Gas hydrate distributions on the marine margins of the U.S. state of Alaska are more poorly known than those on other U.S. margins, where bottom simulating reflections have been systematically mapped on marine seismic data to support modern, quantitative assessments of gas-in-place in gas hydrates. The extent of bottom simulating reflections in the U.S. Beaufort Sea has been known since the late 1970s, and researchers have investigated the possibility that remnant gas hydrate persists in association with decaying subsea permafrost on both the U.S. and Canadian Beaufort continental shelves. In the Bering Sea, possible gas hydrate-related features have been widely mapped, revealing zones of free gas and concentrated gas hydrate within the hydrate stability zone in features called velocity amplitude anomalies (VAMPs). However, there are few reports on bottom simulating reflections along the more than 2500 km of the Aleutian arc and along the transform plate margin in southeast Alaska. Here we examine selected seismic profiles from southeast Alaska, along the Aleutian margin, and on the Bering continental slope, emphasizing surveys acquired with large airgun arrays, and review the results obtained from Bering Sea’s Aleutian Basin and from the U.S. Beaufort Sea. In the new analyses, we detect hydrate-related bottom simulating reflections in southeastern Alaska and the eastern and central parts of the Aleutian arc, but not in the western Aleutian arc or beneath the continental slope from the island arc north into the Aleutian Basin. In the Bering Sea, recognition of hydrate-related bottom simulating reflections is complicated by the widespread existence of a bottom simulating reflector associated with a diagenetic transition (opal CT). Our detection of continental slope hydrate-related bottom simulating reflections in southeast Alaska and the eastern and central Aleutian arcs expands the area of potential gas hydrate distribution on Alaskan margins and underscores the need for more systematic analysis of existing seismic data to inform quantitative evaluation of gas-in-place.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"World atlas of submarine gas hydrates in continental margins","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-81186-0_17","usgsCitation":"Ruppel, C.D., and Hart, P.E., 2022, Gas hydrates on Alaskan marine margins, chap. of World atlas of submarine gas hydrates in continental margins, p. 209-223, https://doi.org/10.1007/978-3-030-81186-0_17.","productDescription":"15 p.","startPage":"209","endPage":"223","ipdsId":"IP-122791","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":394384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bering Sea’s Aleutian basin, U.S. Beaufort Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -159.169921875,\n 69.59589006237648\n ],\n [\n -140.18554687499997,\n 69.59589006237648\n ],\n [\n -140.18554687499997,\n 73.42842364106816\n ],\n [\n -159.169921875,\n 73.42842364106816\n ],\n [\n -159.169921875,\n 69.59589006237648\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -177.01171875,\n 47.87214396888731\n ],\n [\n -153.017578125,\n 47.87214396888731\n ],\n [\n -153.017578125,\n 61.39671887310411\n ],\n [\n -177.01171875,\n 61.39671887310411\n ],\n [\n -177.01171875,\n 47.87214396888731\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2022-01-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruppel, Carolyn D. 0000-0003-2284-6632 cruppel@usgs.gov","orcid":"https://orcid.org/0000-0003-2284-6632","contributorId":195778,"corporation":false,"usgs":true,"family":"Ruppel","given":"Carolyn","email":"cruppel@usgs.gov","middleInitial":"D.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":830832,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hart, Patrick E. 0000-0002-5080-1426 hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5080-1426","contributorId":2879,"corporation":false,"usgs":true,"family":"Hart","given":"Patrick","email":"hart@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":830833,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70228833,"text":"70228833 - 2022 - Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix D: RiverWare analyses","interactions":[],"lastModifiedDate":"2024-03-27T15:12:27.207997","indexId":"70228833","displayToPublicDate":"2022-01-01T10:07:41","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":17147,"text":"Interagency Flood Risk Management Report","active":true,"publicationSubtype":{"id":1}},"title":"Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix D: RiverWare analyses","docAbstract":"RiverWare is a river system modeling tool developed by CADSWES (Center of Advanced Decision Support for Water and Environmental Systems) that allows the user to simulate complex reservoir operations and perform period-of-record analyses for different scenarios. For the InFRM hydrology studies, RiverWare is used to generate a homogeneous regulated POR by simulating the basin as if the reservoirs and their current rule sets had been present in the basin for the entire time period. Statistical analyses can then be performed on the extended records at the gages. This report summarizes the RiverWare portion of the hydrologic analysis being completed for the InFRM Hydrology study of the Neches River Basin.
The RiverWare model described in this chapter presents development of the Neches River Basin hydrology, which mimics current operational conditions. The use of the RiverWare program allows for data extension to periods prior to dam construction. The utilization of longer streamgage record improves discharge frequency results and increases the confidence of the analysis being performed. The modeling evaluation criteria are: (1) evaluate output based on validating policies and functions, and (2) prioritize operation based on surcharge and flood control. A detailed explanation of the Neches River Basin POR hydrology will be in a later section.
Calibration results will also be shown that illustrate model performance since the Salt Water Barrier (SWB) construction was completed in 2005. The time window simulation run is for water year (WY) 2005 – WY 2018. This time window also captures the time when Hurricane Harvey occurred (late August of 2017). Each simulated water year was inspected individually to better validate the results.
After calibration, a general run for January 01, 1929 through WY 2018 was made. Historical pool elevations along with observed inflows and outflows were compared against the model simulated results. More emphasis was put on B.A. Steinhagen’s operations because the dam captures two major rivers (i.e. the Angelina and the Neches Rivers). Results were inspected closely for B.A. Steinhagen’s pool and releases, the simulated discharges at the Neches at Evadale gage, and the simulated discharges at the SWB at Beaumont, Texas.
