Karst subterranean estuaries (KSEs) extend into carbonate platforms along 12% of all coastlines. A recent study has shown that microbial methane (CH4) consumption is an important component of the carbon cycle and food web dynamics within flooded caves that permeate KSEs. In this study, we obtained high‐resolution (~2.5‐day) temporal records of dissolved methane concentrations and its stable isotopic content (δ13C) to evaluate how regional meteorology and hydrology control methane dynamics in KSEs. Our records show that less methane was present in the anoxic fresh water during the wet season (4,361 ± 89 nM) than during the dry season (5,949 ± 132 nM), suggesting that the wet season hydrologic regime enhances mixing of methane and other constituents into the underlying brackish water. The δ13C of the methane (−38.1 ± 1.7‰) in the brackish water was consistently more 13C‐enriched than fresh water methane (−65.4 ± 0.4‰), implying persistent methane oxidation in the cave. Using a hydrologically based mass balance model, we calculate that methane consumption in the KSE was 21–28 mg CH4·m−2·year−1 during the 6‐month dry period, which equates to ~1.4 t of methane consumed within the 102‐ to 138‐km2 catchment basin for the cave. Unless wet season methane consumption is much greater, the magnitude of methane oxidized within KSEs is not likely to affect the global methane budget. However, our estimates constrain the contribution of a critical resource for this widely distributed subterranean ecosystem.
","language":"English","publisher":"AGU","doi":"10.1029/2018GB006026","usgsCitation":"Brankovits, D., Pohlman, J.W., Ganju, N., Iliffe, T., Lowell, N., Roth, E., Sylva, S., Emmert, J., and Lapham, L.L., 2018, Hydrologic controls of methane dynamics in karst subterranean estuaries: Global Biogeochemical Cycles, v. 32, no. 12, p. 1759-1775, https://doi.org/10.1029/2018GB006026.","productDescription":"17 p.","startPage":"1759","endPage":"1775","ipdsId":"IP-102700","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468186,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018gb006026","text":"Publisher Index Page"},{"id":360248,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"12","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-07","publicationStatus":"PW","scienceBaseUri":"5c137dd2e4b006c4f8514874","contributors":{"authors":[{"text":"Brankovits, David 0000-0002-0863-5698","orcid":"https://orcid.org/0000-0002-0863-5698","contributorId":210617,"corporation":false,"usgs":true,"family":"Brankovits","given":"David","email":"","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":753870,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pohlman, John W. 0000-0002-3563-4586 jpohlman@usgs.gov","orcid":"https://orcid.org/0000-0002-3563-4586","contributorId":145771,"corporation":false,"usgs":true,"family":"Pohlman","given":"John","email":"jpohlman@usgs.gov","middleInitial":"W.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":753871,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":753872,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Iliffe, T.M.","contributorId":201287,"corporation":false,"usgs":false,"family":"Iliffe","given":"T.M.","email":"","affiliations":[],"preferred":false,"id":753873,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lowell, N.","contributorId":211383,"corporation":false,"usgs":false,"family":"Lowell","given":"N.","email":"","affiliations":[{"id":38240,"text":"Lowell Instruments","active":true,"usgs":false}],"preferred":false,"id":753874,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roth, E.","contributorId":90499,"corporation":false,"usgs":true,"family":"Roth","given":"E.","affiliations":[],"preferred":false,"id":753875,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sylva, S.P.","contributorId":211384,"corporation":false,"usgs":false,"family":"Sylva","given":"S.P.","email":"","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":753876,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Emmert, J.A.","contributorId":211385,"corporation":false,"usgs":false,"family":"Emmert","given":"J.A.","email":"","affiliations":[{"id":38241,"text":"Moody Gardens Aquarium","active":true,"usgs":false}],"preferred":false,"id":753877,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lapham, L. L.","contributorId":140085,"corporation":false,"usgs":false,"family":"Lapham","given":"L.","email":"","middleInitial":"L.","affiliations":[{"id":13383,"text":"University of Maryland Center for Environmental Science, Chesapeake Biological Laboratory, 6 Solomons, Maryland 20688","active":true,"usgs":false}],"preferred":false,"id":753878,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70201798,"text":"70201798 - 2018 - Book review: Analytical groundwater mechanics","interactions":[],"lastModifiedDate":"2019-01-30T14:04:43","indexId":"70201798","displayToPublicDate":"2018-12-13T14:04:36","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Book review: Analytical groundwater mechanics","docAbstract":"Encapsulating almost 50 years of experience applying mathematics to groundwater flow problems, this latest textbook from Otto Strack (2017) is a tour de force for analytical groundwater approaches. It is comprised of 10 chapters, spanning topics from the basics of groundwater mechanics, to steady state, three‐dimensional, and transient flow, as well as particle tracking and solute transport. The book concludes with a chapter on finite‐difference and finite‐element methods, but the treatment is cursory, focusing on basic principles. The book is high quality, especially with respect to the presentation, due perhaps in no small part to the keen eye of the author's longtime publication resource (and clearly supportive wife) Andrine!
