgages). Of the 71 streamgages with significant annual trends, 70 had at least one season with a significant trend that matched the annual trend direction.
Of 35 Environment and Climate Change Canada streamgages analyzed, 22 had significant upward trends in annual streamflow, and all but 1 of these 22 had at least one season with a significant upward trend. None of the Environment and Climate Change Canada streamgages had significant downward annual trends, and only one had a significant downward seasonal trend.
Trends in lake levels and several major water-budget components affecting lake levels were analyzed for the study period. Significant downward trends in lake level and outflow for Lake Superior are driven primarily by low lake levels and outflows during WYs 1998–2014. A significant downward trend in runoff from the contributing drainage area also is indicated, which is consistent with numerous streamgages northwest of Lake Michigan with significant downward trends in annual streamflow. A significant upward trend in annual overlake evaporation also is indicated, which is consistent with the spatially distributed upward trends in annual Tmin.
The sum of overlake precipitation and runoff from the contributing drainage area for each of the Great Lakes, less overlake evaporation, composes a variable called net basin supply (NBS). A significant downward trend in NBS is indicated for Lake Superior, which is consistent with significant trends for individual components of runoff (downward) and evaporation (upward) that contributed to a significant downward trend for lake outflow. Statistically significant upward trends in NBS for Lake Saint Clair and Lake Ontario offset the downward trend for Lake Superior and combine with nonsignificant upward trends in NBS for Lakes Michigan and Huron and Lake Erie to produce a neutral trend in NBS for the basin.
A predictable pattern in monthly mean lake levels is noted for Lake Superior, with the minimum for each year usually during or near March and the maximum commonly during or near September or October. When an October lake level is in a period of substantial decline, potential for an ensuing short-term period of below-mean lake levels is enhanced. Downstream from Lake Superior, monthly lake levels have sawtooth patterns that somewhat resemble those for Lake Superior but with decreased predictability in timing.
Similar to Lake Superior, Lakes Michigan and Huron, Lake Saint Clair, and Lake Erie all have a prolonged period of low lake levels around WYs 1998–2014; however, a significant downward trend is indicated only for Lakes Michigan and Huron. All these lakes also have a period of low lake levels before about WY 1968, when minimum lake levels were lower than during WYs 1998–2014. The significant downward trend of outflow from Lake Superior is carried downstream into Lakes Michigan and Huron; however, trends in outflow from the next three lakes downstream (Lakes Saint Clair, Erie, and Ontario) are offset by increased precipitation and runoff and are not significant.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195003","collaboration":"Prepared in cooperation with the Great Lakes Restoration Initiative","usgsCitation":"Norton, P.A., Driscoll, D.G., and Carter, J.M., 2019, Climate, streamflow, and lake-level trends in the Great Lakes Basin of the United States and Canada, water years 1960–2015: Scientific Investigations Report 2019–5003, 47 p., https://doi.org/10.3133/sir20195003.","productDescription":"Report: vi, 47 p.; Appendix Figures; Appendix Tables: 5","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-089551","costCenters":[{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true}],"links":[{"id":362031,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5003/coverthb.jpg"},{"id":362032,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003.pdf","text":"Report","size":"22.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5003"},{"id":362033,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003_appendix_figs_1.1_to_1.103.pdf","text":"Appendix figures 1.1–1.103","size":"940 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5003"},{"id":362034,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003_appendix_figs_1.104_to_1.138.pdf","text":"Appendix figures 1.104–1.138","size":"333 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5003"},{"id":362035,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003_appendix_tables_1.1_to_1.5.xlsx","text":"Appendix tables 1.1–1.5","size":"132 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5003"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.4716796875,\n 41.44272637767212\n ],\n [\n -75.7177734375,\n 41.44272637767212\n ],\n [\n -75.7177734375,\n 50.035973672195496\n ],\n [\n -93.4716796875,\n 50.035973672195496\n ],\n [\n -93.4716796875,\n 41.44272637767212\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
About one-quarter of the water supply for the Village of Ruidoso, New Mexico, is derived from groundwater pumping along North Fork Eagle Creek in the Eagle Creek Basin near Alto, New Mexico. Because of concerns regarding the effects of groundwater pumping on surface-water hydrology in the Eagle Creek Basin and the effects of the 2012 Little Bear Fire, which resulted in substantial losses of vegetation in the basin, the monitoring of North Fork Eagle Creek for short-term geomorphic change has been required by the U.S. Department of Agriculture Forest Service, Lincoln National Forest, as part of the permitting decision that allows for the continued pumping of the production wells. The monitoring of short-term geomorphic change in North Fork Eagle Creek began in June 2017 with a geomorphic survey of the stream reach located between the North Fork Eagle Creek near Alto, New Mexico, streamflow-gaging station (USGS site 08387550) and the Eagle Creek below South Fork near Alto, New Mexico, streamflow-gaging station (USGS site 08387600). The 2017 geomorphic survey was conducted by the U.S. Geological Survey (USGS), in cooperation with the Village of Ruidoso, and was the first in a planned series of five annual geomorphic surveys. The results of the 2017 geomorphic survey are summarized and interpreted in this report and are provided in their entirety in its companion data release.
The study reach is 1.86 miles long, and large sections of the reach are characterized by intermittent streamflow. Where water is normally present (including at the upper and lower portions of the reach near the streamflow-gaging stations), the discharge typically remains below 2 cubic feet per second throughout the year. Therefore, if geomorphic change is to occur, it will likely be driven by seasonal high-flow events. Discharge records from streamflow-gaging stations in the Eagle Creek Basin indicated that high-flow events in the basin (with peaks above 50 cubic feet per second) typically occurred during the North American monsoon months of July, August, and September. Additionally, the records appear to indicate that, as expected, overland runoff and “flashy” responses to rainfall have increased in the 5 years since the 2012 Little Bear Fire.
For the 2017 geomorphic survey of North Fork Eagle Creek, cross sections were established and surveyed at 14 locations along the study reach. Cross-section survey results indicated that channel characteristics (including channel width and area) varied widely along the study reach. Also, as part of the survey, woody debris accumulations and pools in the channel of the study reach were identified, cataloged, photographed, and surveyed for location. There were 58 woody debris accumulations and 14 pools found in the study reach. On the basis that debris jams could be a driver of geomorphic change in North Fork Eagle Creek, woody debris accumulations were classified according to their debris jam potential. The burn marks found on some woody debris indicated that the 2012 Little Bear Fire may be a contributing factor to the volume of debris in North Fork Eagle Creek. However, the woody debris present at the time of the survey did not appear to have substantially affected the geomorphic state of the study reach. Further, the structure and composition of the woody debris accumulations indicated that, under high-flow conditions, most woody debris would likely be transported downstream and out of the study reach without causing substantial geomorphic change through further jamming.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181187","collaboration":"Prepared in cooperation with the Village of Ruidoso, New Mexico","usgsCitation":"Graziano, A.P., 2019, Geomorphic survey of North Fork Eagle Creek, New Mexico, 2017: U.S. Geological Survey Open-File Report 2018–1187, 28 p., https://doi.org/10.3133/ofr20181187.","productDescription":"Report: v., 28 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","ipdsId":"IP-093851","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":362041,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PR7TX3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data supporting the 2017 geomorphic survey of North Fork Eagle Creek, New Mexico"},{"id":362039,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1187/coverthb.jpg"},{"id":362040,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1187/ofr20181187.pdf","text":"Report","size":"18.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1187"}],"country":"United States","state":"New Mexico","otherGeospatial":"North Fork Eagle Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.98236083984375,\n 33.02939031998959\n ],\n [\n -104.98260498046875,\n 33.02939031998959\n ],\n [\n -104.98260498046875,\n 33.68549637289138\n ],\n [\n -105.98236083984375,\n 33.68549637289138\n ],\n [\n -105.98236083984375,\n 33.02939031998959\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, NM 87113
Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Marbled murrelets (Brachyramphus marmoratus) have been listed as “endangered” by the State of California and “threatened” by the U.S. Fish and Wildlife Service since 1992 in California, Oregon, and Washington. Information regarding marbled murrelet abundance, distribution, population trends, and habitat associations is critical for risk assessment, effective management, evaluation of conservation efficacy, and ultimately, to meet Federal- and State-mandated recovery efforts for this species. During June–August 2018, the U.S. Geological Survey Western Ecological Research Center continued previously established, long-term (1999–2018), at-sea surveys to estimate abundance and productivity of marbled murrelets in U.S. Fish and Wildlife Service Conservation Zone 6 (San Francisco Bay to Point Sur in central California). Using conventional distance sampling methods, we estimated marbled murrelet abundance using 137 detections of 227 individuals observed on 9 surveys. The abundance estimated for the entire study area using all surveys in 2018 was 370 birds (95-percent confidence interval, 250–546 birds). Estimated abundance from 2018 is comparable to most prior years of study, except for 2001–03, when greater abundances were estimated. In 2018, we estimated reproductive productivity (calculated as the hatch-year [HY] to after-hatch-year [AHY] ratio) using four detections of four HY individuals observed on six surveys. After date-correcting HY and AHY counts to account for birds expected to be absent from the water while inland at nests, the date-corrected juvenile ratio was 0.047 ± 0.024 standard error. We updated a synthesized database of all Zone 6 marbled murrelet survey data since 1999 with 2018 data to allow scientists and managers to evaluate established survey methods and assess trends in abundance and productivity estimates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1107","usgsCitation":"Felis, J.J., Kelsey, E.C., and Adams, J., 2019, Abundance and productivity of marbled murrelets (Brachyramphus marmoratus) off central California during the 2018 breeding season: U.S. Geological Survey Data Series 1107, 10 p., https://doi.org/10.3133/ds1107.","productDescription":"Report: v, 10 p.; Data release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103980","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":362066,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75B01RW","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Annual marbled murrelet abundance and productivity surveys off central California (Zone 6), 1999–2018 (ver. 2.0, March 2019)"},{"id":362050,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1107/coverthb.jpg"},{"id":362051,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1107/ds1107.pdf","text":"Report","size":"1.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1107"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.52090454101564,\n 36.923547681089296\n ],\n [\n -122.02102661132814,\n 36.923547681089296\n ],\n [\n -122.02102661132814,\n 37.52279705525959\n ],\n [\n -122.52090454101564,\n 37.52279705525959\n ],\n [\n -122.52090454101564,\n 36.923547681089296\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
We surveyed for Least Bell’s Vireos (Vireo bellii pusillus; vireo) and Southwestern Willow Flycatchers (Empidonax traillii extimus; flycatcher) in cooperation with the U.S. Army Corps of Engineers along Bull Creek, Haskell Creek, and the Los Angeles River (Sepulveda Dam project area) in Los Angeles County, California, in 2018. Four vireo surveys were conducted between April 27 and July 18, 2018, and three flycatcher surveys were conducted between May 24 and July 18, 2018. We found 14 territorial male vireos, 7 of which were confirmed as paired. Sixty-four percent of vireos were detected along the Los Angeles River, 21 percent along Haskell Creek, and 14 percent along Bull Creek. Eighty-six percent of vireos were detected in habitat characterized as mixed willow, and all vireos were detected in habitat with greater than 50 percent native plant cover. No flycatchers were observed in the survey area in 2018.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1105","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Pottinger, R.E., and Kus, B.E., 2019, Least Bell’s Vireo (Vireo bellii pusillus) and Southwestern Willow Flycatcher (Empidonax traillii extimus) surveys in the Sepulveda Dam Basin, Los Angeles County, California—2018 data summary: U.S. Geological Survey Data Series 1105, 10 p., https://doi.org/10.3133/ds1105. ","productDescription":"iv, 10 p.","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-103359","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":362053,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1105/coverthb.jpg"},{"id":362054,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1105/ds1105.pdf","text":"Report","size":"3.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1105"}],"country":"United States","state":"California","county":"Los Angles County","otherGeospatial":"Sepulveda Dam Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.49387884140015,\n 34.16423284168825\n ],\n [\n -118.47091913223267,\n 34.16423284168825\n ],\n [\n -118.47091913223267,\n 34.1809029051214\n ],\n [\n -118.49387884140015,\n 34.1809029051214\n ],\n [\n -118.49387884140015,\n 34.16423284168825\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The enrichment factor (EF) is a widely used metric for determining how much the presence of an element in a sampling media has increased relative to average natural abundance because of human activity. Calculation of an EF requires the selection of both a background composition and a reference element, choices that can strongly influence the result of the calculation. Here, it is shown how carefully applied, classical principal component analysis (PCA) examined via biplots can guide the selections of background compositions and reference elements. Elemental data were treated using the centered log ratio (CLR) transformation, and multiple subsets of major and trace elements were examined to gain different perspectives. The methodology was applied to a dataset of elemental soil concentrations from around breccia pipe uranium mines in Arizona, U.S.A., with most samples collected via incremental sampling methodology. Storage of ore at the surface creates the potential for wind dispersal of ore-derived material. Uranium was found to be the best individual tracer of dispersal of ore-derived material to nearby soils, with EF values up to 75. Sulfur, As, Mo, and Cu were also enriched but to lesser degrees. The results demonstrate several practical benefits of a PCA in these situations: (1) the ability to identify one or more elements best suited to distinguish a specific source of enrichment from background composition; (2) understanding how background compositions vary within and between sites; (3) identification of samples containing enriched or anthropogenic materials based upon their integrated, multi-element composition. Calculating the most representative EF values is useful for numerical assessment of enrichment, whether anthropogenic or natural. As shown here, however, the PCA and biplot method provide a visual approach that integrates information from all elements for a given subset of data in a manner that yields geochemical insights beyond the power of the EF.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2019.01.122","usgsCitation":"Bern, C.R., Walton-Day, K., and Naftz, D.L., 2019, Improved enrichment factor calculations through principal component analysis: Examples from soils near breccia pipe uranium mines, Arizona, USA: Environmental Pollution, v. 248, p. 90-100, https://doi.org/10.1016/j.envpol.2019.01.122.","productDescription":"11 p.","startPage":"90","endPage":"100","ipdsId":"IP-102119","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":467818,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2019.01.122","text":"Publisher Index Page"},{"id":437543,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KTLXL8","text":"USGS data release","linkHelpText":"Surface Materials Data from Breccia-Pipe Uranium Mine and Reference Sites, Arizona, USA"},{"id":362042,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.04907226562499,\n 35.5\n ],\n [\n -111,\n 35.5\n ],\n [\n -111,\n 37\n ],\n [\n -114.04907226562499,\n 37\n ],\n [\n -114.04907226562499,\n 35.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"248","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bern, Carleton R. 0000-0002-8980-1781 cbern@usgs.gov","orcid":"https://orcid.org/0000-0002-8980-1781","contributorId":201152,"corporation":false,"usgs":true,"family":"Bern","given":"Carleton","email":"cbern@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759220,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walton-Day, Katherine 0000-0002-9146-6193 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":184043,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759221,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Naftz, David L. 0000-0003-1130-6892 dlnaftz@usgs.gov","orcid":"https://orcid.org/0000-0003-1130-6892","contributorId":1041,"corporation":false,"usgs":true,"family":"Naftz","given":"David","email":"dlnaftz@usgs.gov","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759222,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227949,"text":"70227949 - 2019 - Winter precipitation and summer temperature predict lake water quality at macroscales","interactions":[],"lastModifiedDate":"2022-02-02T16:04:07.07506","indexId":"70227949","displayToPublicDate":"2019-03-13T09:52:13","publicationYear":"2019","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":"Winter precipitation and summer temperature predict lake water quality at macroscales","docAbstract":"Climate change can have strong effects on aquatic ecosystems, including disrupting nutrient cycling and mediating processes that affect primary production. Past studies have been conducted mostly on individual or small groups of ecosystems, making it challenging to predict how future climate change will affect water quality at broad scales. We used a subcontinental-scale database to address three objectives: (1) identify which climate metrics best predict lake water quality, (2) examine whether climate influences different nutrient and productivity measures similarly, and (3) quantify the potential effects of a changing climate on lakes. We used climate data to predict lake water quality in ~11,000 north temperate lakes across 17 U.S. states. We developed a novel machine learning method that jointly models different measures of water quality using 48 climate metrics and accounts for properties inherent in macroscale data (e.g., spatial autocorrelation). Our results suggest that climate metrics related to winter precipitation and summer temperature were strong predictors of lake nutrients and productivity. However, we found variation in the magnitude and direction of the relationship between climate and water quality. We predict that a likely future climate change scenario of warmer summer temperatures will lead to increased nutrient concentrations and algal biomass across lakes (median ~3%–9% increase), whereas increased winter precipitation will have highly variable effects. Our results emphasize the importance of heterogeneity in the response of individual ecosystems to climate and are a caution to extrapolating relationships across space.