","language":"English","publisher":"Interagency Flood Risk Management","collaboration":"U.S. Army Corps of Engineers, Federal Emergency Management Agency","usgsCitation":"Wallace, D., 2022, Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix D: RiverWare analyses: Interagency Flood Risk Management Report, 66 p.","productDescription":"66 p.","ipdsId":"IP-113418","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":427144,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396305,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://webapps.usgs.gov/infrm/#ha"}],"country":"United States","state":"Texas","otherGeospatial":"Neches River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96,\n 32\n ],\n [\n -96,\n 30\n ],\n [\n -94,\n 30\n ],\n [\n -94,\n 32\n ],\n [\n -96,\n 32\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wallace, David S. 0000-0002-9134-8197","orcid":"https://orcid.org/0000-0002-9134-8197","contributorId":205198,"corporation":false,"usgs":true,"family":"Wallace","given":"David S.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":835669,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70229817,"text":"70229817 - 2022 - Relative bias in catch among long-term fish monitoring surveys within the San Francisco Estuary","interactions":[],"lastModifiedDate":"2022-03-18T14:34:13.706782","indexId":"70229817","displayToPublicDate":"2022-01-01T09:21:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Relative bias in catch among long-term fish monitoring surveys within the San Francisco Estuary","docAbstract":"Fish monitoring gears rarely capture all available fish, an inherent bias in monitoring programs referred to as catchability. Catchability is a source of bias that can be affected by numerous aspects of gear deployment (e.g., deployment speed, mesh size, and avoidance behavior). Thus, care must be taken when multiple surveys—especially those using different sampling methods—are combined to answer spatio-temporal questions about population and community dynamics. We assessed relative catchability differences among four long-term fish monitoring surveys from the San Francisco Estuary: the Bay Study Otter Trawl (BSOT), the Bay Study Midwater Trawl (BSMT), the Fall Midwater Trawl (FMWT), and the Suisun Marsh Otter Trawl (SMOT). We used generalized additive models with a spatio-temporal smoother and survey as a fixed effect to predict gear-specific estimates of catch for 45 different fish species within large and small size classes. We used estimates of the fixed effect coefficients for each survey (e.g., BSOT) relative to the reference gear (FMWT) to develop relative measures of catchability among taxa, surveys, and fish-size classes, termed the catch-ratio. We found higher relative catchability of 27%, 22%, and 57% of fish species in large size classes from the FMWT than in the BSMT, BSOT, or SMOT, respectively. In the small size class, relative catchability was higher in the FMWT than the BSMT, BSOT, or SMOT for 50%, 18%, and 25% of fish species, respectively. As expected, relative catchability of demersal species was higher in the otter trawls (BSOT, SMOT) while relative catchability of pelagic species was higher in the midwater trawls (FMWT, BSMT). Our results demonstrate that catchability is a source of bias among monitoring efforts within the San Francisco Estuary, and assuming equal catchability among surveys, species, and size classes could result in significant bias when describing spatio-temporal patterns in catch if ignored.
","language":"English","publisher":"University of California Davis","doi":"10.15447/sfews.2022v20iss1art3","usgsCitation":"Huntsman, B., Mahardja, B., and Bashevkin, S., 2022, Relative bias in catch among long-term fish monitoring surveys within the San Francisco Estuary: San Francisco Estuary and Watershed Science, v. 20, no. 1, 3, 17 p., https://doi.org/10.15447/sfews.2022v20iss1art3.","productDescription":"3, 17 p.","ipdsId":"IP-130127","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":449301,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2022v20iss1art3","text":"Publisher Index Page"},{"id":397305,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.684326171875,\n 37.1165261849112\n ],\n [\n -121.2,\n 37.1165261849112\n ],\n [\n -121.2,\n 39.00211029922515\n ],\n [\n -122.684326171875,\n 39.00211029922515\n ],\n [\n -122.684326171875,\n 37.1165261849112\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Huntsman, Brock 0000-0003-4090-1949","orcid":"https://orcid.org/0000-0003-4090-1949","contributorId":223101,"corporation":false,"usgs":true,"family":"Huntsman","given":"Brock","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mahardja, Brian 0000-0003-0695-3745","orcid":"https://orcid.org/0000-0003-0695-3745","contributorId":288940,"corporation":false,"usgs":false,"family":"Mahardja","given":"Brian","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":838467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bashevkin, Samuel M.","contributorId":288941,"corporation":false,"usgs":false,"family":"Bashevkin","given":"Samuel M.","affiliations":[{"id":61910,"text":"Delta Science Program, Delta Stewardship Council","active":true,"usgs":false}],"preferred":false,"id":838468,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229656,"text":"70229656 - 2022 - San Francisco Estuary chlorophyll sensor and sample analysis intercomparison","interactions":[],"lastModifiedDate":"2022-03-11T15:26:53.094975","indexId":"70229656","displayToPublicDate":"2022-01-01T09:19:02","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":10383,"text":"Intercomparison Report","active":true,"publicationSubtype":{"id":3}},"title":"San Francisco Estuary chlorophyll sensor and sample analysis intercomparison","docAbstract":"This report presents an assessment of chlorophyll collection methods and anonymous results of field and laboratory comparisons in 2018 - 2019 by agencies in the San Francisco Estuary (SFE). The methods assessment and comparison exercises, with funding provided by the Delta Regional Monitoring Program and Bay Nutrient Management Strategy and in-kind contributions from participating agencies, are a first step to facilitate future comparisons and syntheses of data and inform best science practices in the region. In situ sonde comparison exercises found general agreement between two models of Yellow Springs Instrument (YSI) sensors, but the newer sensor (EXO v2 - total algae) measured higher chlorophyll fluorescence (fCHL) relative to the older YSI sensor (6-series 6025). Results may be attributed to the use of a two-point calibration and the fluorescence response of algal cultures in sensor development by the manufacturer. The laboratory comparison included participation by 12 distinct field - laboratory pairs (or groups), with one group analyzing filters using two analytical methods. Filters were collected in triplicate across three sampling events in 2018, and all sample results were pooled together. Results of statistical analyses indicated that nominal filter pore size, the grinding method associated with pigment extraction, and analytical methods do not introduce variability to the chlorophyll-a measurement (Chl-a). When Chl-a results were assessed by sample event, however, significant differences between nominal pore size and analytical methods existed; these differences could be attributed to the small sample size per event. Consistent reporting units and high-concentration calibration standards for field sensors among data collection agencies would improve the consistency and comparability of data collected in the SFE. More routine split sampling events, longer term sensor comparison exercises, and further processing and analytical comparisons that control for individual filterers may also enhance comparability in the region.
