Surface rupture in the 2019 Ridgecrest, California, earthquake sequence occurred along two orthogonal cross faults and includes dominantly left‐lateral and northeast‐striking rupture in the
Authorization of lease sales for oil development in the 1002 Area of the Arctic National Wildlife Refuge has highlighted gaps in information about biological communities in the area. The U.S. Fish and Wildlife Service, which is planning a study to evaluate spatial variation in the nest survival of tundra-breeding shorebirds to identify hotspots with high nest survival, sought advice from the U.S. Geological Survey on how to predict which sampling designs might be successful in achieving the study goals. I used previously obtained data on nest survival from Alaska’s North Slope to develop a simulation study that explored the statistical consequences of various sampling designs in the context of expected ranges of values of biological parameters. Of all sampling parameters, the number of nests per plot had the strongest influence on the ability to detect hotspots, followed by number of years of monitoring and the age at which nests were found. The benefit of sampling 98 plots instead of 68 was relatively small. Ages of nests cannot be predicted a priori, so I developed a field reference chart to identify when further nest-searching would be useful for a given plot. This simulation study used the best available information, but values of biological parameters will become better defined following subsequent data collection in the 1002 Area. Data from upcoming field seasons could be used to refine this analysis and improve estimation of the expected results of the field study, thus maximizing the likelihood that data from the 1002 Area nest survival study will meet the monitoring goals.
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Wildland fires have a multitude of ecological effects in forests, woodlands, and savannas across the globe. A major focus of past research has been on tree mortality from fire, as trees provide a vast range of biological services. We assembled a database of individual-tree records from prescribed fires and wildfires in the United States. The Fire and Tree Mortality (FTM) database includes records from 164,293 individual trees with records of fire injury (crown scorch, bole char, etc.), tree diameter, and either mortality or top-kill up to ten years post-fire. Data span 142 species and 62 genera, from 409 fires occurring from 1981-2016. Additional variables such as insect attack are included when available. The FTM database can be used to evaluate individual fire-caused mortality models for pre-fire planning and post-fire decision support, to develop improved models, and to explore general patterns of individual fire-induced tree death. The database can also be used to identify knowledge gaps that could be addressed in future research.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41597-020-0522-7","usgsCitation":"Cansler, C., Hood, S.M., Varner, J., van Mantgem, P., Agne, M.C., Andrus, R.A., Ayres, M.P., Ayres, B.D., Bakker, J., Battaglia, M.A., Bentz, B.J., Breece, C.R., Brown, J.K., Cluck, D.R., Coleman, T.W., Corace, R.G., Covington, W.W., Cram, D.S., Cronan, J.B., Crouse, J.E., Das, A., Davis, R.S., Dickinson, D.M., Fitzgerald, S.A., Fule, P., Ganio, L.M., Grayson, L.M., Halpern, C.B., Hanula, J.L., Harvey, B.J., Hiers, J.K., Huffman, D.W., Keifer, M., Keyser, T.L., Kobziar, L.N., Kolb, T.E., Kolden, C.A., Kopper, K.E., Kreitler, J.R., Kreye, J.K., Latimer, A.M., Lerch, A.P., Lombardero, M.J., McDaniel, V.L., McHugh, C.W., McMillin, J.D., Moghaddas, J.J., O’Brien, J.J., Perrakis, D.D., Peterson, D.W., Pritchard, S.J., Progar, R.A., Raffa, K.F., Reinhardt, E.D., Restaino, J.C., Roccaforte, J.P., Rogers, B.M., Ryan, K.C., Safford, H.D., Santoro, A.E., Shearman, T.M., Shumate, A.M., Sieg, C., 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T.","contributorId":229066,"corporation":false,"usgs":false,"family":"Stoddard","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":41551,"text":"Ecological Restoration Institute, Northern Arizona University, PO Box 15017, Flagstaff, AZ, 86011-5017, USA","active":true,"usgs":false}],"preferred":false,"id":792894,"contributorType":{"id":1,"text":"Authors"},"rank":69},{"text":"Thies, Walter G.","contributorId":229067,"corporation":false,"usgs":false,"family":"Thies","given":"Walter","email":"","middleInitial":"G.","affiliations":[{"id":41576,"text":"USDA Forest Service, Pacific Northwest Research Station, 3200 Jefferson Way, Corvallis, OR, 97331, USA","active":true,"usgs":false}],"preferred":false,"id":792895,"contributorType":{"id":1,"text":"Authors"},"rank":70},{"text":"Vaillant, Nicole M.","contributorId":196237,"corporation":false,"usgs":false,"family":"Vaillant","given":"Nicole","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":792896,"contributorType":{"id":1,"text":"Authors"},"rank":71},{"text":"Weiss, Shelby A.","contributorId":229068,"corporation":false,"usgs":false,"family":"Weiss","given":"Shelby","email":"","middleInitial":"A.","affiliations":[{"id":41577,"text":"Department of Geography, Portland State University, 1721 SW Broadway, Portland, OR, 97201, USA","active":true,"usgs":false}],"preferred":false,"id":792897,"contributorType":{"id":1,"text":"Authors"},"rank":72},{"text":"Westlind, Douglas J.","contributorId":229069,"corporation":false,"usgs":false,"family":"Westlind","given":"Douglas","email":"","middleInitial":"J.","affiliations":[{"id":41576,"text":"USDA Forest Service, Pacific Northwest Research Station, 3200 Jefferson Way, Corvallis, OR, 97331, USA","active":true,"usgs":false}],"preferred":false,"id":792898,"contributorType":{"id":1,"text":"Authors"},"rank":73},{"text":"Woolley, Travis J.","contributorId":229070,"corporation":false,"usgs":false,"family":"Woolley","given":"Travis","email":"","middleInitial":"J.","affiliations":[{"id":41578,"text":"The Nature Conservancy, 114 N., San Francisco Street #205, Flagstaff, Arizona, 86001, USA","active":true,"usgs":false}],"preferred":false,"id":792899,"contributorType":{"id":1,"text":"Authors"},"rank":74},{"text":"Wright, Micah C. 0000-0002-5324-1110","orcid":"https://orcid.org/0000-0002-5324-1110","contributorId":229071,"corporation":false,"usgs":true,"family":"Wright","given":"Micah","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":792900,"contributorType":{"id":1,"text":"Authors"},"rank":75}]}} ,{"id":70210711,"text":"ofr20201067 - 2020 - Water-quality, bed-sediment, and invertebrate tissue trace-element concentrations for tributaries in the Clark Fork Basin, Montana, October 2017–September 2018","interactions":[],"lastModifiedDate":"2020-06-22T14:34:37.361537","indexId":"ofr20201067","displayToPublicDate":"2020-06-22T07:18:53","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":"2020-1067","displayTitle":"Water-Quality, Bed-Sediment, and Invertebrate Tissue Trace-Element Concentrations for Tributaries in the Clark Fork Basin, Montana, October 2017–September 2018","title":"Water-quality, bed-sediment, and invertebrate tissue trace-element concentrations for tributaries in the Clark Fork Basin, Montana, October 2017–September 2018","docAbstract":"Water, bed sediment, and invertebrate tissue were sampled in streams from Butte to near Missoula, Montana, as part of a monitoring program in the Clark Fork Basin. The sampling program was completed by the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, to characterize aquatic resources in the Clark Fork Basin and monitor trace elements associated with historical mining and smelting activities. Sampling sites were on the river and tributaries of the Clark Fork. Water samples were collected periodically at 20 sites from October 2017 through September 2018. Bed-sediment and tissue samples were collected once at 13 sites during August 2018.
