In Switzerland, nearly all historical Mw ~ 6 earthquakes have induced damaging landslides, rockslides and snow avalanches that, in some cases, also resulted in damage to infrastructure and loss of lives. We describe the customisation to Swiss conditions of a globally calibrated statistical approach originally developed to rapidly assess earthquake-induced landslide likelihoods worldwide. The probability of occurrence of such earthquake-induced effects is modelled through a set of geospatial susceptibility proxies and peak ground acceleration. The predictive model is tuned to capture the observations from past events and optimised for near-real-time estimates based on USGS-style ShakeMaps routinely produced by the Swiss Seismological Service. Our emphasis is on the use of high-resolution geospatial datasets along with additional local information on ground failure susceptibility. Even if calibrated on historic events with moderate magnitudes, the methodology presented in this paper yields sensible results also for low-magnitude recent events. The model is integrated in the Swiss ShakeMap framework. This study has a high practical relevance to many Swiss ShakeMap stakeholders, especially those managing lifeline systems, and to other global users interested in conducting a similar customisation for their region of interest.
","language":"English","publisher":"Springer","doi":"10.1007/s11069-018-3248-5","usgsCitation":"Cauzzi, C., Fah, D., Wald, D.J., Clinton, J., Losey, S., and Wiemer, S., 2018, ShakeMap-based prediction of earthquake-induced mass movements in Switzerland calibrated on historical observations: Natural Hazards, v. 92, no. 2, p. 1211-1235, https://doi.org/10.1007/s11069-018-3248-5.","productDescription":"25 p.","startPage":"1211","endPage":"1235","ipdsId":"IP-095789","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":468635,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/20.500.11850/250020","text":"External Repository"},{"id":355312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Switzerland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 6,\n 45.75\n ],\n [\n 10.5,\n 45.75\n ],\n [\n 10.5,\n 47.75\n ],\n [\n 6,\n 47.75\n ],\n [\n 6,\n 45.75\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"92","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-07","publicationStatus":"PW","scienceBaseUri":"5b46e552e4b060350a15d0d1","contributors":{"authors":[{"text":"Cauzzi, Carlo","contributorId":205898,"corporation":false,"usgs":false,"family":"Cauzzi","given":"Carlo","email":"","affiliations":[{"id":37189,"text":"Swiss Seismological Service at ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":738801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fah, Donat","contributorId":205899,"corporation":false,"usgs":false,"family":"Fah","given":"Donat","email":"","affiliations":[],"preferred":false,"id":738802,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":738803,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clinton, John","contributorId":205900,"corporation":false,"usgs":false,"family":"Clinton","given":"John","affiliations":[],"preferred":false,"id":738804,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Losey, Stephane","contributorId":205901,"corporation":false,"usgs":false,"family":"Losey","given":"Stephane","email":"","affiliations":[],"preferred":false,"id":738805,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wiemer, Stefan","contributorId":205902,"corporation":false,"usgs":false,"family":"Wiemer","given":"Stefan","email":"","affiliations":[],"preferred":false,"id":738806,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227705,"text":"70227705 - 2018 - Experimental evidence of long-term reproductive costs in a colonial nesting seabird","interactions":[],"lastModifiedDate":"2022-01-27T14:30:59.155631","indexId":"70227705","displayToPublicDate":"2018-06-21T08:24:36","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2190,"text":"Journal of Avian Biology","active":true,"publicationSubtype":{"id":10}},"title":"Experimental evidence of long-term reproductive costs in a colonial nesting seabird","docAbstract":"Trade-offs between current and future reproduction are central to the evolution of life histories. Experiments that manipulate brood size provide an effective approach to investigating future costs of current reproduction. Most manipulative studies to date, however, have addressed only the short-term effects of brood size manipulation. Our goal was to determine whether survival or breeding costs of reproduction in a long-lived species manifest beyond the subsequent breeding season. To this end, we investigated long-term survival and breeding effects of a multi-year reproductive cost experiment conducted on black-legged kittiwakes Rissa tridactyla, a long-lived colonial nesting seabird. We used multi-state capture–recapture modeling to assess hypotheses regarding the role of experimentally reduced breeding effort and other factors, including climate phase and colony size and productivity, on future survival and breeding probabilities during the 16-yr period following the experiment. We found that forced nest failures had a positive effect on breeding probability over time, but had no effect on long-term survival. This apparent canalization of survival suggests that adult survival is the most important parameter influencing fitness in this long-lived species, and that adults should pay reproductive costs in ways that do not compromise this critical life history parameter. When declines in adult survival rate are observed, they may indicate populations of conservation concern.
","language":"English","publisher":"Wiley","doi":"10.1111/jav.01779","usgsCitation":"McKnight, A., Blomberg, E.J., Golet, G.H., Irons, D.B., Loftin, C., and McKinney, S.T., 2018, Experimental evidence of long-term reproductive costs in a colonial nesting seabird: Journal of Avian Biology, v. 49, no. 8, e01779, 14 p., https://doi.org/10.1111/jav.01779.","productDescription":"e01779, 14 p.","ipdsId":"IP-075525","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":394966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Prince William Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -148.86474609375,\n 59.77852198502987\n ],\n [\n -145.5,\n 59.77852198502987\n ],\n [\n -145.5,\n 61.29398784561188\n ],\n [\n -148.86474609375,\n 61.29398784561188\n ],\n [\n -148.86474609375,\n 59.77852198502987\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"8","noUsgsAuthors":false,"publicationDate":"2018-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"McKnight, Aly","contributorId":220818,"corporation":false,"usgs":false,"family":"McKnight","given":"Aly","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":831948,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blomberg, Erik J.","contributorId":17543,"corporation":false,"usgs":false,"family":"Blomberg","given":"Erik","email":"","middleInitial":"J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":831949,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Golet, Gregory H.","contributorId":89844,"corporation":false,"usgs":false,"family":"Golet","given":"Gregory","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":831950,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irons, David B.","contributorId":220820,"corporation":false,"usgs":false,"family":"Irons","given":"David","email":"","middleInitial":"B.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":831951,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":831842,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McKinney, Shawn T. smckinney@usgs.gov","contributorId":5175,"corporation":false,"usgs":true,"family":"McKinney","given":"Shawn","email":"smckinney@usgs.gov","middleInitial":"T.