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR023088","usgsCitation":"Collins, S.M., Yuan, S., Tan, P.N., Oliver, S.K., Lapierre, J.F., Cheruvelil, K., Fergus, C., Skaff, N.K., Stachelek, J., Wagner, T., and Soranno, P., 2019, Winter precipitation and summer temperature predict lake water quality at macroscales: Water Resources Research, v. 55, no. 4, p. 2708-2721, https://doi.org/10.1029/2018WR023088.","productDescription":"14 p.","startPage":"2708","endPage":"2721","ipdsId":"IP-095436","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395273,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"4","noUsgsAuthors":false,"publicationDate":"2019-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Collins, S. M.","contributorId":273184,"corporation":false,"usgs":false,"family":"Collins","given":"S.","email":"","middleInitial":"M.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":832670,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yuan, S.","contributorId":273185,"corporation":false,"usgs":false,"family":"Yuan","given":"S.","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832671,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tan, P. N.","contributorId":273186,"corporation":false,"usgs":false,"family":"Tan","given":"P.","email":"","middleInitial":"N.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832672,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliver, S. K.","contributorId":273187,"corporation":false,"usgs":false,"family":"Oliver","given":"S.","email":"","middleInitial":"K.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":832673,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lapierre, J. F.","contributorId":273188,"corporation":false,"usgs":false,"family":"Lapierre","given":"J.","email":"","middleInitial":"F.","affiliations":[{"id":41192,"text":"Université de Montreal","active":true,"usgs":false}],"preferred":false,"id":832674,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cheruvelil, K. S.","contributorId":273189,"corporation":false,"usgs":false,"family":"Cheruvelil","given":"K. S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832675,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fergus, C. E.","contributorId":273190,"corporation":false,"usgs":false,"family":"Fergus","given":"C. E.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":832676,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Skaff, N. K.","contributorId":273191,"corporation":false,"usgs":false,"family":"Skaff","given":"N.","email":"","middleInitial":"K.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832677,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stachelek, J.","contributorId":273193,"corporation":false,"usgs":false,"family":"Stachelek","given":"J.","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832678,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832679,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Soranno, P. A.","contributorId":273195,"corporation":false,"usgs":false,"family":"Soranno","given":"P. A.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832680,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70202411,"text":"sir20195009 - 2019 - Flood-inundation maps for the Yellow River from River Drive to Centerville Highway, Gwinnett County, Georgia","interactions":[],"lastModifiedDate":"2019-03-13T16:10:00","indexId":"sir20195009","displayToPublicDate":"2019-03-13T09:00:00","publicationYear":"2019","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":"2019-5009","displayTitle":"Flood-Inundation Maps for the Yellow River from River Drive to Centerville Highway, Gwinnett County, Georgia","title":"Flood-inundation maps for the Yellow River from River Drive to Centerville Highway, Gwinnett County, Georgia","docAbstract":"Digital flood-inundation maps for a 16.4-mile reach of the Yellow River in Gwinnett County, Georgia, from 0.5 mile upstream from River Drive to Centerville Highway (Georgia State Route 124) were developed to depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at two U.S. Geological Survey (USGS) streamgages in the mapped area. The maps for the 9.0-mile reach from 0.5 mile upstream from River Drive to Stone Mountain Highway (U.S. Route 78) are referenced to the streamgage Yellow River near Snellville, Ga. (station 02206500), and the maps for the 7.4-mile reach from Stone Mountain Highway to Centerville Highway are referenced to the streamgage Yellow River at Ga. 124, near Lithonia, Ga. (02207120). Real-time stage information from these streamgages can be used with these maps to estimate near real-time areas of inundation. The forecasted peak-stage information for the USGS streamgages Yellow River near Snellville, Ga. (02206500), and Yellow River at Ga. 124, near Lithonia, Ga. (02207120), can be used in conjunction with the maps developed for this study to show predicted areas of flood inundation.
A one-dimensional step-backwater model was developed using the U.S. Army Corps of Engineers Hydrologic Engineering Center's River Analysis System (HEC–RAS) software for the Yellow River and was used to compute flood profiles for a 16.4-mile reach of the Yellow River. The hydraulic model was then used to simulate 16 water-surface profiles at 1.0-foot (ft) intervals at the Yellow River near Snellville streamgage and 17 water-surface profiles at 1.0-ft intervals at the Yellow River near Lithonia streamgage. At the Yellow River near Snellville streamgage, the profiles ranged from a stage of 18.0 ft, which is 819.1 ft above the North American Vertical Datum of 1988 (NAVD 88), to a stage of 33.0 ft, which is 834.1 ft above NAVD 88. At the Yellow River near Lithonia streamgage, the profiles ranged from the National Weather Service action stage of 13.0 ft, which is 732.5 ft above NAVD 88, to a stage of 29.0 ft, which is 748.5 ft above NAVD 88. The simulated water-surface profiles were then combined with a geographic information system digital elevation model—derived from light detection and ranging (lidar) data having a 5.0-ft horizontal resolution—to delineate the area flooded at each 1.0-ft interval of stream stage for both streamgages.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195009","collaboration":"Prepared in cooperation with Gwinnett County, Georgia","usgsCitation":"Musser, J.W., 2019, Flood-inundation maps for the Yellow River from River Drive to Centerville Highway, Gwinnett County, Georgia: U.S. Geological Survey Scientific Investigations Report 2019–5009, 15 p., https://doi.org/10.3133/sir20195009.","productDescription":"Report: vi, 15 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-100804","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":361975,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KKB3H2","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood inundation and flood depth for the Yellow River in Gwinnett County, Georgia based on water-surface elevation at the U.S. Geological Survey streamgages Yellow River, near Snellville, Georgia (02206500) and Yellow River at Ga. 124, near Lithonia, Georgia (02207120)"},{"id":361973,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5009/coverthb.jpg"},{"id":361974,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5009/sir20195009.pdf","text":"Report","size":"1.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5009"}],"country":"United States","state":"Georgia","county":"Gwinnett County","otherGeospatial":"Yellow River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.1667,\n 33.75\n ],\n [\n -84,\n 33.75\n ],\n [\n -84,\n 33.9167\n ],\n [\n -84.1667,\n 33.9167\n ],\n [\n -84.1667,\n 33.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
We studied some of the relationships between federally listed fall Chinook Salmon, Oncorhynchus tshawytscha, endemic Sand Roller, Percopsis transmontana, and non-native Smallmouth Bass, Micropterus dolomieu, in Lower Granite Reservoir on the Snake River. Because of its recent reappearance and population increase, the Sand Rollers could be filling the role of a “native invader” in the reservoir food web. We speculated that Sand Rollers could either negatively affect fall Chinook Salmon by potentially competing with them for resources in shoreline habitats or, alternatively, benefit the salmon by providing a buffer against Smallmouth Bass predation. Nighttime beach seining showed that habitat use by fall Chinook Salmon and Sand Rollers overlapped completely in spring when both species were present along shorelines. Diet data from stomach samples also showed high overlap, but data on stable isotopes of 13C and 15N suggested that each species could be obtaining much of their dietary energy from different reservoir locations. Although habitat and diet overlap are evidence of competition, diel and spatial partitioning of resource use between fall Chinook Salmon and Sand Rollers may act to reduce potential competition. Analyses of Smallmouth Bass diets showed that fall Chinook Salmon and Sand Rollers comprised the majority of prey fish consumed by bass. Across years, as Smallmouth Bass increased their consumption of Sand Rollers (range 0.219 to 0.392 fish smallmouth-1 day-1), they decreased their consumption of fall Chinook Salmon (range 0.114 to 0.050 fish smallmouth-1 day-1). The greatest effect Sand Rollers may have on fall Chinook Salmon in Lower Granite Reservoir is to serve as a buffer against Smallmouth Bass predation.
