West Africa represents a wide gradient of climates, extending from tropical conditions along the Guinea Coast to the dry deserts of the south Sahara, and it has some of the lowest income, most vulnerable populations on the planet, which increases catastrophic impacts of low and high frequency climate variability. This paper investigates low and high frequency climate variability in West African monthly and seasonal precipitation and reference evapotranspiration from the early 1980s to 2016. We examine the impact of those trends and how they interact with payouts from index insurance products. Understanding low and high frequency variability in precipitation and reference evapotranspiration at these scales can provide insight into trends during periods critical to agricultural performance across the region. For index insurance, it is important to identify low-frequency variability, which can result in radical departures between designed/planned and actual insurance payouts, especially in the later part of a 30-year period, a common climate analysis period. We find that evaporative demand and precipitation are not perfect substitutes for monitoring crop deficits and that there may be space to use both for index insurance design. We also show that low yields—aligned with the need for insurance payouts—can be predicted using classification trees that include both precipitation and reference evapotranspiration.
","language":"English","publisher":"MDPI","doi":"10.3390/RS12152432","usgsCitation":"Blakeley, S., Sweeney, S., Husak, G., Harrison, L., Funk, C., Peterson, P., and Osgood, D.E., 2020, Identifying Precipitation and Reference Evapotranspiration Trends in West Africa to Support Drought Insurance: Remote Sensing, v. 12, no. 15, 2432, 29 p., https://doi.org/10.3390/RS12152432.","productDescription":"2432, 29 p.","ipdsId":"IP-120726","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":456609,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12152432","text":"Publisher Index Page"},{"id":429401,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"West Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 20,\n 20\n ],\n [\n -18,\n 20\n ],\n [\n -18,\n 0\n ],\n [\n 20,\n 0\n ],\n [\n 20,\n 20\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"15","noUsgsAuthors":false,"publicationDate":"2020-07-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Blakeley, Sari","contributorId":337011,"corporation":false,"usgs":false,"family":"Blakeley","given":"Sari","email":"","affiliations":[{"id":80950,"text":"UCSB Climate Hazards Center","active":true,"usgs":false}],"preferred":false,"id":901759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sweeney, Stuart","contributorId":337012,"corporation":false,"usgs":false,"family":"Sweeney","given":"Stuart","email":"","affiliations":[{"id":35298,"text":"UCSB Geography Department","active":true,"usgs":false}],"preferred":false,"id":901760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Husak, Gregory","contributorId":145811,"corporation":false,"usgs":false,"family":"Husak","given":"Gregory","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":901761,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harrison, Laura","contributorId":192382,"corporation":false,"usgs":false,"family":"Harrison","given":"Laura","email":"","affiliations":[],"preferred":false,"id":901762,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":901763,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peterson, Pete","contributorId":337013,"corporation":false,"usgs":false,"family":"Peterson","given":"Pete","affiliations":[{"id":80950,"text":"UCSB Climate Hazards Center","active":true,"usgs":false}],"preferred":false,"id":901764,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Osgood, Daniel E","contributorId":337014,"corporation":false,"usgs":false,"family":"Osgood","given":"Daniel","email":"","middleInitial":"E","affiliations":[{"id":80951,"text":"International Research Institute","active":true,"usgs":false}],"preferred":false,"id":901765,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70211652,"text":"70211652 - 2020 - Capture of environmental DNA (eDNA) from water samples by flocculation","interactions":[],"lastModifiedDate":"2020-08-06T18:55:23.121348","indexId":"70211652","displayToPublicDate":"2020-05-29T08:38:38","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5994,"text":"JOVE Journal Of Visualized Experiments","active":true,"publicationSubtype":{"id":10}},"title":"Capture of environmental DNA (eDNA) from water samples by flocculation","docAbstract":"The analysis of environmental DNA (eDNA) has become a widely used approach to problem solving in species management. The detection of cryptic species including invasive and (or) species at risk is the goal, typically accomplished by testing water and sediment for the presence of characteristic DNA signatures. Reliable and efficient procedures for the capture of eDNA are required, especially those that can be performed easily in the field by personnel with limited training and citizen scientists. The capture of eDNA using membrane filtration is widely used currently. This approach has inherent issues that include the choice of filter material and porosity, filter fouling, and time required on site for the process to be performed. Flocculation offers an alternative that can be easily implemented and applied to sampling regimes that strive to cover broad territories in limited time.
","language":"English","publisher":"JOVE","doi":"10.3791/60967","usgsCitation":"Schill, W., 2020, Capture of environmental DNA (eDNA) from water samples by flocculation: JOVE Journal Of Visualized Experiments, v. 159, e60967, https://doi.org/10.3791/60967.","productDescription":"e60967","onlineOnly":"Y","ipdsId":"IP-117636","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":377079,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"159","noUsgsAuthors":false,"publicationDate":"2020-05-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Schill, W. Bane 0000-0002-9217-984X","orcid":"https://orcid.org/0000-0002-9217-984X","contributorId":213903,"corporation":false,"usgs":true,"family":"Schill","given":"W. Bane","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794938,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70217614,"text":"70217614 - 2020 - Pervasive shifts in forest dynamics in a changing world","interactions":[],"lastModifiedDate":"2021-01-25T14:56:41.188464","indexId":"70217614","displayToPublicDate":"2020-05-29T08:26:21","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"Pervasive shifts in forest dynamics in a changing world","docAbstract":"Forest dynamics arise from the interplay of environmental drivers and disturbances with the demographic processes of recruitment, growth, and mortality, subsequently driving biomass and species composition. However, forest disturbances and subsequent recovery are shifting with global changes in climate and land use, altering these dynamics. Changes in environmental drivers, land use, and disturbance regimes are forcing forests toward younger, shorter stands. Rising carbon dioxide, acclimation, adaptation, and migration can influence these impacts. Recent developments in Earth system models support increasingly realistic simulations of vegetation dynamics. In parallel, emerging remote sensing datasets promise qualitatively new and more abundant data on the underlying processes and consequences for vegetation structure. When combined, these advances hold promise for improving the scientific understanding of changes in vegetation demographics and disturbances.
The effects of extreme concentrations of toxic metalloids, such as arsenic (As) and antimony (Sb), on larval amphibians are not well-understood. We sampled Western Toad tadpoles (Anaxyrus boreas) living in As- and Sb-contaminated wetlands throughout their development. Although the tadpoles completed metamorphosis, they accumulated among the highest concentrations of As and Sb ever reported for a living vertebrate (3866.9 mg/kg; 315.0 mg/kg (dry weight), respectively). Ingestion of contaminated sediment had a more important role in metalloid accumulation than aqueous exposure alone. Metalloids were initially concentrated in the gut; however, by metamorphosis, the majority were found in other tissues. These concentrations subsequently decreased with the onset of metamorphosis, yet remained quite elevated. Sublethal effects, including delayed development and reduced size at metamorphosis, were associated with elevated metalloid exposure. The presence of organic arsenicals in tadpole tissues suggests they have the ability to biomethylate inorganic As compounds. The arsenical trimethyl arsine oxide accounted for the majority of extractable organic As, with lesser amounts of monomethylarsonic acid and dimethylarsinic acid. Our findings demonstrate remarkable tolerance of toad tadpoles to extreme metalloid exposure and implicate physiological processes mediating that tolerance.
Bathymetric and topographic surveys performed annually along the coastlines of northern Oregon and southwestern Washington documented changes in beach and nearshore morphology between 2014 and 2019. Volume change analysis revealed measurable localized erosion and deposition throughout the study area, but significant net erosion at the regional scale (several kilometers [km]) was limited to Benson Beach, Wash., a 3-km-long stretch of coastline immediately north of the Columbia River inlet. Despite the placement of approximately 6.3 million cubic meters (Mm3) of sand dredged from the Columbia River navigational channel at nearshore placement sites located nearby, Benson Beach eroded 2.1±0.8 Mm3 over the 5-year (yr) monitoring time period (420,000 cubic meters/year [m3/yr]). A hydrodynamic and sediment transport model was applied to simulate sediment transport fluxes, and a new visualization technique was developed to evaluate the linkages between nearshore dredge placement sites and adjacent coastlines near the mouth of the Columbia River. The model results indicate the dominance of wave processes on sediment-transport patterns outside of the inlet and suggest that the current configuration of the nearshore dredge placement sites can be improved to more efficiently enhance the sediment budget of Benson Beach to reduce erosion and mitigate associated coastal change hazards.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201045","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers Portland District and Northwest Association of Networked Ocean Observing Systems","usgsCitation":"Stevens, A.W., Elias, E., Pearson, S., Kaminsky, G.M., Ruggiero, P.R., Weiner, H.M., and Gelfenbaum, G.R., 2020, Observations of coastal change and numerical modeling of sediment-transport pathways at the mouth of the Columbia River and its adjacent littoral cell: U.S. Geological Survey Open-File Report 2020–1045, 82 p., https://doi.org//10.3133/ofr20201045.","productDescription":"Report: xii, 82 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-114630","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":375095,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1045/coverthb.jpg"},{"id":375096,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1045/ofr20201045.pdf","text":"Report","size":"18 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":375097,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org//10.5066/P9W15JX8","linkHelpText":"Beach topography and nearshore bathymetry of the Columbia River littoral cell, Washington and Oregon"}],"country":"United States","state":"Oregon, Washinton","otherGeospatial":"Columbia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.07135009765625,\n 46.10180436619509\n ],\n [\n -123.57147216796875,\n 46.10180436619509\n ],\n [\n -123.57147216796875,\n 46.35261512930026\n ],\n [\n -124.07135009765625,\n 46.35261512930026\n ],\n [\n -124.07135009765625,\n 46.10180436619509\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information
Pacific Coastal & Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
The U.S. Geological Survey, in cooperation with the U.S. Centers for Disease Control and Prevention and the Maine Center for Disease Control and Prevention, assessed the chemical characteristics and the occurrence, distribution, and oxidation state of inorganic arsenic in drinking water from selected domestic well-water supplies in Maine in 2001–2 and 2006–7.