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12843","usgsCitation":"Hunt, R., 2018, Book review: Analytical groundwater mechanics: Groundwater, v. 57, no. 1, p. 85-85, https://doi.org/10.1111/gwat.12843.","productDescription":"1 p.","startPage":"85","endPage":"85","ipdsId":"IP-102643","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":360826,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Hunt, Randall J. 0000-0001-6465-9304","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":16118,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":755402,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70202360,"text":"70202360 - 2018 - Intensive oyster aquaculture can reduce disease impacts on sympatric wild oysters","interactions":[],"lastModifiedDate":"2019-02-25T13:33:46","indexId":"70202360","displayToPublicDate":"2018-12-13T13:33:37","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5806,"text":"Aquaculture Environment Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Intensive oyster aquaculture can reduce disease impacts on sympatric wild oysters","docAbstract":"Risks associated with disease spread from fish and shellfish farming have plagued the growth and public perception of aquaculture worldwide. However, by processing nutrients and organic material from the water column, the culture of many suspension-feeding bivalves has been proposed as a novel solution toward mitigating problems facing coastal water quality, including the removal of disease-causing parasites. Here we developed and simulated an epidemiological model describing sympatric oyster Crassostrea virginica populations in aquaculture and the wild impacted by the protozoan parasite Perkinsus marinus. Our model captured the indirect interaction between wild and cultured populations that occurs through sharing water-borne P. marinus transmission stages, and we hypothesized that oyster aquaculture can enhance wild oyster populations through reduced parasitism as long as cultured oysters are harvested prior to spreading disease. We found that the density of oysters in aquaculture, which is commonly thought to lead to the spread of disease through farms and out to nearby populations in the wild, has only indirect effects on P. marinustransmission through its interaction with the rate of aquaculture harvests. Sufficient aquaculture harvest, which varies with the susceptibility of farmed oysters to P. marinus infection and their lifespan once infected, reduces disease by diluting parasites in the environment. Our modeling results offer new insights toward the broader epidemiological implications of oyster aquaculture and effective disease management.
","language":"English","publisher":"Inter-Research","doi":"10.3354/aei00290","usgsCitation":"Ben-Horin, T., Burge, C.A., Bushek, D., Groner, M.L., Proestou, D.A., Huey, L.I., Bidegain, G., and Carnegie, R.B., 2018, Intensive oyster aquaculture can reduce disease impacts on sympatric wild oysters: Aquaculture Environment Interactions, v. 10, p. 557-567, https://doi.org/10.3354/aei00290.","productDescription":"11 p.","startPage":"557","endPage":"567","ipdsId":"IP-098954","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":468187,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/aei00290","text":"Publisher Index Page"},{"id":361500,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ben-Horin, Tal","contributorId":58137,"corporation":false,"usgs":false,"family":"Ben-Horin","given":"Tal","email":"","affiliations":[],"preferred":false,"id":757989,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burge, Colleen A.","contributorId":213539,"corporation":false,"usgs":false,"family":"Burge","given":"Colleen","email":"","middleInitial":"A.","affiliations":[{"id":38781,"text":"Institute of Marine and Environmental Technology, University of Maryland Baltimore County, Baltimore, MD 21202","active":true,"usgs":false}],"preferred":false,"id":757990,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bushek, David","contributorId":213540,"corporation":false,"usgs":false,"family":"Bushek","given":"David","email":"","affiliations":[{"id":38782,"text":"Haskin Shellfish Research Laboratory, Rutgers University, Port Norris, NJ 08349","active":true,"usgs":false}],"preferred":false,"id":757991,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Groner, Maya L. 0000-0002-3381-6415","orcid":"https://orcid.org/0000-0002-3381-6415","contributorId":213541,"corporation":false,"usgs":true,"family":"Groner","given":"Maya","email":"","middleInitial":"L.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":757992,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Proestou, Dina A.","contributorId":213542,"corporation":false,"usgs":false,"family":"Proestou","given":"Dina","email":"","middleInitial":"A.","affiliations":[{"id":38783,"text":"National Cold Water Marine Aquaculture Center, U.S. Department of Agriculture Agricultural Research Service, Kingston, RI 02881","active":true,"usgs":false}],"preferred":false,"id":757993,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Huey, Lauren I.","contributorId":213543,"corporation":false,"usgs":false,"family":"Huey","given":"Lauren","email":"","middleInitial":"I.","affiliations":[{"id":38784,"text":"Virginia Institute of Marine Science, College of William & Mary, P.O. Box 1346, Gloucester Point, VA 23062","active":true,"usgs":false}],"preferred":false,"id":757994,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bidegain, Gorka","contributorId":167008,"corporation":false,"usgs":false,"family":"Bidegain","given":"Gorka","email":"","affiliations":[{"id":13403,"text":"University of Southern Mississippi, Department of Biological Sciences, Hattiesburg, Mississippi, USA","active":true,"usgs":false}],"preferred":false,"id":757995,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Carnegie, Ryan B.","contributorId":213544,"corporation":false,"usgs":false,"family":"Carnegie","given":"Ryan","email":"","middleInitial":"B.","affiliations":[{"id":38784,"text":"Virginia Institute of Marine Science, College of William & Mary, P.O. Box 1346, Gloucester Point, VA 23062","active":true,"usgs":false}],"preferred":false,"id":757996,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70201410,"text":"70201410 - 2018 - Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure","interactions":[],"lastModifiedDate":"2018-12-13T14:59:37","indexId":"70201410","displayToPublicDate":"2018-12-12T14:59:31","publicationYear":"2018","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":"Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure","docAbstract":"Elevation data are critical for assessments of sea-level rise (SLR) and coastal flooding exposure. Previous research has demonstrated that the quality of data used in elevation-based assessments must be well understood and applied to properly model potential impacts. The cumulative vertical uncertainty of the input elevation data substantially controls the minimum increments of SLR and the minimum planning horizons that can be