","language":"English","publisher":"Delta Regional Monitoring Program","usgsCitation":"Stumpner, E.B., Yin, J.S., Heberger, M., Wu, J., Wong, A., and Saraceno, J., 2022, San Francisco Estuary chlorophyll sensor and sample analysis intercomparison: Intercomparison Report, 61 p.","productDescription":"61 p.","ipdsId":"IP-123558","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":397022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397021,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://deltarmp.org/documents/"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.67608642578126,\n 37.36579146999664\n ],\n [\n -121.4,\n 37.36579146999664\n ],\n [\n -121.4,\n 38.348118547988065\n ],\n [\n -122.67608642578126,\n 38.348118547988065\n ],\n [\n -122.67608642578126,\n 37.36579146999664\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stumpner, Elizabeth B. 0000-0003-2356-2244 estumpner@usgs.gov","orcid":"https://orcid.org/0000-0003-2356-2244","contributorId":181854,"corporation":false,"usgs":true,"family":"Stumpner","given":"Elizabeth","email":"estumpner@usgs.gov","middleInitial":"B.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":837825,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yin, Jamie S.","contributorId":288390,"corporation":false,"usgs":false,"family":"Yin","given":"Jamie","email":"","middleInitial":"S.","affiliations":[{"id":61747,"text":"San Francisco Estuary Institute - Aquatic Science Center","active":true,"usgs":false}],"preferred":false,"id":837826,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heberger, Matthew","contributorId":288391,"corporation":false,"usgs":false,"family":"Heberger","given":"Matthew","email":"","affiliations":[{"id":61747,"text":"San Francisco Estuary Institute - Aquatic Science Center","active":true,"usgs":false}],"preferred":false,"id":837827,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Jing","contributorId":191126,"corporation":false,"usgs":false,"family":"Wu","given":"Jing","email":"","affiliations":[],"preferred":false,"id":837828,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wong, Adam","contributorId":288392,"corporation":false,"usgs":false,"family":"Wong","given":"Adam","affiliations":[{"id":61747,"text":"San Francisco Estuary Institute - Aquatic Science Center","active":true,"usgs":false}],"preferred":false,"id":837829,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Saraceno, John Franco 0000-0003-0064-1820","orcid":"https://orcid.org/0000-0003-0064-1820","contributorId":217534,"corporation":false,"usgs":false,"family":"Saraceno","given":"John Franco","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":837830,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70240413,"text":"70240413 - 2022 - Workshops report for mesophotic and deep benthic community fish, mobile invertebrates, sessile invertebrates and infauna","interactions":[],"lastModifiedDate":"2023-02-08T11:59:25.77461","indexId":"70240413","displayToPublicDate":"2022-01-01T09:01:19","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":13296,"text":"DWH MDBC Summary Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"SR-22-01","title":"Workshops report for mesophotic and deep benthic community fish, mobile invertebrates, sessile invertebrates and infauna","docAbstract":"Two workshops with subject matter experts in the appropriate fields, were held in November and December 2021 to elicit guidance and feedback from the broader mesophotic and deep benthic scientific community. These workshops focused on best practices/approaches and identifying data gaps relative to habitat assessment and evaluation goals of the Mesophotic and Deep Benthic Community (MDBC) restoration portfolio. The first workshop was a combined effort of the Habitat Assessment and Evaluation (HAE) Project Team and the Deepwater Horizon (DWH) Program. Industrial Economics, Inc. (IEc) provided extensive workshop planning, organizing, execution, and facilitation support during all stages of the workshop. Based on a questionnaire sent to scientists in August, 2021, the workshop focused on fish and mobile invertebrate habitat associations, abundance trends, community metrics, and food web functionality. Topical presentations and discussions focused not only on demersal fish and mobile invertebrates that are directly associated with mesophotic and deep benthic habitats, but also considered water column species and communities that benefit from these habitats more broadly. The second workshop, intended to complement the first workshop, focused on identifying best practices and critical information gaps for key community metrics, larval dispersal modeling, connectivity, effects and variability of environmental parameters, and recovery trajectories of corals, infauna, and other sessile invertebrates. Through literature review, internal HAE scientists considered these topics to be critical for restoration success. Products from the literature review included topical summaries (see Appendix B) that summarized the current state-of-the-science and provided the framework for the workshop. Information generated from the workshops will assist the MDBC HAE Project, and more broadly the DWH Program, identify data gaps and develop a suite of best practices for restoration activities.","language":"English","publisher":"NOAA","doi":"10.25923/8ph6-j393","usgsCitation":"Bassett, R., Harter, S.L., Clark, R., Zink, I., Hornick, K., Hartman, J., Bliska, H., Carle, M., Sutton, T., Demopoulos, A., David, A., Benson, K., Bourque, J., Nizinski, M.S., Prouty, N.G., Sharuga, S.M., Caporaso, A., Le, J., Herting, J., Morrison, C., and Poti, M., 2022, Workshops report for mesophotic and deep benthic community fish, mobile invertebrates, sessile invertebrates and infauna: DWH MDBC Summary Report SR-22-01, 177 p., https://doi.org/10.25923/8ph6-j393.","productDescription":"177 p.","startPage":"1","endPage":"177","ipdsId":"IP-143984","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":412815,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bassett, Rachel","contributorId":302194,"corporation":false,"usgs":false,"family":"Bassett","given":"Rachel","email":"","affiliations":[{"id":65431,"text":"CSS Inc, under contract to NOAA/NOS","active":true,"usgs":false}],"preferred":false,"id":863705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harter, Stacey L.","contributorId":302195,"corporation":false,"usgs":false,"family":"Harter","given":"Stacey","email":"","middleInitial":"L.","affiliations":[{"id":62397,"text":"NOAA/NMFS","active":true,"usgs":false}],"preferred":false,"id":863706,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clark, Randy","contributorId":218497,"corporation":false,"usgs":false,"family":"Clark","given":"Randy","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":863707,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zink, Ian","contributorId":289796,"corporation":false,"usgs":false,"family":"Zink","given":"Ian","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":863708,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hornick, Katherine","contributorId":302196,"corporation":false,"usgs":false,"family":"Hornick","given":"Katherine","email":"","affiliations":[{"id":65433,"text":"Earth Resources Technology, Inc. Under contract to NOAA/NMFS","active":true,"usgs":false}],"preferred":false,"id":863709,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hartman, Jennifer","contributorId":265721,"corporation":false,"usgs":false,"family":"Hartman","given":"Jennifer","email":"","affiliations":[{"id":54777,"text":"Rogue Detection Teams, Rice, Washington, USA","active":true,"usgs":false}],"preferred":false,"id":863710,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bliska, Hanna","contributorId":302197,"corporation":false,"usgs":false,"family":"Bliska","given":"Hanna","email":"","affiliations":[{"id":65434,"text":"Industrial