The data collected provide support for evaluating arsenic-removal efficiencies of household water-purification systems and provide information to State and local officials that can be used in determining a water-treatment approach for the removal of arsenic from drinking water.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1125","collaboration":"Prepared in cooperation with the U.S. Centers for Disease Control and Prevention and the Maine Center for Disease Control and Prevention","usgsCitation":"Culbertson, C.W., Caldwell, J.M., Schalk, L.F., Manassaram, D., Backer, L.C., and Smith, A.E., 2020, Methods of collection and quality assessment of arsenic data in well-water supplies in Maine, 2001–2 and 2006–7: U.S. Geological Survey Data Series 1125, 11 p., https://doi.org/10.3133/ds1125.","productDescription":"Report: v, 11 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-025715","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":375105,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1125/ds1125.pdf","text":"Report","size":"1.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1125"},{"id":375104,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X5HVDF","text":"USGS data release","linkHelpText":"Arsenic datasets and other physical and chemical measurements for selected domestic well-water supplies in Maine—2001–2 and 2006–7"},{"id":375103,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1125/coverthb.jpg"}],"country":"United States","state":"Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.16943359374999,\n 43.02071359427862\n ],\n [\n -66.15966796874999,\n 43.02071359427862\n ],\n [\n -66.15966796874999,\n 44.87144275016589\n ],\n [\n -71.16943359374999,\n 44.87144275016589\n ],\n [\n -71.16943359374999,\n 43.02071359427862\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The Amphibian Research and Monitoring Initiative (ARMI) was established within the U.S. Geological Survey in 2000 as a result of Congressional funding for Department of the Interior agencies to study amphibians and provide information to help manage amphibians and address threats. As the research arm of the Department of the Interior, the U.S. Geological Survey is providing scientific leadership for this effort with a team of research scientists who are global leaders in amphibian conservation science. This postcard has been developed to commemorate the 20th anniversary of ARMI.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip198","usgsCitation":"Ball, L.C., 2020, Amphibian Research and Monitoring Initiative (ARMI) 20th anniversary postcard: U.S. Geological Survey General Information Product 198, 2 p., https://doi.org/10.3133/gip198.","productDescription":"Postcard; 2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118795","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":375099,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/198/gip198.pdf","text":"Report","size":"922 KB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 198"},{"id":375098,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/198/coverthb2.jpg"}],"contact":"Environments Program
Office of the Associate Director for Ecosystems
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Amphibian Research and Monitoring Initiative (ARMI)
The effects of harvest on cooperatively breeding species are often more complex than simply subtracting the number of animals that died from the group count. Changes in demographic rates, particularly dispersal, could offset some effects of harvest mortality in groups but this is rarely explored with cooperative breeders. We asked whether a cooperatively breeding species known for long-distance dispersal could compensate for the effect of harvest mortality on density by adopting immigrants into the group. We used genetic samples to estimate the minimum density of gray wolves (Canis lupus) and proportion of immigrants in groups in the northern US Rocky Mountains after an annual harvest regime was initiated and in the Canadian Rocky Mountains where wolves were managed consistently under an annual harvest regime. We tested whether immigration (1) compensated, (2) partially compensated or (3) did not compensate numerically for harvest mortality in groups and hypothesized immigration would increase with increasing harvest intensity. Density of wolves in groups declined after harvest was initiated whereas immigration into groups was consistently low and did not change with harvest in the US study area. Immigration into groups was similarly low and density even lower in the Canadian study area compared to the US study area. Our results indicate immigration did not compensate for harvest mortality in groups in two separate populations of a cooperatively breeding carnivore. We hypothesize the social structure of wolf groups may limit the potentially compensatory response of immigration in some populations.
Mesoproterozoic and Neoproterozoic basins in western North America record the evolving position of the Laurentian craton within two supercontinents during their growth and dismemberment: Columbia (Nuna) and Rodinia. The western-most exposures of the Columbia rift-related Belt–Purcell Supergroup are preserved in northeastern Washington, structurally overlain by the Deer Trail Group and depositionally overlying the Neoproterozoic Windermere Supergroup. It has been disputed whether the Deer Trail Group is correlative with the Belt–Purcell Supergroup, or younger. To help resolve the uncertain correlation of these units and their bearing on supercontinent evolution, we characterized the detrital zircon age populations of units from the Deer Trail Group, the Windermere Supergroup, and the Belt–Purcell Supergroup in northeastern Washington. These data show that the western part of the Columbia supercontinent (now located in Australia and eastern Antarctica) remained attached to western Laurentia and continued to supply 1600–1500 Ma detrital zircon grains to the Belt–Purcell Supergroup until after ca. 1391 Ma. The Deer Trail Group is younger than the Belt–Purcell strata, with the basal unit younger than ca. 1362 Ma and a middle unit younger than ca. 1300 Ma. The Deer Trail Group has a pre-Grenville-age provenance from the southwestern USA and possibly east Antarctica. The Buffalo Hump Formation is younger than the Deer Trail Group, with Grenville-age (ca. 1112 Ma) detrital zircon grains and a detrital zircon signature like that of the overlying Neoproterozoic Windermere Supergroup. We interpret the Deer Trail Group to have been deposited during the rift-demise of supercontinent Columbia and before the Grenville-age assembly of the supercontinent Rodinia.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjes-2019-0188","usgsCitation":"Box, S.E., Pritchard, C.J., Stephens, T.S., and O’Sullivan, P.B., 2020, Between the supercontinents: Mesoproterozoic Deer Trail Group, an intermediate age unit between the Mesoproterozoic Belt–Purcell Supergroup and the Neoproterozoic Windermere Supergroup in northeastern Washington, USA: Canadian Journal of Earth Sciences, v. 57, no. 12, p. 1411-1427, https://doi.org/10.1139/cjes-2019-0188.","productDescription":"17 p.","startPage":"1411","endPage":"1427","ipdsId":"IP-112626","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":500791,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1807/101706","text":"External Repository"},{"id":377140,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","city":"Chewelah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.81188964843751,\n 47.89056441663247\n ],\n [\n -117.04833984375001,\n 47.89056441663247\n ],\n [\n -117.04833984375001,\n 48.669198799260045\n ],\n [\n -117.81188964843751,\n 48.669198799260045\n ],\n [\n -117.81188964843751,\n 47.89056441663247\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Box, Stephen E. 0000-0002-5268-8375 sbox@usgs.gov","orcid":"https://orcid.org/0000-0002-5268-8375","contributorId":1843,"corporation":false,"usgs":true,"family":"Box","given":"Stephen","email":"sbox@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795048,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pritchard, Chad J. 0000-0002-4608-7776","orcid":"https://orcid.org/0000-0002-4608-7776","contributorId":237042,"corporation":false,"usgs":false,"family":"Pritchard","given":"Chad","email":"","middleInitial":"J.","affiliations":[{"id":47590,"text":"Eastern Washington University, Dept. of Geology","active":true,"usgs":false}],"preferred":false,"id":795049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stephens, Travis S.","contributorId":237044,"corporation":false,"usgs":false,"family":"Stephens","given":"Travis","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":795050,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Sullivan, Paul B.","contributorId":193544,"corporation":false,"usgs":false,"family":"O’Sullivan","given":"Paul","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":795051,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211973,"text":"70211973 - 2020 - Deglacierization of a marginal basin and implications for outburst floods, Mendenhall Glacier, Alaska","interactions":[],"lastModifiedDate":"2020-08-12T21:56:14.477434","indexId":"70211973","displayToPublicDate":"2020-05-27T16:44:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"Deglacierization of a marginal basin and implications for outburst floods, Mendenhall Glacier, Alaska","docAbstract":"Suicide Basin is a partly glacierized marginal basin of Mendenhall Glacier, Alaska, that has released glacier lake outburst floods (GLOFs) annually since 2011. The floods cause inundation and erosion in the Mendenhall Valley, impacting homes and other infrastructure. Here, we utilize in-situ and remote sensing data to assess the