{"pageNumber":"292","pageRowStart":"7275","pageSize":"25","recordCount":40783,"records":[{"id":70211908,"text":"70211908 - 2020 - Disentangling the potential effects of land-use and climate change on stream conditions","interactions":[],"lastModifiedDate":"2021-07-02T13:41:08.444328","indexId":"70211908","displayToPublicDate":"2020-01-19T13:33:49","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Disentangling the potential effects of land-use and climate change on stream conditions","docAbstract":"<p><span>Land‐use and climate change are significantly affecting stream ecosystems, yet understanding of their long‐term impacts is hindered by the few studies that have simultaneously investigated their interaction and high variability among future projections. We modeled possible effects of a suite of 2030, 2060, and 2090 land‐use and climate scenarios on the condition of 70,772 small streams in the Chesapeake Bay watershed, United States. The Chesapeake Basin‐wide Index of Biotic Integrity, a benthic macroinvertebrate multimetric index, was used to represent stream condition. Land‐use scenarios included four Special Report on Emissions Scenarios (A1B, A2, B1, and B2) representing a range of potential landscape futures. Future climate scenarios included quartiles of future climate changes from downscaled Coupled Model Intercomparison Project ‐ Phase 5 (CMIP5) and a watershed‐wide uniform scenario (Lynch2016). We employed random forests analysis to model individual and combined effects of land‐use and climate change on stream conditions. Individual scenarios suggest that by 2090, watershed‐wide conditions may exhibit anywhere from large degradations (e.g., scenarios A1B, A2, and the CMIP5 25th percentile) to small degradations (e.g., scenarios B1, B2, and Lynch2016). Combined land‐use and climate change scenarios highlighted their interaction and predicted, by 2090, watershed‐wide degradation in 16.2% (A2 CMIP5 25th percentile) to 1.0% (B2 Lynch2016) of stream kilometers. A goal for the Chesapeake Bay watershed is to restore 10% of stream kilometers over a 2008 baseline; our results suggest meeting and sustaining this goal until 2090 may require improvement in 11.0%–26.2% of stream kilometers, dependent on land‐use and climate scenario. These results highlight inherent variability among scenarios and the resultant uncertainty of predicted conditions, which reinforces the need to incorporate multiple scenarios of both land‐use (e.g., development, agriculture, etc.) and climate change in future studies to encapsulate the range of potential future conditions.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/gcb.14961","usgsCitation":"Maloney, K.O., Krause, K.P., Buchanan, C., Hay, L., McCabe, G.J., Smith, Z.M., Sohl, T.L., and Young, J.A., 2020, Disentangling the potential effects of land-use and climate change on stream conditions: Global Change Biology, v. 26, no. 4, p. 2251-2269, https://doi.org/10.1111/gcb.14961.","productDescription":"19 p.","startPage":"2251","endPage":"2269","ipdsId":"IP-108922","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science 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,{"id":70229397,"text":"70229397 - 2020 - Effect of environmental factors on the movement of Rainbow Trout in the Deerfield Reservoir System","interactions":[],"lastModifiedDate":"2022-03-11T17:11:48.638861","indexId":"70229397","displayToPublicDate":"2020-01-18T09:49:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10384,"text":"Journal of FisheriesSciences.com","active":true,"publicationSubtype":{"id":10}},"title":"Effect of environmental factors on the movement of Rainbow Trout in the Deerfield Reservoir System","docAbstract":"<p><span>Spawning movements and the factors affecting those movements are often of interest to fisheries managers and biologists. The objective of this study was to examine the influence of environmental factors on the movements of an adfluvial Rainbow Trout <i>Oncorhynchus mykiss</i> population in the Black Hills, South Dakota. Three unique strains of hatchery-reared Rainbow Trout and resident Rainbow Trout were implanted with passive integrated transponder (PIT) tags and movements between Deerfield Reservoir and the Castle Creek tributary system were monitored from August, 2010-July, 2011. Initial adfluvial movements of Rainbow Trout were detected using a stationary PIT tag reader deployed near the mouth of Castle Creek. Multiple linear regressions were used to model the relationship between PIT tagged Rainbow Trout movement and water temperature, photoperiod, and discharge. Using Akaike’s information criterion (AIC) to compare models, discharge was the top supported model explaining variation in Rainbow Trout movement. Additionally, models containing temperature and photoperiod were also supported. Supported models only explained moderate levels of variation (&lt;23%) in Rainbow Trout movement. Understanding how environmental variables affect the movement patterns of this unique population is essential in determining the proper management strategy for the Deerfield Reservoir system.</span></p>","language":"English","publisher":"IMed Pub LTD","usgsCitation":"Kientz, J., Davis, J., Chipps, S.R., and Simpson, G., 2020, Effect of environmental factors on the movement of Rainbow Trout in the Deerfield Reservoir System: Journal of FisheriesSciences.com, v. 14, no. 1, p. 1-6.","productDescription":"6 p.","startPage":"1","endPage":"6","ipdsId":"IP-124673","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":397026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397024,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fisheriessciences.com/fisheries-aqua/effect-of-environmental-factors-on-the-movement-of-rainbow-trout-in-the-deerfield-reservoir-system.php?aid=26132"}],"country":"United States","state":"South Dakota","otherGeospatial":"Castle Creek, Deerfield Reservoir","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -103.8398551940918,\n              44.00158219755276\n            ],\n            [\n              -103.8017463684082,\n              44.00158219755276\n            ],\n            [\n              -103.8017463684082,\n              44.02726038819847\n            ],\n            [\n              -103.8398551940918,\n              44.02726038819847\n            ],\n            [\n              -103.8398551940918,\n              44.00158219755276\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"14","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kientz, Jeremy","contributorId":205425,"corporation":false,"usgs":false,"family":"Kientz","given":"Jeremy","email":"","affiliations":[{"id":37104,"text":"South Dakota Department of Game, Fish and Parks","active":true,"usgs":false}],"preferred":false,"id":837832,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, Jacob L.","contributorId":275831,"corporation":false,"usgs":false,"family":"Davis","given":"Jacob L.","affiliations":[{"id":56698,"text":"South Dakota Department of Game, Fish, and Parks","active":true,"usgs":false}],"preferred":false,"id":837833,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":837273,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Simpson, Gregory","contributorId":288393,"corporation":false,"usgs":false,"family":"Simpson","given":"Gregory","email":"","affiliations":[{"id":56698,"text":"South Dakota Department of Game, Fish, and Parks","active":true,"usgs":false}],"preferred":false,"id":837834,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70207892,"text":"sim3449 - 2020 - High-resolution airborne geophysical survey of the Shellmound, Mississippi area","interactions":[],"lastModifiedDate":"2022-04-22T20:07:01.788312","indexId":"sim3449","displayToPublicDate":"2020-01-17T16:20:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3449","displayTitle":"High-Resolution Airborne Geophysical Survey of the Shellmound, Mississippi Area","title":"High-resolution airborne geophysical survey of the Shellmound, Mississippi area","docAbstract":"<p>In late February to early March 2018, the U.S. Geological Survey acquired 2,364 line-kilometers (km) of airborne electromagnetic, magnetic, and radiometric data in the Shellmound, Mississippi study area. The purpose of this survey is to contribute high-resolution information about subsurface geologic structure to inform groundwater models, water resource infrastructure studies, and local decision making. The Shellmound region hosts a managed aquifer recharge (MAR) pilot project, developed by the Agricultural Research Service of the U.S. Department of Agriculture. The MAR pilot project is investigating the use of bank filtration along the Tallahatchie River as a source for recharge in areas of significant groundwater decline. Direct injection into the Mississippi River Valley Alluvial aquifer (MRVA) occurs about 3 km from the extraction gallery. Understanding the structure of the aquifer, including both shallow and deep confining units, is important for the success of this pilot MAR study and may be even more important for potential future large-scale MAR projects and groundwater model development efforts.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3449","usgsCitation":"Burton, B.L., Minsley, B.J., Bloss, B.R., Kress, W.H., Rigby, J.R., and Smith, B.D., 2020, High-resolution airborne geophysical survey of the Shellmound, Mississippi area: U.S. Geological Survey Scientific Investigations Map 3449, 2 sheets, https://doi.org/10.3133/sim3449.","productDescription":"2 Sheets: 28.09 x 21.01 inches and 29.96 x 24.19 inches; Data Release; ReadMe","onlineOnly":"Y","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":399521,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109607.htm"},{"id":371340,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D4EA9W","text":"USGS data release","linkHelpText":"Airborne electromagnetic, magnetic, and radiometric survey, Shellmound, Mississippi, March 2018"},{"id":371339,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3449/sim3449_ReadMe.txt","text":"Read Me","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3449 Read Me"},{"id":371338,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3449/sim3449_sheet2.pdf","text":"Sheet 2—","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3449 Sheet 2","linkHelpText":"High-Resolution Airborne Geophysical Survey of the Shellmound, Mississippi Area"},{"id":371337,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3449/sim3449_sheet1.pdf","text":"Sheet 1—","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3449 Sheet 1","linkHelpText":"High-Resolution Airborne Geophysical Survey of the Shellmound, Mississippi Area"},{"id":371336,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3449/coverthb.jpg"}],"country":"United States","state":"Mississippi","county":"Leflore County","city":"Shellmound","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -90.5333,\n              33.5242\n            ],\n            [\n              -90.1628,\n              33.5242\n            ],\n            [\n              -90.1628,\n              33.8\n            ],\n            [\n              -90.5333,\n              33.8\n            ],\n            [\n              -90.5333,\n              33.5242\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"http:/www.usgs.gov/centers/gggsc/\" data-mce-href=\"http:/www.usgs.gov/centers/gggsc/\">Geology, Geophysics, and Geochemistry Science Center</a><br>U.S. Geological Survey<br>Box 25046, MS-973<br>Denver, CO 80225-0046</p>","publishedDate":"2020-01-17","noUsgsAuthors":false,"publicationDate":"2020-01-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Burton, Bethany L. 0000-0001-5011-7862 blburton@usgs.gov","orcid":"https://orcid.org/0000-0001-5011-7862","contributorId":1341,"corporation":false,"usgs":true,"family":"Burton","given":"Bethany L.","email":"blburton@usgs.gov","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":779674,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":779675,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bloss, Benjamin R. 0000-0002-1678-8571 bbloss@usgs.gov","orcid":"https://orcid.org/0000-0002-1678-8571","contributorId":139981,"corporation":false,"usgs":true,"family":"Bloss","given":"Benjamin","email":"bbloss@usgs.gov","middleInitial":"R.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":779676,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kress, Wade H. 0000-0002-6833-028X wkress@usgs.gov","orcid":"https://orcid.org/0000-0002-6833-028X","contributorId":1576,"corporation":false,"usgs":true,"family":"Kress","given":"Wade","email":"wkress@usgs.gov","middleInitial":"H.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":779677,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rigby, James R. 0000-0002-5611-6307","orcid":"https://orcid.org/0000-0002-5611-6307","contributorId":196374,"corporation":false,"usgs":false,"family":"Rigby","given":"James R.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":false,"id":779678,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Bruce D. 0000-0002-1643-2997 bsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-1643-2997","contributorId":845,"corporation":false,"usgs":true,"family":"Smith","given":"Bruce","email":"bsmith@usgs.gov","middleInitial":"D.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":779679,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70249835,"text":"70249835 - 2020 - Alpine plant community diversity in species-area relations at fine scale","interactions":[],"lastModifiedDate":"2023-11-01T20:51:01.204397","indexId":"70249835","displayToPublicDate":"2020-01-16T15:49:07","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":899,"text":"Arctic, Antarctic, and Alpine Research","active":true,"publicationSubtype":{"id":10}},"title":"Alpine plant community diversity in species-area relations at fine scale","docAbstract":"<div class=\"hlFld-Abstract\"><p class=\"last\">Observations of diversity in alpine vegetation appear to be scale dependent. The relations of plant species richness with surface processes and geomorphology have been studied, but patterns of beta diversity are less known. In Glacier National Park, Montana, diversity has been examined within 1 m<sup>2</sup><span>&nbsp;</span>plots and for 16 m<sup>2</sup><span>&nbsp;</span>plots across two ranges, with within-plot and across-range explanatory factors, respectively. The slopes of species–area equations for nested 4, 8, 12, and 16 m<sup>2</sup><span>&nbsp;</span>plots were used as an indicator of beta diversity in Glacier National Park, where smaller and larger scales have been examined. The slopes were negatively related to a field assessment of surface stability and positively to the presence of talus—two sides of the same coin. A positive relationship with bedrock outcrops may be due to a misrepresentation of area for plants. The relationship of species–area slopes to plot-level gamma diversity was negative, weak, and marginally significant, and this variable did not enter the general linear model (GLM). Beyond simple differences in diversity with differences in environment, examination of beta diversity at a scale between that of earlier studies revealed surface processes and geomorphology as drivers that were also at a scale between those previously reported.</p></div>","language":"English","publisher":"Taylor and Francis","doi":"10.1080/15230430.2019.1698894","usgsCitation":"Malanson, G.P., Nelson, E.L., Zimmerman, D.L., and Fagre, D., 2020, Alpine plant community diversity in species-area relations at fine scale: Arctic, Antarctic, and Alpine Research, v. 52, no. 1, p. 41-46, https://doi.org/10.1080/15230430.2019.1698894.","productDescription":"6 p.","startPage":"41","endPage":"46","ipdsId":"IP-108601","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":458107,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15230430.2019.1698894","text":"Publisher Index Page"},{"id":422314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-01-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Malanson, George P.","contributorId":189162,"corporation":false,"usgs":false,"family":"Malanson","given":"George","email":"","middleInitial":"P.