The study was conducted in the Northern Atlantic Coastal Plain aquifer system, in the eastern USA.
Groundwater pH and redox conditions are fundamental chemical characteristics controlling the distribution of many contaminants of concern for drinking water or the ecological health of receiving waters. In this study, pH and redox conditions were modeled and mapped in a complex, layered aquifer system. Machine-learning methods (boosted regression trees) were applied to data from 3000 to 5000 wells. Predicted pH and the probability of anoxic conditions, defined by three thresholds of dissolved oxygen (0.5, 1, and 2 mg/L), were mapped at the 1-km2 scale for each of 10 regional aquifer layers.
Maps depict the extent of acidic groundwater and oxic conditions in the shallow, unconfined surficial aquifer and in unconfined, recharge-proximal areas of underlying aquifers, in contrast to alkaline and anoxic groundwater elsewhere. Geographic patterns and influential predictors–including elevation, overlying confining-units thickness, and simulated groundwater age and flux–are consistent with prior understanding of the processes controlling pH and redox in the aquifer system. The model-based maps support robust estimates of aquifer proportions, either areal or volumetric, likely to contain groundwater of a specified quality or be vulnerable to specific pH- or redox-sensitive contaminants. The machine-learning methods were an effective tool to map groundwater quality at the regional scale.
Seismometers are highly sensitive instruments to not only ground motion but also many other nonseismic noise sources (e.g., temperature, pressure, and magnetic field variations). We show that the Alaska component of the Transportable Array is particularly susceptible to recording magnetic storms and other space weather events because the sensors used in this network are unshielded and magnetic flux variations are stronger at higher latitudes. We also show that vertical‐component seismic records across Alaska are directly recording magnetic field variations between 40 and 800 s period as opposed to actual ground motion during geomagnetic events with sensitivities ranging from 0.004 to
Climate change is expected to result in the tropicalization of coastal wetlands in the northern Gulf of Mexico, as warming winters allow tropical mangrove forests to expand their distribution poleward at the expense of temperate salt marshes. Data limitations near mangrove range limits have hindered understanding of the effects of winter temperature extremes on mangrove distribution and structure. Here, we investigated the influence of extreme freeze events on the abundance, height and coverage of black mangroves (Avicennia germinans ) near their northern range limit in Louisiana.
Coastal Louisiana, USA.
We quantified the relationships between the frequency of extreme freeze events and A. germinans abundance, height and coverage using: (a) mangrove observation points recorded via aerial surveys from a fixed‐wing aircraft; (b) 30 years of temperature data; and (c) mangrove mortality and leaf damage temperature thresholds. We used freeze frequency data and mangrove–climate relationships to evaluate and spatially depict the risk of A. germinans freeze damage across Louisiana.
We identified strong negative relationships between the frequency of extreme freeze events and A. germinans abundance, height and coverage. Avicennia germinans is most abundant, tall and continuous along the south‐eastern outer coast of Louisiana, where the frequency of extreme freeze events is reduced (i.e., lower risk of mangrove freeze damage) by the buffering effects of comparatively warm Gulf of Mexico waters. Conversely, the risk of A. germinans freeze damage has historically been very high across Louisiana's Chenier Plain and within more inland wetlands in the Deltaic Plain.
Our analyses advance understanding of how the frequency of extreme freeze events controls the distribution, height and coverage of A. germinans near its northern range limit. In addition to informing climate‐smart coastal restoration efforts, our findings can be used to better anticipate and prepare for the tropicalization of temperate wetlands due to climate change.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13119","usgsCitation":"Osland, M., Day, R., and Michot, T.C., 2020, Frequency of extreme freeze events controls the distribution and structure of black mangroves (Avicennia germinans) near their northern range limit in coastal Louisiana: Diversity and Distributions, v. 26, no. 10, p. 1366-1382, https://doi.org/10.1111/ddi.13119.","productDescription":"Article: 17 p.; Data Release","startPage":"1366","endPage":"1382","ipdsId":"IP-116815","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":456209,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13119","text":"Publisher Index Page"},{"id":376104,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":379388,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RC8EIE"}],"country":"United States","state":"Louisiana","otherGeospatial":"Coastal Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.779296875,\n 29.152161283318915\n ],\n [\n -92.46093749999999,\n 28.998531814051795\n ],\n [\n -90.615234375,\n 28.8831596093235\n ],\n [\n -89.07714843749999,\n 29.305561325527698\n ],\n [\n -89.384765625,\n 30.29701788337205\n ],\n [\n -89.82421875,\n 30.600093873550072\n ],\n [\n -91.62597656249999,\n 30.44867367928756\n ],\n [\n -93.6474609375,\n 30.259067203213018\n ],\n [\n -94.130859375,\n 30.031055426540206\n ],\n [\n -93.779296875,\n 29.152161283318915\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":214842,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":792079,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day, Richard 0000-0002-5959-7054","orcid":"https://orcid.org/0000-0002-5959-7054","contributorId":221895,"corporation":false,"usgs":true,"family":"Day","given":"Richard","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":792080,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Michot, Thomas C.","contributorId":228798,"corporation":false,"usgs":false,"family":"Michot","given":"Thomas","email":"","middleInitial":"C.","affiliations":[{"id":41511,"text":"USGS WARC (retired)","active":true,"usgs":false}],"preferred":false,"id":792081,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210868,"text":"70210868 - 2020 - A rasterized building footprint dataset for the United States","interactions":[],"lastModifiedDate":"2020-06-30T12:31:08.886688","indexId":"70210868","displayToPublicDate":"2020-06-29T07:16:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"A rasterized building footprint dataset for the United States","docAbstract":"Microsoft released a U.S.-wide vector building dataset in 2018. Although the vector building layers provide relatively accurate geometries, their use in large-extent geospatial analysis comes at a high computational cost. We used High-Performance Computing (HPC) to develop an algorithm that calculates six summary values for each cell in a raster representation of each U.S. state, excluding Alaska and Hawaii: (1) total footprint coverage, (2) number of unique buildings intersecting each cell, (3) number of building centroids falling inside each cell, and area of the (4) average, (5) smallest, and (6) largest area of buildings that intersect each cell. These values are represented as raster layers with 30 m cell size covering the 48 conterminous states. We also identify errors in the original building dataset. We evaluate precision and recall in the data for three large U.S. urban areas. Precision is high and comparable to results reported by Microsoft while recall is high for buildings with footprints larger than 200 m2 but lower for progressively smaller buildings.
The National Energy Technology Laboratory, the Japan Oil, Gas and Metals National Corporation, and the U.S. Geological Survey are leading an effort to conduct an extended gas hydrate production test in northern Alaska. The proposed production test required the drilling of an initial stratigraphic test well (STW) to confirm the geologic conditions of the proposed test site. This well was completed in December 2018 in cooperation with the Prudhoe Bay Unit Interest Owners. With the success of the STW, the project leadership group is developing plans to drill a geologic data well and a production test well. Drilling plans for the STW were advanced in late 2018. The Prudhoe Bay Unit Hydrate-01 well was spudded on 10-December-2018. Downhole data acquisition was completed on 25-December-2018 and the rig was released on 01-January-2019. The STW was drilled in two sections. The surface hole was drilled to a depth of 2248 ft (MD, measured depth) and cased, and the “production hole section” was drilled to a depth of 3558 ft (MD) and also cased. A thermally chilled mineral-oilbased mud was used to maintain drillhole stability and quality of the borehole acquired data. The primary borehole data were acquired using a suite of Schlumberger logging-while-drilling tools. To gather grain size and other data needed to inform the design of the production test well, sidewall pressure cores were collected using Halliburton’s CoreVault tool. In addition to confirming the geologic conditions at the test site, the Hydrate-01 well was designed to serve as a monitoring well during future field operations. Therefore, two sets of fiber optic cables, each including bundled Distributed Acoustic Sensors (DAS) and Distributed Temperature Sensors (DTS), were clamped to the outside of the well casing and cemented in place. In March 2019, the project team worked with SAExploration to acquire 3D DAS Vertical Seismic Profiling (VSP) data in the Hydrate-01 well, which was the largest 3D DAS-VSP ever conducted. Additionally, since the December 2018 completion of the STW, several borehole temperature surveys have been acquired with the DTS deployed in the Hydrate-01 well.