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":831952,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70197401,"text":"ofr20181073 - 2018 - Regional spectral analysis of moderate earthquakes in northeastern North America—Final Report to the Nuclear Regulatory Commission, Project V6240, Task 3","interactions":[],"lastModifiedDate":"2018-06-22T09:44:35","indexId":"ofr20181073","displayToPublicDate":"2018-06-21T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1073","title":"Regional spectral analysis of moderate earthquakes in northeastern North America—Final Report to the Nuclear Regulatory Commission, Project V6240, Task 3","docAbstract":"We analyze the Fourier spectra of S+Lg+surface wave groups from the horizontal and vertical components of broadband and accelerogram recordings of 120 small and moderate (2< Mw <6) earthquakes recorded by Canadian and American stations sited on rock at distances from 3 to 600 kilometers. There are seven Mw 4.0–4.5, six Mw 4.5–5.0, and three Mw ≥5 earthquakes in this event set. We test the regional spectral analysis by comparing the moment magnitudes with the moment magnitudes from the earthquake moment tensors determined by Bob Herrmann (St. Louis University) for 27 events, obtaining dMw=0.004±0.074. We determine the Lg attenuation in seven regions within northeastern North America: Charlevoix, lower St. Lawrence, Maine, Northern New York, lower Great Lakes, Ontario, and Nunavut. These attenuation estimates yield an average attenuation Q= (368±13)f (0.54±0.02) for the Appalachian region, a stronger attenuation Q= (317±16)f (0.54±0.03) for the Appalachian lowlands, and a weaker attenuation Q=(455±20)f (0.51±0.02) for Ontario and western Quebec. For events in Nunavut and northernmost Quebec, we estimate a similar attenuation for r <450 km, but a weaker attenuation Q= (773±70)f (0.27±0.06) for Lg propagation for 450< r <1700 kilometers. This far-regional attenuation allows us to analyze recordings of the 1989 Ungava and Payne Bay earthquakes obtained in Ontario and southern Quebec. We use these regional attenuations to determine the corner frequencies, stress drops, and radiated energies of the 120 earthquakes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181073","usgsCitation":"Boatwright, J., 2018, Regional spectral analysis of moderate earthquakes in northeastern North America—Final report to the Nuclear Regulatory Commission, project V6240, task 3: U.S. Geological Survey Open-File Report 2018–1073, 39 p., https://doi.org/10.3133/ofr20181073.","productDescription":"Report: vi, 39 p.","numberOfPages":"39","onlineOnly":"Y","ipdsId":"IP-096269","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":355095,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1073/ofr20181073.pdf","text":"Report","size":"2.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1073"},{"id":355094,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1073/coverthb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.2666015625,\n 39.977120098439634\n ],\n [\n -49.0869140625,\n 39.977120098439634\n ],\n [\n -49.0869140625,\n 54.059387886623576\n ],\n [\n -78.2666015625,\n 54.059387886623576\n ],\n [\n -78.2666015625,\n 39.977120098439634\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
The Lower Brule Reservation in central South Dakota is losing land because of shoreline erosion along Lake Sharpe, a reservoir on the Missouri River, which has caused detrimental effects for the Lower Brule Sioux Tribe including losses of cultural sites, recreation access points, wildlife habitat, irrigated cropland, and landmass. To better understand and quantify shoreline erosion, the Lower Brule Sioux Tribe and the U.S. Geological Survey cooperated on a series of data-collection efforts and study of shoreline erosion along Lake Sharpe. Data collected or compiled for 1966–2015 were used to describe and quantify shoreline erosion along Lake Sharpe. The progression of shoreline erosion near the community of Lower Brule, South Dakota, was tracked by comparing current or recent aerial imagery with existing historical maps. At 33 evaluation lines along a 7-mile reach of Lake Sharpe shoreline near Lower Brule, cumulative change of shoreline from 1966 to 2010 ranged from about −224 feet of deposition to 770 feet of erosion.
Photographic and location data were collected for this study to understand the processes affecting erosion and estimate erosion rates. Photographs were collected only in the 7-mile reach near Lower Brule, but locations of the bank over time were collected at the 7-mile reach and two additional reaches within the Lower Brule Reservation. Global navigation satellite system equipment was used in real-time kinematic mode to collect bank locations along three reaches of interest. Reach-length data were collected four times between November 2011 and November 2012. A small, unmanned aerial system (drone) was used to capture digital video along the shoreline of the 7-mile reach.
Water-level fluctuations contribute to the number of wet-dry cycles experienced by the soils at the shoreline or bank. The soils present under the current (2017) location of the reservoir are predominantly terrace alluvium, consisting of sand and silt. Detailed soils data for Lyman County indicate that the dominant soil type along the southern part of the shoreline in the 7-mile reach is Bullcreek clay. Weather within the study area can affect the erosion rate. Air temperature can potentially affect erosion rates by freezing and thawing water and soils. Mean hourly wind speeds vary somewhat throughout the year but averaged 13.3 miles per hour. The direction of prevailing winds near Lower Brule indicates that there are several miles of fetch to build large waves.
Annual erosion rates calculated or measured throughout this study varied by location. Long-term annual average erosion rates of the 7-mile reach, as calculated by image analysis, ranged from −5.1 feet per year (deposition) to 17.5 feet per year (erosion). Short-term annual erosion rates measured using global navigation satellite system equipment during 2010–12 ranged from about 0 to 31.7 feet per year for the 7-mile reach. Existing scour countermeasures have been effective variably. Fieldstone rip-rap seems to have stabilized the shoreline, whereas tree strips paralleling the shoreline seem to have slowed erosion.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185058","collaboration":"Prepared in cooperation with the Lower Brule Sioux Tribe","usgsCitation":"Thompson, R.F., and Stamm, J.F., 2018, Shoreline erosion at selected areas along Lake Sharpe on the Lower Brule Reservation, South Dakota, 1966–2015: U.S. Geological Survey Scientific Investigations Report 2018–5058, 29 p., https://doi.org/10.3133/sir20185058.","productDescription":"Report: vi, 29 p.; Plate: 33.11 x 46.81 inches; Data Release","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080051","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":355250,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2018/5058/sir20185058_plate01.pdf","text":"Plate 1 -","size":"7.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5058 Plate","linkHelpText":"Soil Type and Land Cover Interactions with Erosion Measurements near Lower Brule, South Dakota"},{"id":355251,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7H130XV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data to document shoreline erosion at selected locations along Lake Sharpe on the Lower Brule Reservation in South Dakota, 1966–2015"},{"id":355249,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5058/sir20185058.pdf","text":"Report","size":"5.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5058"},{"id":355248,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5058/coverthb.jpg"}],"country":"United States","state":"South Dakota","city":"Lower Brule","otherGeospatial":"Lake Sharpe","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.1678466796875,\n 43.81471121600004\n ],\n [\n -99.16259765625,\n 43.81471121600004\n ],\n [\n -99.16259765625,\n 44.38669150215206\n ],\n [\n -100.1678466796875,\n 44.38669150215206\n ],\n [\n -100.1678466796875,\n 43.81471121600004\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
South Dakota Office
U.S. Geological Survey
1608 Mountain View Road
Rapid City, SD 57702
The 14 November 2016
