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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\"}}]}","volume":"73","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","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":794861,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":794862,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@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":794863,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wylie, Bruce 0000-0002-7374-1083","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":201929,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":794864,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"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. M","contributorId":156299,"corporation":false,"usgs":false,"family":"Harrington","given":"R.","email":"","middleInitial":"M","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":797739,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dougherty, S.","contributorId":220221,"corporation":false,"usgs":false,"family":"Dougherty","given":"S.","email":"","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":797740,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pena Castro, A.","contributorId":215264,"corporation":false,"usgs":false,"family":"Pena Castro","given":"A.","email":"","affiliations":[{"id":36610,"text":"McGill","active":true,"usgs":false}],"preferred":false,"id":797741,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70210243,"text":"ofr20201038 - 2020 - Evaluation of the analytical methods used to determine the elemental concentrations found in the stream geochemical dataset compiled for Alaska","interactions":[],"lastModifiedDate":"2020-05-27T11:42:57.164561","indexId":"ofr20201038","displayToPublicDate":"2020-05-26T14:36:58","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1038","displayTitle":"Evaluation of the Analytical Methods Used to Determine the Elemental Concentrations Found in the Stream Geochemical Dataset Compiled for Alaska","title":"Evaluation of the analytical methods used to determine the elemental concentrations found in the stream geochemical dataset compiled for Alaska","docAbstract":"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
This study contributes to the existing literature on valuing visitation to an important cultural heritage site. Pearl Harbor National Memorial in Hawaii remembers and honors those that served the United States in the Pacific battles of World War II. Although historic and cultural monuments and memorials comprise a substantial portion of the U.S. National Park System, there is little research into the economic benefits such sites provide to visitors. These benefits are a critical component of planning and management decisions based on an economic efficiency criterion. This study contributes to the literature by quantifying the economic value derived from visitation to Pearl Harbor National Memorial, home of the USS Arizona Memorial. Using data from a recent visitor survey, we explore the motivations for visiting the site and how such motivations may influence these values. These findings have significant implications, demonstrating a substantial return on the public's investment in a unique historic site and recent investments in maintaining the site for safety and an enhanced visitor experience.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.culher.2020.04.007","usgsCitation":"Sinclair, W., Huber, C., and Richardson, L., 2020, Valuing tourism to a historic World War II national memorial: Journal of Cultural Heritage, v. 45, p. 334-338, https://doi.org/10.1016/j.culher.2020.04.007.","productDescription":"5 p.","startPage":"334","endPage":"338","ipdsId":"IP-107057","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":380065,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Pearl Harbor, Honolulu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.03386688232422,\n 21.31080568603828\n ],\n [\n -157.91851043701172,\n 21.31080568603828\n ],\n [\n -157.91851043701172,\n 21.400015935143976\n ],\n [\n -158.03386688232422,\n 21.400015935143976\n ],\n [\n -158.03386688232422,\n 21.31080568603828\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sinclair, Wilson","contributorId":219796,"corporation":false,"usgs":false,"family":"Sinclair","given":"Wilson","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":803760,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":803761,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richardson, Leslie","contributorId":197525,"corporation":false,"usgs":false,"family":"Richardson","given":"Leslie","affiliations":[],"preferred":false,"id":803762,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228493,"text":"70228493 - 2020 - Estimation of metademographic rates and landscape connectivity for a conservation-reliant anuran","interactions":[],"lastModifiedDate":"2022-02-11T17:12:15.833486","indexId":"70228493","displayToPublicDate":"2020-05-23T11:05:55","publicationYear":"2020","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":"Estimation of metademographic rates and landscape connectivity for a conservation-reliant anuran","docAbstract":"Amphibian conservation efforts commonly assume populations are tied to waterbodies that collectively function as a metapopulation. This assumption is rarely evaluated, and there is a need to understand the degree of connectivity among patches to appropriately define, manage, and conserve biological populations.
Our objectives were to quantify local persistence, colonization, and recruitment (metademographic rates) in relation to habitat attributes, evaluate the influence of the spatial arrangement of patches on landscape-scale population dynamics, and estimate the scale at which metapopulation dynamics are occurring for Oregon spotted frog (Rana pretiosa).
We collected R. pretiosa detection/non-detection data and habitat information from 93 sites spread throughout the species’ extant range in Oregon, USA, 2010–2018. We developed a spatial multistate dynamic occupancy model to analyze these data.
The proportion of sites occupied by R. pretiosa was relatively stable despite regular turnover in site occupancy. Connectivity was greatest when the distance between sites was within 4.49–7.70 km, and the results suggested that populations within 1 km are at the appropriate spatial scale for effective population management. Rana pretiosa metademographic rates were strongly tied to water availability, vegetation characteristics, and beaver dams.
Our analysis provides critical information to identify the appropriate spatial scale for effective population management, estimates the distance at which populations are connected, and quantifies the effects of hypothesized threats to species at a landscape scale. We believe this model will prove to be useful to inform conservation and management strategies for multiple species.
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Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Peak ground velocity spatial variability revealed by dense seismic array in southern California","docAbstract":"Understanding and modeling variability of ground motion is essential for building accurate and precise ground motion prediction equations, which can net site‐specific characterization and reduced hazard levels. Here, we explore the spatial variability in peak ground velocity (PGV) at Sage Brush Flats along the San Jacinto Fault in southern California. We use data from a dense array (0.6 x 0.6 km2, 1,108 geophones, station spacings 10‐30 m) deployed in 2014 for ~1‐month. These data offer an opportunity to study small scale variability in this region. We examine 38 2≤ML≤4.2 earthquakes within 200 km of the array. Fault strands and a small basin impact the ground motions, producing PGV variations up to 22% of the mean and a 40% reduction in P and S wave near‐surface velocities. We find along‐fault rupture directivity, source, and path effects can increase PGVs by 167%. Surface PGV measurements exceed the co‐located borehole station (depth 148 m) PGV by factors of 3‐10, confirming the impact on PGV from near surface fault structures, basins, topography, and amplifications from soft sediments. Consistently we find high PGVs within the basin structure. A pair of ~co‐located ML2.6 events produce repeatable PGV values with similar spatial patterns. The average corner frequencies of these two events are 11‐16 Hz and viable measurements of stress drop can differ by 6.45MPa. Within this small array, the PGV values are variable implying spatial extrapolation of PGV to regions of known faults and basins, even across a small area, should be done with caution.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JB019157","usgsCitation":"Johnson, C.E., Kilb, D., Baltay Sundstrom, A.S., and Vernon, F., 2020, Peak ground velocity spatial variability revealed by dense seismic array in southern California: Journal of Geophysical Research B: Solid Earth, v. 125, no. 6, e2019JB019157, 17 p., https://doi.org/10.1029/2019JB019157.","productDescription":"e2019JB019157, 17 p.","ipdsId":"IP-114215","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":456667,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019jb019157","text":"Publisher Index Page"},{"id":375522,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sage Brush Flats, San Jacinto Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.95838928222656,\n 33.56199537293026\n ],\n [\n -116.76681518554689,\n 33.56199537293026\n ],\n [\n -116.76681518554689,\n 33.701492795584365\n ],\n [\n -116.95838928222656,\n 33.701492795584365\n ],\n [\n -116.95838928222656,\n 33.56199537293026\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Christopher E","contributorId":178221,"corporation":false,"usgs":false,"family":"Johnson","given":"Christopher","email":"","middleInitial":"E","affiliations":[],"preferred":false,"id":790749,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kilb, Debi","contributorId":206552,"corporation":false,"usgs":false,"family":"Kilb","given":"Debi","affiliations":[{"id":37339,"text":"Scripps/UCSD","active":true,"usgs":false}],"preferred":false,"id":790750,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":790751,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vernon, Frank","contributorId":217839,"corporation":false,"usgs":false,"family":"Vernon","given":"Frank","affiliations":[{"id":27208,"text":"UC San Diego","active":true,"usgs":false}],"preferred":false,"id":790752,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211858,"text":"70211858 - 2020 - Linking subsurface to surface using gas emission and melt inclusion data at Mount Cleveland volcano, Alaska","interactions":[],"lastModifiedDate":"2020-08-10T16:35:54.421875","indexId":"70211858","displayToPublicDate":"2020-05-21T11:12:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Linking subsurface to surface using gas emission and melt inclusion data at Mount Cleveland volcano, Alaska","docAbstract":"Mount Cleveland is one of Alaska's most active volcanoes, yet little is known about the magmatic system driving persistent and dynamic volcanic activity. Volcanic gas and melt inclusion (MI) data from 2016 were combined to investigate shallow magmatic processes. SO2 emission rates were between 166 and 324 t/day and the H2O/SO2 was 600 ± 53, whereas CO2 and H2S were below detection. Olivine‐, clinopyroxene‐, and plagioclase‐hosted MIs have up to 3.8 wt.% H2O, 514 ppm CO2, and 2,320 ppm S. Equilibration depths, based on MI H2O contents, suggest that a magmatic column extended from 0.5 to 3.0 km (~10–60 MPa). We used MI data to empirically model open‐system H‐C‐S degassing from 0 to 12 km and found that a column of magma between 0.5 and 3 km could produce the measured gas H2O/SO2 ratio. However, additional magma deeper than 3 km is required to sustain emissions over periods greater than days to weeks, if the observed vent dimension is a valid proxy for the conduit. Assuming an initial S content of 2,320 ppm, the total magma supply needed to sustain the annual SO2 flux was 5 to 9.8 Mm3/yr, suggesting a maximum intrusive‐to‐extrusive ratio of 13:1. The model predicts degassing of <50 t/day CO2 for July 2016, which corresponds to a maximum predicted CO2/SO2 of 0.2. Ultimately, frequent recharge from deeper, less degassed magma is required to drive the continuous activity observed over multiple years. During periods of recharge we would expect lower H2O/SO2 and measurable volcanic CO2.