Water-quality data included concentrations of major ions, dissolved organic carbon, nitrogen (nitrate plus nitrite), trace elements, and suspended sediment. Daily values of turbidity were determined at four sites. Bed-sediment data included trace-element concentrations in the fine-grained (less than 0.063 millimeter) fraction. Biological data included trace-element concentrations in whole-body tissue of aquatic benthic invertebrates. Statistical summaries of water-quality, bed-sediment, and invertebrate tissue trace element data for sites in the Clark Fork Basin were provided for the period of record: March 1985–September 2018.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201067","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Clark, G.D., Hornberger, M.I., Cleasby, T.E., Heinert, T.L., and Turner, M.A., 2020, Water-quality, bed-sediment, and invertebrate tissue trace-element concentrations for tributaries in the Clark Fork Basin, Montana, October 2017–September 2018: U.S. Geological Survey Open-File Report 2020–1067, 16 p., https://doi.org/10.3133/ofr20201067.","productDescription":"Report: vi, 16 p.; Data Release; Dataset","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-115189","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":375701,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98IRLJF","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-quality, bed-sediment, and invertebrate tissue trace element concentrations for tributaries in the Clark Fork Basin, Montana, October 2017–September 2018"},{"id":375700,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1067/ofr20201067.pdf","text":"Report","size":"1.00 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1067"},{"id":375699,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1067/coverthb.jpg"},{"id":375702,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System","description":"NWIS","linkHelpText":"— USGS Water Data for the Nation"}],"country":"United States","state":"Montana","otherGeospatial":"Clark Fork Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.345703125,\n 45.24395342262324\n ],\n [\n -111.181640625,\n 45.24395342262324\n ],\n [\n -111.181640625,\n 47.264320080254805\n ],\n [\n -114.345703125,\n 47.264320080254805\n ],\n [\n -114.345703125,\n 45.24395342262324\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Boseman Avenue
Helena, MT 59601
Ensuring that oysters remain sustainable in the face of significant coastal restoration activities, high local subsidence rates, and predicted sea-level rise requires a deeper understanding of basic population demographics, including reproductive potential. We quantified fecundity (eggs ind−1) of oysters at high- and low-salinity sites during a fall and spring spawn season. We assessed the relationships between oyster size, the relative proportion of females across size classes, and fecundity. Finally, we quantified reproductive potential (eggs m−2) of an engineered reef by connecting fecundity with annual oyster population demographic data as a means to assess population sustainability. The proportion of females generally increased with shell height, achieving a population with >50% females in Biloxi oysters >75 mm, and Grand Isle oysters >100 mm. Fecundity across both sites and seasons ranged from approximately 2,000 to >55 million eggs oyster−1. Mean fecundity generally increased with shell height, varying significantly by site, with Grand Isle (high salinity) oysters having greater fecundity than Biloxi (low salinity) oysters. Fecundity did not differ by season. Mean reproductive potential (eggs m−2) was driven by density and size distribution. Reefs with high densities and higher counts of market-sized oysters had reproductive potentials 5× greater than those with low densities and low counts of juvenile oysters. With increasing changes in water quality from coastal management and climate, impacts on oyster reproduction may critically impact population sustainability. Reproductive potential provides critical data to assess individual reef ecosystem services, and to assess the potential for maintenance of local metapopulations.