recent evolution and current state of Suicide Basin. We focus on the 2018 and 2019 melt seasons, during which we collected most of our data, partly using unmanned aerial vehicles (UAVs). To provide longer-term context, we analyze DEMs collected since 2006 and model glacier surface mass balance over the 2006–2019 period. During the 2018 and 2019 outburst flood events, Suicide Basin released ~30 × 106 m3 of water within approximately 4–5 days. Since lake drainage was partial in both years, these ~30 × 106 m3 represent only a fraction (~60%) of the basin's total storage capacity. In contrast to previous years, subglacial drainage was preceded by supraglacial outflow over the ice dam, which lasted ~1 day in 2018 and 6 days in 2019. Two large calving events occurred in 2018 and 2019, with submerged ice breaking off the main glacier during lake filling, thereby increasing the basin's storage capacity. In 2018, the floating ice in the basin was 36 m thick on average. In 2019, ice thickness was 29 m, suggesting rapid decay of the ice tongue despite increasing ice inflow from Mendenhall Glacier. The ice dam at the basin entrance thinned by more than 5 m a–1 from 2018 to 2019, which is approximately double the rate of the reference period 2006–2018. While ice-dam thinning reduces water storage capacity in the basin, that capacity is increased by declining ice volume in the basin and longitudinal lake expansion, with the latter process challenging to predict. The potential for premature drainage onset (i.e., drainage before the lake's storage capacity is reached), intermittent drainage decelerations, and early drainage termination further complicates prediction of future GLOF events.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2020.00137","usgsCitation":"Kienholz, C., Pierce, J., Hood, E., Amundson, J.M., Wolken, G., Jacobs, A., Hart, S., Wikstrom-Jones, K., Abdel-Fattah, D., Johnson, C., and Conaway, J.S., 2020, Deglacierization of a marginal basin and implications for outburst floods, Mendenhall Glacier, Alaska: Frontiers in Earth Science, v. 8, 137, 21 p., https://doi.org/10.3389/feart.2020.00137.","productDescription":"137, 21 p.","ipdsId":"IP-114163","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":456622,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.00137","text":"Publisher Index Page"},{"id":377453,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Mendenhall Glacier, Suicide Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -134.78164672851562,\n 58.32679897129091\n ],\n [\n -134.06890869140625,\n 58.32679897129091\n ],\n [\n -134.06890869140625,\n 58.73186643857013\n ],\n [\n -134.78164672851562,\n 58.73186643857013\n ],\n [\n -134.78164672851562,\n 58.32679897129091\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-05-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Kienholz, Christian","contributorId":220416,"corporation":false,"usgs":false,"family":"Kienholz","given":"Christian","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":796031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pierce, Jamie","contributorId":218174,"corporation":false,"usgs":true,"family":"Pierce","given":"Jamie","email":"","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":796032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hood, Eran","contributorId":106802,"corporation":false,"usgs":false,"family":"Hood","given":"Eran","affiliations":[],"preferred":false,"id":796033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Amundson, Jason M.","contributorId":26944,"corporation":false,"usgs":true,"family":"Amundson","given":"Jason","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":796034,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wolken, Gabriel","contributorId":204863,"corporation":false,"usgs":false,"family":"Wolken","given":"Gabriel","affiliations":[{"id":37000,"text":"DGGS","active":true,"usgs":false}],"preferred":false,"id":796035,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jacobs, Aaron","contributorId":204855,"corporation":false,"usgs":false,"family":"Jacobs","given":"Aaron","email":"","affiliations":[{"id":36995,"text":"NWS","active":true,"usgs":false}],"preferred":false,"id":796036,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hart, Skye","contributorId":238101,"corporation":false,"usgs":false,"family":"Hart","given":"Skye","email":"","affiliations":[],"preferred":false,"id":796037,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wikstrom-Jones, Katreen","contributorId":238102,"corporation":false,"usgs":false,"family":"Wikstrom-Jones","given":"Katreen","email":"","affiliations":[],"preferred":false,"id":796038,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Abdel-Fattah, Dina","contributorId":238103,"corporation":false,"usgs":false,"family":"Abdel-Fattah","given":"Dina","email":"","affiliations":[],"preferred":false,"id":796039,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Johnson, Crane","contributorId":238104,"corporation":false,"usgs":false,"family":"Johnson","given":"Crane","email":"","affiliations":[],"preferred":false,"id":796040,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Conaway, Jeffrey S. 0000-0002-3036-592X jconaway@usgs.gov","orcid":"https://orcid.org/0000-0002-3036-592X","contributorId":2026,"corporation":false,"usgs":true,"family":"Conaway","given":"Jeffrey","email":"jconaway@usgs.gov","middleInitial":"S.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":796041,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70211524,"text":"70211524 - 2020 - Rethinking foundation species in a changing world: The case for Rhododendron maximum as an emerging foundation species in shifting ecosystems of the southern Appalachians","interactions":[],"lastModifiedDate":"2020-07-31T13:15:51.186703","indexId":"70211524","displayToPublicDate":"2020-05-27T12:00:02","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Rethinking foundation species in a changing world: The case for Rhododendron maximum as an emerging foundation species in shifting ecosystems of the southern Appalachians","title":"Rethinking foundation species in a changing world: The case for Rhododendron maximum as an emerging foundation species in shifting ecosystems of the southern Appalachians","docAbstract":"“Foundation species” are widespread, abundant species that play critical roles in structuring ecosystem characteristics and processes. Ecosystem change in response to human activities, climate change, disease introduction, or other environmental conditions may promote the emergence of new foundation species or the decline of previously important foundation species. We present rhododendron (Rhododendron maximum) as an example of an emerging foundation species in riparian forest and headwater stream ecosystems of the southern Appalachian Mountains and use its example to propose a dynamic approach to recognizing foundation species. As other species have declined, rhododendron has increased in abundance, biomass, and ecosystem importance, and now dominates the riparian zones and mesic uplands of much of the region. Rhododendron structures, stabilizes, and modulates functions within both terrestrial and aquatic ecosystems. Studies of forest ecosystem response to environmental conditions indicate that rhododendron may increase the resistance and resilience of its associated ecosystems to predicted anthropogenic stress, including climate change, nitrogen enrichment, and invasive species. A more dynamic conception of foundation species as dependent on ecosystem states will help ecologists to focus on ecosystem processes and services, rather than on historically dominant species, for restoration strategies.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2020.118240","usgsCitation":"Dudley, M.P., Freeman, M., Wenger, S., Jackson, C.R., and Pringle, C.M., 2020, Rethinking foundation species in a changing world: The case for Rhododendron maximum as an emerging foundation species in shifting ecosystems of the southern Appalachians: Forest Ecology and Management, v. 472, 118240, 9 p., https://doi.org/10.1016/j.foreco.2020.118240.","productDescription":"118240, 9 p.","ipdsId":"IP-116677","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":456625,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2020.118240","text":"Publisher Index Page"},{"id":376915,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Georgia, North Carolina, South Carolina, Tennessee, Virginia, West Virginia","otherGeospatial":"southern Applachian forests","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.837646484375,\n 36.89719446989036\n ],\n [\n -81.6064453125,\n 37.56199695314352\n ],\n [\n -85.60546875,\n 35.32633026307483\n ],\n [\n -86.59423828125,\n 33.054716488042736\n ],\n [\n -85.286865234375,\n 32.95336814579932\n ],\n [\n -79.837646484375,\n 36.89719446989036\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"472","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dudley, Maura P. 0000-0001-9574-8844","orcid":"https://orcid.org/0000-0001-9574-8844","contributorId":236862,"corporation":false,"usgs":false,"family":"Dudley","given":"Maura","email":"","middleInitial":"P.