","language":"English","publisher":"Geological Society of America","doi":"10.1029/2019GC008882","usgsCitation":"Werner, C., Rasmussen, D.J., Plank, T., Kelly, P.J., Kern, C., Lopez, T., Gliss, J., Power, J., Roman, D., Izbekov, P., and Lyons, J.J., 2020, Linking subsurface to surface using gas emission and melt inclusion data at Mount Cleveland volcano, Alaska: Geochemistry, Geophysics, Geosystems, v. 21, no. 7, e2019GC008882, 33 p., https://doi.org/10.1029/2019GC008882.","productDescription":"e2019GC008882, 33 p.","ipdsId":"IP-114946","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":456669,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doaj.org/article/0330702a2b9b4b9bb85171b1a5ab3440","text":"Publisher Index Page"},{"id":436956,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DRMV0U","text":"USGS data release","linkHelpText":"Volcanic Gas Measurements at Mount Cleveland, Alaska 2016"},{"id":377282,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Mount Cleveland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -170.0082778930664,\n 52.78802219709245\n ],\n [\n -169.87781524658203,\n 52.78802219709245\n ],\n [\n -169.87781524658203,\n 52.856486091099804\n ],\n [\n -170.0082778930664,\n 52.856486091099804\n ],\n [\n -170.0082778930664,\n 52.78802219709245\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Werner, Cynthia 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Data and information from thousands of sources have increased our scientific understanding of the character of the margin surface, but rarely have those data been combined and integrated. Initially, the U.S. Geological Survey (USGS), in cooperation with the Institute of Arctic and Alpine Research at the University of Colorado Boulder, created the usSEABED database to provide surficial sea-floor-characterization data for USGS assessments of marine-based aggregates and for studies of sea-floor habitat. Since then, the USGS has continued to build up the database as a nationwide resource for many uses and applications.
Previously published data derived from the usSEABED database have been released as three USGS data series publications containing data covering the U.S. Atlantic margin, the Gulf of Mexico and Caribbean regions, and the Pacific coast. An updated USGS data release unifies the three publications, incorporates additional data and sources including data from Alaska, Hawaii, and U.S. overseas territories, and provides revised output files that fix known errors and add known or inferred sampling dates. This report accompanies the data release and contains information on the methodology and products of the usSEABED database.
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and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543–1598
A mantle of both active and stable aeolian sand covers approximately 34,000 km2 of northern Arizona, western New Mexico and southern Utah on the southern Colorado Plateau. From west to east, these deposits can be subdivided into the Kaibab-Moenkopi dunes, Chinle Valley dunes, and Chaco dunes, all of which include relict, partly stable and mobile aeolian sand. Locally, these deposits have distinct compositional characteristics. An examination of previous studies into disparate aspects of Colorado Plateau dunes, taken in the context of local geology, Quaternary landscape history and geomorphic processes, provides new insights into interpretation of this regional aeolian sedimentary record. Additional new data about the characteristics of the deposits, and an assessment of present-day climatic conditions enhances our ability to interpret the relative influences of ecosystem and geomorphologic processes with climate variability that continue to influence both new dune formation and reactivation of older deposits. Taken as a whole, the data emphasizes the role that local landscape conditions and history play in providing the context for correctly interpreting aeolian activity and depositional environments, and whether sediment supply or climate play a dominant role in sand dune formation. This is particularly true in the Little Colorado River Valley of northeastern Arizona, where Quaternary volcanic activity has significantly influenced the local landscape processes, deposit characteristics, and dune paleohistory.