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":887300,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelson, Emma L","contributorId":331310,"corporation":false,"usgs":false,"family":"Nelson","given":"Emma","email":"","middleInitial":"L","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":887301,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zimmerman, Dale L.","contributorId":166811,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Dale","email":"","middleInitial":"L.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":887303,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fagre, Daniel B. 0000-0001-8552-9461","orcid":"https://orcid.org/0000-0001-8552-9461","contributorId":224632,"corporation":false,"usgs":true,"family":"Fagre","given":"Daniel B.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":887302,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70207314,"text":"sir20195137 - 2020 - Precipitation, temperature, groundwater-level elevation, streamflow, and potential flood storage trends within the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River basins in Texas through 2017","interactions":[],"lastModifiedDate":"2022-04-25T19:47:32.575058","indexId":"sir20195137","displayToPublicDate":"2020-01-16T15:40:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5137","displayTitle":"Precipitation, Temperature, Groundwater-Level Elevation, Streamflow, and Potential Flood Storage Trends Within the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River Basins in Texas Through 2017","title":"Precipitation, temperature, groundwater-level elevation, streamflow, and potential flood storage trends within the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River basins in Texas through 2017","docAbstract":"<p>The U.S. Geological Survey (USGS), in cooperation with the U.S. Army Corps of Engineers (USACE), analyzed streamflow trends and streamflow-related variables through 2017 in seven important water-supply basins to provide information that can help water managers with the USACE and river authorities make future water management decisions. The primary purpose of this report is to document trends in long-term streamflow data at 114 selected USGS streamflow-gaging stations and 36 simulated reservoir-inflow stations in 7 river basins primarily in Texas: Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity. In this report, trends were considered statistically significant if their <i>p</i>-values were less than or equal to 0.05 (<i>p</i>-value ≤0.05). Streamflow data selected for temporal trend analyses included annual minimum streamflow, annual peak streamflow, and streamflow volume. Precipitation, air temperature, and groundwater-level-elevation data were analyzed for trends that may help to explain changes observed in the streamflow statistics. Basins were divided into sections along county lines for precipitation analyses. Streamflow volumes were analyzed for associations with potential flood storage. The potential flood storage, defined as the difference between maximum storage and normal storage, was computed for each dam from the National Inventory of Dams database and accumulated over time based on the completion date of the dam.</p><p>Precipitation and air temperature trends were analyzed for each of the eight climate divisions (High Plains, Trans-Pecos, Low Rolling Hills, Edwards Plateau, North Central Texas, South Central Texas, East Texas, and Upper Coast). Results of precipitation trend analyses indicated moderate upward trends in the Upper Coast and East Texas Climate Divisions analyzed on an annual time step from 1900 through 2017. These two climate divisions are in the eastern and southeastern parts of the State, and they receive more mean annual precipitation (45.88 and 46.09 inches, respectively) than the other climate divisions. The results of air temperature analyses indicated upward trends in annual mean air temperature within all climate divisions, with a mean slope of 0.02 degree Fahrenheit per year, or 1 degree every 50 years.</p><p>Within the Brazos River Basin, results of precipitation trend analyses on an annual time step indicated that precipitation amounts are most likely increasing in the lower and middle sections of the basin. Downward trends in annual streamflow and in the ratio of streamflow volume to precipitation volume were indicated at 7 of the 15 stations in the upper sections of the basin. The lower sections of the basin had mostly downward trends in annual minimum streamflow, whereas upward trends in annual minimum streamflow were indicated in the upper sections of the basin. Downward trends in annual peak streamflow were indicated at many of the stations in the upper sections of the basin. At the same seven stations in the upper sections of the basin where there were downward trends in annual streamflow, there were also downward trends in the ratio of streamflow volume to precipitation volume. The data from the same seven stations indicated negative associations between potential flood storage volume and annual streamflow volume and downward trends in the ratio of annual streamflow volume to potential flood storage volume. With the known addition of 13,006,394 acre-feet of potential flood storage between 1900 and 2010 in the subbasins analyzed, streamflow volumes have decreased in the upper sections of the Brazos River Basin.</p><p>Within the Colorado River Basin, results of precipitation trend analyses on an annual time step indicated no trends in the basin. Downward trends in annual streamflow were indicated at 16 stations in the upper sections of the basin, whereas no trends in annual streamflow were indicated in the lower section of the basin. In the lower section of the basin, one station that was operated as a continuous streamflow-gaging station through 2017 had a downward trend in annual minimum streamflow, and another station (operated through 2007) had an upward trend in annual minimum streamflow. In the upper sections of the basin, data from seven stations indicated upward trends in annual minimum streamflow, and data from six stations indicated downward trends. Data from 18 stations in the upper sections of the basin indicated downward trends in annual peak streamflow. Thirteen of the 16 stations in the upper sections of the basin with data that indicated downward trends in annual streamflow also have data that indicated downward trends in the ratio of streamflow volume to precipitation volume. Data from the same 13&nbsp;stations indicated negative associations between potential flood storage volume and annual streamflow volume and downward trends in the ratio of annual streamflow volume to potential flood storage volume. With the known addition of 7,193,147 acre-feet of potential flood storage between 1891 and 2014 in the subbasins analyzed, streamflow volumes have decreased in the upper sections of the Colorado River Basin.</p><p>Within the Big Cypress Basin, results of precipitation trend analyses on annual, seasonal, and monthly time steps indicated almost no trends in the basin as defined in this report. However, the annual precipitation <i>p</i>-value only slightly exceeded the <i>p</i>-value threshold for a statistically significant trend. Given the upward trend in precipitation in the East Texas Climate Division, which includes the Big Cypress Basin, and the low <i>p</i>-value for annual precipitation within the basin, precipitation in the basin may be increasing over time. Two annual streamflow trends, one upward and one downward, were in the upper parts of the basin. Data from USGS streamflow-gaging station 07346000 Big Cypress Bayou near Jefferson, Texas, indicated an upward trend in annual minimum streamflow and a downward trend in annual peak streamflow. The station is immediately downstream from Lake O’ the Pines; presumably, minimums have increased because of regulated releases, and annual peaks have decreased because of storage from the lake for flood control. Despite the known addition of 2,737,154 acre-feet of potential flood storage between 1898 and 2011 in the subbasins analyzed, there have not been widespread reductions in streamflow volumes in the Big Cypress Basin, except for within the drainage area for the farthest upstream station on the main stem downstream from Mount Pleasant, Texas.</p><p>Within the Guadalupe River Basin, results of precipitation trend analyses on an annual time step indicated an upward trend in the lower section of the basin, but no trends in annual streamflow were indicated in the lower section of the basin. In the upper section of the basin, data from 1 of the 13 stations indicated an upward trend in annual streamflow. Data from 6 of the 13 stations in the upper section of the basin indicated a trend in annual minimum streamflow with 4&nbsp;upward and 2 downward trends. Data from 2 of the 13&nbsp;stations in the upper section of the basin indicated downward trends in annual peak streamflow. Despite the known addition of 2,016,534 acre-feet of potential flood storage between 1849 and 2013 in the subbasins analyzed, streamflow volumes have not decreased in the Guadalupe River Basin.</p><p>Within the Neches River Basin, results of precipitation trend analyses on an annual time step indicated upward trends in the basin. None of the data from stations analyzed in the Neches River Basin indicated annual trends in streamflow despite upward trends in annual precipitation within the basin. Data from 9 of the 19 stations analyzed in the basin indicated upward trends in annual minimum streamflow. Data from one of the simulated-inflow stations indicated a downward trend in annual minimum streamflow into Sam Rayburn Reservoir. Data from two stations indicated downward trends in annual peak streamflow, and data from one small subbasin indicated an upward trend in annual peak streamflow. Despite the known addition of 4,839,609 acre-feet of potential flood storage between 1888 and 2008 in the subbasins analyzed, there have not been widespread reductions in streamflow volumes in the Neches River Basin.</p><p>Within the Sulphur River Basin, results of precipitation trend analyses on an annual time step indicated a moderate upward trend within the basin. Data from only one of the stations, the simulated inflow to Jim Chapman Lake, indicated an annual upward trend in streamflow despite an upward trend in annual precipitation throughout the basin. Data from three of the six stations in the Sulphur River Basin indicated upward trends in annual minimum streamflow, and data from one of the six stations indicated a downward trend in annual peak streamflow. Despite the known addition of 6,933,361 acre-feet of potential flood storage between 1904 and 2006 in the subbasins analyzed, streamflow volumes have not decreased in the Sulphur River Basin.</p><p>Within the Trinity River Basin, results of precipitation trend analyses on an annual time step indicated upward trends in most sections of the basin. Data from 8 of the 36 stations analyzed for trends in annual streamflow indicated upward trends, and all 8 stations are in the upper sections of the basin. None of the data from stations in the lower sections of the basin indicated trends in annual streamflow. Data from 16 of the 36 stations indicated upward trends in annual minimum streamflow. Upward trends in annual minimum streamflow could be the result of managed reservoir releases in combination with wastewater treatment plant releases in the large Dallas-Fort Worth metroplex in the upper sections of the basin. All the trends in annual peak streamflow were in the sections of the basin that include the Dallas-Fort Worth metroplex. Data from two stations, one USGS streamflow-gaging station and one simulated-inflow station, indicated upward trends in annual peak streamflow, and data from one streamflow-gaging station indicated a downward trend in annual peak streamflow. Of the basins included in this study, the Trinity River Basin has the second largest amount of potential flood storage of 8,947,349 acre-feet from dams added between 1890 and 2013. Eleven stations in the Trinity River Basin had positive associations between potential flood storage volume and annual streamflow volume, indicating that annual streamflow increases as potential flood storage increases. Data from 7 of the 11 stations also indicated upward trends in annual streamflow. The positive associations may be the result of increases in minimum streamflow, which could be the result of any combination of managed reservoir releases, wastewater treatment plant releases, or increased runoff from urbanized areas, particularly in the urbanized area of the Dallas-Fort Worth metroplex.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195137","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Fort Worth District","usgsCitation":"Harwell, G.R., McDowell, J.S., Gunn, C.L., and Garrett, B.S., 2020, Precipitation, temperature, groundwater-level elevation, streamflow, and potential flood storage trends within the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River basins in Texas through 2017 (ver. 1.1, April 2020): U.S. Geological Survey Scientific Investigations Report 2019–5137, 94 p., https://doi.org/10.3133/sir20195137.","productDescription":"Report: x, 94 p.; 5 Tables; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102896","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":399613,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109606.htm"},{"id":374071,"rank":9,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5137/coverthb2.jpg"},{"id":373986,"rank":8,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5137/versionHist.txt","text":"Version History","size":"1.35 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2019–5137 Version History"},{"id":371261,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5137/sir20195137_table9.xlsx","text":"Table 9—","size":"120 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 9","linkHelpText":"Summary of annual, seasonal, and monthly trends in the ratio of streamflow volume to precipitation volume in the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River Basins"},{"id":371258,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5137/sir20195137_table7.xlsx","text":"Table 7—","size":"64 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 7","linkHelpText":"Summary of precipitation temporal trends around the time of annual peak streamflow in the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River Basins"},{"id":371255,"rank":2,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5137/sir20195137_table5.xlsx","text":"Table 5—","size":"80 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 5","linkHelpText":"Summary of annual, seasonal, and monthly associations between precipitation volume and streamflow volume in the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River Basins"},{"id":371252,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L1F7PT","text":"USGS data release","description":"USGS data release","linkHelpText":"Data used to assess precipitation, temperature, groundwater-level elevation, streamflow, and potential flood storage trends within the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River Basins in Texas through 2017"},{"id":373985,"rank":7,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5137/sir20195137_v1.1.pdf","text":"Report","size":"20.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5137"},{"id":371259,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5137/sir20195137_table8.xlsx","text":"Table 8—","size":"144 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 8","linkHelpText":"Summary of annual, seasonal, and monthly streamflow volume trends in the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River Basins"},{"id":371262,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5137/sir20195137_table10.xlsx","text":"Table 10—","size":"48 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 10","linkHelpText":"Summary of trends in annual minimum streamflow and annual peak streamflow and relations between streamflow volume and potential flood storage volume in the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River Basins"}],"country":"United States","state":"Texas","otherGeospatial":"Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River basins","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -101.4667,\n              28.4167\n            ],\n            [\n              -93.0619,\n              28.4167\n            ],\n            [\n              -93.0619,\n              33.6667\n            ],\n            [\n              -101.4667,\n              33.6667\n            ],\n            [\n              -101.4667,\n              28.4167\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","edition":"Version 1.0: January 2020; Version 1.1: April 2020","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/tx-water/\" data-mce-href=\"https://www.usgs.gov/centers/tx-water/\">Oklahoma-Texas Water Science Center</a><br>U.S. Geological Survey<br>1505 Ferguson Lane<br>Austin, TX 78754–4501</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Precipitation and Temperature Trends by Climate Division</li><li>Groundwater-Level Elevation Trends for Major Aquifers</li><li>Precipitation, Streamflow, and Potential Flood Storage Trends by River Basin</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2020-01-16","revisedDate":"2020-04-16","noUsgsAuthors":false,"publicationDate":"2020-01-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Harwell, Glenn R. 0000-0003-4265-2296","orcid":"https://orcid.org/0000-0003-4265-2296","contributorId":221295,"corporation":false,"usgs":true,"family":"Harwell","given":"Glenn R.