","language":"English","publisher":"Society for Ecological Restoration","doi":"10.1111/rec.13225","usgsCitation":"Marshall, D., Moore, S., Sutor, M., La Peyre, J.F., and La Peyre, M., 2020, Using reproductive potential to assess oyster population sustainability: Restoration Ecology, v. 28, no. 6, p. 1621-1632, https://doi.org/10.1111/rec.13225.","productDescription":"12 p.","startPage":"1621","endPage":"1632","ipdsId":"IP-118044","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":499854,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.lsu.edu/animalsciences_pubs/795","text":"External Repository"},{"id":395940,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.2691650390625,\n 29.563901551414418\n ],\n [\n -89.3353271484375,\n 29.563901551414418\n ],\n [\n -89.3353271484375,\n 30.259067203213018\n ],\n [\n -90.2691650390625,\n 30.259067203213018\n ],\n [\n -90.2691650390625,\n 29.563901551414418\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Marshall, Danielle A.","contributorId":239867,"corporation":false,"usgs":false,"family":"Marshall","given":"Danielle A.","affiliations":[{"id":48014,"text":"School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA","active":true,"usgs":false}],"preferred":false,"id":834590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Samuel C.","contributorId":276133,"corporation":false,"usgs":false,"family":"Moore","given":"Samuel C.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":834591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sutor, Malinda","contributorId":276134,"corporation":false,"usgs":false,"family":"Sutor","given":"Malinda","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":834592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"La Peyre, Jerome F.","contributorId":177346,"corporation":false,"usgs":false,"family":"La Peyre","given":"Jerome","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":834593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"La Peyre, Megan 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":79375,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan","email":"mlapeyre@usgs.gov","affiliations":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834594,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210730,"text":"ofr20201056 - 2020 - Envisioning a multi-agency and multi-academic institution geomorphology data exchange portal","interactions":[],"lastModifiedDate":"2020-06-22T11:33:00.458228","indexId":"ofr20201056","displayToPublicDate":"2020-06-19T11:14:24","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":"2020-1056","displayTitle":"Envisioning a Multi-Agency and Multi-Academic Institution Geomorphology Data Exchange Portal","title":"Envisioning a multi-agency and multi-academic institution geomorphology data exchange portal","docAbstract":"Access to bathymetry and geomorphology data for rivers and reservoirs is a critical need in multiple agencies and academia. These data are needed to make water-resource-management decisions regarding river restoration, resource protection, infrastructure design and sustainability, and flood-risk reduction, and during natural disasters. Sharing of data increases decision-making capacity by incorporating information from entire watersheds, provides knowledge from similar settings being managed or studied by other entities, and helps meet the goals of the Federal Open Water Data Initiative. Addressing these needs across broad spatial and temporal scales would be made more efficient if these data were available in consistent formats with standardized metadata and were either stored in a centralized database or integrated with existing geospatial datasets. Because of renewed interest and technological advances, representatives from multiple Federal agencies and academic institutions have created a new working group to scope the development of a Geomorphology Data Exchange Portal to increase access to needed data. The working group has developed a vision for the Portal and outlined possible approaches to achieve the vision. Short-term approaches may include leveraging existing data-access portals and data-processing tools and integrating geomorphology data with existing national geospatial datasets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201056","collaboration":"USGS Water Observing Systems ProgramOffice of Associate Director, Water
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The Leica Geosystems CountryMapper hybrid system has the potential to collect data that satisfy the U.S. Geological Survey (USGS) National Geospatial Program (NGP) and 3D Elevation Program (3DEP) and the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) requirements in a single collection. This research will help 3DEP determine if this sensor has the potential to meet current and future 3DEP topographic lidar collection requirements. We performed an accuracy analysis and assessment on the lidar point cloud produced from CountryMapper. The boresighting calibration and co-registration by georeferencing correction based on ground control points are assumed to be performed by the data provider. The scope of the accuracy assessment is to apply the following variety of ways to measure the accuracy of the delivered point cloud to obtain the error statistics. Intraswath uncertainty from a flat surface was computed to evaluate the point cloud precision. Intraswath difference between opposite scan directions and the interswath overlap difference were evaluated to find boresighting or any systematic errors. Absolute vertical accuracy over vegetated and non-vegetated areas were also assessed. Both horizontal and vertical absolute errors were assessed using the 3D absolute error analysis methodology of comparing conjugate points derived from geometric features. A three-plane feature makes a single unique intersection point. Intersection points were computed from ground-based lidar and airborne lidar point clouds for comparison. The difference between two intersection points form one error vector. The geometric feature-based error analysis was applied to intraswath, interswath, and absolute error analysis. The CountryMapper pilot data appear to satisfy the accuracy requirements suggested by the USGS lidar specification, based upon the error analysis results. The focus of this research was to demonstrate various conventional accuracy measures and novel 3D accuracy techniques using two different error computation methods on the CountryMapper airborne lidar point cloud.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12121974","usgsCitation":"Kim, M., Park, S., Irwin, J., McCormick, C., Danielson, J.J., Stensaas, G.L., Sampath, A., Bauer, M.A., and Burgess, M.A., 2020, Positional accuracy assessment of lidar point cloud from NAIP/3DEP pilot project: Remote Sensing, v. 12, no. 12, 1974, 20 p., https://doi.org/10.3390/rs12121974.","productDescription":"1974, 20 p.","ipdsId":"IP-119473","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":456357,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12121974","text":"Publisher Index Page"},{"id":436924,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CPDWUU","text":"USGS data release","linkHelpText":"Hybrid Lidar/Imagery Sensor Validation Survey Data"},{"id":408027,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-06-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Kim, Minsu 0000-0003-4472-0926 minsukim@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-4472-0926","contributorId":216429,"corporation":false,"usgs":true,"family":"Kim","given":"Minsu","email":"minsukim@contractor.usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":853950,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Park, Seonkyung 0000-0003-3203-1998","orcid":"https://orcid.org/0000-0003-3203-1998","contributorId":223182,"corporation":false,"usgs":true,"family":"Park","given":"Seonkyung","email":"","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":853951,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irwin, Jeffrey 0000-0001-5828-0787 jrirwin@usgs.gov","orcid":"https://orcid.org/0000-0001-5828-0787","contributorId":222485,"corporation":false,"usgs":true,"family":"Irwin","given":"Jeffrey","email":"jrirwin@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":853952,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCormick, Collin","contributorId":270002,"corporation":false,"usgs":false,"family":"McCormick","given":"Collin","email":"","affiliations":[],"preferred":false,"id":853953,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Danielson, Jeffrey J. 0000-0003-0907-034X daniels@usgs.gov","orcid":"https://orcid.org/0000-0003-0907-034X","contributorId":3996,"corporation":false,"usgs":true,"family":"Danielson","given":"Jeffrey","email":"daniels@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":853954,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stensaas, Gregory L. 0000-0001-6679-2416 stensaas@usgs.gov","orcid":"https://orcid.org/0000-0001-6679-2416","contributorId":2551,"corporation":false,"usgs":true,"family":"Stensaas","given":"Gregory","email":"stensaas@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":853955,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sampath, Aparajithan 0000-0002-6922-4913 asampath@usgs.gov","orcid":"https://orcid.org/0000-0002-6922-4913","contributorId":3622,"corporation":false,"usgs":true,"family":"Sampath","given":"Aparajithan","email":"asampath@usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":853956,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bauer, Mark A. 0000-0002-4156-5759 mabauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4156-5759","contributorId":224288,"corporation":false,"usgs":true,"family":"Bauer","given":"Mark","email":"mabauer@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":853957,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Burgess, Matthew Alexander 0000-0003-3487-4972 mburgess@usgs.gov","orcid":"https://orcid.org/0000-0003-3487-4972","contributorId":225090,"corporation":false,"usgs":true,"family":"Burgess","given":"Matthew","email":"mburgess@usgs.gov","middleInitial":"Alexander","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":853958,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70211531,"text":"70211531 - 2020 - Modified QuEChERS extraction for the analysis of young-of-year smallmouth bass using GC × GC-TOFMS","interactions":[],"lastModifiedDate":"2020-07-30T16:47:05.265837","indexId":"70211531","displayToPublicDate":"2020-06-18T11:43:45","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":763,"text":"Analytical Methods","active":true,"publicationSubtype":{"id":10}},"title":"Modified QuEChERS extraction for the analysis of young-of-year smallmouth bass using GC × GC-TOFMS","docAbstract":"Signs of disease, such as external lesions, have been prevalent in smallmouth bass throughout the Susquehanna River Basin, USA. Previous targeted chemical studies in this system have identified known persistent organic pollutants, but a common explanatory link across multiple affected sites remains undetermined. A fast and robust extraction method that can be applied to young-of-year fish is needed to effectively screen for target and non-target compounds that may be impacting organism health. The quick, easy, cheap, effective, rugged, and safe (QuEChERS) extraction methodology was optimized to perform both targeted and non-targeted chemical analyses from a single extraction of whole young-of-year fish. Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) was used for extract analysis. Sample extraction was performed using the solvent ethyl acetate, followed by a two-step cleanup in which samples were frozen for lipid removal and subjected to dispersive solid phase extraction using Florisil. A sample of 21 young-of-year smallmouth bass collected from areas with disease and exhibiting different types of external lesions were evaluated for 233 target compounds. A total of 34 organic contaminants, including polychlorinated biphenyls, brominated diphenyl ethers, organochlorinated pesticides, and personal care products, were detected. Data from this sample set was then analyzed for non-targets. Using the Fisher ratio method and multivariate analysis, an additional 10 significant features were identified specific to either fish with visible lesions or with no visible disease characteristics.