Globally, our knowledge on lake fisheries is still limited despite their importance to food security and livelihoods. Here we show that fish catches can respond either positively or negatively to climate and land-use changes, by analyzing time-series data (1970–2014) for 31 lakes across five continents. We find that effects of a climate or land-use driver (e.g., air temperature) on lake environment could be relatively consistent in directions, but consequential changes in a lake-environmental factor (e.g., water temperature) could result in either increases or decreases in fish catch in a given lake. A subsequent correlation analysis indicates that reductions in fish catch was less likely to occur in response to potential climate and land-use changes if a lake is located in a region with greater access to clean water. This finding suggests that adequate investments for water-quality protection and water-use efficiency can provide additional benefits to lake fisheries and food security.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41467-020-14624-2","usgsCitation":"Kao, Y., Rogers, M.W., Bunnell, D., Cowx, I.G., Qian, S.S., Anneville, O., Beard, Brinker, A., Britton, J., Chura-Crusz, R., Gownaris, N.J., Jackson, J., Kangur, K., Kolding, J., Lukin, A., Lynch, A., Mercado-Silva, N., Moncayo-Estrada, R., Njaya, F.J., Ostrovsky, I., Rudstam, L., Sandstrom, A.L., Sato, Y., Siguayro-Mamani, H., Thorpe, A., van Zwieten, P.A., Volta, P., Wang, Y.Q., Weiperth, A., Weyl, O., and Young, J.D., 2020, Effects of climate and land-use changes on fish catches across lakes at a global scale: Nature Communications, v. 11, 2526, 14 p., https://doi.org/10.1038/s41467-020-14624-2.","productDescription":"2526, 14 p.","ipdsId":"IP-108774","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456678,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-020-14624-2","text":"Publisher Index Page"},{"id":382523,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2020-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Kao, Y.","contributorId":243522,"corporation":false,"usgs":false,"family":"Kao","given":"Y.","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":802469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogers, Mark W. 0000-0001-7205-5623 mwrogers@usgs.gov","orcid":"https://orcid.org/0000-0001-7205-5623","contributorId":4590,"corporation":false,"usgs":true,"family":"Rogers","given":"Mark","email":"mwrogers@usgs.gov","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":802470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bunnell, David 0000-0003-3521-7747","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":217344,"corporation":false,"usgs":true,"family":"Bunnell","given":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":802471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cowx, I. G.","contributorId":243523,"corporation":false,"usgs":false,"family":"Cowx","given":"I.","email":"","middleInitial":"G.","affiliations":[{"id":40174,"text":"University of Hull","active":true,"usgs":false}],"preferred":false,"id":802472,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Qian, S. S.","contributorId":243524,"corporation":false,"usgs":false,"family":"Qian","given":"S.","email":"","middleInitial":"S.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":802473,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anneville, O.","contributorId":243525,"corporation":false,"usgs":false,"family":"Anneville","given":"O.","affiliations":[{"id":48714,"text":"Université Savoie","active":true,"usgs":false}],"preferred":false,"id":802474,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Beard, Jr. 0000-0003-2632-2350 dbeard@usgs.gov","orcid":"https://orcid.org/0000-0003-2632-2350","contributorId":169459,"corporation":false,"usgs":true,"family":"Beard","suffix":"Jr.","email":"dbeard@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":802475,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brinker, A.","contributorId":243526,"corporation":false,"usgs":false,"family":"Brinker","given":"A.","email":"","affiliations":[{"id":48715,"text":"Fisheries Research Station of Baden-Württemberg","active":true,"usgs":false}],"preferred":false,"id":802476,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Britton, J. 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These marking devices have improved our understanding of wildlife spatial ecology and demographic parameters and provided new data facilitating model development for species conservation and management. Although these tools have been used on virtually all North American ungulates, their deployment on feral horses (Equus ferus caballus) or burros (E. asinus) has been limited. To determine if radio-collars and radio-tags could be safely deployed on feral equids, we conducted a 1-year observational study in 2015 to investigate fit and wear of radio-collars on feral horses and burros kept in pastures/pens at the Bureau of Land Management contracted adoption facility in Pauls Valley, Oklahoma, USA. We assessed the impact of radio-collars and transmitter tags on individual behavior, body condition, and evaluated neck surface for effects. We tested 2 radio-collar shapes (teardrop and oval) and a radio-tag (i.e., avian backpack) braided into the mane and tail of horses. Behavior of mares did not differ between radio-collared (n = 12) and control (uncollared; n = 12) individuals. Despite the small sample size, collared burro jennies (n = 4) spent more time standing than controls (n = 4). Stallions wearing radio-collars (n = 9) fed less, moved less, and stood more than controls (n = 8). During the study, we did not detect injuries to the necks of mares or burro jennies, but stallions developed small sores (that healed while still wearing radio-collars and re-haired within 3 months). Two radio-collars occasionally flipped forward over the ears onto the foreheads of stallions. Although our study confirmed that radio-collars could be safely deployed on captive mares and jennies, stallions proved challenging for a variety of reasons. While our conclusions were optimistic, longer studies will be required to ensure radio-collar safety on free-ranging feral horses and burros.
","language":"English","publisher":"Berryman Institute","doi":"10.26077/127m-4x33","usgsCitation":"Schoenecker, K., King, S.R., and Collins, G.C., 2020, Evaluation of the impacts of radio-marking devices on feral horses and burros in a captive setting: Human Wildlife Interactions, v. 14, no. 1, p. 73-86, https://doi.org/10.26077/127m-4x33.","productDescription":"14 p.","startPage":"73","endPage":"86","ipdsId":"IP-104331","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":436958,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEUE9I","text":"USGS data release","linkHelpText":"Body condition score of horses wearing radio collars, weekly behavior data of treatments and controls, and monthly descriptive data of collar and radio tag effects, 2015-2016, Oklahoma, USA"},{"id":396430,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","city":"Pauls Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.27432250976562,\n 34.649025753526985\n ],\n [\n -97.16033935546875,\n 34.649025753526985\n ],\n [\n -97.16033935546875,\n 34.77545980961412\n ],\n [\n -97.27432250976562,\n 34.77545980961412\n ],\n [\n -97.27432250976562,\n 34.649025753526985\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":202531,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":835958,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, Sarah R. 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B.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":835959,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Gail C.","contributorId":280060,"corporation":false,"usgs":false,"family":"Collins","given":"Gail","email":"","middleInitial":"C.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":835960,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211291,"text":"70211291 - 2020 - Hydrothermal activity in the southwest Yellowstone Plateau Volcanic Field","interactions":[],"lastModifiedDate":"2020-07-22T15:02:02.47563","indexId":"70211291","displayToPublicDate":"2020-05-20T09:59:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Hydrothermal activity in the southwest Yellowstone Plateau Volcanic Field","docAbstract":"In the past two decades, the U.S. Geological Survey and the National Park Service have studied hydrothermal activity across the Yellowstone Plateau Volcanic Field (YPVF) to improve the understanding of the magmatic-hydrothermal system and to provide a baseline for detecting future anomalous activity. In 2017 and 2018 we sampled water and gas over a large area in the southwest YPVF and used Landsat 8 thermal infrared data to estimate radiative heat flow. Most of the thermal activity in this region is in close proximity to the Yellowstone Caldera boundary. Springs and fumaroles discharge from a variety of lithologies including some of the youngest rhyolites in the YPVF. Gas compositions and helium isotope ratios of most samples resemble those in other parts of the YPVF. The waters have meteoric origins and tritium was detected in several samples. Thermal waters from some areas have compositions that plot along a line connecting thermal and non-thermal water endmember compositions. The thermal water endmember equilibrated at 160-170 °C, lower than waters in Yellowstone’s geyser basins. Heat discharged by springs and fumaroles originates from within the Yellowstone Caldera and is transported laterally by advection, mainly along the base of rhyolite flows that cover the inferred caldera boundaries.","language":"English","publisher":"Geological Society of America","doi":"10.1029/2019GC008848","usgsCitation":"Hurwitz, S., McCleskey, R., Bergfeld, D., Peek, S., Susong, D., Roth, D.A., Hungerford, J., White, E.B., Harrison, L., Hosseini, B., Vaughan, R.G., Hunt, A., and Paces, J.B., 2020, Hydrothermal activity in the southwest Yellowstone Plateau Volcanic Field: Geochemistry, Geophysics, Geosystems, v. 21, no. 7, e2019GC008848, 26 p., https://doi.org/10.1029/2019GC008848.","productDescription":"e2019GC008848, 26 p.","ipdsId":"IP-114664","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":456680,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019gc008848","text":"Publisher Index Page"},{"id":436959,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MJ0HYM","text":"USGS data release","linkHelpText":"Water chemistry data for selected hot springs and rivers in Southwest Yellowstone National Park, Wyoming"},{"id":376633,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.060791015625,\n 43.88205730390537\n ],\n [\n -109.3304443359375,\n 43.88205730390537\n ],\n [\n -109.3304443359375,\n 44.999767019181284\n ],\n [\n -111.060791015625,\n 44.999767019181284\n ],\n [\n -111.060791015625,\n 43.88205730390537\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-07-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":793539,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCleskey, R. Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. 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Greg 0000-0002-0850-6669","orcid":"https://orcid.org/0000-0002-0850-6669","contributorId":69030,"corporation":false,"usgs":true,"family":"Vaughan","given":"R.","email":"","middleInitial":"Greg","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":793549,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hunt, Andrew G. 0000-0001-9175-2432","orcid":"https://orcid.org/0000-0001-9175-2432","contributorId":229555,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew G.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":793550,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Paces, James B. 0000-0002-9809-8493","orcid":"https://orcid.org/0000-0002-9809-8493","contributorId":215864,"corporation":false,"usgs":true,"family":"Paces","given":"James","email":"","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":793551,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70210185,"text":"sir20205047 - 2020 - Quantifying trends in arsenic, nitrate, and dissolved solids from selected wells in Utah","interactions":[],"lastModifiedDate":"2020-05-21T11:51:18.956539","indexId":"sir20205047","displayToPublicDate":"2020-05-20T09:45:16","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5047","displayTitle":"Quantifying Trends in Arsenic, Nitrate, and Dissolved Solids from Selected Wells in Utah","title":"Quantifying trends in arsenic, nitrate, and dissolved solids from selected wells in Utah","docAbstract":"Groundwater makes up a primary portion of the water supply in many parts of Utah, with annual withdrawals estimated at more than 1,000,000 acre-feet per year. Increases to groundwater withdrawal and land use may negatively impact water availability. Ensuring availability of clean water requires understanding how water quality has changed over time and how natural and human activities and processes influence water quality. Changes in arsenic, nitrate, and dissolved-solids concentrations in the groundwater in basins with high groundwater withdrawals were evaluated between 1975 and 2015 as indicators of basinwide water quality and the suitability of water for drinking. Data were used from the U.S. Geological Survey’s National Water Information System (NWIS) database and the Safe Drinking Water Information System (SDWIS) maintained by the Utah Department of Environmental Quality, Division of Drinking Water. Mann-Kendall trend tests were used to assess temporal trends in decadal and 5-year (sub-decadal) median analyte concentrations in basins. Trends also were assessed in smaller parts of larger basins to focus on changes occurring at a smaller spatial scale. To evaluate the relationship between land-use change and water-quality changes, trends also were evaluated for wells where land use has changed. Trends in decadal and sub-decadal median arsenic, nitrate, and dissolved-solids concentrations over time were identified throughout the basins and sub-basins in this study. For combined NWIS and SDWIS data, rates of median arsenic concentration change in basins and sub-basins ranged between decreases of –0.24 microgram per liter (μg/L) per year and increases of 0.48 μg/L per year. Rates of median nitrate-concentration change ranged between decreases of –0.08 milligram per liter (mg/L) per year and increases of 0.02 mg/L per year. Rates of median dissolved solids concentration change ranged between decreases of –5 mg/L per year and increases of 7 mg/L per year. The rates of change for nitrate and dissolved solids were similar to or less than rates of change observed in other parts of the country. Trends were not directly related to land-use change approximal to a well, although more data from wells where land use has changed would improve this evaluation. These findings highlight that water quality at a well is related to a range of factors including land, demographics, and water use over a larger area surrounding and up-gradient from the well; rates and direction of groundwater movement; and geologic and hydrologic conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205047","collaboration":"Prepared in Cooperation with the Utah Department of Environmental Quality","usgsCitation":"Miller, O.L., 2020, Quantifying trends in arsenic, nitrate, and dissolved solids from selected wells in Utah: U.S. Geological Survey Scientific Investigations Report 2020–5047, 80 p., https://doi.org/10.3133/sir20205047.","productDescription":"viii, 80 p.","numberOfPages":"80","onlineOnly":"Y","ipdsId":"IP-108685","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":374936,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5047/sir20205047.pdf","text":"Report","size":"7.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374937,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5047/coverthb.jpg"}],"country":"United 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\"}}]}","contact":"Director,
Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
801-908-5000
Many coral reef-lined coasts are low-lying with elevations <4 m above mean sea level. Climate-change-driven sea-level rise, coral reef degradation, and changes in storm wave climate will lead to greater occurrence and impacts of wave-driven flooding. This poses a significant threat to their coastal communities. While greatly at risk, the complex hydrodynamics and bathymetry of reef-lined coasts make flood risk assessment and prediction costly and difficult. Here we use a large (>30,000) dataset of measured coral reef topobathymetric cross-shore profiles, statistics, machine learning, and numerical modeling to develop a set of representative cluster profiles (RCPs) that can be used to accurately represent the shoreline hydrodynamics of a large variety of coral reef-lined coasts around the globe. In two stages, the large dataset is reduced by clustering cross-shore profiles based on morphology and hydrodynamic response to typical wind and swell wave conditions. By representing a large variety of coral reef morphologies with a reduced number of RCPs, a computationally feasible number of numerical model simulations can be done to obtain wave runup estimates, including setup at the shoreline and swash separated into infragravity and sea-swell components, of the entire dataset. The predictive capability of the RCPs is tested against 5,000 profiles from the dataset. The wave runup is predicted with a mean error of 9.7–13.1%, depending on the number of cluster profiles used, ranging from 312 to 50. The RCPs identified here can be combined with probabilistic tools that can provide an enhanced prediction given a multivariate wave and water level climate and reef ecology state. Such a tool can be used for climate change impact assessments and studying the effectiveness of reef restoration projects, as well as for the provision of coastal flood predictions in a simplified (global) early warning system.
","language":"English","publisher":"Frontiers","doi":"10.3389/fmars.2020.00361","usgsCitation":"Scott, F., Antolinez, J.A., McCall, R.T., Storlazzi, C.D., Reiners, A., and Pearson, S., 2020, Hydro-morphological characterization of coral reefs for wave runup prediction: Frontiers in Marine Science, v. 7, 361, 20 p., https://doi.org/10.3389/fmars.2020.00361.","productDescription":"361, 20 p.","ipdsId":"IP-116940","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":456686,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2020.00361","text":"Publisher Index Page"},{"id":436960,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C39WNE","text":"USGS data release","linkHelpText":"Coral reef profiles for wave-runup prediction"},{"id":376661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","noUsgsAuthors":false,"publicationDate":"2020-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Scott, Fred","contributorId":229615,"corporation":false,"usgs":false,"family":"Scott","given":"Fred","email":"","affiliations":[{"id":27619,"text":"TU Delft","active":true,"usgs":false}],"preferred":false,"id":793676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Antolinez, Jose A.A.","contributorId":177510,"corporation":false,"usgs":false,"family":"Antolinez","given":"Jose","email":"","middleInitial":"A.A.","affiliations":[],"preferred":false,"id":793677,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCall, Robert T.","contributorId":148986,"corporation":false,"usgs":false,"family":"McCall","given":"Robert","email":"","middleInitial":"T.","affiliations":[{"id":12474,"text":"Deltares, Netherlands","active":true,"usgs":false}],"preferred":false,"id":793678,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":793679,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reiners, Ad","contributorId":229616,"corporation":false,"usgs":false,"family":"Reiners","given":"Ad","email":"","affiliations":[{"id":27619,"text":"TU