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":794501,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":794502,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wenger, Seth J.","contributorId":177838,"corporation":false,"usgs":false,"family":"Wenger","given":"Seth J.","affiliations":[],"preferred":false,"id":794503,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, C. Rhett","contributorId":236863,"corporation":false,"usgs":false,"family":"Jackson","given":"C.","email":"","middleInitial":"Rhett","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":794504,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pringle, Catherine M.","contributorId":176292,"corporation":false,"usgs":false,"family":"Pringle","given":"Catherine","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":794505,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70260155,"text":"70260155 - 2020 - Capturing, preserving and digitizing legacy seismic data from the Montserrat Volcano Observatory analog seismic network, July 1995 – December 2004","interactions":[],"lastModifiedDate":"2024-10-30T22:19:06.067897","indexId":"70260155","displayToPublicDate":"2020-05-27T11:18:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Capturing, preserving and digitizing legacy seismic data from the Montserrat Volcano Observatory analog seismic network, July 1995 – December 2004","docAbstract":"An eruption of the Soufrière Hills Volcano (SHV) on the eastern Caribbean island of Montserrat began on 18 July 1995 and continued until February 2010. Within nine days of the eruption onset, an existing four‐station analog seismic network (ASN) was expanded to 10 sites. Telemetered data from this network were recorded, processed, and archived locally using a system developed by scientists from the U.S. Geological Survey (USGS) Volcano Disaster Assistance Program (VDAP). In October 1996, a digital seismic network (DSN) was deployed with the ability to capture larger amplitude signals across a broader frequency range. These two networks operated in parallel until December 2004, with separate telemetry and acquisition systems (analysis systems were merged in March 2001). Although the DSN provided better quality data for research, the ASN featured superior real‐time monitoring tools and captured valuable data including the only seismic data from the first 15 months of the eruption. These successes of the ASN have been rather overlooked. This article documents the evolution of the ASN, the VDAP system, the original data captured, and the recovery and conversion of more than 230,000 seismic events from legacy SUDS, Hypo71, and Seislog formats into Seisan database with waveform data in miniSEED format. No digital catalog existed for these events, but students at the University of South Florida have classified two-thirds of the 40,000 events that were captured between July 1995 and October 1996. Locations and magnitudes were recovered for ~10,000 of these events. Real-time seismic amplitude measurement, seismic spectral amplitude measurement, and tiltmeter data were also captured. The result is that the ASN seismic dataset is now more discoverable, accessible, and reusable, in accordance with FAIR data principles. These efforts could catalyze new research on the 1995–2010 SHV eruption. Furthermore, many observatories have data in these same legacy data formats and might benefit from procedures and codes documented here.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220200012","usgsCitation":"Thompson, G., Power, J., Braunmiller, J., Lockhart, A., Lynch, L., McCausland, W., Rowe, C., Shea, T., White, R., and Breithaupt, C., 2020, Capturing, preserving and digitizing legacy seismic data from the Montserrat Volcano Observatory analog seismic network, July 1995 – December 2004: Seismological Research Letters, v. 91, no. 4, p. 2127-2140, https://doi.org/10.1785/0220200012.","productDescription":"14 p.","startPage":"2127","endPage":"2140","ipdsId":"IP-115441","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":463354,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Monserrat","otherGeospatial":"Soufrière Hills Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -62.20648979760041,\n 16.723228121283853\n ],\n [\n -62.20648979760041,\n 16.684378490612588\n ],\n [\n -62.15099565340141,\n 16.684378490612588\n ],\n [\n -62.15099565340141,\n 16.723228121283853\n ],\n [\n -62.20648979760041,\n 16.723228121283853\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"91","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-05-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Glenn","contributorId":345675,"corporation":false,"usgs":false,"family":"Thompson","given":"Glenn","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":917233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Power, John 0000-0002-7233-4398","orcid":"https://orcid.org/0000-0002-7233-4398","contributorId":215240,"corporation":false,"usgs":true,"family":"Power","given":"John","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Braunmiller, Jochen","contributorId":345676,"corporation":false,"usgs":false,"family":"Braunmiller","given":"Jochen","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":917235,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lockhart, Andrew 0000-0002-1591-3254 ablock@usgs.gov","orcid":"https://orcid.org/0000-0002-1591-3254","contributorId":204748,"corporation":false,"usgs":true,"family":"Lockhart","given":"Andrew","email":"ablock@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917236,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lynch, Lloyd","contributorId":345677,"corporation":false,"usgs":false,"family":"Lynch","given":"Lloyd","affiliations":[{"id":82692,"text":"Seismic Research Unit","active":true,"usgs":false}],"preferred":false,"id":917237,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McCausland, Wendy 0000-0002-8683-1440","orcid":"https://orcid.org/0000-0002-8683-1440","contributorId":345678,"corporation":false,"usgs":true,"family":"McCausland","given":"Wendy","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917238,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rowe, Charlotte","contributorId":345679,"corporation":false,"usgs":false,"family":"Rowe","given":"Charlotte","email":"","affiliations":[{"id":82693,"text":"Los Alamos National Labs","active":true,"usgs":false}],"preferred":false,"id":917239,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shea, Thomas","contributorId":345680,"corporation":false,"usgs":false,"family":"Shea","given":"Thomas","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":917240,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"White, Randall A. 0000-0003-4074-8577","orcid":"https://orcid.org/0000-0003-4074-8577","contributorId":344964,"corporation":false,"usgs":false,"family":"White","given":"Randall A.","affiliations":[{"id":82444,"text":"none, retired USGS","active":true,"usgs":false}],"preferred":false,"id":917241,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Breithaupt, Charles","contributorId":345681,"corporation":false,"usgs":false,"family":"Breithaupt","given":"Charles","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":917242,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70210593,"text":"70210593 - 2020 - When source and path components trade off in ground-motion prediction equations","interactions":[],"lastModifiedDate":"2020-07-10T12:39:00.623841","indexId":"70210593","displayToPublicDate":"2020-05-27T11:10:39","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"When source and path components trade off in ground-motion prediction equations","docAbstract":"Current research on ground‐motion models (also known as ground‐motion prediction equations [GMPEs]) and their uncertainties focus on the separate contributions of source, path, and site to both median values and their variability. Implicit here is the assumption that the event term, path term, and site term reflect only properties of the source, path, and site, respectively. Events with larger stress drop generate more high‐frequency energy, and thus more ground motion. Therefore, the correlation of high‐frequency (i.e., peak ground acceleration [PGA] or peak ground velocity [PGV]) event terms in GMPEs with stress drop is taken to be genuine. However, PGA and PGV ground‐motion observations of the 2014
Climate change is altering the spatial and temporal patterns of temperature and discharge in rivers, which is expected to have implications for the life stages of anadromous fish using those rivers. We developed an individual-based model to track American Shad Alosa sapidissima offspring within a coarse template of spatially and temporally variable habitat conditions defined by a combination of temperature, river velocity, and prey availability models. We simulated spawning at each river kilometer along a 142-km reach of the Connecticut River on each day (April 1–August 31) to understand how spawning date and location drive larval recruitment differentially across years and decades (1993–2002 and 2007–2016). For both temperature and flow, interannual variation was large in comparison to interdecadal differences. Variation in simulated recruitment was best explained by a combination of season-specific spawning temperature and location along the course of the river. The greatest potential recruitment occurred during years in which June temperatures were relatively high. In years when June and July were warmer than average, maximum recruitment resulted from spawning taking place at the upstream portion of the modeled reach. Model scenarios (stationary or passive-drift larvae; and dams or no dams) had predictable effects. We assumed that the pools above dams had negative impacts on eggs and yolk-sac larvae that may have been deposited there. Allowing eggs and larvae to drift passively with the current reduced spatial differences in recruitment success among spawning sites relative to stationary eggs and larvae. Our results demonstrate the importance of spatiotemporal environmental heterogeneity for producing positive recruitment over the long term. In addition, our results suggest the importance of successful passage of spawners to historical spawning sites in the Connecticut River upstream of Vernon Dam, especially as conditions shift with climate change.