Economics, Inc","active":true,"usgs":false}],"preferred":false,"id":863711,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Carle, 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coupling","interactions":[],"lastModifiedDate":"2024-09-25T15:54:28.499952","indexId":"70233204","displayToPublicDate":"2022-01-01T08:38:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":11126,"text":"Cuban Journal of Meteorology (Revista Cubana de Meteorología)","active":true,"publicationSubtype":{"id":10}},"title":"Analysis of ocean dynamics during the impact of Hurricane Matthew using ocean-atmosphere coupling","docAbstract":"The main goal of this investigation is to improve the understanding of ocean-atmosphere coupling during hurricanes. The present work involves the integration of the ocean-atmosphere coupled components of the Coupled Ocean-Atmosphere-Wave-Sediment Transport Modeling System in the Very Short Term Prediction System (SisPI). Three experiments are performed: First, using a dynamic sea surface temperature, consistent with the daily updated atmospheric model Weather Research and Forecast (SisPI); second, using the Regional Oceanic Modeling System and third, using a dynamic coupling between the atmospheric and the oceanic models. The coupled system improves the tracks of the hurricane simulations respect to the SisPI. The use of the oceanic model allows a more detailed representation of the sea surface temperature. Using the coupled model, a more precise diurnal cycle of the surface net heat fluxes is obtained.","language":"English","publisher":"Instituto de Meteorología de Cuba","doi":"2377/v28n1e05","usgsCitation":"Vazquez Proveyer, L., Sierra Lorenzo, M., Cruz Rodriguez, R.C., and Warner, J.C., 2022, Analysis of ocean dynamics during the impact of Hurricane Matthew using ocean-atmosphere coupling: Cuban Journal of Meteorology (Revista Cubana de Meteorología), v. 28, no. 1, e05, 11 p., https://doi.org/2377/v28n1e05.","productDescription":"e05, 11 p.","ipdsId":"IP-133711","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":404011,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Cuba","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-82.26815,23.18861],[-81.40446,23.11727],[-80.61877,23.10598],[-79.67952,22.7653],[-79.28149,22.3992],[-78.34743,22.51217],[-77.9933,22.27719],[-77.14642,21.65785],[-76.52382,21.20682],[-76.19462,21.22057],[-75.59822,21.01662],[-75.67106,20.73509],[-74.9339,20.69391],[-74.17802,20.28463],[-74.29665,20.05038],[-74.96159,19.92344],[-75.63468,19.87377],[-76.32366,19.95289],[-77.75548,19.85548],[-77.08511,20.41335],[-77.49265,20.67311],[-78.13729,20.73995],[-78.48283,21.02861],[-78.71987,21.59811],[-79.285,21.55918],[-80.21748,21.82732],[-80.51753,22.03708],[-81.82094,22.19206],[-82.16999,22.38711],[-81.795,22.63696],[-82.7759,22.68815],[-83.49446,22.16852],[-83.9088,22.15457],[-84.05215,21.91058],[-84.54703,21.80123],[-84.97491,21.89603],[-84.44706,22.20495],[-84.23036,22.56575],[-83.77824,22.78812],[-83.26755,22.98304],[-82.51044,23.07875],[-82.26815,23.18861]]]},\"properties\":{\"name\":\"Cuba\"}}]}","volume":"28","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vazquez Proveyer, Liset","contributorId":293212,"corporation":false,"usgs":false,"family":"Vazquez Proveyer","given":"Liset","email":"","affiliations":[{"id":63246,"text":"Center for Atmospheric Physics, Institute of Meteorology, Casablanca, 10900, Havana, Cuba","active":true,"usgs":false}],"preferred":false,"id":846779,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sierra Lorenzo, Maibys","contributorId":293213,"corporation":false,"usgs":false,"family":"Sierra Lorenzo","given":"Maibys","email":"","affiliations":[{"id":63246,"text":"Center for Atmospheric Physics, Institute of Meteorology, Casablanca, 10900, Havana, Cuba","active":true,"usgs":false}],"preferred":false,"id":846780,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cruz Rodriguez, Roberto Carlos","contributorId":293214,"corporation":false,"usgs":false,"family":"Cruz Rodriguez","given":"Roberto","email":"","middleInitial":"Carlos","affiliations":[{"id":63247,"text":"Department of Atmospheric Physics, National Autonomous University of Mexico, Av. 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To better understand these droughts, we used tree-ring-based reconstructions of the Palmer drought severity index (PDSI) for the period 1475–2017 to examine droughts that covered at least 33% of the conterminous United States (CONUS). We identified 37 spatially extensive drought events for the CONUS and examined their spatial and temporal patterns. The duration of the extensive drought events ranged from 3 to 12 yr and on average affected 43% of the CONUS. The recent (2000–08) drought in the southwestern CONUS, often referred to as the turn-of-the-century drought, is likely one of the longest droughts in the CONUS during the past 500 years. A principal components analysis of the PDSI data from 1475 through 2017 resulted in three principal components (PCs) that explain about 48% of the variability of PDSI and are helpful to understand the temporal and spatial variability of the 37 extensive droughts in the CONUS. Analyses of the relations between the three PCs and well-known climate indices, such as indices of El Niño–Southern Oscillation, indicate statistically significant correlations; however, the correlations do not appear to be large enough (all with an absolute value less than 0.45) to be useful for the development of drought prediction models.
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Branch","active":true,"usgs":true},{"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":850242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolock, David M. 0000-0002-6209-938X","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":219213,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":850243,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70225504,"text":"70225504 - 2022 - Impact of spectral resolution on quantifying cyanobacteria in lakes and reservoirs: A machine-learning assessment","interactions":[],"lastModifiedDate":"2024-05-17T17:00:12.08779","indexId":"70225504","displayToPublicDate":"2022-01-01T05:55:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9530,"text":"IEEE Transactions in Geoscience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Impact of spectral resolution on quantifying cyanobacteria in lakes and reservoirs: A machine-learning assessment","docAbstract":"Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of best-available multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large (N = 905) database of colocated in situ radiometric spectra and PC are employed. We first examine the performance of selected widely used machine-learning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance ( Rrs) spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a full-width at half-maximum (FWHM) of < 6 nm. Results show that the multilayer perceptron (MLP) neural network applied to HICO spectral configurations (median errors < 65%) outperforms other ML models. This model is subsequently applied to Rrs spectra resampled to the band configuration of existing satellite instruments and of the one proposed for the next Landsat sensor. These results confirm that employing MLP models to estimate PC from hyperspectral data delivers tangible improvements compared with retrievals from multispectral data and benchmark algorithms (with median errors between ~73% and 126%) and shows promise for developing a globally applicable cyanobacteria measurement approach.