Ecological occupancy modeling has historically relied on high-quality, low-quantity designed-survey data for estimation and prediction. In recent years, there has been a large increase in the amount of high-quantity, unknown-quality opportunistic data. This has motivated research on how best to combine these two data sources in order to optimize inference. Existing methods can be infeasible for large datasets or require opportunistic data to be located where designed-survey data exist. These methods map species occupancies, motivating a need to properly evaluate covariate effects (e.g., land cover proportion) on their distributions. We describe a spatial estimation method for supplementarily including additional opportunistic data using mediation analysis concepts. The opportunistic data mediate the effect of the covariate on the designed-survey data response, decomposing it into a direct and indirect effect. A component of the indirect effect can then be quickly estimated via regressing the mediator on the covariate, while the other components are estimated through a spatial occupancy model. The regression step allows for use of large quantities of opportunistic data that can be collected in locations with no designed-survey data available. Simulation results suggest that the mediated method produces an improvement in relative MSE when the data are of reasonable quality. However, when the simulated opportunistic data are poorly correlated with the true spatial process, the standard, unmediated method is still preferable. A spatiotemporal extension of the method is also developed for analyzing the effect of deciduous forest land cover on red-eyed vireo distribution in the southeastern United States and find that including the opportunistic data do not lead to a substantial improvement. Opportunistic data quality remains an important consideration when employing this method, as with other data integration methods.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3165","usgsCitation":"Huberman, D.B., Reich, B.J., Pacifici, K., and Collazo, J.A., 2020, Estimating the drivers of species distributions with opportunistic data using mediation analysis: Ecosphere, v. 11, no. 6, e03165, 13 p., https://doi.org/10.1002/ecs2.3165.","productDescription":"e03165, 13 p.","ipdsId":"IP-113854","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456360,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3165","text":"Publisher Index Page"},{"id":396100,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"eastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.03125,\n 24.84656534821976\n ],\n [\n -66.09375,\n 24.84656534821976\n ],\n [\n -66.09375,\n 49.26780455063753\n ],\n [\n -97.03125,\n 49.26780455063753\n ],\n [\n -97.03125,\n 24.84656534821976\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Huberman, D. B.","contributorId":279615,"corporation":false,"usgs":false,"family":"Huberman","given":"D.","email":"","middleInitial":"B.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835221,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reich, B. J.","contributorId":279616,"corporation":false,"usgs":false,"family":"Reich","given":"B.","email":"","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835222,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pacifici, Krishna","contributorId":244494,"corporation":false,"usgs":false,"family":"Pacifici","given":"Krishna","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835223,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":835224,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210681,"text":"sir20205045 - 2020 - Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16","interactions":[],"lastModifiedDate":"2020-06-18T14:21:59.738964","indexId":"sir20205045","displayToPublicDate":"2020-06-17T15:15:22","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5045","displayTitle":"Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16","title":"Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16","docAbstract":"The city of Omaha, Nebraska, has a combined sewer system in some areas of the city. In Omaha, Nebr., a moderate amount of rainfall will lead to the combination of stormwater and untreated sewage or wastewater being discharged directly into the Missouri River and Papillion Creek and is called a combined sewer overflow (CSO) event. In 2009, the city of Omaha began the implementation of their Long Term Control Plan (LTCP) to mitigate the effects of CSOs on the Missouri River and Papillion Creek. As part of the LTCP, the city partnered with the U.S. Geological Survey (USGS) in 2012 to begin monitoring in the Missouri River. Since 2012, monthly discrete water-quality samples for many constituents have been collected from the Missouri River at four sites. At 3 of the 4 sites, water quality has been monitored continuously for selected constituents and physical properties. These discrete water-quality samples and continuous water-quality monitoring data (from July 2012 to 2020) have been collected to better understand the water quality of the Missouri River, how it is changing with time, how it changes upstream from the city of Omaha to downstream, and how it varies during base-flow conditions and during periods of runoff.
The purpose of this report is to document the development of Escherichia coli (E. coli) concentration models for these four Missouri River sites. Analysis was completed using the first 5 years of data (through 2016) to determine if the current approach is sufficient to meet future analysis goals and to understand if proposed models such as Load Estimator (LOADEST) models will be able to represent water-quality changes in the Missouri River.
Multiple linear regression models were developed to estimate E. coli concentration using LOADEST as implemented in the rloadest package in the R statistical software program. A set of explanatory variables, including streamflow and streamflow anomalies, precipitation, information about CSOs, and continuous water quality, were evaluated for potential inclusion in regression models. The best model at Missouri River at NP Dodge Park at Omaha, Nebr. (USGS station 412126095565201; hereafter “NP Dodge”) included basin explanatory variables of upstream antecedent precipitation index measured at Tekamah, Nebr.; decimal time; season; and turbidity. The best model at Missouri River at Freedom Park Omaha, Nebr. (USGS station 411636095535401; hereafter “Freedom Park”) included the same explanatory variables as the NP Dodge model with the addition of turbidity anomalies and flow anomalies. The best models at the two downstream sites (Missouri River near Council Bluffs, Iowa, USGS station 06610505 and Missouri River near La Platte, Nebr., USGS station 410333095530101) included the same explanatory variables as the Freedom Park model with the addition of local antecedent precipitation index as measured at Eppley Airport in Omaha, Nebr., and additional turbidity and flow anomalies. The final selected models were the best models given our modeling design constraint in which explanatory variables included in the model for the upstream site were included in the downstream models.