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":777673,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McDowell, Jeremy 0000-0002-8132-9806","orcid":"https://orcid.org/0000-0002-8132-9806","contributorId":221296,"corporation":false,"usgs":true,"family":"McDowell","given":"Jeremy","email":"","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":777674,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gunn-Rosas, Cathina 0000-0002-6633-3735","orcid":"https://orcid.org/0000-0002-6633-3735","contributorId":221298,"corporation":false,"usgs":true,"family":"Gunn-Rosas","given":"Cathina","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":777676,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garrett, Brett 0000-0003-0132-2426","orcid":"https://orcid.org/0000-0003-0132-2426","contributorId":221297,"corporation":false,"usgs":true,"family":"Garrett","given":"Brett","email":"","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":777675,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70228356,"text":"70228356 - 2020 - Use of underwater videography to quantify conditions utilized by endangered Moapa Dace While spawning","interactions":[],"lastModifiedDate":"2022-02-09T18:06:42.187099","indexId":"70228356","displayToPublicDate":"2020-01-16T11:58:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Use of underwater videography to quantify conditions utilized by endangered Moapa Dace While spawning","docAbstract":"<p><span>Advances in underwater camera technology provide an affordable means to quantify the environmental conditions under which fish spawn. This information is important for investigating spawning ecology, managing habitat, or providing information for captive breeding programs. We deployed 12 modified security cameras underwater to identify environmental conditions related to the spawning behavior of the critically endangered Moapa Dace&nbsp;</span><i>Moapa coriacea</i><span>, a Mojave Desert stream-dwelling cyprinid that had never been observed spawning and that had fallen to a low of 459 individual fish 4&nbsp;years prior to this study. Camera sites were selected systematically along the stream to represent the variety of conditions available. We divided the field of view in front of each camera into a grid, and we estimated both the available environment and the habitat over which Moapa Dace showed spawning behavior. From over 4,000 10-min video clips that were randomly selected for analysis, 13 spawning events were identified. Using nonparametric contingency table analyses, we found that Moapa Dace selected depths between 30 and 34&nbsp;cm, water velocities between 0.11 and 0.17&nbsp;m/s, cobble substrate, and overhead instream cover. Although the recorded sample size of spawning events was small (13), our sample represents a large proportion of events given that the world's entire population of Moapa Dace at the time was approximately 650 fish distributed over multiple kilometers of stream length. Environmental conditions identified by this study were replicated in laboratory facilities to successfully propagate Moapa Dace for the first time in captivity. These propagation methods are now used in a management setting by the Nevada Department of Wildlife to maintain a captive population of this rare fish. Camera methods can be effective in helping to identify spawning conditions where water clarity is sufficient.</span></p>","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10356","usgsCitation":"Ruggirello, J.E., Bonar, S.A., Feuerbacher, O.G., and Simons, L.H., 2020, Use of underwater videography to quantify conditions utilized by endangered Moapa Dace While spawning: North American Journal of Fisheries Management, v. 40, no. 1, p. 17-28, https://doi.org/10.1002/nafm.10356.","productDescription":"12 p.","startPage":"17","endPage":"28","ipdsId":"IP-110930","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395702,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Plumer Stream, Warm Springs area","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -114.74601745605469,\n              36.696502641380036\n            ],\n            [\n              -114.66361999511719,\n              36.696502641380036\n            ],\n            [\n              -114.66361999511719,\n              36.74108512094412\n            ],\n            [\n              -114.74601745605469,\n              36.74108512094412\n            ],\n            [\n              -114.74601745605469,\n              36.696502641380036\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"40","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-01-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruggirello, Jack E.","contributorId":30526,"corporation":false,"usgs":true,"family":"Ruggirello","given":"Jack","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":833924,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bonar, Scott A. 0000-0003-3532-4067 sbonar@usgs.gov","orcid":"https://orcid.org/0000-0003-3532-4067","contributorId":3712,"corporation":false,"usgs":true,"family":"Bonar","given":"Scott","email":"sbonar@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833923,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Feuerbacher, Olin G.","contributorId":275282,"corporation":false,"usgs":false,"family":"Feuerbacher","given":"Olin","email":"","middleInitial":"G.","affiliations":[{"id":40855,"text":"UA","active":true,"usgs":false}],"preferred":false,"id":833925,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Simons, Lee H.","contributorId":264621,"corporation":false,"usgs":false,"family":"Simons","given":"Lee","email":"","middleInitial":"H.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":833926,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70207451,"text":"ofr20191147 - 2020 - Kelp forest monitoring at Naval Base Ventura County, San Nicolas Island, California: Fall 2017 and Spring 2018, Fourth Annual Report","interactions":[],"lastModifiedDate":"2022-04-21T20:21:41.150501","indexId":"ofr20191147","displayToPublicDate":"2020-01-16T09:37:32","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1147","displayTitle":"Kelp Forest Monitoring at Naval Base Ventura County, San Nicolas Island, California: Fall 2017 and Spring 2018, Fourth Annual Report","title":"Kelp forest monitoring at Naval Base Ventura County, San Nicolas Island, California: Fall 2017 and Spring 2018, Fourth Annual Report","docAbstract":"<p><span>To assess and track changes to the rocky subtidal communities surrounding San Nicolas Island, the U.S. Navy entered into an agreement with the U.S. Geological Survey (USGS) in 2014 to conduct an ecological monitoring program at several sites around the island. Four permanent sites—Nav Fac 100, West End, Dutch Harbor, and Daytona 100—were established. The sites were based on ones that had been monitored since 1980 by USGS and were combined or expanded for better comparability with monitoring programs conducted at the other California Channel Islands. At the sites, scientists from USGS and our cooperator, the University of California, Santa Cruz, measured bottom cover of algae and sessile invertebrate species in quadrats, counted and sized fish on swimming transects, and counted a suite of kelps and invertebrates on benthic band transects. Holdfast diameter and number of stipes of giant kelp (<i>Macrocystis pyrifera</i>) were recorded on these transects, and size data were collected for urchins, sea stars, and shelled mollusks. Bottom temperatures were recorded at hourly intervals by archival data loggers that were deployed at the sites. This report focuses primarily on data collected in fall 2017 and spring 2018 and makes comparisons with data collected in previous years, beginning in fall 2014.</span></p><p><span>Nav Fac 100 is a site with a relatively low benthic profile, situated on the north side of San Nicolas Island. It was previously urchin dominated but underwent a dramatic decline in purple sea urchins in 2015 and 2016. Since then, macroalgae has become more prevalent as both annual brown algae, such as Dictyota, and perennials (for example, <i>Cystoseira</i>) have become established. The invasive brown alga <i>Sargassum horneri</i> has also become established. West End, on the southwest side of the island, also lacks much bottom relief but has more crevice habitat associated with boulders. It remains dominated by kelps and red algae, but red algae have decreased recently. Dutch Harbor, on the south side, has many high relief rocky reefs and had the greatest fish and non-motile invertebrate densities. It remains the most stable of the sites. Daytona 100, on the southeast side, has moderate relief and has remained a patchwork of kelp and urchin dominated areas with moderate fish density.</span></p><p><span>The main change at the sites during the last 4 years was the decline in urchin numbers at Nav Fac 100. There was storm-related mortality and subsequent recruitment in the <i>M. pyrifera</i> population at several of the sites in both 2016 and 2017. The winter of 2018, however, was relatively mild, with less destructive storm-related disturbance. The invasive brown alga <i>S. horneri</i>, first seen at San Nicolas Island at Nav Fac 100 in fall 2015, has become firmly established there during the last 2 sampling years. Finally, moderate increases were observed in purple urchin densities at all sites this spring. Long-term data are presented to illustrate trends and changes over the past three decades. Results indicate continued monitoring to evaluate ecosystem effects from perturbations owing to natural processes and anthropomorphic factors, including recovery of the sea otter population, changes in fisheries, invasive species and changing environmental conditions, could be valuable to inform managers’ decision-making.</span></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191147","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Kenner, M.C., and Tomoleoni, J., 2020, Kelp forest monitoring at Naval Base Ventura County, San Nicolas Island, California: Fall 2017 and Spring 2018, Fourth Annual Report: U.S. Geological Survey Open-File Report 2019–1147, 76 p., https://doi.org/10.3133/ofr20191147.","productDescription":"vi, 76 p.","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-111796","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":399434,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109596.htm"},{"id":371253,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1147/coverthb.jpg"},{"id":371254,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1147/ofr20191147.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1147"}],"country":"United States","state":"California","otherGeospatial":"San Nicolas Island","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -119.64111328125,\n              33.18353672893615\n            ],\n            [\n              -119.37744140625,\n              33.18353672893615\n            ],\n            [\n              -119.37744140625,\n              33.32134852669881\n            ],\n            [\n              -119.64111328125,\n              33.32134852669881\n            ],\n            [\n              -119.64111328125,\n              33.18353672893615\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://www.usgs.gov/centers/werc/connect\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/werc/connect\">Director</a>,<br><a href=\"https://www.usgs.gov/centers/werc\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/werc\">Western Ecological Research Center</a><br><a href=\"https://www.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/\">U.S. Geological Survey</a><br>3020 State University Drive East<br>Sacramento, California 95819</p>","tableOfContents":"<p></p><ul><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Site Descriptions</li><li>Trip Conditions and Accomplishments</li><li>Results</li><li>Conclusions and Management Considerations</li><li>References Cited</li><li>Appendix 1. Sampling History</li></ul><p></p>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2020-01-15","noUsgsAuthors":false,"publicationDate":"2020-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Kenner, Michael C. 0000-0003-4659-461X","orcid":"https://orcid.org/0000-0003-4659-461X","contributorId":208151,"corporation":false,"usgs":true,"family":"Kenner","given":"Michael","email":"","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":778104,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tomoleoni, Joseph A. 0000-0001-6980-251X jtomoleoni@usgs.gov","orcid":"https://orcid.org/0000-0001-6980-251X","contributorId":167551,"corporation":false,"usgs":true,"family":"Tomoleoni","given":"Joseph","email":"jtomoleoni@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":778105,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70208459,"text":"70208459 - 2020 - Amphibian chytrid prevalence on boreal toads in SE Alaska and NW British Columbia: Tests of habitat, life stages, and temporal trends","interactions":[],"lastModifiedDate":"2020-02-12T06:13:53","indexId":"70208459","displayToPublicDate":"2020-01-16T07:52:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1396,"text":"Diseases of Aquatic Organisms","active":true,"publicationSubtype":{"id":10}},"title":"Amphibian chytrid prevalence on boreal toads in SE Alaska and NW British Columbia: Tests of habitat, life stages, and temporal trends","docAbstract":"Tracking and understanding variation in pathogens such as Batrachochytrium dendrobatidis ([Bd]), which causes amphibian chytridiomycosis and has caused population declines globally, is a priority for many land managers. However, there has been relatively little sampling of amphibian communities at high latitudes. We used skin swabs collected during 2005–2017 from boreal toads (Anaxyrus boreas; N = 248), in southeast Alaska (USA; primarily in Klondike Gold Rush National Historical Park [KLGO]) and northwest British Columbia (Canada) to determine how Bd prevalence varied across life stages, habitat characteristics, local species richness, and time. Across all years, Bd prevalence peaked in June and was >3 times greater for adult toads (37.5%) vs. juveniles and metamorphs (11.2%). Bd prevalence for toads in the KLGO area, where other amphibian species are rare or absent, was highest from river habitats (55.0%), followed by human-modified upland wetlands (32.3%) and natural upland wetlands (12.7%) — the same rank-order these habitats are used for toad breeding. No Columbia spotted frogs (N = 12) or wood frogs (N = 2) from the study area tested Bd-positive, although all were from an area of low host density where Bd has not been detected. Prevalence of Bd on toads in the KLGO area decreased during 2005–2015. This trend from a largely single-species system may be encouraging or concerning, depending on how Bd is affecting vital rates, and emphasizes the need to understand effects of pathogens before translating disease prevalence into management actions.","language":"English","publisher":"Inter-Research","doi":"10.3354/dao03430","usgsCitation":"Hossack, B.R., Adams, M.J., Honeycutt, R., Belt, J.J., and Pyare, S., 2020, Amphibian chytrid prevalence on boreal toads in SE Alaska and NW British Columbia: Tests of habitat, life stages, and temporal trends: Diseases of Aquatic Organisms, v. 137, p. 159-165, https://doi.org/10.3354/dao03430.","productDescription":"7 p.","startPage":"159","endPage":"165","ipdsId":"IP-109582","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":372209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","otherGeospatial":"Southeastern Alaska, Northwestern British Columbia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -141.416015625,\n              60.02095215374802\n            ],\n            [\n              -139.04296875,\n              58.03137242177637\n            ],\n            [\n              -133.9453125,\n              52.26815737376817\n            ],\n            [\n              -130.166015625,\n              50.62507306341435\n            ],\n            [\n              -124.1015625,\n              51.6180165487737\n            ],\n            [\n              -127.79296875,\n              53.330872983017066\n            ],\n            [\n              -129.814453125,\n              57.89149735271034\n            ],\n            [\n              -137.4609375,\n              61.18562468142281\n            ],\n            [\n              -141.416015625,\n              60.02095215374802\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"137","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hossack, Blake R. 0000-0001-7456-9564 blake_hossack@usgs.gov","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":1177,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake","email":"blake_hossack@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":781975,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, Michael J. 0000-0001-8844-042X","orcid":"https://orcid.org/0000-0001-8844-042X","contributorId":211916,"corporation":false,"usgs":true,"family":"Adams","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":781976,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Honeycutt, R Ken","contributorId":222362,"corporation":false,"usgs":false,"family":"Honeycutt","given":"R Ken","affiliations":[],"preferred":false,"id":781977,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belt, Jami J","contributorId":222363,"corporation":false,"usgs":false,"family":"Belt","given":"Jami","email":"","middleInitial":"J","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":781978,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pyare, S","contributorId":222364,"corporation":false,"usgs":false,"family":"Pyare","given":"S","affiliations":[{"id":40534,"text":"University of Alaska Southeast, Juneau","active":true,"usgs":false}],"preferred":false,"id":781979,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70209182,"text":"70209182 - 2020 - Is your ad hoc model selection strategy affecting your multimodel inference?","interactions":[],"lastModifiedDate":"2020-03-23T07:06:30","indexId":"70209182","displayToPublicDate":"2020-01-16T07:05:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Is your ad hoc model selection strategy affecting your multimodel inference?","docAbstract":"(Yackulic)  1.\tEcologists routinely fit complex models with multiple parameters of interest, where hundreds or more competing models are plausible. To limit the number of fitted models, ecologists often define a model selection strategy composed of a series of stages in which certain features of a model are compared while other features are held constant. Defining these multi-stage strategies requires making a series of decisions, which may potentially impact inferences, but have not been critically evaluated.\n2.\tWe begin by identifying key features of strategies, introducing descriptive terms when they did not already exist in the literature. Strategies differ in how they define and order model building stages. Sequential-by-sub-model strategies focus on one sub-model (parameter) at a time with modeling of subsequent sub-models dependent on the selected model structures from the previous stages. Secondary candidate set strategies model sub-models independently and combine the top set of models from each sub-model for selection in a final stage. Build-up approaches define stages across sub-models and increase in complexity at each stage. Strategies also differ in how the top set of models is selected in each stage and whether they use null or more complex model structures for non-target sub-models.\n3.\tWe tested the performance of different model selection strategies using four datasets and three model types. For each dataset, we determined the “true” distribution of AIC weights by fitting all plausible models. Then, we calculated the number of models that would have been fitted and the portion of “true” AIC weight we recovered under different model selection strategies.\n4.\tSequential-by-sub-model strategies often performed poorly. Build-up or secondary candidate sets were more reliable, provided all models within 5 AIC of the top model were carried forward to subsequent stages.  The structure of non-target sub-models was less important. \n5.\t Multi-stage approaches cannot compensate for a lack of critical thought in selecting covariates and building models to represent competing a priori hypotheses. However, even when competing hypotheses for different sub-models are limited, thousands or more models may be possible so strategies to explore candidate model space reliably and efficiently will be necessary.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2997","usgsCitation":"Morin, D.J., Yackulic, C.B., Diffendorfer, J., Lesmeister, D.B., Nielsen, C., Reid, J., and Schauber, E.M., 2020, Is your ad hoc model selection strategy affecting your multimodel inference?: Ecosphere, v. 11, no. 1, e02997, https://doi.org/10.1002/ecs2.2997.","productDescription":"e02997","ipdsId":"IP-106290","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":458118,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2997","text":"Publisher Index Page"},{"id":373428,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2020-01-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Morin, Dana J.","contributorId":200306,"corporation":false,"usgs":false,"family":"Morin","given":"Dana","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":785265,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":785266,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":223504,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James","email":"jediffendorfer@usgs.gov","middleInitial":"E.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":785267,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lesmeister, Damon B. 0000-0003-1102-0122","orcid":"https://orcid.org/0000-0003-1102-0122","contributorId":205006,"corporation":false,"usgs":false,"family":"Lesmeister","given":"Damon","email":"","middleInitial":"B.","affiliations":[{"id":37019,"text":"USDA Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":785268,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nielsen, Clayton","contributorId":223505,"corporation":false,"usgs":false,"family":"Nielsen","given":"Clayton","email":"","affiliations":[{"id":40724,"text":"Cooperative Wildlife Research Laboratory and Department of Forestry, Southern Illinois University, 251 Life Science II, Mail Code 6504, Carbondale, Illinois 62901 USA","active":true,"usgs":false}],"preferred":false,"id":785269,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reid, Janice","contributorId":89391,"corporation":false,"usgs":false,"family":"Reid","given":"Janice","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":785270,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schauber, Eric M.","contributorId":223506,"corporation":false,"usgs":false,"family":"Schauber","given":"Eric","email":"","middleInitial":"M.","affiliations":[{"id":40725,"text":"Illinois Natural History Survey, Prairie Research Institute, University of Illinois Urbana-Champaign, 1816 S. Oak St., Champaign, IL 61820 USA","active":true,"usgs":false}],"preferred":false,"id":785271,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70228439,"text":"70228439 - 2020 - A flexible survey design for monitoring spatiotemporal fish richness in nonwadeable rivers: optimizing efficiency by integrating gears","interactions":[],"lastModifiedDate":"2022-02-10T13:12:06.682769","indexId":"70228439","displayToPublicDate":"2020-01-16T07:04:21","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6455,"text":"Canadian Journal Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"A flexible survey design for monitoring spatiotemporal fish richness in nonwadeable rivers: optimizing efficiency by integrating gears","docAbstract":"<div>We designed a flexible protocol for monitoring fish species richness in nonwadeable rivers. Nine sites were sampled seasonally with six gears in two physiographic regions in Missouri (USA). Using resampling procedures and mixed-effects modeling, we quantified richness and compositional overlap among gears, identified efficient gear combinations, and evaluated protocol performance across regions and seasons. We detected 25–75 species per sample and 89 185 fish. On average, no single gear detected &gt;62% of observed species, but an optimized, integrated-gear protocol with four complementary gears on average detected 90% of species while only requiring 51.9% of initial sampling effort. Neither season nor physiographic region explained low spatiotemporal variation in percent richness detected by the integrated-gear protocol. In contrast, equivalent effort with an electrofishing-only protocol was 53.5% less efficient, seasonally biased and imprecise (36.1%–82.3% of richness), and on average detected 15.9% less of observed richness. Altogether, riverine fish richness is likely underestimated with single-gear survey designs. When paired with existing wadeable-stream inventories, our customizable approach could benefit regional monitoring by comprehensively documenting riverine contributions to riverscape biodiversity.</div>","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2019-0315","usgsCitation":"Dunn, C., and Paukert, C.P., 2020, A flexible survey design for monitoring spatiotemporal fish richness in nonwadeable rivers: optimizing efficiency by integrating gears: Canadian Journal Fisheries and Aquatic Sciences, v. 77, no. 6, p. 978-990, https://doi.org/10.1139/cjfas-2019-0315.","productDescription":"13 p.","startPage":"978","endPage":"990","ipdsId":"IP-111381","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"77","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dunn, Corey G.","contributorId":275809,"corporation":false,"usgs":false,"family":"Dunn","given":"Corey G.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":834297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834298,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70228899,"text":"70228899 - 2020 - An agricultural water use package for MODFLOW and GSFLOW","interactions":[],"lastModifiedDate":"2022-02-23T12:45:47.212533","indexId":"70228899","displayToPublicDate":"2020-01-16T06:43:59","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7599,"text":"Environmental Modeling and Software","active":true,"publicationSubtype":{"id":10}},"title":"An agricultural water use package for MODFLOW and GSFLOW","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"abs0010\" class=\"abstract author\" lang=\"en\"><div id=\"abssec0010\"><p id=\"abspara0010\"><span>The Agricultural Water Use (AG) Package was developed for simulating demand-driven and supply-constrained agricultural water use in MODFLOW and GSFLOW models. The AG Package uses pre-existing hydrologic simulation provided by MODFLOW and GSFLOW. Three options are available for simulating water use for agriculture: (1) user-specified demands, (2) demands determined by a user-specified irrigation trigger value that is compared to the ratio of the simulated actual to&nbsp;potential evapotranspiration&nbsp;(ET), and (3) demands determined by minimizing the difference between potential and actual&nbsp;ET. The latter two approaches use energy and soil-water balance to determine crop-water demands. Irrigation withdrawals are diverted into canals and routed to fields using the MODFLOW&nbsp;</span>SFR<span>&nbsp;</span>Package, or irrigation water is provided/supplemented by groundwater. Combined with MODFLOW or GSFLOW, the AG Package can simulate dynamic water use by agriculture in developed basins while providing flexibility to represent a range of irrigation practices.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2019.104617","usgsCitation":"Niswonger, R.G., 2020, An agricultural water use package for MODFLOW and GSFLOW: Environmental Modeling and Software, v. 125, 104617, 16 p., https://doi.org/10.1016/j.envsoft.2019.104617.","productDescription":"104617, 16 p.","ipdsId":"IP-109425","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":458119,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2019.104617","text":"Publisher Index Page"},{"id":396332,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"125","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":197892,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":835828,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70236131,"text":"70236131 - 2020 - Peak ground motions and site response at Anza and Imperial Valley, California","interactions":[],"lastModifiedDate":"2022-08-30T14:00:58.547116","indexId":"70236131","displayToPublicDate":"2020-01-15T08:55:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3208,"text":"Pure and Applied Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Peak ground motions and site response at Anza and Imperial Valley, California","docAbstract":"<p><span>Power spectra of shear-waves for eighteen earthquakes from the Anza-Imperial Valley region were inverted for source, mid-path Q, site attenuation and site response. The motivation was whether differences in site attenuation (parameterized as&nbsp;</span><i>t*, r/cQ,</i><span>&nbsp;where&nbsp;</span><i>r</i><span>&nbsp;is distance along ray path near the site,&nbsp;</span><i>c</i><span>&nbsp;is shear velocity and&nbsp;</span><i>Q</i><span>&nbsp;is the quality factor that parameterizes attenuation) and site response could be correlated with residuals in peak values of velocity or acceleration after removing the affect of distance-dependent attenuation. We decomposed spectra of S-waves from horizontal components of 18 earthquakes from 2010 to 2018 into a common source for each event with ω</span><sup>−2</sup><span>&nbsp;spectral fall-off at high frequencies and then projected the residuals onto path and site terms following the methodology of Boatwright et al. (Bull Seismol Soc Am 81:1754–1782, 1991). The site terms were constrained to have an amplification at a particular frequency governed by V</span><sub>S30</sub><span>&nbsp;at two of the sites which had downhole shear-wave logs. The 18 events, 3 &lt; M &lt; 4, had moments between approximately 10</span><sup>20</sup><span>&nbsp;and 10</span><sup>22</sup><span>&nbsp;dyne-cm, and stress drops between 1 and 100&nbsp;bars. Average mid-crust attenuation had a Q of 844 reflecting the average path through the crystalline rock of the San Jacinto Mountains.&nbsp;</span><i>t*</i><span>&nbsp;for each station corresponded to the geologic environment such that stations on hard rock had low&nbsp;</span><i>t*</i><span>&nbsp;(e.g. stations KNW, PFO and RDM) a station in the San Jacinto fault zone (station SND) had a moderate&nbsp;</span><i>t*</i><span>&nbsp;of 0.035&nbsp;s and stations in the Imperial Valley usually had higher&nbsp;</span><i>t*s</i><span>. Generally&nbsp;</span><i>t*</i><span>&nbsp;correlated with average amplification suggesting that sites characterized by low surface velocities and higher attenuation also have more amplification in the 1–6&nbsp;Hz band. Residuals of peak values were determined by subtracting the prediction of Boore and Atkinson (</span>2008<span>). There is a correlation between average amplification and peak velocity, but not peak acceleration. Interestingly, there is less scatter at high values of amplification although there is also less data. Scatter in values of peak velocity and peak acceleration are higher at shorter compared to longer durations. When using a frequency-dependent form for&nbsp;</span><i>Q</i><span>, variances are higher, sometimes much higher; the dataset does not support frequency-dependent&nbsp;</span><i>Q</i><span>, which is not similar to results from the Imperial Valley and northeastern North America.