Autonomous underwater vehicles (AUVs) and animal telemetry have become important tools for understanding the relationships between aquatic organisms and their environment, but more information is needed to guide the development and use of AUVs as effective animal tracking platforms. A forward-facing acoustic telemetry receiver (VR2Tx 69 kHz; VEMCO, Bedford, Nova Scotia) attached to a novel AUV (gliding robotic fish) was tested in a freshwater lake to (1) compare its detection efficiency (i.e., the probability of detecting an acoustic signal emitted by a tag) of acoustic tags (VEMCO model V8-4H 69 kHz) to stationary receivers and (2) determine if detection efficiency was related to distance between tag and receiver, direction of movement (toward or away from transmitter), depth, or pitch.
Detection efficiency for mobile (robot-mounted) and stationary receivers were similar at ranges less than 300 m, on average across all tests, but detection efficiency for the mobile receiver decreased faster than for stationary receivers at distances greater than 300 m. Detection efficiency was higher when the robot was moving toward the transmitter than when moving away from the transmitter. Detection efficiency decreased with depth (surface to 4 m) when the robot was moving away from the transmitter, but depth had no significant effect on detection efficiency when the robot was moving toward the transmitter. Detection efficiency was higher when the robot was descending (pitched downward) than ascending (pitched upward) when moving toward the transmitter, but pitch had no significant effect when moving away from the transmitter.
Results suggested that much of the observed variation in detection efficiency is related to shielding of the acoustic signal by the robot body depending on the positions and orientation of the hydrophone relative to the transmitter. Results are expected to inform hardware, software, and operational changes to gliding robotic fish that will improve detection efficiency. Regardless, data on the size and shape of detection efficiency curves for gliding robotic fish will be useful for planning future missions and should be relevant to other AUVs for telemetry. With refinements, gliding robotic fish could be a useful platform for active tracking of acoustic tags in certain environments.
","language":"English","publisher":"Springer","doi":"10.1186/s40317-020-00219-7","usgsCitation":"Ennasr, O., Holbrook, C., Hondorp, D.W., Krueger, C., Coleman, D., Solanki, P., Thon, J., and Tan, X., 2020, Characterization of acoustic detection efficiency using a gliding robotic fish as a mobile receiver platform: Animal Biotelemetry, v. 8, no. 32, 13 p., https://doi.org/10.1186/s40317-020-00219-7.","productDescription":"13 p.","ipdsId":"IP-122951","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":454977,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-020-00219-7","text":"Publisher Index Page"},{"id":436745,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S75TSB","text":"USGS data release","linkHelpText":"Acoustic detection performance of gliding robotic fish in Higgins Lake, Michigan, USA, 2016-2018"},{"id":380901,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"32","noUsgsAuthors":false,"publicationDate":"2020-10-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Ennasr, Osama 0000-0002-8353-6446","orcid":"https://orcid.org/0000-0002-8353-6446","contributorId":245318,"corporation":false,"usgs":false,"family":"Ennasr","given":"Osama","email":"","affiliations":[{"id":49149,"text":"Department of Electrical and Computer Engineering, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":805903,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":805904,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":805905,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krueger, Charles C.","contributorId":67821,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles C.","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":805906,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coleman, Demetris","contributorId":245319,"corporation":false,"usgs":false,"family":"Coleman","given":"Demetris","email":"","affiliations":[{"id":49149,"text":"Department of Electrical and Computer Engineering, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":805907,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Solanki, Pratap","contributorId":245320,"corporation":false,"usgs":false,"family":"Solanki","given":"Pratap","email":"","affiliations":[{"id":49149,"text":"Department of Electrical and Computer Engineering, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":805908,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thon, John","contributorId":245321,"corporation":false,"usgs":false,"family":"Thon","given":"John","email":"","affiliations":[{"id":49149,"text":"Department of Electrical and Computer Engineering, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":805909,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tan, Xiaobo 0000-0002-5542-6266","orcid":"https://orcid.org/0000-0002-5542-6266","contributorId":214765,"corporation":false,"usgs":false,"family":"Tan","given":"Xiaobo","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":805910,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70216563,"text":"70216563 - 2020 - A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species","interactions":[],"lastModifiedDate":"2020-11-25T15:25:24.210332","indexId":"70216563","displayToPublicDate":"2020-10-23T09:22:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species","docAbstract":"Predictive models of post-fire tree and stem mortality are vital for management planning and understanding fire effects. Post-fire tree and stem mortality have been traditionally modeled as a simple empirical function of tree defenses (e.g., bark thickness) and fire injury (e.g., crown scorch). We used the Fire and Tree Mortality database (FTM)—which includes observations of tree mortality in obligate seeders and stem mortality in basal resprouting species from across the USA—to evaluate the accuracy of post-fire mortality models used in the First Order Fire Effects Model (FOFEM) software system. The basic model in FOFEM, the Ryan and Amman (R-A) model, uses bark thickness and percentage of crown volume scorched to predict post-fire mortality and can be applied to any species for which bark thickness can be calculated (184 species-level coefficients are included in the program). FOFEM (v6.7) also includes 38 species-specific tree mortality models (26 for gymnosperms, 12 for angiosperms), with unique predictors and coefficients. We assessed accuracy of the R-A model for 44 tree species and accuracy of 24 species-specific models for 13 species, using data from 93 438 tree-level observations and 351 fires that occurred from 1981 to 2016.
For each model, we calculated performance statistics and provided an assessment of the representativeness of the evaluation data. We identified probability thresholds for which the model performed best, and the best thresholds with either ≥80% sensitivity or specificity. Of the 68 models evaluated, 43 had Area Under the Receiver Operating Characteristic Curve (AUC) values ≥0.80, indicating excellent performance, and 14 had AUCs <0.7, indicating poor performance. The R-A model often over-predicted mortality for angiosperms; 5 of 11 angiosperms had AUCs <0.7. For conifers, R-A over-predicted mortality for thin-barked species and for small diameter trees. The species-specific models had significantly higher AUCs than the R-A models for 10 of the 22 models, and five additional species-specific models had more balanced errors than R-A models, even though their AUCs were not significantly different or were significantly lower.