Explanatory variables currently (2020) being collected and included in the selected models through 2016 explained 64–75 percent of the variability of E. coli concentration in the Missouri River. Explaining 64–75 percent of the variability might be considered low when working with physical constituents (total nitrogen or sediment), but with the natural variability of biological constituents such as E. coli, the uncertainty of E. coli laboratory measurements, and the added complexity of modeling in a large drainage basin with multiple sources, these results are adequate and indicate that the explanatory variables being collected and models such as LOADEST can represent water-quality changes in the Missouri River for E. coli concentration from 2012 to 2016.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205045","collaboration":"Prepared in cooperation with the city of Omaha, Nebraska","usgsCitation":"Densmore, B.K., Hall, B.M., and Moser, M.T., 2020, Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16: U.S. Geological Survey Scientific Investigations Report 2020–5045, 24 p., https://doi.org/10.3133/sir20205045.","productDescription":"Report: vi, 24 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-098296","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":375621,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5045/coverthb.jpg"},{"id":375622,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5045/sir20205045.pdf","text":"Report","size":"9.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5045"},{"id":375623,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97S6WSV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16: Model Inputs and Outputs"}],"country":"United States","state":"Nebraska","city":"Omaha","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.23611450195312,\n 41.166249339092\n ],\n [\n -95.78155517578124,\n 41.15901221836655\n ],\n [\n -95.7843017578125,\n 41.37783904584602\n ],\n [\n -95.95321655273436,\n 41.37886950966323\n ],\n [\n -96.23611450195312,\n 41.37165592008984\n ],\n [\n -96.23611450195312,\n 41.166249339092\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
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The North Dakota hydrology manual prepared by the U.S. Department of Agriculture, Soil Conservation Service, presents methodologies primarily used for developing hydrology for onfarm conservation practices, watershed projects, Resource Conservation and Development project measures, and river basin studies. The manual includes data necessary for determining hydrologic factors and developing a design discharge for a given site and intended purpose. The U.S. Geological Survey, in cooperation with the North Dakota Natural Resources Conservation Service, developed methods to reproduce and update the annual yield maps for chapter 7 of the North Dakota hydrology manual. Annual yields, in acre-feet per square mile, for the 50- and 80-percent exceedance probabilities and expected percentage of snowmelt runoff isolines were estimated using U.S. Geological Survey streamflow data from 1931 to 2016 for 71 selected streamgages with drainage areas of 505 square miles or less. An application of a modified Maintenance of Variance Extension Type III was used to estimate missing annual streamflow volumes. An alternate expected percentage of snowmelt runoff isolines was estimated using High Plains Climatic Center precipitation and snowmelt data from 1931 to 2016 for 85 selected sites. The final expected percentage of snowmelt runoff isolines was estimated using streamflow data instead of precipitation and snowfall depth data. A snowmelt runoff seasonal period of March–May produced better isoline slopes than a November–May runoff seasonal period. Slopes of the expected percentage of snowmelt runoff isolines were sensitive to amounts of missing record. Suitable isoline slopes appeared when the missing record was set to 50 percent (43 years) and 66 percent (57 years) for the 86-year period of 1931–2016.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195144","collaboration":"Prepared in cooperation with the Natural Resources Conservation Service—North Dakota","usgsCitation":"Williams-Sether, T., and Wheeling, S.L., 2020, Small basin annual yield and percentage of snowmelt runoff in North Dakota, 1931–2016: U.S. Geological Survey Scientific Investigations Report 2019–5144, 37 p., https://doi.org/10.3133/sir20195144.","productDescription":"Report: vii, 38 p.; Dataset; 2 Appendixes","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-104356","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":375620,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5144/sir20195144.pdf","text":"Report","size":"6.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5144"},{"id":375416,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5144/coverthb.jpg"},{"id":375418,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5144/sir20195144_appendix_1.xlsx","text":"Appendix 1","size":"136 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5144 Appendix 1","linkHelpText":"—Table 1.1. 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Dakota\",\"nation\":\"USA \"}}]}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503–1608
Mountain View Road
Rapid City, SD 57702
The ascent and extrusion of crystal-rich magma is commonly facilitated by deformation partitioned within annular, conduit-parallel shear zones. The physical properties and textures of the shear zone materials, where exposed at surface, provide a record of ascent and eruption dynamics. We describe the shear zone developed in Dome C, part of Chaos Crags in the Lassen Volcanic Center (California, USA). The extruded shear zone comprises volcanic fault gouge and variably densified cataclasites. The competent cataclasites evidence deep-seated gouge production followed by gouge densification within the conduit on the timescale of lava dome ascent. Textural, geochemical and mineralogical data identify solid-state sintering as the densification mechanism. At the temperatures and pressures in the volcanic conduit, solid-state sintering causes rapid porosity and permeability loss within the gouge and concomitant material strengthening. Longer dwell times (i.e., slower ascent) allow for more sintering, producing stronger, denser and less permeable cataclasites. At Chaos Crags, we use the extent of sintering, quantified by residual porosity, to recover minimum in-conduit dwell times necessary to produce the observed cataclasites. Our analysis of the Dome C cataclasites suggests a maximum linear ascent rate of 10 m/day and a minimum ascent time of 100 days. We evaluate the consequences of shear zone lithification by solid-state sintering for the eruption of other crystal-rich, glass-poor magmas. Chaos Crags cataclasites preserve evidence of multiple cycles of fracturing, cataclasis and (re-)sintering suggesting a mechanism for transitions between effusive and explosive phases of dome-building eruptions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2020.106935","usgsCitation":"Ryan, A., Heap, M.J., Russell, J.K., Kennedy, L.A., and Clynne, M.A., 2020, Cyclic shear zone cataclasis and sintering during lava dome extrusion: Insights from Chaos Crags, Lassen Volcanic Center (USA): Journal of Volcanology and Geothermal Research, v. 401, 106935, 14 p., https://doi.org/10.1016/j.jvolgeores.2020.106935.","productDescription":"106935, 14 p.","ipdsId":"IP-118124","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467288,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://insu.hal.science/insu-03093680","text":"External Repository"},{"id":462542,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Chaos Crags, Lassen Volcanic Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.55292549839629,\n 40.56700203547902\n ],\n [\n -121.55292549839629,\n 40.49261388583824\n ],\n [\n -121.47706180792726,\n 40.49261388583824\n ],\n [\n -121.47706180792726,\n 40.56700203547902\n ],\n [\n -121.55292549839629,\n 40.56700203547902\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"401","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ryan, Amy","contributorId":300368,"corporation":false,"usgs":false,"family":"Ryan","given":"Amy","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":914833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heap, Michael