The purpose of this report is to present a potentiometric-surface map for the Mississippi River Valley alluvial aquifer (MRVA). The source data for the map were groundwater-altitude data from wells measured manually or continuously generally in spring 2020 and from the altitude of the top of the water surface measured generally on April 9, 2020, in rivers in the area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3478","programNote":"Water Availability and Use Science Program","usgsCitation":"McGuire, V.L., Seanor, R.C., Asquith, W.H., Strauch, K.R., Nottmeier, A.M., Thomas, J.C., Tollett, R.W., and Kress, W.H., 2021, Altitude of the potentiometric surface in the Mississippi River Valley alluvial aquifer, spring 2020: U.S. Geological Survey Scientific Investigations Map 3478, 5 sheets, includes 14-p. pamphlet, https://doi.org/10.3133/sim3478.","productDescription":"Pamphlet: vi, 14p.; 5 Sheets: 30.00 x 46.00 inches or smaller; Data Release; Dataset","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119302","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":388770,"rank":9,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":388769,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CXDIPL","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Datasets used to map the potentiometric surface, Mississippi River Valley alluvial aquifer, spring 2020"},{"id":388768,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3478/sim3478_sheet5.pdf","text":"Sheet 5—Atchafalaya and Deltaic and Chenier Plain MAP regions","size":"6.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3478 Sheet 5"},{"id":388762,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3478/coverthb2.jpg"},{"id":388767,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3478/sim3478_sheet4.pdf","text":"Sheet 4—Delta MAP region","size":"4.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3478 Sheet 4"},{"id":388763,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3478/sim3478_pamphlet.pdf","text":"Pamphlet","size":"13.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3478 Pamphlet"},{"id":388764,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3478/sim3478_sheet1.pdf","text":"Sheet 1—All Mississippi Alluvial Plain (MAP) regions","size":"14.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3478 Sheet 1"},{"id":388765,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3478/sim3478_sheet2.pdf","text":"Sheet 2—St. Francis and Cache MAP regions","size":"5.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3478 Sheet 2"},{"id":388766,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3478/sim3478_sheet3.pdf","text":"Sheet 3—Boeuf and Grand Prairie MAP regions","size":"6.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3478 Sheet 3"}],"country":"United States","state":"Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi River Valley Alluvial Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.62597656249999,\n 29.152161283318915\n ],\n [\n -88.76953125,\n 28.8831596093235\n ],\n [\n -88.9453125,\n 29.726222319395504\n ],\n [\n -90.439453125,\n 30.751277776257812\n ],\n [\n -90,\n 33.247875947924385\n ],\n [\n -88.857421875,\n 35.17380831799959\n ],\n [\n -88.59374999999999,\n 36.421282443649496\n ],\n [\n -89.12109375,\n 37.43997405227057\n ],\n [\n -90.1318359375,\n 37.26530995561875\n ],\n [\n -91.318359375,\n 35.10193405724606\n ],\n [\n -91.845703125,\n 32.84267363195431\n ],\n [\n -91.8896484375,\n 31.87755764334002\n ],\n [\n -92.2412109375,\n 29.99300228455108\n ],\n [\n -91.62597656249999,\n 29.152161283318915\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
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Process-based ecosystem carbon cycle models typically incorporate vegetation growth, vegetation mortality, and soil respiration as well as the biotic and environmental drivers that influence these variables. However, few spatially explicit process models can efficiently incorporate the influence of land cover change and carbon lateral movement at regional scales or high spatial resolution. This study uses the Land Use and Carbon Scenario Simulator (LUCAS) to demonstrate the development of a fast ecosystem model that not only considers the basic carbon cycle but also incorporates the impact of land cover change, soil erosion, and soil deposition. As input to the LUCAS modeling framework, we used the integrated biosphere simulator (IBIS) to simulate a non-spatial reference carbon cycling scenario without considering land cover change for the Nisqually River watershed in the northwestern United States. We then used the Land Change Monitoring, Assessment, and Projection (LCMAP) remotely sensed 30-m sequential land cover data to generate annual land change history for the Nisqually River area from 1985 to 2017 and used the Unit Stream Powered Erosion and Deposition model (USPED) to estimate annual soil carbon lateral movement. Finally, we combined the annual carbon outputs from IBIS, the land change history from LCMAP, and the soil erosion and deposition from USPED within the LUCAS simulation framework. Results showed that from 1985 to 2017, along with the dynamic land cover changes, total ecosystem biomass carbon increased from 11.4 to 18.6 TgC, mainly due to forest growth. Total ecosystem soil carbon declined from 31.7 to 29.7 TgC, but the overall loss in soil carbon was not uniform across land cover types. Forestland (forest sector) and grassland lost carbon, while wetland, developed land and agricultural land gained carbon. Forest, grassland, and developed land lost 0.0553 TgC during the study period (1.73 Gg of C per year; 1 Gg = 0.001 Tg) from erosion, while wetland gained 0.0071 TgC (0.22 Gg C per year) from deposition. Agricultural land was neutral in terms of soil erosion.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2021.109724","usgsCitation":"Liu, J., Sleeter, B.M., Selmants, P., Diao, J., Zhou, Q., Worstell, B., and Moritsch, M.M., 2021, Modeling watershed carbon dynamics as affected by land cover change and soil erosion: Ecological Modelling, v. 459, 109724, 11 p., https://doi.org/10.1016/j.ecolmodel.2021.109724.","productDescription":"109724, 11 p.","ipdsId":"IP-129044","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":450838,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2021.109724","text":"Publisher Index Page"},{"id":436201,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A27GFH","text":"USGS data release","linkHelpText":"Simulated Nisqually River Watershed 30-m resolution 2017 ecosystem carbon variables from the LUCAS model"},{"id":417208,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nisqually River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.16253640390157,\n 47.168168689027254\n ],\n [\n -123.16253640390157,\n 46.28394294633836\n ],\n [\n -121.7181395192194,\n 46.28394294633836\n ],\n [\n -121.7181395192194,\n 47.168168689027254\n ],\n [\n -123.16253640390157,\n 47.168168689027254\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"459","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Jinxun 0000-0003-0561-8988 jxliu@usgs.gov","orcid":"https://orcid.org/0000-0003-0561-8988","contributorId":3414,"corporation":false,"usgs":true,"family":"Liu","given":"Jinxun","email":"jxliu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873045,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sleeter, Benjamin M. 0000-0003-2371-9571 bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":873046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Selmants, Paul 0000-0001-6211-3957 pselmants@usgs.gov","orcid":"https://orcid.org/0000-0001-6211-3957","contributorId":192591,"corporation":false,"usgs":true,"family":"Selmants","given":"Paul","email":"pselmants@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873047,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Diao, Jiaojiao","contributorId":305505,"corporation":false,"usgs":false,"family":"Diao","given":"Jiaojiao","email":"","affiliations":[{"id":33416,"text":"Nanjing Forestry University, China","active":true,"usgs":false}],"preferred":false,"id":873048,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":265886,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":54817,"text":"AFDS, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":873049,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Worstell, Bruce 0000-0001-8927-3336","orcid":"https://orcid.org/0000-0001-8927-3336","contributorId":305506,"corporation":false,"usgs":false,"family":"Worstell","given":"Bruce","affiliations":[{"id":66235,"text":"SGT Inc. Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":873050,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moritsch, Monica Mei Jeen 0000-0002-3890-1264","orcid":"https://orcid.org/0000-0002-3890-1264","contributorId":225210,"corporation":false,"usgs":true,"family":"Moritsch","given":"Monica","email":"","middleInitial":"Mei Jeen","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873051,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223695,"text":"sir20215084 - 2021 - Forecasting drought probabilities for streams in the northeastern United States","interactions":[],"lastModifiedDate":"2021-09-13T12:01:34.031419","indexId":"sir20215084","displayToPublicDate":"2021-09-10T14:10:00","publicationYear":"2021","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":"2021-5084","displayTitle":"Forecasting Drought Probabilities for Streams in the Northeastern United States","title":"Forecasting drought probabilities for streams in the northeastern United States","docAbstract":"Maximum likelihood logistic regression (MLLR) models for the northeastern United States forecast drought probability estimates for water flowing in rivers and streams using methods previously identified and developed. Streamflow data from winter months are used to estimate chances of hydrological drought during summer months. Daily streamflow data collected from 1,143 streamgages from April 1, 1877, through October 31, 2018, are used to provide hydrological drought streamflow probabilities for July, August, and September as functions of streamflows during October, November, December, January, and February. This allows estimates of outcomes from 5 to 11 months ahead of their occurrence. Models specific to the northeastern United States were investigated and updated. The MLLR models of drought stream-flow probabilities utilize the explanatory power of temporally linked water flows. Models with strong drought streamflow probability correct-classification rates were produced for streams throughout the northeastern United States. A test of northeastern United States drought streamflow probability predictions found that overall correct-classification rates for drought streamflow probabilities in the northeastern United States exceeded 97 percent when predicting July 2019 drought probability using February 2019 monthly mean streamflow data. Using hydrological drought probability estimates in a water-management context informs understandings of possible future streamflow drought conditions in the northeastern United States, provides warnings of potential future drought conditions, and aids water-management decision making and responses to changing circumstances.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215084","usgsCitation":"Austin, S.H., 2021, Forecasting drought probabilities for streams in the northeastern United States: U.S. Geological Survey Scientific Investigations Report 2021–5084, 11 p., https://doi.org/10.3133/sir20215084.","productDescription":"Report: vi, 12 p.; Data Release","numberOfPages":"11","ipdsId":"IP-113685","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":388741,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5084/sir20215084.pdf","text":"Report","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5084"},{"id":388740,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5084/coverthb.jpg"},{"id":388742,"rank":3,"type":{"id":30,"text":"Data 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\"}}]}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Mayon is a basaltic andesitic, open-vent volcano characterized by persistent passive degassing from the summit at 2463 m above sea level. Mid-size (<0.1 km3) and mildly explosive eruptions and occasional phreatic eruptions have occurred approximately every 10 years for over a hundred years. Mayon’s plumbing system structure, processes, and time scales driving its eruptions are still not well-known, despite being the most active volcano in the Philippines. We investigated the petrology and geochemistry of its crystal-rich lavas (~50 vol% phenocrysts) from nine historical eruptions between 1928 and 2009 and propose a conceptual model of the processes and magmatic architecture that led to the eruptions. The whole-rock geochemistry and mineral assemblage (plagioclase + orthopyroxene + clinopyroxene + Fe-Ti oxide ± olivine) of the lavas have remained remarkably homogenous (54 wt% SiO2,~4 wt% MgO) from 1928 to 2009. However, electron microscope images and microprobe analyses of the phenocrysts and the existence of three types of glomerocrysts testify to a range of magmatic processes, including long-term magma residence, magma mixing, crystallization, volatile fluxing, and degassing. Multiple mineral-melt geothermobarometers suggest a relatively thermally buffered system at 1050±25 °C, with several magma residence zones, ranging from close to the surface, through reservoirs at ~4–5 km, and as deep as ~ 20 km. Diffusion chronometry on >200 orthopyroxene crystals reveal magma mixing timescales that range from a few days to about 65 years, but the majority are shorter than the decadal inter-eruptive repose period. This implies that magma intrusion at Mayon has been nearly continuous over the studied time period, with limited crystal recycling from one eruption to the next. The variety of plagioclase textures and zoning patterns reflect fluxing of volatiles from depth to shallower melts through which they eventually reach the atmosphere through an open conduit. The crystal-rich nature of the erupted magmas may have developed during each inter-eruptive period. We propose that Mayon has behaved over almost 100 years as a steady state system, with limited variations in eruption frequency, degassing flux, magma composition, and crystal content that are mainly determined by the amount and composition of deep magma and volatile input in the system. We explore how Mayon volcano’s processes and working model can be related to other open-vent mafic and water-rich systems such as Etna, Stromboli, Villarrica, or Llaima. Finally, our understanding of open-vent, persistently active volcanoes is rooted in historical observations, but volcano behavior can evolve over longer time frames. We speculate that these volcanoes produce specific plagioclase textures that can be used to identify similar volcanic behavior in the geologic record.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-021-01486-9","usgsCitation":"Ruth, D.C., and Costa, F., 2021, A petrological and conceptual model of Mayon volcano (Philippines) as an example of an open-vent volcano: Bulletin of Volcanology, v. 83, 62, 28 p., https://doi.org/10.1007/s00445-021-01486-9.","productDescription":"62, 28 p.","ipdsId":"IP-123082","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467226,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00445-021-01486-9","text":"Publisher Index Page"},{"id":463239,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Philippines","otherGeospatial":"Mayon volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 123.5101731400274,\n 13.501020877721444\n ],\n [\n 123.5101731400274,\n 13.33999591750549\n ],\n [\n 123.70654204522504,\n 13.33999591750549\n ],\n [\n 123.70654204522504,\n 13.501020877721444\n ],\n [\n 123.5101731400274,\n 13.501020877721444\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"83","noUsgsAuthors":false,"publicationDate":"2021-09-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruth, Dawn Catherine Sweeney 0000-0001-9369-9364","orcid":"https://orcid.org/0000-0001-9369-9364","contributorId":334908,"corporation":false,"usgs":true,"family":"Ruth","given":"Dawn","email":"","middleInitial":"Catherine Sweeney","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":916874,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Costa, Fidel","contributorId":184169,"corporation":false,"usgs":false,"family":"Costa","given":"Fidel","email":"","affiliations":[],"preferred":false,"id":916875,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227783,"text":"70227783 - 2021 - The potential of satellite remote sensing time series to uncover wetland phenology under unique challenges of tidal setting","interactions":[],"lastModifiedDate":"2022-01-31T16:09:37.311812","indexId":"70227783","displayToPublicDate":"2021-09-09T09:54:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"The potential of satellite remote sensing time series to uncover wetland phenology under unique challenges of tidal setting","docAbstract":"While growth history of vegetation within upland systems is well studied, plant phenology within coastal tidal systems is less understood. Landscape-scale, satellite-derived indicators of plant greenness may not adequately represent seasonality of vegetation biomass and productivity within tidal wetlands due to limitations of cloud cover, satellite temporal frequency and attenu-ation of plant signals by tidal flooding. However, understanding plant phenology is necessary to gain insight into aboveground biomass, photosynthetic activity, and carbon sequestration. In this study we use a modeling approach to estimate plant greenness throughout a year in tidal wet-lands located within the San Francisco Bay Area, USA. We used variables such as EVI history, temperature, and elevation to predict plant greenness on a 14-day timestep. We found this ap-proach accurately estimated plant greenness, with larger error observed within more dynamic restored wetlands, particularly at early post-restoration stages. We also found modeled EVI can be used as an input variable into greenhouse gas models, allowing for an estimate of carbon se-questration and gross primary production. Our strategy can be further developed in future re-search by assessing restoration and management effects on wetland phenological dynamics and through incorporating the entire Sentinel-2 time-series once it becomes available