Approximately 75% of models tested had acceptable, excellent, or outstanding predictive ability. The models that performed poorly were primarily models predicting stem mortality of angiosperms or tree mortality of thin-barked conifers. This suggests that different approaches—such as different model forms, better estimates of bark thickness, and additional predictors—may be warranted for these taxa. Future data collection and research should target the geographical and taxonomic data gaps and poorly performing models identified in this study. Our evaluation of post-fire tree mortality models is the most comprehensive effort to date and allows users to have a clear understanding of the expected accuracy in predicting tree death from fire for 44 species.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s42408-020-00082-0","usgsCitation":"Cansler, C., Hood, S.M., van Mantgem, P., and Varner, J.M., 2020, A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species: Fire Ecology, v. 16, 25, 37 p., https://doi.org/10.1186/s42408-020-00082-0.","productDescription":"25, 37 p.","ipdsId":"IP-115072","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":454980,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-020-00082-0","text":"Publisher Index Page"},{"id":380782,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","noUsgsAuthors":false,"publicationDate":"2020-10-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Cansler, C. Alina","contributorId":245203,"corporation":false,"usgs":false,"family":"Cansler","given":"C. Alina","affiliations":[{"id":49115,"text":"USDA Forest Service, Rocky Mountain Research Station, Fire, Fuel, and Smoke Science Program, 5775 US Highway 10 W, Missoula, Montana, 59808, USA","active":true,"usgs":false}],"preferred":false,"id":805617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hood, Sharon M.","contributorId":221183,"corporation":false,"usgs":false,"family":"Hood","given":"Sharon","email":"","middleInitial":"M.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":805618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Mantgem, Phillip J. 0000-0002-3068-9422","orcid":"https://orcid.org/0000-0002-3068-9422","contributorId":204320,"corporation":false,"usgs":true,"family":"van Mantgem","given":"Phillip J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":805619,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Varner, J. Morgan 0000-0003-3781-5839","orcid":"https://orcid.org/0000-0003-3781-5839","contributorId":244802,"corporation":false,"usgs":false,"family":"Varner","given":"J.","email":"","middleInitial":"Morgan","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":805620,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215983,"text":"70215983 - 2020 - Double exposure and dynamic vulnerability: Assessing economic well-being, ecological change and the development of the oil and gas industry in coastal Louisiana","interactions":[],"lastModifiedDate":"2020-11-02T14:02:27.322344","indexId":"70215983","displayToPublicDate":"2020-10-23T07:58:47","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3385,"text":"Shore & Beach","printIssn":"0037-4237","active":true,"publicationSubtype":{"id":10}},"title":"Double exposure and dynamic vulnerability: Assessing economic well-being, ecological change and the development of the oil and gas industry in coastal Louisiana","docAbstract":"The oil and gas industry has been a powerful driver of economic change in coastal Louisiana for the latter half of the 20th century and into the 21st. Yet, the overall impact of the industry on the economic well-being of host communities is varied, both spatially and temporally. While the majority of Louisiana’s oil and gas production now occurs offshore, processing the extracted product is an energy-intensive undertaking requiring an expansive network of land-based infrastructure. Despite the positive economic aspects of this development, there are also potential negatives posed to coastal ecosystems and to communities located adjacent to oil and gas infrastructure. This research utilizes a double exposure framework to explore the relationship between oil and gas infrastructure development, fish and shellfish habitat, and economic well-being in Louisiana’s coastal zone from 1950 to 2010. The approach followed four main steps: (1) Developing a hazardousness of place model to identify areas of magnified risk due to the combined hazards of multiple potential exposure sites related to the extraction and processing of crude oil and natural gas; (2) developing a model of ecological functioning to measure the ability of aquatic habitat to support key fish and shellfish species; (3) utilizing an integrated community economic well-being index to assess change on a decadal timescale; and (4) analyzing selected oil-dependent communities to illustrate how change processes occurring in different energy sectors result in differential outcomes. The results suggest that, for many communities, the dependence on the oil and gas industry has increased economic well-being but also increased sensitivity to natural and human-induced changes, including fluctuating economic conditions, environmental stress, coastal habitat destruction, and increasing social and economic pressures.","language":"English","publisher":"American Shore and Beach Preservation Association (ASBPA)","doi":"10.34237/1008819","usgsCitation":"Hemmerling, S., Carruthers, T., Hijuelos, A., and Bienn, H.C., 2020, Double exposure and dynamic vulnerability: Assessing economic well-being, ecological change and the development of the oil and gas industry in coastal Louisiana: Shore & Beach, v. 88, no. 1, p. 72-82, https://doi.org/10.34237/1008819.","productDescription":"11 p.","startPage":"72","endPage":"82","ipdsId":"IP-112627","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":380018,"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 -93.779296875,\n 28.43971381702788\n ],\n [\n -89.05517578125,\n 28.43971381702788\n ],\n [\n -89.05517578125,\n 30.543338954230222\n ],\n [\n -93.779296875,\n 30.543338954230222\n ],\n [\n -93.779296875,\n 28.43971381702788\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"88","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Hemmerling, Scott","contributorId":221274,"corporation":false,"usgs":false,"family":"Hemmerling","given":"Scott","affiliations":[],"preferred":false,"id":803667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carruthers, Tim J. B.","contributorId":140566,"corporation":false,"usgs":false,"family":"Carruthers","given":"Tim J. B.","affiliations":[],"preferred":false,"id":803668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hijuelos, Ann 0000-0003-0922-6754","orcid":"https://orcid.org/0000-0003-0922-6754","contributorId":201525,"corporation":false,"usgs":true,"family":"Hijuelos","given":"Ann","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":803669,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bienn, Harris C.","contributorId":244280,"corporation":false,"usgs":false,"family":"Bienn","given":"Harris","email":"","middleInitial":"C.","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":803670,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219095,"text":"70219095 - 2020 - Diverse cataclysmic floods from Pleistocene glacial Lake Missoula","interactions":[],"lastModifiedDate":"2021-04-27T11:52:46.725403","indexId":"70219095","displayToPublicDate":"2020-10-23T07:32:33","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7779,"text":"GSA Special Volume on Pleistocene megafloods","active":true,"publicationSubtype":{"id":10}},"title":"Diverse cataclysmic floods from Pleistocene glacial Lake Missoula","docAbstract":"In late Wisconsin time, the Purcell Trench lobe of the Cordilleran ice sheet dammed the Clark Fork of the Columbia River in western Montana, creating glacial Lake Missoula. During part of this epoch, the Okanogan lobe also dammed the Columbia River downstream, creating glacial Lake Columbia in northeast Washington. Repeated failure of the Purcell Trench ice dam released glacial Lake Missoula, causing dozens of catastrophic floods in eastern Washington that can be distinguished by the geologic record they left behind. These floods removed tens of meters of pale loess from dark basalt substrate, forming scars along flowpaths visible from space.
Different positions of the Okanogan lobe are required for modeled Missoula floods to inundate the diverse channels that show field evidence for flooding, as shown by accurate dam-break flood modeling using a roughly 185 m digital terrain model of existing topography (with control points dynamically varied using automatic mesh refinement). The maximum extent of the Okanogan lobe, which blocked inundation of the upper Grand Coulee and the Columbia River valley, is required to flood all channels in the Telford scablands and to produce highest flood stages in Pasco Basin. Alternatively, the Columbia River valley must have been open and the upper Grand Coulee blocked to nearly match evidence for high water on Pangborn bar near Wenatchee, Washington, and to flood Quincy Basin from the west. Finally, if the Columbia River valley and upper Grand Coulee were both open, Quincy Basin would have flooded from the northeast.