Sea cliff retreat rates are expected to accelerate with rising sea levels during the 21st century. Here we develop an approach for a multi‐model ensemble that efficiently projects time‐averaged sea cliff retreat over multi‐decadal time scales and large (>50 km) spatial scales. The ensemble consists of five simple 1‐D models adapted from the literature that relate sea cliff retreat to wave impacts, sea level rise (SLR), historical cliff behavior, and cross‐shore profile geometry. Ensemble predictions are based on Monte Carlo simulations of each individual model, which account for the uncertainty of model parameters. The consensus of the individual models also weights uncertainty, such that uncertainty is greater when predictions from different models do not agree. A calibrated, but unvalidated, ensemble was applied to the 475 km‐long coastline of Southern California (USA), with 4 SLR scenarios of 0.5, 0.93, 1.5, and 2 m by 2100. Results suggest that future retreat rates could increase relative to mean historical rates by more than two‐fold for the higher SLR scenarios, causing an average total land loss of 19 – 41 m by 2100. However, model uncertainty ranges from +/‐ 5 – 15 m, reflecting the inherent difficulties of projecting cliff retreat over multiple decades. To enhance ensemble performance, future work could include weighting each model by its skill in matching observations in different morphological settings
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2017JF004401","usgsCitation":"Limber, P.W., Barnard, P., Vitousek, S., and Erikson, L.H., 2018, A model ensemble for projecting multi‐decadal coastal cliff retreat during the 21st century: Journal of Geophysical Research F: Earth Surface, v. 123, no. 7, p. 1566-1589, https://doi.org/10.1029/2017JF004401.","productDescription":"24 p.","startPage":"1566","endPage":"1589","ipdsId":"IP-088080","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468640,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017jf004401","text":"Publisher Index Page"},{"id":355235,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"123","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-20","publicationStatus":"PW","scienceBaseUri":"5b46e555e4b060350a15d0e7","contributors":{"authors":[{"text":"Limber, Patrick W. 0000-0002-8207-3750 plimber@usgs.gov","orcid":"https://orcid.org/0000-0002-8207-3750","contributorId":196794,"corporation":false,"usgs":true,"family":"Limber","given":"Patrick","email":"plimber@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":738644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":147147,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick L.","email":"pbarnard@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":738645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vitousek, Sean 0000-0002-3369-4673 svitousek@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-4673","contributorId":149065,"corporation":false,"usgs":true,"family":"Vitousek","given":"Sean","email":"svitousek@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":738646,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":738647,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70198088,"text":"70198088 - 2018 - Hydrological regime and climate interactively shape riparian vegetation composition along the Colorado River, Grand Canyon","interactions":[],"lastModifiedDate":"2018-11-21T15:35:06","indexId":"70198088","displayToPublicDate":"2018-06-19T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":849,"text":"Applied Vegetation Science","active":true,"publicationSubtype":{"id":10}},"title":"Hydrological regime and climate interactively shape riparian vegetation composition along the Colorado River, Grand Canyon","docAbstract":"Question
How closely do riparian plant communities track hydrological and climatic variation in space, and how do interactions among hydrological and climatic filters influence success of flow management strategies?
Location
Grand Canyon, Arizona, USA.
Methods
Multi‐year vegetation surveys were conducted across three hydrological zones – active channel, active floodplain and inactive floodplain – within each of 42 sandbars that vary geographically in temperature and precipitation along a 400‐km river segment. Ecological niche models were used to estimate locally optimal conditions of maximum inundation duration, elevation above daily peak flow, mean annual precipitation, and mean maximum and minimum temperature for 16 of the most abundant woody and 58 most abundant herbaceous plant species. These estimates were used to calculate community‐weighted mean (CWM) environmental preferences, which were used to determine how closely vegetation preferences tracked local variation in environmental factors, and to assess interactive responses of species and communities to variation in hydrology and climate.
Results
Communities closely tracked hydrological variation across zones, but less so within zones. Communities tracked variation in minimum temperature more closely than maximum temperature or precipitation. At the species level, woody plants that were more abundant in wetter hydrological conditions were also more abundant in wetter climatic conditions, and vice versa. This relationship was even stronger at the community level, where there were significant negative relationships between CWM preferences of inundation duration and temperature for both woody and herbaceous vegetation.
Conclusions
The climate‐hydrology linkages found in this system suggest that increasing temperatures and drought are likely to reduce the inundation tolerance of riparian vegetation within the Grand Canyon. Increasing the duration of high flow events would likely reduce the abundance of encroaching woody vegetation, but could also reduce the resilience of remaining vegetation to heat waves and drought. The reinforcing effects of climatic and hydrological filters are likely to generally result in greater sensitivity of species composition to environmental change than if those environmental filters acted independently. These results have implications for predicting resource responses to environmental change, as well as prescriptions for direct vegetation management to enhance resilience.
In situ Al-silicate formation, also known as “reverse weathering,” is an important sink of many of the major and minor cations in seawater (e.g. Mg, K, and Li). However, the importance of this sink in global geochemical cycles and isotopic budgets of these elements remains poorly constrained. Here, we report on the potassium isotopic composition (
Communities resident in urban areas located near active volcanoes can experience volcanic ash exposures during, and following, an eruption, in addition to sustained exposures to high concentrations of anthropogenic air pollutants (e.g., vehicle exhaust emissions). Inhalation of anthropogenic pollution is known to cause the onset of, or exacerbate, respiratory and cardiovascular diseases. It is further postulated similar exposure to volcanic ash can also affect such disease states. Understanding of the impact of combined exposure of volcanic ash and anthropogenic pollution to human health, however, remains limited.
The aim of this study was to assess the biological impact of combined exposure to respirable volcanic ash (from Soufrière Hills volcano (SHV), Montserrat and Chaitén volcano (ChV), Chile; representing different magmatic compositions and eruption styles) and freshly-generated complete exhaust from a gasoline vehicle. A multicellular human lung model (an epithelial cell-layer composed of A549 alveolar type II-like cells complemented with human blood monocyte-derived macrophages and dendritic cells cultured at the air-liquid interface) was exposed to diluted exhaust (1:10) continuously for 6 h, followed by immediate exposure to the ash as a dry powder (0.54 ± 0.19 μg/cm2 and 0.39 ± 0.09 μg/cm2 for SHV and ChV ash, respectively). After an 18 h incubation, cells were exposed again for 6 h to diluted exhaust, and a final 18 h incubation (at 37 °C and 5% CO2). Cell cultures were then assessed for cytotoxic, oxidative stress and (pro-)inflammatory responses.