","conferenceTitle":"10th International Conference on Gas Hydrates (ICGH10)","conferenceDate":"June 21-26, 2020","conferenceLocation":"Singapore","language":"English","publisher":"National Energy Technology Laboratory","usgsCitation":"Collett, T.S., Zyrianova, M.V., Okinaka, N., Wakatsuki, M., Boswell, R., Marsteller, S., Minge, D., Crumley, S., Itter, D., and Hunter, R.D., 2020, Design and operations of the Hydrate 01 Stratigraphic test well, Alaska North Slope, 10th International Conference on Gas Hydrates (ICGH10), Singapore, June 21-26, 2020, 8 p.","productDescription":"8 p.","ipdsId":"IP-115172","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":378430,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":378429,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.netl.doe.gov/node/10037"}],"country":"United States","state":"Alaska","otherGeospatial":"North Slope","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -169.013671875,\n 67.03316279015063\n ],\n [\n -140.888671875,\n 67.03316279015063\n ],\n [\n -140.888671875,\n 72.04683989379397\n ],\n [\n -169.013671875,\n 72.04683989379397\n ],\n [\n -169.013671875,\n 67.03316279015063\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Collett, Timothy S. 0000-0002-7598-4708 tcollett@usgs.gov","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":1698,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","email":"tcollett@usgs.gov","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":798833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zyrianova, Margarita V. 0000-0002-3669-1320 rita@usgs.gov","orcid":"https://orcid.org/0000-0002-3669-1320","contributorId":198970,"corporation":false,"usgs":true,"family":"Zyrianova","given":"Margarita","email":"rita@usgs.gov","middleInitial":"V.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":798834,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Okinaka, Norihiro","contributorId":240054,"corporation":false,"usgs":false,"family":"Okinaka","given":"Norihiro","email":"","affiliations":[{"id":17917,"text":"Japan Oil, Gas and Metals National Corporation","active":true,"usgs":false}],"preferred":false,"id":798835,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wakatsuki, Motoi","contributorId":216075,"corporation":false,"usgs":false,"family":"Wakatsuki","given":"Motoi","email":"","affiliations":[{"id":39359,"text":"JOGMEC","active":true,"usgs":false}],"preferred":false,"id":798836,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boswell, Ray","contributorId":224746,"corporation":false,"usgs":false,"family":"Boswell","given":"Ray","affiliations":[{"id":28000,"text":"National Energy Technology Laboratory, Pittsburgh, PA, USA","active":true,"usgs":false}],"preferred":false,"id":798837,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marsteller, Scott","contributorId":216073,"corporation":false,"usgs":false,"family":"Marsteller","given":"Scott","email":"","affiliations":[{"id":34152,"text":"US Department of Energy","active":true,"usgs":false}],"preferred":false,"id":798838,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Minge, David","contributorId":240716,"corporation":false,"usgs":false,"family":"Minge","given":"David","email":"","affiliations":[],"preferred":false,"id":798839,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Crumley, Stephen","contributorId":240080,"corporation":false,"usgs":false,"family":"Crumley","given":"Stephen","affiliations":[{"id":48087,"text":"BP Exploration Alaska, Inc.","active":true,"usgs":false}],"preferred":false,"id":798840,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Itter, David","contributorId":240081,"corporation":false,"usgs":false,"family":"Itter","given":"David","email":"","affiliations":[{"id":48087,"text":"BP Exploration Alaska, Inc.","active":true,"usgs":false}],"preferred":false,"id":798841,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hunter, Robert D. 0000-0002-6021-4479 rhunter@usgs.gov","orcid":"https://orcid.org/0000-0002-6021-4479","contributorId":5749,"corporation":false,"usgs":true,"family":"Hunter","given":"Robert","email":"rhunter@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":798842,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70210768,"text":"sir20205042 - 2020 - Procedure for calculating estimated ultimate recoveries of wells in the Wolfcamp shale of the Midland Basin, Permian Basin Province, Texas","interactions":[],"lastModifiedDate":"2020-08-05T18:37:00.289283","indexId":"sir20205042","displayToPublicDate":"2020-06-26T13:50:00","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":"2020-5042","displayTitle":"Procedure for Calculating Estimated Ultimate Recoveries of Wells in the Wolfcamp Shale of the Midland Basin, Permian Basin Province, Texas","title":"Procedure for calculating estimated ultimate recoveries of wells in the Wolfcamp shale of the Midland Basin, Permian Basin Province, Texas","docAbstract":"In 2016, the U.S. Geological Survey published an assessment of technically recoverable continuous oil and gas resources of the Wolfcamp shale in the Midland Basin, Permian Basin Province, Texas. Estimated ultimate recoveries (EURs) were calculated with production data from IHS MarkitTM using DeclinePlus software in the Harmony interface. These EURs were a major component of the quantitative resource assessment. For five of the six assessment units in the study, an industry operator in the Midland Basin provided information that was used to differentiate the Wolfcamp horizontal well landing zones. The IHS MarkitTM production database does not distinguish between the Wolfcamp A, B, C, and D well landing zones. These different units of the Wolfcamp have different production patterns that are important for calculation of EURs. The calculated mean EURs for each assessment unit ranged from 99,000 barrels of oil in the Wolfcamp C to 142,000 barrels of oil in the Wolfcamp A.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205042","usgsCitation":"Leathers-Miller, H.M., 2020, Procedure for calculating estimated ultimate recoveries of wells in the Wolfcamp shale of the Midland Basin, Permian Basin Province, Texas: U.S. Geological Survey Scientific Investigations Report 2020–5042, 5 p., https://doi.org/10.3133/sir20205042.","productDescription":"iii, 5 p.","onlineOnly":"Y","ipdsId":"IP-091005","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":375829,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5042/coverthb.jpg"},{"id":375830,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5042/sir20205042.pdf","text":"Report","size":"1.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5042"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.974609375,\n 30.031055426540206\n ],\n [\n -98.1298828125,\n 30.031055426540206\n ],\n [\n -98.0859375,\n 33.61461929233378\n ],\n [\n -101.05224609374999,\n 33.63291573870479\n ],\n [\n -103.9306640625,\n 33.687781758439364\n ],\n [\n -103.974609375,\n 30.031055426540206\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
In recent decades, many bumble bee species have declined due to changes in habitat, climate, and pressures from pathogens, pesticides, and introduced species. The western bumble bee (Bombus occidentalis ), once common throughout western North America, is a species of concern and will be considered for listing by the U.S. Fish and Wildlife Service (USFWS) under the Endangered Species Act (ESA). We attempt to improve alignment of data collection and research with USFWS needs to consider redundancy, resiliency, and representation in the upcoming species status assessment. We reviewed existing data and literature on B. occidentalis , highlighting information gaps and priority topics for research. Priorities include increased knowledge of trends, basic information on several life‐history stages, and improved understanding of the relative and interacting effects of stressors on population trends, especially the effects of pathogens, pesticides, climate change, and habitat loss. An understanding of how and where geographic range extent has changed for the two subspecies of B. occidentalis is also needed. We outline data that could be easily collected in other research projects that would increase their utility for understanding range‐wide trends of bumble bees. We modeled the overall trend in occupancy from 1998 to 2018 of Bombus occidentalis within the continental United States using existing data. The probability of local occupancy declined by 93% over 21 yr from 0.81 (95% CRI = 0.43, 0.98) in 1998 to 0.06 (95% CRI = 0.02, 0.16) in 2018. The decline in occupancy varied spatially by landcover and other environmental factors. Detection rates vary in both space and time, but peak detection across the continental United States occurs in mid‐July. We found considerable spatial gaps in recent sampling, with limited sampling in many regions, including most of Alaska, northwestern Canada, and the southwestern United States. We therefore propose a sampling design to address these gaps to best inform the ESA species status assessment through improved assessment of how the spatial distribution of stressors influences occupancy changes. Finally, we request involvement via data sharing, participation in occupancy sampling with repeated visits to distributed survey sites, and complementary research to address priorities outlined in this paper.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3141","usgsCitation":"Graves, T., Janousek, W.M., Gaulke, S., Nicholas, A., Keinath, D., Bell, C.M., Cannings, S., Hatfield, R.G., Heron, J.M., Koch, J.B., Loffland, H.L., Richardson, L., Rohde, A., Rykken, J., Strange, J.P., Tronstead, L., and Sheffield, C., 2020, Western bumble bee: Declines in United States and range-wide information gaps: Ecosphere, v. 11, no. 6, e03141, 13 p., https://doi.org/10.1002/ecs2.3141.","productDescription":"e03141, 13 p.","ipdsId":"IP-113225","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":456241,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3141","text":"Publisher Index Page"},{"id":436910,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QY8ZA0","text":"USGS data release","linkHelpText":"Western bumble bee predicted occupancy and detection probability rasters for the western continental United States from 1998 to 2018"},{"id":377366,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.017578125,\n 31.728167146023935\n ],\n [\n -102.919921875,\n 32.175612478499325\n ],\n [\n -103.271484375,\n 37.020098201368114\n ],\n [\n -102.216796875,\n 37.020098201368114\n ],\n [\n -102.216796875,\n 41.04621681452063\n ],\n [\n -95.44921875,\n 40.64730356252251\n ],\n [\n -96.85546875,\n 48.980216985374994\n ],\n [\n -109.16015624999999,\n 66.16051056018838\n ],\n [\n -126.5625,\n 69.3493386397765\n ],\n [\n -153.45703125,\n 70.58341752317065\n ],\n [\n -157.060546875,\n 71.10254274232307\n ],\n [\n -165.76171875,\n 68.942606818121\n ],\n [\n -167.6953125,\n 65.47650756256367\n ],\n [\n -164.70703125,\n 59.93300042374631\n ],\n [\n -157.32421875,\n 57.75107598132104\n ],\n [\n -167.958984375,\n 53.64463782485651\n ],\n [\n -152.05078125,\n 57.326521225217064\n ],\n [\n -146.162109375,\n 60.02095215374802\n ],\n [\n -133.154296875,\n 53.330872983017066\n ],\n [\n -125.595703125,\n 47.635783590864854\n ],\n [\n -125.068359375,\n 40.111688665595956\n ],\n [\n -121.904296875,\n 34.016241889667015\n ],\n [\n -117.158203125,\n 32.54681317351514\n ],\n [\n -114.345703125,\n 32.84267363195431\n ],\n [\n -110.302734375,\n 31.052933985705163\n ],\n [\n -108.10546875,\n 31.353636941500987\n ],\n [\n -108.017578125,\n 31.728167146023935\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":795738,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Janousek, William Michael 0000-0003-3978-1775","orcid":"https://orcid.org/0000-0003-3978-1775","contributorId":237980,"corporation":false,"usgs":true,"family":"Janousek","given":"William","email":"","middleInitial":"Michael","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":795739,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gaulke, Sarah M. 0000-0002-2657-5844","orcid":"https://orcid.org/0000-0002-2657-5844","contributorId":237981,"corporation":false,"usgs":true,"family":"Gaulke","given":"Sarah M.","affiliations":[],"preferred":false,"id":795740,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nicholas, Amy","contributorId":237982,"corporation":false,"usgs":false,"family":"Nicholas","given":"Amy","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":795741,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Keinath, Douglas","contributorId":12747,"corporation":false,"usgs":true,"family":"Keinath","given":"Douglas","affiliations":[],"preferred":false,"id":795742,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bell, Christine M.","contributorId":237984,"corporation":false,"usgs":false,"family":"Bell","given":"Christine","email":"","middleInitial":"M.