In all these scenarios, the discrepancy between modeled flood stages and field evidence for maximum flood stages increases in all channels downstream, from Spokane to Umatilla Basin. The pattern of discrepancies indicates that bulking of floods by loess increased flow volume across the scablands, but this alone does not explain low modeled flow stages along the Columbia River valley near Wenatchee. This latter discrepancy between modeled flood stages and field data requires either additional bulking of flow by sediment along the Columbia reach downstream of glacial Lake Columbia, or coincident dam failures of glacial Lake Columbia and glacial Lake Missoula.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/2021.2548(17)","usgsCitation":"Denlinger, R.P., George, D.L., Cannon, C.M., O'Connor, J., and Waitt, R.B., 2020, Diverse cataclysmic floods from Pleistocene glacial Lake Missoula: GSA Special Volume on Pleistocene megafloods, v. 548, 18 p., https://doi.org/10.1130/2021.2548(17).","productDescription":"18 p.","ipdsId":"IP-101636","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":384572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Washington","otherGeospatial":"Lake Missoula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.92675781249999,\n 46.08847179577592\n ],\n [\n -113.3349609375,\n 46.08847179577592\n ],\n [\n -113.3349609375,\n 48.22467264956519\n ],\n [\n -119.92675781249999,\n 48.22467264956519\n ],\n [\n -119.92675781249999,\n 46.08847179577592\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"548","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Denlinger, Roger P. 0000-0003-0930-0635 roger@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-0635","contributorId":2679,"corporation":false,"usgs":true,"family":"Denlinger","given":"Roger","email":"roger@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":812746,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"George, David L. 0000-0002-5726-0255 dgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-5726-0255","contributorId":3120,"corporation":false,"usgs":true,"family":"George","given":"David","email":"dgeorge@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":812747,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cannon, Charles M. 0000-0003-4136-2350 ccannon@usgs.gov","orcid":"https://orcid.org/0000-0003-4136-2350","contributorId":247680,"corporation":false,"usgs":true,"family":"Cannon","given":"Charles","email":"ccannon@usgs.gov","middleInitial":"M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":812748,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O'Connor, Jim E. 0000-0002-7928-5883 oconnor@usgs.gov","orcid":"https://orcid.org/0000-0002-7928-5883","contributorId":140771,"corporation":false,"usgs":true,"family":"O'Connor","given":"Jim E.","email":"oconnor@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":812749,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Waitt, Richard B. 0000-0002-6392-5604 waitt@usgs.gov","orcid":"https://orcid.org/0000-0002-6392-5604","contributorId":2343,"corporation":false,"usgs":true,"family":"Waitt","given":"Richard","email":"waitt@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":812750,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215976,"text":"70215976 - 2020 - Geomorphic and sedimentary effects of modern climate change: Current and anticipated future conditions in the western United States","interactions":[],"lastModifiedDate":"2020-12-14T16:49:17.200792","indexId":"70215976","displayToPublicDate":"2020-10-23T07:02:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3283,"text":"Reviews of Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Geomorphic and sedimentary effects of modern climate change: Current and anticipated future conditions in the western United States","docAbstract":"Hydroclimatic changes associated with global warming over the past 50 years have been documented widely, but physical landscape responses are poorly understood thus far. Detecting sedimentary and geomorphic signals of modern climate change presents challenges owing to short record lengths, difficulty resolving signals in stochastic natural systems, influences of land use and tectonic activity, long‐lasting effects of individual extreme events, and variable connectivity in sediment‐routing systems. We review existing literature to investigate the nature and extent of sedimentary and geomorphic responses to modern climate change, focusing on the western United States, a region with generally high relief and high sediment yield likely to be sensitive to climatic forcing. Based on fundamental geomorphic theory and empirical evidence from other regions, we anticipate climate‐driven changes to slope stability, watershed sediment yields, fluvial morphology, and aeolian sediment mobilization in the western U.S. We find evidence for recent climate‐driven changes to slope stability and increased aeolian dune and dust activity, whereas changes in sediment yields and fluvial morphology have been linked more commonly to non‐climatic drivers thus far. Detecting effects of climate change will require better understanding how landscape response scales with disturbance, how lag times and hysteresis operate within sedimentary systems, and how to distinguish the relative influence and feedbacks of superimposed disturbances. The ability to constrain geomorphic and sedimentary response to rapidly progressing climate change has widespread implications for human health and safety, infrastructure, water security, economics, and ecosystem resilience.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019RG000692","usgsCitation":"East, A.E., and Sankey, J.B., 2020, Geomorphic and sedimentary effects of modern climate change: Current and anticipated future conditions in the western United States: Reviews of Geophysics, v. 58, no. 4, e2019RG000692, 59 p., https://doi.org/10.1029/2019RG000692.","productDescription":"e2019RG000692, 59 p.","ipdsId":"IP-115204","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":454985,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019rg000692","text":"Publisher Index Page"},{"id":380009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.5078125,\n 31.052933985705163\n ],\n [\n -103.6669921875,\n 31.052933985705163\n ],\n [\n -103.6669921875,\n 48.951366470947725\n ],\n [\n -125.5078125,\n 48.951366470947725\n ],\n [\n -125.5078125,\n 31.052933985705163\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-12-03","publicationStatus":"PW","contributors":{"authors":[{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":803644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":803645,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215132,"text":"70215132 - 2020 - Spectral wave-driven bedload transport across a coral reef flat/lagoon complex","interactions":[],"lastModifiedDate":"2020-10-29T15:04:55.965749","indexId":"70215132","displayToPublicDate":"2020-10-22T10:04:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Spectral wave-driven bedload transport across a coral reef flat/lagoon complex","docAbstract":"Coral reefs are an important source of sediment for reef-lined coasts by helping to maintain beaches while also providing protection in the form of wave energy dissipation. Understanding the mechanisms by which sediment is delivered to the coast as well as better constraining the total volumes generated are critical for projecting future coastal change. A month-long hydrodynamics and sediment transport study on a fringing reef/lagoon complex in Western Australia indicates that lower frequency constituents of wave energy are important to the total bedload transport of sediment across the reef flat and lagoon to the shoreline. The reef flat and the lagoon are characterized by distinctly different transport regimes, resulting in an offset in the timing of bedform migration between the two. Short-term storage of sediment is noted on the reef flat, which is subsequently washed out into the lagoon when offshore wave heights increase and strong currents due to wave breaking at the reef crest develop. This sudden influx of sediment is a significant control on bedform migration rates in the lagoon. Infragravity wave energy on the reef flat and lagoon make an important contribution to the migration of bedforms and resultant bedload transport. Given the complexity of the hydrodynamics of fringing reefs, the transfer of energy to lower frequency bands, as well as accurate estimates of sources and sinks of sediment, must but considered in order to correctly model the transport of sediment from the reef to the coast.
Quantifying the distribution of animals and identifying underlying characteristics that define suitable habitat are essential for effective conservation of free-ranging species. Prioritizing areas for conservation is important in managing a geographic extent that has a high level of disturbance and limited conservation resources. We examined the potential use of a species distribution model ensemble for multi-species conservation in marine habitats. Using satellite telemetry locations during foraging as input data, and ensemble ecological niche models, we predicted foraging areas for 2 nesting marine turtle species within the Gulf of Mexico (GoM): Kemp’s ridley Lepidochelys kempii (n = 63) and loggerhead Caretta caretta (n = 63). We considered 7 geophysical, biological, and climatic variables and compared contributing factors for each species’ foraging habitat selection. For both species, predicted suitable foraging habitats encompassed large areas along the GoM coast, but only intersected with each other in relatively small areas. Highly parameterized models resulted in overall greater fits, suggesting that multiple factors influence habitat selection by these species. Model validation results were mixed: cross-validation resulted in high prediction accuracy for both species, but an evaluation against independent data resulted in a low omission rate (5%) for Kemp’s ridleys and a high omission rate (72%) for loggerheads. The relatively small intersection of model-predicted foraging areas for these 2 species within the study area may indicate possible niche differentiations. The high omission rate for loggerheads indicates our samples likely underrepresent the population and illustrates the challenges in predicting suitable foraging extents for species that make dynamic movements and have greater individual variability.
","language":"English","publisher":"Inter Research","doi":"10.3354/esr01059","usgsCitation":"Fujisaki, I., Hart, K., Bucklin, D.N., Iverson, A., Rubio, C., Lamont, M.M., Miron, R.D., Burchfield, P., Pena, J., and Shaver, D.J., 2020, Predicting multi-species foraging hotspots for marine turtles in the Gulf of Mexico: Endangered Species Research, v. 43, p. 253-266, https://doi.org/10.3354/esr01059.","productDescription":"14 p.","startPage":"253","endPage":"266","ipdsId":"IP-120330","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":454990,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01059","text":"Publisher Index Page"},{"id":380022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Mexico","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.701171875,\n 21.779905342529645\n ],\n [\n -96.0205078125,\n 18.22935133838668\n ],\n [\n -93.2958984375,\n 17.518344187852218\n ],\n [\n -91.0546875,\n 18.104087015773956\n ],\n [\n -80.5517578125,\n 24.966140159912975\n ],\n [\n -82.6171875,\n 30.977609093348686\n ],\n [\n -97.822265625,\n 30.486550842588485\n ],\n [\n -98.701171875,\n 21.779905342529645\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fujisaki, Ikuko","contributorId":38359,"corporation":false,"usgs":false,"family":"Fujisaki","given":"Ikuko","affiliations":[],"preferred":false,"id":803648,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":206816,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":803649,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bucklin, David N.","contributorId":175273,"corporation":false,"usgs":false,"family":"Bucklin","given":"David","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":803650,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Iverson, Autumn R. 0000-0002-8353-6745","orcid":"https://orcid.org/0000-0002-8353-6745","contributorId":173555,"corporation":false,"usgs":false,"family":"Iverson","given":"Autumn R.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":803651,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rubio, Cynthia","contributorId":244274,"corporation":false,"usgs":false,"family":"Rubio","given":"Cynthia","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":803652,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lamont, Margaret M. 0000-0001-7520-6669","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":218323,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","email":"","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":803653,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miron, Raul de Jesus G.D.","contributorId":244275,"corporation":false,"usgs":false,"family":"Miron","given":"Raul","email":"","middleInitial":"de Jesus G.D.","affiliations":[{"id":48880,"text":"Acuario de Veracruz A.C., Veracruz, Veracruz Mexico","active":true,"usgs":false}],"preferred":false,"id":803654,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Burchfield, Patrick M.","contributorId":244276,"corporation":false,"usgs":false,"family":"Burchfield","given":"Patrick M.","affiliations":[{"id":48881,"text":"Gladys Porter Zoo","active":true,"usgs":false}],"preferred":false,"id":803655,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pena, Jaime","contributorId":168392,"corporation":false,"usgs":false,"family":"Pena","given":"Jaime","email":"","affiliations":[],"preferred":false,"id":803656,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Shaver, Donna J.","contributorId":191186,"corporation":false,"usgs":false,"family":"Shaver","given":"Donna","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":803657,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70224302,"text":"70224302 - 2020 - Spatial fingerprint of younger dryas cooling and warming in eastern North America","interactions":[],"lastModifiedDate":"2021-09-21T13:03:33.362473","indexId":"70224302","displayToPublicDate":"2020-10-22T08:00:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Spatial fingerprint of younger dryas cooling and warming in eastern North America","docAbstract":"The Younger Dryas (YD, 12.9–11.7 ka) is the most recent, near-global interval of abrupt climate change with rates similar to modern global warming. Understanding the causes and biodiversity effects of YD climate changes requires determining the spatial fingerprints of past temperature changes. Here we build pollen-based and branched glycerol dialkyl glycerol tetraether-based temperature reconstructions in eastern North America (ENA) to better understand deglacial temperature evolution. YD cooling was pronounced in the northeastern United States and muted in the north central United States. Florida sites warmed during the YD, while other southeastern sites maintained a relatively stable climate. This fingerprint is consistent with an intensified subtropical high during the YD and demonstrates that interhemispheric responses were more complex spatially in ENA than predicted by the bipolar seesaw model. Reduced-amplitude or antiphased millennial-scale temperature variability in the southeastern United States may support regional hotspots of biodiversity and endemism.