within Google Earth Engine.","language":"English","publisher":"MDPI","doi":"10.3390/rs13183589","collaboration":"=","usgsCitation":"Miller, G.J., Dronova, I., Oikawa, P., Knox, S., Windham-Myers, L., Shahan, J., and Stuart-Haëntjens, E., 2021, The potential of satellite remote sensing time series to uncover wetland phenology under unique challenges of tidal setting: Remote Sensing, v. 13, no. 18, p. 1-28, https://doi.org/10.3390/rs13183589.","productDescription":"3589, 28 p.","startPage":"1","endPage":"28","ipdsId":"IP-133045","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":450854,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13183589","text":"Publisher Index Page"},{"id":395144,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Francisco","otherGeospatial":"San Francisco Bay Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.728271484375,\n 37.276238364942955\n ],\n [\n -121.75872802734375,\n 37.276238364942955\n ],\n [\n -121.75872802734375,\n 38.26406296833961\n ],\n [\n -122.728271484375,\n 38.26406296833961\n ],\n [\n -122.728271484375,\n 37.276238364942955\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"18","noUsgsAuthors":false,"publicationDate":"2021-09-09","publicationStatus":"PW","contributors":{"editors":[{"text":"Bostater, Charles R. Jr.","contributorId":272837,"corporation":false,"usgs":false,"family":"Bostater","given":"Charles","suffix":"Jr.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":832310,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Miller, Gwendolyn Joelle 0000-0002-5712-945X","orcid":"https://orcid.org/0000-0002-5712-945X","contributorId":272606,"corporation":false,"usgs":false,"family":"Miller","given":"Gwendolyn","email":"","middleInitial":"Joelle","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":832226,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dronova, Iryna 0000-0003-3339-3704","orcid":"https://orcid.org/0000-0003-3339-3704","contributorId":272607,"corporation":false,"usgs":false,"family":"Dronova","given":"Iryna","email":"","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":832227,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oikawa, Patricia","contributorId":272608,"corporation":false,"usgs":false,"family":"Oikawa","given":"Patricia","affiliations":[{"id":56387,"text":"CSU East Bay","active":true,"usgs":false}],"preferred":false,"id":832228,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knox, Sara Helen 0000-0003-2255-5835","orcid":"https://orcid.org/0000-0003-2255-5835","contributorId":272609,"corporation":false,"usgs":false,"family":"Knox","given":"Sara Helen","affiliations":[{"id":56388,"text":"U. British Columbia","active":true,"usgs":false}],"preferred":false,"id":832229,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Windham-Myers, Lisamarie","contributorId":272610,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":832230,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shahan, Julie","contributorId":272611,"corporation":false,"usgs":false,"family":"Shahan","given":"Julie","email":"","affiliations":[{"id":56387,"text":"CSU East Bay","active":true,"usgs":false}],"preferred":false,"id":832231,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stuart-Haëntjens, Ellen 0000-0001-9901-7643","orcid":"https://orcid.org/0000-0001-9901-7643","contributorId":265857,"corporation":false,"usgs":true,"family":"Stuart-Haëntjens","given":"Ellen","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832232,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70224262,"text":"70224262 - 2021 - If you give a clam an estuary: The story of potamocorbula","interactions":[],"lastModifiedDate":"2021-09-16T13:06:17.362712","indexId":"70224262","displayToPublicDate":"2021-09-09T08:04:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9348,"text":"Frontiers for Young Minds","active":true,"publicationSubtype":{"id":10}},"title":"If you give a clam an estuary: The story of potamocorbula","docAbstract":"When you look at San Francisco Bay, what animals do you see? You may see lots of fish swimming around and birds flying above. What you DON’T see is Potamocorbula, a little clam that has had a big impact. Many years ago, ships accidentally brought Potamocorbula into the Bay. Pretty soon, Potamocorbula spread out all over in large numbers! Clams pump water over their gills and eat small particles of food, like phytoplankton, that pass through with the water. Potamocorbula can pump water much faster than other clams that live in the Bay, and they can eat more than their share of phytoplankton. Sometimes Potamocorbula eats phytoplankton faster than phytoplankton can grow! What problems does that cause for other animals, like birds and fish, that also need phytoplankton? Does Potamocorbula’s invasion only have negative impacts? In this article, we dive to the bottom of the Bay to find some answers.\n\nBook series publishing the chapter: https://kids.frontiersin.org/collection/13528/where-the-river-meets-the-ocean-stories-from-san-francisco-estuary","language":"English","publisher":"Frontiers","doi":"10.3389/frym.2021.599289","usgsCitation":"Shrader, K., Zierdt Smith, E.L., Parchaso, F., and Thompson, J.K., 2021, If you give a clam an estuary: The story of potamocorbula: Frontiers for Young Minds, https://doi.org/10.3389/frym.2021.599289.","ipdsId":"IP-120502","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":450858,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frym.2021.599289","text":"Publisher Index Page"},{"id":389334,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.134765625,\n 37.405073750176925\n ],\n [\n -121.55273437499999,\n 37.405073750176925\n ],\n [\n -121.55273437499999,\n 38.37611542403604\n ],\n [\n -123.134765625,\n 38.37611542403604\n ],\n [\n -123.134765625,\n 37.405073750176925\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2021-09-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Shrader, Kelly H. 0000-0001-6550-7425","orcid":"https://orcid.org/0000-0001-6550-7425","contributorId":215872,"corporation":false,"usgs":true,"family":"Shrader","given":"Kelly H.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":823393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zierdt Smith, Emily L. 0000-0003-0787-1856 ezierdtsmith@usgs.gov","orcid":"https://orcid.org/0000-0003-0787-1856","contributorId":220320,"corporation":false,"usgs":true,"family":"Zierdt Smith","given":"Emily","email":"ezierdtsmith@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":true,"id":823394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Parchaso, Francis 0000-0002-9471-7787 parchaso@usgs.gov","orcid":"https://orcid.org/0000-0002-9471-7787","contributorId":150620,"corporation":false,"usgs":true,"family":"Parchaso","given":"Francis","email":"parchaso@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":823395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Janet K. 0000-0002-1528-8452 jthompso@usgs.gov","orcid":"https://orcid.org/0000-0002-1528-8452","contributorId":1009,"corporation":false,"usgs":true,"family":"Thompson","given":"Janet","email":"jthompso@usgs.gov","middleInitial":"K.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":823396,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223891,"text":"70223891 - 2021 - The structure and volume of large geysers in Yellowstone National Park, USA and the mineralogy and chemistry of their silica sinter deposits","interactions":[],"lastModifiedDate":"2021-10-06T15:58:41.800728","indexId":"70223891","displayToPublicDate":"2021-09-09T07:50:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"The structure and volume of large geysers in Yellowstone National Park, USA and the mineralogy and chemistry of their silica sinter deposits","docAbstract":"Siliceous sinter is formed by biogenic and abiogenic opal deposition around hot springs and geysers. Using Structure-from-Motion photogrammetry we generated three-dimensional models of Giant and Castle Geysers from the Upper Geyser Basin of Yellowstone National Park. We use these models to calculate an approximate mass of sinter for each (~2 and ~ 5 kton, respectively) and estimate a range of plausible long-term deposition rates for Castle Geyser (470 to 940 kg·yr−1). We estimate ~2% of the silica discharged from Castle Geyser is deposited as sinter in the cone and proximal terraces. We collected 15 sinter samples following the stratigraphy of each geyser from an older terrace to a younger cone and examined them using a variety of analytical methods. We find that young opaline sinter with a water content of <12 wt% (from loss on ignition) contains higher concentrations of major and trace elements, notably As, Sb, Rb, Ga and Cs, relative to older dehydrated sinter. Rare earth element (REE) concentrations in sinter are 2–3 orders of magnitude higher than in the thermal water from which they are deposited. Sinter deposits are enriched in light REE, Gd and Yb when normalized to concentrations in thermal water and enriched in Eu, Tm, and Yb when normalized to the underlying rhyolite. Sinter samples with the highest REE concentrations are also enriched in organic material, implying either microbial uptake of REE, or that organic molecules are efficient ligands that form metal complexes.