effectively used in assessments. For regional, continental, or global assessments, several digital elevation models (DEMs) are available for the required topographic information to project potential impacts of increased coastal water levels, whether a simple inundation model is used or a more complex process-based or probabilistic model is employed. When properly characterized, the vertical accuracy of the DEM can be used to report assessment results with the uncertainty stated in terms of a specific confidence level or likelihood category. An accuracy evaluation has been conducted of global DEMs to quantify their inherent vertical uncertainty to demonstrate how accuracy information should be considered when planning and implementing a SLR or coastal flooding assessment. The evaluation approach includes comparison of the DEMs with high-accuracy geodetic control points as the independent reference data over a variety of coastal relief settings. The global DEMs evaluated include SRTM, ASTER GDEM, ALOS World 3D, TanDEM-X, NASADEM, and MERIT. High-resolution, high-accuracy DEM sources, such as airborne lidar and stereo imagery, are also included to give context to the results from the global DEMs. The accuracy characterization results show that current global DEMs are not adequate for high confidence mapping of exposure to fine increments (<1 m) of SLR or with shorter planning horizons (<100 years) and thus they should not be used for such mapping, but they are suitable for general delineation of low elevation coastal zones. In addition to the best practice of rigorous accounting for vertical uncertainty, other recommended procedures are presented for delineation of different types of impact areas (marine and groundwater inundation) and use of regional relative SLR scenarios. The requirement remains for a freely available, high-accuracy, high-resolution global elevation model that supports quantitative SLR and coastal inundation assessments at high confidence levels.
","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2018.00230","usgsCitation":"Gesch, D.B., 2018, Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure: Frontiers in Earth Science, v. 6, p. 1-19, https://doi.org/10.3389/feart.2018.00230.","productDescription":"Article 230; 19 p.","startPage":"1","endPage":"19","ipdsId":"IP-099709","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468189,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2018.00230","text":"Publisher Index Page"},{"id":360254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-12","publicationStatus":"PW","scienceBaseUri":"5c137dd3e4b006c4f851487e","contributors":{"authors":[{"text":"Gesch, Dean B. 0000-0002-8992-4933 gesch@usgs.gov","orcid":"https://orcid.org/0000-0002-8992-4933","contributorId":2956,"corporation":false,"usgs":true,"family":"Gesch","given":"Dean","email":"gesch@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":754063,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70201353,"text":"70201353 - 2018 - The metabolic regimes of 356 rivers in the United States","interactions":[],"lastModifiedDate":"2020-09-02T12:52:01.189155","indexId":"70201353","displayToPublicDate":"2018-12-11T10:56:25","publicationYear":"2018","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":"The metabolic regimes of 356 rivers in the United States","docAbstract":"A national-scale quantification of metabolic energy flow in streams and rivers can improve understanding of the temporal dynamics of in-stream activity, links between energy cycling and ecosystem services, and the effects of human activities on aquatic metabolism. The two dominant terms in aquatic metabolism, gross primary production (GPP) and aerobic respiration (ER), have recently become practical to estimate for many sites due to improved modeling approaches and the availability of requisite model inputs in public datasets. We assembled inputs from the U.S. Geological Survey and National Aeronautics and Space Administration for October 2007 to January 2017. We then ran models to estimate daily GPP, ER, and the gas exchange rate coefficient for 356 streams and rivers across the continental United States. We also gathered potential explanatory variables and spatial information for cross-referencing this dataset with other datasets of watershed characteristics. This dataset offers a first national assessment of many-day time series of metabolic rates for up to 9 years per site, with a total of 490,907 site-days of estimates.
","language":"English","publisher":"Nature","doi":"10.1038/sdata.2018.292","usgsCitation":"Appling, A.P., Read, J.S., Winslow, L., Arroita, M., Bernhardt, E.S., Griffiths, N.A., Hall, R., Harvey, J.W., Heffernan, J.B., Stanley, E.H., Stets, E.G., and Yackulic, C.B., 2018, The metabolic regimes of 356 rivers in the United States: Scientific Data, v. 5, 18029, 14 p., https://doi.org/10.1038/sdata.2018.292.","productDescription":"18029, 14 p.","ipdsId":"IP-098533","costCenters":[{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":468191,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/sdata.2018.292","text":"Publisher Index Page"},{"id":437653,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70864KX","text":"USGS data 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CWP is an important metric to test and monitor water-saving strategies in agroecosystems across the globe. Red and near-infrared broadbands have been used to estimate CWP, because they capture biophysical constraints based on crop-light interaction principles at pixel level (e.g., 30-meter resolution) over large areas through time. Hyperspectral remote sensing, which allows for the more precise measurement of crop-light interactions at higher spectral resolution, should in theory provide higher accuracy in CWP estimation but has been underutilized by the remote sensing community due to computational challenges and lack of availability. In this study, a simple methodology is presented to demonstrate how CWP could be estimated using hyperspectral remote sensing. Due to a lack of hyperspectral data, Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data were used for the demonstration. Landsat is a broadband sensor that provides considerable spectral information for CWP estimation. New bands were identified in the workflow outside the typical Landsat bands used to estimate CWP and its components (Y and ETC). Landsat bands 1 and 3 were the most effective at estimating CWP and Y with an R2 of 0.72 (RMSE = 0.50 kg m−3) and 0.64 (RMSE = 0.31 kg m−2), respectively. All of the bands were poor at estimating ETC, with Landsat bands 1 and 7 being the most highly correlated (R2 = 0.13, RMSE = 0.08 m). Future work should train models with multiple estimates of CWP and Y over the growing season, while ETC may be better estimated with thermal infrared bands not considered in this study. Finally, studies should also consider estimating CWP categorically, instead of continuously, if the same objectives of testing and monitoring are met.