Chlorinated volatile organic compounds (CVOCs) have migrated to groundwater beneath a former 9-acre landfill at Operable Unit 1 (OU-1) on Naval Base Kitsap, which was active from the 1930s through 1973 on the Keyport Peninsula, in Kitsap County, Washington. Biodegradation of CVOCs at OU-1 limits the mass of dissolved-phase CVOCs in groundwater that discharges to surface water, but contaminant concentrations up to 630 milligrams per liter persist in localized areas, likely from the dissolution of residual, non-aqueous phase liquids. Variable-density groundwater-flow and contaminant-transport models were developed using the SEAWAT-Version 4 computer program to simulate the direction and rate of groundwater flow in a 5.9 square-mile (mi2) - area surrounding the Keyport Peninsula, to estimate the CVOC mass in groundwater and the rate of mass loading, and to assess possible remedial activities at OU-1.
The study area is underlain by Quaternary deposits consisting of alternating glacial and interglacial sediments ranging from 500 to 1,500 feet (ft) thick. A hydrogeologic model delineated a sequence of 10 units including a relatively thin package (less than 100 ft) of recent sediments (Vashon Stade and younger) beneath the Keyport Peninsula that are underlain by the much thicker (more than 300 ft) Clover Park Aquitard, which overlies a confined, sea-level aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205066","collaboration":"Prepared in cooperation with the Department of the Navy, Naval Facilities Engineering Command, Northwest","usgsCitation":"Yager, R.M., Welch, W.B., Headman, A., and Dinicola, R.S., 2020, Variable-density groundwater flow and contaminant transport, Operable Unit 1, Naval Base Kitsap, Keyport, Washington: U.S. Geological Survey Scientific Investigations Report 2020–5066, 58 p., https://doi.org/10.3133/sir20205066.","productDescription":"x, 62 p.","onlineOnly":"Y","ipdsId":"IP-112628","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":379666,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95WQ7TM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil water balance (SWB) model of Keyport, Washington"},{"id":379617,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5066/sir20205066.pdf","text":"Report","size":"10.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5066"},{"id":379667,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YNPPNL","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-2005, MODFLOW-NWT, and SEAWAT V.4 models used to simulate variable-density groundwater flow and contaminant transport at Naval Base Kitsap, Keyport, Washington"},{"id":379616,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5066/coverthb2.jpg"}],"country":"United States","state":"Washington","city":"Keyport","otherGeospatial":"Naval Base Kitsap","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.65,\n 47.6666\n ],\n [\n -122.60833,\n 47.6666\n ],\n [\n -122.60833,\n 47.71666\n ],\n [\n -122.65,\n 47.71666\n ],\n [\n -122.65,\n 47.6666\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Declining water levels and reduced natural discharge at springs, seeps, and phreatophyte areas primarily are the result of decades of groundwater development in the Death Valley regional flow system, in Nevada and California. A calibrated groundwater-flow model was used to simulate potential future effects of groundwater pumping on water levels and natural groundwater discharge in the study area. Effects of climate change on future groundwater pumping were not considered and were beyond the scope of the study. Four groundwater-pumping scenarios were developed by stakeholders to predict and compare (1) the extent of regional water-level declines; (2) drawdown at Devils Hole; and (3) reductions in natural discharge at select discharge areas, including the Amargosa Wild and Scenic River, the Ash Meadows discharge area, the Furnace Creek area, and Stump Spring. Scenarios were simulated from 1913 to 2120, with historical pumping occurring from 1913 to 2010, historical 2010 pumping rates projected from 2010 to 2020, and scenario pumping beginning in 2020. Pumping scenarios included a base case and scenarios A, B, and C. The base case projected 2010 pumping rates from 2010 to 2120, and scenarios A, B, and C projected base case pumping plus additional pumping at various locations from 2020 to 2120. By 2020, historical (1913–2020) pumping resulted in the propagation of simulated drawdown of 1 foot (ft) or more westward from Pahrump Valley to areas north of Shoshone in the Pahrump to Death Valley South (PDVS) groundwater basin and the merging of simulated 1-ft drawdown contours between the Alkali Flat–Furnace Creek Ranch (AFFCR) and Ash Meadows groundwater basins. In the base case scenario, extent and magnitude of simulated drawdown continued to increase in the Ash Meadows and AFFCR groundwater basins from 2020 to 2120. In the base case, the magnitude of simulated drawdown continued to increase in western Pahrump Valley from 2020 to 2120, whereas simulated water levels rose in eastern Pahrump Valley from 2020 to 2070 and then stabilized from 2070 to 2120. Scenarios A and B primarily affected the PDVS and AFFCR groundwater basins by increasing the magnitude of drawdown in 2120, compared to the base case. In scenario C, drawdown propagated throughout a high-transmissivity part of the carbonate aquifer known as the megachannel, greatly affecting water levels in the Ash Meadows discharge area. Scenario C resulted in an additional 10–100 ft of drawdown (compared to the base case) throughout the southeastern part of the Ash Meadows groundwater basin by 2120. Simulated drawdowns in Devils Hole in 2120 were 3.2, 3.4, 3.8, and 25.4 ft for the base case and scenarios A, B, and C, respectively. The federally mandated minimum water level for Devils Hole is 2.7 ft below a reference point. In 2020, the simulated water level in Devils Hole was above the minimum water level, at 1.7 ft below the reference. Simulated water levels in Devils Hole fell below the federally mandated water level by 2078, 2073, 2058, and 2025 for the base case and scenarios A, B, and C, respectively, assuming a hypothetical recharge scenario of constant natural recharge. Simulated reductions in predevelopment (natural) discharge at select discharge areas ranged from 3 to 38 percent by 2120 for all scenarios. Amargosa Wild and Scenic River was the least affected discharge area with simulated capture rates ranging from 3 to 4 percent of predevelopment discharge by 2120. Ash Meadows discharge area was greatly affected by groundwater pumping in scenario C with a simulated capture rate of 38 percent, compared to simulated capture rates of 8, 8, and 9 percent for the base case, scenario A, and scenario B, respectively, in 2120. Simulated capture rates in the Furnace Creek area ranged from 10 to 11 percent for all scenarios in 2120. Simulated capture rates at Stump Spring ranged from 32 to 36 percent for all scenarios in 2120.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205103","collaboration":"Prepared in cooperation with the Bureau of Land Management; National Park Service; Nevada Division of Wildlife; Nye County, Nevada; and U.S. Fish and Wildlife Service","usgsCitation":"Nelson, N.C., and Jackson, T.R., 2020, Simulated effects of pumping in the Death Valley Regional Groundwater Flow System, Nevada and California—Selected management scenarios projected to 2120: U.S. Geological Survey Scientific Investigations Report 2020–5103, 30 p., https://doi.org/10.3133/sir20205103.","productDescription":"Report: vii, 30 p.; Data Releases","onlineOnly":"Y","ipdsId":"IP-112177","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":379438,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5103/coverthb.jpg"},{"id":379439,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5103/sir20205103.pdf","text":"Report","size":"6.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5103"},{"id":379440,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OBUPXU","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-2005 models used to simulate effects of pumping in the Death Valley Regional Groundwater Flow System, Nevada and California—Selected management scenarios projected to 2120"},{"id":379476,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75H7FH3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Update to the groundwater withdrawals database for the Death Valley regional groundwater flow system, Nevada and California, 1913 -2010"},{"id":379477,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HIYVG2","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-2005 model and supplementary data used to characterize groundwater flow and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Death Valley Regional Groundwater Flow System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.3779296875,\n 33.62376800118811\n ],\n [\n -114.08203125,\n 33.62376800118811\n ],\n [\n -114.08203125,\n 38.62545397209084\n ],\n [\n -117.3779296875,\n 38.62545397209084\n ],\n [\n -117.3779296875,\n 33.62376800118811\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