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":795743,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cannings, Syd","contributorId":237985,"corporation":false,"usgs":false,"family":"Cannings","given":"Syd","email":"","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":795744,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hatfield, Richard G.","contributorId":237986,"corporation":false,"usgs":false,"family":"Hatfield","given":"Richard","email":"","middleInitial":"G.","affiliations":[{"id":37554,"text":"Xerces Society","active":true,"usgs":false}],"preferred":false,"id":795745,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Heron, Jennifer M","contributorId":237987,"corporation":false,"usgs":false,"family":"Heron","given":"Jennifer","email":"","middleInitial":"M","affiliations":[{"id":47670,"text":"British Columbia Ministry of Environment and Climate Change","active":true,"usgs":false}],"preferred":false,"id":795746,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Koch, Jonathan B","contributorId":237988,"corporation":false,"usgs":false,"family":"Koch","given":"Jonathan","email":"","middleInitial":"B","affiliations":[{"id":47671,"text":"University of Hawai'i, Hilo","active":true,"usgs":false}],"preferred":false,"id":795747,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Loffland, Helen L","contributorId":237989,"corporation":false,"usgs":false,"family":"Loffland","given":"Helen","email":"","middleInitial":"L","affiliations":[{"id":37290,"text":"The Institute for Bird Populations","active":true,"usgs":false}],"preferred":false,"id":795748,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Richardson, Leif L","contributorId":237990,"corporation":false,"usgs":false,"family":"Richardson","given":"Leif L","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":795749,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rohde, Ashley T. 0000-0003-4939-3047","orcid":"https://orcid.org/0000-0003-4939-3047","contributorId":204143,"corporation":false,"usgs":false,"family":"Rohde","given":"Ashley T.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":795750,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Rykken, Jessica","contributorId":150931,"corporation":false,"usgs":false,"family":"Rykken","given":"Jessica","email":"","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":795751,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Strange, James P.","contributorId":224183,"corporation":false,"usgs":false,"family":"Strange","given":"James","email":"","middleInitial":"P.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":795752,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Tronstead, Lusha","contributorId":237991,"corporation":false,"usgs":false,"family":"Tronstead","given":"Lusha","email":"","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":795753,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Sheffield, Cory","contributorId":237992,"corporation":false,"usgs":false,"family":"Sheffield","given":"Cory","email":"","affiliations":[{"id":47672,"text":"Royal Saskatchewan Museum","active":true,"usgs":false}],"preferred":false,"id":795754,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70213136,"text":"70213136 - 2020 - Gas hydrate saturation estimation from acoustic log data in the 2018 Alaska North Slope Hydrate-01 stratigraphic test well","interactions":[],"lastModifiedDate":"2020-09-10T14:43:04.308755","indexId":"70213136","displayToPublicDate":"2020-06-26T09:37:57","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Gas hydrate saturation estimation from acoustic log data in the 2018 Alaska North Slope Hydrate-01 stratigraphic test well","docAbstract":"Completed in December 2018, the Alaska North Slope Hydrate 01 stratigraphic test well provides a wealth of logging-while-drilling (LWD) data for strata to below the base of gas hydrate stability (BGHS). This well is intended to be the first of three wells drilled for a long-term gas hydrate production test to be conducted by the U.S. Department of Energy National Energy Technology Laboratory, the Japan Oil, Gas and Metals National Corporation, and the U.S. Geological Survey (USGS). The Hydrate 01 stratigraphic test well confirmed the presence of gas hydrate in two sand reservoirs within the hydrate stability zone, indicating the suitability of this location for a long-term gas hydrate production test. \nThe USGS, using an effective-medium-theory rock-physics approach, has estimated gas hydrate saturations from compressional (P) and shear (S) wave log data acquired in the Hydrate 01 well. We assume that gas hydrate occurs as pore-filling load-bearing material (i.e., part of the grain matrix). For Unit D, approximately 500 feet above the BGHS, both P-wave and S-wave acoustic logs indicate high gas hydrate saturations with S-wave results slightly lower than those for P-waves. For Unit B, located just above the BGHS, we obtain high gas hydrate saturation estimates from both sonic logs. Our P-wave saturation estimates agree well with results from electrical-resistivity-based estimates, whereas estimates from nuclear magnetic resonance LWD data generally suggest 5 to 10 percent higher saturations; our S-wave results suggest lower saturations. These differences likely indicate complexities in the form of gas hydrate occurrence within the sediment pore space, potentially including differences between hydrate occurrence in Units B and D.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 10th International Conference on Gas Hydrates (ICGH10","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"10th International Conference on Gas Hydrates (ICGH10)","conferenceDate":"June 21-26, 2020","conferenceLocation":"Singapore","language":"English","publisher":"US Department of Energy – NETL Program","usgsCitation":"Haines, S.S., Collett, T., Boswell, R., Lim, T., Okinaka, N., Suzuki, K., and Fujimoto, A., 2020, Gas hydrate saturation estimation from acoustic log data in the 2018 Alaska North Slope Hydrate-01 stratigraphic test well, in Proceedings of the 10th International Conference on Gas Hydrates (ICGH10, Singapore, June 21-26, 2020, 5 p.","productDescription":"5 p.","ipdsId":"IP-115106","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":378310,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":378294,"type":{"id":15,"text":"Index Page"},"url":"https://www.netl.doe.gov/node/10037"}],"country":"United States","state":"Alaska","otherGeospatial":"North Slope","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -166.46484375,\n 67.20403234340081\n ],\n [\n -141.6796875,\n 67.20403234340081\n ],\n [\n -141.6796875,\n 70.37785394109224\n ],\n [\n -156.796875,\n 71.69129271863999\n ],\n [\n -166.46484375,\n 70.37785394109224\n ],\n [\n -166.46484375,\n 67.20403234340081\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Haines, Seth S. 0000-0003-2611-8165 shaines@usgs.gov","orcid":"https://orcid.org/0000-0003-2611-8165","contributorId":1344,"corporation":false,"usgs":true,"family":"Haines","given":"Seth","email":"shaines@usgs.gov","middleInitial":"S.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":798376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collett, Timothy 0000-0002-7598-4708","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":220806,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":798377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boswell, Ray","contributorId":240083,"corporation":false,"usgs":false,"family":"Boswell","given":"Ray","affiliations":[{"id":48091,"text":"NETL, DOE","active":true,"usgs":false}],"preferred":false,"id":798378,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lim, Teck","contributorId":240084,"corporation":false,"usgs":false,"family":"Lim","given":"Teck","affiliations":[{"id":48092,"text":"TOYO Engineering","active":true,"usgs":false}],"preferred":false,"id":798379,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Okinaka, Nori","contributorId":240085,"corporation":false,"usgs":false,"family":"Okinaka","given":"Nori","affiliations":[{"id":39359,"text":"JOGMEC","active":true,"usgs":false}],"preferred":false,"id":798380,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Suzuki, Kiyofumi","contributorId":240086,"corporation":false,"usgs":false,"family":"Suzuki","given":"Kiyofumi","affiliations":[{"id":39359,"text":"JOGMEC","active":true,"usgs":false}],"preferred":false,"id":798381,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fujimoto, Akira","contributorId":240087,"corporation":false,"usgs":false,"family":"Fujimoto","given":"Akira","email":"","affiliations":[{"id":39359,"text":"JOGMEC","active":true,"usgs":false}],"preferred":false,"id":798382,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70210995,"text":"70210995 - 2020 - Critical evaluation of stable isotope mixing end-members for estimating groundwater recharge sources: Case study from the South Rim of the Grand Canyon, Arizona, USA","interactions":[],"lastModifiedDate":"2020-08-05T13:35:17.005891","indexId":"70210995","displayToPublicDate":"2020-06-26T08:37:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Critical evaluation of stable isotope mixing end-members for estimating groundwater recharge sources: Case study from the South Rim of the Grand Canyon, Arizona, USA","docAbstract":"Springs and groundwater seeps along the South Rim of the Grand Canyon serve an important function for the region’s ecosystems, residents (both human and wild animal), and economy. However, these springs and seeps are potentially vulnerable to contamination, increased groundwater extraction, or reduced recharge due to climate change. Protection of South Rim groundwater resources requires improved understanding of the regional groundwater system. In this study, statistical methods are used to investigate δ2H and δ18O in precipitation, surface water, and groundwater. A mixing model for δ18O is developed using statistically distinct seasonal end-members represented by modeled winter (Nov-Apr.) precipitation and summer (May-Oct.) surface water run-off. The calculated fraction of winter recharge (Fwin) indicates that South Rim groundwater is primarily sourced from snow-melt and winter rains with an average Fwin of 0.97 ± 0.09. Groundwater sourced from the highest elevations of the study area are more depleted than the winter end-member suggesting values of Fwin are overestimated or a meaningful portion of recharge occurs at lower elevations. Lower elevation recharge from the Coconino Plateau is supported by consistent spatial trends in δ2H and δ18O with respect to longitude, Fwin values less than 0.9 for 9 of the 50 samples, and age tracer data indicating young groundwater discharging from springs which is distinct from old groundwater observed in the regional flow system. These results suggest a new conceptual model is needed to account for recharge sources from low elevation and summer precipitation. Results imply resource managers need to reconsider current land-use and water management practices on the South Rim to protect future water quantity and quality.","language":"English","publisher":"Springer","doi":"10.1007/s10040-020-02194-y","usgsCitation":"Solder, J.E., and Beisner, K.R., 2020, Critical evaluation of stable isotope mixing end-members for estimating groundwater recharge sources: Case study from the South Rim of the Grand Canyon, Arizona, USA: Hydrogeology Journal, v. 28, p. 1575-1591, https://doi.org/10.1007/s10040-020-02194-y.","productDescription":"17 p.","startPage":"1575","endPage":"1591","ipdsId":"IP-110272","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":456249,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-020-02194-y","text":"Publisher Index Page"},{"id":436913,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G7INFB","text":"USGS data release","linkHelpText":"Stable isotopic ratios of hydrogen and oxygen in groundwater and calculated fraction of recharge from winter precipitation, South Rim Grand Canyon, Arizona"},{"id":376253,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"South Rim of the Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.4835205078125,\n 35.7019167328534\n ],\n [\n -111.65679931640625,\n 35.7019167328534\n ],\n [\n -111.65679931640625,\n 36.18000806322456\n ],\n [\n -112.4835205078125,\n 36.18000806322456\n ],\n [\n -112.4835205078125,\n 35.7019167328534\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","noUsgsAuthors":false,"publicationDate":"2020-06-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Solder, John E. 0000-0002-0660-3326","orcid":"https://orcid.org/0000-0002-0660-3326","contributorId":201953,"corporation":false,"usgs":true,"family":"Solder","given":"John","email":"","middleInitial":"E.