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s00024-019-02366-2","usgsCitation":"Fletcher, J.P., and Boatwright, J., 2020, Peak ground motions and site response at Anza and Imperial Valley, California: Pure and Applied Geophysics, v. 177, p. 2753-2769, https://doi.org/10.1007/s00024-019-02366-2.","productDescription":"17 p.","startPage":"2753","endPage":"2769","ipdsId":"IP-103872","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":458127,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00024-019-02366-2","text":"Publisher Index Page"},{"id":405902,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Anza","otherGeospatial":"Imperial Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -117,\n              32.7\n            ],\n            [\n              -115.2,\n              32.7\n            ],\n            [\n              -115.2,\n              33.8\n            ],\n            [\n              -117,\n              33.8\n            ],\n            [\n              -117,\n              32.7\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"177","noUsgsAuthors":false,"publicationDate":"2020-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Fletcher, Jon Peter B. 0000-0001-8885-6177 jfletcher@usgs.gov","orcid":"https://orcid.org/0000-0001-8885-6177","contributorId":1216,"corporation":false,"usgs":true,"family":"Fletcher","given":"Jon","email":"jfletcher@usgs.gov","middleInitial":"Peter B.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":850200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boatwright, John","contributorId":219666,"corporation":false,"usgs":false,"family":"Boatwright","given":"John","affiliations":[{"id":40044,"text":"USGS, deceased","active":true,"usgs":false}],"preferred":false,"id":850201,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70207810,"text":"sir20195151 - 2020 - Storage capacity and sedimentation characteristics of the San Antonio Reservoir, California, 2018","interactions":[],"lastModifiedDate":"2022-04-25T20:37:40.39933","indexId":"sir20195151","displayToPublicDate":"2020-01-15T08:04:46","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5151","displayTitle":"Storage Capacity and Sedimentation Characteristics of the San Antonio Reservoir, California, 2018","title":"Storage capacity and sedimentation characteristics of the San Antonio Reservoir, California, 2018","docAbstract":"<p>The San Antonio Reservoir is a large water storage facility in Alameda County, California, and is a major component of the Hetch Hetchy Regional Water System (RWS). The RWS is a water-supply system owned and operated by the San Francisco Public Utilities Commission (SFPUC) and provides water for about 2.7 million people in the San Francisco, Santa Clara, Alameda, and San Mateo Counties. The San Antonio Reservoir is one of two RWS reservoirs in Alameda County and the third largest of the RWS reservoirs in the San Francisco Bay Area. The reservoir was formed by the James H. Turner Dam, which was completed in 1965. At the time of construction, the reservoir was estimated to have 50,500 acre-feet (acre-ft) of storage capacity. That early estimate was based on a 1963 pre-construction topographic map, which was drawn from aerial photographs. The capacity of the reservoir was later surveyed in 1994 and 2000. These two later surveys did not include the upper 18 feet (ft) of the reservoir, which represents roughly 30 percent of the overall storage volume. To determine the storage capacity and provide updated stage-capacity curves up to the spillway, the U.S. Geological Survey, in cooperation with the SFPUC, surveyed the bathymetry and shoreline of the reservoir in April 2018.</p><p>The bathymetric survey was performed by making depth soundings using a boat-mounted, multibeam echosounder. At the time of the survey, the water level was between 13 and 14 ft below the spillway elevation. To measure capacity between the water line up to the spillway elevation, topography along most of the shoreline was surveyed from the boat using a terrestrial Light Detection and Ranging (LiDAR) scanner and in other areas by using ground-survey techniques. Location during bathymetric and topographic data collection was determined using a Global Navigation Satellite System-Real Time Network system. Vertical profiles of sound speed were collected periodically. The sound-speed profiles were used to spatially and temporally adjust the sound-speed calculations used to determine depth from the soundings. Approximately 125 kilometers (78 miles) of transects with a total of about 560 million depth soundings and topographic LiDAR points were collected (about 160 per square meter). In addition, approximately 500 topographic survey points were collected in shallow, wadable areas and on land near the upper reservoir area using a Global Navigation Satellite System receiver attached to a fixed length survey rod. Depth soundings, terrestrial LiDAR points, topographic survey points, and a digitized shoreline were merged and interpolated to generate a digital elevation model (DEM) of the reservoir. Gridded elevation data extracted from the DEM were then tabulated to determine total reservoir capacity and create reservoir stage-surface area and stage-storage capacity tables.</p><p>Results of the reservoir capacity analysis indicated that the reservoir has 53,266 (plus or minus 140) acre-ft of storage capacity, which is an increase of 2,766 acre-ft (or 5.5 percent) greater than the original 1965 estimate; the increase is likely due to improved survey methods. Also, at the time of this 2018 survey, Intake #1 (the lowest intake) was not in operation. Intake #1 is estimated to be buried approximately 10 ft below the bed, whereas Intake #2 is about 20 ft above the bed. There are five intakes at different elevation levels; however, when consecutive lower intakes become inoperable due to sedimentation, the live storage capacity (capacity available for use) is reduced. At the time of this survey, the remaining live storage (above Intake #2) was approximately 52,363 acre-ft.</p><p>The 2018 stage-capacity curve was compared to the original 1965 stage-capacity curve. Although overall, the changes indicate an increase in storage capacity, the change in volume at 372.7 ft North American Vertical Datum of 1988 (370 ft National Geodetic Vertical Datum of 1929, NGVD 29) shows a decrease of 733 acre-ft (the elevation of 370 ft NGVD 29 was used because it is the lowest elevation available for the 1965 stage-capacity curves). This finding agrees with the observed accumulation of sediment over Intake #1. That volume was converted to an annual sediment yield of 0.35 acre-ft per square mile (or 165 cubic meters per square kilometer), which is of the same order of magnitude as that found in other watersheds for the Coast Ranges in California. A decrease of 733 acre-ft between 1965 and 2018 thus represents a loss of 1.5 percent of the overall storage capacity in the reservoir. The updated stage-surface area and stage-capacity tables provided in this report and online (<a href=\"https://doi.org/10.5066/P9KC9DU8\" data-mce-href=\"https://doi.org/10.5066/P9KC9DU8\">https://doi.org/10.5066/P9KC9DU8</a>) can be used by the SFPUC to improve reservoir operations and serve as an accurate baseline to monitor bathymetric changes in the future.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195151","collaboration":"Prepared in cooperation with the San Francisco Public Utilities Commission","usgsCitation":"Marineau, M.D., Wright, S.A, and Lopez, J.V., 2020, Storage capacity and sedimentation characteristics of the San Antonio Reservoir, California, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5151, 34 p., https://doi.org/10.3133/sir20195151.","productDescription":"Report: vi, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-105258","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":399623,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109595.htm"},{"id":371223,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KC9DU8","linkHelpText":"Bathymetry, Stage-Area, and Stage-Volume Tables for the San Antonio Reservoir, California, 2018"},{"id":371222,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5151/sir20195151.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5151"},{"id":371221,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5151/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Antonio Reservoir","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.81846618652345,\n              37.596415965954684\n            ],\n            [\n              -121.85348510742188,\n              37.57138553454929\n            ],\n            [\n              -121.841983795166,\n              37.565262680889965\n            ],\n            [\n              -121.8335723876953,\n              37.56186087804736\n            ],\n            [\n              -121.82378768920898,\n              37.5711134184077\n            ],\n            [\n              -121.81726455688477,\n              37.582541440297746\n            ],\n            [\n              -121.80301666259766,\n              37.5814531328266\n            ],\n            [\n              -121.80473327636719,\n              37.59083926161267\n            ],\n            [\n           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V. 0000-0003-4477-7025 jvlopez@usgs.gov","orcid":"https://orcid.org/0000-0003-4477-7025","contributorId":221656,"corporation":false,"usgs":true,"family":"Lopez","given":"Joan","email":"jvlopez@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":779410,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70224972,"text":"70224972 - 2020 - Estimation of nonlinear water-quality trends in high-frequency monitoring data","interactions":[],"lastModifiedDate":"2021-10-11T13:02:50.792784","indexId":"70224972","displayToPublicDate":"2020-01-15T07:58:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Estimation of nonlinear water-quality trends in high-frequency monitoring data","docAbstract":"<div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0100\">Recent advances in high-frequency water-quality sensors have enabled direct measurements of physical and chemical attributes in rivers and streams nearly continuously. Water-quality trends can be used to identify important watershed-scale changes driven by natural and anthropogenic influences. Statistical methods to estimate trends using high-frequency data are lacking. To address this gap, an evaluation of the generalized additive model (GAM) approach to test for trends in high-frequency data was conducted. Our proposed framework includes methods for handling serial correlation, trend estimation and slope-change detection, and trend interpretation at arithmetic scale for log-transformed variables. Water-temperature and turbidity data, representing two analytes with different temporal patterns, collected from the James River at Cartersville, Virginia, USA, were chosen for this analysis. Results indicated that the model, including flow, season, time covariates, and interaction between flow and season performed well for both analytes. The same model structure was applied to specific conductance data, collected from a small highly urbanized watershed, with satisfactory model performance. The water temperature GAM results indicated that the significant decreasing-then-increasing patterns after 2012 were mainly driven by air temperature changes. The turbidity trend was not significant over time. The specific conductance results showed a consistently upward trend over the last decade due to ever-increasing urbanization in the small watershed. This study suggests that the GAM method has great potential as a useful tool for trend analysis on high-frequency data, and for informing watershed managers of hydro-climatic and human influences on water quality by detecting crucial signal variation over time.</p></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.136686","usgsCitation":"Yang, G., and Moyer, D.L., 2020, Estimation of nonlinear water-quality trends in high-frequency monitoring data: Science of the Total Environment, v. 715, 136686, 12 p., https://doi.org/10.1016/j.scitotenv.2020.136686.","productDescription":"136686, 12 p.","ipdsId":"IP-113815","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":467305,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.136686","text":"Publisher Index Page"},{"id":390382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":390371,"type":{"id":15,"text":"Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.136686"}],"country":"United States","otherGeospatial":"Chesapeake Bay watershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -75.1904296875,\n              38.41916639395372\n            ],\n            [\n              -75.223388671875,\n              38.64261790634527\n            ],\n            [\n              -75.35522460937499,\n              38.79690830348427\n            ],\n            [\n              -75.498046875,\n              38.87392853923629\n            ],\n            [\n              -75.5419921875,\n              39.0533181067413\n    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gyang@usgs.gov","orcid":"https://orcid.org/0000-0001-5587-3683","contributorId":197859,"corporation":false,"usgs":true,"family":"Yang","given":"Guoxiang","email":"gyang@usgs.gov","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824950,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moyer, Douglas L. 0000-0001-6330-478X dlmoyer@usgs.gov","orcid":"https://orcid.org/0000-0001-6330-478X","contributorId":174389,"corporation":false,"usgs":true,"family":"Moyer","given":"Douglas","email":"dlmoyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824951,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70209626,"text":"70209626 - 2020 - The use of support vectors from support vector machines for hydrometeorologic monitoring network analyses","interactions":[],"lastModifiedDate":"2020-04-16T12:03:44.002862","indexId":"70209626","displayToPublicDate":"2020-01-14T06:58:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"The use of support vectors from support vector machines for hydrometeorologic monitoring network analyses","docAbstract":"Hydrometeorologic monitoring networks are ubiquitous in contemporary earth-system science. Network stakeholders often inquire about the importance of sites and their locations when discussing funding and monitoring design. Support vector machines (SVMs) can be useful by their assigning each monitoring site as either a support or nonsupport vector. A potentiometric surface was created from synthetic data and 800 random observation locations (sites) as an analog to a groundwater-level network. Using generalized additive models for potentiometric surface prediction, simulations show that a subsample of support vectors from the 800 sites will out perform random samples of sample size equaling the support vector count. Support vector percentages from simulation quantify the recurrence that SVMs assign each site as a support vector, and these percentages in turn measure site importance. An example application of support vector percentages identifies important monitoring sites needed to regionalize the 0.1 annual exceedance probability peak streamflow. The results indicate that 152 of 283 streamgages with support vector percentages equalling 100 percent have not operated since about 2000 and generally have much smaller drainage areas than the greater streamgage network in Texas. The drainage area disparity is an indication of historical imbalance in peak streamflow data acquisition from various stream sizes in Texas.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2019.124522","collaboration":"","usgsCitation":"Asquith, W.H., 2020, The use of support vectors from support vector machines for hydrometeorologic monitoring network analyses: Journal of Hydrology, v. 583, 124522, 10 p., https://doi.org/10.1016/j.jhydrol.2019.124522.","productDescription":"124522, 10 p.","ipdsId":"IP-104552","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":374045,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"583","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Asquith, William H. 0000-0002-7400-1861 wasquith@usgs.gov","orcid":"https://orcid.org/0000-0002-7400-1861","contributorId":1007,"corporation":false,"usgs":true,"family":"Asquith","given":"William","email":"wasquith@usgs.gov","middleInitial":"H.