Seagrass communities are a vital component of estuarine ecosystems, but are threatened by projected sea level rise (SLR) and temperature increases with climate change. To understand these potential effects, we developed a spatially explicit model that represents seagrass (Zostera marina) habitat and estuary-wide productivity for Barnegat Bay-Little Egg Harbor (BB-LEH) in New Jersey, United States. Our modeling approach included an offline coupling of a numerical seagrass biomass model with the spatially variable environmental conditions from a hydrodynamic model to calculate above and belowground biomass at each grid cell of the hydrodynamic model domain. Once calibrated to represent present day seagrass habitat and estuary-wide annual productivity, we applied combinations of increasing air temperature and sea level following regionally specific climate change projections, enabling analysis of the individual and combined impacts of these variables on seagrass biomass and spatial coverage. Under the SLR scenarios, the current model domain boundaries were maintained, as the land surrounding BB-LEH is unlikely to shift significantly in the future. SLR caused habitat extent to decrease dramatically, pushing seagrass beds toward the coastline with increasing depth, with a 100% loss of habitat by the maximum SLR scenario. The dramatic loss of seagrass habitat under SLR was in part due to the assumption that surrounding land would not be inundated, as the model did not allow for habitat expansion outside the current boundaries of the bay. Temperature increases slightly elevated the rate of summer die-off and decreased habitat area only under the highest temperature increase scenarios. In combined scenarios, the effects of SLR far outweighed the effects of temperature increase. Sensitivity analysis of the model revealed the greatest sensitivity to changes in parameters affecting light limitation and seagrass mortality, but no sensitivity to changes in nutrient limitation constants. The high vulnerability of seagrass in the bay to SLR exceeded that demonstrated for other systems, highlighting the importance of site- and region-specific assessments of estuaries under climate change.
Timing of activity can reveal an organism's efforts to optimize foraging either by minimizing energy loss through passive movement or by maximizing energetic gain through foraging. Here, we assess whether signals of either of these strategies are detectable in the timing of activity of daily, local movements by birds. We compare the similarities of timing of movement activity among species using six temporal variables: start of activity relative to sunrise, end of activity relative to sunset, relative speed at midday, number of movement bouts, bout duration, and proportion of active daytime hours. We test for the influence of flight mode and foraging habitat on the timing of movement activity across avian guilds. We used 64570 days of GPS movement data collected between 2002 and 2019 for local (non‐migratory) movements of 991 birds from 49 species, representing 14 orders. Dissimilarity among daily activity patterns was best explained by flight mode. Terrestrial soaring birds began activity later and stopped activity earlier than pelagic soaring or flapping birds. Broad‐scale foraging habitat explained less of the clustering patterns because of divergent timing of active periods of pelagic surface and diving foragers. Among pelagic birds, surface foragers were active throughout the day while diving foragers matched their active hours more closely to daylight hours. Pelagic surface foragers also had the greatest daily foraging distances, which was consistent with their daytime activity patterns. This study demonstrates that flight mode and foraging habitat influence temporal patterns of daily movement activity of birds.
","language":"English","publisher":"Wiley","doi":"10.1111/jav.02612","usgsCitation":"Mallon, J.M., Tucker, M.A., Beard, A., Bierregaard, R.O., Bildstein, K.L., Böhning-Gaese, K., Brzorad, J.N., Buechley, E., Bustamante, J., Carrapato, C., Castillo-Guerrero, J.A., Clingham, E., Desholm, M., DeSorbo, C.R., Domenech, R., Douglas, H., Duriez, O., Enggist, P., Farwig, N., Fiedler, W., Gagliardo, A., García‐Ripollés, C., Gil Gallus, J.A., Gilmour, M., Harel, R., Harrison, A., Henry, L., Katzner, T., Kays, R., Kleyheeg, E., Limiñana, R., Lopez-Lopez, P., Lucia, G., Maccarone, A., Mallia, E., Mellone, U., Mojica, E., Nathan, R., Newman, S., Oppel, S., Orchan, Y., Prosser, D.J., Riley, H., Rösner, S., Schabo, D.G., Schulz, H., Shaffer, S.A., Shreading, A., Silva, J., Sim, J., Skov, H., Spiegel, O., Stuber, M.J., Takekawa, J.Y., Urios, V., Vidal-Mateo, J., Warner, K., Watts, B.D., Weber, N., Weber, S., Wikelski, M., Zydelis, R., Mueller, T., and Fagan, W., 2020, Diurnal timing of nonmigratory movement by birds: The importance of foraging spatial scales: Journal of Avian Biology, v. 51, no. 12, e02612, 11 p., https://doi.org/10.1111/jav.02612.","productDescription":"e02612, 11 p.","ipdsId":"IP-115942","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":455018,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/jav.02612","text":"External Repository"},{"id":380648,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Mallon, Julie M.","contributorId":150853,"corporation":false,"usgs":false,"family":"Mallon","given":"Julie","email":"","middleInitial":"M.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia 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Although the existence and geometrical properties of SAG have been well documented, an understanding of the hydrodynamics over them is limited. Here, the three-dimensional flow patterns over SAG formations, and a sensitivity of those patterns to waves, currents, and SAG geometry were characterized using the physics-based Delft3D-FLOW and SWAN models. Shore-normal shoaling waves over SAG formations were shown to drive two circulation cells: a cell on the lower fore reef with offshore flow over the spurs and onshore flow over the grooves, except near the seabed where velocities were always onshore, and a cell on the upper fore reef with offshore surface velocities and onshore bottom currents, which result in depth-averaged onshore and offshore flow over the spurs and grooves, respectively. The mechanism driving this flow results from the net of the radiation stress gradients and pressure gradient, which is balanced by the Reynolds stress gradients and bottom friction that differ over the spur and over the groove. Waves were the primary driver of variations in modelled flow over SAG, with the flow strength increasing for increasing wave heights and periods. Spur height, SAG wavelength, and the water depth at peak spur height were the dominant influences on the hydrodynamics, with spur heights directly proportional to the strength of SAG circulation cells. SAG formations with shorter SAG wavelengths only presented one circulation cell on the shallower portion of the reef, as opposed to the two circulation cells for longer SAG wavelengths. SAG formations with peak spur heights occurring in shallower water had stronger circulation than those with peak spur heights occurring in deeper water. These hydrodynamic patterns also likely affect coral and reef development through sediment and nutrient fluxes.