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Conservation implementation has been substantial in the time period for which digital records are available (from 2007 through 2017). Farmer participation in USDA conservation programs is voluntary and the implementation data are privacy protected. In 2010, the U.S. Geological Survey (USGS) and USDA formed a cooperative partnership to analyze the effects of agricultural conservation on sediment, nutrient, and pesticide transport to the Chesapeake Bay. The USDA provides conservation implementation records for Chesapeake Bay farms to the USGS, with strict limitations on the use of the data to maintain confidentiality of site-specific farm data. The USGS aggregates the data to maintain farmer privacy, and subsequently provides the aggregated datasets to the public to inform conservation decision making processes. As part of that process, the USGS collaborates with the USDA to increase the understanding and quality of the USDA datasets and informs the interpretation of data records by Chesapeake Bay Program partners. The USGS obtains USDA conservation datasets in October of each year, performs data handling and quality checks as described in this document, and delivers aggregated summaries to the six Chesapeake Bay state jurisdictions for use in reporting conservation implementation to the Chesapeake Bay Partnership’s Annual Progress Review, which occurs in December of each year. The privacy protected, site-specific datasets are also used by USGS scientists to understand the effects of agricultural conservation on sediment, nutrient, and pesticide transport to the Chesapeake Bay at the small watershed scale. This publication describes the methods used to aggregate the datasets herein made available to the public at county and eight-digit hydrologic unit code watershed scales, reporting annual implementation from 2007 through 2017. 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U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Hatch-year Semipalmated Sandpipers (Calidris pusilla) use river deltas along the Beaufort Sea as their first stops during fall migration. However, these sites are subject to extreme changes in water levels that affect available foraging habitat. We examined relationships between timing of fall migration and storm surges, with respect to forage availability, using different water level scenarios to predict impacts on food availability for fueling migration at three river deltas. We compared available calories at observed water levels to modeled values derived from changes due to lunar tides (35% decline) and storm surges (58% decline). Peak use by shorebirds varied temporally among sites, while the peak in forage availability occurred late in the season, mismatched with the largest peak in migration at the most used river delta. Shifts in breeding phenology due to climate warming may allow shorebirds to migrate earlier and miss some storm surges, but this may create a mismatch between peak migration and greater food availability. Additionally, changes in climate will likely increase frequency and severity of storm surges that negatively impact availability of foraging habitat for migrant shorebirds.
","language":"English","publisher":"International Wader Study Group","doi":"10.18194/ws.00121","usgsCitation":"Churchwell, R., Kendall, S., Brown, S., and Powell, A., 2018, Will increased storm surge frequency impact food availability for Semipalmated Sandpipers (Calidris pusilla) at the beginning of fall migration?: Wader Study, v. 125, p. 195-204, https://doi.org/10.18194/ws.00121.","productDescription":"10 p.","startPage":"195","endPage":"204","ipdsId":"IP-084979","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":366198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"125","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Churchwell, R.T.","contributorId":217823,"corporation":false,"usgs":false,"family":"Churchwell","given":"R.T.","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":767593,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, S.","contributorId":217824,"corporation":false,"usgs":false,"family":"Kendall","given":"S.","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":767594,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, S.C.","contributorId":217825,"corporation":false,"usgs":false,"family":"Brown","given":"S.C.","email":"","affiliations":[{"id":39696,"text":"Manomet Inc.","active":true,"usgs":false}],"preferred":false,"id":767595,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Powell, Abby 0000-0002-9783-134X abby_powell@usgs.gov","orcid":"https://orcid.org/0000-0002-9783-134X","contributorId":176843,"corporation":false,"usgs":true,"family":"Powell","given":"Abby","email":"abby_powell@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":767592,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70199837,"text":"sir20185126 - 2018 - Evaluation of whole-water churn splitters for suspended-sediment sample collection and analysis","interactions":[],"lastModifiedDate":"2018-12-10T10:24:48","indexId":"sir20185126","displayToPublicDate":"2018-12-07T16:30:00","publicationYear":"2018","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":"2018-5126","displayTitle":"Evaluation of Whole-Water Churn Splitters for Suspended-Sediment Sample Collection and Analysis","title":"Evaluation of whole-water churn splitters for suspended-sediment sample collection and analysis","docAbstract":"The U.S. Geological Survey (USGS) collects a wide range of whole-water samples to test for the many physical and chemical constituents that represent stream conditions at the time of sampling to assess the quality of the Nation’s waters. During sampling efforts, in which a suspended-sediment concentration is one result among a broader suite of constituents, a sample is sometimes composited into a churn splitter and then subdivided for analysis. Five churn splitters—comprising three sizes and two different materials—used by the USGS were tested for single-withdrawal accuracy from one-half of full capacity and for multiple-withdrawal accuracy at varied volumes of fullness to see if churn splitters introduce bias during the collection of sediment samples. Both tests were similar to previously conducted tests for consistency, but the tests conducted in this report also attempted to answer questions that arose during previous evaluations of churn splitters. The purpose of this report is to inform sediment analysts about the capabilities and limitations of all available churn-splitter sizes and materials used by the USGS for the analysis of suspended sediment.