J. 0000-0002-4748-735X","orcid":"https://orcid.org/0000-0002-4748-735X","contributorId":297882,"corporation":false,"usgs":false,"family":"Heap","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":64429,"text":"Université de Strasbourg","active":true,"usgs":false}],"preferred":false,"id":914834,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Russell, James K. 0000-0002-2062-3155","orcid":"https://orcid.org/0000-0002-2062-3155","contributorId":344810,"corporation":false,"usgs":false,"family":"Russell","given":"James","email":"","middleInitial":"K.","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":914835,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kennedy, Lori A. 0000-0001-5583-1264","orcid":"https://orcid.org/0000-0001-5583-1264","contributorId":344811,"corporation":false,"usgs":false,"family":"Kennedy","given":"Lori","email":"","middleInitial":"A.","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":914836,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clynne, Michael A. 0000-0002-4220-2968 mclynne@usgs.gov","orcid":"https://orcid.org/0000-0002-4220-2968","contributorId":2032,"corporation":false,"usgs":true,"family":"Clynne","given":"Michael","email":"mclynne@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":914837,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210673,"text":"sir20205040 - 2020 - Missouri StreamStats—St. Louis County and the City of St. Louis urban application","interactions":[],"lastModifiedDate":"2020-06-16T20:33:11.424554","indexId":"sir20205040","displayToPublicDate":"2020-06-16T09:37:03","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5040","displayTitle":"Missouri StreamStats—St. Louis County and the City of St. Louis Urban Application","title":"Missouri StreamStats—St. Louis County and the City of St. Louis urban application","docAbstract":"To address a major limitation of the functionality of the Missouri statewide StreamStats application in the urban areas of St. Louis County and the City of St. Louis, Missouri, the U.S. Geological Survey, in cooperation with the Metropolitan St. Louis Sewer District, defined watershed boundaries and hydrography for the study area using high-resolution 3-meter digital elevation data derived from light detection and ranging sources, high-resolution 6-inch imagery, and storm sewer network geospatial data. The combined sanitary sewers, a part of the storm sewer network, were integrated into the open channel hydrography and elevation data using a new Arc Hydro stormwater tool developed to facilitate the incorporation of the combined sanitary sewer network into the StreamStats application.
The combined sanitary sewer network was edited for connectivity and flow direction before integration into the Missouri-St. Louis StreamStats application. Inlet structures in the geospatial data were defined as HydroJunction features that allow for stormwater runoff to enter the combined sanitary sewer network. An Arc Hydro stormwater processing workflow and a sewershed delineation tool were developed to integrate the combined sanitary sewer network with the hydrographic dataset and digital elevation model in the study area.
The StreamStats application developed for the study area provides various data exploration tools that can be used to examine the spatial data and to obtain general descriptive information and flow statistics at streamgages in the study area. Watersheds and sewersheds can be delineated and basin characteristics can be determined at any point on the open channel network or the combined sanitary sewer network in the study area. Peak-flow statistics can be computed at any point on the open channel network. A report summarizing the results is generated by the StreamStats application and can be downloaded and used in other software.
The Missouri-St. Louis StreamStats application is limited to the area inside St. Louis County and the City of St. Louis and excludes locations on the main stem of the Mississippi, Missouri, and Meramec Rivers. The limitations of the Missouri-St. Louis StreamStats application include possible inaccuracies using regression equations for peak-flow statistics developed assuming natural flow conditions and topographically derived watersheds determined from a coarser resolution of data than is used in this application. Additionally, published regression equations for peak-flow statistics did not incorporate any pipe flow or sewershed delineations when they were developed, which limits the applicability of peak-flow statistics to basins based on primarily topographic delineation. Inaccuracies in resolution, completeness, location, or attribution of geospatial elevation data, hydrographic data, derived stream lines, derived watershed boundaries, and combined sanitary sewer data can limit the accuracy and functionality of the Missouri-St. Louis StreamStats application.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205040","collaboration":"Prepared in cooperation with the Metropolitan St. Louis Sewer District","usgsCitation":"Southard, R.E., Haluska, T., Richards, J.M., Ellis, J.T., Dartiguenave, C., and Djokic, D., 2020, Missouri StreamStats—St. Louis County and the City of St. Louis urban application: U.S. Geological Survey Scientific Investigations Report 2020–5040, 27 p., https://doi.org/10.3133/sir20205040.","productDescription":"Report: vii, 27 p.; Appendix; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-098907","costCenters":[{"id":36532,"text":"Central Midwest Water Science 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This report presents a novel conceptual framework and approach for conducting a geologically based environmental assessment, or geoenvironmental assessment, of undiscovered uranium resources within an area likely to contain uranium deposits. The framework is based on a source-to-receptor model that prioritizes the most likely contaminant sources, contaminant pathways, and affected environmental media for three common uranium extraction methods—open pit or underground mining with milling and in situ recovery (ISR). Data on regional geology, hydrology, and climate, as well as historical uranium mining and milling records are used to estimate the probable amounts of waste rock, tailings, wastewater, surface land disturbance, and subsurface aquifer disturbance for likely mining methods. Constituents of concern that might take the form of leachates, dust, radon, and sediments formed by chemical and physical weathering are also identified in the geoenvironmental assessment. Finally, areas where constituents of concern are likely to occur and persist in air, land, surface water, and groundwater are indicated by the potential for dispersion of dust by wind, accumulation of radon because of air stagnation, dispersion of sediments and wastewater by runoff, and infiltration of wastewater or leachates with consideration of the likely mobility of contaminants in surface water and groundwater. The geoenvironmental assessment output can be summarized in the following primary products: (1) a descriptive geoenvironmental model; (2) maps and statistics of variables that indicate the potential for constituents of concern to occur and persist in air, land, surface water, and groundwater within a tract that is geologically permissive for the occurrence of uranium; and (3) tables providing estimated or indicated quantities of waste rock, tailings, wastewater, dust, and radon emissions that could be associated with undiscovered uranium resources, if extracted, for each permissive tract. The uranium geoenvironmental assessment could help natural resource managers to prioritize and (or) identify (1) important potential contaminant pathways, (2) management practices required depending on the types of constituents that could be of concern, (3) areas for response in the event of accidental release, and (4) future directions for study. Furthermore, indicators of rock and water volumes potentially associated with an undiscovered uranium deposit may be evaluated to make quantitative comparisons of water required for uranium production or potential waste products generated during uranium extraction from areas permissive for uranium resource occurrence throughout the United States.