Delft","active":true,"usgs":false}],"preferred":false,"id":793680,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pearson, Stuart","contributorId":193835,"corporation":false,"usgs":false,"family":"Pearson","given":"Stuart","affiliations":[],"preferred":false,"id":793681,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208929,"text":"sir20205053 - 2020 - Using remotely sensed data to map Joshua Tree distributions at Naval Air Weapons Station China Lake, California, 2018","interactions":[],"lastModifiedDate":"2020-05-21T11:43:03.892535","indexId":"sir20205053","displayToPublicDate":"2020-05-20T08:23:12","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5053","displayTitle":"Using Remotely Sensed Data to Map Joshua Tree Distributions at Naval Air Weapons Station China Lake, California, 2018","title":"Using remotely sensed data to map Joshua Tree distributions at Naval Air Weapons Station China Lake, California, 2018","docAbstract":"Species distribution models (SDMs) that are derived through inference have been used to provide important insights toward species distributions. Their inferences can be robust in relation to known presences, but SDMs have error rates that cannot be quantified with certainty. For large plant species with unique signatures and in sparsely vegetated habitats, object-oriented satellite image interpretation provides a useful alternative to the more commonly used SDM approach. We tested visual image interpretation techniques in a pilot project to map the distribution of the Joshua tree (Yucca brevifolia), an arborescent succulent plant endemic to the Mojave Desert of North America. Naval Air Weapons Station China Lake (NAWS–CL) required assistance in mapping the distribution of Joshua trees across the 4,715 square kilometer (km2) military installation in support of their national security mission. Joshua trees were present on 1,307 1-km2 cells in the species distribution model, or 27.7 percent of the military installation. This increases the published range of Joshua trees at NAWS–CL by 90 percent and corrects for two stands of Joshua trees that were previously identified but do not exist. Remotely sensed satellite data in combination with ground surveys of Joshua trees produced a more accurate distribution map at a 1-kilometer resolution than did previous SDMs based on correlative modeling (area under the curve [AUC] 0.9064 versus 0.5848, respectively). Ancillary comparison with light detection and ranging (lidar) data indicated that satellite and lidar data were equally successful with slightly different sources of error, but that using them in combination produced the best results.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205053","usgsCitation":"Esque, T.C., Baird, P.E., Chen, F.C., Housman, D., and Holton, J.T., 2020, Using remotely sensed data to map Joshua Tree distributions at Naval Air Weapons Station China Lake, California, 2018: U.S. Geological Survey Scientific Investigations Report 2020–5053, 13 p., https://doi.org/10.3133/sir20205053.","productDescription":"vi, 13 p.","numberOfPages":"13","onlineOnly":"Y","ipdsId":"IP-106778","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":374807,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5053/sir20205053.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374806,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5053/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Naval Air Weapons Station China Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.0316162109375,\n 35.04573815523954\n ],\n [\n -116.66931152343749,\n 34.99625375979014\n ],\n [\n -116.79565429687499,\n 36.461054075054314\n ],\n [\n -117.99316406249999,\n 36.474306755095235\n ],\n [\n -118.0316162109375,\n 35.04573815523954\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
This study created digital terrain models (DTMs) from historical aerial images using Structure from Motion (SfM) for a variety of image dates, resolutions, and photo scales. Accuracy assessments were performed on the SfM DTMs, and they were compared to the United States Geological Survey’s three-dimensional digital elevation program (3DEP) light detection and ranging (LiDAR) DTMs to evaluate geomorphic change thresholds based on vertical accuracy assessments and elevation change methodologies. The results of this study document a relationship between historical aerial photo scales and predicted vertical accuracy of the resultant DTMs. The results may be used to assess geomorphic change thresholds over multi-decadal timescales depending on spatial scale, resolution, and accuracy requirements. This study shows that if elevation changes of approximately ±1 m are to be mapped, historical aerial photography collected at 1:20,000 scale or larger would be required for comparison to contemporary LiDAR derived DTMs.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12101625","usgsCitation":"Chirico, P.G., DeWitt, J.D., and Bergstresser, S.E., 2020, Evaluating elevation change thresholds between structure-from-motion DEMs derived from historical aerial photos and 3DEP LiDAR data: Remote Sensing, v. 10, no. 12, 1625, 16 p., https://doi.org/10.3390/rs12101625.","productDescription":"1625, 16 p.","ipdsId":"IP-118392","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":456696,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12101625","text":"Publisher Index Page"},{"id":376482,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","county":"Fairfax County","otherGeospatial":"Piney Branch","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.30409622192383,\n 38.8538792131213\n ],\n [\n -77.23749160766602,\n 38.8538792131213\n ],\n [\n -77.23749160766602,\n 38.93230667504973\n ],\n [\n -77.30409622192383,\n 38.93230667504973\n ],\n [\n -77.30409622192383,\n 38.8538792131213\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Chirico, Peter G. 0000-0001-8375-5342","orcid":"https://orcid.org/0000-0001-8375-5342","contributorId":63838,"corporation":false,"usgs":true,"family":"Chirico","given":"Peter","email":"","middleInitial":"G.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":793216,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeWitt, Jessica D. 0000-0002-8281-8134 jdewitt@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-8134","contributorId":5804,"corporation":false,"usgs":true,"family":"DeWitt","given":"Jessica","email":"jdewitt@usgs.gov","middleInitial":"D.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":793217,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bergstresser, Sarah E. 0000-0003-0182-5779 sbergstresser@usgs.gov","orcid":"https://orcid.org/0000-0003-0182-5779","contributorId":195556,"corporation":false,"usgs":true,"family":"Bergstresser","given":"Sarah","email":"sbergstresser@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":793218,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210166,"text":"70210166 - 2020 - Estimating the effect of winter cover crops on nitrogen leaching using cost-share enrollment data, satellite remote sensing, and Soil and Water Assessment Tool (SWAT) modeling","interactions":[],"lastModifiedDate":"2020-05-19T14:46:15.127607","indexId":"70210166","displayToPublicDate":"2020-05-19T09:41:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the effect of winter cover crops on nitrogen leaching using cost-share enrollment data, satellite remote sensing, and Soil and Water Assessment Tool (SWAT) modeling","docAbstract":"This study employed a novel combination of data (winter cover crop cost-share enrollment records, satellite remote sensing of wintertime vegetation, and results of Soil and Water Assessment Tool (SWAT) water quality simulations) to estimate the environmental performance of winter cover crops (WCC) at the watershed scale, from 2008 through 2017, within the Tuckahoe sub-watershed of the Choptank River. The Choptank is a river basin within the Chesapeake Bay watershed and, as a focus watershed for the U.S. Department of Agriculture’s Conservation Effects Assessment Project (CEAP), has been the subject of considerable study assessing linkages between land use and water quality. Farm enrollment data from the Maryland Agricultural Cost Share (MACS) program documented a strong increase in the use of WCC within the Tuckahoe watershed during the study period, from 27% of corn fields and 9% of soybean fields in 2008 to 89% of corn fields and 46% of soybean fields in 2016. Satellite remote sensing of wintertime ground cover detected increased wintertime vegetation following corn crops, in comparison to full season and double cropped soybean, consistent with patterns of cover crop implementation. Although inter-annual variation in climate strongly affected observed levels of vegetation, with warm winters resulting in increased vegetative cover, a 30-year analysis of wintertime greenness revealed significant increases in wintertime vegetation associated increased adoption of WCC. The predominant WCC species recorded by the MACS program as planted in the Tuckahoe watershed were wheat (68.1%), barley (16.1%), and rye (7.2%). The MACS WCC enrollment data were combined with output from the SWAT model, calibrated to streamflow and nutrient loading from the Tuckahoe watershed, to estimate water quality impacts based on known distribution of cover crop species and planting dates (2008 to 2017). Results indicated a 25% overall 10-year reduction in nitrate leaching from cropland resulting from cover crop adoption, rising to an estimated 38% load reduction in 2016 when 64% of fields were planted to cover crops. A large portion of WCC (39.3%) were planted late (after October 15) and planted to wheat (68.1%). Increased environmental benefits would be achieved by shifting