The results indicate that suspended-sediment samples and constituents absorbed into suspended sediment may have substantial bias errors when withdrawn from churn splitters. Results were affected by the settling velocity of sediment particles relative to the resuspension velocities induced by the churning, the effects of prior withdrawals of the water-sediment mixture, and the remaining volume in a churn splitter after samples were withdrawn.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185126","usgsCitation":"Barr, M.N., 2018, Evaluation of whole-water churn splitters for suspended-sediment sample collection and analysis: U.S. Geological Survey Scientific Investigations Report 2018–5126, 25 p., https://doi.org/10.3133/sir20185126.","productDescription":"iv, 25 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-086055","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":359821,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5126/coverthb.jpg"},{"id":359822,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5126/sir20185126.pdf","text":"Report","size":"2.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5126"}],"contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1440 Independence Road
Rolla, MO 65401
The objective of this study was to assess the impact of different tagging methods and transmitter types on juvenile salmonid behavior, mortality, physiology, and swimming performance over a range of water temperatures and fish sizes.
In Chapter 1, two laboratory experiments were conducted to assess maximum burst-swimming speeds, the probability of gulping air, swimming angles, and the probability of resting on a screen in a swim tunnel. For the burst swim speed experiment, we identified a slightly reduced, but statistically significant difference in burst-swimming speeds for gastric- and surgical-tagged fish implanted with dummy radio and acoustic transmitters. For the swim tunnel experiment, surgical-tagged fish were one-half as likely to gulp air at the surface of the swim tunnel as untagged and gastric-tagged fish. We observed higher probabilities of fish gulping air at the surface for fish with tag ratios greater than 5 percent, suggesting that smaller fish required greater adjustment to their buoyancy than larger fish. We also observed that gastric-tagged fish had, on average, steeper swimming angles than untagged and surgical-tagged fish in the swim tunnel.
In Chapter 2, we conducted a field-based laboratory experiment at John Day Dam to assess the sustained swimming performance (i.e., critical swimming speed or Ucrit) of in-river migrating subyearling Chinook salmon that were surgically implanted with dummy radio and acoustic transmitters. Statistical tests indicated a significant reduction (about 8.3 centimeters per second [cm/s] or 1 body length per second) in sustained swimming performance for fish implanted with either radio or acoustic transmitters. We also found a significant reduction in Ucrit of -1.38 cm/s for every 1 degree Celsius (°C) increase in temperature.
In Chapter 3, we assessed the effects of water temperature on the physiology, mortality, and swimming performance of juvenile Chinook salmon in laboratory and field experiments. Juvenile Chinook salmon generally showed elevated stress response, elevated mortality, and reduced swimming performance as water temperature increased. We concluded that the water temperature threshold for handling and tagging fish with minimal impacts seems to be near 23 °C. At 25 °C, we documented very high mortality and dramatically reduced swimming performance of tagged fish relative to controls. Telemetry studies conducted at 25 °C would not meet the critical assumption that the transmitter has minimal impacts on the study fish.
In the Chapter 4, we evaluated the effects of antenna length and antenna material on the subsequent tag output power, reception, and detection of tagged fish. In a laboratory, we compared the relative signal strengths in water of 150-megahertz transmitters over a range of antenna lengths (from 6 to 30 cm) and materials (one weighing about one-half of the other). The peak relative signal strengths were at 20 and 22 cm, which are about 1 wavelength underwater at the test frequency. The peak relative signal strengths at these antenna lengths were about 50 percent greater than those of 30-cm antennas, a length commonly used in fisheries research.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181186","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Perry, R.W., and Liedtke, T.L., eds., 2018, Effects of transmitter type, tagging method, body size, and temperature on behavior, physiology, and swimming performance of juvenile Chinook salmon (Oncorhynchus tshawytscha): U.S. Geological Survey Open Report 2018–1186, 74 p., https://doi.org/10.3133/ofr20181186.","productDescription":"viii, 74 p.","onlineOnly":"Y","ipdsId":"IP-076451","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":360005,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1186/coverthb.jpg"},{"id":360006,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1186/ofr20181186.pdf","text":"Report","size":"1.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1186"}],"contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
When applied to a snow-covered surface, aerodynamic roughness length, z0, is typically considered as a static parameter within energy balance equations. However, field observations show that z0 changes spatially and temporally, and thus z0 incorporated as a dynamic parameter may greatly improve models. To evaluate methods for characterizing snow surface roughness, we compared concurrent estimates of z0 based on (1) terrestrial light detection and ranging derived surface geometry of the snowpack surface (geometric, z0G) and (2) vertical wind profile measurements (anemometric, z0A). The value of z0Gwas computed from Lettau’s equation and underestimated z0A but compared well when scaled by a factor of 2.34. The Counihan method for computing z0G was found to be unsuitable for estimating z0 on a snow surface. During snowpack accumulation in early winter, z0 varied as a function of the snow-covered area (SCA). Our results show that as the SCA increases, z0 decreases, indicating there is a topographic influence on this relation.