","language":"English","publisher":"IEEE","doi":"10.1109/TGRS.2021.3114635","usgsCitation":"Zolfaghari, K., Pahlevan, N., Binding, C., Gurlin, D., Simis, S.G., Verdu, A.R., Li, L., Crawford, C., VanderWoude, A., Errera, R., Zastepa, A., and Duguay, C.R., 2022, Impact of spectral resolution on quantifying cyanobacteria in lakes and reservoirs: A machine-learning assessment: IEEE Transactions in Geoscience and Remote Sensing, v. 60, 5515520, 20 p., https://doi.org/10.1109/TGRS.2021.3114635.","productDescription":"5515520, 20 p.","ipdsId":"IP-132686","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":449319,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1109/tgrs.2021.3114635","text":"Publisher Index Page"},{"id":390590,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zolfaghari, Kiana","contributorId":267804,"corporation":false,"usgs":false,"family":"Zolfaghari","given":"Kiana","email":"","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":825333,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pahlevan, Nima","contributorId":267805,"corporation":false,"usgs":false,"family":"Pahlevan","given":"Nima","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":825334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Binding, Caren","contributorId":267806,"corporation":false,"usgs":false,"family":"Binding","given":"Caren","affiliations":[],"preferred":false,"id":825335,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gurlin, Daniela","contributorId":267807,"corporation":false,"usgs":false,"family":"Gurlin","given":"Daniela","email":"","affiliations":[],"preferred":false,"id":825336,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Simis, Stefan G.H.","contributorId":267808,"corporation":false,"usgs":false,"family":"Simis","given":"Stefan","email":"","middleInitial":"G.H.","affiliations":[],"preferred":false,"id":825337,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Verdu, Antonio Ruiz","contributorId":267809,"corporation":false,"usgs":false,"family":"Verdu","given":"Antonio","email":"","middleInitial":"Ruiz","affiliations":[],"preferred":false,"id":825338,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Li, Lin","contributorId":267810,"corporation":false,"usgs":false,"family":"Li","given":"Lin","email":"","affiliations":[],"preferred":false,"id":825339,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":825340,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"VanderWoude, Andrea","contributorId":267811,"corporation":false,"usgs":false,"family":"VanderWoude","given":"Andrea","email":"","affiliations":[],"preferred":false,"id":825341,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Errera, Reagan","contributorId":267812,"corporation":false,"usgs":false,"family":"Errera","given":"Reagan","email":"","affiliations":[],"preferred":false,"id":825342,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Zastepa, Arthur","contributorId":267813,"corporation":false,"usgs":false,"family":"Zastepa","given":"Arthur","email":"","affiliations":[],"preferred":false,"id":825343,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Duguay, Claude R.","contributorId":267814,"corporation":false,"usgs":false,"family":"Duguay","given":"Claude","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":825344,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70262046,"text":"70262046 - 2022 - Demography and site fidelity of a grassland bird, the Henslow’s Sparrow, in powerline right-of-way habitat","interactions":[],"lastModifiedDate":"2025-01-10T16:40:33.895421","indexId":"70262046","displayToPublicDate":"2022-01-01T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2284,"text":"Journal of Field Ornithology","active":true,"publicationSubtype":{"id":10}},"title":"Demography and site fidelity of a grassland bird, the Henslow’s Sparrow, in powerline right-of-way habitat","docAbstract":"Grassland birds are among the fastest declining avian species in North America, primarily due to habitat loss. In the southeastern U.S., much grassland and open savanna habitat has been converted to timber production or agriculture, neither of which typically provides habitat for breeding or wintering grassland birds. Powerline right-of-ways could provide suitable habitat for many grassland species as these areas are maintained to be treeless. We studied the population dynamics of Henslow’s Sparrows (Centronyx henslowii) wintering in powerline right-of-ways in southeastern Georgia through an 11-year mark-recapture study. We used a robust design Cormack-Jolly-Seber model to estimate probability of detection and apparent survival. Abundance varied substantially among years at each site, with density varying from 1.7 to 8.5 birds/ha. Within-year detection probability was moderately high at 28% (24-33%, 95% credible interval [CI]), but apparent survival was very low at 13% (9-17%, 95% CI). This low apparent survival was likely due to low return rates (and not necessarily low survival). However, birds that did return to the study sites had extremely high site fidelity, with 82% of across-year recaptures < 200 m apart. This apparent incongruity between low apparent survival rates (likely due to emigration from the study sites) and high site fidelity for returning individuals could be explained by the dependability of the right-of-way habitat, which differs from typically patchy and temporally variable grassland and savanna wintering habitats. Dependable habitat may allow for higher site fidelity than this species would otherwise have, potentially resulting in the high densities we observed. Thousands of miles of right-of-ways in Georgia, and other southeastern states, could be managed to maximize potential habitat for declining grassland bird species.
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\"}}]}","volume":"93","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922808,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dwire, Abigail","contributorId":348000,"corporation":false,"usgs":false,"family":"Dwire","given":"Abigail","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":922809,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schneider, Todd M.","contributorId":348001,"corporation":false,"usgs":false,"family":"Schneider","given":"Todd M.","affiliations":[{"id":36378,"text":"Georgia Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":922810,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227422,"text":"70227422 - 2022 - Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics","interactions":[],"lastModifiedDate":"2022-01-14T15:32:34.362133","indexId":"70227422","displayToPublicDate":"2021-12-31T09:21:06","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9944,"text":"Remote Sensing of the Environment","active":true,"publicationSubtype":{"id":10}},"title":"Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics","docAbstract":"Mounting evidence indicates dryland ecosystems play an important role in driving the interannual variability and trend of the terrestrial carbon sink. Nevertheless, our understanding of the seasonal dynamics of dryland ecosystem carbon uptake through photosynthesis [gross primary productivity (GPP)] remains relatively limited due in part to the limited availability of long-term data and unique challenges associated with satellite remote sensing across dryland ecosystems. Here, we comprehensively evaluated longstanding and emerging satellite vegetation proxies in their ability to capture seasonal dryland GPP dynamics. Specifically, we evaluated: 1) reflectance-based proxies normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), near infrared reflectance index (NIRv), and kernel NDVI (kNDVI) from the MODerate resolution Imaging Spectroradiometer (MODIS); and 2) newly available physiologically-based proxy solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI). As a performance benchmark, we used GPP estimates from a robust network of 21 western United States eddy covariance tower sites that span representative gradients in dryland ecosystem climate and functional composition. We found that NIRv and SIF were the best performing GPP proxies and captured complementary aspects of seasonal GPP dynamics across dryland ecosystem types. NIRv offered better performance than the other proxies across relatively low-productivity, sparsely non-evergreen vegetated sites (R2 = 0.59 ± 0.13); whereas SIF best captured seasonal dynamics across relatively high-productivity sites, including evergreen-dominated sites (R2 = 0.74 ± 0.07). Notably, across grass-dominated sites, all reflectance-based proxies (NDVI, SAVI, NIRv and kNDVI) showed significant seasonal bias (hysteresis) that strengthened with the total fraction of woody vegetation cover, likely due to seasonal patterns in woody vegetation reflectance that are unrelated to or decoupled from GPP. Future efforts to fully integrate the complementary strengths of NIRv and SIF could significantly improve our understanding and representation of dryland GPP dynamics in satellite-based models.