agronomic methods away from late-planted wheat.","language":"English","publisher":"Soil and Water Conservation Society","doi":"10.2489/jswc.75.3.362","usgsCitation":"Hively, W.D., Lee, S., Sadeghi, A.M., McCarty, G.W., Lamb, B.T., Soroka, A.M., Keppler, J., Yeo, I., and Moglen, G.E., 2020, Estimating the effect of winter cover crops on nitrogen leaching using cost-share enrollment data, satellite remote sensing, and Soil and Water Assessment Tool (SWAT) modeling: Journal of Soil and Water Conservation, v. 75, no. 3, p. 362-375, https://doi.org/10.2489/jswc.75.3.362.","productDescription":"14 p.","startPage":"362","endPage":"375","ipdsId":"IP-106326","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":456701,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2489/jswc.75.3.362","text":"Publisher Index Page"},{"id":374921,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland","otherGeospatial":"Chesapeake Bay watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.783203125,\n 36.98500309285596\n ],\n [\n -75.0146484375,\n 36.98500309285596\n ],\n [\n -75.0146484375,\n 39.57182223734374\n ],\n [\n -77.783203125,\n 39.57182223734374\n ],\n [\n -77.783203125,\n 36.98500309285596\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"75","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-05-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789371,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Sangchul","contributorId":201237,"corporation":false,"usgs":false,"family":"Lee","given":"Sangchul","email":"","affiliations":[],"preferred":false,"id":789372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sadeghi, Ali M.","contributorId":131147,"corporation":false,"usgs":false,"family":"Sadeghi","given":"Ali","email":"","middleInitial":"M.","affiliations":[{"id":7262,"text":"USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705","active":true,"usgs":false}],"preferred":false,"id":789373,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCarty, Gregory W.","contributorId":192367,"corporation":false,"usgs":false,"family":"McCarty","given":"Gregory","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":789374,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lamb, Brian T.","contributorId":211092,"corporation":false,"usgs":false,"family":"Lamb","given":"Brian","email":"","middleInitial":"T.","affiliations":[{"id":38178,"text":"City College of New York","active":true,"usgs":false}],"preferred":false,"id":789375,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Soroka, Alexander M. 0000-0002-8002-5229","orcid":"https://orcid.org/0000-0002-8002-5229","contributorId":201664,"corporation":false,"usgs":true,"family":"Soroka","given":"Alexander","email":"","middleInitial":"M.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789376,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Keppler, Jason","contributorId":218039,"corporation":false,"usgs":false,"family":"Keppler","given":"Jason","email":"","affiliations":[{"id":39731,"text":"Maryland Department of Agriculture, Office of Resource Conservation","active":true,"usgs":false}],"preferred":false,"id":789377,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yeo, In-Young","contributorId":131145,"corporation":false,"usgs":false,"family":"Yeo","given":"In-Young","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":789378,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Moglen, Glenn E.","contributorId":106585,"corporation":false,"usgs":false,"family":"Moglen","given":"Glenn","email":"","middleInitial":"E.","affiliations":[{"id":13220,"text":"The Charles E. Via, Jr. Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":789379,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70227318,"text":"70227318 - 2020 - Golden Eagle perch site use in the U.S. southern plains: Understanding electrocution risk","interactions":[],"lastModifiedDate":"2022-01-10T13:38:35.904154","indexId":"70227318","displayToPublicDate":"2020-05-19T07:35:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"title":"Golden Eagle perch site use in the U.S. southern plains: Understanding electrocution risk","docAbstract":"Electrocution on overhead electric systems is a primary cause of anthropogenic mortality for Golden Eagles (Aquila chrysaetos) in North America. Distribution poles supporting energized equipment are most often involved in electrocutions, but the frequency with which Golden Eagles perch on pole supporting equipment is unknown. To resolve questions of perch frequency, and by extension, electrocution risk and mitigation prioritization, we used Google Earth to identify perch locations of GPS-transmittered preadult Golden Eagles, and specifically to identify perching on poles supporting transformers. We used transformer poles as a proxy for electrocution risk because transformers are visible in Google Earth imagery. We examined 105 randomly selected “perch events” for each of 10 Golden Eagles (n = 1050 perch events total) tracked for a mean of 16 consecutive mo after fledging. The most frequently used perch sites were cliffs (24.6%), trees (21.2%), and hills (16.6%). Across individuals, 10.8% of perches were on overhead electric systems (individual ranges = 0.0–34.3%). Seven Golden Eagles perched on a distribution pole at least once. Of these, five perched on a transformer pole at least once. Perching on transformer poles occurred more frequently than expected given the proportion of transformer poles present (Yates’ χ2 = 26.5, P < 0.001). Given the frequency of perching on transformer poles revealed in this study and the frequency of electrocution on equipment poles revealed in previous studies, the data suggest that electrocution mitigation measures should be focused on equipment poles. Future research should quantify perching across a wider variety of habitats and Golden Eagle age and sex to identify whether the patterns reported here occur more broadly.
Strong seismic waves from the July 2019 Ridgecrest, California, earthquakes displaced rocks in proximity to the M 7.1 mainshock fault trace at several locations. In this report, we document large boulders that were displaced at the Wagon Wheel Staging Area (WWSA), approximately 4.5 km southeast of the southern terminus of the large M 6.4 foreshock rupture (hereafter “the large foreshock”) and 9 km southwest of the nearest approach of the M 7.1 mainshock surface rupture. Some boulders appear to have slid along essentially flat surfaces, which suggest that dynamic stresses overcame the coefficient of friction. Other boulders appear to have rocked within their sockets. In both cases, we use simple mechanical models to estimate total peak dynamic accelerations between 0.5 and 1g, commensurate with modified Mercalli intensity 9. It is unclear if the strongest shaking at this location occurred during the large foreshock or the M 7.1 mainshock. The inferred accelerations are higher than predicted mainshock ground motions at WWSA, although local high accelerations could have been generated by path, site, or source effects. Gaps between boulders and their sockets are easily visible in the immediate aftermath of earthquakes and provide a quick indication of strong shaking. More importantly, the gaps quickly fill with surficial organic debris, including seeds and leaves of the year, that quickly become entombed. Boulders may thus potentially be extracted to examine gap fillings associated with past earthquakes, providing a new datable paleoseismic method.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200029","usgsCitation":"Sleep, N., and Hough, S.E., 2020, Mild displacements of boulders during the 2019 Ridgecrest Earthquakes: Bulletin of the Seismological Society of America, v. 110, no. 4, p. 1579-1588, https://doi.org/10.1785/0120200029.","productDescription":"10 p.","startPage":"1579","endPage":"1588","ipdsId":"IP-119088","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482218,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.71929444649237,\n 35.663769671293025\n ],\n [\n -117.71929444649237,\n 35.5783607422547\n ],\n [\n -117.59492272074345,\n 35.5783607422547\n ],\n [\n -117.59492272074345,\n 35.663769671293025\n ],\n [\n -117.71929444649237,\n 35.663769671293025\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"110","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Sleep, Norman","contributorId":245424,"corporation":false,"usgs":false,"family":"Sleep","given":"Norman","affiliations":[{"id":49192,"text":"Stanford","active":true,"usgs":false}],"preferred":false,"id":927666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":263442,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927667,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211000,"text":"70211000 - 2020 - Specialized meltwater biodiversity persists despite widespread deglaciation","interactions":[],"lastModifiedDate":"2020-07-10T13:25:29.284896","indexId":"70211000","displayToPublicDate":"2020-05-18T08:23:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3165,"text":"Proceedings of the National Academy of Sciences of the United States of America","active":true,"publicationSubtype":{"id":10}},"title":"Specialized meltwater biodiversity persists despite widespread deglaciation","docAbstract":"Glaciers are important drivers of environmental heterogeneity and biological diversity across mountain landscapes. Worldwide, glaciers are receding rapidly due to climate change, with important consequences for biodiversity in mountain ecosystems. However, the effects of glacier loss on biodiversity have never been quantified across a mountainous region, primarily due to a lack of adequate data at large spatial and temporal scales. Here, we combine high-resolution biological and glacier change (ca. 1850–2015) datasets for Glacier National Park, USA, to test the prediction that glacier retreat reduces biodiversity in mountain ecosystems through the loss