","language":"English","publisher":"MDPI","doi":"10.3390/geosciences8120463","usgsCitation":"Sanow, J.E., Fassnacht, S.R., Kamin, D.J., Sexstone, G., Bauerle, W.L., and Oprea, I., 2018, Geometric versus anemometric surface roughness for a shallow accumulating snowpack: Geosciences, v. 8, no. 12, p. 1-10, https://doi.org/10.3390/geosciences8120463.","productDescription":"Article 463; 10 p.","startPage":"1","endPage":"10","ipdsId":"IP-103052","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":468200,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/geosciences8120463","text":"Publisher Index Page"},{"id":359976,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"12","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-06","publicationStatus":"PW","scienceBaseUri":"5c0a4356e4b0815414d2812a","contributors":{"authors":[{"text":"Sanow, Jessica E.","contributorId":211149,"corporation":false,"usgs":false,"family":"Sanow","given":"Jessica","email":"","middleInitial":"E.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":true,"id":753274,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fassnacht, Steven R.","contributorId":177135,"corporation":false,"usgs":false,"family":"Fassnacht","given":"Steven","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":753275,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kamin, David J.","contributorId":211150,"corporation":false,"usgs":false,"family":"Kamin","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":753276,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sexstone, Graham A. 0000-0001-8913-0546","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":203850,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":753273,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bauerle, William L.","contributorId":211151,"corporation":false,"usgs":false,"family":"Bauerle","given":"William","email":"","middleInitial":"L.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":753277,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oprea, Iuliana","contributorId":211152,"corporation":false,"usgs":false,"family":"Oprea","given":"Iuliana","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":753278,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70203095,"text":"70203095 - 2018 - What environmental conditions reduce predation vulnerability for juvenile Colorado River native fishes?","interactions":[],"lastModifiedDate":"2019-06-18T11:44:06","indexId":"70203095","displayToPublicDate":"2018-12-05T16:23:18","publicationYear":"2018","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":"What environmental conditions reduce predation vulnerability for juvenile Colorado River native fishes?","docAbstract":"The incompatibility of native Colorado River fishes and nonnative warm-water sport fishes is well documented with predation by nonnative species causing rapid declines and even extirpation of native species in most locations. In a few rare instances native fishes are able to survive and recruit despite the presence of nonnative warm water predators, indicating that specific environmental conditions may help reduce predation vulnerability. We experimented with turbidity, artificial blue water colorant, woody debris, rocks, and aquatic vegetation in a laboratory setting to determine if any of these types of cover could reduce predation vulnerability and confer survival advantages for juvenile bonytail Gila elegans, (mean = 70 mm TL), roundtail chub Gila robusta, (mean = 35 mm TL), humpback chub Gila cypha, (mean = 67 mm TL), and razorback sucker Xyrauchen texanus (mean = 74 mm TL). Juvenile native fishes were exposed to predation by adult largemouth bass Micropterus salmoides, smallmouth bass Micropterus dolomieu, green sunfish Lepomis cyanellus, flathead catfish Pylodictis olivaris, and black bullhead catfish Ameiurus melas, in overnight trials. Turbidity above 500 NTU reduced predation vulnerability by up to 50%, for the sight-feeding predators, but increased predation vulnerability to non-sight feeding predators such as flathead catfish and bullhead catfish. Turbidity was the only treatment which appeared to significantly alter predation mortality of native prey. These results may help to explain recent patterns of wild juvenile razorback sucker recruitment at the inflow of the San Juan River into Lake Powell and the inflow of the Colorado River into Lake Mead. These are both areas of high turbidity where flathead catfish are not currently present but other nonnative sportfish are relatively abundant.