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2021.112858","usgsCitation":"Wang, X., Biederman, J.A., Knowles, J.F., Scott, R.L., Turner, A.J., Dannenberg, M.P., Kohler, P., Frankenberg, C., Litvak, M.E., Flerchinger, G.N., Law, B.E., Kwon, H., Reed, S., Parton, W.J., Barron-Gafford, G.A., and Smith, W.K., 2022, Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics: Remote Sensing of the Environment, v. 270, 112858, 11 p., https://doi.org/10.1016/j.rse.2021.112858.","productDescription":"112858, 11 p.","ipdsId":"IP-133234","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":449323,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1981744","text":"Publisher Index 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Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":830804,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Flerchinger, Gerald N.","contributorId":257377,"corporation":false,"usgs":false,"family":"Flerchinger","given":"Gerald","email":"","middleInitial":"N.","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":830805,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Law, Beverly E.","contributorId":222527,"corporation":false,"usgs":false,"family":"Law","given":"Beverly","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":830806,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kwon, Hyojung","contributorId":271096,"corporation":false,"usgs":false,"family":"Kwon","given":"Hyojung","email":"","affiliations":[{"id":56277,"text":"Department of Forest Ecosystems and Society, College of Forestry, Oregon State University, Corvallis, OR, 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A.","contributorId":19058,"corporation":false,"usgs":false,"family":"Barron-Gafford","given":"Greg","email":"","middleInitial":"A.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":830810,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Smith, William K. 0000-0002-5785-6489","orcid":"https://orcid.org/0000-0002-5785-6489","contributorId":239667,"corporation":false,"usgs":false,"family":"Smith","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":47959,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":830811,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70227508,"text":"70227508 - 2022 - Nitrogen reductions have decreased hypoxia in the Chesapeake Bay: Evidence from empirical and numerical modeling","interactions":[],"lastModifiedDate":"2022-01-20T13:30:49.473216","indexId":"70227508","displayToPublicDate":"2021-12-31T07:30:14","publicationYear":"2022","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":"Nitrogen reductions have decreased hypoxia in the Chesapeake Bay: Evidence from empirical and numerical modeling","docAbstract":"Seasonal hypoxia is a characteristic feature of the Chesapeake Bay due to anthropogenic nutrient input from agriculture and urbanization throughout the watershed. Although coordinated management efforts since 1985 have reduced nutrient inputs to the Bay, oxygen concentrations at depth in the summer still frequently fail to meet water quality standards that have been set to protect critical estuarine living resources. To quantify the impact of watershed nitrogen reductions on Bay hypoxia during a recent period including both average discharge and extremely wet years (2016–2019), this study employed both statistical and three-dimensional (3-D) numerical modeling analyses. Numerical model results suggest that if the nitrogen reductions since 1985 had not occurred, annual hypoxic volumes (O2 < 3 mg L−1) would have been ~50–120% greater during the average discharge years of 2016–2017 and ~20–50% greater during the wet years of 2018–2019. The effect was even greater for O2 < 1 mg L−1, where annual volumes would have been ~80–280% greater in 2016–2017 and ~30–100% greater in 2018–2019. These results were supported by statistical analysis of empirical data, though the magnitude of improvement due to nitrogen reductions was greater in the numerical modeling results than in the statistical analysis. This discrepancy is largely accounted for by warming in the Bay that has exacerbated hypoxia and offset roughly 6–34% of the improvement from nitrogen reductions. Although these results may reassure policymakers and stakeholders that their efforts to reduce hypoxia have improved ecosystem health in the Bay, they also indicate that greater reductions are needed to counteract the ever-increasing impacts of climate change.
Plants are critical mediators of terrestrial mass and energy fluxes, and their structural and functional traits have profound impacts on local and global climate, biogeochemistry, biodiversity, and hydrology. Yet Earth System Models (ESMs), our most powerful tools for predicting the effects of humans on the coupled biosphere-atmosphere system, simplify the incredible diversity of land plants into a handful of coarse categories of ‘Plant Functional Types’ (PFTs) that often fail to capture ecological dynamics such as biome distributions. The inclusion of more realistic functional diversity is a recognized goal for ESMs, yet there is currently no consistent, widely accepted way to add diversity to models, i.e. to determine what new PFTs to add and with what data to constrain their parameters. We review approaches to representing plant diversity in ESMs and draw on recent ecological and evolutionary findings to present an evolution-based functional type approach for further disaggregating functional diversity. Specifically, the prevalence of niche conservatism, or the tendency of closely related taxa to retain similar ecological and functional attributes through evolutionary time, reveals that evolutionary relatedness is a powerful framework for summarizing functional similarities and differences among plant types. We advocate that Plant Functional Types based on dominant evolutionary lineages (‘Lineage Functional Types’) will provide an ecologically defensible, tractable, and scalable framework for representing plant diversity in next-generation ESMs, with the potential to improve parameterization, process representation, and model benchmarking. We highlight how the importance of evolutionary history for plant function can unify the work of disparate fields to improve predictive modeling of the Earth system.
Frequency‐dependent horizontal‐to‐vertical spectral ratios (HVSRs) of Fourier amplitudes from three‐component recordings can provide useful information for site response modeling. However, such information is not incorporated into most ground‐motion models, including those from Next‐Generation Attenuation projects, which instead use the time‐averaged shear‐wave velocity (
During the 2018 Kīlauea eruption and caldera collapse, M ∼ 5 caldera collapse earthquakes occurred almost daily from mid-May until the beginning of August. While caldera collapses happen infrequently, the collapse-related seismicity damaged nearby structures, and so these events should be included in a complete seismic hazard assessment. Here, we present an approach to forecast the seismic hazard of the collapse earthquakes. We model their occurrence by combining a Poisson distribution for the number of collapses with a negative binomial for the number of earthquakes in a collapse, based on observations at Kīlauea. This rate model is then combined with a ground motion model to assess the seismic hazard posed by caldera collapse events. The rate model is non-Poisson but a Poisson model is adequate for low exceedance probabilities (e.g., <10% in 50 years). This approach could be generalized to model the hazard from earthquakes triggered by other underlying processes.