","language":"English","publisher":"Society for Northwestern Vertebrate Biology","doi":"10.1898/NWN18-13","usgsCitation":"Hemingway, R.J., Tiffan, K.F., Erhardt, J.M., Rhodes, T., and Bickford, B.K., 2019, Fall Chinook salmon (Oncorhynchus tshawytscha), sand roller (Percopsis transmontana), and smallmouth bass (Micropterus dolomieu) interactions in a Snake River reservoir: A tale of three species: Northwestern Naturalist, v. 100, no. 1, p. 26-36, https://doi.org/10.1898/NWN18-13.","productDescription":"11 p.","startPage":"26","endPage":"36","ipdsId":"IP-097818","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":362014,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.25021362304689,\n 46.29476444388206\n ],\n [\n -116.9769287109375,\n 46.29476444388206\n ],\n [\n -116.9769287109375,\n 46.4752265177719\n ],\n [\n -117.25021362304689,\n 46.4752265177719\n ],\n [\n -117.25021362304689,\n 46.29476444388206\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"100","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hemingway, Rulon J. 0000-0001-8143-0325 rhemingway@usgs.gov","orcid":"https://orcid.org/0000-0001-8143-0325","contributorId":194697,"corporation":false,"usgs":true,"family":"Hemingway","given":"Rulon","email":"rhemingway@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":759198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":759199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Erhardt, John M. 0000-0002-5170-285X jerhardt@usgs.gov","orcid":"https://orcid.org/0000-0002-5170-285X","contributorId":5380,"corporation":false,"usgs":true,"family":"Erhardt","given":"John","email":"jerhardt@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":759200,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rhodes, Tobyn N. 0000-0002-4023-4827","orcid":"https://orcid.org/0000-0002-4023-4827","contributorId":210057,"corporation":false,"usgs":true,"family":"Rhodes","given":"Tobyn N.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":759201,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bickford, Brad K. 0000-0003-3756-6588 bbickford@usgs.gov","orcid":"https://orcid.org/0000-0003-3756-6588","contributorId":140889,"corporation":false,"usgs":true,"family":"Bickford","given":"Brad","email":"bbickford@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":759202,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202445,"text":"sir20195002 - 2019 - Contemporary environmental assessment using a viability analysis in a large river system to inform restoration and adaptive management decisions","interactions":[],"lastModifiedDate":"2019-03-13T16:05:04","indexId":"sir20195002","displayToPublicDate":"2019-03-12T15:18:28","publicationYear":"2019","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":"2019-5002","displayTitle":"Contemporary Environmental Assessment Using a Viability Analysis in a Large River System to Inform Restoration and Adaptive Management Decisions","title":"Contemporary environmental assessment using a viability analysis in a large river system to inform restoration and adaptive management decisions","docAbstract":"As large-scale restoration plans for degraded aquatic habitats evolve, it is essential that multiorganizational collaborations have a common vision to achieve consensus on restoration goals. Development of restoration targets and postrestoration monitoring strategies can be focused using a viability analysis framework that supports an adaptive management process. Viability analysis is a robust and accommodating framework, adaptable to any restoration monitoring program and, through the determination of common desired endpoints, can aid consensus building and collaboration across jurisdictional boundaries. In the St. Clair-Detroit River System, which is the Great Lakes connecting channel between southern Lake Huron and western Lake Erie, a viability analysis framework was used to evaluate environmental parameters associated with fisheries and aquatic restoration efforts and to gauge the overall health of the aquatic environment. Steps to derive the viability analysis were as follows: (1) establishing meaningful baseline metrics, (2) identifying information deficiencies, and (3) placing the context of current conditions into a usable format for managers and practitioners. Most geographic segments were designated in overall fair condition, and the conservation targets were designated in either good or fair condition, based on available assessed indicators. Many indicators were unable to be assessed or assigned condition status, which identified research and monitoring data gaps. Metrics associated with native migratory fishes, Lake St. Clair, and islands are generally in better condition than metrics associated with the coastal terrestrial systems, aerial migrants, and coastal wetlands. These results were not unexpected given the highly urbanized landscape of the St. Clair-Detroit River System. Resource managers in the corridor can use these results to identify knowledge gaps, research and restoration priorities, and to assess progress towards meeting restoration goals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195002","usgsCitation":"DeBruyne, R.L., Roseman, E.F., Ross, J.E., Newman, K.R., and Strach, R.M., 2019, Contemporary environmental assessment using a viability analysis in a large river system to inform restoration and adaptive management decisions: U.S. Geological Survey Scientific Investigations Report 2019–5002, 59 p., https://doi.org/10.3133/sir20195002.","productDescription":"xi, 59 p.","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-071170","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":362006,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5002/sir20195002.pdf","text":"Report","size":"1.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5002"},{"id":362005,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5002/coverthb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"St. Clair-Detroit River System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.5,\n 42\n ],\n [\n -82,\n 42\n ],\n [\n -82,\n 43\n ],\n [\n -83.5,\n 43\n ],\n [\n -83.5,\n 42\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Great Lakes Science Center
U.S. Geological Survey
1451 Green Road
Ann Arbor, Michigan 48105
Glacial−interglacial variations in CO2 and methane in polar ice cores have been attributed, in part, to changes in global wetland extent, but the wetland distribution before the Last Glacial Maximum (LGM, 21 ka to 18 ka) remains virtually unknown. We present a study of global peatland extent and carbon (C) stocks through the last glacial cycle (130 ka to present) using a newly compiled database of 1,063 detailed stratigraphic records of peat deposits buried by mineral sediments, as well as a global peatland model. Quantitative agreement between modeling and observations shows extensive peat accumulation before the LGM in northern latitudes (>40°N), particularly during warmer periods including the last interglacial (130 ka to 116 ka, MIS 5e) and the interstadial (57 ka to 29 ka, MIS 3). During cooling periods of glacial advance and permafrost formation, the burial of northern peatlands by glaciers and mineral sediments decreased active peatland extent, thickness, and modeled C stocks by 70 to 90% from warmer times. Tropical peatland extent and C stocks show little temporal variation throughout the study period. While the increased burial of northern peats was correlated with cooling periods, the burial of tropical peat was predominately driven by changes in sea level and regional hydrology. Peat burial by mineral sediments represents a mechanism for long-term terrestrial C storage in the Earth system. These results show that northern peatlands accumulate significant C stocks during warmer times, indicating their potential for C sequestration during the warming Anthropocene.
","language":"English","publisher":"PNAS","doi":"10.1073/pnas.1813305116","usgsCitation":"Treat, C.C., Kleinen, T., Broothaerts , N., Dalton, A.S., Dommain, R., Douglas, T.A., Drexler, J.Z., Finkelstein, S., Grosse, G., Hope, G., Hutchings, J., Jones, M.C., Kuhry, P., Lacourse, T., Lahteenoja, O., Loisel, J., Notebaert, B., Payne, R., Peteet, D.M., Sannel, A.B., Stelling, J.M., Strauss, J., Swindles, G.T., Talbot, J., Tarnocai, C., Verstraeten, G., Williams , C., Xia, Z., Yu, Z., Valiranta, M., Hattestrand, M., Alexanderson, H., and Brovkin, V., 2019, Widespread global peatland establishment and persistence over the last 130,000 y: PNAS, v. 116, no. 11, p. 4822-4827, https://doi.org/10.1073/pnas.1813305116.","productDescription":"6 p.","startPage":"4822","endPage":"4827","ipdsId":"IP-091584","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":467822,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.1813305116","text":"Publisher Index Page"},{"id":373582,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"116","issue":"11","noUsgsAuthors":false,"publicationDate":"2019-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Treat, Claire C.","contributorId":96606,"corporation":false,"usgs":true,"family":"Treat","given":"Claire","email":"","middleInitial":"C.","affiliations":[{"id":25501,"text":"University of Eastern Finland","active":true,"usgs":false}],"preferred":false,"id":785865,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kleinen, Thomas 0000-0001-9550-5164","orcid":"https://orcid.org/0000-0001-9550-5164","contributorId":50427,"corporation":false,"usgs":true,"family":"Kleinen","given":"Thomas","email":"","affiliations":[{"id":32387,"text":"Max Planck Institute for Meteorology, Hamburg, Germany","active":true,"usgs":false}],"preferred":false,"id":785866,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Broothaerts , Nils ","contributorId":223665,"corporation":false,"usgs":true,"family":"Broothaerts ","given":"Nils ","affiliations":[],"preferred":false,"id":785867,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dalton, April S.","contributorId":223666,"corporation":false,"usgs":true,"family":"Dalton","given":"April","email":"","middleInitial":"S.","affiliations":[{"id":13300,"text":"3Department of Earth Sciences, University of Toronto, Toronto, Ontario M5S 3B1, Canada.","active":true,"usgs":false}],"preferred":false,"id":785868,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dommain, Rene","contributorId":220666,"corporation":false,"usgs":false,"family":"Dommain","given":"Rene","email":"","affiliations":[{"id":40220,"text":"University of Potsdam, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":785869,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Douglas, Thomas A. 0000-0003-1314-1905","orcid":"https://orcid.org/0000-0003-1314-1905","contributorId":64553,"corporation":false,"usgs":false,"family":"Douglas","given":"Thomas","email":"","middleInitial":"A.","affiliations":[{"id":33087,"text":"Cold