Results indicate that, at all tested (sub-lethal) concentrations, co-exposures with both ash samples induced no significant expression of genes associated with oxidative stress (HMOX1, NQO1) or production of (pro-)inflammatory markers (IL-1β, IL-8, TNF-α) at the gene and protein levels. In summary, considering the employed experimental conditions, combined exposure of volcanic ash and gasoline vehicle exhaust has a limited short-term biological impact to an advanced lung cell in vitro model.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2018.01.115","usgsCitation":"Tomasek, I., Horwell, C.J., Bisig, C., Damby, D., Comte, P., Czerwinski, J., Petri-Fink, A., Clift, M., Drasler, B., and Rothen-Rutishauer, B., 2018, Respiratory hazard assessment of combined exposure to complete gasoline exhaust and respirable volcanic ash in a multicellular human lung model at the air-liquid interface: Environmental Pollution, v. 238, p. 977-987, https://doi.org/10.1016/j.envpol.2018.01.115.","productDescription":"12 p.","startPage":"977","endPage":"987","ipdsId":"IP-094236","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":468648,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2018.01.115","text":"Publisher Index Page"},{"id":355177,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"238","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e559e4b060350a15d103","contributors":{"authors":[{"text":"Tomasek, Ines","contributorId":205741,"corporation":false,"usgs":false,"family":"Tomasek","given":"Ines","email":"","affiliations":[{"id":37158,"text":"Institute of Hazard, Risk & Resilience, Department of Earth Sciences, Durham University, UK","active":true,"usgs":false}],"preferred":false,"id":738328,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Horwell, Claire J.","contributorId":177455,"corporation":false,"usgs":false,"family":"Horwell","given":"Claire","email":"","middleInitial":"J.","affiliations":[{"id":16770,"text":"Dept. Earth Sciences, Durham University, UK","active":true,"usgs":false}],"preferred":false,"id":738329,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bisig, Christoph","contributorId":205742,"corporation":false,"usgs":false,"family":"Bisig","given":"Christoph","email":"","affiliations":[{"id":37159,"text":"Adolphe Merkle Institute, University of Fribourg, Switzerland","active":true,"usgs":false}],"preferred":false,"id":738330,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Damby, David 0000-0002-3238-3961 ddamby@usgs.gov","orcid":"https://orcid.org/0000-0002-3238-3961","contributorId":177453,"corporation":false,"usgs":true,"family":"Damby","given":"David","email":"ddamby@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":738327,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Comte, Pierre","contributorId":205743,"corporation":false,"usgs":false,"family":"Comte","given":"Pierre","email":"","affiliations":[{"id":37160,"text":"Bern University for Applied Sciences, Nidau, Switzerland","active":true,"usgs":false}],"preferred":false,"id":738331,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Czerwinski, Jan","contributorId":205744,"corporation":false,"usgs":false,"family":"Czerwinski","given":"Jan","email":"","affiliations":[{"id":37160,"text":"Bern University for Applied Sciences, Nidau, Switzerland","active":true,"usgs":false}],"preferred":false,"id":738332,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Petri-Fink, Alke","contributorId":177458,"corporation":false,"usgs":false,"family":"Petri-Fink","given":"Alke","email":"","affiliations":[],"preferred":false,"id":738333,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Clift, Martin J D","contributorId":205745,"corporation":false,"usgs":false,"family":"Clift","given":"Martin J D","affiliations":[{"id":37161,"text":"Swansea University Medical School, Swansea, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":738334,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Drasler, Barbara","contributorId":205746,"corporation":false,"usgs":false,"family":"Drasler","given":"Barbara","email":"","affiliations":[{"id":37159,"text":"Adolphe Merkle Institute, University of Fribourg, Switzerland","active":true,"usgs":false}],"preferred":false,"id":738335,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rothen-Rutishauer, Barbara","contributorId":205747,"corporation":false,"usgs":false,"family":"Rothen-Rutishauer","given":"Barbara","email":"","affiliations":[{"id":37159,"text":"Adolphe Merkle Institute, University of Fribourg, Switzerland","active":true,"usgs":false}],"preferred":false,"id":738336,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70200630,"text":"70200630 - 2018 - A method to value nature-related webcam viewing: The value of virtual use with application to brown bear webcam viewing","interactions":[],"lastModifiedDate":"2018-10-26T09:47:55","indexId":"70200630","displayToPublicDate":"2018-06-18T12:37:08","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5771,"text":"Journal of Environmental Economics and Policy","active":true,"publicationSubtype":{"id":10}},"title":"A method to value nature-related webcam viewing: The value of virtual use with application to brown bear webcam viewing","docAbstract":"There are an estimated 16,000 nature related remote web cameras that provide users around the world with an opportunity to view wildlife. Because there is no monetary price to view the webcams, we utilise variations in the viewers’ opportunity cost of time to estimate consumer surplus. We apply this model to a large sample (n = 2649) of the more than 10 million viewers of Alaska's Katmai National Park and Preserve brown bear webcams. The resulting consumer surplus is around \\$11 per hour of viewing. When applied to the 2.42 million viewer hours, this yields a benefit of \\$27 million annually. Since there are limits on the number of visitors as well as high costs of visiting this remote site, the aggregate webcam viewing value is more than twice the aggregate on-site viewing value. With minimal survey data required to apply this model, we believe it has broad applicability to other nature-related webcams around the world.
","language":"English","publisher":"Taylor and Francis","doi":"10.1080/21606544.2018.1483842","usgsCitation":"Loomis, J.B., Richardson, L., Huber, C., Skibins, J., and Sharp, R., 2018, A method to value nature-related webcam viewing: The value of virtual use with application to brown bear webcam viewing: Journal of Environmental Economics and Policy, v. 7, no. 4, p. 452-462, https://doi.org/10.1080/21606544.2018.1483842.","productDescription":"11 p.","startPage":"452","endPage":"462","ipdsId":"IP-090967","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":358820,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-18","publicationStatus":"PW","scienceBaseUri":"5c10a99ae4b034bf6a7e5359","contributors":{"authors":[{"text":"Loomis, John B.","contributorId":197268,"corporation":false,"usgs":false,"family":"Loomis","given":"John","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":749758,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richardson, Leslie","contributorId":197525,"corporation":false,"usgs":false,"family":"Richardson","given":"Leslie","affiliations":[],"preferred":false,"id":749759,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huber, Christopher 0000-0001-8446-8134 chuber@usgs.gov","orcid":"https://orcid.org/0000-0001-8446-8134","contributorId":127600,"corporation":false,"usgs":true,"family":"Huber","given":"Christopher","email":"chuber@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":749757,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skibins, Jeffrey","contributorId":210077,"corporation":false,"usgs":false,"family":"Skibins","given":"Jeffrey","email":"","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":749760,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sharp, Ryan","contributorId":168598,"corporation":false,"usgs":false,"family":"Sharp","given":"Ryan","email":"","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":749761,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70204848,"text":"70204848 - 2018 - A framework for identifying and characterising coral reef “oases” against a backdrop of degradation","interactions":[],"lastModifiedDate":"2020-09-01T14:08:16.952561","indexId":"70204848","displayToPublicDate":"2018-06-18T08:10:12","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A framework for identifying and characterising coral reef “oases” against a backdrop of degradation","docAbstract":"This paper examines adaptation responses to climate change through adjustment of agricultural land use. The climate drivers we examine are changes in long-term climate normals (e.g., 10-year moving averages) and changes in inter-annual climate variability. Using US county level data over 1982 to 2012 from Census of Agriculture, we find that impacts of long-term climate normals are as important as that of inter-annual climate variability. Projecting into the future, we find projected climate change will lead to an expansion in crop land share across the northern and interior western United States with decreases in the south. We also find that grazing land share increases in southern regions and Inland Pacific Northwest and declines in the northern areas. However, the extent to which the adaptation potential would be is dependent on the climate model, emission scenario and time horizon under consideration.