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":787258,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70225829,"text":"70225829 - 2020 - Using the Lomb-Scargle method for wave statistics from gappy time series","interactions":[],"lastModifiedDate":"2021-11-10T14:50:24.002282","indexId":"70225829","displayToPublicDate":"2020-01-13T08:43:38","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Using the Lomb-Scargle method for wave statistics from gappy time series","docAbstract":"<p><span>Sandwich Town Neck Beach in Sandwich, MA, has experienced substantial erosion and has been the subject of efforts by the town and private landowners to limit the sand loss. Erosion has been particularly dramatic in the past five years with the loss of dwellings. Sandwich's nourishment efforts presented a unique opportunity for scientists at the U.S. Geological Survey Woods Hole Coastal and Marine Science Center to monitor beach morphology and to test new technologies and techniques such as geo-referenced drone imaging. Two bottom lander deployments were performed in Cape Cod Bay at a location that was key to model the fate of waves at Sandwich Town Neck Beach and to support the study of beach morphological evolution. The study period was after the town nourished the beach and during a time when several intense winter storms reshaped the beach and removed much of the nourished sand. A TRDI Workhorse Sentinel V ADCP was used for both deployments. For wave bursts, the instruments collected 2048 samples at 2 Hz every hour. The first deployment during the winter of 2016 returned good quality data. The second deployment during the following winter had gaps throughout the time series from a wiring problem in the external battery pack. The timing of the gaps was random, the duration approximately 100 s. While most of the bursts started at the top of each hour, many had 1-3 gaps within. Time series data with random gaps are problematic for computing spectral density, and thus, wave statistics. This kind of situation is familiar in other scientific disciplines such as astrophysics [1], where techniques exist to find stationary signals in sparse data. One of these methods is the Lomb-Scargle technique for computing periodograms. The most useful feature of the Lomb-Scargle (LS) method is that it allows the spectral analysis of incomplete records, without having to manipulate the record to extrapolate from or replace missing data. We compared the effectiveness of LS against common methods of averaging Fourier transforms such as a simple un-windowed Fast Fourier transform (FFT), Welch's method, and TRDI's Wavesmon software; methods that are commonly used in oceanography for non-gappy data. Synthetic data series that have been artificially modified to introduce gaps were used to evaluate the performance of each method. The LS approach was able to recover spectral density even with several 100-s gaps present. The method was applied here to the gappy and non-gappy data from both Sandwich deployments, and wave statistics were obtained and compared to the wave-buoy data. LS was used to process data that contains gaps that was rejected by Wavesmon, which was approximately 39% of the dataset. Significant wave height and peak period from LS compared well with buoy data. Mean period computed on gappy data using LS produced values biased low, compared with other methods when gaps were filled with the mean value. The LS technique has potential to uncover low-frequency signals such as infragravity waves from gappy records where the non-gappy segments are not long enough to resolve them. It has potential to unlock new information from older data sets.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"2019 IEEE/OES twelfth current, waves and turbulence measurement (CWTM)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"IEEE Oceanic Engineering Society - Current, Waves, Turbulence and Measurement Applications Workshop","conferenceDate":"Mar 10-13, 2019","language":"English","publisher":"IEEE","doi":"10.1109/CWTM43797.2019.8955285","usgsCitation":"Martini, M.A., Aretxabaleta, A., and Sherwood, C.R., 2020, Using the Lomb-Scargle method for wave statistics from gappy time series, <i>in</i> 2019 IEEE/OES twelfth current, waves and turbulence measurement (CWTM), Mar 10-13, 2019, 9 p., https://doi.org/10.1109/CWTM43797.2019.8955285.","productDescription":"9 p.","ipdsId":"IP-105269","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":391573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","city":"Sandwich","otherGeospatial":"Sandwich Town Neck Beach","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -70.48888206481934,\n              41.762413206292656\n            ],\n            [\n              -70.47154426574707,\n              41.762413206292656\n            ],\n            [\n              -70.47154426574707,\n              41.77297600540535\n            ],\n            [\n              -70.48888206481934,\n              41.77297600540535\n            ],\n            [\n              -70.48888206481934,\n              41.762413206292656\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Martini, Marinna A. 0000-0002-7757-5158 mmartini@usgs.gov","orcid":"https://orcid.org/0000-0002-7757-5158","contributorId":2456,"corporation":false,"usgs":true,"family":"Martini","given":"Marinna","email":"mmartini@usgs.gov","middleInitial":"A.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":826574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":826575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":826576,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70208449,"text":"70208449 - 2020 - Effects of montane watershed development on vulnerability of domestic groundwater supply during drought","interactions":[],"lastModifiedDate":"2020-02-10T18:22:15","indexId":"70208449","displayToPublicDate":"2020-01-11T18:13:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Effects of montane watershed development on vulnerability of domestic groundwater supply during drought","docAbstract":"Climate change is expected to reduce recharge to montane aquifers in the western United States, but it is unclear how this will impact groundwater resources in watersheds where intensive surface-water development has disrupted the natural hydrologic regime. To better understand sources of recharge and associated vulnerabilities of groundwater supply in this setting, we made a detailed geochemical survey of domestic wells finished in fractured bedrock throughout the Yuba and Bear River watersheds (Sierra Nevada foothills, northern California)during historic drought (2015–2016). Stable isotopes of water and noble gas recharge temperatures closely tracked atmospheric lapse rates across a broad elevation gradient (100–2000 m), indicating groundwater inputs are dominated by local precipitation that rapidly recharges fractured bedrock during the winter wet-season. However, nearly one-quarter of wells had water isotopes that were fractionated by evaporation and warm recharge temperatures, indicative of mixing with dry-season recharge by surface water. Monte Carlo mixing models suggest evaporation-impacted groundwater samples are mixtures of local rain with an average of 28% ± 13% from diverted surface water that can recharge bedrock aquifers during the dry-season by either irrigation return flow or seepage from extensive distribution infrastructure. Wells that received recharge subsidies from diverted surface water had elevated levels of nitrate and coliform bacteria compared to those replenished exclusively by local precipitation,\nwhich are more vulnerable to supply shortage during drought. It is important to consider the impacts of increased surface-water development on the quantity and quality of groundwater recharge in rapidly developing montane watersheds.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2020.124567","usgsCitation":"Levy, Z., Fram, M.S., Faulkner, K., Alpers, C.N., Soltero, E.M., and Taylor, K.A., 2020, Effects of montane watershed development on vulnerability of domestic groundwater supply during drought: Journal of Hydrology, v. 583, 124567, 18 p., https://doi.org/10.1016/j.jhydrol.2020.124567.","productDescription":"124567, 18 p.","ipdsId":"IP-107517","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":458154,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2020.124567","text":"Publisher Index Page"},{"id":437168,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YETK9P","text":"USGS data release","linkHelpText":"Dissolved Noble Gas Concentrations and Modeled Recharge Temperatures for Groundwater from Northern Sierra Nevada Foothills Shallow Aquifer Assessment Study Units, 2015-2017: Results from the California GAMA Priority Basin Project"},{"id":372204,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Bear River watershed, Yuba River watershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.2451171875,\n              38.86323626888358\n            ],\n            [\n              -120.13275146484374,\n              38.86323626888358\n            ],\n            [\n              -120.13275146484374,\n              39.85282948915942\n            ],\n            [\n              -121.2451171875,\n              39.85282948915942\n            ],\n            [\n              -121.2451171875,\n              38.86323626888358\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"583","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Levy, Zeno F. 0000-0003-4580-2309","orcid":"https://orcid.org/0000-0003-4580-2309","contributorId":222340,"corporation":false,"usgs":true,"family":"Levy","given":"Zeno","middleInitial":"F.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781920,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fram, Miranda S. 0000-0002-6337-059X mfram@usgs.gov","orcid":"https://orcid.org/0000-0002-6337-059X","contributorId":1156,"corporation":false,"usgs":true,"family":"Fram","given":"Miranda","email":"mfram@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781921,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Faulkner, Kirsten 0000-0003-1628-2877","orcid":"https://orcid.org/0000-0003-1628-2877","contributorId":222341,"corporation":false,"usgs":true,"family":"Faulkner","given":"Kirsten","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781922,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alpers, Charles N. 0000-0001-6945-7365 cnalpers@usgs.gov","orcid":"https://orcid.org/0000-0001-6945-7365","contributorId":411,"corporation":false,"usgs":true,"family":"Alpers","given":"Charles","email":"cnalpers@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781923,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soltero, Evelyn M","contributorId":222342,"corporation":false,"usgs":false,"family":"Soltero","given":"Evelyn","email":"","middleInitial":"M","affiliations":[{"id":40530,"text":"All About Wells","active":true,"usgs":false}],"preferred":false,"id":781924,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Taylor, Kimberly A. 0000-0002-0095-6403 ktaylor@usgs.gov","orcid":"https://orcid.org/0000-0002-0095-6403","contributorId":1601,"corporation":false,"usgs":true,"family":"Taylor","given":"Kimberly","email":"ktaylor@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781925,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70260133,"text":"70260133 - 2020 - Mechanisms for ballistic block ejection during the 2016–2017 shallow submarine eruption of Bogoslof volcano, Alaska","interactions":[],"lastModifiedDate":"2024-10-29T16:44:36.747968","indexId":"70260133","displayToPublicDate":"2020-01-11T11:38:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Mechanisms for ballistic block ejection during the 2016–2017 shallow submarine eruption of Bogoslof volcano, Alaska","docAbstract":"<p><span>Ejection of ballistic blocks was a characteristic feature of the 2016–2017 Bogoslof eruption. High-resolution satellite images acquired throughout the duration of the 9-month long eruptive period permitted the recognition and mapping of ballistic blocks on the surface of Bogoslof Island. Many of the satellite images recorded the accumulation of ballistic material over several individual eruptive events, but a few images recorded the effects of a single event. The nonuniform spatial distribution of blocks suggests that some of the eruption columns were inclined. Ballistic trajectories were estimated using the Eject! model and indicate that accumulation of blocks on Bogoslof Island required launch angles of 45–80° and initial velocities of 50–100&nbsp;ms</span><sup>−1</sup><span>&nbsp;to reproduce observed travel distances. The amount of ballistic fallout observed in satellite data indicates that there must have been a shallow submarine source of rock within the conduit/upper edifice system. Dense, accidental cryptodome trachyandesite, and juvenile basalt to trachybasalt scoria make up the bulk of the surface ejecta. Abundant accidental fragments and inclined eruption columns point to periodic vent-wall collapse and jetting around edges of temporarily blocked vents as the likely cause of ballistic ejection.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s00445-019-1351-4","usgsCitation":"Waythomas, C.F., and Mastin, L.G., 2020, Mechanisms for ballistic block ejection during the 2016–2017 shallow submarine eruption of Bogoslof volcano, Alaska: Bulletin of Volcanology, v. 82, 13, 20 p., https://doi.org/10.1007/s00445-019-1351-4.","productDescription":"13, 20 p.","ipdsId":"IP-113632","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":463356,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bogoslof volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -168.04572245314344,\n              53.93741980478987\n            ],\n            [\n              -168.04572245314344,\n              53.92361535465113\n            ],\n            [\n              -168.0255939265047,\n              53.92361535465113\n            ],\n            [\n              -168.0255939265047,\n              53.93741980478987\n            ],\n            [\n              -168.04572245314344,\n              53.93741980478987\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"82","noUsgsAuthors":false,"publicationDate":"2020-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Waythomas, Christopher F. 0000-0002-3898-272X cwaythomas@usgs.gov","orcid":"https://orcid.org/0000-0002-3898-272X","contributorId":640,"corporation":false,"usgs":true,"family":"Waythomas","given":"Christopher","email":"cwaythomas@usgs.gov","middleInitial":"F.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mastin, Larry G. 0000-0002-4795-1992","orcid":"https://orcid.org/0000-0002-4795-1992","contributorId":265985,"corporation":false,"usgs":true,"family":"Mastin","given":"Larry","email":"","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917131,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70237952,"text":"70237952 - 2020 - Conjoint use of hydraulic head and groundwater age data to detect hydrogeologic barriers","interactions":[],"lastModifiedDate":"2022-11-01T14:06:22.857819","indexId":"70237952","displayToPublicDate":"2020-01-11T08:57:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Conjoint use of hydraulic head and groundwater age data to detect hydrogeologic barriers","docAbstract":"<p><span>Hydraulic head and groundwater age data are effective in building understanding of groundwater systems. Yet their joint role in detecting and characterising low-permeability geological structures, i.e. hydrogeologic barriers such as faults and dykes, has not been widely studied. Here, numerical flow and transport models, using MODFLOW-NWT and MT3D-USGS, were developed with different hydrogeologic barrier configurations in a hypothetical aquifer. Computed hydraulic head and groundwater age distributions were compared to those without a barrier. The conjoint use of these datasets helps in detecting vertically-oriented barriers. Two forms of recharge were compared: (1) applied across the entire aquifer surface (uniform), and (2) applied to the upstream part of the aquifer (upgradient). The hydraulic head distribution is significantly impacted by a barrier that penetrates the aquifer’s full vertical thickness. This barrier also perturbs the groundwater age distribution when upgradient recharge prevails; however, with uniform recharge, groundwater age is not successful in detecting the barrier. When a barrier is buried, such as by younger sediment, hydraulic head data also do not clearly identify the barrier. Groundwater age data could, on the other hand, prove to be useful if sampled at depth-specific intervals. These results are important for the detection and characterisation of hydrogeologic barriers, which may play a significant role in the compartmentalisation of groundwater flow, spring dynamics, and drawdown and recovery associated with groundwater extraction.