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792368,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beisner, Kimberly R. 0000-0002-2077-6899 kbeisner@usgs.gov","orcid":"https://orcid.org/0000-0002-2077-6899","contributorId":2733,"corporation":false,"usgs":true,"family":"Beisner","given":"Kimberly","email":"kbeisner@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792369,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70210822,"text":"70210822 - 2020 - Migratory behavior and winter geography drive differential range shifts of eastern birds in response to recent climate change","interactions":[],"lastModifiedDate":"2020-06-29T12:45:16.577045","indexId":"70210822","displayToPublicDate":"2020-06-26T08:36:20","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":"Migratory behavior and winter geography drive differential range shifts of eastern birds in response to recent climate change","docAbstract":"Over the past half century, migratory birds in North America have shown divergent population trends relative to resident species, with the former declining rapidly and the latter increasing. The role that climate change has played in these observed trends is not well understood, despite significant warming over this period. We used 43 y of monitoring data to fit dynamic species distribution models and quantify the rate of latitudinal range shifts in 32 species of birds native to eastern North America. Since the early 1970s, species that remain in North America throughout the year, including both resident and migratory species, appear to have responded to climate change through both colonization of suitable area at the northern leading edge of their breeding distributions and adaption in place at the southern trailing edges. Neotropical migrants, in contrast, have shown the opposite pattern: contraction at their southern trailing edges and no measurable shifts in their northern leading edges. As a result, the latitudinal distributions of temperate-wintering species have increased while the latitudinal distributions of neotropical migrants have decreased. These results raise important questions about the mechanisms that determine range boundaries of neotropical migrants and suggest that these species may be particularly vulnerable to future climate change. Our results highlight the potential importance of climate change during the nonbreeding season in constraining the response of migratory species to temperature changes at both the trailing and leading edges of their breeding distributions. Future research on the interactions between breeding and nonbreeding climate change is urgently needed.","language":"English","publisher":"PNAS","doi":"10.1073/pnas.2000299117","usgsCitation":"Clark Rushing, Royle, A., Ziolkowski, D., and Pardieck, K.L., 2020, Migratory behavior and winter geography drive differential range shifts of eastern birds in response to recent climate change: Proceedings of the National Academy of Sciences of the United States of America, v. 117, no. 23, p. 12897-12903, https://doi.org/10.1073/pnas.2000299117.","productDescription":"7 p.","startPage":"12897","endPage":"12903","ipdsId":"IP-115090","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":456252,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2000299117","text":"Publisher Index Page"},{"id":375949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Eastern North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -69.9609375,\n 58.44773280389084\n ],\n [\n -80.33203125,\n 41.77131167976407\n ],\n [\n -85.25390625,\n 30.14512718337613\n ],\n [\n -81.9140625,\n 24.367113562651262\n ],\n [\n -74.00390625,\n 38.95940879245423\n ],\n [\n -60.1171875,\n 45.583289756006316\n ],\n [\n -53.26171875,\n 47.39834920035926\n ],\n [\n -64.16015624999999,\n 59.977005492196\n ],\n [\n -69.9609375,\n 58.44773280389084\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"117","issue":"23","noUsgsAuthors":false,"publicationDate":"2020-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Clark Rushing","contributorId":225554,"corporation":false,"usgs":false,"family":"Clark Rushing","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":791593,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791594,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ziolkowski, David 0000-0002-2500-4417 dziolkowski@usgs.gov","orcid":"https://orcid.org/0000-0002-2500-4417","contributorId":195409,"corporation":false,"usgs":true,"family":"Ziolkowski","given":"David","email":"dziolkowski@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791595,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pardieck, Keith L. 0000-0003-2779-4392 kpardieck@usgs.gov","orcid":"https://orcid.org/0000-0003-2779-4392","contributorId":4104,"corporation":false,"usgs":true,"family":"Pardieck","given":"Keith","email":"kpardieck@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791596,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210820,"text":"70210820 - 2020 - Changes to Monitoring Trends in Burn Severity Program’s production procedures and data products","interactions":[],"lastModifiedDate":"2024-04-23T16:46:28.247778","indexId":"70210820","displayToPublicDate":"2020-06-26T08:29:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Changes to Monitoring Trends in Burn Severity Program’s production procedures and data products","docAbstract":"The Monitoring Trends in Burn Severity (MTBS) program has been providing the fire science community with large fire perimeter and burn severity data for the past 14 years. As of October 2019, 22 969 fires have been mapped by the MTBS program and are available on the MTBS website (https://www.mtbs.gov). These data have been widely used by researchers to examine a variety of fire and climate science topics. However, MTBS has undergone significant changes to its fire mapping methodology, the remotely sensed imagery used to map fires, and the subsequent fire occurrence, burned boundary, and severity databases. To gather a better understanding of these changes and the potential impacts that they may have on the user community, we examined the changes to the MTBS burn mapping protocols and whether remapped burned area boundary and severity products differ significantly from the original MTBS products.
","language":"English","publisher":"Springer","doi":"10.1186/s42408-020-00076-y","usgsCitation":"Picotte, J.J., Bhattarai, K.P., Howard, D., Lecker, J., Epting, J., Quayle, B., Benson, N., and Nelson, K., 2020, Changes to Monitoring Trends in Burn Severity Program’s production procedures and data products: Fire Ecology, v. 16, 16, 12 p., https://doi.org/10.1186/s42408-020-00076-y.","productDescription":"16, 12 p.","ipdsId":"IP-112537","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":456256,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-020-00076-y","text":"Publisher Index Page"},{"id":436914,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97UMU6K","text":"USGS data release","linkHelpText":"Burn Severity Portal, a clearing house of fire severity and extent information (ver. 8.0, August 2024)"},{"id":395379,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IED7RZ","text":"USGS data release","description":"USGS data release","linkHelpText":"Monitoring Trends in Burn Severity from 1984-2018"},{"id":375946,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","noUsgsAuthors":false,"publicationDate":"2020-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Picotte, Joshua J. 0000-0002-4021-4623 jpicotte@usgs.gov","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":4626,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua","email":"jpicotte@usgs.gov","middleInitial":"J.","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":791579,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bhattarai, Krishna P. kbhattarai@usgs.gov","contributorId":3487,"corporation":false,"usgs":true,"family":"Bhattarai","given":"Krishna","email":"kbhattarai@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":true,"id":791622,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howard, Daniel 0000-0002-7563-7538","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":56946,"corporation":false,"usgs":true,"family":"Howard","given":"Daniel","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":791581,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lecker, Jennifer","contributorId":199101,"corporation":false,"usgs":false,"family":"Lecker","given":"Jennifer","email":"","affiliations":[],"preferred":false,"id":791582,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Epting, Justin","contributorId":225552,"corporation":false,"usgs":false,"family":"Epting","given":"Justin","email":"","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":791583,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Quayle, Brad","contributorId":146381,"corporation":false,"usgs":false,"family":"Quayle","given":"Brad","email":"","affiliations":[],"preferred":false,"id":791584,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Benson, Nate","contributorId":225028,"corporation":false,"usgs":false,"family":"Benson","given":"Nate","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":791585,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nelson, Kurtis 0000-0003-4911-4511 knelson@usgs.gov","orcid":"https://orcid.org/0000-0003-4911-4511","contributorId":3602,"corporation":false,"usgs":true,"family":"Nelson","given":"Kurtis","email":"knelson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":791586,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70211026,"text":"70211026 - 2020 - Accurate bathymetric maps from underwater digital imagery without ground control","interactions":[],"lastModifiedDate":"2020-07-10T13:06:24.414755","indexId":"70211026","displayToPublicDate":"2020-06-26T08:03:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Accurate bathymetric maps from underwater digital imagery without ground control","docAbstract":"Structure-from-Motion (SfM) photogrammetry can be used with digital underwater photographs to generate high-resolution bathymetry and orthomosaics with millimeter-to-centimeter scale resolution at relatively low cost. Although these products are useful for assessing species diversity and health, they have additional utility for quantifying benthic community structure, such as coral growth and fine-scale elevation change over time, if accurate length scales and georeferencing are included. This georeferencing is commonly provided with “ground control,” such as pre-installed seafloor benchmarks or identifiable “static” features, which can be difficult and time consuming to install, survey, and