Rapid infiltration following precipitation may result in groundwater contamination from surface contaminants or pathogens. In fractured rock, contaminants can migrate rapidly to points of groundwater withdrawals. In contrast to the temporal availability of groundwater quality chemical indicators, meteorological and groundwater level observations are available in real-time to estimate time-varying recharge, which can act as a surrogate to identify periods of rapid infiltration that may indicate contamination susceptibility. Estimating recharge using methods, such as base-flow recession, unsaturated infiltration models, or Water-Table Fluctuations (WTF), cannot capitalize on currently available technologies and telecommunication infrastructure to conduct real-time recharge estimation at scales relevant to characterizing rapid infiltration. We present a linear, physics-based State-Space (SS) model of one-dimensional infiltration to estimate recharge, which includes preferential and diffuse-flow to the water table. The model can take advantage of real-time data for water-table altitude, precipitation, and evapotranspiration. Model parameters are calibrated over an observation period, and the Kalman Filter (KF) is subsequently applied to continuously update the observed (water-table altitude) and unobserved (groundwater recharge) system states and predict future states as new data become available. The SS/KF algorithm is demonstrated at the Masser Groundwater Recharge Site in Pennsylvania, USA and comparisons are made with recharge estimates from WTF methods. Model results indicate that the frequency of observations (daily versus sub-daily) dictates the allocation between preferential and diffuse flow. Additionally, because infiltration processes encompass many nonlinearities, model parameters estimated from observation periods need to be updated at least seasonally to account for changing recharge conditions.
","language":"English","publisher":"Wiley","doi":"10.1029/2020WR029110","usgsCitation":"Shapiro, A.M., and Day-Lewis, F., 2021, Estimating and forecasting time-varying groundwater recharge in fractured rock: A state-space formulation with preferential and diffuse flow to the water table: Water Resources Research, v. 57, no. 9, e2020WR029110, 30 p., https://doi.org/10.1029/2020WR029110.","productDescription":"e2020WR029110, 30 p.","ipdsId":"IP-122279","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":450863,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020wr029110","text":"Publisher Index Page"},{"id":436205,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VBR9V8","text":"USGS data release","linkHelpText":"Algorithms for model parameter estimation, state estimation, and forecasting applied to a State-Space model coupled with the Kalman Filter for one-dimensional vertical infiltration to fractured rock aquifers"},{"id":436204,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LLXCIC","text":"USGS data release","linkHelpText":"Water Level Altitude in Bedrock Wells and Meteorological Data at the Masser Groundwater Recharge Site between February 1 and December 31, 1999"},{"id":389205,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Shapiro, Allen M. 0000-0002-6425-9607 ashapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-6425-9607","contributorId":2164,"corporation":false,"usgs":true,"family":"Shapiro","given":"Allen","email":"ashapiro@usgs.gov","middleInitial":"M.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":823234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day-Lewis, Frederick 0000-0003-3526-886X","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":216359,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":823235,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70224627,"text":"70224627 - 2021 - Hotspot dune erosion on an intermediate beach","interactions":[],"lastModifiedDate":"2021-10-01T13:25:35.688431","indexId":"70224627","displayToPublicDate":"2021-09-08T08:21:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Hotspot dune erosion on an intermediate beach","docAbstract":"A large, low pressure Nor’easter storm and Hurricane Joaquin contributed to multiple weeks of sustained, elevated wave and water level conditions along the southeastern Atlantic coast of the United States in Fall 2015. Sea level anomalies in excess of 1 m and offshore wave heights of up to 4 m were recorded during these storms, as observed at the U.S. Army Corps of Engineers’ Field Research Facility in Duck, NC, USA. In response to these energetic oceanographic conditions, there were highly variable morphologic changes to the dune over short spatial scales (<km) which included a range of responses from vertical dune scarping to no measureable response. The portion of the study area with the largest dune erosion occurred at a location fronted by an abnormally deep nearshore bathymetric feature, which altered surf-zone waves and hydrodynamics. The pre-storm beach and dune topography also varied throughout the study area, additionally influencing the frequency of dune collision and contributing to the spatially variable erosion patterns. This work uses field datasets and numerical modeling tools to investigate the causation of hotspot dune erosion at the Field Research Facility. Three different numerical models were tested against the available data in order to assess model skill at resolving complex spatial dune erosion patterns. The three models successfully reproduce the general spatial trends in alongshore variable responses, although not necessarily the details of profile response or net erosion magnitude. Analysis of the model outputs, in conjunction with the available field data, suggests that the observed hotspot dune erosion is related to a complex combination of both topographic and bathymetric controls on the processes driving dune erosion. Therefore, the most simplistic model tested, which only accounts for alongshore variations in topographic profile details, can only predict hotspot dune erosion in locations where steep beach and/or dune topography is the primary control on collisional dune impacts. The higher fidelity models, which account for feedback effects from subaqueous morphology, are similarly able to predict the locations of maximum hotspot erosion, but are sensitive to beach over-steepening and/or errors in wave runup calculations that can lead to over-prediction of simulated dune erosion. This work highlights that numerous existing tools are capable of identifying the foredune regions at most risk from hotspot erosion, as well as the need for continued research to improve representation of all relevant intra-storm morphodynamic processes.
River managers strive to use the best available science to sustain biodiversity and ecosystem function. To achieve this goal requires consideration of processes at different scales. Metacommunity theory describes how multiple species from different communities potentially interact with local-scale environmental drivers to influence population dynamics and community structure. However, this body of knowledge has only rarely been used to inform management practices for river ecosystems. In this article, we present a conceptual model outlining how the metacommunity processes of local niche sorting and dispersal can influence the outcomes of management interventions and provide a series of specific recommendations for applying these ideas as well as research needs. In all cases, we identify situations where traditional approaches to riverine management could be enhanced by incorporating an understanding of metacommunity dynamics. A common theme is developing guidelines for assessing the metacommunity context of a site or region, evaluating how that context may affect the desired outcome, and incorporating that understanding into the planning process and methods used. To maximize the effectiveness of management activities, scientists, and resource managers should update the toolbox of approaches to riverine management to reflect theoretical advances in metacommunity ecology.