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10040-019-02095-9","usgsCitation":"Marshall, S.K., Cook, P., Konikow, L.F., Simmons, C., and Dogramaci, S., 2020, Conjoint use of hydraulic head and groundwater age data to detect hydrogeologic barriers: Hydrogeology Journal, v. 28, p. 1003-1019, https://doi.org/10.1007/s10040-019-02095-9.","productDescription":"17 p.","startPage":"1003","endPage":"1019","ipdsId":"IP-109151","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":408987,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","noUsgsAuthors":false,"publicationDate":"2020-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Marshall, Sarah K.","contributorId":298728,"corporation":false,"usgs":false,"family":"Marshall","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":40595,"text":"Flinders University","active":true,"usgs":false}],"preferred":false,"id":856337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cook, Peter G.","contributorId":298729,"corporation":false,"usgs":false,"family":"Cook","given":"Peter G.","affiliations":[{"id":40595,"text":"Flinders University","active":true,"usgs":false}],"preferred":false,"id":856338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Konikow, Leonard F. 0000-0002-0940-3856 lkonikow@usgs.gov","orcid":"https://orcid.org/0000-0002-0940-3856","contributorId":158,"corporation":false,"usgs":true,"family":"Konikow","given":"Leonard","email":"lkonikow@usgs.gov","middleInitial":"F.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":856339,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Simmons, Craig T.","contributorId":298730,"corporation":false,"usgs":false,"family":"Simmons","given":"Craig T.","affiliations":[{"id":40595,"text":"Flinders University","active":true,"usgs":false}],"preferred":false,"id":856340,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dogramaci, Shawan","contributorId":298731,"corporation":false,"usgs":false,"family":"Dogramaci","given":"Shawan","email":"","affiliations":[{"id":64684,"text":"Rio Tinto Iron Ore Co.","active":true,"usgs":false}],"preferred":false,"id":856341,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70208357,"text":"70208357 - 2020 - Phosphorus, nitrogen and dissolved organic carbon fluxes from sediments in freshwater rivermouths entering Green Bay (Lake Michigan; USA)","interactions":[],"lastModifiedDate":"2020-02-05T16:05:31","indexId":"70208357","displayToPublicDate":"2020-01-10T15:56:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1007,"text":"Biogeochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Phosphorus, nitrogen and dissolved organic carbon fluxes from sediments in freshwater rivermouths entering Green Bay (Lake Michigan; USA)","docAbstract":"<p><span>Transitional areas between ecosystem types are often active biogeochemically due to resource limitation changes. Lotic-to-lentic transitions in freshwaters appear active biogeochemically, but few studies have directly measured nutrient processing rates to assess whether processing within the rivermouth is important for load estimates or the local communities. We measured oxic fluxes of inorganic nitrogen and phosphorus and dissolved organic carbon (DOC) from sediments in two rivermouths of Green Bay (Lake Michigan, USA). Soluble reactive phosphorus (SRP) flux was positive in most cases (overall mean 1.74 mg SRP m</span><sup>− 2</sup><span>&nbsp;day</span><sup>− 1</sup><span>), as was ammonium (NH</span><sub>4</sub><span>) flux (40.6 mg NH</span><sub>4</sub><span>&nbsp;m</span><sup>− 2</sup><span>&nbsp;day</span><sup>− 1</sup><span>). Partial least square regression (PLSR) indicated a latent variable associated with both sediment [loosely bound phosphorus (P), iron bound P, organic content] and water column properties [temperature, DOC:dissolved inorganic nitrogen (DIN) and DOC:SRP ratios (negatively)] that was moderately associated with variation in SRP flux. PLSR analysis also indicated several sediment characteristics were moderately related to NH</span><sub>4</sub><span>&nbsp;flux, especially organic content, density (negative), and porosity. Flux of nitrates/nitrites (NO</span><sub>X</sub><span>) and DOC were positively associated with the water column concentrations of NO</span><sub>X</sub><span>&nbsp;and DOC and qualitative estimates of the labile, non-humic types of DOC. In early summer, water column NO</span><sub>X</sub><span>&nbsp;and DOC concentrations were high and labile DOC may have fueled denitrification, resulting in net flux into sediments of both NO</span><sub>X</sub><span>&nbsp;and DOC. By late summer, water column NO</span><sub>X</sub><span>&nbsp;and DOC were very low and both these constituents were fluxing out of sediments into the water column. Based on our estimates for the entire period from April through September, rivermouth sediments were a net source of SRP and DIN, with a DIN:SRP ratio of ~ 44 and a NH</span><sub>4</sub><span>:NO</span><sub>X</sub><span>&nbsp;&gt; 1. We estimated that the sediments in the Fox rivermouth probably contributed a small proportion of the total Fox River load during the growing season 2016 (&lt; 5%), but at times may have contributed as much as 14% of the daily load. Despite the small size of the Fox rivermouth (&lt; 0.5% of the watershed area), these results indicate that at times sediments can contribute substantially to the overall delivery of nitrogen and phosphorus to the nearshore zone.</span></p>","language":"English","publisher":"Springer Nature Switzerland AG","doi":"10.1007/s10533-020-00635-0","usgsCitation":"Larson, J.H., James, W.F., Fitzpatrick, F.A., Frost, P.C., Evans, M.A., Reneau, P., and Xenopoulos, M.A., 2020, Phosphorus, nitrogen and dissolved organic carbon fluxes from sediments in freshwater rivermouths entering Green Bay (Lake Michigan; USA): Biogeochemistry, v. 147, p. 179-197, https://doi.org/10.1007/s10533-020-00635-0.","productDescription":"19 p.","startPage":"179","endPage":"197","ipdsId":"IP-101349","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":437171,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LVTWS8","text":"USGS data release","linkHelpText":"Data from 92 sediment incubation experiments using sediments collected from the Fox and Duck rivermouths (adjacent to Green Bay, Lake Michigan; 2016 data)"},{"id":437170,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P995SMVW","text":"USGS data release","linkHelpText":"\tR Code to analyze data from sediment incubation experiments (Fox and Duck Rivermouths; 2016)"},{"id":372096,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","city":"Green Bay","otherGeospatial":"Duck Creek, Fox River, Green Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -88.077392578125,\n              44.44162421758805\n            ],\n            [\n              -87.99121856689453,\n              44.44162421758805\n            ],\n            [\n              -87.99121856689453,\n              44.57873024377564\n            ],\n            [\n              -88.077392578125,\n              44.57873024377564\n            ],\n            [\n              -88.077392578125,\n              44.44162421758805\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"147","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2020-01-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":781554,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"James, William F.","contributorId":213265,"corporation":false,"usgs":false,"family":"James","given":"William","email":"","middleInitial":"F.","affiliations":[{"id":38729,"text":"University of Wisconsin-Stout","active":true,"usgs":false}],"preferred":false,"id":781555,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075 fafitzpa@usgs.gov","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":196543,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith","email":"fafitzpa@usgs.gov","middleInitial":"A.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":false,"id":781556,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frost, Paul C.","contributorId":138628,"corporation":false,"usgs":false,"family":"Frost","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":12467,"text":"Department of Biology, Trent University, Peterborough, ON  CA","active":true,"usgs":false}],"preferred":false,"id":781557,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Evans, Mary Anne 0000-0002-1627-7210 maevans@usgs.gov","orcid":"https://orcid.org/0000-0002-1627-7210","contributorId":149358,"corporation":false,"usgs":true,"family":"Evans","given":"Mary","email":"maevans@usgs.gov","middleInitial":"Anne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":781558,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reneau, Paul C.","contributorId":222219,"corporation":false,"usgs":false,"family":"Reneau","given":"Paul C.","affiliations":[{"id":40507,"text":"former employee, Wisconsin Water Science Center","active":true,"usgs":false}],"preferred":false,"id":781559,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Xenopoulos, Marguerite A.","contributorId":138629,"corporation":false,"usgs":false,"family":"Xenopoulos","given":"Marguerite","email":"","middleInitial":"A.","affiliations":[{"id":12467,"text":"Department of Biology, Trent University, Peterborough, ON  CA","active":true,"usgs":false}],"preferred":false,"id":781560,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70249354,"text":"70249354 - 2020 - Potential underestimation of satellite fire radiative power retrievals over gas flares and wildland fires","interactions":[],"lastModifiedDate":"2023-10-05T00:14:25.937585","indexId":"70249354","displayToPublicDate":"2020-01-10T12:32:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Potential underestimation of satellite fire radiative power retrievals over gas flares and wildland fires","docAbstract":"<p><span>Fire Radiative Power (FRP) is related to fire combustion rates and is used to quantify the atmospheric emissions of greenhouse gases and aerosols. FRP over gas flares and wildfires can be retrieved remotely using satellites that observe in shortwave infrared (SWIR) to middle infrared (MIR) wavelengths. Heritage techniques to retrieve FRP developed for wildland fires using the MIR 4 μm radiances have been adapted for the hotter burning gas flares using the SWIR 2 μm observations. Effects of atmosphere, including smoke and aerosols, are assumed to be minimal in these algorithms because of the use of longer than visual wavelengths. Here we use Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS) and Landsat 8 observations acquired before and during emergency oil and gas flaring in eastern Saudi Arabia to show that dark, sooty smoke affects both 4 μm and 2 μm observations. While the 2 μm observations used to retrieve gas FRP may be reliable during clear atmospheric conditions, performance is severely impacted by dark smoke. Global remote sensing-based inventories of wildfire and gas flaring need to consider the possibility that soot and dark smoke can potentially lead to an underestimation of FRP over fires.</span></p>","language":"English","publisher":"MPDI","doi":"10.3390/rs12020238","usgsCitation":"Kumar, S.S., Hult, J.E., Picotte, J., and Peterson, B., 2020, Potential underestimation of satellite fire radiative power retrievals over gas flares and wildland fires: Remote Sensing, v. 12, no. 2, 238, 9 p., https://doi.org/10.3390/rs12020238.","productDescription":"238, 9 p.","ipdsId":"IP-113025","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":458161,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12020238","text":"Publisher Index Page"},{"id":421611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Saudi Arabia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[42.77933,16.34789],[42.64957,16.77464],[42.34799,17.07581],[42.27089,17.47472],[41.75438,17.83305],[41.22139,18.6716],[40.93934,19.48649],[40.24765,20.17463],[39.80168,20.33886],[39.1394,21.2919],[39.0237,21.98688],[39.06633,22.57966],[38.49277,23.68845],[38.02386,24.07869],[37.48363,24.28549],[37.15482,24.85848],[37.20949,25.08454],[36.93163,25.60296],[36.6396,25.82623],[36.24914,26.57014],[35.64018,27.37652],[35.13019,28.06335],[34.63234,28.05855],[34.78778,28.60743],[34.83222,28.95748],[34.95604,29.35655],[36.06894,29.19749],[36.50121,29.50525],[36.74053,29.86528],[37.50358,30.00378],[37.66812,30.33867],[37.99885,30.5085],[37.00217,31.50841],[39.00489,32.01022],[39.19547,32.16101],[40.39999,31.88999],[41.88998,31.19001],[44.7095,29.17889],[46.56871,29.09903],[47.45982,29.00252],[47.70885,28.52606],[48.41609,28.552],[48.80759,27.68963],[49.29955,27.46122],[49.47091,27.11],[50.15242,26.68966],[50.21294,26.27703],[50.1133,25.94397],[50.23986,25.60805],[50.52739,25.32781],[50.66056,24.9999],[50.81011,24.75474],[51.11242,24.55633],[51.38961,24.62739],[51.57952,24.2455],[51.61771,24.01422],[52.00073,23.00115],[55.0068,22.49695],[55.20834,22.70833],[55.66666,22],[54.99998,19.99999],[52.00001,19],[49.11667,18.61667],[48.18334,18.16667],[47.46669,17.11668],[47,16.95],[46.74999,17.28334],[46.36666,17.23332],[45.4,17.33334],[45.21665,17.43333],[44.06261,17.41036],[43.79152,17.31998],[43.38079,17.57999],[43.1158,17.08844],[43.21838,16.66689],[42.77933,16.34789]]]},\"properties\":{\"name\":\"Saudi Arabia\"}}]}","volume":"12","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-01-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Kumar, Sanath S. 0000-0003-4067-4926","orcid":"https://orcid.org/0000-0003-4067-4926","contributorId":330540,"corporation":false,"usgs":true,"family":"Kumar","given":"Sanath","email":"","middleInitial":"S.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":885282,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hult, John Edward 0000-0001-8895-3727","orcid":"https://orcid.org/0000-0001-8895-3727","contributorId":330551,"corporation":false,"usgs":true,"family":"Hult","given":"John","email":"","middleInitial":"Edward","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":885283,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Picotte, Joshua J. 0000-0002-4021-4623","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":202800,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":885284,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peterson, Birgit 0000-0002-4356-1540 bpeterson@usgs.gov","orcid":"https://orcid.org/0000-0002-4356-1540","contributorId":192353,"corporation":false,"usgs":true,"family":"Peterson","given":"Birgit","email":"bpeterson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":885285,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70209414,"text":"70209414 - 2020 - Calcite precipitation in Lake Powell reduces alkalinity and total salt loading to the Lower Colorado River Basin","interactions":[],"lastModifiedDate":"2020-08-04T13:59:38.294865","indexId":"70209414","displayToPublicDate":"2020-01-10T08:25:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Calcite precipitation in Lake Powell reduces alkalinity and total salt loading to the Lower Colorado River Basin","docAbstract":"<p><span>Reservoirs can retain and transform carbon, nitrogen, phosphorus, and silica, but less is known about their effects on other biogeochemically relevant solutes. The salinization of freshwater ecosystems is a growing concern in many regions, and the role of reservoirs in salinity transport is an important research frontier. Here, we examine how a large desert southwest reservoir, Lake Powell, has altered the downstream transport of total dissolved solids (TDSs) as well as the dominant cations and anions comprising the TDS pool (</span><img class=\"section_image\" src=\"https://aslopubs.onlinelibrary.wiley.com/cms/asset/e804c3ff-bfd7-48f1-aae4-42cd05a557b1/lno11399-math-0001.png\" alt=\"urn:x-wiley:00243590:media:lno11399:lno11399-math-0001\" data-mce-src=\"https://aslopubs.onlinelibrary.wiley.com/cms/asset/e804c3ff-bfd7-48f1-aae4-42cd05a557b1/lno11399-math-0001.png\" width=\"28\" height=\"16\"><span>,&nbsp;</span><img class=\"section_image\" src=\"https://aslopubs.onlinelibrary.wiley.com/cms/asset/6502c6e0-db3d-4fd2-b57f-0736ce6bea4a/lno11399-math-0002.png\" alt=\"urn:x-wiley:00243590:media:lno11399:lno11399-math-0002\" data-mce-src=\"https://aslopubs.onlinelibrary.wiley.com/cms/asset/6502c6e0-db3d-4fd2-b57f-0736ce6bea4a/lno11399-math-0002.png\" width=\"32\" height=\"12\"><span>, and Ca</span><sup>2+</sup><span>). Average downstream TDS concentrations have declined significantly since river impoundment and seasonal fluctuations in TDS concentrations have become more modulated, but year to year variation in TDS concentrations has remained similar. While some of the reductions in TDS concentration can be attributed to watershed management, we find that Lake Powell retains about 10% of the TDS loaded to the system (1991 Mg TDS d</span><sup>−1</sup><span>). Much of this retention is occurring in the forms of calcium and bicarbonate, likely via calcite precipitation, and is equivalent to an average burial of 522 mg C m</span><sup>−2</sup><span>&nbsp;d</span><sup>−1</sup><span>, thus reducing the alkalinity of downstream water. Flow‐weighted modeling suggests that, in the absence of Lake Powell, downstream salinity limits would be surpassed at the outflow to Lake Powell 41% of the time (vs. 0% of the time currently). Understanding the dominant mechanisms regulating solute transport through the reservoir is important given the relevance for downstream drinking water and irrigation concerns, biogeochemical cycling, and the high potential for reduced flows in the future.