maintain. To address these challenges, we developed the SfM Quantitative Underwater Imaging Device with Five Cameras (SQUID-5), a towed surface vehicle with an onboard survey-grade Global Navigation Satellite System (GNSS) and five rigidly mounted downward-looking cameras with overlapping views of the seafloor. The cameras are tightly synchronized with both the GNSS and each other to collect quintet photo sets and record the precise location of every collection event. The system was field tested in July 2019 in the U.S. Florida Keys, in water depths ranging from 3 to 9 m over a variety of bottom types. Surveying accuracy was assessed using pre-installed stations with known coordinates, machined scale bars, and two independent surveys of a site to evaluate repeatability. Under a range of sea conditions, ambient lighting, and water clarity, we were able to map living and senile coral reef habitats and sand waves at mm-scale resolution. Data were processed using best practice SfM techniques without ground control and local measurement errors of horizontal and vertical scales were consistently sub-millimeter, equivalent to 0.013% RMSE relative to water depth. Survey-to-survey repeatability RMSE was on the order of 3 cm without georeferencing but could be improved to several millimeters with the incorporation of one or more non-surveyed marker points. We demonstrate that the SQUID-5 platform can map complex coral reef and other seafloor habitats and measure mm-to-cm scale changes in the morphology and location of seafloor features over time without pre-existing ground control.","language":"English","publisher":"Frontiers","doi":"10.3389/fmars.2020.00525","usgsCitation":"Hatcher, G.A., Warrick, J.A., Ritchie, A.C., Dailey, E.T., Zawada, D., Kranenburg, C.J., and Yates, K.K., 2020, Accurate bathymetric maps from underwater digital imagery without ground control: Frontiers in Marine Science, v. 7, 525, 20 p., https://doi.org/10.3389/fmars.2020.00525.","productDescription":"525, 20 p.","ipdsId":"IP-117107","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":456262,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2020.00525","text":"Publisher Index Page"},{"id":436916,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WYVEJ6","text":"USGS data release","linkHelpText":"squid5-software"},{"id":436915,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V7K7EG","text":"USGS data release","linkHelpText":"SQUID-5 structure-from-motion point clouds, bathymetric maps, orthomosaics, and underwater photos of coral reefs in Florida, 2019"},{"id":376245,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Florida Keys","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.771240234375,\n 24.43714786161562\n ],\n [\n -80.892333984375,\n 24.657002173279082\n ],\n [\n -80.277099609375,\n 24.95119964792312\n ],\n [\n -80.18920898437499,\n 25.35891851754525\n ],\n [\n -80.343017578125,\n 25.37877231509496\n ],\n [\n -80.7550048828125,\n 25.085598897064752\n ],\n [\n -81.2713623046875,\n 24.84656534821976\n ],\n [\n -81.91955566406249,\n 24.696934226366672\n ],\n [\n -81.97998046875,\n 24.44714958973082\n ],\n [\n -81.771240234375,\n 24.43714786161562\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","noUsgsAuthors":false,"publicationDate":"2020-06-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Hatcher, Gerry A. 0000-0001-7705-1509 ghatcher@usgs.gov","orcid":"https://orcid.org/0000-0001-7705-1509","contributorId":208239,"corporation":false,"usgs":true,"family":"Hatcher","given":"Gerry","email":"ghatcher@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":792468,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warrick, Jonathan A. 0000-0002-0205-3814 jwarrick@usgs.gov","orcid":"https://orcid.org/0000-0002-0205-3814","contributorId":167736,"corporation":false,"usgs":true,"family":"Warrick","given":"Jonathan","email":"jwarrick@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":792469,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ritchie, Andrew C. aritchie@usgs.gov","contributorId":4984,"corporation":false,"usgs":true,"family":"Ritchie","given":"Andrew","email":"aritchie@usgs.gov","middleInitial":"C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":792470,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dailey, Evan T. 0000-0002-4382-3870 edailey@usgs.gov","orcid":"https://orcid.org/0000-0002-4382-3870","contributorId":195607,"corporation":false,"usgs":true,"family":"Dailey","given":"Evan","email":"edailey@usgs.gov","middleInitial":"T.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":792471,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zawada, David G. 0000-0003-4547-4878 dzawada@usgs.gov","orcid":"https://orcid.org/0000-0003-4547-4878","contributorId":1898,"corporation":false,"usgs":true,"family":"Zawada","given":"David G.","email":"dzawada@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":792472,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kranenburg, Christine J. 0000-0002-2955-0167 ckranenburg@usgs.gov","orcid":"https://orcid.org/0000-0002-2955-0167","contributorId":169234,"corporation":false,"usgs":true,"family":"Kranenburg","given":"Christine","email":"ckranenburg@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":792473,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yates, Kimberly K. 0000-0001-8764-0358","orcid":"https://orcid.org/0000-0001-8764-0358","contributorId":214349,"corporation":false,"usgs":true,"family":"Yates","given":"Kimberly","email":"","middleInitial":"K.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":792474,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70238863,"text":"70238863 - 2020 - Carbon dioxide-induced mortality of four species of North American fishes","interactions":[],"lastModifiedDate":"2022-12-14T13:21:47.751686","indexId":"70238863","displayToPublicDate":"2020-06-26T07:18:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Carbon dioxide-induced mortality of four species of North American fishes","docAbstract":"Fisheries managers have a growing interest in the use of carbon dioxide (CO2) as a tool for controlling invasive fishes. However, limited published data exist on susceptibility of many commonly encountered species to elevated CO2 concentrations. Our objective was to estimate the 24-h 50% lethal concentration (LC50) and 95% lethal concentration (LC95) of CO2 for four fishes (Rainbow Trout Oncorhynchus mykiss, Common Carp Cyprinus carpio, Channel Catfish Ictalurus punctatus, and Westslope Cutthroat Trout Oncorhynchus clarkii lewisi). In the laboratory, we exposed juvenile fish to a range of CO2 concentrations for 24 h in unpressurized, flow-through tanks. We developed a Bayesian hierarchical model to estimate the dose-response relationship for each fish species with associated uncertainty, and estimated 24-h LC50 and LC95 values based on laboratory trials for each species. The minimum concentration inducing mortality differed among cold water–adapted species and warm water–adapted species groups: 150 mg CO2/L for Westslope Cutthroat Trout and Rainbow Trout and 225 mg CO2/L for Common Carp and Channel Catfish. We observed complete mortality at 275 mg CO2/L (38,672 microatmospheres [μatm]), 225 mg CO2/L (30,711 μatm), and 495 mg CO2/L (65,708 μatm [Common Carp]; 77,213 μatm [Channel Catfish]) for Westslope Cutthroat Trout, Rainbow Trout, and both Common Carp and Channel Catfish, respectively. There was evidence of a statistical difference between the 24-h LC95 values of Westslope Cutthroat Trout and Rainbow Trout (245.0 [222.2–272.2] and 190.6 [177.2–207.8] mg CO2/L, respectively). Additionally, these values were almost half the estimated 24-h LC95 values for Common Carp and Channel Catfish (422.5 [374.7–474.5] and 434.2 [377.2–492.2] mg CO2/L, respectively). Although the experimental findings show strong relationships between increased CO2 concentration and higher mortality, additional work is required to assess the efficacy and feasibility of a CO2 application in a field setting.
To understand the behavior of potentially damaging ground motions during earthquakes, seismic-site effects are routinely characterized by using the dispersion of surface waves. Many methods exist to measure dispersion; these methods have various advantages and disadvantages, but they all yield dispersion data that must be inverted for shear-wave velocity. This report presents a graphical tool for efficiently removing spurious data as well as combining data from multiple methods prior to inversion.
The Dispersion Plot Editor (DPE) program presented here (version 1.5) is coded in Python 3, which is open source and platform independent. DPE accepts input dispersion data as one or more delimited text files. The program plots the data in useful forms, including both scattered points and an interpolated heat map. The user selects points to delete by drawing arbitrary shapes with the mouse cursor. After the spurious data are removed, the user may represent the acceptable data with a dispersion curve. The acceptable data and the representative dispersion curve are output as separate comma-delimited text files. Images of the plotted data and the representative dispersion curve may also be saved.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201065","usgsCitation":"McPhillips, D., Yong, A.K., Martin, A., and Stephenson, W.J., 2020, Graphical Dispersion Plot Editor (DPE) for seismic-site characterization by using multiple surface-wave methods: U.S. Geological Survey Open-File Report 2020–1065, 8 p., https://doi.org/10.3133/ofr20201065.","productDescription":"Report: iii, 8 p.; Appendix","numberOfPages":"8","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113656","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":375790,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1065/coverthb.jpg"},{"id":375791,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1065/ofr20201065.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":375792,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1065/ofr20201065_appendix.zip","size":"110 KB","linkFileType":{"id":6,"text":"zip"}}],"contact":"Earthquake Science Center
U.S. Geological Survey
350 N. Akron Road
Moffett Field, CA 94035
Science-based management strategies are needed to halt or reverse the global decline of amphibians. In many cases, sound management requires reliable models built using monitoring data. Historically, monitoring and statistical modeling efforts have focused on estimating occupancy using detection–nondetection data. Spatial occupancy models are useful for studying colonization–extinction dynamics, but richer insights can be gained from estimating abundance and density-dependent demographic rates. We developed an integrated abundance-based metapopulation model of the processes contributing to spatiotemporal variation in patch population density. We fit our model to a combination of detection–nondetection and count data from a 14-yr study of a reintroduced metapopulation of federally threatened Chiricahua Leopard Frogs (Lithobates chiricahuensis). Pond-specific population growth rate was influenced by pond hydroperiod and frog density, such that permanent and semipermanent ponds with low densities of adult frogs experienced the highest annual population growth rates. Immigration rate declined as the distance among ponds increased. After reintroduction in 2003, metapopulation-level abundance increased and appeared to stabilize around 1300 adult frogs (95% CI = 1192–1471) by year 2015. Further, changes in metapopulation abundance were driven mostly by changes in abundance at a few ponds. These high-density populations, which would not have been identifiable with traditional occupancy-based metapopulation models, are likely especially important for species recovery in the area. Abundance-based metapopulation models can be widely applied to inform conservation efforts, by providing higher quality information needed to prioritize habitat patches for management and can be used to make more accurate predictions of metapopulation extinction risk.