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.landusepol.2018.05.057","usgsCitation":"Mu, J.E., McCarl, B.A., Sleeter, B.M., Abatzoglou, J.T., and Zhang, H., 2018, Adaptation with climate uncertainty: An examination of agricultural land use in the United States: Land Use Policy, v. 77, p. 392-401, https://doi.org/10.1016/j.landusepol.2018.05.057.","productDescription":"10 p.","startPage":"392","endPage":"401","ipdsId":"IP-076144","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468652,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.landusepol.2018.05.057","text":"Publisher Index Page"},{"id":355131,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"77","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e559e4b060350a15d10b","contributors":{"authors":[{"text":"Mu, Jianhong E.","contributorId":75840,"corporation":false,"usgs":true,"family":"Mu","given":"Jianhong","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":738230,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCarl, Bruce A.","contributorId":58173,"corporation":false,"usgs":true,"family":"McCarl","given":"Bruce","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":738228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sleeter, Benjamin M. 0000-0003-2371-9571 bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":738226,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Abatzoglou, John T.","contributorId":191729,"corporation":false,"usgs":false,"family":"Abatzoglou","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":33345,"text":" University of Idaho","active":true,"usgs":false}],"preferred":false,"id":738227,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhang, Hongliang","contributorId":205709,"corporation":false,"usgs":false,"family":"Zhang","given":"Hongliang","email":"","affiliations":[{"id":37150,"text":"University of Neuchâtel","active":true,"usgs":false}],"preferred":false,"id":738229,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221454,"text":"70221454 - 2018 - Estimating lag to peak between rainfall and peak streamflow with a mixed-effects model","interactions":[],"lastModifiedDate":"2021-06-16T14:14:34.261411","indexId":"70221454","displayToPublicDate":"2018-06-16T08:52:24","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7168,"text":"Journal of the American Water Resources Association (JAWRA)","active":true,"publicationSubtype":{"id":10}},"title":"Estimating lag to peak between rainfall and peak streamflow with a mixed-effects model","docAbstract":"We test the use of a mixed-effects model for estimating lag to peak for small basins in Maine (drainage areas from 0.8 to 78 km2). Lag to peak is defined as the time between the center of volume of the excess rainfall during a storm event and the resulting peak streamflow. A mixed-effects model allows for multiple observations at sites without violating model assumptions inherent in traditional ordinary least squares models, which assume each observation is independent. The mixed model includes basin drainage area and maximum 15-min rainfall depth for individual storms as explanatory features. Based on a remove-one-site cross-validation analysis, the prediction errors of this model ranged from 42% to +73%. The mixed model substantially outperformed three published models for lag to peak and one published model for centroid lag for estimating lag to peak for small basins in Maine. Lag to peak estimates are a key input to rainfallrunoff models used to design hydraulic infrastructure. The improved accuracy and consistency with model assumptions indicates that mixed models may provide increased data utilization that could enhance models and estimates of lag to peak in other regions.","language":"English","publisher":"American Water Resources Association","doi":"10.1111/1752-1688.12653","usgsCitation":"Lombard, P.J., and Holtschlag, D., 2018, Estimating lag to peak between rainfall and peak streamflow with a mixed-effects model: Journal of the American Water Resources Association (JAWRA), v. 54, no. 4, p. 949-961, https://doi.org/10.1111/1752-1688.12653.","productDescription":"13 p.","startPage":"949","endPage":"961","ipdsId":"IP-089128","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":437859,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PK0F3D","text":"USGS data release","linkHelpText":"Precipitation and streamflow data for computing lag to peak at selected stations in Maine"},{"id":386535,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.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 -69.08203125,\n 47.517200697839414\n ],\n [\n -70.0048828125,\n 46.558860303117164\n ],\n [\n -70.9716796875,\n 45.27488643704891\n ],\n [\n -70.751953125,\n 43.100982876188546\n ],\n [\n -66.8408203125,\n 44.84029065139799\n ],\n [\n -67.3681640625,\n 45.82879925192134\n ],\n [\n -67.9833984375,\n 47.368594345213374\n ],\n [\n -69.08203125,\n 47.517200697839414\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"4","noUsgsAuthors":false,"publicationDate":"2018-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Lombard, Pamela J. 0000-0002-0983-1906","orcid":"https://orcid.org/0000-0002-0983-1906","contributorId":205225,"corporation":false,"usgs":true,"family":"Lombard","given":"Pamela","email":"","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817754,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holtschlag, David 0000-0001-5185-4928","orcid":"https://orcid.org/0000-0001-5185-4928","contributorId":215360,"corporation":false,"usgs":true,"family":"Holtschlag","given":"David","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817755,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221449,"text":"70221449 - 2018 - Suspended-sediment concentrations and loads in the lower Mississippi and Atchafalaya rivers decreased by half between 1980 and 2015","interactions":[],"lastModifiedDate":"2021-06-17T10:29:24.325243","indexId":"70221449","displayToPublicDate":"2018-06-16T07:48:30","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Suspended-sediment concentrations and loads in the lower Mississippi and Atchafalaya rivers decreased by half between 1980 and 2015","docAbstract":"The Weighted Regressions on Time, Discharge, and Season (WRTDS) model was used to derive estimates of suspended-sediment concentration (SSC) and suspended-sediment load (SSL), their dependence on discharge, and their trends with confidence intervals, for one site each on the lowermost Mississippi and Atchafalaya Rivers. The WRTDS model reduces uncertainty in SSCs related to variable streamflow conditions. Flow-normalized SSCs in each river were similar, and decreased from about 260 mg/L to 130 mg/L from 1980 through 2015; combined annual SSL in the two rivers decreased from about 200 Megatons per year (MT/y) to about 100 MT/y. Declines in SSC and SSL were more gradual from 2005 through 2015 and show signs of stabilizing. Our estimates of SSL in 2015 differ markedly from several recently published estimates of current and projected future Mississippi River SSLs, which were generally around 200 MT/y. However, these values came mostly from a different site upstream on the Mississippi River. The relationship between SSC and streamflow differed in an important way between the two rivers. SSC increased as streamflow increased for the entire range of observed streamflow in the Atchafalaya River. In the Mississippi River, SSC followed the same pattern during low and moderate streamflow but decreased at the highest streamflow that tended to occur between January and July. Since much of the water flowing in the Atchafalaya originates from the Mississippi River, the difference suggests a within-basin source of suspended sediment for the Atchafalaya River that is absent in the lower Mississippi River. These findings have important implications for the restoration of deltaic wetlands in coastal Louisiana. Accurate estimates of the SSL available in each river are crucial for understanding how effective diversions of river water into adjacent estuaries will be in sustaining these wetlands. Our study demonstrates that there might be far less sediment available than previously reported. Further, the difference in the relationship between SSC and streamflow in the two rivers is highly relevant to the ongoing discussion of coastal restoration strategies because the delta building that is occurring at the mouth of the Atchafalaya River is frequently used as a model of what could be expected with controlled diversions in the lower Mississippi River delta. The differences in the SSC behavior with changes in streamflow between the two rivers needs to be considered when results from the Atchafalaya River system are projected to those of the Mississippi River.