","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10460","usgsCitation":"Marschall, E.A., Glover, D., Mather, M.E., and Parrish, D.L., 2020, Modeling larval American Shad recruitment in a large river: North American Journal of Fisheries Management, v. 41, no. 4, p. 939-954, https://doi.org/10.1002/nafm.10460.","productDescription":"16 p.","startPage":"939","endPage":"954","ipdsId":"IP-109474","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":456629,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10460","text":"Publisher Index Page"},{"id":395637,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Hampshite, Massachutsetts","otherGeospatial":"Connecticut River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.81463623046875,\n 42.18375873465217\n ],\n [\n -72.13348388671875,\n 42.18375873465217\n ],\n [\n -72.13348388671875,\n 43.12504316740127\n ],\n [\n -72.81463623046875,\n 43.12504316740127\n ],\n [\n -72.81463623046875,\n 42.18375873465217\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-05-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Marschall, Elizabeth A.","contributorId":274924,"corporation":false,"usgs":false,"family":"Marschall","given":"Elizabeth","email":"","middleInitial":"A.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":833512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glover, David C.","contributorId":274925,"corporation":false,"usgs":false,"family":"Glover","given":"David C.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":833513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mather, Martha E. 0000-0003-3027-0215 mather@usgs.gov","orcid":"https://orcid.org/0000-0003-3027-0215","contributorId":2580,"corporation":false,"usgs":true,"family":"Mather","given":"Martha","email":"mather@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833514,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parrish, Donna L. 0000-0001-9693-6329 dparrish@usgs.gov","orcid":"https://orcid.org/0000-0001-9693-6329","contributorId":138661,"corporation":false,"usgs":true,"family":"Parrish","given":"Donna","email":"dparrish@usgs.gov","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833511,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70222338,"text":"70222338 - 2020 - Challenges in quantifying air-water carbon dioxide flux using estuarine water quality data: Case study for Chesapeake Bay","interactions":[],"lastModifiedDate":"2021-07-22T15:12:18.885038","indexId":"70222338","displayToPublicDate":"2020-05-27T10:10:47","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7159,"text":"JGR Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Challenges in quantifying air-water carbon dioxide flux using estuarine water quality data: Case study for Chesapeake Bay","docAbstract":"Estuaries play an uncertain but potentially important role in the global carbon cycle via CO2 outgassing. The uncertainty mainly stems from the paucity of studies that document the full spatial and temporal variability of estuarine surface water partial pressure of carbon dioxide ( pCO2). Here, we explore the potential of utilizing the abundance of pH data from historical water quality monitoring programs to fill the data void via a case study of the mainstem Chesapeake Bay (eastern United States). We calculate pCO2 and the air-water CO2 flux at monthly resolution from 1998 to 2018 from tidal fresh to polyhaline waters, paying special attention to the error estimation. The biggest error is due to the pH measurement error, and errors due to the gas transfer velocity, temporal sampling, the alkalinity mixing model, and the organic alkalinity estimation are 72%, 27%, 15%, and 5%, respectively, of the error due to pH. Seasonal, interannual, and spatial variability in the air-water flux and surface pCO2 is high, and a correlation analysis with oxygen reveals that this variability is driven largely by biological processes. Averaged over 1998–2018, the mainstem bay is a weak net source of CO2 to the atmosphere of 1.2 (1.1, 1.4) mol m−2 yr−1 (best estimate and 95% confidence interval). Our findings suggest that the abundance of historical pH measurements in estuaries around the globe should be mined in order to constrain the large spatial and temporal variability of the CO2 exchange between estuaries and the atmosphere.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JC015610","usgsCitation":"Herrmann, M., Najjar, R.G., Da, F., Friedman, J.R., Friedrichs, M.A., Goldberger, S., Menendez, A., Shadwick, E.H., Stets, E.G., and St-Laurent, P., 2020, Challenges in quantifying air-water carbon dioxide flux using estuarine water quality data: Case study for Chesapeake Bay: JGR Oceans, v. 125, no. 7, e2019JC015610, 19 p., https://doi.org/10.1029/2019JC015610.","productDescription":"e2019JC015610, 19 p.","ipdsId":"IP-119043","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":456632,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2019jc015610","text":"External Repository"},{"id":387386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.11328125,\n 36.96744946416934\n ],\n [\n -75.970458984375,\n 37.501010429493284\n ],\n [\n -75.65185546874999,\n 37.90953361677018\n ],\n [\n -75.82763671875,\n 37.96152331396614\n ],\n [\n -75.816650390625,\n 38.07404145941957\n ],\n [\n -76.278076171875,\n 38.40194908237822\n ],\n [\n -76.168212890625,\n 38.8225909761771\n ],\n [\n -76.256103515625,\n 39.104488809440475\n ],\n [\n -76.1572265625,\n 39.29179704377487\n ],\n [\n -75.9375,\n 39.50404070558415\n ],\n [\n -75.9814453125,\n 39.57182223734374\n ],\n [\n -76.11328125,\n 39.53793974517628\n ],\n [\n -76.168212890625,\n 39.39375459224348\n ],\n [\n -76.3330078125,\n 39.36827914916014\n ],\n [\n -76.519775390625,\n 39.18117526158749\n ],\n [\n -76.5966796875,\n 38.805470223177466\n ],\n [\n -76.431884765625,\n 38.35888785866677\n ],\n [\n -76.35498046875,\n 38.07404145941957\n ],\n [\n -76.35498046875,\n 37.65773212628272\n ],\n [\n -76.453857421875,\n 37.33522435930639\n ],\n [\n -76.409912109375,\n 37.09900294387622\n ],\n [\n -76.11328125,\n 36.96744946416934\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-07-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Herrmann, Maria","contributorId":198519,"corporation":false,"usgs":false,"family":"Herrmann","given":"Maria","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":819664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Najjar, Raymond G. 0000-0002-3770-2300","orcid":"https://orcid.org/0000-0002-3770-2300","contributorId":261280,"corporation":false,"usgs":false,"family":"Najjar","given":"Raymond","email":"","middleInitial":"G.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":819665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Da, Fei 0000-0002-5330-5962","orcid":"https://orcid.org/0000-0002-5330-5962","contributorId":261282,"corporation":false,"usgs":false,"family":"Da","given":"Fei","email":"","affiliations":[{"id":40564,"text":"Virginia Institute of Marine Science, William & Mary","active":true,"usgs":false}],"preferred":false,"id":819666,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Friedman, Jaclyn R. 0000-0001-8120-2541","orcid":"https://orcid.org/0000-0001-8120-2541","contributorId":222587,"corporation":false,"usgs":false,"family":"Friedman","given":"Jaclyn","email":"","middleInitial":"R.","affiliations":[{"id":40564,"text":"Virginia Institute of Marine Science, William & Mary","active":true,"usgs":false}],"preferred":false,"id":819668,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Friedrichs, Marjorie A. 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Our response focused primarily on the United States’ Navy’s China Lake Naval Air Weapons Station base (NAWSCL). This focus was because much of the surface rupture occurred on the NAWSCL and because of NAWSCL access restrictions only permitting Federal and State of California personnel. In total, we measured or are still measuring at 24 sites, 14 of which were on the NAWSCL and, as of this writing, operational. The majority of sites were set up as continuous stations logging at either 1 sample per second or 1 sample per 15 s. Two stations were recording a 200 m cross‐rupture aperture starting ∼10 hr after the M 6.4 event, and they recorded the coseismic displacements of the M 7.1. Approximately, 1 hr after the M 7.1 event, two new stations were recording a ∼200 m cross‐rupture aperture of the surface rupture. In the days following, we established the rest of the stations ranging to a distance of ∼15 km from the M 7.1 principal rupture trace. The lack of differential displacement across the M 6.4 rupture during the M 7.1 event suggests that it did not reactivate the M 6.4 plane. The lack of differential cross‐fault displacement for both events suggests that rapid shallow afterslip did not occur at those two locations. The postseismic time series from these stations shows centimeters of horizontal displacement over periods of a few months. They record a mixture of fault‐parallel and fault‐normal displacements that, in conjunction with analysis of more spatially complete Interferometric Synthetic Aperture Radar displacement fields, suggest that both poroelastic and afterslip phenomena occur along the M 6.4 and 7.1 rupture planes. Using preliminary data from these and other regional stations, we also explore the Ridgecrest sequence’s effect on regional GNSS time series and the differentiation of long‐term postseismic motions and secular deformation rates. We find that redefining a common‐mode noise filter using different GNSS stations that are assumed to be unaffected by the earthquakes results in small but systematic differences in the regional velocity field estimate.