","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1557","usgsCitation":"Patrick, C.J., Anderson, K.E., Brown, B.L., Hawkins, C.P., Metcalfe, A.N., Saffarinia, P., Siqueira, T., Swan, C.M., Tonkin, J.D., and Yuan, L.L., 2021, The application of metacommunity theory to the management of riverine ecosystems: WIREs Water, v. 8, no. 6, e1557, 21 p., https://doi.org/10.1002/wat2.1557.","productDescription":"e1557, 21 p.","ipdsId":"IP-119036","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":450888,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wat2.1557","text":"Publisher Index Page"},{"id":389050,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-09-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Patrick, Christopher J.","contributorId":199778,"corporation":false,"usgs":false,"family":"Patrick","given":"Christopher","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":822932,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Kurt E.","contributorId":265545,"corporation":false,"usgs":false,"family":"Anderson","given":"Kurt","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":822933,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Brown L.","contributorId":265546,"corporation":false,"usgs":false,"family":"Brown","given":"Brown","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":822934,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hawkins, Charles P.","contributorId":198331,"corporation":false,"usgs":false,"family":"Hawkins","given":"Charles","email":"","middleInitial":"P.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":822935,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Metcalfe, Anya N. 0000-0002-6286-4889 ametcalfe@usgs.gov","orcid":"https://orcid.org/0000-0002-6286-4889","contributorId":5271,"corporation":false,"usgs":true,"family":"Metcalfe","given":"Anya","email":"ametcalfe@usgs.gov","middleInitial":"N.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":822936,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Saffarinia, Parsa","contributorId":265547,"corporation":false,"usgs":false,"family":"Saffarinia","given":"Parsa","email":"","affiliations":[],"preferred":false,"id":822937,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Siqueira, Tadeu","contributorId":265548,"corporation":false,"usgs":false,"family":"Siqueira","given":"Tadeu","email":"","affiliations":[],"preferred":false,"id":822938,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Swan, Christopher M.","contributorId":265549,"corporation":false,"usgs":false,"family":"Swan","given":"Christopher","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":822939,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tonkin, Jonathan D.","contributorId":260624,"corporation":false,"usgs":false,"family":"Tonkin","given":"Jonathan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":822940,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Yuan, Lester L.","contributorId":198316,"corporation":false,"usgs":false,"family":"Yuan","given":"Lester","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":822941,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70223780,"text":"fs20213046 - 2021 - Streamflow—Water year 2020","interactions":[],"lastModifiedDate":"2021-09-08T11:46:34.780751","indexId":"fs20213046","displayToPublicDate":"2021-09-07T19:14:29","publicationYear":"2021","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":"2021-3046","displayTitle":"Streamflow—Water Year 2020","title":"Streamflow—Water year 2020","docAbstract":"The maps and graphs in this summary describe national streamflow conditions for water year 2020 (October 1, 2019, to September 30, 2020) in the context of streamflow ranks relative to the 91-year period of water years 1930–2020. Annual runoff in the Nation’s rivers and streams during water year 2020 (11.10 inches) was higher than the long-term (1930–2020) mean annual runoff of 9.40 inches for the contiguous United States. Nationwide, the 2020 streamflow ranked the 10th highest out of the 91 years.
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Scant availability of streamflow data can impede the utility of streamflow as a variable in ecological models of aquatic and terrestrial species, especially when studying small streams in watersheds that lack streamgages. Streamflow data at fine resolution and broad extent were needed by collaborators for ecological research on small streams in several ungaged watersheds of southwestern Wyoming, where streamflow data are sparse.
To improve the utility of sparse streamflow data to ecological research in ungaged watersheds, we developed a machine learning approach in R for modeling spatially and temporally continuous monthly streamflow from 2012 through 2017 in three semiarid montane-steppe watersheds (with drainage areas of 26–55 square miles and mean elevations of 8,031–8,455 feet) on the Wyoming Range in the upper Green River Basin. A machine learning streamflow (MLFLOW) model was calibrated and validated with 971 discrete streamflow observations and 24 static and dynamic predictor variables derived from geospatial and time series data on climatic, physiographic, and anthropogenic characteristics affecting streamflow. The predictor variables were temporally and spatially conditioned to amplify the relation of predictor variables to monthly streamflow.
The MLFLOW model had satisfactory agreement between observed and predicted streamflow (coefficient of determination [R2]=0.80, Nash-Sutcliffe efficiency [NSE]=0.79, NSE with log-transformed data [logNSE]=0.82, and percent bias [PBIAS]=0.7 percent). NSE and logNSE indicated the MLFLOW model performed equally well for high and low flows, and PBIAS indicated the MLFLOW model did not overpredict or underpredict monthly streamflow. Streamflow predictions seemed to well represent the annual hydrograph within the study area during the study period.
The most important variables (statistically important in the MLFLOW model) for explaining monthly streamflow were temporally and spatially conditioned dynamic climatic variables, mostly precipitation and snow water equivalent. Importance of the static and dynamic variables did not differ substantially among the three watersheds but differed considerably among the 6 years. Monthly streamflow increased with increasing precipitation, snow water equivalent, and drainage area but decreased with increasing forest cover, elevation, evapotranspiration, and temperature.
The MLFLOW model was most sensitive to selection of dynamic climatic variables. Unconditioned dynamic climatic variables alone explained 54 percent of the variance (R2=0.54) in monthly streamflow, whereas adding static physiographic and anthropogenic variables only explained 12 percent more of the variance (R2=0.66). Also, spatial conditioning of all variables together with temporal conditioning of dynamic variables increased the variance explained in the MLFLOW model by another 14 percent (R2=0.80). The MLFLOW model also had greater sensitivity to temporal than to spatial differences in the data. For the MLFLOW model trained with observations from all watersheds and years or for models trained with observations from all except one watershed or 1 year left out sequentially, performance was better in testing on observations from each watershed than from each year separately. Also, performance was better for models fitted to fewer sites than to fewer months of observations.
The greatest utility of the modeling approach is the ease of use and the speed of processing input data, running the model, and interpreting the model output, whereas the greatest limitation is the need for spatially and temporally representative streamflow observations to drive the model. Although familiarity with R is necessary, only a working knowledge of hydrology (for selecting appropriate predictor variables and evaluating the quality of streamflow observations) and a rudimentary understanding of machine learning models are needed. Therefore, this modeling approach is practicable for other scientists who work with water but who are not hydrologists.
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3162 Bozeman Avenue
Helena, MT 59601
From the shores of Jamestown and spreading north, south, and west, the lands that became the State of Virginia were some of the first in North America top experience rapid landscape change from European settlement. Imagery and data from the USGS Landsat series of satellites offer an unparalleled resource for the study, understanding, and preservation of Virginia’s land and water resources. From monitoring the health of water bodies to managing invasive species to planning for a range of climate change effects, the USGS National Land Imaging Program’s stewardship and public delivery of Landsat data have benefitted Virginians in myriad ways.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
About 14.5 months after the 2018 eruption and summit collapse of Kīlauea Volcano, Hawaiʻi, liquid water started accumulating in the deepened summit crater, forming a lake that attained 51 m depth before rapidly boiling off on December 20, 2020, when an eruption from the crater wall poured lava into the lake. Modeling the growth of the crater lake at Kīlauea summit is important for assessing the potential for explosive volcanism. Our current understanding of the past 2500 years of eruptive activity at Kīlauea suggests a slight dominance of explosive behavior over effusive. The deepened summit crater and presence of the crater lake in 2019 raised renewed concerns about explosive activity. Groundwater models using hydraulic-property data from a nearby drillhole successfully forecast the timing and rate of lake filling. Here we compare the groundwater-model predictions with observational data through the demise of the crater lake, examine the implications for local water-table configuration, consider the potential role of evaporation and recharge (neglected in previous models), and briefly discuss the energetics of the rapid boil-off. This post audit of groundwater-flow models of Kīlauea summit shows that simple models can sometimes be used effectively to simulate complex settings such as volcanoes.