Regions Research and Engineering Laboratory","active":true,"usgs":false}],"preferred":true,"id":785870,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785871,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Finkelstein, Sarah A","contributorId":217548,"corporation":false,"usgs":true,"family":"Finkelstein","given":"Sarah A","affiliations":[{"id":7044,"text":"University of Toronto","active":true,"usgs":false}],"preferred":false,"id":785872,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Grosse, Guido","contributorId":101475,"corporation":false,"usgs":true,"family":"Grosse","given":"Guido","affiliations":[{"id":34291,"text":"University of Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":785873,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hope, Geoffrey","contributorId":223668,"corporation":false,"usgs":false,"family":"Hope","given":"Geoffrey","email":"","affiliations":[{"id":16807,"text":"Australian National University","active":true,"usgs":false}],"preferred":false,"id":785874,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hutchings, Jack","contributorId":223669,"corporation":false,"usgs":true,"family":"Hutchings","given":"Jack","email":"","affiliations":[{"id":36338,"text":"University of Florida Department of Geological Sciences","active":true,"usgs":false}],"preferred":false,"id":785875,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Jones, Miriam C. 0000-0002-6650-7619 miriamjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6650-7619","contributorId":4056,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","email":"miriamjones@usgs.gov","middleInitial":"C.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":785876,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kuhry, Peter","contributorId":9513,"corporation":false,"usgs":true,"family":"Kuhry","given":"Peter","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":785877,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Lacourse, Terri","contributorId":220254,"corporation":false,"usgs":true,"family":"Lacourse","given":"Terri","email":"","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":785878,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Lahteenoja, Outi","contributorId":223670,"corporation":false,"usgs":true,"family":"Lahteenoja","given":"Outi","email":"","affiliations":[{"id":29807,"text":"Arizona State University, School of Life Sciences,Tempe, AZ 85287","active":true,"usgs":false}],"preferred":false,"id":785879,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Loisel, Julie","contributorId":166672,"corporation":false,"usgs":false,"family":"Loisel","given":"Julie","email":"","affiliations":[{"id":18162,"text":"University of Helsinki","active":true,"usgs":false}],"preferred":false,"id":785880,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Notebaert, Bastiaan","contributorId":223671,"corporation":false,"usgs":false,"family":"Notebaert","given":"Bastiaan","email":"","affiliations":[],"preferred":false,"id":785881,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Payne, Richard","contributorId":200868,"corporation":false,"usgs":true,"family":"Payne","given":"Richard","email":"","affiliations":[{"id":35536,"text":"University of York","active":true,"usgs":false}],"preferred":false,"id":785882,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Peteet, Dorothy M. 0000-0003-3029-7506","orcid":"https://orcid.org/0000-0003-3029-7506","contributorId":147523,"corporation":false,"usgs":false,"family":"Peteet","given":"Dorothy","email":"","middleInitial":"M.","affiliations":[{"id":16858,"text":"Goddard Institute","active":true,"usgs":false}],"preferred":false,"id":785883,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Sannel, A. Britta K. 0000-0002-1350-6516","orcid":"https://orcid.org/0000-0002-1350-6516","contributorId":223672,"corporation":false,"usgs":false,"family":"Sannel","given":"A.","email":"","middleInitial":"Britta K.","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":785886,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Stelling, Jonathan M.","contributorId":223673,"corporation":false,"usgs":false,"family":"Stelling","given":"Jonathan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":785887,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Strauss, Jens","contributorId":223674,"corporation":false,"usgs":false,"family":"Strauss","given":"Jens","email":"","affiliations":[],"preferred":false,"id":785888,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Swindles, Graeme T.","contributorId":220282,"corporation":false,"usgs":false,"family":"Swindles","given":"Graeme","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":785889,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Talbot, Julie","contributorId":223675,"corporation":false,"usgs":false,"family":"Talbot","given":"Julie","email":"","affiliations":[],"preferred":false,"id":785890,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Tarnocai, Charles","contributorId":199154,"corporation":false,"usgs":false,"family":"Tarnocai","given":"Charles","email":"","affiliations":[],"preferred":false,"id":785892,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Verstraeten, Gert","contributorId":223676,"corporation":false,"usgs":false,"family":"Verstraeten","given":"Gert","email":"","affiliations":[],"preferred":false,"id":785893,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Williams , Christopher J. ","contributorId":223677,"corporation":false,"usgs":false,"family":"Williams ","given":"Christopher J. ","affiliations":[],"preferred":false,"id":785894,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Xia, Zhengyu","contributorId":223678,"corporation":false,"usgs":false,"family":"Xia","given":"Zhengyu","email":"","affiliations":[],"preferred":false,"id":785896,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Yu, Zicheng 0000-0003-2358-2712","orcid":"https://orcid.org/0000-0003-2358-2712","contributorId":147521,"corporation":false,"usgs":false,"family":"Yu","given":"Zicheng","email":"","affiliations":[{"id":16857,"text":"Lehigh Univ.","active":true,"usgs":false}],"preferred":false,"id":785897,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Valiranta, Minna","contributorId":223679,"corporation":false,"usgs":false,"family":"Valiranta","given":"Minna","email":"","affiliations":[{"id":18162,"text":"University of Helsinki","active":true,"usgs":false}],"preferred":false,"id":785898,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Hattestrand, Martina","contributorId":223680,"corporation":false,"usgs":false,"family":"Hattestrand","given":"Martina","email":"","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":785899,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Alexanderson, Helena","contributorId":223681,"corporation":false,"usgs":false,"family":"Alexanderson","given":"Helena","email":"","affiliations":[{"id":13428,"text":"Lund University","active":true,"usgs":false}],"preferred":false,"id":785900,"contributorType":{"id":1,"text":"Authors"},"rank":32},{"text":"Brovkin, Victor 0000-0001-6420-3198","orcid":"https://orcid.org/0000-0001-6420-3198","contributorId":223682,"corporation":false,"usgs":true,"family":"Brovkin","given":"Victor","email":"","affiliations":[{"id":32387,"text":"Max Planck Institute for Meteorology, Hamburg, Germany","active":true,"usgs":false}],"preferred":false,"id":785901,"contributorType":{"id":1,"text":"Authors"},"rank":33}]}} ,{"id":70202202,"text":"sir20195004 - 2019 - Flood-inundation maps of the Meramec River from Eureka to Arnold, Missouri, 2018","interactions":[],"lastModifiedDate":"2019-10-23T09:27:27","indexId":"sir20195004","displayToPublicDate":"2019-03-11T13:38:08","publicationYear":"2019","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":"2019-5004","displayTitle":"Flood-Inundation Maps of the Meramec River from Eureka to Arnold, Missouri, 2018","title":"Flood-inundation maps of the Meramec River from Eureka to Arnold, Missouri, 2018","docAbstract":"Libraries of digital flood-inundation maps that spanned a combined 37.2-mile reach of the Meramec River that extended upstream from Eureka, Missouri, to downstream near the confluence of the Meramec and Mississippi Rivers were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Army Corps of Engineers, Metropolitan St. Louis Sewer District, Missouri Department of Transportation, Missouri American Water, Federal Emergency Management Agency Region 7, and the cities of Pacific, Eureka, Wildwood, and Arnold. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the cooperative USGS streamgages for the Meramec River near Eureka, Mo. (USGS station 07019000), the Meramec River at Valley Park, Mo. (USGS station 07019130), the Meramec River at Fenton, Mo. (USGS station 07019210), and the Meramec River at Arnold, Mo. (USGS station 07019300). Near-real-time stage data at these streamgages may be obtained from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/, which also forecasts flood hydrographs at these sites (listed as NWS sites erkm7, vllm7, fnnm7, and arnm7, respectively).
Flood profiles were computed for the stream reach by means of a calibrated one-dimensional step-backwater hydraulic model. The model was calibrated using a stage-discharge relation at the Meramec River near Eureka, Mo., streamgage (USGS station 07019000) and documented high-water marks from the flood of December 2015 through January 2016.
The calibrated hydraulic model was used to compute water-surface profiles: 1 set for the Meramec River near Eureka, Mo., streamgage (USGS station 07019000); 1 set for the Meramec River at Valley Park, Mo., streamgage (USGS station 07019130); 7 sets for the Meramec River at Fenton, Mo., streamgage (USGS station 07019210) for a range of Mississippi River conditions; and 8 sets for the Meramec River at Arnold, Mo., streamgage (USGS station 07019300) for a range of Mississippi River conditions. The water-surface profiles were produced for stages at 1-foot (ft) intervals referenced to the datum from each streamgage and ranging from the NWS action stage, or near bankfull discharge, to the stage corresponding to the estimated 0.2-percent annual exceedance probability (500-year recurrence interval) flood, as determined at the Meramec River near Eureka, Mo., streamgage (USGS station 07019000). The simulated water-surface profiles then were combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.28-ft vertical accuracy and 3.28-ft horizontal resolution) to delineate the area flooded at each simulated 1-ft stage increment. Previously published flood-inundation maps were updated and incorporated in the flood map libraries for USGS stations 07019130 and 07019210 to complete the map sets corresponding to eight Mississippi River conditions.