","language":"English","publisher":"The Herpetologists' League","doi":"10.1655/0018-0831-76.2.240","usgsCitation":"Howell, P.E., Hossack, B., Muths, E., Sigafus, B.H., and Chandler, R., 2020, Informing amphibian conservation efforts with abundance-based metapopulation models: Herpetologica, v. 76, no. 2, p. 240-250, https://doi.org/10.1655/0018-0831-76.2.240.","productDescription":"11 p.","startPage":"240","endPage":"250","ipdsId":"IP-111558","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":383399,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Howell, Paige E","contributorId":251713,"corporation":false,"usgs":false,"family":"Howell","given":"Paige","email":"","middleInitial":"E","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":810414,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":810415,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muths, Erin L. 0000-0002-5498-3132","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":243368,"corporation":false,"usgs":true,"family":"Muths","given":"Erin L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":810416,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sigafus, Brent H. 0000-0002-7422-8927 bsigafus@usgs.gov","orcid":"https://orcid.org/0000-0002-7422-8927","contributorId":4534,"corporation":false,"usgs":true,"family":"Sigafus","given":"Brent","email":"bsigafus@usgs.gov","middleInitial":"H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":810417,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chandler, Richard B.","contributorId":251714,"corporation":false,"usgs":false,"family":"Chandler","given":"Richard B.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":810418,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211174,"text":"70211174 - 2020 - Effects of snowpack, temperature, and disease on the demography of a wild population of amphibians","interactions":[],"lastModifiedDate":"2020-08-06T19:20:03.217264","indexId":"70211174","displayToPublicDate":"2020-06-23T10:51:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1892,"text":"Herpetologica","active":true,"publicationSubtype":{"id":10}},"title":"Effects of snowpack, temperature, and disease on the demography of a wild population of amphibians","docAbstract":"Understanding the demographic consequences of interactions among pathogens, hosts, and weather conditions is critical in determining how amphibian populations respond to disease and in identifying site-specific conservation actions that can be developed to bolster persistence of amphibian populations. We investigated population dynamics in Boreal Toads (Anaxyrus boreas) relative to abiotic (fall temperatures and snowpack) and biotic (the abundance of another anuran host and disease) characteristics of the local environment in Wyoming, USA. We used capture–recapture data and a multistate model where state was treated as a hidden Markov process to incorporate disease state uncertainty and assess our a priori hypotheses. Our results indicated that snowpack during the coldest week of winter is more influential to toad survival, disease transition probabilities, and the population-level prevalence of the amphibian chytrid fungus (Batrachochytrium dendrobatidis) in the spring, than temperatures in the fall or the presence of another host. As hypothesized, apparent survival at low (i.e., <25 cm) snowpack (0.22; confidence interval [CI] = 0.15–0.31) was lower than apparent survival at high snowpack (90.65; CI = 0.50–0.78). Our findings highlight the potential for local environmental factors, like snowpack, to influence disease and host persistence, and demonstrate the ecological complexity of disease effects on population demography in natural environments. This work further emphasizes the need for improved understanding of how climate change may influence the relationships among pathogens, hosts, and their environment for wild animal populations challenged by disease.
","language":"English","publisher":"BioOne","doi":"10.1655/0018-0831-76.2.132","usgsCitation":"Muths, E., Hossack, B., Grant, E.H., Pilliod, D., and Mosher, B.A., 2020, Effects of snowpack, temperature, and disease on the demography of a wild population of amphibians: Herpetologica, v. 76, no. 2, p. 132-143, https://doi.org/10.1655/0018-0831-76.2.132.","productDescription":"12 p.","startPage":"132","endPage":"143","ipdsId":"IP-111041","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":436920,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VACHX0","text":"USGS data release","linkHelpText":"Capture-recapture, disease and covariate data for boreal toads from Blackrock Wyoming 2019"},{"id":376433,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Bridger-Teton National Forest, Togwetee Pass","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.37139892578125,\n 43.49676775343911\n ],\n [\n -109.86602783203125,\n 43.49676775343911\n ],\n [\n -109.86602783203125,\n 43.866218006556394\n ],\n [\n -110.37139892578125,\n 43.866218006556394\n ],\n [\n -110.37139892578125,\n 43.49676775343911\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"76","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Muths, Erin L. 0000-0002-5498-3132","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":229346,"corporation":false,"usgs":true,"family":"Muths","given":"Erin L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":792944,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":792945,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grant, Evan H. 0000-0003-4401-6496","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":229348,"corporation":false,"usgs":true,"family":"Grant","given":"Evan","email":"","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":792946,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":229349,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":792947,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mosher, Brittany A.","contributorId":189579,"corporation":false,"usgs":false,"family":"Mosher","given":"Brittany","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":792948,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211180,"text":"70211180 - 2020 - A synthesis of evidence of drivers of amphibian declines","interactions":[],"lastModifiedDate":"2020-07-16T15:46:40.508724","indexId":"70211180","displayToPublicDate":"2020-06-23T10:43:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1892,"text":"Herpetologica","active":true,"publicationSubtype":{"id":10}},"title":"A synthesis of evidence of drivers of amphibian declines","docAbstract":"Early calls for robust long-term time series of amphibian population data, stemming from discussion following the first World Congress of Herpetology, are now being realized after 25 yr of focused research. Inference from individual studies and locations have contributed to a basic consensus on drivers of amphibian declines. Until recently there were no large-scale syntheses of long-term time series data to test hypotheses about the generality of factors driving population dynamics at broad spatial scales. Through the U.S. Geological Survey's Powell Center for Analysis and Synthesis, we brought together a group of scientists to elucidate mechanisms underlying amphibian declines in North America and Europe. We used time series of field data collected across dozens of study areas to make inferences with these combined data using hierarchical and spatial models. We bring together results from four syntheses of these data to summarize our state of knowledge of amphibian declines, identify commonalities that suggest further avenues of study, and suggest a way forward in addressing amphibian declines—by looking beyond specific drivers to how to achieve stability in remaining populations. The common thread of the syntheses is that declines are real but not ubiquitous, and that multiple factors drive declines but the relative importance of each factor varies among species, populations, and regions. We also found that climate is an important driver of amphibian population dynamics. However, the direction and magnitude of sensitivity to change vary among species in ways unlikely to explain overall rates of decline. Thirty years after the initial identification of a major catastrophe for global biodiversity, the scientific community has empirically demonstrated the reality of the problem, identified putative causes, provided evidence of their impacts, invested in broader-scale actions, and attempted meta-analyses to search out global drivers. We suggest an approach that focuses on key demographic rates that may improve amphibian population trends at multiple sites across the landscape.
","language":"English","publisher":"BioOne","doi":"10.1655/0018-0831-76.2.101","usgsCitation":"Grant, E.H., Miller, D., and Muths, E., 2020, A synthesis of evidence of drivers of amphibian declines: Herpetologica, v. 76, no. 2, p. 101-107, https://doi.org/10.1655/0018-0831-76.2.101.","productDescription":"7 p.","startPage":"101","endPage":"107","ipdsId":"IP-111040","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":376430,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Grant, Evan H. 0000-0003-4401-6496","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":229348,"corporation":false,"usgs":true,"family":"Grant","given":"Evan","email":"","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":792978,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, D. A. W.","contributorId":216930,"corporation":false,"usgs":false,"family":"Miller","given":"D. A. W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":792979,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muths, Erin L. 0000-0002-5498-3132","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":224061,"corporation":false,"usgs":true,"family":"Muths","given":"Erin L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":792980,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210791,"text":"70210791 - 2020 - The predictive skills of elastic Coulomb rate-and-state aftershock forecasts during the 2019 Ridgecrest, California, earthquake sequence","interactions":[],"lastModifiedDate":"2020-08-26T19:12:50.615604","indexId":"70210791","displayToPublicDate":"2020-06-23T10:23:54","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}},"title":"The predictive skills of elastic Coulomb rate-and-state aftershock forecasts during the 2019 Ridgecrest, California, earthquake sequence","docAbstract":"Operational earthquake forecasting protocols commonly use statistical models for their recognized ease of implementation and robustness in describing the short-term spatiotemporal patterns of triggered seismicity. However, recent advances on physics-based aftershock forecasting reveal comparable performance to the standard statistical counterparts with significantly improved predictive skills when fault and stress field heterogeneities are considered. Here, we perform a pseudo-prospective forecasting experiment during the first month of the 2019 Ridgecrest (California) earthquake sequence. We develop seven Coulomb rate-and-state models that couple static stress change estimates with continuum mechanics expressed by the rate-and-state friction laws. Our model parametrization supports a gradually increasing complexity; we start from a preliminary model implementation with simplified slip distributions and spatially homogeneous receiver faults to reach an enhanced one featuring optimized fault constitutive parameters, finite-fault slip models, secondary triggering effects, and spatially heterogenous planes informed by pre-existing ruptures. The data-rich environment of Southern California allows us to test whether incorporating data collected in near real-time during an unfolding earthquake sequence boosts our predictive power. We assess the absolute and relative performance of the forecasts by means of statistical tests used within the Collaboratory for the Study of Earthquake Predictability (CSEP) and compare their skills against a standard benchmark ETAS model for the short (24 hours after the two Ridgecrest mainshocks) and intermediate-term (one month). Stress-based forecasts expect heightened rates along the whole near-fault region and increased expected seismicity rates in Central Garlock Fault. Our comparative model evaluation supports that faulting heterogeneities coupled with secondary triggering effects are the most critical success components behind physics-based forecasts, but also underlines the importance of model updates incorporating near real-time available aftershock data reaching better performance than ETAS models. We explore the physical basis behind our results by investigating the localized shut down of pre-existing normal faults in the Ridgecrest near-source area.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200028","usgsCitation":"Mancini, S., Segou, M., Werner, M., and Parsons, T.E., 2020, The predictive skills of elastic Coulomb rate-and-state aftershock forecasts during the 2019 Ridgecrest, California, earthquake sequence: Bulletin of the Seismological Society of America, v. 110, no. 4, p. 1736-1751, https://doi.org/10.1785/0120200028.","productDescription":"16 p.","startPage":"1736","endPage":"1751","ipdsId":"IP-117717","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":456304,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research-information.bris.ac.uk/en/publications/b86ef22d-e493-45b3-b98c-b20b940530be","text":"External Repository"},{"id":375920,"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 \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.33923339843749,\n 35.14237113713991\n ],\n [\n -116.83959960937499,\n 35.14237113713991\n ],\n [\n -116.83959960937499,\n 36.37706783983682\n ],\n [\n -118.33923339843749,\n 36.37706783983682\n ],\n [\n -118.33923339843749,\n 35.14237113713991\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"110","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-06-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Mancini, Simone 0000-0003-3415-2080","orcid":"https://orcid.org/0000-0003-3415-2080","contributorId":225525,"corporation":false,"usgs":false,"family":"Mancini","given":"Simone","email":"","affiliations":[{"id":37322,"text":"University of Bristol","active":true,"usgs":false}],"preferred":false,"id":791436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Segou, Margarita","contributorId":199044,"corporation":false,"usgs":false,"family":"Segou","given":"Margarita","affiliations":[],"preferred":false,"id":791437,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Werner, Maximillian J","contributorId":195950,"corporation":false,"usgs":false,"family":"Werner","given":"Maximillian J","affiliations":[],"preferred":false,"id":791438,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":791439,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227716,"text":"70227716 - 2020 - Making ‘chemical cocktails’ – Evolution of urban geochemical processes across the periodic table of elements","interactions":[],"lastModifiedDate":"2022-01-27T15:38:18.764858","indexId":"70227716","displayToPublicDate":"2020-06-23T09:34:49","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Making ‘chemical cocktails’ – Evolution of urban geochemical processes across the periodic table of elements","docAbstract":"Urbanization contributes to the formation of novel elemental combinations and signatures in terrestrial and aquatic watersheds, also known as ‘chemical cocktails.’ The composition of chemical cocktails evolves across space and time due to: (1) elevated concentrations from anthropogenic sources, (2) accelerated weathering and corrosion of the built environment, (3) increased drainage density and intensification of urban water conveyance systems, and (4) enhanced rates of geochemical transformations due to changes in temperature, ionic strength, pH, and redox potentials. Characterizing chemical cocktails and underlying geochemical processes is necessary for: (1) tracking pollution sources using complex chemical mixtures instead of individual elements or compounds; (2) developing new strategies for co-managing groups of contaminants; (3) identifying proxies for predicting transport of chemical mixtures using continuous sensor data; and (4) determining whether interactive effects of chemical cocktails produce ecosystem-scale impacts greater than the sum of individual chemical stressors. First, we discuss some unique urban geochemical processes which form chemical cocktails, such as urban soil formation, human-accelerated weathering, urban acidification-alkalinization, and Freshwater Salinization Syndrome. Second, we review and synthesize global patterns in concentrations of major ions, carbon and nutrients, and trace elements in urban streams across different world regions and make comparisons with reference conditions. In addition to our global analysis, we highlight examples from watersheds in the Baltimore-Washington DC area, USA, which show increased transport of major ions, trace metals, and nutrients across streams draining a well-defined land-use gradient. Urbanization increased the concentrations of multiple major and trace elements in streams draining human-dominated watersheds compared to reference conditions. Chemical cocktails of major and trace elements were formed over diurnal cycles coinciding with changes in streamflow, dissolved oxygen, pH, and other variables measured by high-frequency sensors. Some chemical cocktails of major and trace elements were also significantly related to specific conductance (p < 0.05), which can be measured by sensors. Concentrations of major and trace elements increased, peaked, or decreased longitudinally along streams as watershed urbanization increased, which is consistent with distinct shifts in chemical mixtures upstream and downstream of other major cities in the world. Our global analysis of urban streams shows that concentrations of multiple elements along the periodic table significantly increase when compared with reference conditions. Furthermore, similar biogeochemical patterns and processes can be grouped among distinct mixtures of elements of major ions, dissolved organic matter, nutrients, and trace elements as chemical cocktails. Chemical cocktails form in urban waters over diurnal cycles, decades, and throughout drainage basins. We conclude our global review and synthesis by proposing strategies for monitoring and managing chemical cocktails using source control, ecosystem restoration, and green infrastructure. We discuss future research directions applying the watershed chemical cocktail approach to diagnose and manage environmental problems. Ultimately, a chemical cocktail approach targeting sources, transport, and transformations of different and distinct elemental combinations is beneficial to more holistically monitor and manage the emerging impacts of chemical mixtures in the world's fresh waters.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2020.104632","usgsCitation":"Kaushal, S., Wood, K.L., Galella, J.G., Gion, A.M., Haq, S., Goodling, P.J., Haviland, K., Reimer, J.E., Morel, C.J., Wessel, B., Nguyen, W., Hollingsworth, J.W., Mei, K., Leal, J., Widmer, J., Sharif, R., Mayer, P.M., Newcomer Johnson, T.A., Newcomb, K.D., Smith, E., and Belt, K., 2020, Making ‘chemical cocktails’ – Evolution of urban geochemical processes across the periodic table of elements: Applied Geochemistry, v. 119, 104632, 23 p., https://doi.org/10.1016/j.apgeochem.2020.104632.","productDescription":"104632, 23 p.","ipdsId":"IP-114278","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":456305,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7970522","text":"Publisher Index Page"},{"id":394971,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"119","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kaushal, Sujay S.","contributorId":210125,"corporation":false,"usgs":false,"family":"Kaushal","given":"Sujay S.","affiliations":[{"id":38074,"text":"Univ. of Maryland","active":true,"usgs":false}],"preferred":false,"id":831875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Kelsey L.","contributorId":210140,"corporation":false,"usgs":false,"family":"Wood","given":"Kelsey","email":"","middleInitial":"L.","affiliations":[{"id":38074,"text":"Univ. of Maryland","active":true,"usgs":false}],"preferred":false,"id":831876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Galella, Joseph G.","contributorId":210135,"corporation":false,"usgs":false,"family":"Galella","given":"Joseph","email":"","middleInitial":"G.","affiliations":[{"id":38074,"text":"Univ. of Maryland","active":true,"usgs":false}],"preferred":false,"id":831877,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gion, Austin M.","contributorId":272260,"corporation":false,"usgs":false,"family":"Gion","given":"Austin","email":"","middleInitial":"M.","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":831878,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haq, Shahan","contributorId":210138,"corporation":false,"usgs":false,"family":"Haq","given":"Shahan","email":"","affiliations":[{"id":38074,"text":"Univ. of Maryland","active":true,"usgs":false}],"preferred":false,"id":831879,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Goodling, Phillip J. 0000-0001-5715-8579","orcid":"https://orcid.org/0000-0001-5715-8579","contributorId":239738,"corporation":false,"usgs":true,"family":"Goodling","given":"Phillip","email":"","middleInitial":"J.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831880,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Haviland, Katherine","contributorId":210137,"corporation":false,"usgs":false,"family":"Haviland","given":"Katherine","email":"","affiliations":[{"id":38081,"text":"Cornell Univ.","active":true,"usgs":false}],"preferred":false,"id":831881,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reimer, Jenna E.","contributorId":272261,"corporation":false,"usgs":false,"family":"Reimer","given":"Jenna","email":"","middleInitial":"E.","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":831882,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Morel, Carol J. 0000-0002-0179-3159","orcid":"https://orcid.org/0000-0002-0179-3159","contributorId":217343,"corporation":false,"usgs":true,"family":"Morel","given":"Carol","email":"","middleInitial":"J.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831883,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wessel, Barret","contributorId":210139,"corporation":false,"usgs":false,"family":"Wessel","given":"Barret","email":"","affiliations":[{"id":38074,"text":"Univ. of Maryland","active":true,"usgs":false}],"preferred":false,"id":831884,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Nguyen, William","contributorId":272262,"corporation":false,"usgs":false,"family":"Nguyen","given":"William","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":831885,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hollingsworth, John W.","contributorId":272263,"corporation":false,"usgs":false,"family":"Hollingsworth","given":"John","email":"","middleInitial":"W.","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":831886,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Mei, Kevin","contributorId":272264,"corporation":false,"usgs":false,"family":"Mei","given":"Kevin","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":831887,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Leal, Julian","contributorId":272265,"corporation":false,"usgs":false,"family":"Leal","given":"Julian","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":831888,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Widmer, Jacob","contributorId":258308,"corporation":false,"usgs":false,"family":"Widmer","given":"Jacob","affiliations":[{"id":28165,"text":"No affiliation","active":true,"usgs":false}],"preferred":false,"id":831889,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Sharif, Rahat","contributorId":272266,"corporation":false,"usgs":false,"family":"Sharif","given":"Rahat","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":831890,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Mayer, Paul M.","contributorId":272267,"corporation":false,"usgs":false,"family":"Mayer","given":"Paul","email":"","middleInitial":"M.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":831891,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Newcomer Johnson, Tamara A.","contributorId":272268,"corporation":false,"usgs":false,"family":"Newcomer Johnson","given":"Tamara","email":"","middleInitial":"A.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":831892,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Newcomb, Katie D.","contributorId":272269,"corporation":false,"usgs":false,"family":"Newcomb","given":"Katie","email":"","middleInitial":"D.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":831893,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Smith, Evan","contributorId":272270,"corporation":false,"usgs":false,"family":"Smith","given":"Evan","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":831894,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Belt, Kenneth T.","contributorId":210142,"corporation":false,"usgs":false,"family":"Belt","given":"Kenneth T.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":831895,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70210729,"text":"fs20203033 - 2020 - NHDPlus High Resolution (NHDPlus HR)---A hydrography framework for the Nation","interactions":[],"lastModifiedDate":"2020-06-23T14:32:17.573219","indexId":"fs20203033","displayToPublicDate":"2020-06-23T09:20:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3033","displayTitle":"NHDPlus High Resolution (NHDPlus HR)—A Hydrography Framework for the Nation","title":"NHDPlus High Resolution (NHDPlus HR)---A hydrography framework for the Nation","docAbstract":"Reliable and accurate high-resolution mapping of the Nation’s waters are critical inputs to models and decision support systems used to predict risk and enable response to impacts on water resources. It is necessary to know where the water is and how it relates to features beyond the stream network like forests, cities, and infrastructure. An up-to-date, high-resolution national hydrography framework is required for modeling the occurrence of water and to provide the ability to connect detailed information from the surrounding landscape to the stream network. To support this, the U.S. Geological Survey is developing NHDPlus High Resolution (NHDPlus HR), the next generation of NHDPlus using updated, high-resolution datasets to create a modern, scalable, and openly accessible hydrography framework for the inland waters of the Nation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203033","usgsCitation":"Buto, S.G., and Anderson, R.D., 2020, NHDPlus High Resolution (NHDPlus HR)---A hydrography framework for the Nation: U.S. Geological Survey Fact Sheet 2020-3033, 2 p., https://doi.org/10.3133/fs20203033.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112468","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":375780,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3033/coverthb.jpg"},{"id":375781,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3033/fs20203033.pdf","text":"Report","size":"641 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020-3033"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","contact":"National Geospatial Program
Core Science Systems
U.S. Geological Survey
12201 Sunrise Valley Dr., MS 511
Reston, VA 20192
Email National Hydrography at nhd@usgs.gov
","tableOfContents":"An overreliance on groundwater resources in the Houston (Texas) metropolitan area led to aquifer drawdowns and land subsidence, so regional water suppliers have been turning to surface water resources to meet water demand. Lake Houston, an important water supply reservoir 24 kilometers (15 miles) northeast of downtown Houston, requires new water supply sources to continue to meet water supply demands for the next several decades. The upcoming Luce Bayou Interbasin Transfer Project will divert up to 500 million gallons per day of Trinity River water into Lake Houston. Trinity River water has significantly different water quality than the Lake Houston tributaries. To evaluate the project’s potential effect on water quality, the U.S. Geological Survey used an enhanced version of a previously released Lake Houston hydrodynamic model. With a focus on salinity and water-surface elevations, the model combined data from 2009 to 2017 with simulated flow from the Luce Bayou Interbasin Transfer to evaluate potential outcomes from three hypothetical flow scenarios. Overall, these scenarios found that the Luce Bayou Interbasin Transfer would cause salinities to moderately rise over most of the modeled time (2009–2017), although salinities were buffered under 2011 drought conditions. Large inflow events equalized salinities under baseline conditions as well as the enhanced flow scenarios.