The travel time or “age” of groundwater affects catchment responses to hydrologic changes, geochemical reactions, and time lags between management actions and responses at down-gradient streams and wells. Use of atmospheric tracers has facilitated the characterization of groundwater ages, but most wells lack such measurements. This study applied machine learning to predict ages in wells across a large region around the Great Lakes Basin using well, chemistry, and landscape characteristics. For a dataset of age tracers in 961 samples, the travel time from the land surface to the sample location was estimated for each sample using parametric functions. The mean travel times were then modeled using a gradient boosting machine (GBM) algorithm with cross validation tuning of model metaparameters. The GBM approach was able to closely match estimated ages for the training data (RMSE = 0.26 natural-log scale years) and provided a reasonable match to testing data (RMSE = 0.84). Of the variables tested, well characteristics (e.g. depth), land use, hydrologic indicators (e.g. topographic wetness index), and water chemistry (e.g. nitrate, fluoride, and pH), substantially affected the predictions of age. GBM prediction was applied to 14,335 groundwater samples with median sample depth of 5.4 m, indicating for the Great Lakes Basin a broad distribution of ages among wells with a median of 32.9 years. Lag times of decades are likely for these wells to respond to changing solute fluxes near land surface. While depth variables most strongly affected predicted mean ages, chemical constituents exhibited smooth trends with age, consistent with prevailing conceptual models of evolving sources and geochemistry flowpaths. The results provide proof of concept for use of readily available variables of well, landscape, and chemical characteristics to improve groundwater age estimates across large regions.
The U.S. Geological Survey, in cooperation with the National Park Service, developed a three-dimensional groundwater-flow model for Assateague Island in eastern Maryland and Virginia to assess the effects of sea-level rise on the groundwater system. Sea-level rise is expected to increase the altitude of the water table in barrier island aquifer systems, possibly leading to adverse effects to ecosystems on the barrier islands. The potential effects of sea-level rise were evaluated by simulating groundwater conditions under sea-level-rise scenarios of 20 centimeters (cm), 40 cm, and 60 cm. Results show that as sea level rises, low-lying areas of the island originally represented as receiving freshwater recharge in the baseline scenario are inundated by saltwater. This change from freshwater recharge to saltwater decreases the overall amount of freshwater recharging the system. As the water table rises in response to the higher sea levels, freshwater flow out of the system changes, with more freshwater leaving as submarine groundwater discharge and less freshwater leaving as seeps and evapotranspiration. At the current land-surface altitude, as much as 50 percent of the island may be inundated with a 60-cm rise in sea level, and the low-lying marshes may change from freshwater to saltwater.
Groundwater levels at 32 wells were monitored for as long as 12 months between October 2014 and September 2015 on Assateague Island. Results from objective classification analysis of 14 shallow monitoring wells show two dominant processes affecting groundwater levels in two different settings on the island. On the western side of the island, between the primary dune and the inland bays, water levels clearly respond to precipitation events. This side of the island is more protected from ocean tides and typically is more vegetated than the eastern side. On the eastern side of the island, between the Atlantic Ocean and the primary dune, water levels clearly respond to tidal events. Specific conductance was measured at four wells, two on the western part of the island and two on the eastern part of the island. Specific conductance values in the two wells west of the primary dune show episodic decreases, coinciding with precipitation events. Specific conductance values in the two wells on the eastern side of the primary dune show episodic increases, coinciding with high-tide events. These high frequency monitoring data are intended to aid in designing a monitoring network that can document both short-term and long-term hydrologic processes on Assateague Island National Seashore.
This study uses a modeling approach consistent with models developed for Gateway National Recreation Area, Sandy Hook Unit (New Jersey) and Fire Island National Seashore (New York). Combined, these models are meant to improve the regional capabilities for predicting climate-change effects on barrier islands and provide resource managers with a common set of tools for adaptation and mitigation of potentially adverse effects of sea-level rise.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205104","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Fleming, B.J., Raffensperger, J.P., Goodling, P.J., and Masterson, J., 2021, Simulated effects of sea-level rise on the shallow, fresh groundwater system of Assateague Island, Maryland and Virginia: U.S. Geological Survey Scientific Investigations Report 2020–5104, 62 p., https://doi.org/10.3133/sir20205104.","productDescription":"Report: viii, 62 p.; Data Release","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094959","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":387028,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AJOLRK","text":"USGS data release","linkHelpText":"MODFLOW-NWT model with SWI2 used to evaluate the water-table response to sea-level rise and change in recharge, Assateague Island, Maryland and Virginia"},{"id":387027,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5104/sir20205104.pdf","text":"Report","size":"21.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5104"},{"id":387026,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5104/coverthb.jpg"},{"id":387041,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205117","text":"Scientific Investigations Report 2020–5117","linkHelpText":"- Simulation of Water-Table and Freshwater/Saltwater Interface Response to Climate-Change-Driven Sea-Level Rise and Changes in Recharge at Fire Island National Seashore, New York"},{"id":387040,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205080","text":"Scientific Investigations Report 2020–5080","linkHelpText":"- Simulation of Water-Table Response to Sea-Level Rise and Change in Recharge, Sandy Hook Unit, Gateway National Recreation Area, New Jersey"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Assateague Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.42388916015625,\n 37.87376937332855\n ],\n [\n -75.3826904296875,\n 37.83473402375478\n ],\n [\n -75.30441284179688,\n 37.88027325525864\n ],\n [\n -75.15335083007812,\n 38.11727165830543\n ],\n [\n -75.12039184570312,\n 38.29101446582335\n ],\n [\n -75.17120361328125,\n 38.22847167526397\n ],\n [\n -75.28793334960938,\n 38.0513353697269\n ],\n [\n -75.3826904296875,\n 37.93769926732864\n ],\n [\n -75.42388916015625,\n 37.87376937332855\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