Wastes from the world's largest manufacturer of DDT (1-chloro-4-[2,2,2-trichloro-1-(4-chlorophenyl)ethyl]benzene) were released into the Los Angeles County municipal sewer system from 1947 to 1971. Following primary treatment, the effluent was discharged through a submarine outfall system whereupon a portion of the DDT and associated degradation products were deposited in sediments of the Palos Verdes Shelf (PVS). Parent DDT is present only in trace amounts in the sediments today, the vast majority having been transformed to DDE (1-chloro-4-[2,2-dichloro-1-(4-chlorophenyl)ethenyl]benzene) shortly following deposition. Previously believed to be inert, DDE is slowly being converted to DDMU (1-chloro-4-[2-chloro-1-(4-chlorophenyl)ethenyl]benzene) and DDMU to DDNU (1-chloro-4-[1-(4-chlorophenyl)ethenyl]benzene) via microbially-mediated reductive dechlorination (RDC). Kinetic and compositional data suggest that this process began sometime in the mid- to late 1970s. Rates of DDE RDC in shelf sediments are spatially variable and have proven difficult to determine accurately. This limits our ability to understand the factors controlling RDC rates and to predict the course of natural recovery. In the present study, concentrations of ten DDT compounds and twelve PCB (polychlorinated biphenyl) congeners were determined in cores collected at two locations on the PVS (stations 3C, 6C, ~7km and ~2km downcurrent from the outfalls, respectively). DDE inventories, normalized to those of non-degrading PCB congeners having similar physico-chemical properties, were modeled to yield first-order RDC rates for the period 1981–2010. Average rates at stations 3C and 6C were 0.044±0.004 and 0.008±0.002yr−1, respectively, with depth-dependent RDC rates at station 3C (1992–2003) ranging from 0.0025 to 0.102yr−1. Comparison of RDC and total loss (i.e., RDC+physical loss) rates suggests that the average per cent loss of DDE due to RDC is ~90% at station 3C (1981–2010) and ~57% at station 6C (1992–2010). Trajectories of adjusted molar inventories of DDE, DDMU, and DDNU were forecast using a first-order multi-step reaction series (M-SRS) model. The results for DDE are consistent with the normalization procedure; RDC rates at stations 3C and 6C were 0.036±0.002yr−1 and 0.010±0.001yr−1, respectively. At station 6C, the DDE to DDMU transformation appears to be the rate limiting step in the reaction sequence, DDE k1→ DDMU k2→ DDNU k3→ unidentified compound(s), whereas at station 3C RDC rates for DDE and DDMU are roughly equivalent. At both locations the transformation rate of DDNU is 7–20 times that of the other steps. Estimated half-lives of DDE at stations 3C and 6C based on the M-SRS model results are ~19 and 72 years, respectively.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marchem.2017.12.005","usgsCitation":"Eganhouse, R.P., Sherwood, C.R., Pontolillo, J., Edwards, B., and Dickhudt, P., 2018, Reductive dechlorination rates of 4,4′-DDE (1-chloro-4-[2,2-dichloro-1-(4-chlorophenyl)ethenyl]benzene) in sediments of the Palos Verdes Shelf, CA: Marine Chemistry, v. 203, p. 10-21, https://doi.org/10.1016/j.marchem.2017.12.005.","productDescription":"12 p.","startPage":"10","endPage":"21","ipdsId":"IP-088923","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":460891,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marchem.2017.12.005","text":"Publisher Index Page"},{"id":355656,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Palos Verde Shelf","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -118.35111111111111,33.66777777777777 ], [ -118.35111111111111,33.7175 ], [ -118.28444444444445,33.7175 ], [ -118.28444444444445,33.66777777777777 ], [ -118.35111111111111,33.66777777777777 ] ] ] } } ] }","volume":"203","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc431e4b0f5d57878ea15","contributors":{"authors":[{"text":"Eganhouse, Robert P. 0000-0002-2075-5908 eganhous@usgs.gov","orcid":"https://orcid.org/0000-0002-2075-5908","contributorId":206243,"corporation":false,"usgs":true,"family":"Eganhouse","given":"Robert","email":"eganhous@usgs.gov","middleInitial":"P.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":739872,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739873,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pontolillo, James 0000-0002-1075-1313 jpontoli@usgs.gov","orcid":"https://orcid.org/0000-0002-1075-1313","contributorId":206244,"corporation":false,"usgs":true,"family":"Pontolillo","given":"James","email":"jpontoli@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":739874,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Edwards, Brian 0000-0002-4655-8208 bedwards@usgs.gov","orcid":"https://orcid.org/0000-0002-4655-8208","contributorId":206245,"corporation":false,"usgs":true,"family":"Edwards","given":"Brian","email":"bedwards@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739875,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dickhudt, Patrick J. ","contributorId":169593,"corporation":false,"usgs":false,"family":"Dickhudt","given":"Patrick J. ","affiliations":[{"id":25562,"text":"(former) Woods Hole Coastal and Marine Science Center employee","active":true,"usgs":false}],"preferred":false,"id":739876,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197650,"text":"70197650 - 2018 - Integrating animal movement with habitat suitability for estimating dynamic migratory connectivity","interactions":[],"lastModifiedDate":"2021-08-11T18:52:48.609974","indexId":"70197650","displayToPublicDate":"2018-06-15T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Integrating animal movement with habitat suitability for estimating dynamic migratory connectivity","docAbstract":"High-resolution animal movement data are becoming increasingly available, yet having a multitude of empirical trajectories alone does not allow us to easily predict animal movement. To answer ecological and evolutionary questions at a population level, quantitative estimates of a species’ potential to link patches or populations are of importance.
We introduce an approach that combines movement-informed simulated trajectories with an environment-informed estimate of the trajectories’ plausibility to derive connectivity. Using the example of bar-headed geese we estimated migratory connectivity at a landscape level throughout the annual cycle in their native range.
We used tracking data of bar-headed geese to develop a multi-state movement model and to estimate temporally explicit habitat suitability within the species’ range. We simulated migratory movements between range fragments, and calculated a measure we called route viability. The results are compared to expectations derived from published literature.
Simulated migrations matched empirical trajectories in key characteristics such as stopover duration. The viability of the simulated trajectories was similar to that of the empirical trajectories. We found that, overall, the migratory connectivity was higher within the breeding than in wintering areas, corroborating previous findings for this species.