The availability of these maps, along with internet information regarding current stage from the USGS streamgages and forecasted high-flow stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures and for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195004","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Metropolitan St. Louis Sewer District, Missouri Department of Transportation, Missouri American Water, Federal Emergency Management Agency Region 7, the city of Pacific, the city of Eureka, the city of Wildwood, and the city of Arnold","usgsCitation":"Dietsch, B.J., and Strauch, K.R., 2019, Flood-inundation maps of the Meramec River from Eureka to Arnold, Missouri, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5004, 12 p., https://doi.org/10.3133/sir20195004.","productDescription":"Report: vi, 12 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-096951","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":361924,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5004/sir20195004.pdf","text":"Report","size":"1.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5004"},{"id":361925,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B0XLJL","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Flood-Inundation Maps of the Meramec River from Eureka to Arnold, Missouri, 2018"},{"id":361923,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5004/coverthb.jpg"}],"country":"United States","state":"Missouri","city":"Arnold, Eureka","otherGeospatial":"Meramec River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.66398620605469,\n 38.38795699631396\n ],\n [\n -90.33405303955078,\n 38.38795699631396\n ],\n [\n -90.33405303955078,\n 38.565347844885466\n ],\n [\n -90.66398620605469,\n 38.565347844885466\n ],\n [\n -90.66398620605469,\n 38.38795699631396\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Water levels in lakes and wetlands in the central North Dakota Missouri Coteau region that were either dry or only sporadically held water since before the 1930s have been rising since the early 1990s in response to an extended wet period. The lakes have remained full since the mid-1990s, which has provided benefits to migratory waterfowl, fisheries, and wildlife. A small shift in climate conditions, either to drier or wetter conditions, can have a large effect on the lake levels of these water bodies. The North Dakota Game and Fish Department identified five lakes as candidates for sustaining long-term fisheries. The lakes are in Kidder, Stutsman, and Logan Counties, and some lakes might receive inflow from mostly freshwater aquifers, such as the Central Dakota and Streeter aquifers, and were mostly dry during the early 1990s. After about 1995, the lakes had filled up and were deep enough to sustain populations of game fish such as walleye, perch, and northern pike. Before investing in development of permanent fisheries and associated infrastructure, such as campgrounds and boat ramps, fisheries biologists needed to know if the lake levels are likely to remain high in coming decades.
The U.S. Geological Survey, in cooperation with the North Dakota Game and Fish Department, developed a water-balance model to determine the effects of precipitation, evapotranspiration, and groundwater interaction on lake volumes. The model was developed using climate input data and lake volumes for the calibration period 1992 through 2016, during which historical lake volumes could be estimated using land surface elevation data and Landsat images. Long-term (1940–2018) climate input data were used with the water-balance model to reconstruct historical lake volumes prior to the calibration period, and block-bootstrapping was used to simulate potential future climate input data and lake volumes for 2017 through 2067. The simulated future lake volumes were used to estimate the likelihood of annual lake volumes remaining consistent, increasing, or decreasing through the year 2067.
Of the five lakes, Sibley Lake was the most likely to sustain a long-term fishery for a period longer than 50 years. The simulated lake volumes for Alkaline Lake, Big Mallard Marsh, and Remmick Lake indicated the lakes have a 50-percent chance to fall below 75 percent of their 2016 volume by about 2030, 2067, and 2025, respectively. Simulation results for Marvin Miller Lake were substantially different compared to the other four lakes and indicated the lake has a 50-percent chance to fall below 75 percent of its 2016 volume prior to 2025.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195007","collaboration":"Prepared in cooperation with the North Dakota Game and Fish Department","usgsCitation":"Lundgren, R.F., York, B.C., Stroh, N.A., and Vecchia, A.V., 2019, Water-balance modeling of selected lakes for evaluating viability as long-term fisheries in Kidder, Logan, and Stutsman Counties, North Dakota: U.S. Geological Survey Scientific Investigations Report 2019–5007, 22 p., https://doi.org/10.3133/sir20195007.","productDescription":"Report: v, 22 p.; Downloads","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102504","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":361913,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5007/coverthb.jpg"},{"id":361914,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5007/sir20195007.pdf","text":"Report","size":"1.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5007"},{"id":361915,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2019/5007/downloads/","text":"Water-Balance Model R code scripts","linkFileType":{"id":6,"text":"zip"},"description":"Water-Balance Model R code scripts"}],"country":"United States","state":"North Dakota","county":"Kidder County, Logan County, Stutsman County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.1667,\n 46.5\n ],\n [\n -99.1667,\n 46.5\n ],\n [\n -99.1667,\n 47.5\n ],\n [\n -100.1667,\n 47.5\n ],\n [\n -100.1667,\n 46.5\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
On February 27–28, 2018, the U.S. Geological Survey and Department of the Interior (DOI) Office of Emergency Management (OEM) hosted a workshop to gather input from DOI subject matter experts (SMEs), resource managers, facility managers, emergency managers, and law enforcement personnel. Workshop goals were to (1) determine how DOI Bureaus and Offices use risk information for strategic planning and decision-making; (2) understand what types of information are most useful to DOI Bureaus and Offices; (3) establish what data, information, and products are desired; (4) identify the most effective methods for delivery and visualization; and (5) collect ideas for future project directions. The workshop findings presented in this report will influence the development of risk-information products created by the Strategic Hazard Identification and Risk Assessment of Department of the Interior Resources (SHIRA) Project team.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1453","collaboration":"Prepared in cooperation with the Office of Emergency Management, U.S. Department of the Interior","usgsCitation":"Wood, N., Pennaz, A., Ludwig, K., Jones, J., Henry, K, Sherba, J., Ng, P., Marineau, J., and Juskie, J., 2019, Assessing hazards and risks at the Department of the Interior—A workshop report: U.S. Geological Survey Circular 1453, 42 p., https://doi.org/10.3133/cir1453.","productDescription":"v, 42 p.","onlineOnly":"Y","ipdsId":"IP-101292","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":361993,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1453/coverthb.jpg"},{"id":361994,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1453/circ1453.pdf","text":"Report","size":"6.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1453"}],"contact":"Contact Information, Western Geographic Science Center
U.S. Geological Survey
345 Middlefield Road, MS 531
Menlo Park, CA 94025
The Mw 6.0 South Napa earthquake of August 24, 2014, produced complex and extensive surface faulting and other ground deformation features. Following the event, geologists made more than 1,200 field observations at locations where tectonic faulting and ground failure produced visible deformation that fractured and disturbed the ground surface. At a few locations, large-scale, detailed, field-based maps of fault rupture and ground deformation were produced. The South Napa earthquake response was one of the first times when post-earthquake reconnaissance data were mostly collected and disseminated electronically. The advantages and opportunities these new methods bring to our research also pose new challenges to large-scale compilation efforts and demonstrate the value of developing guidelines and better standardization across the community to more optimally utilize developing technology in future post-earthquake investigations. Some suggestions for standardizing the collection and dissemination of post-earthquake field reconnaissance data are provided herein.
Field observations and maps were integrated with airborne imagery, lidar, and InSAR to produce a comprehensive, large-scale digital map of fault rupture and zones of ground deformation. The map, observations, and photo database are summarized here in appendixes and figures and are also available as a series of digital data products within a companion U.S. Geological Survey data release (Ponti and others, 2019); the characteristics of fault rupture and ground deformation features are summarized in detail in the body of this report.
The results of this compilation reveal that faulting occurred within a 2-km-wide zone on six, roughly parallel traces within the West Napa Fault System. Most of the fault slip, and all the afterslip, occurred on the 21-km-long westernmost trace (Trace A). Maximum coseismic slip was greater than 40 cm and possibly as great as 60 cm, with the slip maximum located about 10 km north of the epicenter. Extensive ground deformation also occurred off the principal fault traces. Deformation characteristics of these features were not consistent with either primary faulting or shaking-induced ground failure and remain enigmatic, although this report includes speculation about possible origins.