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220200007","usgsCitation":"Brooks, B.A., Murray, J.R., Svarc, J.L., Phillips, E.L., Turner, R., Murray, M.H., Ericksen, T., Wang, K., Minson, S.E., Burgmann, R., Pollitz, F., Hudnut, K.W., Nevitt, J., Roeloffs, E., Hernandez, J., and Olson, B., 2020, Rapid geodetic observations of spatiotemporally varying postseismic deformation following the Ridgecrest earthquake sequence: The U.S. Geological Survey response: Seismological Research Letters, v. 9, no. 4, p. 2108-2123, https://doi.org/10.1785/0220200007.","productDescription":"16 p.","startPage":"2108","endPage":"2123","ipdsId":"IP-113806","costCenters":[{"id":237,"text":"Earthquake Science 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jrmurray@usgs.gov","orcid":"https://orcid.org/0000-0002-6144-1681","contributorId":2759,"corporation":false,"usgs":true,"family":"Murray","given":"Jessica","email":"jrmurray@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927766,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Svarc, Jerry L. 0000-0002-2802-4528","orcid":"https://orcid.org/0000-0002-2802-4528","contributorId":212736,"corporation":false,"usgs":true,"family":"Svarc","given":"Jerry","email":"","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927767,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Phillips, Ellen L. 0000-0003-3381-5428","orcid":"https://orcid.org/0000-0003-3381-5428","contributorId":331482,"corporation":false,"usgs":true,"family":"Phillips","given":"Ellen","email":"","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927768,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Turner, Ryan Clayton 0000-0003-0732-5951","orcid":"https://orcid.org/0000-0003-0732-5951","contributorId":351029,"corporation":false,"usgs":true,"family":"Turner","given":"Ryan Clayton","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927769,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murray, Mark Hunter 0000-0003-4862-5547","orcid":"https://orcid.org/0000-0003-4862-5547","contributorId":300982,"corporation":false,"usgs":true,"family":"Murray","given":"Mark","email":"","middleInitial":"Hunter","affiliations":[{"id":237,"text":"Earthquake 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0000-0002-3168-4797 hudnut@usgs.gov","orcid":"https://orcid.org/0000-0002-3168-4797","contributorId":2550,"corporation":false,"usgs":true,"family":"Hudnut","given":"Kenneth","email":"hudnut@usgs.gov","middleInitial":"W.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927776,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Nevitt, Johanna 0000-0003-3819-1773 jnevitt@usgs.gov","orcid":"https://orcid.org/0000-0003-3819-1773","contributorId":198144,"corporation":false,"usgs":true,"family":"Nevitt","given":"Johanna","email":"jnevitt@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927777,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Roeloffs, Evelyn 0000-0002-4761-0469","orcid":"https://orcid.org/0000-0002-4761-0469","contributorId":215340,"corporation":false,"usgs":true,"family":"Roeloffs","given":"Evelyn","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927778,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hernandez, Janis","contributorId":216335,"corporation":false,"usgs":false,"family":"Hernandez","given":"Janis","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":927779,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Olson, Brian","contributorId":217365,"corporation":false,"usgs":false,"family":"Olson","given":"Brian","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":927780,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70211626,"text":"70211626 - 2020 - Departures of rangeland fractional component cover and land cover from landsat-based ecological potential in Wyoming USA","interactions":[],"lastModifiedDate":"2020-11-13T15:47:47.342487","indexId":"70211626","displayToPublicDate":"2020-05-27T09:33:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3228,"text":"Rangeland Ecology and Management","onlineIssn":"1551-5028","printIssn":"1550-7424","active":true,"publicationSubtype":{"id":10}},"title":"Departures of rangeland fractional component cover and land cover from landsat-based ecological potential in Wyoming USA","docAbstract":"Monitoring rangelands by identifying the departure of contemporary conditions from long-term ecological potential allows for the disentanglement of natural biophysical gradients driving change from changes associated with land uses and other disturbance types. We developed maps of ecological potential (EP) for shrub, sagebrush (Artemisia spp.), perennial herbaceous, litter, and bare ground fractional cover in Wyoming, USA. EP maps correspond to the potential natural vegetation cover expected by environmental conditions in the absence of anthropogenic and natural disturbance as represented by the greenest and least disturbed period of the Landsat archive. EP was predicted using regression tree models with inputs of soil maps and spectral data associated with the 75th percentile of the Normalized Difference Vegetation Index in the Landsat archive. We trained our EP models with 2015 component cover maps on ecologically intact sites with relatively lower bare ground than expected. We generated departure of vegetation cover by comparing the EP and 2015 fractional cover. The departures represent land cover change from potential land cover and/or within-state changes in 2015. Next, we converted EP and 2015 fractional cover maps into thematic land cover and evaluated departure to determine if it was great enough to result in land cover change. The 2015 conditions showed reduced shrub, sagebrush, litter, and perennial herbaceous cover and increased bare ground relative to EP. Known disturbances, such as energy development, fires, and vegetation treatments, are clearly visible on the departure maps, but not on EP component maps. The most frequent departure from EP land cover was shrubland conversion to grassland. Land cover departures can be explained only in small part by known disturbance, and instead are ostensibly related to climate and land management practices. These drivers result in land cover departures that broadened the ecotone between shrubland and grassland relative to EP.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2020.03.009","usgsCitation":"Rigge, M.B., Homer, C.G., Shi, H., and Wylie, B., 2020, Departures of rangeland fractional component cover and land cover from landsat-based ecological potential in Wyoming USA: Rangeland Ecology and Management, v. 73, no. 6, p. 856-870, https://doi.org/10.1016/j.rama.2020.03.009.","productDescription":"15 p.","startPage":"856","endPage":"870","ipdsId":"IP-114686","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":456635,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2020.03.009","text":"Publisher Index Page"},{"id":436954,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IKI4XV","text":"USGS data release","linkHelpText":"Using Targeted Training Data to Develop Site Potential for the Upper Colorado River Basin from 2000 - 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(EROS) Center","active":true,"usgs":true}],"preferred":true,"id":794864,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216851,"text":"70216851 - 2020 - Landscape matters: Predicting the biogeochemical effects of permafrost thaw on aquatic networks with a state factor approach","interactions":[],"lastModifiedDate":"2020-12-09T13:29:53.289874","indexId":"70216851","displayToPublicDate":"2020-05-27T08:27:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3032,"text":"Permafrost and Periglacial Processes","active":true,"publicationSubtype":{"id":10}},"title":"Landscape matters: Predicting the biogeochemical effects of permafrost thaw on aquatic networks with a state factor approach","docAbstract":"Permafrost thaw has been widely observed to alter the biogeochemistry of recipient aquatic ecosystems. However, research from various regions has shown considerable variation in effect. In this paper, we propose a state factor approach to predict the release and transport of materials from permafrost through aquatic networks. Inspired by Hans Jenny's seminal description of soil‐forming factors, and based on the growing body of research on the subject, we propose that a series of state factors—including relief, ice content, permafrost extent, and parent material—will constrain and direct the biogeochemical effect of thaw over time. We explore state‐factor‐driven variation in thaw response using a series of case studies from diverse regions of the permafrost‐affected north, and also describe unique scaling considerations related to the mobile and integrative nature of aquatic networks. While our cross‐system review found coherent responses to thaw for some biogeochemical constituents, such as nutrients, others, such as dissolved organics and particles, were much more variable in their response. We suggest that targeted, hypothesis‐driven investigation of the effects of state factor variation will bolster our ability to predict the biogeochemical effects of thaw across diverse and rapidly changing northern landscapes.
","language":"English","publisher":"Wiley","doi":"10.1002/ppp.2057","usgsCitation":"Tank, S.E., Vonk, J.E., Walvoord, M.A., McClelland, J.W., Laurion, I., and Abbott, B., 2020, Landscape matters: Predicting the biogeochemical effects of permafrost thaw on aquatic networks with a state factor approach: Permafrost and Periglacial Processes, v. 31, no. 3, p. 358-370, https://doi.org/10.1002/ppp.2057.","productDescription":"13 p.","startPage":"358","endPage":"370","numberOfPages":"13","ipdsId":"IP-112819","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":456641,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ppp.2057","text":"External Repository"},{"id":381159,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-05-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Tank, Suzanne E.","contributorId":150795,"corporation":false,"usgs":false,"family":"Tank","given":"Suzanne","email":"","middleInitial":"E.","affiliations":[{"id":18102,"text":"University of Alberta, Edmonton, Canada","active":true,"usgs":false}],"preferred":false,"id":806614,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vonk, Jorien E.","contributorId":150794,"corporation":false,"usgs":false,"family":"Vonk","given":"Jorien","email":"","middleInitial":"E.","affiliations":[{"id":18101,"text":"Utrecht University, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":806615,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":806616,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McClelland, James W. 0000-0001-9619-8194","orcid":"https://orcid.org/0000-0001-9619-8194","contributorId":238027,"corporation":false,"usgs":false,"family":"McClelland","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":47685,"text":"Marine Science Institute, University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":806617,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Laurion, Isabelle","contributorId":245611,"corporation":false,"usgs":false,"family":"Laurion","given":"Isabelle","email":"","affiliations":[{"id":49236,"text":"Centre Eau Terre Environnement, Institut national de la recherche scientifique, Québec, QC, Canada","active":true,"usgs":false}],"preferred":false,"id":806618,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Abbott, Benjamin W.","contributorId":218049,"corporation":false,"usgs":false,"family":"Abbott","given":"Benjamin W.","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":806619,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70212872,"text":"70212872 - 2020 - Minimal clustering of injection-induced earthquakes observed with a large-n seismic array","interactions":[],"lastModifiedDate":"2020-10-12T17:26:57.804493","indexId":"70212872","displayToPublicDate":"2020-05-26T19:54:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Minimal clustering of injection-induced