We show how empirical tracking data and environmental information can be fused for meaningful predictions of animal movements throughout the year and even outside the spatial range of the available data. Beyond predicting migratory connectivity, our framework will prove useful for modelling ecological processes facilitated by animal movement, such as seed dispersal or disease ecology.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-018-0637-9","usgsCitation":"van Toor, M.L., Kranstauber, B., Newman, S.H., Prosser, D.J., Takekawa, J., Technitis, G., Weibel, R., Wikelski, M., and Safi, K., 2018, Integrating animal movement with habitat suitability for estimating dynamic migratory connectivity: Landscape Ecology, v. 33, no. 6, p. 879-893, https://doi.org/10.1007/s10980-018-0637-9.","productDescription":"15 p.","startPage":"879","endPage":"893","ipdsId":"IP-084732","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":468656,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-018-0637-9","text":"Publisher Index Page"},{"id":355084,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"6","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-26","publicationStatus":"PW","scienceBaseUri":"5b46e566e4b060350a15d117","contributors":{"authors":[{"text":"van Toor, Marielle L.","contributorId":205670,"corporation":false,"usgs":false,"family":"van Toor","given":"Marielle","email":"","middleInitial":"L.","affiliations":[{"id":37137,"text":"Department of Migration and Immuno-Ecology, Max Planck Institute for Ornithology","active":true,"usgs":false}],"preferred":false,"id":738069,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kranstauber, Bart","contributorId":205671,"corporation":false,"usgs":false,"family":"Kranstauber","given":"Bart","email":"","affiliations":[{"id":37138,"text":"Department of Evolutionary Biology and Environmental Studies, University of Zurich","active":true,"usgs":false}],"preferred":false,"id":738070,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Newman, Scott H.","contributorId":199129,"corporation":false,"usgs":false,"family":"Newman","given":"Scott","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":738071,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prosser, Diann J. 0000-0002-5251-1799 dprosser@usgs.gov","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":2389,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","email":"dprosser@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":738068,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Takekawa, John Y. 0000-0003-0217-5907","orcid":"https://orcid.org/0000-0003-0217-5907","contributorId":203805,"corporation":false,"usgs":false,"family":"Takekawa","given":"John Y.","affiliations":[{"id":36724,"text":"Audubon California, Richardson Bay Audubon Center and Sanctuary, Tiburon, CA","active":true,"usgs":false}],"preferred":false,"id":738072,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Technitis, Georgios","contributorId":205672,"corporation":false,"usgs":false,"family":"Technitis","given":"Georgios","email":"","affiliations":[{"id":37139,"text":"Department of Geography, University of Zurich","active":true,"usgs":false}],"preferred":false,"id":738073,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Weibel, Robert","contributorId":205673,"corporation":false,"usgs":false,"family":"Weibel","given":"Robert","email":"","affiliations":[{"id":37139,"text":"Department of Geography, University of Zurich","active":true,"usgs":false}],"preferred":false,"id":738074,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wikelski, Martin","contributorId":205674,"corporation":false,"usgs":false,"family":"Wikelski","given":"Martin","email":"","affiliations":[{"id":37137,"text":"Department of Migration and Immuno-Ecology, Max Planck Institute for Ornithology","active":true,"usgs":false}],"preferred":false,"id":738075,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Safi, Kamran","contributorId":205675,"corporation":false,"usgs":false,"family":"Safi","given":"Kamran","email":"","affiliations":[{"id":37137,"text":"Department of Migration and Immuno-Ecology, Max Planck Institute for Ornithology","active":true,"usgs":false}],"preferred":false,"id":738076,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70200470,"text":"70200470 - 2018 - Harnessing big data to rethink land heterogeneity in Earth system models","interactions":[],"lastModifiedDate":"2018-10-18T14:26:46","indexId":"70200470","displayToPublicDate":"2018-06-14T14:26:38","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Harnessing big data to rethink land heterogeneity in Earth system models","docAbstract":"The continual growth in the availability, detail, and wealth of environmental data provides an invaluable asset to improve the characterization of land heterogeneity in Earth system models – a persistent challenge in macroscale models. However, due to the nature of these data (volume and complexity) and computational constraints, these data are underused for global applications. As a proof of concept, this study explores how to effectively and efficiently harness these data in Earth system models over a 1/4° ( ∼ 25km) grid cell in the western foothills of the Sierra Nevada in central California. First, a novel hierarchical multivariate clustering approach (HMC) is introduced that summarizes the high-dimensional environmental data space into hydrologically interconnected representative clusters (i.e., tiles). These tiles and their associated properties are then used to parameterize the sub-grid heterogeneity of the Geophysical Fluid Dynamics Laboratory (GFDL) LM4-HB land model. To assess how this clustering approach impacts the simulated water, energy, and carbon cycles, model experiments are run using a series of different tile configurations assembled using HMC. The results over the test domain show that (1) the observed similarity over the landscape makes it possible to converge on the macroscale response of the fully distributed model with around 300 sub-grid land model tiles; (2) assembling the sub-grid tile configuration from available environmental data can have a large impact on the macroscale states and fluxes of the water, energy, and carbon cycles; for example, the defined subsurface connections between the tiles lead to a dampening of macroscale extremes; (3) connecting the fine-scale grid to the model tiles via HMC enables circumvention of the classic scale discrepancies between the macroscale and field-scale estimates; this has potentially significant implications for the evaluation and application of Earth system models.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/hess-22-3311-2018","usgsCitation":"Chaney, N.W., Van Huijgevoort, M.H., Shevliakova, E., Malyshev, S., Milly, P.C., Gauthier, P., and Sulman, B.N., 2018, Harnessing big data to rethink land heterogeneity in Earth system models: Hydrology and Earth System Sciences, v. 22, p. 3311-3330, https://doi.org/10.5194/hess-22-3311-2018.","productDescription":"20 p.","startPage":"3311","endPage":"3330","ipdsId":"IP-090830","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":468658,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-22-3311-2018","text":"Publisher Index Page"},{"id":358546,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-14","publicationStatus":"PW","scienceBaseUri":"5c10a99ae4b034bf6a7e535d","contributors":{"authors":[{"text":"Chaney, Nathaniel W.","contributorId":169242,"corporation":false,"usgs":false,"family":"Chaney","given":"Nathaniel","email":"","middleInitial":"W.","affiliations":[{"id":25453,"text":"Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA","active":true,"usgs":false}],"preferred":false,"id":749025,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Huijgevoort, Marjolein H. J.","contributorId":209888,"corporation":false,"usgs":false,"family":"Van Huijgevoort","given":"Marjolein","email":"","middleInitial":"H. J.","affiliations":[{"id":7108,"text":"Princeton Univ.","active":true,"usgs":false}],"preferred":false,"id":749026,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shevliakova, Elena","contributorId":201589,"corporation":false,"usgs":false,"family":"Shevliakova","given":"Elena","email":"","affiliations":[{"id":36211,"text":"GFDL/NOAA","active":true,"usgs":false}],"preferred":false,"id":749027,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Malyshev, Sergey","contributorId":201588,"corporation":false,"usgs":false,"family":"Malyshev","given":"Sergey","affiliations":[{"id":36211,"text":"GFDL/NOAA","active":true,"usgs":false}],"preferred":false,"id":749028,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Milly, Paul C. D. 0000-0003-4389-3139 cmilly@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-3139","contributorId":176836,"corporation":false,"usgs":true,"family":"Milly","given":"Paul","email":"cmilly@usgs.gov","middleInitial":"C. D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":749024,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gauthier, Paul P. G.","contributorId":209889,"corporation":false,"usgs":false,"family":"Gauthier","given":"Paul P. G.","affiliations":[{"id":7108,"text":"Princeton Univ.","active":true,"usgs":false}],"preferred":false,"id":749029,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sulman, Benjamin N. 0000-0002-3265-6691","orcid":"https://orcid.org/0000-0002-3265-6691","contributorId":209890,"corporation":false,"usgs":false,"family":"Sulman","given":"Benjamin","email":"","middleInitial":"N.","affiliations":[{"id":7108,"text":"Princeton Univ.","active":true,"usgs":false}],"preferred":false,"id":749030,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70217156,"text":"70217156 - 2018 - Exposure to human-associated chemical markers of fecal contamination and self-reported illness among swimmers at recreational beaches","interactions":[],"lastModifiedDate":"2021-01-07T13:39:52.424305","indexId":"70217156","displayToPublicDate":"2018-06-14T07:34:54","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Exposure to human-associated chemical markers of fecal contamination and self-reported illness among swimmers at recreational beaches","docAbstract":"Anthropogenic chemicals have been proposed as potential markers of human fecal contamination in recreational water. However, to date, there are no published studies describing their relationships with illness risks. Using a cohort of swimmers at seven U.S. beaches, we examined potential associations between the presence of chemical markers of human fecal pollution and self-reported gastrointestinal (GI) illness, diarrhea, and respiratory illness. Swimmers were surveyed about their beach activities, water exposure, and baseline symptoms on the day of their beach visit, and about any illness experienced 10–12 days later. Risk differences were estimated using model-based standardization and adjusted for the swimmer’s age, beach site, sand contact, rainfall, and water temperature. Sixty-two chemical markers were analyzed from daily water samples at freshwater and marine beaches. Of those, 20 were found consistently. With the possible exception of bisphenol A and cholesterol, no chemicals were consistently associated with increased risks of illness. These two chemicals were suggestively associated with 2% and 1% increased risks of GI illness and diarrhea in both freshwater and marine beaches. Additional research using the more sensitive analytic methods currently available for a wider suite of analytes is needed to support the use of chemical biomarkers to quantify illness risk and identify fecal pollution sources.
Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing season. Through a process of incrementally removing weekly and monthly independent variables from the CCM and implementing a ‘two model mapping’ approach, we found it viable to generate conterminous United States-wide rapid crop cover maps at a resolution of 250 m for the current year by the month of September. In this approach, we divided the CCM model into one ‘crop type model’ to handle the classification of nine specific crops and a second, binary model to classify the presence or absence of ‘other’ crops. Under the two model mapping approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4%, respectively. With spatial mapping accuracies for annual maps reaching upwards of 70%, this approach demonstrated a strong potential for generating rapid crop cover maps by the 1st of September.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-018-26284-w","usgsCitation":"Dahal, D., Wylie, B.K., and Howard, D., 2018, Rapid crop cover mapping for the conterminous United States: Scientific Reports, v. 8, Article number: 8631; 12 p., https://doi.org/10.1038/s41598-018-26284-w.","productDescription":"Article number: 8631; 12 p.","ipdsId":"IP-089563","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468660,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-018-26284-w","text":"Publisher Index Page"},{"id":355060,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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The effect of seagrasses on coastal bay resilience and sediment transport dynamics is understudied. Here we use six historical maps of seagrass distribution in Barnegat Bay, USA, to investigate the role of these vegetated surfaces on the sediment storage capacity of shallow bays. Analyses are carried out by means of the Coupled-Ocean-Atmosphere-Wave-Sediment Transport (COAWST) numerical modeling framework. Results show that a decline in the extent of seagrass meadows reduces the sediment mass potentially stored within bay systems. The presence of seagrass reduces shear stress values across the entire bay, including unvegetated areas, and promotes sediment deposition on tidal flats. On the other hand, the presence of seagrasses decreases suspended sediment concentrations, which in turn reduces the delivery of sediment to marsh platforms. Results highlight the relevance of seagrasses for the long-term survival of coastal ecosystems, and the complex dynamics regulating the interaction between subtidal and intertidal landscapes.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018GL078056","usgsCitation":"Donatelli, C., Ganju, N.K., Fagherazzi, S., and Leonardi, N., 2018, Seagrass impact on sediment exchange between tidal flats and salt Marsh, and the sediment budget of shallow bays: Geophysical Research Letters, v. 45, no. 10, p. 4933-4943, https://doi.org/10.1029/2018GL078056.","productDescription":"11 p.","startPage":"4933","endPage":"4943","ipdsId":"IP-093431","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":460893,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018gl078056","text":"Publisher Index Page"},{"id":355044,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Barnegat Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.25178527832031,\n 39.67759833072648\n ],\n [\n -74.07840728759766,\n 39.67759833072648\n ],\n [\n -74.07840728759766,\n 39.87048617098581\n ],\n [\n -74.25178527832031,\n 39.87048617098581\n ],\n [\n -74.25178527832031,\n 39.67759833072648\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","issue":"10","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-20","publicationStatus":"PW","scienceBaseUri":"5b46e567e4b060350a15d11f","contributors":{"authors":[{"text":"Donatelli, Carmine","contributorId":202870,"corporation":false,"usgs":false,"family":"Donatelli","given":"Carmine","affiliations":[{"id":36541,"text":"University of Liverpool, Department of Geography and Planning, 74 Bedford St S.","active":true,"usgs":false}],"preferred":false,"id":737973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ganju, Neil Kamal 0000-0002-1096-0465 nganju@usgs.gov","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":192273,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil","email":"nganju@usgs.gov","middleInitial":"Kamal","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":737972,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fagherazzi, Sergio","contributorId":89282,"corporation":false,"usgs":true,"family":"Fagherazzi","given":"Sergio","affiliations":[],"preferred":false,"id":737974,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leonardi, Nicoletta","contributorId":202868,"corporation":false,"usgs":false,"family":"Leonardi","given":"Nicoletta","email":"","affiliations":[{"id":36541,"text":"University of Liverpool, Department of Geography and Planning, 74 Bedford St S.","active":true,"usgs":false}],"preferred":false,"id":737975,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70201714,"text":"70201714 - 2018 - Preface to the Focus Section on the Collaboratory for the Study of Earthquake Predictability (CSEP): New results and future directions","interactions":[],"lastModifiedDate":"2019-01-29T10:30:56","indexId":"70201714","displayToPublicDate":"2018-06-13T13:01:51","publicationYear":"2018","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":"Preface to the Focus Section on the Collaboratory for the Study of Earthquake Predictability (CSEP): New results and future directions","docAbstract":"The Collaboratory for the Study of Earthquake Predictability (CSEP; Jordan, 2006) carries out fully prospective tests of earthquake forecasts, using fixed and standardized statistical tests and authoritative data sets, to assess the predictive skill of forecast models and to make objective comparisons between models. CSEP conducts prospective experiments at four testing centers around the world, at which more than 400 models and model versions are currently under evaluation. These models include a range of methods and scales from long‐term global earthquake forecasts to short‐term regional forecasts used for Operational Earthquake Forecasting (OEF). CSEP has also conducted retrospective tests and developed new testing methods in its quest to answer fundamental scientific questions, improve seismic hazard assessments, and develop new forecast methods for OEF.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220180161","usgsCitation":"Michael, A.J., and Werner, M.J., 2018, Preface to the Focus Section on the Collaboratory for the Study of Earthquake Predictability (CSEP): New results and future directions: Seismological Research Letters, v. 89, no. 4, p. 1226-1228, https://doi.org/10.1785/0220180161.","productDescription":"3 p.","startPage":"1226","endPage":"1228","ipdsId":"IP-098396","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":468662,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research-information.bris.ac.uk/en/publications/5ef252d7-aa19-4039-bc43-051e520e7e29","text":"External Repository"},{"id":360739,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"89","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-13","publicationStatus":"PW","scienceBaseUri":"5c5022c5e4b0708288f7e826","contributors":{"authors":[{"text":"Michael, Andrew J. 0000-0002-2403-5019 michael@usgs.gov","orcid":"https://orcid.org/0000-0002-2403-5019","contributorId":1280,"corporation":false,"usgs":true,"family":"Michael","given":"Andrew","email":"michael@usgs.gov","middleInitial":"J.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":754957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Werner, Maximillian J.","contributorId":211807,"corporation":false,"usgs":false,"family":"Werner","given":"Maximillian","email":"","middleInitial":"J.","affiliations":[{"id":38325,"text":"University of Bristol, UK","active":true,"usgs":false}],"preferred":false,"id":754958,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}