","language":"English","publisher":"American Fisheries Society","doi":"10.3996/042018-JFWM-031","usgsCitation":"Ward, D.L., and Vaage, B., 2018, What environmental conditions reduce predation vulnerability for juvenile Colorado River native fishes?: Journal of Fish and Wildlife Management, v. 10, no. 1, p. 196-205, https://doi.org/10.3996/042018-JFWM-031.","productDescription":"10 p.","startPage":"196","endPage":"205","ipdsId":"IP-095633","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":468202,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/042018-jfwm-031","text":"Publisher Index Page"},{"id":437657,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94ANHV3","text":"USGS data release","linkHelpText":"Laboratory Predation Data (various nonnative warm-water sport fishes), Arizona"},{"id":363085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"10","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Ward, David L. 0000-0002-3355-0637 dlward@usgs.gov","orcid":"https://orcid.org/0000-0002-3355-0637","contributorId":3879,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dlward@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":761156,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vaage, Benjamin 0000-0003-1730-4302 bvaage@usgs.gov","orcid":"https://orcid.org/0000-0003-1730-4302","contributorId":211598,"corporation":false,"usgs":true,"family":"Vaage","given":"Benjamin","email":"bvaage@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":761157,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70201380,"text":"70201380 - 2018 - An experimental comparison of composite and grab sampling of stream water for metagenetic analysis of environmental DNA","interactions":[],"lastModifiedDate":"2018-12-13T14:40:53","indexId":"70201380","displayToPublicDate":"2018-12-05T14:40:47","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"An experimental comparison of composite and grab sampling of stream water for metagenetic analysis of environmental DNA","docAbstract":"Use of environmental DNA (eDNA) to assess distributions of aquatic and semi-aquatic macroorganisms is promising, but sampling schemes may need to be tailored to specific objectives. Given the potentially high variance in aquatic eDNA among replicate grab samples, compositing smaller water volumes collected over a period of time may be more effective for some applications. In this study, we compared eDNA profiles from composite water samples aggregated over three hours with grab water samples. Both sampling patterns were performed with identical autosamplers paired at two different sites in a headwater stream environment, augmented with exogenous fish eDNA from an upstream rearing facility. Samples were filtered through 0.8 μm cellulose nitrate filters and DNA was extracted with a cetyl trimethylammonium bromide procedure. Eukaryotic and bacterial community profiles were derived by amplicon sequencing of 12S ribosomal, 16S ribosomal, and cytochrome oxidase I loci. Operational taxa were assigned to genus with a lowest common ancestor approach for eukaryotes and to family with the RDP Classifier software for prokaryotes. Eukaryotic community profiles were more consistent with composite sampling than grab sampling. Downstream, rarefaction curves suggested faster taxon accumulation for composite samples, and estimated richness was higher for composite samples as a set than for grab samples. Upstream, composite sampling produced lower estimated richness than grab samples, but with overlapping standard errors. Furthermore, a bimodal pattern of richness as a function of sequence counts suggested the impact of clumped particles on upstream samples. Bacterial profiles were insensitive to sample method, consistent with the more even dispersion expected for bacteria compared with eukaryotic eDNA. Overall, samples composited over 3 h performed equal to or better than triplicate grab sampling for quantitative community metrics, despite the higher total sequencing effort provided to grab replicates. On the other hand, taxon-specific detection rates did not differ appreciably and the two methods gave similar estimates of the ratio of the common fish genera Salmo and Coregonus at each site. Unexpectedly, Salmo eDNA dropped out substantially faster than Coregonus eDNA between the two sites regardless of sampling method, suggesting that differential settling affects the estimation of relative abundance. We identified bacterial patterns that were associated with eukaryotic diversity, suggesting potential roles as biomarkers of sample representativeness.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.5871","usgsCitation":"Cornman, R.S., McKenna, J.E., Fike, J., Oyler-McCance, S.J., and Johnson, R., 2018, An experimental comparison of composite and grab sampling of stream water for metagenetic analysis of environmental DNA: PeerJ, v. 6, p. 1-28, https://doi.org/10.7717/peerj.5871.","productDescription":"e5871; 28 p.","startPage":"1","endPage":"28","ipdsId":"IP-098663","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":468203,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.5871","text":"Publisher Index Page"},{"id":437658,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93NIUYM","text":"USGS data release","linkHelpText":"Metagenetic analysis of stream community composition based on environmental DNA"},{"id":360249,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-05","publicationStatus":"PW","scienceBaseUri":"5c137dd3e4b006c4f8514882","contributors":{"authors":[{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":753900,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKenna, James E. Jr. 0000-0002-1428-7597 jemckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-1428-7597","contributorId":195894,"corporation":false,"usgs":true,"family":"McKenna","given":"James","suffix":"Jr.","email":"jemckenna@usgs.gov","middleInitial":"E.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":753901,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fike, Jennifer A. 0000-0001-8797-7823","orcid":"https://orcid.org/0000-0001-8797-7823","contributorId":207268,"corporation":false,"usgs":true,"family":"Fike","given":"Jennifer A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":753902,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":753903,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Robin 0000-0003-4314-3792","orcid":"https://orcid.org/0000-0003-4314-3792","contributorId":211387,"corporation":false,"usgs":false,"family":"Johnson","given":"Robin","affiliations":[{"id":38242,"text":"Integrated Statistics (Contractor)","active":true,"usgs":false}],"preferred":false,"id":753904,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70201146,"text":"sir20185129 - 2018 - Vibration monitoring results near a bat hibernaculum at Mammoth Cave National Park, Kentucky, March 2016","interactions":[],"lastModifiedDate":"2018-12-05T14:39:59","indexId":"sir20185129","displayToPublicDate":"2018-12-04T15:45:00","publicationYear":"2018","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":"2018-5129","displayTitle":"Vibration Monitoring Results near a Bat Hibernaculum at Mammoth Cave National Park, Kentucky, March 2016","title":"Vibration monitoring results near a bat hibernaculum at Mammoth Cave National Park, Kentucky, March 2016","docAbstract":"Vibrations originating from construction of a new walkway in a passage of Mammoth Cave, from walking personnel simulating a bat survey, and from ambient sources were measured near a bat hibernaculum beneath Mammoth Cave National Park, Kentucky, to determine if the vibrations were disturbing the hibernating bats. Data presented indicate direction and magnitude of the vibrations. The seven sources of vibration that were recorded include hammer drill (one location), plate compactor (two locations), jackhammer (two locations), personnel simulating a bat survey near the hibernaculum (walking throughout the cave), and background levels. Vibrations were measured for approximately 10 seconds during each triggering of the source and each source was recorded 5–10 times to represent the reproducibility of the vibrations.