The use of InSAR was invaluable for identifying and mapping secondary traces with small displacements, and for delineating the overall details of the extensive rupture. InSAR data also highlighted other areas with possible ground deformation—some of which are found coincident with previously mapped fault traces, whereas others are in areas where no faults were previously mapped. Several of these regions had no visible ground deformation, whereas others did produce features that were inconsistent with tectonic faulting, so care must be taken not to over interpret the InSAR data without careful, corroborating field investigations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191018","usgsCitation":"Ponti, D.J, Rosa, C.M, and Blair, J.L., 2019, The Mw 6.0 South Napa earthquake of August 24, 2014—Observations of surface faulting and ground deformation, with recommendations for improving post-earthquake field investigations: U.S. Geological Survey Open-File Report 2019–1018, 50 p., 15 appendixes, https://doi.org/10.3133/ofr20191018.","productDescription":"Report: v, 57 p.; Appendixes 1-15","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-081675","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":361845,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix10.pdf","text":"Appendix 10","size":"5.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 10","linkHelpText":"— Shaking-related features resulting from lateral spreads and bank failures"},{"id":361841,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix06.pdf","text":"Appendix 6","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 6","linkHelpText":"— Surface faulting along Trace E"},{"id":361846,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix12.pdf","text":"Appendix 12","size":"5.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 12","linkHelpText":"— Isolated cracking on slopes"},{"id":361838,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix03.pdf","text":"Appendix 3","size":"1.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 3","linkHelpText":"— Surface faulting along Trace B"},{"id":361840,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix05.pdf","text":"Appendix 5","size":"1.1MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 5","linkHelpText":"— Surface faulting along Trace D"},{"id":361843,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix08.pdf","text":"Appendix 8","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 8","linkHelpText":"— Surface faulting along Trace G"},{"id":361836,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr2019-1018.pdf","text":"Report","size":"29.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018","linkHelpText":" (Includes Appendix 1)"},{"id":361847,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix11.pdf","text":"Appendix 11","size":"2.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 11","linkHelpText":"— Ridge-top fractures"},{"id":361848,"rank":14,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix13.pdf","text":"Appendix 13","size":"5.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 13","linkHelpText":"— Fractures associated with UAVSAR lineaments"},{"id":361849,"rank":15,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix14.pdf","text":"Appendix 14","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 14","linkHelpText":"— Areas of extensive curb and sidewalk damage"},{"id":361850,"rank":16,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix15.pdf","text":"Appendix 15","size":"1.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 15","linkHelpText":"— Pavement cracks south of the Soda Creek Fault"},{"id":361851,"rank":17,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendixes1_15.zip","text":"Appendixes 1–15","size":"81.3 MB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2019-1018 Appendixes 2–15"},{"id":361852,"rank":18,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P26W84","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Digital datasets documenting fault rupture and ground deformation features produced by the Mw 6.0 South Napa Earthquake of August 24, 2014"},{"id":361842,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix07.pdf","text":"Appendix 7","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 7","linkHelpText":"— Surface faulting along Trace F"},{"id":361835,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1018/coverthb.jpg"},{"id":361844,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix09.pdf","text":"Appendix 9","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 9","linkHelpText":"— Shaking-induced deformation owing to landslide reactivation or fill settlement"},{"id":361837,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix02.pdf","text":"Appendix 2","size":"41.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 2","linkHelpText":"— Surface faulting along Trace A"},{"id":361839,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix04.pdf","text":"Appendix 4","size":"10.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 4","linkHelpText":"— Surface faulting along Trace C"}],"country":"United States","state":"California","county":"Napa County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.46871948242186,\n 38.1237539824224\n ],\n [\n -122.34100341796875,\n 38.1237539824224\n ],\n [\n -122.21603393554688,\n 38.1237539824224\n ],\n [\n -122.21878051757811,\n 38.3868805698475\n ],\n [\n -122.47009277343749,\n 38.3868805698475\n ],\n [\n -122.46871948242186,\n 38.1237539824224\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Earthquake Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
The painted turtle (Chrysemys picta) is widely distributed from coast to coast in North America with each of four subspecies generally occupying different regions. In the southwestern USA and northern Mexico, where C. p. bellii is the expected native race, populations are small and widelyscattered. Introduced populations of other painted turtle subspecies are reported from various locations in the USA. We discovered a small but dense introduced population of C. p. marginata on the Colorado Plateau in northern Arizona, a region with few, if any, turtles due to aridity and an elevated topography with little surface water. The turtles were in a remote pond constructed to provide cattle with water. Chrysemys p. marginata occur naturally east of the Mississippi River, over 2,000 km away. The nearest native population of C. p. bellii in Arizona is over 160 km away. We observed nesting females, juveniles, and the presence of shelled eggs in females via Xradiography confirming a self-sustaining population. The body sizes and nesting season we observed were consistent with data for those variables from native populations of the taxon. It is unknown exactly how the turtles came to be established in such a remote location, but it is unlikely that they will spread due to the scarcity of perennial water sources in the semi-arid region. Due to increasing drought frequency and duration in the region, small populations like this one, introduced into a novel environment, may be bellwethers for monitoring the effects of climate change.
","language":"English","publisher":"The Herpetological Society of Japan","doi":"10.5358/hsj.38.91","usgsCitation":"Lovich, J.E., Christman, B.L., Cummings, K.L., Norris, J., Puffer, S., and Jones, C., 2019, An introduced breeding population of Chrysemys picta marginata in the Kaibab National Forest, northern Arizona: Current Herpetology, v. 38, no. 1, p. 91-98, https://doi.org/10.5358/hsj.38.91.","productDescription":"8 p.","startPage":"91","endPage":"98","ipdsId":"IP-101843","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":361868,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Kaibab National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.63458251953125,\n 34.951241964789645\n ],\n [\n -111.73095703125,\n 34.951241964789645\n ],\n [\n -111.73095703125,\n 35.655064568953875\n ],\n [\n -112.63458251953125,\n 35.655064568953875\n ],\n [\n -112.63458251953125,\n 34.951241964789645\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":759042,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christman, Bruce L.","contributorId":207392,"corporation":false,"usgs":false,"family":"Christman","given":"Bruce","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":759043,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cummings, Kristy L. 0000-0002-8316-5059","orcid":"https://orcid.org/0000-0002-8316-5059","contributorId":202061,"corporation":false,"usgs":true,"family":"Cummings","given":"Kristy","email":"","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":759044,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Norris, Jenna 0000-0003-1312-4478","orcid":"https://orcid.org/0000-0003-1312-4478","contributorId":214059,"corporation":false,"usgs":false,"family":"Norris","given":"Jenna","email":"","affiliations":[{"id":38973,"text":"Formerly USGS SBSC Flagstaff, AZ now at NAU","active":true,"usgs":false}],"preferred":false,"id":759045,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Puffer, Shellie R. 0000-0003-4957-0963","orcid":"https://orcid.org/0000-0003-4957-0963","contributorId":193099,"corporation":false,"usgs":true,"family":"Puffer","given":"Shellie R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":759046,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jones, Christina","contributorId":214060,"corporation":false,"usgs":false,"family":"Jones","given":"Christina","affiliations":[{"id":38974,"text":"Arizona Game and Fish Department, Terrestrial Wildlife Branch, 5000 W. Carefree Highway, Phoenix, AZ 85086-5000","active":true,"usgs":false}],"preferred":false,"id":759047,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70202005,"text":"sir20185169 - 2019 - Flood-inundation maps for Lake Champlain in Vermont and New York","interactions":[{"subject":{"id":70170965,"text":"sir20165060 - 2016 - Flood-inundation maps for Lake Champlain in Vermont and in northern Clinton County, New York","indexId":"sir20165060","publicationYear":"2016","noYear":false,"title":"Flood-inundation maps for Lake Champlain in Vermont and in northern Clinton County, New York"},"predicate":"SUPERSEDED_BY","object":{"id":70202005,"text":"sir20185169 - 2019 - Flood-inundation maps for Lake Champlain in Vermont and New York","indexId":"sir20185169","publicationYear":"2019","noYear":false,"title":"Flood-inundation maps for Lake Champlain in Vermont and New York"},"id":1}],"lastModifiedDate":"2019-03-11T13:07:35","indexId":"sir20185169","displayToPublicDate":"2019-03-07T16:15:00","publicationYear":"2019","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":"2018-5169","displayTitle":"Flood-Inundation Maps for Lake Champlain in Vermont and New York","title":"Flood-inundation maps for Lake Champlain in Vermont and New York","docAbstract":"In 2016, digital flood-inundation maps along the shoreline of Lake Champlain in Addison, Chittenden, Franklin, and Grand Isle Counties in Vermont and northern Clinton County in New York were created by the U.S. Geological Survey (USGS) in cooperation with the International Joint Commission (IJC). This report discusses the creation of updated static digital flood-inundation mapping, in 2018, to include the entire shoreline of Lake Champlain in the United States. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent of flooding corresponding to selected water-surface elevations (stages) at the USGS lake gages on Lake Champlain.
As a result of the record setting floods of May 2011 in Lake Champlain and the Richelieu River, the U.S. and Canadian governments requested that the IJC issue a reference for a study to identify how flood forecasting, preparedness, and mitigation could be improved in the Lake Champlain–Richelieu River Basin. The IJC submitted the Lake Champlain–Richelieu River Plan of Study to the governments of Canada and the United States in 2013. The flood-inundation maps in this study are one aspect of the task work outlined in the IJC 2013 Plan of Study.