No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Applied hierarchical modeling in ecology: Analysis of distribution, abundance and species richness in R and BUGS","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Academic Press","usgsCitation":"Royle, A., and Kery, M., 2020, Modeling population dynamics with multinomial count data, chap. 2 of Applied hierarchical modeling in ecology: Analysis of distribution, abundance and species richness in R and BUGS, p. 65-156.","productDescription":"92 p.","startPage":"65","endPage":"156","ipdsId":"IP-101095","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":379643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":379628,"type":{"id":15,"text":"Index Page"},"url":"https://www.elsevier.com/books/applied-hierarchical-modeling-in-ecology-analysis-of-distribution-abundance-and-species-richness-in-r-and-bugs/kery/978-0-12-809585-0"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":802622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kery, Marc","contributorId":168361,"corporation":false,"usgs":false,"family":"Kery","given":"Marc","affiliations":[{"id":12551,"text":"Swiss Ornithological Institute, Sempach, Switzerland","active":true,"usgs":false}],"preferred":false,"id":802707,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216504,"text":"70216504 - 2020 - Injection‐induced earthquakes near Milan, Kansas, controlled by Karstic Networks","interactions":[],"lastModifiedDate":"2020-11-24T13:38:00.985824","indexId":"70216504","displayToPublicDate":"2020-10-19T07:34:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Injection‐induced earthquakes near Milan, Kansas, controlled by Karstic Networks","docAbstract":"Induced earthquakes from waste disposal operations in otherwise tectonically stable regions significantly increases seismic hazard. It remains unclear why injections induce large earthquakes on non‐optimally oriented faults kilometers below the injection horizon, particularly since fluids are not injected under pressure, but rather poured, into the well as observed in the Milan, Kansas area. Here we propose a mechanism for induced earthquakes whereby the karstic lower Arbuckle provides the short‐circuit that establishes a tens of MPa stepwise fluid pressure increase within the basement upon arrival of the hydraulic connection to the free surface and ultimately induce slip on the deeper fault. We investigate this scenario through modeling and mechanical analysis and show that earthquakes near Milan are likely induced by large (and sudden) fluid pressure changes when the karst network links two previously isolated hydrological systems.
Large-domain hydrological models are increasingly needed to support water-resource assessment and management in large river basins. Here, we describe results for the first Brazilian application of the SPAtially Referenced Regression On Watershed attributes (SPARROW) model using a new open-source modeling and interactive decision support system tool (RSPARROW) to quantify the origin, flux, and fate of total nitrogen (TN) in two sub-basins of the Grande River Basin (GRB; 43,000 km2). Land under cultivation for sugar cane, urban land, and point source inputs from wastewater treatment plants was estimated to each contribute approximately 30% of the TN load at the outlet, with pasture land contributing about 10% of the load. Hypothetical assessments of wastewater treatment plant upgrades and the building of new facilities that could treat currently untreated urban runoff suggest that these management actions could potentially reduce loading at the outlet by as much as 20–25%. This study highlights the ability of SPARROW and the RSPARROW mapping tool to assist with the development and evaluation of management actions aimed at reducing nutrient pollution and eutrophication. The freely available RSPARROW modeling tool provides new opportunities to improve understanding of the sources, delivery, and transport of water-quality contaminants in watersheds throughout the world.
","language":"English","publisher":"MDPI","doi":"10.3390/w12102911","usgsCitation":"Miller, M., de Souza, M.L., Alexander, R.B., Gorman Sanisaca, L.E., de Amorim Teixeira, A., and Appling, A.P., 2020, Application of the RSPARROW modeling tool to estimate total nitrogen sources to streams and evaluate source reduction management scenarios in the Grande River Basin, Brazil: Water, v. 12, no. 10, 2911, 20 p., https://doi.org/10.3390/w12102911.","productDescription":"2911, 20 p.","ipdsId":"IP-122604","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":455023,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12102911","text":"Publisher Index Page"},{"id":436752,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FZV0Z0","text":"USGS data release","linkHelpText":"RSPARROW Model Archive Files for the Grande River Basin TN SPARROW Model"},{"id":379649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","otherGeospatial":"Grande River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -50.95458984374999,\n -20.324023603422507\n ],\n [\n -49.32861328125,\n -21.46329344189928\n ],\n [\n -48.284912109375,\n -22.451648819126202\n ],\n [\n -46.73583984375,\n -23.29181053244191\n ],\n [\n -45.37353515625,\n -22.61401087437028\n ],\n [\n -44.05517578124999,\n -21.881889807629257\n ],\n [\n -43.5498046875,\n -21.125497636606266\n ],\n [\n -45.736083984375,\n -20.33432561683554\n ],\n [\n -46.35131835937499,\n -20.478481600090554\n ],\n [\n -46.966552734375,\n -20.014645445341355\n ],\n [\n -47.647705078125,\n -19.797717490704724\n ],\n [\n -48.944091796875,\n -19.9526963975442\n ],\n [\n -49.32861328125,\n -19.652934210612436\n ],\n [\n -50.28442382812499,\n -19.425153718960143\n ],\n [\n -50.86669921875,\n -19.756364230752375\n ],\n [\n -50.95458984374999,\n -20.324023603422507\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Matthew P. 0000-0002-2537-1823","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":220622,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew P.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Souza, Marcelo L","contributorId":243598,"corporation":false,"usgs":false,"family":"de Souza","given":"Marcelo","email":"","middleInitial":"L","affiliations":[{"id":48748,"text":"Brazilian National Water and Sanitation Agency","active":true,"usgs":false}],"preferred":false,"id":802666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, Richard B 0000-0001-9166-0626","orcid":"https://orcid.org/0000-0001-9166-0626","contributorId":243599,"corporation":false,"usgs":false,"family":"Alexander","given":"Richard","email":"","middleInitial":"B","affiliations":[{"id":38108,"text":"NA","active":true,"usgs":false}],"preferred":false,"id":802667,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gorman Sanisaca, Lillian E. 0000-0003-1711-3864","orcid":"https://orcid.org/0000-0003-1711-3864","contributorId":210381,"corporation":false,"usgs":true,"family":"Gorman Sanisaca","given":"Lillian","middleInitial":"E.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802668,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"de Amorim Teixeira, Alexandre","contributorId":243600,"corporation":false,"usgs":false,"family":"de Amorim Teixeira","given":"Alexandre","email":"","affiliations":[{"id":48748,"text":"Brazilian National Water and Sanitation Agency","active":true,"usgs":false}],"preferred":false,"id":802669,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":802670,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70216657,"text":"70216657 - 2020 - From pools to flow: The PROMISE framework for new insights on soil carbon cycling in a changing world","interactions":[],"lastModifiedDate":"2020-11-27T17:04:13.695051","indexId":"70216657","displayToPublicDate":"2020-10-16T11:01:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"From pools to flow: The PROMISE framework for new insights on soil carbon cycling in a changing world","docAbstract":"Soils represent the largest terrestrial reservoir of organic carbon, and the balance between soil organic carbon (SOC) formation and loss will drive powerful carbon‐climate feedbacks over the coming century. To date, efforts to predict SOC dynamics have rested on pool‐based models, which assume classes of SOC with internally homogenous physicochemical properties. However, emerging evidence suggests that soil carbon turnover is not dominantly controlled by the chemistry of carbon inputs, but rather by restrictions on microbial access to organic matter in the spatially heterogeneous soil environment. The dynamic processes that control the physicochemical protection of carbon translate poorly to pool‐based SOC models; as a result, we are challenged to mechanistically predict how environmental change will impact movement of carbon between soils and the atmosphere. Here, we propose a novel conceptual framework to explore controls on belowground carbon cycling: Probabilistic Representation of Organic Matter Interactions within the Soil Environment (PROMISE). In contrast to traditional model frameworks, PROMISE does not attempt to define carbon pools united by common thermodynamic or functional attributes. Rather, the PROMISE concept considers how SOC cycling rates are governed by the stochastic processes that influence the proximity between microbial decomposers and organic matter, with emphasis on their physical location in the soil matrix. We illustrate the applications of this framework with a new biogeochemical simulation model that traces the fate of individual carbon atoms as they interact with their environment, undergoing biochemical transformations and moving through the soil pore space. We also discuss how the PROMISE framework reshapes dialogue around issues related to SOC management in a changing world. We intend the PROMISE framework to spur the development of new hypotheses, analytical tools, and model structures across disciplines that will illuminate mechanistic controls on the flow of carbon between plant, soil, and atmospheric pools.