of uniquely adapted meltwater stream species. We identified a specialized cold-water invertebrate community restricted to the highest elevation streams primarily below glaciers, but also snowfields and groundwater springs. We show that this community and endemic species have unexpectedly persisted in cold, high-elevation sites, even in catchments that have not been glaciated in ∼170 y. Future projections suggest substantial declines in suitable habitat, but not necessarily loss of this community with the complete disappearance of glaciers. Our findings demonstrate that high-elevation streams fed by snow and other cold-water sources continue to serve as critical climate refugia for mountain biodiversity even after glaciers disappear.","language":"English","publisher":"PNAS","doi":"10.1073/pnas.2001697117","usgsCitation":"Muhlfeld, C.C., Cline, T.J., Giersch, J.J., Peitzsch, E.H., Florentine, C., Jacobsen, D., and Hotaling, S., 2020, Specialized meltwater biodiversity persists despite widespread deglaciation: Proceedings of the National Academy of Sciences of the United States of America, v. 117, no. 22, p. 12208-12214, https://doi.org/10.1073/pnas.2001697117.","productDescription":"7 p.","startPage":"12208","endPage":"12214","ipdsId":"IP-114696","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":456727,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2001697117","text":"Publisher Index Page"},{"id":436963,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RCMMKL","text":"USGS data release","linkHelpText":"Glacier National Park alpine aquatic invertebrates, 2011-2013"},{"id":376249,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.17517089843749,\n 46.86770273172814\n ],\n [\n -112.1319580078125,\n 46.86770273172814\n ],\n [\n -112.1319580078125,\n 48.99463598353405\n ],\n [\n -115.17517089843749,\n 48.99463598353405\n ],\n [\n -115.17517089843749,\n 46.86770273172814\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"117","issue":"22","noUsgsAuthors":false,"publicationDate":"2020-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Muhlfeld, Clint C. 0000-0002-4599-4059 cmuhlfeld@usgs.gov","orcid":"https://orcid.org/0000-0002-4599-4059","contributorId":924,"corporation":false,"usgs":true,"family":"Muhlfeld","given":"Clint","email":"cmuhlfeld@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":792386,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cline, Timothy Joseph 0000-0002-4955-654X","orcid":"https://orcid.org/0000-0002-4955-654X","contributorId":228871,"corporation":false,"usgs":true,"family":"Cline","given":"Timothy","email":"","middleInitial":"Joseph","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":792387,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Giersch, J. Joseph 0000-0001-7818-3941 jgiersch@usgs.gov","orcid":"https://orcid.org/0000-0001-7818-3941","contributorId":198074,"corporation":false,"usgs":true,"family":"Giersch","given":"J.","email":"jgiersch@usgs.gov","middleInitial":"Joseph","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":792388,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":792389,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":792390,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jacobsen, Dean 0000-0001-5137-297X","orcid":"https://orcid.org/0000-0001-5137-297X","contributorId":198314,"corporation":false,"usgs":false,"family":"Jacobsen","given":"Dean","email":"","affiliations":[],"preferred":false,"id":792391,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hotaling, Scott 0000-0002-5965-0986","orcid":"https://orcid.org/0000-0002-5965-0986","contributorId":176860,"corporation":false,"usgs":false,"family":"Hotaling","given":"Scott","email":"","affiliations":[],"preferred":false,"id":792392,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70215085,"text":"70215085 - 2020 - Aligning climate models with stakeholder needs: Advances in communicating future rainfall uncertainties for south Florida decision makers","interactions":[],"lastModifiedDate":"2020-10-07T13:12:42.218246","indexId":"70215085","displayToPublicDate":"2020-05-18T08:08:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Aligning climate models with stakeholder needs: Advances in communicating future rainfall uncertainties for south Florida decision makers","docAbstract":"Changes in future precipitation are of great importance to climate data users in South Florida. A recent U.S. Geological Survey workshop, “Increasing Confidence in Precipitation Projections for Everglades Restoration,” highlighted a gap between standard climate model outputs and the climate information needs of some key Florida natural resource managers. These natural resource managers (hereafter broadly defined as “climate data users”) need more tailored output than is commonly provided by the climate modeling community. This study responds to these user needs by outlining and testing an adaptable methodology to select output from ensemble climate‐model simulations based on user‐defined precipitation drivers, using statistical methods common across scientific disciplines. This methodology is developed to provide a “decision matrix” that guides climate data users to specify the subset of models most important to their work based on each user's season (winter, summer, and annual) and the condition (dry, wet, neutral, and no threshold events) of interest. The decision matrix is intended to better communicate the subset of models best representing precipitation drivers. This information could increase users' confidence in climate models as a resource for natural resource planning and can be used to direct future dynamical downscaling efforts. This methodology is based in dynamical processes controlling precipitation via remote and local teleconnections. We also suggest that future climate studies in South Florida include high‐resolution climate model runs (i.e., ocean eddy resolving) in conjunction with dynamical downscaling to adequately capture precipitation variability.
In August 2010, Sinabung volcano began erupting after more than a thousand years of dormancy. Following several weeks of phreatic eruptions, the eruptions ceased and Sinabung entered what became an inter-eruptive period of dominantly seismic unrest. While standard equations for understanding the size of an earthquake (local magnitude (ML), coda magnitude (MC), and seismic energy release (ER)) have long been developed, it is best practice to fine tune these relations for a given region and period of study to more accurately describe seismicity and to directly compare it with other volcanic systems. More accurate descriptions of magnitudes and energy release are vital to accurate volcanic eruption forecasting and evaluation of seismic and volcanic risk. In this study, we use high-frequency volcano-tectonic (VT) earthquakes recorded on a temporary three-component network installed between October 2010 and December 2011 in the region around Sinabung volcano to better constrain the seismic parameters of and better understand this previously unstudied volcano. We determine region-specific formulas for ML, MC, and ER as follows:
where A, r, and tcoda are maximum amplitude on a Wood-Anderson seismogram, hypocentral distance (km), and the coda duration (s), respectively. Constants in the ML equation have physically interpretable meanings. The constant for the geometrical spreading term (log10r term) equals one for perfect spherical spreading of the waveform. Our value is greater than one and thus suggests that wavefronts spread at a slightly different rate than for simple spherical spreading. The constant for the attenuation term (r term) is consistent with locally mapped attenuative deposits (limestones and tuffs) and previous 3D tomographic results. Our MC equation differs from a previous study, likely because different data in a different time period were used. Earthquake hypocenters are consistent with those located in previous tomographic studies, and we interpret the earthquakes in this study as distal VT earthquakes induced by continued magmatic intrusion at Sinabung over the period of October 2010–December 2011.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-020-01383-7","usgsCitation":"Pagacancang, A., McCausland, W.A., Hamidah, N.N., Kristianto, Basuki, A., and Indrastuti, N., 2020, Local magnitude, coda magnitude, and radiated energy of volcanic tectonic earthquakes from October 2010 to December 2011 at Sinabung volcano, Indonesia: Bulletin of Volcanology, v. 83, 45, 16 p., https://doi.org/10.1007/s00445-020-01383-7.","productDescription":"45, 16 p.","ipdsId":"IP-112724","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":421164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Indonesia","otherGeospatial":"Sinabung volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 94.80408011956274,\n 6.071558619518001\n ],\n [\n 94.80408011956274,\n 1.292805138355149\n ],\n [\n 101.37390433831303,\n 1.292805138355149\n ],\n [\n 101.37390433831303,\n 6.071558619518001\n ],\n [\n 94.80408011956274,\n 6.071558619518001\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"83","noUsgsAuthors":false,"publicationDate":"2020-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Pagacancang, Afnimar","contributorId":330169,"corporation":false,"usgs":false,"family":"Pagacancang","given":"Afnimar","email":"","affiliations":[{"id":78836,"text":"Bandung Institute of Technology (ITB)","active":true,"usgs":false}],"preferred":false,"id":884204,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCausland, Wendy A. 0000-0002-8683-1440","orcid":"https://orcid.org/0000-0002-8683-1440","contributorId":204380,"corporation":false,"usgs":true,"family":"McCausland","given":"Wendy","email":"","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":884205,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hamidah, Nimas