Wind and seiche effects (standing oscillating wave with a long wavelength) that can influence flooding along the Lake Champlain shoreline were not represented. The flood-inundation maps reflect 11 stages for Lake Champlain that are static for the entire area of the lake. Near-real-time stages at the USGS gages on Lake Champlain may be obtained from the USGS National Water Information System website at http://waterdata.usgs.gov/ (https://doi.org/10.5066/F7P55KJN) or from the National Weather Service Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/.
Updated static flood-inundation boundary extents were created for Lake Champlain in Franklin, Chittenden, Addison, Rutland, and Grand Isle Counties in Vermont and Clinton, Essex, and Washington Counties in New York by using recently acquired (2009, 2012, 2014, and 2015) light detection and ranging (lidar) data. The corresponding flood-inundation maps may be referenced to any of the four active USGS lake gages on Lake Champlain. Of these four active lake gages, USGS lake gage 04295000, Richelieu River (Lake Champlain) at Rouses Point, N.Y.; USGS lake gage 04294500, Lake Champlain at Burlington, Vt.; USGS lake gage 04279085 Lake Champlain north of Whitehall, N.Y.; and USGS lake gage 04294413, Lake Champlain at Port Henry, N.Y., only the Richelieu River (Lake Champlain) at Rouses Point, N.Y., gage also serves as a National Weather Service prediction location. Lake Champlain static flood-inundation map boundary extents corresponding to the May 2011 peak flood stage (103.20 feet [ft], National Geodetic Vertical Datum of 1929 [NGVD 29], as recorded at the USGS Rouses Point lake gage, were compared to the flood-inundation area extents determined from satellite imagery for the May 2011 flood (which incorporated documented high-water marks from the flood of May 2011) and were found to be in good agreement. The May 2011 flood is the highest recorded lake water level (stage) at the Rouses Point, N.Y., and Burlington, Vt., lake gages. Flood stages greater than 101.5 ft (NGVD 29) exceed the “major flood stage” as defined by the National Weather Service for USGS lake gage 04295000.
Updated digital elevation models (DEMs) were created from the recent lidar data for Lake Champlain in Vermont and New York. These DEMs were used in determining the flood-inundation boundary and associated depth grids for 11 flood stages at 0.5-ft or 1-ft intervals from 100.0 to 106.0 ft (NGVD 29) as referenced to the USGS lake gages. In addition, the May 2011 flood-inundation area for elevation 103.20 ft (NGVD 29) (102.77 ft, North American Vertical Datum of 1988) was determined from these updated DEMs.
The availability of these maps, along with online information regarding current stages at the USGS lake gages and forecasted high-flow stages from the National Weather Service at USGS lake gage 04295000, Richelieu River (Lake Champlain) at Rouses Point, N.Y., will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185169","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Flynn, R.H., and Hayes, L., 2019, Flood-inundation maps for Lake Champlain in Vermont and New York: U.S. Geological Survey Scientific Investigations Report 2018–5169, 14 p., https://doi.org/10.3133/sir20185169. [Supersedes USGS Scientific Investigations Report 2016–5060.]","productDescription":"Report: v, 14 p.; Application Site; Data release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101452","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":437545,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZBDF6S","text":"USGS data release","linkHelpText":"Flood-Inundation Shapefiles and Grids for Lake Champlain in Vermont and New York"},{"id":361774,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://wimcloud.usgs.gov/apps/FIM/FloodInundationMapper.html ","linkHelpText":"- Flood Inundation Mapper"},{"id":361771,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5169/coverthb.jpg"},{"id":361772,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5169/sir20185169.pdf","text":"Report","size":"1.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5169"},{"id":361773,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZBDF6S ","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood-inundation shapefiles and grids for Lake Champlain in Vermont and New York"}],"country":"United States","state":"New York, Vermont","county":"Addison, Chittenden, Clinton, Franklin, Grand Isle ","otherGeospatial":"Lake Champlain","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -73.7081,43.5785 ], [ -73.7081,45.0891 ], [ -72.8948,45.0891 ], [ -72.8948,43.5785 ], [ -73.7081,43.5785 ] ] ] } } ] }","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
The Florida Coastal Mapping Program is a nascent but highly relevant program that has the potential to greatly enhance the “Blue Economy” of Florida by coordinating and facilitating sea-floor mapping efforts and aligning partner and stakeholder activities for increased efficiency and cost reduction. Sustained acquisition of modern coastal mapping information for Florida may improve management of resources and reduce costs by eliminating redundancy. Economic growth could be aided by improved data to support emerging sectors such as aquaculture and renewable energy.
The present focus of the Florida Coastal Mapping Program is on modern, high-resolution bathymetric and coastal topobathymetric data, which can be immediately used to update navigational charts and identify navigation hazards, provide fundamental baseline data for scientific research, and provide information for use by emergency managers and responders. Derivative products include identifying sand resources for beach nourishment, creating vastly improved models for coastal erosion and flooding, identifying coastal springs, and creating benthic habitat maps. The uses and applications of the data generated could grow over time. The process of creating a steering committee and technical team, conducting an inventory and gaps analysis, soliciting feedback from the stakeholder and partner communities, and developing a prioritization process has provided a framework on which a successful program can develop a sustainable funding strategy that may be an investment the citizens of Florida could benefit from for decades.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191017","collaboration":"Prepared in cooperation with the Florida Institute of Oceanography, Florida Fish and Wildlife Research Institute, and Florida Department of Environmental Protection","usgsCitation":"Hapke, C.J., Kramer, P.A., Fetherston-Resch, E.H., Baumstark, R.D., Druyor, R., Fredericks, X., and Fitos, E., 2019, Florida Coastal Mapping Program—Overview and 2018 workshop report: U.S. Geological Survey Open-File Report 2019–1017, 19 p., https://doi.org/10.3133/ofr20191017.","productDescription":"vii, 19 p.","numberOfPages":"28","ipdsId":"IP-099357","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":361829,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1017/ofr20191017.pdf","text":"Report","size":"5.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1017"},{"id":361828,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1017/coverthb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.00,\n 24.5\n ],\n [\n -80,\n 24.5\n ],\n [\n -80,\n 30.75\n ],\n [\n -88.00,\n 30.75\n ],\n [\n -88.00,\n 24.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 Fourth Street South
St. Petersburg, FL 33701
We mapped yearly (2000–2016) estimates of annual grass percent cover for much of the sagebrush ecosystem of the western United States using remotely sensed, climate, and geophysical data in regression-tree models. Annual grasses senesce and cure by early summer and then become beds of fine fuel that easily ignite and spread fire through rangeland systems. Our annual maps estimate the extent of these fuels and can serve as a tool to assist land managers and scientists in understanding the ecosystem’s response to weather variations, disturbances, and management. Validating the time series of annual maps is important for determining the usefulness of the data. To validate these maps, we compare Bureau of Land Management Assessment Inventory and Monitoring (AIM) data to mapped estimates and use a leave-one-out spatial assessment technique that is effective for validating maps that cover broad geographical extents. We hypothesize that the time series of annual maps exhibits high spatiotemporal variability because precipitation is highly variable in arid and semiarid environments where sagebrush is native, and invasive annual grasses respond to precipitation. The remotely sensed data that help drive our regression-tree model effectively measures annual grasses’ response to precipitation. The mean absolute error (MAE) rate varied depending on the validation data and technique used for comparison. The AIM plot data and our maps had substantial spatial incongruence, but despite this, the MAE rate for the assessment equaled 12.62%. The leave-one-out accuracy assessment had an MAE of 8.43%. We quantified bias, and bias was more substantial at higher percent cover. These annual maps can help management identify actions that may alleviate the current cycle of invasive grasses because it enables the assessment of the variability of annual grass − percent cover distribution through space and time, as part of dynamic systems rather than static systems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2018.09.004","usgsCitation":"Boyte, S.P., Wylie, B.K., and Major, D.J., 2019, Validating a time series of annual grass percent cover in the sagebrush ecosystem: Rangeland Ecology and Management, v. 72, no. 2, p. 347-359, https://doi.org/10.1016/j.rama.2018.09.004.","productDescription":"13 p.","startPage":"347","endPage":"359","ipdsId":"IP-101002","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":467831,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2018.09.004","text":"Publisher Index Page"},{"id":437546,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71J98QK","text":"USGS data release","linkHelpText":"A Time Series of Herbaceous Annual Cover in the Sagebrush Ecosystem"},{"id":361853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"72","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Boyte, Stephen P. 0000-0002-5462-3225 sboyte@usgs.gov","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":139238,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen","email":"sboyte@usgs.gov","middleInitial":"P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":758988,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":758989,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Major, Donald J.","contributorId":83405,"corporation":false,"usgs":false,"family":"Major","given":"Donald","email":"","middleInitial":"J.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":758990,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}