Sea lamprey (SL; Petromyzon marinus), one of the oldest living vertebrates, have a complex metamorphic life cycle. Following hatching, SL transition into a microphagous, sediment burrowing larval stage, and after 2–10+ years, the larvae undergo a dramatic metamorphosis, transforming into parasitic juveniles that feed on blood and bodily fluids of fishes; adult lamprey cease feeding, spawn, and die. Since gut microbiota are critical for the overall health of all animals, we examined the microbiota associated with SLs in each life history stage. We show that there were significant differences in the gut bacterial communities associated with the larval, parasitic juvenile, and adult life stages. The transition from larval to the parasitic juvenile stage was marked with a significant shift in bacterial community structure and reduction in alpha diversity. The most abundant SL-associated phyla were Proteobacteria, Fusobacteria, Bacteroidetes, Verrucomicrobia, Actinobacteria, and Firmicutes, with their relative abundances varying among the stages. Moreover, while larval SL were enriched with unclassified Fusobacteriaceae, unclassified Verrucomicrobiales and Cetobacterium, members of the genera with fastidious nutritional requirements, such as Streptococcus, Haemophilus, Cutibacterium, Veillonella, and Massilia, were three to four orders of magnitude greater in juveniles than in larvae. In contrast, adult SLs were enriched with Aeromonas, Iodobacter, Shewanella, and Flavobacterium. Collectively, our findings show that bacterial communities in the SL gut are dramatically different among its life stages. Understanding how these communities change over time within and among SL life stages may shed more light on the role that these gut microbes play in host growth and fitness.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmicb.2021.706683","usgsCitation":"Mathai, P., Byappanahalli, M., Johnson, N.S., and Sadowsky, M.J., 2021, Gut microbiota associated with different sea lamprey (Petromyzon marinus) life stages: Frontiers in Microbiology, v. 12, 706683, 11 p., https://doi.org/10.3389/fmicb.2021.706683.","productDescription":"706683, 11 p.","ipdsId":"IP-129553","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":450943,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmicb.2021.706683","text":"Publisher Index Page"},{"id":388873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"Lake Huron watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.24316406249999,\n 42.85180609584705\n ],\n [\n -79.56298828125,\n 42.85180609584705\n ],\n [\n -79.56298828125,\n 47.502358951968574\n ],\n [\n -84.24316406249999,\n 47.502358951968574\n ],\n [\n -84.24316406249999,\n 42.85180609584705\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2021-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Mathai, P 0000-0001-5261-9911","orcid":"https://orcid.org/0000-0001-5261-9911","contributorId":265312,"corporation":false,"usgs":false,"family":"Mathai","given":"P","email":"","affiliations":[{"id":12644,"text":"University of Minnesota, St. Paul","active":true,"usgs":false}],"preferred":false,"id":822538,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byappanahalli, Muruleedhara 0000-0001-5376-597X","orcid":"https://orcid.org/0000-0001-5376-597X","contributorId":241924,"corporation":false,"usgs":true,"family":"Byappanahalli","given":"Muruleedhara","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":822539,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":597,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas","email":"njohnson@usgs.gov","middleInitial":"S.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":822540,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sadowsky, Michael J.","contributorId":34003,"corporation":false,"usgs":false,"family":"Sadowsky","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":12644,"text":"University of Minnesota, St. Paul","active":true,"usgs":false}],"preferred":false,"id":822541,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70224925,"text":"70224925 - 2021 - Watershed sediment yield following the 2018 Carr Fire, Whiskeytown National Recreation Area, northern California","interactions":[],"lastModifiedDate":"2021-10-05T12:21:37.890807","indexId":"70224925","displayToPublicDate":"2021-09-03T07:18:54","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Watershed sediment yield following the 2018 Carr Fire, Whiskeytown National Recreation Area, northern California","docAbstract":"Wildfire risk has increased in recent decades over many regions, due to warming climate and other factors. Increased sediment export from recently burned landscapes can jeopardize downstream infrastructure and water resources, but physical landscape response to fire has not been quantified for some at-risk areas, including much of northern California, USA. We measured sediment yield from three watersheds (13–29 km2) that drain to Whiskeytown Lake, California, within the area burned by the 2018 Carr Fire. Structure-from-Motion photogrammetry on aerial images combined with sonar bathymetric mapping of submerged areas indicated first-year post-fire sediment yields of 4,080 ± 598 t/km2 (Brandy Creek), 2,700 ± 527 t/km2 (Boulder Creek), and 305 ± 58.0 t/km2 (Whiskey Creek)—some of the first post-fire yields measured in northern California and 64, 42, and 4.8 times greater than pre-fire yields, respectively. These were measured during a wet year and resulted largely from rilling erosion and fluvial sediment transport, without post-fire debris flows. Rilling preferentially developed in contact with dirt roads, aided by thin soils and exposed bedrock, and on slopes vegetated by chaparral pre-fire. The second post-fire year (a dry year) was characterized by fluvial reworking and delta progradation of the first-year deposits and relatively little new sediment export. First-year sedimentation of 111,000 m3 represented minor loss of storage capacity in Whiskeytown Lake but would be detrimental to smaller reservoirs; in general, increased sediment yields from western US watersheds as fire and extreme rainfall increase will likely pose risks to water quality and storage.
Livebearers in the family Poeciliidae are some of the most widely introduced fishes. Native poeciliid translocations within the U.S. are mostly due to deliberate stocking for mosquito control. Introductions of exotic poeciliids, those not native to the U.S., are more likely to be due to release from aquaria or escape from farms. Many of these non-natives originate from warm climate regions, contrasting with the relatively cold climates in the U.S. Thus, thermal springs may increase the possible range of these species. Our primary objective was to examine the importance of climate and thermal springs in affecting the distribution of translocated and non-native poeciliids in the U.S. This objective was addressed using a national database of poeciliid introductions. Records were dominated by a handful of states and most introductions led to established populations. While translocated mosquitofish were found across many states and climates, non-natives were found almost exclusively in warm climate states and territories (e.g., Florida, Hawaii, Puerto Rico), especially where air temperatures remained above freezing. Outside warm climate states, 46% of established non-native populations were located at thermal spring sources. These results indicate that thermal springs extend the distribution of non-natives, but were relatively unimportant for translocated poeciliids.