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12201 Sunrise Valley Drive
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We model the kinematic rupture process of the 2019
A method to estimate the probable high groundwater level in Massachusetts, excluding Cape Cod and the islands, was developed in 1981. The method uses a groundwater measurement from a test site, groundwater measurements from an index well, and a distribution of high groundwater levels from wells in similar geologic and topographic settings. The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, conducted an update to the Frimpter method for estimating the probable high groundwater levels in Massachusetts. The study evaluated the potential changes to the method resulting from four decades of additional groundwater-level data and the expansion of the network of wells for monitoring groundwater levels. The differences and potential benefits of daily, as opposed to monthly, measurements in the application of the method were examined because of the increased availability of high-frequency (subdaily) groundwater-level data. The study also considered long-term trends in groundwater levels that may alter the accuracy of the method. Finally, the accuracy of the estimated high groundwater levels was evaluated, and improved implementation guidance was prepared.
For this study, groundwater levels in 153 wells in Massachusetts and surrounding States with records with lengths of 16 to 78 years were analyzed. The highest recorded groundwater levels ranged from 1.2 feet (ft) above land surface (flooded conditions) to 45.8 ft below land surface, with a median of 4.6 ft below land surface. The maximum annual groundwater-level range was 1.4 to 17.9 ft, with a median of 5.5 ft.
The within-month variation, maximum annual groundwater-level range, and highest recorded groundwater level were computed using daily mean groundwater-level values from 28 wells with continuous records. The use of daily data resulted in larger maximum annual groundwater-level ranges (0.02 to 2.94 ft larger, with a median of 0.58 ft larger) and shallower highest-recorded groundwater levels (0.0 to 1.60 ft shallower, with a median of 0.18 ft shallower) than computations based on monthly measurements in the same wells.
Statistical tests showed moderate to strong evidence of trends in measurements of both high and low groundwater levels within most of the periods during which water levels were analyzed. High groundwater levels rose beneath the land surface at most sites during four of the six periods used for analysis (1966–2015, 1986–2015, 1991–2010, and 1981–2010). Low groundwater levels also increased at many sites during most of the periods evaluated, but this trend was less widespread than the similar trends in high groundwater levels, and the trend was to deeper low groundwater levels at more sites than the trend to deeper high groundwater levels. There was no clear trend in annual groundwater-level ranges at most sites during the six periods analyzed.
In general, the Frimpter method predicted shallower (higher) high groundwater levels than were observed but correctly classified sites according to their suitabilities for unmounded septic systems. The mean error of the predictions (difference between the estimated and observed groundwater levels) ranged from −3.23 ft to −1.40 ft for various approaches to estimating the groundwater-level range and selecting an index well. The method correctly classified 83 to 86 percent of monitoring-well sites according to their suitability for an unmounded septic system for many approaches to estimating the annual groundwater-level range and selecting an index well. The approach selected for estimating the annual groundwater-level range and selecting an index well will depend upon the importance of an accurate estimate of the high groundwater level as compared to the importance of an estimated high groundwater level that is less likely to be exceeded.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205036","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Barclay, J.R., and Mullaney, J.R., 2020, Updating data inputs, assessing trends, and evaluating a method to estimate probable high groundwater levels in selected areas of Massachusetts: U.S. Geological Survey Scientific Investigations Report 2020–5036, 45 p., https://doi.org/10.3133/sir20205036.","productDescription":"Report: viii, 45 p.; Data Release","numberOfPages":"45","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103689","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":375551,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NM2PHP","text":"USGS data release","linkHelpText":"Data on well characteristics and well-pair characteristics for estimating high groundwater levels in selected areas of Massachusetts"},{"id":375553,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5036/sir20205036.pdf","text":"Report","size":"7.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5036"},{"id":375554,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5036/coverthb2.jpg"}],"country":"United States","state":"Connecticut, Massachusetts, New Hampshire, Rhode Island, Vermont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.8343505859375,\n 42.90011265525328\n ],\n [\n -73.2952880859375,\n 42.9524020856897\n ],\n [\n -73.27880859375,\n 42.65820178455667\n ],\n [\n -73.5150146484375,\n 42.12267315117256\n ],\n [\n -73.5479736328125,\n 41.393294288784865\n ],\n [\n -73.54248046875,\n 41.29431726315258\n ],\n [\n -73.487548828125,\n 41.20345619205131\n ],\n [\n -73.7347412109375,\n 41.10005163093046\n ],\n [\n -73.65234375,\n 41.000629848685385\n ],\n [\n -72.9547119140625,\n 41.14143302653628\n ],\n [\n -72.0538330078125,\n 41.17451935556443\n ],\n [\n -71.43310546875,\n 41.29431726315258\n ],\n [\n -70.6475830078125,\n 41.21585377825921\n ],\n [\n -69.7686767578125,\n 41.16211393939692\n ],\n [\n -69.8785400390625,\n 41.87774145109676\n ],\n [\n -70.1806640625,\n 42.17968819665961\n ],\n [\n -70.57617187499999,\n 42.718768102606326\n ],\n [\n -70.8343505859375,\n 42.90011265525328\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
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Snow interception by the forest canopy controls the spatial heterogeneity of subcanopy snow accumulation leading to significant differences between forested and nonforested areas at a variety of scales. Snow intercepted by the forest canopy can also drastically change the surface albedo. As such, accurately modeling snow interception is of importance for various model applications such as hydrological, weather, and climate predictions. Due to difficulties in the direct measurements of snow interception, previous empirical snow interception models were developed at just the point scale. The lack of spatially extensive data sets has hindered the validation of snow interception models in different snow climates, forest types, and at various spatial scales and has reduced the accurate representation of snow interception in coarse-scale models. We present two novel empirical models for the spatial mean and one for the standard deviation of snow interception derived from an extensive snow interception data set collected in an evergreen coniferous forest in the Swiss Alps. Besides open-site snowfall, subgrid model input parameters include the standard deviation of the DSM (digital surface model) and/or the sky view factor, both of which can be easily precomputed. Validation of both models was performed with snow interception data sets acquired in geographically different locations under disparate weather conditions. Snow interception data sets from the Rocky Mountains, US, and the French Alps compared well to the modeled snow interception with a normalized root mean square error (NRMSE) for the spatial mean of ≤10 % for both models and NRMSE of the standard deviation of ≤13 %. Compared to a previous model for the spatial mean interception of snow water equivalent, the presented models show improved model performances. Our results indicate that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale DSM is available to derive subgrid forest parameters.