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.15365","usgsCitation":"Waring, B.G., Sulman, B.N., Reed, S., Smith, A.P., Averill, C., Creamer, C., Cusack, D.F., Hall, S.J., Jastrow, J., Kemner, K.M., Kleber, M., Liu, X.A., Pett-Ridge, J., and Schulz, M., 2020, From pools to flow: The PROMISE framework for new insights on soil carbon cycling in a changing world: Global Change Biology, v. 26, no. 12, p. 6631-6643, https://doi.org/10.1111/gcb.15365.","productDescription":"13 p.","startPage":"6631","endPage":"6643","ipdsId":"IP-112861","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":455027,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1782302","text":"External Repository"},{"id":380844,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Waring, Bonnie G. 0000-0002-8457-5164","orcid":"https://orcid.org/0000-0002-8457-5164","contributorId":245284,"corporation":false,"usgs":false,"family":"Waring","given":"Bonnie","email":"","middleInitial":"G.","affiliations":[{"id":49130,"text":"Utah State University, Department of Biology and Ecology Center, Logan UT 84322","active":true,"usgs":false}],"preferred":false,"id":805742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sulman, Benjamin N. 0000-0002-3265-6691","orcid":"https://orcid.org/0000-0002-3265-6691","contributorId":209890,"corporation":false,"usgs":false,"family":"Sulman","given":"Benjamin","email":"","middleInitial":"N.","affiliations":[{"id":7108,"text":"Princeton Univ.","active":true,"usgs":false}],"preferred":false,"id":805743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":805744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, A. Peyton","contributorId":245298,"corporation":false,"usgs":false,"family":"Smith","given":"A.","email":"","middleInitial":"Peyton","affiliations":[],"preferred":false,"id":805745,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Averill, Colin","contributorId":245299,"corporation":false,"usgs":false,"family":"Averill","given":"Colin","email":"","affiliations":[],"preferred":false,"id":805746,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Creamer, Courtney 0000-0001-8270-9387","orcid":"https://orcid.org/0000-0001-8270-9387","contributorId":201952,"corporation":false,"usgs":true,"family":"Creamer","given":"Courtney","email":"","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":805747,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cusack, Daniela F. 0000-0003-4681-7449","orcid":"https://orcid.org/0000-0003-4681-7449","contributorId":245300,"corporation":false,"usgs":false,"family":"Cusack","given":"Daniela","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":805822,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hall, Steven J. 0000-0002-7841-2019","orcid":"https://orcid.org/0000-0002-7841-2019","contributorId":244336,"corporation":false,"usgs":false,"family":"Hall","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":805823,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jastrow, Julie","contributorId":243114,"corporation":false,"usgs":false,"family":"Jastrow","given":"Julie","affiliations":[{"id":17946,"text":"Argonne National Laboratory","active":true,"usgs":false}],"preferred":false,"id":805824,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kemner, Kenneth M.","contributorId":245301,"corporation":false,"usgs":false,"family":"Kemner","given":"Kenneth","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":805825,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kleber, Markus","contributorId":92182,"corporation":false,"usgs":true,"family":"Kleber","given":"Markus","email":"","affiliations":[],"preferred":false,"id":805826,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Liu, Xiao-Jun Allen","contributorId":245302,"corporation":false,"usgs":false,"family":"Liu","given":"Xiao-Jun","email":"","middleInitial":"Allen","affiliations":[],"preferred":false,"id":805827,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Pett-Ridge, Jennifer","contributorId":6726,"corporation":false,"usgs":true,"family":"Pett-Ridge","given":"Jennifer","email":"","affiliations":[],"preferred":false,"id":805828,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Schulz, Marjorie S. 0000-0001-5597-6447 mschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-5597-6447","contributorId":3720,"corporation":false,"usgs":true,"family":"Schulz","given":"Marjorie S.","email":"mschulz@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":805829,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70215294,"text":"sir20205082 - 2020 - Delineation of flood-inundation areas in Grapevine Canyon near Scotty’s Castle, Death Valley National Park, California","interactions":[],"lastModifiedDate":"2024-06-05T14:01:50.726878","indexId":"sir20205082","displayToPublicDate":"2020-10-16T10:48:16","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":"2020-5082","displayTitle":"Delineation of Flood-Inundation Areas in Grapevine Canyon Near Scotty’s Castle, Death Valley National Park, California","title":"Delineation of flood-inundation areas in Grapevine Canyon near Scotty’s Castle, Death Valley National Park, California","docAbstract":"On October 18, 2015, a large flood caused considerable damage in Grapevine Canyon near Death Valley Scotty Historic District, in Death Valley National Park, California. Significant channel changes had limited the applicability of previously created flood-inundation maps to current conditions. Predicted flood-inundation maps for Scotty’s Castle were updated using one-dimensional hydraulic models. A digital terrain model was created for the study area using a terrestrial laser scanner for use in the hydraulic models. Estimations of the 4, 2, 1, 0.5, and 0.2-percent annual exceedance probability flood streamflows (previously known as the 25, 50, 100, 250, and 500-year floods) were computed from regional flood regression equations. The estimated flood streamflows were used with the hydraulic models to compute water surface elevations that were mapped on the digital terrain model. The results indicate inundation of the visitor center and park offices occurs by the 4-percent annual exceedance probability flood. Bridge and embankment overtopping occurs by the 2-percent annual exceedance probability flood. Sections of Grapevine Canyon Road and the parking lot are inundated by the 4-percent annual exceedance probability flood and above streamflows. None of the computed streamflows reach Scotty’s Castle main building.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205082","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Morris, C.M., Welborn, T.L., and Minear, J.T., 2020, Delineation of flood-inundation areas in Grapevine Canyon near Scotty’s Castle, Death Valley National Park, California: U.S. Geological Survey Scientific Investigations Report 2020–5082, 27 p., https://doi.org/10.3133/sir20205082.","productDescription":"Report: vi, 27 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-091560","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":379474,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IPKW55","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial Data, Tabular Data, and Surface-Water Model Archive for Delineation of Flood-Inundation Areas in Grapevine Canyon Near Scotty's Castle, Death Valley National Park, California"},{"id":379390,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5082/sir20205082.pdf","text":"Report","size":"4.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5082"},{"id":379389,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5082/coverthb2.jpg"}],"country":"United States","state":"California","otherGeospatial":"Death Valley National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.960205078125,\n 36.5670120564234\n ],\n [\n -116.8670654296875,\n 36.5670120564234\n ],\n [\n -116.8670654296875,\n 37.19095471582605\n ],\n [\n -117.960205078125,\n 37.19095471582605\n ],\n [\n -117.960205078125,\n 36.5670120564234\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 95819
Results are presented and compared for the Community Earth System Model version 2 (CESM2) simulations of the middle Holocene (MH, 6 ka) and Last Interglacial (LIG, 127 ka). These simulations are designated as Tier 1 experiments (midHolocene and lig127k) for the Coupled Model Intercomparison Project phase 6 (CMIP6) and the Paleoclimate Modeling Intercomparison Project phase 4 (PMIP4). They use the low-top, standard 1° version of CESM2 contributing to CMIP6 DECK, historical, and future projection simulations, and to other modeling intercomparison projects. The midHolocene and lig127k provide the opportunity to examine the responses in CESM2 to the orbitally induced changes in the seasonal and latitudinal distribution of insolation. The insolation anomalies result in summer warming over the Northern Hemisphere continents, reduced Arctic summer minimum sea ice, and increased areal extent of the North African monsoon. The Arctic remains warm throughout the year. These changes are greater in the lig127k than midHolocene simulation. Other notable changes are reduction of the Niño3.4 variability and Drake Passage transport and a small increase in the Atlantic Meridional Overturning Circulation from the piControl to midHolocene to lig127k simulation. Comparisons to paleo-data and to simulations from previous model versions are discussed. Possible reasons for mismatches with the paleo-observations are proposed, including missing processes in CESM2, simplifications in the CMIP6 protocols for these experiments, and dating and calibration uncertainties in the data reconstructions.