Land management decisions need context about how landscapes will respond to different circumstances or actions. As ecologists’ understanding of nonlinear ecological dynamics has evolved into state-and-transition models (STMs), they have put more emphasis on defining and mapping the soil, geomorphological, and climate parameters that mediate these dynamics. The US Department of Agriculture Natural Resources Conservation Service ecological site descriptions (ESDs) have become the foremost system in classifying lands into ecological units based on STMs. However, an exhaustive inventory of ESDs has proved challenging to complete in the United States, and there have been questions about the consistency of detail in areas completed and the ability to objectively support some assertions made in existing ESDs. To address these issues, this study examines ESDs in the diverse Upper Colorado River region, where ESDs are only partially complete, to look at quantitative approaches to generalizing ecological site concepts based on unifying underlying soil, geomorphology, and climate patterns. Using existing ESDs and vegetation monitoring plot data, results show that a simple hierarchical soil geomorphic unit (SGU) framework based on topographic mediation of moisture, soil salinity, soil depth, slope, rock content, and soil texture can represent much of the ecological dynamics cataloged in ESDs. Analyses of reference plant production data, ecological state attribution, and regional monitoring data show that the new SGUs represent more variation than common climate parameters. This study also included predictively mapping SGUs at 30-m resolution (Kappa of 0.53, 74% agreement with top two predictions in validation). An optimized combination of SGUs with climate zones derived from an aridity index and maximum temperature of the hottest month resulted in an ecological site group framework that condensed over 826 unique ecological site records at various stages of completeness in the regional soil survey down to 35 intuitive and mappable ecological site groups.
From a management perspective, reptiles are relatively novel invasive taxa. Few methods for reptile control have been developed and very little is known about their effectiveness for reducing reptile populations, particularly when the goal is eradication. Many reptiles, and especially snakes, are cryptic, secretive, and undergo extended periods of inactivity, traits that decrease detection probabilities and create challenges in estimating population size or evaluating management effects. The brown treesnake (Boiga irregularis) is a notorious invasive species that continues to cause major ecological and economic harm following their introduction to the island of Guam after World War II. They have been the subject of intensive research on the effectiveness of various techniques to control snakes, including the first ever aerial system for the distribution of toxic acetaminophen baits for reptile control. We provide a cohort-based life table for a cryptic and invasive reptile undergoing extended population control using toxic baits from March 2017–2020. We also evaluated the effects of single (toxic bait) versus multi-tool (toxic bait and live trapping) management efforts on population trajectories, and estimated which population vital rates are most important for influencing population growth or decline in a treated landscape. Treatment of the population with acetaminophen-laced baits resulted in an immediate reduction followed by a gradual population decline that suggested that eradication was the probable outcome given sufficient treatment time but that the period of treatment was decades in magnitude. Inclusion of live trapping reduced the predicted time required to achieve eradication by more than half. Preventing the transition of 1,000-mm snout-vent length (SVL) females to larger sizes was predicted to have the greatest effect on population reduction based on integral projection modeling. Our results suggest that toxic baits are capable of eradicating brown treesnakes in an enclosure, although inclusion of trapping reduced overall treatment time required. Tools that effectively target females >1,000 mm SVL may have the greatest effect on reducing overall treatment timelines.
The uranium (U) content, and more recently, the ratio between 238U and 235U in black shales are commonly applied as a proxy to determine redox conditions and infer organic-richness. Uranium contents typically display a linear relationship with total organic carbon (TOC) in shales. This relationship is due to the processes and mechanisms responsible for the incorporation of U into the sediment during the deposition and remineralization of organic matter. This U/TOC relationship can vary, however, and some shales display uncharacteristically low U content despite having high TOC content, while others show large enrichments of U relative to TOC. Here we examine the U to TOC ratios and U-isotope compositions of three Upper Devonian-Lower Mississippian shales: the Woodford Shale, the Cleveland Shale, and the Bakken Shale, with two study sites in Oklahoma, one site in eastern Kentucky, and three sites in eastern Montana and western North Dakota, respectively. The U/TOC ratios of each shale are distinct from one another exhibiting average ratios ranging from 3 in the Cleveland Shale, to over 10 in the Bakken Shale. The distinct geochemical composition of the three shales suggests that, although lithologically similar, each study site represents a markedly different and dynamic depositional environment. The low average U/TOC (~3) along with the relatively high δ238U values (~0.03‰) of the Cleveland Shale core suggests deposition along the basin margin under normal marine conditions with periods of reduced bottom water oxygenation, likely due to fluctuations in the location of the pycnocline. The Woodford Shale on the other hand, shows higher U/TOC ratios (~4, George core, ~9, Poe core) and δ238U (~0.02‰ average, George core, ~0.06‰ average, Poe core), which suggests an unrestricted setting with intermittent euxinic conditions. In contrast, high U/TOC ratios (2–15), and very high δ238U values (up to 0.55‰) in the Bakken Shale cores indicate intense metal draw-down into sediments under sulfidic waters. The results show that when the U/TOC ratios and U-isotopic compositions of each studied shale are compared to modern anoxic basins and upwelling areas, it allows for an enhanced understanding of the paleoenvironmental conditions such as basin restriction and redox state of waters within the Late Devonian epicontinental seas of North America.
Manganese (Mn) concentrations and the probability of arsenic (As) exceeding the drinking-water standard of 10 μg/L were predicted in the Mississippi River Valley alluvial aquifer (MRVA) using boosted regression trees (BRT). BRT, a type of ensemble-tree machine-learning model, were created using predictor variables that affect Mn and As distribution in groundwater. These variables included iron (Fe) concentrations and specific conductance predicted from previously developed BRT models, groundwater flux and age estimates from MODFLOW, and hydrologic characteristics. The models also included results from the first airborne geophysical survey conducted in the United States to target an entire aquifer system. Predictions of high Mn and As occurred where Fe was high. Predicted high Mn concentrations were correlated with fraction of young groundwater (less than 65 years) computed from MODFLOW results. High probabilities of As exceedance were predicted where groundwater was relatively old and airborne electromagnetic resistivity was high, typically proximal to streams. Two-variable partial-dependence plots and sensitivity analysis were used to provide insight into the factors controlling Mn and As distribution in groundwater. The maps of predicted Mn concentrations and As exceedance probabilities can be used to identify areas where these constituents may be high, and that could be targeted for further study. This paper shows that incorporation of a selected set of process-informed data, such as MODFLOW results and airborne geophysics, into a machine-learning model improves model interpretability. Incorporation of process-rich information into machine-learning models will likely be useful for addressing a wide range of problems of interest to groundwater hydrologists.