earthquakes observed with a large-n seismic array","title":"Minimal clustering of injection-induced earthquakes observed with a large-n seismic array","docAbstract":"The clustering behavior of injection‐induced earthquakes is examined using one month of data recorded by the LArge‐n Seismic Survey in Oklahoma (LASSO) array. The 1829‐node seismic array was deployed in a 25 km×32 km area of active saltwater disposal in northern Oklahoma between 14 April and 10 May 2016. Injection rates in the study area are nearly constant around the time of the deployment. We develop a local magnitude (ML) equation for the region and estimate magnitudes for 1104 earthquakes recorded by the deployment. The determined earthquake magnitudes range from ML 0.01 to 3.0. The majority of earthquakes occurred between 1.5 and 5.5 km depth, and the shallowest earthquake depths overlap with the base of injection wells at depths between 1.5 and 2.5 km. We compute focal mechanisms of the largest events (ML>2.0), and find a mix of normal‐ and strike‐slip‐faulting types. Earthquakes occur regularly in time during the deployment, but are not evenly distributed in space across the study area, that is, they are spatially clustered. Analysis of the nearest‐neighbor distances in the space–time–magnitude domain shows the seismicity is dominated by single‐event clusters (i.e., independent events). This high proportion of single‐event clusters compared with multievent clusters has been previously noted for induced events at geothermal sites. When clustering occurs, the number of events in a cluster is typically small. We observe only four clusters with 10 or more events. For these larger clusters, we find equivalent numbers of foreshocks and aftershocks; however, the foreshock sequences are significantly longer in duration lasting days to tens of days, while aftershock sequences are observed only on the order of one day. The minimal clustering observed for events in the LASSO array suggests that the majority of events are being directly driven by stress changes due to local saltwater disposal.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200101","usgsCitation":"Cochran, E.S., Wickham-Piotrowski, A., Kemna, K., Harrington, R.M., Dougherty, S., and Pena Castro, A., 2020, Minimal clustering of injection-induced earthquakes observed with a large-n seismic array: Bulletin of the Seismological Society of America, v. 110, no. 5, p. 2005-2017, https://doi.org/10.1785/0120200101.","productDescription":"13 p.","startPage":"2005","endPage":"2017","ipdsId":"IP-117066","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":378082,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.3,\n 36.5\n ],\n [\n -97.6,\n 36.5\n ],\n [\n -97.6,\n 37.1\n ],\n [\n -98.3,\n 37.1\n ],\n [\n -98.3,\n 36.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"110","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wickham-Piotrowski, A.","contributorId":239705,"corporation":false,"usgs":false,"family":"Wickham-Piotrowski","given":"A.","email":"","affiliations":[{"id":47980,"text":"Ecole Nationale Superieure","active":true,"usgs":false}],"preferred":false,"id":797737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kemna, K.","contributorId":239706,"corporation":false,"usgs":false,"family":"Kemna","given":"K.","email":"","affiliations":[{"id":47982,"text":"Ruhr-Universitat Bochum","active":true,"usgs":false}],"preferred":false,"id":797738,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harrington, R. 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A reintroduction program was established to restore healthy and harvestable populations of steelhead because volitional access to the area was blocked in the 1960s after construction of dams in the lower river. A trap-and-haul program is used to move adult steelhead and salmon (Oncorhynchus spp.) upstream, around the dams and large reservoirs, and release them in the upper basin to spawn naturally. Fish are released into Lake Scanewa, the uppermost reservoir in the system, and into the Cowlitz and Cispus Rivers. The goal of this study was to describe the behavior, movement, and tributary use of adult steelhead in the upper Cowlitz River Basin to assist in evaluating the trap-and-haul program and reintroduction program. We were specifically interested in learning more about the locations where steelhead spawn. Individual fish were assigned one of four fates, based on their location during the spawning period: Cowlitz River, Cispus River, Lake Scanewa, or fallback below the dam that impounds the reservoir. A total of 215 steelhead were tagged for the 2017–18 study. Of these, 5 fish regurgitated their transmitters before or shortly after release, so 210 tagged fish were used for analyses, including 121 fish (57.6 percent) released into the Cispus River and 89 fish (42.4 percent) released into Lake Scanewa. The Cowlitz River release site was not evaluated. Hatchery-origin (HOR) and natural-origin (NOR) steelhead were included in the study. Within the first 10 days after release, most steelhead had moved at least 2 river kilometers away from their release site. Fish released into Lake Scanewa, however, moved from their release site significantly sooner than fish released into the Cispus River, regardless of origin or sex. Most radio-tagged steelhead (93–100 percent) made no more than one trip between the reservoir and one of the rivers prior to spawning.
Steelhead were predominantly assigned fates in the river closest to where they were released, but origin also played a role. Steelhead released into the Cispus River were assigned Cispus River fates more than 80 percent of the time, including both origins and both years. About 13 percent of NOR fish had fates in the Cowlitz River, but the proportion was lower for HOR fish (0–2 percent). Fallback was the least common fate for Cispus-released steelhead (three HOR fish in 2017), followed by the reservoir fate, ranging from 5 to 9 percent in 2017, and zero in 2018. The steelhead released into Lake Scanewa were almost exclusively NOR fish, which had primarily Cowlitz River fates (55–57 percent). We released only six HOR steelhead into Lake Scanewa, and they all had Cowlitz River fates. The reservoir fate was uncommon for both release sites, and it was consistently lower in 2018 compared to 2017. Flow conditions were higher in 2017, which may have affected steelhead movement patterns or timing. The three HOR steelhead released in the Cispus River in 2017 were the only fish that fell back over Cowlitz Falls Dam, which represented 1.4 percent of the total study fish, 5.7 percent of the Cispus-released fish in 2017, and 9.4 percent of the Cispus-released HOR fish in 2017.
About 62 percent of the steelhead released in Lake Scanewa spawned in the Cowlitz River, with the remaining 38 percent in the Cispus River. Within the Cowlitz River, close to one-half (about 47 percent) of the fish spawned in the upper reach, and about 16 percent spawned in the lower reach. Steelhead released into Lake Scanewa that spawned in the Cispus River were minimal in reach 5 (4 percent) and distributed in approximately equal proportions in the middle (reach 6; 16 percent) and upper (reach 7; 18 percent) reaches. Steelhead released into the Cispus River spawned almost exclusively in the Cispus River (94 percent). The upper Cispus River reach (reach 7) was used by more fish (72 percent) than the middle Cispus reach (reach 6; 16 percent). Taken together, the upper Cowlitz River reach (reach 4) and upper Cispus River reach (reach 7) accounted for more than 71 percent of the spawning locations for radio-tagged steelhead. When the middle Cispus River reach (reach 6) is included with the upper reaches, they account for 87 percent of our described spawning sites. We described 12 tributaries in the Cowlitz River and 8 tributaries in the Cispus River where steelhead spawned. After spawning, about 53–63 percent of steelhead moved downstream as kelts, either being collected at Cowlitz Falls Dam or being detected downstream from the dam. More fish were collected from both release sites in 2018 compared to 2017. Across release sites and years, more NOR fish and females moved downstream as kelts. This study added to the understanding of the behavior and movement of adult steelhead in the upper Cowlitz River Basin, supporting the findings of previous studies in the basin and describing spawning sites in the system’s two main rivers and their tributaries. Future research efforts in this system may use additional telemetry studies, genetic analyses, and spawning ground surveys to provide further insights into the progress of the reintroduction effort.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201054","usgsCitation":"Liedtke, T.L., Kock, T.J., Hansen, A.C., Ekstrom, B.K., and Tomka, R.G., 2020, Behavior and movement of adult winter steelhead (Oncorhynchus mykiss) in the upper Cowlitz River Basin, Washington, 2017–18: U.S. Geological Survey Open-File Report 2020–1054, 35 p., https://doi.org/10.3133/ofr20201054.","productDescription":"vi, 35 p.","numberOfPages":"35","onlineOnly":"Y","ipdsId":"IP-116465","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":375037,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1054/coverthb.jpg"},{"id":375038,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1054/ofr20201054.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Washington","otherGeospatial":"Upper Cowlitz River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.22265625000001,\n 45.767522962149876\n ],\n [\n -120.498046875,\n 45.767522962149876\n ],\n [\n -120.498046875,\n 46.98025235521881\n ],\n [\n -123.22265625000001,\n 46.98025235521881\n ],\n [\n -123.22265625000001,\n 45.767522962149876\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
A recent U.S. Geological Survey data compilation of stream-sediment geochemistry for Alaska contains decades of analyses collected under numerous Federal and State programs. The compiled data were determined by various analytical methods. Some samples were reanalyzed by a different analytical method than the original, resulting in some elements having concentrations reported by multiple analytical methods. Consideration of the analytical methods used to determine the elemental concentrations is an important step in a mineral prospectivity analysis. We used the compiled data to compare concentrations of barium (Ba), cobalt (Co), copper (Cu), chromium (Cr), nickel (Ni), lead (Pb), and zinc (Zn) determined by different analytical methods to show how simple data comparisons can identify bias and provide a general sense of the comparability of different analytical methods. The elements were selected because they have a range of geochemical properties that may affect the performance of different analytical procedures.