</span></p>","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lno.11399","usgsCitation":"Deemer, B., Stets, E.G., and Yackulic, C.B., 2020, Calcite precipitation in Lake Powell reduces alkalinity and total salt loading to the Lower Colorado River Basin: Limnology and Oceanography, v. 65, no. 7, p. 1439-1455, https://doi.org/10.1002/lno.11399.","productDescription":"17 p.","startPage":"1439","endPage":"1455","ipdsId":"IP-112663","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":437173,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A9P44R","text":"USGS data release","linkHelpText":"Calcium, magnesium and total dissolved solids data as well as modeled salinity and mass balance estimates for Lake Powell, 1952-2017"},{"id":373749,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"Lower Colorado River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -113.4228515625,\n              36.87962060502676\n            ],\n            [\n              -109.3798828125,\n              35.02999636902566\n            ],\n            [\n              -104.765625,\n              35.639441068973944\n            ],\n            [\n              -104.19433593749999,\n              37.996162679728116\n            ],\n            [\n              -104.4580078125,\n              40.74725696280421\n            ],\n            [\n              -107.5341796875,\n              43.42100882994726\n            ],\n            [\n              -110.56640625,\n              43.739352079154706\n            ],\n            [\n              -112.54394531249999,\n              43.58039085560784\n            ],\n            [\n              -113.115234375,\n              41.672911819602085\n            ],\n            [\n              -112.412109375,\n              40.3130432088809\n            ],\n            [\n              -112.1484375,\n              39.13006024213511\n            ],\n            [\n              -112.8955078125,\n              37.61423141542417\n            ],\n            [\n              -113.4228515625,\n              36.87962060502676\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"65","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-01-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":786378,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stets, Edward G. 0000-0001-5375-0196 estets@usgs.gov","orcid":"https://orcid.org/0000-0001-5375-0196","contributorId":194490,"corporation":false,"usgs":true,"family":"Stets","given":"Edward","email":"estets@usgs.gov","middleInitial":"G.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":786379,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":786380,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70208310,"text":"70208310 - 2020 - How often can Earthquake Early Warning systems alert sites with high intensity ground motion?","interactions":[],"lastModifiedDate":"2020-02-04T07:34:49","indexId":"70208310","displayToPublicDate":"2020-01-10T07:33:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"How often can Earthquake Early Warning systems alert sites with high intensity ground motion?","docAbstract":"Although numerous Earthquake Early Warning (EEW) algorithms have been developed we still lack a detailed understanding of how often and under what circumstances useful ground motion alerts can be provided to end-users. Here we analyze the alerting performance of the PLUM, EPIC and FinDer algorithms by running them retrospectively on the seismic strong motion data of the 219 earthquakes in Japan since 1996 that exceeded Modified Mercalli Intensity (MMI) of 4.5 on at least 10 sites (Mw 4.5-9.1). Our analysis suggests that, irrespective of the algorithm, EEW end-users should be prepared that EEW can often but not always provide useful ground motion alerts. A majority of sites with moderate-strong ground motion (MMI 5-6) can generally get at least a few seconds of warning time from all algorithms. If such shaking is caused by a shallow crustal event, around 50% of such sites receive alerts with warning times >5 s. Many sites with severe-extreme ground motion (MMI >=8) can be alerted successfully in the case of very large offshore earthquakes, but less than 20% can be alerted ahead of time if such shaking is caused by a shallow crustal event. Our results provide detailed quantitative insight into the expected alerting performance for EEW algorithms under realistic conditions. The main caveat is that the largest shallow crustal event in our data set has Mw7.0, i.e. the data set does not contain very large strike slip events.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JB017718","usgsCitation":"Meier, M., Kodera, Y., Bose, M., Chung, A.I., Hoshiba, M., Cochran, E.S., Minson, S.E., Hauksson, E., and Heaton, T., 2020, How often can Earthquake Early Warning systems alert sites with high intensity ground motion?: Journal of Geophysical Research, v. 125, e2019JB017718, 17 p., https://doi.org/10.1029/2019JB017718.","productDescription":"e2019JB017718, 17 p.","ipdsId":"IP-107685","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":458167,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019jb017718","text":"Publisher Index Page"},{"id":371988,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"125","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2020-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Meier, M.-A.","contributorId":222138,"corporation":false,"usgs":false,"family":"Meier","given":"M.-A.","email":"","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":781351,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kodera, Y.","contributorId":216381,"corporation":false,"usgs":false,"family":"Kodera","given":"Y.","affiliations":[{"id":39398,"text":"JMA","active":true,"usgs":false}],"preferred":false,"id":781352,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bose, M.","contributorId":222139,"corporation":false,"usgs":false,"family":"Bose","given":"M.","email":"","affiliations":[{"id":40494,"text":"ETH-Zurich","active":true,"usgs":false}],"preferred":false,"id":781353,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chung, A. I.","contributorId":39293,"corporation":false,"usgs":false,"family":"Chung","given":"A.","email":"","middleInitial":"I.","affiliations":[{"id":7033,"text":"School of Earth Sciences, Stanford University","active":true,"usgs":false}],"preferred":false,"id":781354,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hoshiba, M.","contributorId":222140,"corporation":false,"usgs":false,"family":"Hoshiba","given":"M.","affiliations":[{"id":39398,"text":"JMA","active":true,"usgs":false}],"preferred":false,"id":781355,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":781350,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":781356,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hauksson, E.","contributorId":196003,"corporation":false,"usgs":false,"family":"Hauksson","given":"E.","affiliations":[],"preferred":false,"id":781357,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Heaton, T.","contributorId":222141,"corporation":false,"usgs":false,"family":"Heaton","given":"T.","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":781358,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70227742,"text":"70227742 - 2020 - Combined influence of intrinsic and environmental factors in shaping productivity in a small pelagic gull, the black-legged kittiwake Rissa tridactyla","interactions":[],"lastModifiedDate":"2022-01-28T16:01:47.756094","indexId":"70227742","displayToPublicDate":"2020-01-09T09:57:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Combined influence of intrinsic and environmental factors in shaping productivity in a small pelagic gull, the black-legged kittiwake <i>Rissa tridactyla</i>","title":"Combined influence of intrinsic and environmental factors in shaping productivity in a small pelagic gull, the black-legged kittiwake Rissa tridactyla","docAbstract":"<p><span>While we have a good understanding in many systems of the effects of single variable changes on organisms, we understand far less about how variables act in concert to affect living systems, where interactions among variables can lead to unanticipated results. We used mixed-effect models to evaluate the effects of multiple variables that we expected to play a role in the early reproductive stages of a North Pacific seabird, the black-legged kittiwake&nbsp;</span><i>Rissa tridactyla,</i><span>&nbsp;during 1992-2008 using data collected on known-aged individuals. Our work revealed the potential for contrasting stressor effects across successive stages of reproduction. Bird age, timing of egg laying, and winter ENSO conditions best explained individual laying success, such that laying success was greater when parents were older, the average winter ENSO index was positive (as occurs during El Niño episodes), and the median laying date for the colony was earlier. Age and salmon run timing (a proxy for predator presence at the colony) best explained hatching success, such that hatching success was greater when parents were older and when salmon runs were early. Identifying such differential effects of multiple stressors across consecutive reproductive stages can greatly enhance our ability to interpret trends and manage populations in the face of changes currently occurring in living systems.</span></p>","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps13162","usgsCitation":"McKnight, A., Irons, D., Loftin, C., McKinney, S., and Olsen, B., 2020, Combined influence of intrinsic and environmental factors in shaping productivity in a small pelagic gull, the black-legged kittiwake Rissa tridactyla: Marine Ecology Progress Series, v. 633, p. 207-223, https://doi.org/10.3354/meps13162.","productDescription":"17 p.","startPage":"207","endPage":"223","ipdsId":"IP-088508","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"633","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McKnight, Aly","contributorId":272505,"corporation":false,"usgs":false,"family":"McKnight","given":"Aly","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":832006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irons, David B.","contributorId":272506,"corporation":false,"usgs":false,"family":"Irons","given":"David B.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":832007,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832005,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKinney, Shawn T.","contributorId":272507,"corporation":false,"usgs":false,"family":"McKinney","given":"Shawn T.","affiliations":[],"preferred":false,"id":832008,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olsen, Brian J.","contributorId":272508,"corporation":false,"usgs":false,"family":"Olsen","given":"Brian J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":832009,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70207989,"text":"70207989 - 2020 - Challenges for leveraging citizen science to support statistically robust monitoring programs","interactions":[],"lastModifiedDate":"2020-01-23T06:36:56","indexId":"70207989","displayToPublicDate":"2020-01-09T06:35:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Challenges for leveraging citizen science to support statistically robust monitoring programs","docAbstract":"Large samples and long time series are often needed for effective broad-scale monitoring of status and trends in wild populations. Obtaining those sample sizes can be more feasible when volunteers contribute to the dataset, but volunteer-selected sites are not always representative of a population. Previous work to account for biased site selection has relied on knowledge of covariates to explain differences between site types, but such knowledge is often unavailable. For cases where relevant covariates have not been defined, we used a simulation study to identify the consequences of including non-probabilistically selected sites (NP sites) in addition to sites selected from a probability-based design (P sites), test modeling frameworks that might correct for biases, and evaluate whether those frameworks could allow NP sites to reduce the sampling requirement for P sites and potentially reduce costs of monitoring. We informed the simulation with pilot data from surveys of monarch butterflies and their obligate larval host plant, milkweed. We found strong biases in NP sites versus P sites in density and trends of monarchs and milkweed. Modeling frameworks that accounted for site type with a group effect or that strongly downweighted NP sites successfully produced unbiased estimates. However, sampling more NP sites typically did not improve accuracy or precision, and adding NP sites sometimes required also adding P sites to prevent biases. Further work on novel modeling frameworks would be useful to allow citizen-science data to contribute useful information to conservation.","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2020.108411","usgsCitation":"Weiser, E.L., Diffendorfer, J., Lopez-Hoffman, L., Semmens, D., and Thogmartin, W.E., 2020, Challenges for leveraging citizen science to support statistically robust monitoring programs: Biological Conservation, v. 242, 108411, 10 p., https://doi.org/10.1016/j.biocon.2020.108411.","productDescription":"108411, 10 p.","ipdsId":"IP-112580","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":458175,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2020.108411","text":"Publisher Index Page"},{"id":371491,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"242","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Weiser, Emily L. 0000-0003-1598-659X","orcid":"https://orcid.org/0000-0003-1598-659X","contributorId":213770,"corporation":false,"usgs":true,"family":"Weiser","given":"Emily","email":"","middleInitial":"L.","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":780046,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780047,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lopez-Hoffman, Laura","contributorId":149127,"corporation":false,"usgs":false,"family":"Lopez-Hoffman","given":"Laura","affiliations":[{"id":17654,"text":"School of Natural Resources & the Environment and Udall Center for Studies in Public Policy, The University of Arizona, Tucson","active":true,"usgs":false}],"preferred":false,"id":780048,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Semmens, Darius J. 0000-0001-7924-6529","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":64201,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":780049,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":780050,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
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