","language":"English","publisher":"Texas Water Resources Institute","doi":"10.21423/twj.v11i1.7094","usgsCitation":"Smith, E.A., and Shah, S.D., 2020, Hydrodynamic modeling results showing the effects of the Luce Bayou interbasin transfer on salinity in Lake Houston, TX: Texas Water Journal, v. 11, no. 1, p. 64-88, https://doi.org/10.21423/twj.v11i1.7094.","productDescription":"25 p.","startPage":"64","endPage":"88","ipdsId":"IP-107391","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":456306,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21423/twj.v11i1.7094","text":"Publisher Index Page"},{"id":436921,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AVUJ73","text":"USGS data release","linkHelpText":"Lake Houston (Texas) EFDC hydrodynamic model for water-surface elevation and specific conductance simulations, 2009-2017"},{"id":375850,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Lake Houston, Luce Bayou","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.11533737182616,\n 30.044430015213965\n ],\n [\n -95.10314941406249,\n 30.045767374787093\n ],\n [\n -95.09679794311523,\n 30.052453901811464\n ],\n [\n -95.08563995361328,\n 30.081423634757307\n ],\n [\n -95.07431030273438,\n 30.09894991821156\n ],\n [\n -95.0592041015625,\n 30.10592986084689\n ],\n [\n -95.0533676147461,\n 30.11944281648474\n ],\n [\n -95.03860473632812,\n 30.128351446338055\n ],\n [\n -95.01869201660156,\n 30.140376821599734\n ],\n [\n -95.0126838684082,\n 30.148392924428762\n ],\n [\n -95.04392623901367,\n 30.133250850150496\n ],\n [\n -95.0621223449707,\n 30.121224606751863\n ],\n [\n -95.06658554077148,\n 30.107117887092357\n ],\n [\n -95.08289337158203,\n 30.10236569641242\n ],\n [\n -95.09920120239256,\n 30.08216633691056\n ],\n [\n -95.10074615478516,\n 30.07280788225917\n ],\n [\n -95.1060676574707,\n 30.066865546842724\n ],\n [\n -95.10932922363281,\n 30.053642570466106\n ],\n [\n -95.1222038269043,\n 30.052899654229005\n ],\n [\n -95.12392044067383,\n 30.043835627386027\n ],\n [\n -95.11533737182616,\n 30.044430015213965\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.13198852539062,\n 29.91090055463952\n ],\n [\n -95.11344909667969,\n 29.92696926999306\n ],\n [\n -95.1361083984375,\n 29.94660528850718\n ],\n [\n -95.11207580566406,\n 29.9793233715619\n ],\n [\n -95.11276245117188,\n 29.99478635165809\n ],\n [\n -95.10177612304688,\n 30.010841512528764\n ],\n [\n -95.10726928710938,\n 30.0405664305846\n ],\n [\n -95.14778137207031,\n 30.088107753367257\n ],\n [\n -95.16288757324219,\n 30.080978010788556\n ],\n [\n -95.15602111816406,\n 30.06255713055085\n ],\n [\n -95.13473510742186,\n 30.041160838027047\n ],\n [\n -95.15602111816406,\n 30.050670872077248\n ],\n [\n -95.19378662109375,\n 30.035811042667792\n ],\n [\n -95.24940490722656,\n 30.028083050581905\n ],\n [\n -95.2459716796875,\n 30.012030680358613\n ],\n [\n -95.21713256835938,\n 30.015003537560943\n ],\n [\n -95.19584655761719,\n 30.012625258927418\n ],\n [\n -95.16288757324219,\n 30.01738175916562\n ],\n [\n -95.15190124511719,\n 30.007273923504556\n ],\n [\n -95.14915466308594,\n 29.989434054144688\n ],\n [\n -95.17662048339844,\n 29.952554831950987\n ],\n [\n -95.16838073730469,\n 29.91923280484215\n ],\n [\n -95.13198852539062,\n 29.91090055463952\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-06-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Erik A. 0000-0001-8434-0798 easmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8434-0798","contributorId":1405,"corporation":false,"usgs":true,"family":"Smith","given":"Erik","email":"easmith@usgs.gov","middleInitial":"A.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791375,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shah, Sachin D. 0000-0002-5440-5535 sdshah@usgs.gov","orcid":"https://orcid.org/0000-0002-5440-5535","contributorId":194450,"corporation":false,"usgs":true,"family":"Shah","given":"Sachin","email":"sdshah@usgs.gov","middleInitial":"D.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791376,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211703,"text":"70211703 - 2020 - A newly emerging thermal area in Yellowstone","interactions":[],"lastModifiedDate":"2020-08-07T13:44:28.762634","indexId":"70211703","displayToPublicDate":"2020-06-23T08:39:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"A newly emerging thermal area in Yellowstone","docAbstract":"Yellowstone is a large restless caldera that contains many dynamic thermal areas that are the surface expression of the deeper magmatic system. In 2018, using a Landsat 8 nighttime thermal infrared image, we discovered the emergence of a new thermal area located near Tern Lake on the northeast margin of the Sour Creek dome. A high-spatial-resolution airborne visible image from August 2017 revealed a large (~33,000 m2) area of recently fallen trees, mostly devoid of vegetation, with bright soil, similar to other nearby thermal areas. Field observations in August 2019 confirmed that this was a steam-heated acid-sulfate thermal area, with an arc-shaped zone of hydrothermally altered soil and heated ground, with surface temperatures of 60-80 °C, several steaming fumaroles, and boiling temperatures (93 °C) just beneath the surface. Fallen trees in contact with warm ground were being carbonized, yet there were some cooler areas with new trees growing. Observations of stressed or dying vegetation from archived satellite and airborne remote sensing data going back to 1994 indicated that this thermal area started emerging around 2000. It increased in size slowly until around 2005, when the radiative heat output started measurably increasing. From 2005 to 2012 it grew more rapidly; and from 2012 through 2019 the growth rate slowed and the heat output stabilized. We predict that this stabilizing trend will continue in the coming years. The initial formation of this new thermal area was not clearly linked to any distinct seismic or geodetic events, although the period of rapid growth partly coincided with a period of rapid local uplift, possibly suggesting a causative relationship. The identification of this emerging thermal area illustrates the importance of satellite thermal infrared imaging combined with high-spatial-resolution remote sensing data and field observations for mapping, measuring, and monitoring Yellowstone's thermal areas. It is also an example of the dynamics we expect to observe within large caldera systems like Yellowstone, where changes in the size and distribution of thermal areas are normal and do not indicate an impending eruption nor any significant changes in the broader magmatic system.","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2020.00204","usgsCitation":"Vaughan, R.G., Hungerford, J., and Keller, B., 2020, A newly emerging thermal area in Yellowstone: Frontiers in Earth Science, v. 8, 204, 19 p., https://doi.org/10.3389/feart.2020.00204.","productDescription":"204, 19 p.","ipdsId":"IP-115041","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":456310,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.00204","text":"Publisher Index Page"},{"id":377169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","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 -111.05529785156249,\n 43.31718491566705\n ],\n [\n -108.907470703125,\n 43.31718491566705\n ],\n [\n -108.907470703125,\n 45.01141864227728\n ],\n [\n -111.05529785156249,\n 45.01141864227728\n ],\n [\n -111.05529785156249,\n 43.31718491566705\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-06-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Vaughan, R. 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":795178,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hungerford, Jefferson 0000-0003-2651-2285","orcid":"https://orcid.org/0000-0003-2651-2285","contributorId":229552,"corporation":false,"usgs":false,"family":"Hungerford","given":"Jefferson","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":795179,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keller, Bill","contributorId":237086,"corporation":false,"usgs":false,"family":"Keller","given":"Bill","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":795180,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}