Generally, agreement between Ba, Co, Cu, Cr, Ni, Pb, and Zn concentrations is good for most quantitative methods that use a total decomposition of the sample. However, Cr concentrations typically were lower for methods using quantitative-instrumental analysis following a multi-acid dissolution technique that included hydrofluoric acid compared to those using sinter decomposition. Additionally, low- to middle-range concentrations for Co, Cr, Cu, Ni, Pb, and Zn by instrumental neutron activation (NA) and energy-dispersive x-ray spectroscopy (EDX) analyzed by the National Uranium Resource Evaluation (NURE) program have high uncertainty. Concentrations determined by methods that use partial decomposition of the sample generally correspond well to concentrations determined by methods that use a total decomposition technique, except for Ba and Cr. For Ba and Cr, partial decomposition techniques yield lower concentrations than those determined by methods that use a total decomposition technique. Comparison of Ba, Co, Cr, Cu, Ni, Pb, and Zn concentrations determined by semiquantitative visual six-step direct-current arc emission spectrography (ES_SQ) to those determined by quantitative methods using either a total or partial decomposition technique consistently show scatter that exceeds the values expected based on the range represented by the semiquantitative concentration.
The data compilation includes a best-value determination that was selected based on the analytical method from the all concentration data for that sample. Ba, Cr, Co, and Zn concentrations determined by NA usually are selected as the best-value determination. However, the NURE-NA method was designed for high throughput and the uncertainty associated with low- and mid-range concentrations is greater than that of the multi-acid method used to reanalyze many samples. Selection of the multi-acid method over the NURE-NA method for Ba, Co, and Zn could be warranted. Additionally, concentrations determined by ES_SQ usually are selected as the best-value determination over all methods that use a partial decomposition of the sample. Substitution of concentrations determined by methods that use a partial decomposition for those of ES_SQ may be warranted for Co, Cu, Ni, Pb, and Zn. Regardless of the selection of the best-value determination, the dataset remains a mixed method dataset and the uncertainty due to differences in analytical methodology must be considered when using the dataset.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201038","usgsCitation":"Wang, B, Ellefsen, K.J., Granitto, M., Kelley, K.D., Karl, S.M., Case, G.N.D., Kreiner, D.C., and Amundson, C.L., 2020, Evaluation of the analytical methods used to determine the elemental concentrations found in the stream geochemical dataset compiled for Alaska: U.S. Geological Survey Open-File Report 2020-1038, 66 p., https://doi.org/10.3133/ofr20201038.","productDescription":"xii, 66 p.","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-109726","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":375022,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1038/coverthb.jpg"},{"id":375023,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1038/ofr20201038.pdf","text":"Report","size":"8 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Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Nutrient and soil loss, defined herein as the loss of nutrients or soil to streams and other downstream receiving waters, affect watersheds around the globe. Although governments make large investments mitigating nutrient and soil loss through watershed management efforts, the efficacy of these efforts is often difficult to assess, in part because streamflow variability obscures the effects.
This study investigates the effects of watershed management on nutrient and soil losses in the State of Illinois during two periods: 1978 to 2017, and 2008 to 2017. The former period provides an important test case for assessing the efficacy of major Federal programs like the Clean Water Act and the Conservation Reserve Program at mitigating nutrient and soil loss, whereas the latter spans the years after these policies were well established, thereby providing an assessment of whether these programs have kept pace with ongoing trends in climate and watershed management.
The effect of interannual streamflow variability on long-term nutrient and soil loss trends was removed using an extension of the Weighted Regressions on Time, Discharge, and Season methodology, called generalized flow normalization. This process also partitions trends into components attributable to long-term changes in streamflow and watershed management. The Weighted Regressions on Time, Discharge, and Season trend analysis indicated significant, widespread trends in nutrient and soil loss in Illinois since 1978. From 1978 to 2017, improvements in watershed management reduced nitrogen and soil loss from watersheds within Illinois, but this effect was partially or entirely negated by increasing losses due to changing streamflow. During the same period, phosphorus loss also increased owing to a combination of inadequate management efforts and changing streamflow. During 2008–17, however, nutrient and soil losses have all accelerated, threatening to undo previous reductions if the current trends continue.
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U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The ecological effects of tropical cyclones on mangrove forests are diverse and highly location- and cyclone-dependent. Ecological resistance, resilience, and enhancement are terms that describe most mangrove forest responses to tropical cyclones. However, in the most extreme cases, tropical cyclones can trigger abrupt and irreversible ecological transformations (i.e., ecological regime shifts). Here, we examine a cyclone-induced ecological regime shift that occurred in Everglades National Park (USA), where forest mortality and peat collapse due to a powerful tropical cyclone (the 1935 Labor Day Hurricane) led to the conversion of mangrove forests to mudflats and an estimated elevation loss of approximately 75 cm. We investigated soil elevation change measured in these mangrove forests and adjacent mudflats during a twenty-year period [1998–2018] using Surface Elevation Table-Marker Horizon (SET-MH) methods. This period encompasses the effects of Hurricanes Wilma (2005) and Irma (2017). We also used historical sea-level rise rates and future sea-level rise scenarios to estimate surface elevation changes in the past (1930–1998) and to illustrate elevation gains needed for these ecosystems to adapt to future change. Collectively, our findings advance understanding of the long-term effects of cyclone-induced ecological regime shifts due to forest mortality, peat collapse, and conversion of mangrove forests to mudflats.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-020-01291-8","usgsCitation":"Osland, M., Feher, L., Anderson, G., Vervaeke, W., Krauss, K., Whelan, K.R., Balentine, K.S., Tiling-Range, G., Smith, T., and Cahoon, D., 2020, A tropical cyclone-induced ecological regime shift: Mangrove forest conversion to mudflat in Everglades National Park (Florida, USA): Wetlands, v. 40, p. 1445-1458, https://doi.org/10.1007/s13157-020-01291-8.","productDescription":"14 p.","startPage":"1445","endPage":"1458","ipdsId":"IP-102521","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":375472,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n 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S.","contributorId":225172,"corporation":false,"usgs":false,"family":"Balentine","given":"Karen","email":"","middleInitial":"S.","affiliations":[{"id":41066,"text":"U.S. Fish and Wildlife Service, Suffolk, VA 23434 USA","active":true,"usgs":false}],"preferred":false,"id":790624,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tiling-Range, G.","contributorId":225173,"corporation":false,"usgs":false,"family":"Tiling-Range","given":"G.","affiliations":[{"id":41067,"text":"National Marine Fisheries Service (contracted through Jamison Professional Services), NOAA Southeast Regional Office, St. Petersburg, FL","active":true,"usgs":false}],"preferred":false,"id":790625,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Smith, Thomas J.","contributorId":225174,"corporation":false,"usgs":false,"family":"Smith","given":"Thomas J.","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":790626,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Cahoon, Donald R. 0000-0002-2591-5667","orcid":"https://orcid.org/0000-0002-2591-5667","contributorId":219657,"corporation":false,"usgs":true,"family":"Cahoon","given":"Donald","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":790627,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70228181,"text":"70228181 - 2020 - Surface soil temperature seasonal variation estimation in a forested area using combined satellite observations and in-situ measurements","interactions":[],"lastModifiedDate":"2022-02-07T17:37:56.521281","indexId":"70228181","displayToPublicDate":"2020-05-26T11:33:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2027,"text":"International Journal of Applied Earth Observation and Geoinformation","active":true,"publicationSubtype":{"id":10}},"title":"Surface soil temperature seasonal variation estimation in a forested area using combined satellite observations and in-situ measurements","docAbstract":"Surface soil temperature is the soil temperature from the surface to 10 cm in depth. Surface soil temperature plays a significant role in agricultural drought monitoring, ecosystem energy transfer modeling, and global carbon cycle evaluation. Studies have been proposed to estimate surface soil temperature, but surface soil temperature monitoring within forested areas still poses a significant challenge. In this study, we proposed a surface soil temperature retrieval method using combined satellite observations and in-situ measurements for the Great Dismal Swamp (GDS). The GDS is a U.S. protected area managed and protected by the U.S. Fish and Wildlife Service. It is located along the boundary of Virginia and North Carolina, with maple gum, Atlantic white cedar, and pine pocosin as the main forest cover types. Ground-based surface soil temperature measurements were collected for these forest types from May 2015 to April 2017. Both the Land Remote Sensing Satellite (Landsat) Thermal Infrared Sensor (TIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) carry two thermal infrared (TIR) channels. The TIR channels with similar corresponding wavelengths were first fused using an improved fusing model to generate high resolution TIR measurements. Then the enterprise algorithm was applied to calculate land surface temperature (LST) from the fused TIR bands. An improved soil temperature retrieval method was applied to generate surface soil temperature based on LST and vegetation index (VI) within the study area for the three forest types. In-situ measurements were used to build the surface soil temperature retrieval method, and results were then validated. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were integrated separately as VIs in the model to monitor surface soil temperature. The R2 for retrieved surface soil temperature through satellite observations was 0.76, and the RMSE was 1.96