Despite ongoing Lake Sturgeon recovery efforts, little is known about the role of stocking location on survival and dispersal to nursery habitats. We stocked age-0 Lake Sturgeon at four sites in two adjacent Missouri River tributaries and used telemetry to examine whether survival and dispersal differed among stocking sites and rivers. Survival estimates from Barker Cormack-Jolly-Seber models that incorporated both receiver detections and auxiliary manual detections were higher than spatial capture-recapture models that only included receiver detections. Barker model overwinter survival averaged 53% and provided information to adjust individual censoring in spatial capture-recapture model dispersal estimates. Within the two rivers, stocking site had little effect on activity centers with individuals from both sites converging upon similar locations by the end of the study period. However, dispersal distance and direction differed among stocking locations. Our overwinter survival estimates of stocked age-0 Lake Sturgeon in Missouri River tributaries were equal to or higher than other studied populations suggesting stocked juveniles may be contributing to the recovering population. Tributaries were important overwintering nursery locations with high stream fidelity that may contribute to future homing among adults.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3925","usgsCitation":"Moore, M., Paukert, C.P., Bonnot, T., Brooke, B., and Moore, T., 2022, Does where they start affect where they finish? A multimethod investigation of the role of stocking location on survival and dispersal of hatchery-reared Lake Sturgeon in Missouri River tributaries: River Research and Applications, v. 38, no. 4, p. 627-638, https://doi.org/10.1002/rra.3925.","productDescription":"12 p.","startPage":"627","endPage":"638","ipdsId":"IP-124542","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433443,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Gasconade River, Missouri River, Osage River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93,\n 39.25\n ],\n [\n -93,\n 38\n ],\n [\n -91,\n 38\n ],\n [\n -91,\n 39.25\n ],\n [\n -93,\n 39.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"38","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, M.J.","contributorId":341714,"corporation":false,"usgs":false,"family":"Moore","given":"M.J.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":908824,"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":908825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bonnot, T.W.","contributorId":274985,"corporation":false,"usgs":false,"family":"Bonnot","given":"T.W.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":908826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brooke, B.","contributorId":341723,"corporation":false,"usgs":false,"family":"Brooke","given":"B.","email":"","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":908827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moore, T.","contributorId":257287,"corporation":false,"usgs":false,"family":"Moore","given":"T.","affiliations":[],"preferred":false,"id":908828,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227402,"text":"70227402 - 2022 - Improving groundwater model calibration with repeat microgravity measurements","interactions":[],"lastModifiedDate":"2022-05-13T14:37:28.20751","indexId":"70227402","displayToPublicDate":"2021-12-23T06:52:44","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Improving groundwater model calibration with repeat microgravity measurements","docAbstract":"Groundwater-flow models depend on hydraulic head and flux observations for evaluation and calibration. A different type of observation—change in storage measured using repeat microgravity—can also be used for parameter estimation by simulating the expected change in gravity from a groundwater model and including the observation misfit in the objective function. The method is demonstrated using new software linked to MODFLOW input and output files and field data from the vicinity of the All American Canal in southeast California, USA. Over a 10-year period following lining of the previously highly permeable canal with concrete, gravity decreased by over 100 μGal (equivalent to about 2.5 m of free-standing water) at some locations as seepage decreased and the remnant groundwater mound dissipated into the aquifer or was removed by groundwater pumping. Simulated gravity from a MODFLOW model closely matched observations, and repeat microgravity data proved useful for constraining both hydraulic conductivity and specific yield estimates. Specific yield estimated using the infinite-horizontal slab approximation agreed well with model-derived values, and the departure from the linear, flat-water-table approximation was small, less than 2%, despite relatively large and dynamic water-table slope. First-order second-moment parameter uncertainty analysis shows reduction in uncertainty for all hydraulic conductivity and specific yield parameter estimates with the addition of repeat microgravity data, as compared to drawdown data alone.
Because the amount of available ground‐motion data has increased over the last decades, the need for automated processing algorithms has also increased. One difficulty with automated processing is to screen clipped records. Clipping occurs when the ground‐motion amplitude exceeds the dynamic range of the linear response of the instrument. Clipped records in which the amplitude exceeds the dynamic range are relatively easy to identify visually yet challenging for automated algorithms. In this article, we seek to identify a reliable and fully automated clipping detection algorithm tailored to near‐real‐time earthquake response needs. We consider multiple alternative algorithms, including (1) an algorithm based on the percentage difference in adjacent data points, (2) the standard deviation of the data within a moving window, (3) the shape of the histogram of the recorded amplitudes, (4) the second derivative of the data, and (5) the amplitude of the data. To quantitatively compare these algorithms, we construct development and holdout datasets from earthquakes across a range of geographic regions, tectonic environments, and instrument types. We manually classify each record for the presence of clipping and use the classified records. We then develop an artificial neural network model that combines all the individual algorithms. Testing on the holdout dataset, the standard deviation and histogram approaches are the most accurate individual algorithms, with an overall accuracy of about 93%. The combined artificial neural network method yields an overall accuracy of 95%, and the choice of classification threshold can balance precision and recall.
Anthropogenic climate change during the 21st century presents a significant challenge to the protection of cultural resources (CRs) on federal lands that encompass ∼ 28% of the U.S. In particular, CRs on this land base may be adversely affected by a wide range of climate-change hazards, including damage by sea-level rise, enhanced deterioration by increasing temperature and precipitation, and destruction by more-frequent and severe wildfire. Most current measures to manage the impacts of hazards on CRs use vulnerability assessments, but because these require that all CRs be treated as having an equal chance of being affected by climate-change hazards (i.e., equal exposure) across large landscapes, the cost and resources required for such analyses are overwhelming to land management agencies. Projections of changes in many hazards, however, show that the probability of hazard occurrence will be unevenly distributed on the landscape. Incorporating this information into a risk assessment thus allows CR managers to prioritize their efforts on assessing impacts to CRs in those areas where the probability of the hazard is greatest, thus increasing efficiency. We provide several heuristic examples of implementing the first part of a CR risk assessment by using 21st-century projections of several hazards most likely to adversely affect CRs on nine National Forests (NFs) managed by the U.S. Forest Service in northern Idaho and Montana. Overlaying the projected distribution of hazards on these NFs with the distribution of CRs identifies CR exposure that, with information on their vulnerability, is required to determine risk. Additional policy and field studies will be needed to determine how to prioritize those CRs that are most at risk according to their significance as well as identify how impacts can be reduced and managed through adaptation planning and implementation. Adaptation will follow the iterative risk management process particularly by improving projection resolution. Finer scale, process-based modeling informed by the highest priority CRs would also provide a means to assess various adaptation options that might change the estimated risk and increase the odds of CRs being as little affected as possible.