","language":"English","doi":"10.5194/hess-24-2545-2020","usgsCitation":"Helbig, N., Moeser, C.D., Teich, M., Vincent, L., Lejeune, Y., Sicart, J., and Monnet, J., 2020, Snow processes in mountain forests: Interception modeling for coarse-scale applications: Hydrology and Earth System Sciences, v. 24, p. 2545-2560, https://doi.org/10.5194/hess-24-2545-2020.","productDescription":"16 p.","startPage":"2545","endPage":"2560","ipdsId":"IP-111174","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":456397,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-24-2545-2020","text":"Publisher Index Page"},{"id":375684,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"France, United States","state":"Utah","otherGeospatial":"French Alps, Rocky Mountains","volume":"24","noUsgsAuthors":false,"publicationDate":"2020-05-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Helbig, N. 0000-0002-8663-7306","orcid":"https://orcid.org/0000-0002-8663-7306","contributorId":225392,"corporation":false,"usgs":false,"family":"Helbig","given":"N.","email":"","affiliations":[{"id":41093,"text":"WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland","active":true,"usgs":false}],"preferred":false,"id":791020,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moeser, C. 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Grenoble Alpes, Irstea, LESSEM, 38000 Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":791026,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70225148,"text":"70225148 - 2020 - Using a bayesian multistate occupancy model to assess seabird and shorebird status in Glacier Bay, Alaska","interactions":[],"lastModifiedDate":"2021-10-14T12:44:11.60355","indexId":"70225148","displayToPublicDate":"2020-06-15T07:41:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Using a bayesian multistate occupancy model to assess seabird and shorebird status in Glacier Bay, Alaska","docAbstract":"The U.S. Department of Interior National Park Service is charged with both monitoring avian communities and evaluating the influence of visitors to National Parks on sensitive species; however, this task is challenging considering that sampling programs often involve multiple species, each with differing behavior, habitat requirements, and detectability. Our objectives were to build a model to describe the status of waterbirds in Glacier Bay National Park, Alaska, USA, and assess effects of area closures on these species. We used a Bayesian multistate occupancy model to describe the status of multiple species and make the best possible use of existing survey data. We modeled up to 5 states per species and evaluated predictors of occupancy, nesting, and abundance, as well as survey-related predictors of state-dependent detection probability. We found that occupancy probability varied across species and habitats (islands vs. glacial outwashes). For most species, occupancy probability was substantially greater at sites occupied in the year previous (site persistence). We found weak evidence that area closures affected the occurrence of species in the study, but this was largely because most sites were closed for the entirety of the study period. The probability of detecting occurrence, nesting, and abundance varied across species and survey methods (ground vs. vessel). Detection parameters provided valuable information for enhancing the efficiency of future surveys, by identifying preferred survey methods and sampling periods for specific waterbird species. © 2020 The Wildlife Society.
To better understand the Earth system, it is important to investigate the interactions between precipitation, land use/land cover (LULC), and the land surface, especially vegetation. An improved understanding of these land-atmosphere interactions can aid understanding of the climate system and modeling of time series satellite data. Here, we investigate the effect of precipitation and LULC on the reflectance of the land surface in the northern U.S. Great Plains. We utilize time series satellite data from the 45 year Landsat archive. The length of the Landsat record allows for analysis of multiple periods of drought and wet conditions (reflecting climate, as well as weather), such that the precipitation-reflectance relationship can be investigated robustly for every individual pixel in the study area. The high spatial resolution of Landsat (30 m) allows for investigation of spatial patterns in weather (i.e., precipitation extremes) interactions with land surface reflectance at the scale of individual fields. Weather history is represented by a drought index that describes effective moisture availability, the Standardized Precipitation and Evaporation Index (SPEI). We find that effective moisture has a robust and consistent effect on reflectance over many types of land cover, with ∼90% of all pixels having significantly (
The western pond turtle (WPT), recently separated into two paripatrically distributed species (Emys pallida and Emys marmorata), is experiencing significant reductions in its range and population size. In addition to habitat loss, two potential causes of decline are female-biased road mortality and high juvenile mortality from non-native predatory bullfrogs (Rana catesbeiana). However, quantitative analyses of these threats have never been conducted for either species of WPT. We used a combination of historical museum samples and published and unpublished field studies shared with us through personal communications with WPT field researchers (B. Shaffer, P. Scott, R. Fisher, C. Brown, R. Dagit, L. Patterson, T. Engstrom, 2019, personal communications) to quantify the effect of roads and bullfrogs on WPT populations along the west coast of the United States. Both species of WPT shift toward increasingly male biased museum collections over the last century, a trend consistent with increasing, female-biased road mortality. Recent WPT population studies revealed that road density and proximity were significantly associated with increasingly male-biased sex ratios, further suggesting female-biased road mortality. The mean body size of museum collections of E. marmorata, but not E. pallida, has increased over the last 100 years, consistent with reduced recruitment and aging populations that could be driven by invasive predators. Contemporary WPT population sites that co-occur with bullfrogs had significantly greater average body sizes than population sites without bullfrogs, suggesting strong bullfrog predation on small WPT hatchlings and juveniles. Overall, our findings indicate that both species of WPT face demographic challenges which would have been difficult to document without the use of both historical data from natural history collections and contemporary demographic field data. Although correlational, our analyses suggest that female-biased road mortality and predation on small turtles by non-native bullfrogs are occurring, and that conservation strategies reducing both may be important for WPT recovery.
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