Costs to producing sexual signals can create selective pressures on males to invest signaling effort in particular contexts. When the benefits of signaling vary consistently across time, males can optimize signal investment to specific temporal contexts using biological rhythms. Sea lamprey, Petromyzon marinus, have a semelparous life history, are primarily nocturnal, and rely on pheromone communication for reproduction; however, whether male investment in pheromone transport and release matches increases in spawning activity remains unknown. By measuring (1) 3keto-petromyzonol sulfate (3kPZS, a main pheromone component) and its biosynthetic precursor PZS in holding water and tissue samples at six points over the course of 24 hours and (2) 3kPZS release over the course of several days, we demonstrate that 3kPZS release exhibits a consistent diel pattern across several days with elevated pheromone release just prior to sunset and at night. Trends in hepatic concentrations and circulatory transport of PZS and 3kPZS were relatively consistent with patterns of 3kPZS release and suggest the possibility of direct upregulation in pheromone transport and release rather than observed release patterns being solely a byproduct of increased behavioral activity. Our results suggest males evolved a signaling strategy that synchronizes elevated pheromone release with nocturnal increases in sea lamprey behavior. This may be imperative to ensure that male signaling effort is not wasted in a species having a single, reproductive event.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/icb/icab190","usgsCitation":"Fissette, S.D., Bussy, U., Huerta, B., Brant, C.O., Li, K., Johnson, N.S., and Li, W., 2021, Diel patterns of pheromone release by male sea lamprey: Integrative and Comparative Biology, v. 61, no. 5, p. 1795-1810, https://doi.org/10.1093/icb/icab190.","productDescription":"16 p.","startPage":"1795","endPage":"1810","ipdsId":"IP-131044","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":450955,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/icb/icab190","text":"Publisher Index Page"},{"id":412801,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"61","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Fissette, Skye D.","contributorId":150994,"corporation":false,"usgs":false,"family":"Fissette","given":"Skye","email":"","middleInitial":"D.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":863623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bussy, Ugo","contributorId":150993,"corporation":false,"usgs":false,"family":"Bussy","given":"Ugo","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":863624,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huerta, Belinda","contributorId":222210,"corporation":false,"usgs":false,"family":"Huerta","given":"Belinda","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":863625,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brant, Cory O.","contributorId":126746,"corporation":false,"usgs":false,"family":"Brant","given":"Cory","email":"","middleInitial":"O.","affiliations":[{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":863626,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Li, Ke","contributorId":172267,"corporation":false,"usgs":false,"family":"Li","given":"Ke","email":"","affiliations":[],"preferred":false,"id":863804,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":597,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas","email":"njohnson@usgs.gov","middleInitial":"S.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":863628,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Li, Weiming","contributorId":126748,"corporation":false,"usgs":false,"family":"Li","given":"Weiming","email":"","affiliations":[{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":863629,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223740,"text":"70223740 - 2021 - Unexpected diversity of Endozoicomonas in deep-sea corals","interactions":[],"lastModifiedDate":"2021-09-03T12:12:53.673111","indexId":"70223740","displayToPublicDate":"2021-09-02T07:09:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"title":"Unexpected diversity of Endozoicomonas in deep-sea corals","docAbstract":"ABSTRACT: The deep ocean hosts a large diversity of azooxanthellate cold-water corals whose associated microbiomes remain to be described. While the bacterial genus Endozoicomonas has been widely identified as a dominant associate of tropical and temperate corals, it has rarely been detected in deep-sea corals. Determining microbial baselines for these cold-water corals is a critical first step to understanding the ecosystem services their microbiomes contribute, while providing a benchmark against which to measure responses to environmental change or anthropogenic effects. Samples of Acanthogorgia aspera, A. spissa, Desmophyllum dianthus, and D. pertusum (Lophelia pertusa) were collected from western Atlantic sites off the US east coast and from the northeastern Gulf of Mexico. Microbiomes were characterized by 16S rRNA gene amplicon surveys. Although D. dianthus and D. pertusum have recently been combined into a single genus due to their genetic similarity, their microbiomes were significantly different. The Acanthogorgia spp. were collected from submarine canyons in different regions, but their microbiomes were extremely similar and dominated by Endozoicomonas. This is the first report of coral microbiomes dominated by Endozoicomonas occurring below 1000 m, at temperatures near 4°C. D. pertusum from 2 Atlantic sites were also dominated by distinct Endozoicomonas, unlike D. pertusum from other sites described in previous studies, including the Gulf of Mexico, the Mediterranean Sea and a Norwegian fjord.
","language":"English","publisher":"Inter-Research","doi":"10.3354/meps13844","usgsCitation":"Kellogg, C.A., and Pratte, Z.A., 2021, Unexpected diversity of Endozoicomonas in deep-sea corals: Marine Ecology Progress Series, v. 673, p. 1-15, https://doi.org/10.3354/meps13844.","productDescription":"15 p.","startPage":"1","endPage":"15","ipdsId":"IP-126734","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":450959,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps13844","text":"Publisher Index Page"},{"id":436213,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z1HPKR","text":"USGS data release","linkHelpText":"Cold-water Coral Microbiomes (Acanthogorgia spp. Desmophyllum dianthus, and Lophelia pertusa) from the Gulf of Mexico and Atlantic Ocean off the Southeast Coast of the United States-Raw Data"},{"id":388829,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.8828125,\n 37.405073750176925\n ],\n [\n -74.6630859375,\n 35.42486791930558\n ],\n [\n -75.673828125,\n 34.161818161230386\n ],\n [\n -78.046875,\n 33.247875947924385\n ],\n [\n -79.453125,\n 32.32427558887655\n ],\n [\n -79.4970703125,\n 31.541089879585808\n ],\n [\n -77.87109375,\n 31.316101383495624\n ],\n [\n -74.4873046875,\n 32.509761735919426\n ],\n [\n -71.71875,\n 34.77771580360469\n ],\n [\n -71.455078125,\n 36.4566360115962\n ],\n [\n -71.89453125,\n 37.405073750176925\n ],\n [\n -72.5537109375,\n 37.50972584293751\n ],\n [\n -73.47656249999999,\n 37.82280243352756\n ],\n [\n -74.1796875,\n 37.96152331396614\n ],\n [\n -74.8828125,\n 37.405073750176925\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"673","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kellogg, Christina A. 0000-0002-6492-9455 ckellogg@usgs.gov","orcid":"https://orcid.org/0000-0002-6492-9455","contributorId":391,"corporation":false,"usgs":true,"family":"Kellogg","given":"Christina","email":"ckellogg@usgs.gov","middleInitial":"A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":822526,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pratte, Zoe A.","contributorId":214260,"corporation":false,"usgs":false,"family":"Pratte","given":"Zoe","email":"","middleInitial":"A.","affiliations":[{"id":27526,"text":"Georgia Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":822527,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70230525,"text":"70230525 - 2021 - Historical changes in plant water use and need in the continental United States","interactions":[],"lastModifiedDate":"2022-04-15T12:11:42.091767","indexId":"70230525","displayToPublicDate":"2021-09-02T07:07:39","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Historical changes in plant water use and need in the continental United States","docAbstract":"A robust method for characterizing the biophysical environment of terrestrial vegetation uses the relationship between Actual Evapotranspiration (AET) and Climatic Water Deficit (CWD). These variables are usually estimated from a water balance model rather than measured directly and are often more representative of ecologically-significant changes than temperature or precipitation. We evaluate trends and spatial patterns in AET and CWD in the Continental United States (CONUS) during 1980–2019 using a gridded water balance model. The western US had linear regression slopes indicating increasing CWD and decreasing AET (drying), while the eastern US had generally opposite trends. When limits to plant performance characterized by AET and CWD are exceeded, vegetation assemblages change. Widespread increases in aridity throughout the west portends shifts in the distribution of plants limited by available moisture. A detailed look at Sequoia National Park illustrates the high degree of fine-scale spatial variability that exists across elevation and topographical gradients. Where such topographical and climatic diversity exists, appropriate use of our gridded data will require sub-setting to an appropriate area and analyzing according to categories of interest such as vegetation communities or across obvious physical gradients. Recent studies have successfully applied similar water balance models to fire risk and forest structure in both western and eastern U.S. forests, arid-land spring discharge, amphibian colonization and persistence in wetlands, whitebark pine mortality and establishment, and the distribution of arid-land grass species and landscape scale vegetation condition. Our gridded dataset is available free for public use. Our findings illustrate how a simple water balance model can identify important trends and patterns at site to regional scales. However, at finer scales, environmental heterogeneity is driving a range of responses that may not be simply characterized by a single trend.
Chemical contamination of freshwaters is a global problem. In the United States alone, millions of kilometers of rivers and hectares of lakes and wetlands are impaired from contamination by chemicals including mercury, pesticides, polychlorinated biphenyls (PCBs), and trace metals (US Environmental Protection Agency, 2017). Efforts to mitigate the risks of contamination have largely focused on aquatic endpoints. However, these contaminants pose a risk not only to life in freshwater ecosystems but also to the terrestrial organisms that depend on freshwater ecosystems for food.
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