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020PA003957","usgsCitation":"Otto-Bliesner, B., Brady, E.C., Tomas, R.A., Albani, S., Bartlein, P.J., Mahowald, N.M., Shafer, S., Kluzek, E., Lawrence, P.J., Leguy, G., Rothstein, M., and Sommers, A., 2020, A comparison of the CMIP6 midHolocene and lig127k simulations in CESM2: Paleoceanography and Paleoclimatology, v. 35, e2020PA003957, 30 p., https://doi.org/10.1029/2020PA003957.","productDescription":"e2020PA003957, 30 p.","ipdsId":"IP-116661","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":455028,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020pa003957","text":"Publisher Index Page"},{"id":436753,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D9S4EY","text":"USGS data release","linkHelpText":"Biomes simulated by BIOME4 using CESM2 lig127k, midHolocene, and piControl climate data on a global 0.5-degree grid"},{"id":397601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","noUsgsAuthors":false,"publicationDate":"2020-11-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Otto-Bliesner, Bette L.","contributorId":279720,"corporation":false,"usgs":false,"family":"Otto-Bliesner","given":"Bette L.","affiliations":[{"id":57353,"text":"Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":838791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brady, Esther C. 0000-0001-7833-2249","orcid":"https://orcid.org/0000-0001-7833-2249","contributorId":289169,"corporation":false,"usgs":false,"family":"Brady","given":"Esther","email":"","middleInitial":"C.","affiliations":[{"id":6648,"text":"National Center for Atmospheric 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College","active":true,"usgs":false}],"preferred":false,"id":838802,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70216781,"text":"70216781 - 2020 - Variables affecting resource subsidies from streams and rivers to land and their susceptibility to global change stressors","interactions":[],"lastModifiedDate":"2020-12-07T14:50:09.483403","indexId":"70216781","displayToPublicDate":"2020-10-16T08:48:08","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Variables affecting resource subsidies from streams and rivers to land and their susceptibility to global change stressors","docAbstract":"Stream and river ecosystems provide subsidies of emergent adult aquatic insects and other resources to terrestrial food webs, and this lotic–land subsidy has garnered much attention in recent research. Here, we critically examine a list of biotic and abiotic variables—including productivity, dominant taxa, geomorphology, and weather—that should be important in affecting the nature of these subsidy dynamics between lotic and terrestrial ecosystems, especially the pathway from emergent aquatic insects to terrestrial predators. We also explore how interactions between these variables can lead to otherwise unexpected patterns in the importance of aquatic subsidies to terrestrial food webs. Utilizing a match-mismatch framework developed previously, we identify how these variables and interactions may be affected by a broad suite of stressors in addition to contaminants: climate change, land-use conversion, damming and water abstraction, and species invasions and extinctions. These stressors may all act to modify and potentially exacerbate the effects of contaminants on subsidies. The available literature on many variables is sparse, despite strong theoretical underpinnings supporting their importance for lotic–land subsidies. Notably, these understudied variables include those related to physical geomorphology and the structure of the stream/river and floodplain/riparian zone as well as species-specific interactions between aquatic and terrestrial organisms. We suggest that more explicit characterization of these variables and more research directly linking broad-scale stressors to subsidy resource–consumer interactions can help provide a more mechanistic understanding to lotic–land subsidy dynamics within a changing environment.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Contaminants and ecological subsidies","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-49480-3_7","usgsCitation":"Muehlbauer, J., Larsen, S., Jonsson, M., and Emilson, E.J., 2020, Variables affecting resource subsidies from streams and rivers to land and their susceptibility to global change stressors, chap. of Contaminants and ecological subsidies, p. 129-155, https://doi.org/10.1007/978-3-030-49480-3_7.","productDescription":"27 p.","startPage":"129","endPage":"155","ipdsId":"IP-090826","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":381024,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Muehlbauer, Jeffrey 0000-0003-1808-580X","orcid":"https://orcid.org/0000-0003-1808-580X","contributorId":221739,"corporation":false,"usgs":true,"family":"Muehlbauer","given":"Jeffrey","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":806231,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larsen, Stefano","contributorId":169188,"corporation":false,"usgs":false,"family":"Larsen","given":"Stefano","email":"","affiliations":[{"id":13099,"text":"German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany","active":true,"usgs":false}],"preferred":false,"id":806232,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jonsson, Micael","contributorId":245462,"corporation":false,"usgs":false,"family":"Jonsson","given":"Micael","email":"","affiliations":[{"id":49198,"text":"Department of Ecology and Environmental Science, Umeå University, Umeå, Sweden","active":true,"usgs":false}],"preferred":false,"id":806233,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Emilson, Erik J.S.","contributorId":245463,"corporation":false,"usgs":false,"family":"Emilson","given":"Erik","email":"","middleInitial":"J.S.","affiliations":[{"id":49199,"text":"Natural Resources Canada, Canadian Forest ServiceGreat Lakes Forestry Centre, Sault Ste. Marie, Canada","active":true,"usgs":false}],"preferred":false,"id":806234,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216156,"text":"70216156 - 2020 - Synthesis: A framework for predicting the dark side of ecological subsidies","interactions":[],"lastModifiedDate":"2020-11-06T14:20:09.778211","indexId":"70216156","displayToPublicDate":"2020-10-16T08:17:35","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Synthesis: A framework for predicting the dark side of ecological subsidies","docAbstract":"In this chapter, we synthesize the state of the science regarding ecological subsidies and contaminants at the land-water interface and suggest research and management approaches for linked freshwater-terrestrial ecosystems. Specifically, we focus on movements of animals with complex life histories and the detrital inputs associated with animal and plant matter delivered to freshwaters. We present a framework based on the physicochemical parameters of contaminants and how they shape the relationship between contaminant persistence within resource subsidies (“dark side” of subsidies) and movement of resource subsidies (“bright side” of subsidies) across ecosystem boundaries. This relationship between the “dark side” and “bright side” of subsidies defines an important parameter space that allows researchers and practitioners to predict the potential impacts of aquatic contaminants on resource subsidies and their interaction with other stressors on consumers. Ecological factors such as ecosystem productivity, community composition, and consumer prey preference shape the ecotoxicological outcomes of aquatic contamination on subsidies. Landscape factors such as lithology, hydrogeomorphology, hydroperiod, and land use underlie chemical, toxicological, and ecological patterns and provide the context within which effects of contaminants play out. Finally, effects of contaminants combine with effects of other global stressors on timing, quality, and quantity of subsidies that drive responses to contaminants at the land-water interface. Understanding the “dark side” of ecological subsidies requires expertise from multiple disciplines. We attempt to synthesize current knowledge from those disciplines and generate conceptual models that ecologists can use to guide future research in understanding cross-ecosystem subsidies and contaminant fate and effects.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Contaminants and ecological subsidies","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-49480-3_14","usgsCitation":"Kraus, J.M., Wessner, J., and Walters, D., 2020, Synthesis: A framework for predicting the dark side of ecological subsidies, chap. of Contaminants and ecological subsidies, p. 343-372, https://doi.org/10.1007/978-3-030-49480-3_14.","productDescription":"30 p.","startPage":"343","endPage":"372","ipdsId":"IP-114721","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":380257,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Kraus, Johanna M. 0000-0002-9513-4129 jkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-9513-4129","contributorId":4834,"corporation":false,"usgs":true,"family":"Kraus","given":"Johanna","email":"jkraus@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":804243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wessner, Jeff","contributorId":244602,"corporation":false,"usgs":false,"family":"Wessner","given":"Jeff","email":"","affiliations":[{"id":16684,"text":"University of South Dakota","active":true,"usgs":false}],"preferred":false,"id":804244,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walters, David 0000-0002-4237-2158","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":205915,"corporation":false,"usgs":true,"family":"Walters","given":"David","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":804245,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}