Nurul","contributorId":330170,"corporation":false,"usgs":false,"family":"Hamidah","given":"Nimas","email":"","middleInitial":"Nurul","affiliations":[{"id":78836,"text":"Bandung Institute of Technology (ITB)","active":true,"usgs":false}],"preferred":false,"id":884206,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kristianto","contributorId":330171,"corporation":false,"usgs":false,"family":"Kristianto","affiliations":[{"id":40024,"text":"Center for Volcanology and Geologic Hazard Mitigation","active":true,"usgs":false}],"preferred":false,"id":884207,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Basuki, Ahmad","contributorId":330172,"corporation":false,"usgs":false,"family":"Basuki","given":"Ahmad","email":"","affiliations":[{"id":40024,"text":"Center for Volcanology and Geologic Hazard Mitigation","active":true,"usgs":false}],"preferred":false,"id":884208,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Indrastuti, Novianti","contributorId":204389,"corporation":false,"usgs":false,"family":"Indrastuti","given":"Novianti","email":"","affiliations":[{"id":36928,"text":"Center for Volcanology and Geological Hazard Mitigation, Bandung, Indonesia","active":true,"usgs":false}],"preferred":false,"id":884209,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209232,"text":"ofr20201030 - 2020 - Louisiana Barrier Island Comprehensive Monitoring Program: Mapping habitats in beach, dune, and intertidal environments along the Louisiana Gulf of Mexico shoreline, 2008 and 2015–16","interactions":[],"lastModifiedDate":"2020-05-19T11:53:31.168523","indexId":"ofr20201030","displayToPublicDate":"2020-05-18T07:11:23","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1030","displayTitle":"Louisiana Barrier Island Comprehensive Monitoring Program: Mapping Habitats in Beach, Dune, and Intertidal Environments Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16","title":"Louisiana Barrier Island Comprehensive Monitoring Program: Mapping habitats in beach, dune, and intertidal environments along the Louisiana Gulf of Mexico shoreline, 2008 and 2015–16","docAbstract":"Barrier islands, headlands, and coastal shorelines provide numerous valuable ecosystem goods and services, including storm protection and erosion control for the mainland, habitat for fish and wildlife, salinity regulation in estuaries, carbon sequestration in marshes, and areas for recreation and tourism. These coastal features are dynamic environments because of their position at the land-sea interface. Storms, wave energy, tides, currents, and relative sea-level rise are powerful forces that shape local geomorphology and habitat distribution. In order to make more informed decisions, coastal resource managers require insights into how these dynamic systems are changing through time.
In 2005, Louisiana’s Coastal Protection and Restoration Authority, in partnership with the University of New Orleans and the U.S. Geological Survey, developed the Barrier Island Comprehensive Monitoring (BICM) Program. The goal of the BICM Program is to develop long-term datasets for habitat coverage, shoreline assessments, shoreline position, topobathymetric changes, and sediment characterization to assist with planning, designing, evaluating, and maintaining current and future barrier shorelines. The overall objectives of the study described in this report were to (1) map habitats for 2008 and 2015–16 for BICM coastal reaches and (2) map habitat change between these two time periods.
This report highlights the second phase of habitat analyses for the BICM Program. This work builds on a previous habitat analysis conducted by the University of New Orleans, which included the development of habitat maps for 1996/1998, 2001, 2004, and 2005, along with habitat change maps. For this current effort, a new 15-class habitat scheme was developed from the original BICM scheme to further delineate various dune habitats, including meadow habitat found along the backslopes of dunes, to distinguish between marsh and mangrove, and to distinguish between beach and unvegetated barrier flat habitats. Additionally, a geographic object-based image analysis-based mapping framework was used to incorporate relative topography and address elevation uncertainty in light detection and ranging data to assist with mapping dune and intertidal habitats.
For the entire BICM region, the area experiencing a change in a land/water category (that is, land gain or land loss) was 3.4 percent, of which, 59.2 percent was land gain and 40.8 percent was land loss. Areal coverages of meadow, mangrove, scrub/shrub, and vegetated dune increased from 2008 to 2015–16, whereas areal coverages of beach, grassland, and intertidal decreased. The decrease in intertidal, however, was largely due to differing water levels in the orthophotography between the two time periods. Regional analyses of habitat coverage and habitat change captured the dynamic nature of these systems and the effects of restoration efforts, most notably in the Late Lafourche Delta, Modern Delta, and Chandeleur Islands regions. For instance, in the Modern Delta region there was a marked increase in unvegetated flat, meadow, mangrove, scrub/shrub, beach, unvegetated dune, and vegetated dune. As a result, this region experienced the highest percent change for land/water classes (6.6 percent) with land gain accounting for much of this change (70.8 percent). In contrast, the Acadiana Bays region had the highest relative percent loss of all regions. The region had a percent change for land/water classes of 2.8 percent, of which, 79.7 percent was land loss.
The results of this study provide information about the areal coverage and distribution of habitats for two recent time periods and change over about an 8-year period. These data can be used to evaluate changes along the Louisiana Gulf of Mexico shoreline, including gradual changes caused by coastal processes, restoration actions, and (or) episodic events, such as hurricanes and extreme storms.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201030","collaboration":"Prepared in cooperation with the Louisiana Coastal Protection and Restoration Authority","usgsCitation":"Enwright, N.M., SooHoo, W.M., Dugas, J.L., Conzelmann, C.P., Laurenzano, C., Lee, D.M., Mouton, K., and Stelly, S.J., 2020, Louisiana Barrier Island Comprehensive Monitoring Program—Mapping habitats in beach, dune, and intertidal environments along the Louisiana Gulf of Mexico shoreline, 2008 and 2015–16: U.S. Geological Survey Open-File Report 2020–1030, 57 p., https://doi.org/10.3133/ofr20201030.","productDescription":"Report: ix, 57 p.; Data Release","numberOfPages":"72","onlineOnly":"Y","ipdsId":"IP-114268","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":436983,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YRT54Z","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008 habitat map, Chandeleur Islands Region"},{"id":436982,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E94E33","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2016 habitat map, Chandeleur Islands Region"},{"id":436981,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UBUO7C","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008-2016 habitat change, Chandeleur Islands Region"},{"id":436980,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LUPB9N","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008-2015 habitat change, East Chenier Region"},{"id":436979,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N0GKPB","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008-2016 habitat change, Acadiana Bays Region"},{"id":436978,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ABPHMC","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008 Habitat Map, Acadiana Bays Region"},{"id":436977,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91F6GQY","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008 habitat map, East Chenier Region"},{"id":436976,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KSG6WX","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2015 Habitat Map, East Chenier Region"},{"id":436975,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SKS31W","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2015/16 Habitat Map, Acadiana Bays Region"},{"id":436974,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DW2Y25","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008-2016 habitat change, Late Lafourche Delta Region"},{"id":436973,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UDQ0U0","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008 to 2016 habitat change, Modern Delta Region"},{"id":436972,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L2GU4R","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008 habitat map, West Chenier Region"},{"id":436971,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95TQ72U","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2015 habitat map, West Chenier Region"},{"id":436970,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ERLQ1V","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008 to 2016 habitat change, Early Lafourche Delta Region"},{"id":436969,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P960YCTY","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2016 habitat map, Late Lafourche Delta Region"},{"id":436968,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98VJ9R6","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2016 habitat map, Modern Delta Region"},{"id":436967,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T1B66S","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2016 Habitat Map, Early Lafourche Delta Region"},{"id":436966,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LKFIJ5","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 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U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506–3152