The plate compactor produced the largest velocity of 0.00226 inch per second on one of the longitudinal components. The simulated bat survey produced the largest value of acceleration of 0.34 inch per square second in the vertical component. Maximum vertical velocities and accelerations did not exceed literature values for human perception or visible agitation in laboratory mice.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185129","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Adams, R.F., Morrow, W.S., and Koebel, C.M., 2018, Vibration monitoring results near a bat hibernaculum at Mammoth Cave National Park, Kentucky, March 2016: U.S. Geological Survey Scientific Investigations Report 2018–5129, 16 p., https://doi.org/10.3133/sir20185129.","productDescription":"Report: iv, 16 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-074738","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":359864,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94LHZHR","text":"USGS data release","description":"USGS data release","linkHelpText":"Vibration Monitoring Data from a Bat Hibernaculum at Mammoth Cave National Park, Kentucky, March 2016"},{"id":359862,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5129/coverthb.jpg"},{"id":359863,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5129/sir20185129.pdf","text":"Report","size":"19.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5129"}],"country":"United States","state":"Kentucky","otherGeospatial":"Mammoth Cave National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.10980987548828,\n 37.17071303242321\n ],\n [\n -86.08234405517578,\n 37.17071303242321\n ],\n [\n -86.08234405517578,\n 37.19368966240492\n ],\n [\n -86.10980987548828,\n 37.19368966240492\n ],\n [\n -86.10980987548828,\n 37.17071303242321\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, Kentucky 40299
This report describes an interactive tool (NDakGWtool) in which a statistical model is developed using locally weighted regression to estimate monthly mean groundwater elevations for a specified latitude and longitude, referred to as the “user-specified location.” For each user-specified location, seven models are developed for each month from April through October. Localized, high spatial-resolution maps of estimated monthly mean groundwater surface elevations are produced from the models. The tool was evaluated for glacial drift aquifers of the 32-county study area in central and eastern North Dakota. Although groundwater elevations from 1960 to 2017 were available to develop the tool, groundwater elevations from 1995 to 2015 were used for model testing and development of the model domain. There are 413 grid cells of 0.1-degree latitude by 0.1-degree longitude size in the model domain, and the tool produces maps of estimated monthly mean groundwater surface elevations for the cell containing the user-specified location. Additionally, the NDakGWtool produces maps of estimated groundwater depth below land surface and ArcGIS files of estimated groundwater surface elevations and groundwater depth below land surface. The tool is composed of four main components: data input, statistical model, output, and user-interactive process.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181185","collaboration":"Prepared in cooperation with Natural Resources Conservation Service","usgsCitation":"Nustad, R.A., Damschen, W.C., and Vecchia, A.V., 2018, Interactive tool to estimate groundwater elevations in central and eastern North Dakota: U.S. Geological Survey Open-File Report 2018–1185, 24 p., https://doi.org/10.3133/ofr20181185.","productDescription":"Report: vi, 24; Appendix","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-090716","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":359877,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1185/coverthb.jpg"},{"id":359878,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1185/ofr20181185.pdf","text":"Report","size":"6.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1185"},{"id":359880,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1185/ofr20181185_appendix.zip","text":"Appendix","size":"27.6 MB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2018–1185 Appendix","linkHelpText":"R Documentation"}],"country":"United States","state":"North Dakota","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
Avian influenza has advanced from a regional concern to a global health issue with significant economic, trade, and public health implications. Wild birds, particularly waterfowl (Anseriformes), are known reservoirs for low-pathogenic avian influenza viruses (AIV) and recent studies have shown their potential in the spread of highly pathogenic forms of virus. East Asia remains an epicenter for the emergence of novel strains of AIV, however, information on movement ecology of waterfowl, and subsequently a clearer understanding of disease transmission risks in this region has been greatly lacking. To address this, we marked two species of wild waterfowl, northern pintail (Anas acuta) and Eurasian wigeon (Anas penelope), with satellite transmitters on their wintering grounds in Hong Kong, China to study the northward spring migration in the East Asian-Australasian Flyway in relation to disease transmission factors. Northern pintail were found to initiate migration 42 days earlier, travel 2,150 km farther, and perform 4.4 more stopovers than Eurasian wigeon. We found both species used similar stopover locations including areas along the Yangtze River near Shanghai, Bohai Bay and Korea Bay in rapidly developing regions of the Yellow Sea, and the Sea of Okhotsk where the species appeared to funnel through a migratory bottleneck. Both species appeared to exhibit strong habitat selection for rice paddies during migration stopovers, a habitat preference which has the potential to influence risks of AIV outbreaks as rapid land use and land cover changes occur throughout China. Both species had greatest association with H5N1 outbreaks during the early stages of migration when they were at lower latitudes. While Eurasian wigeon were not associated with outbreaks after the mean date of wintering ground departures, northern pintail were associated with outbreaks until the majority of individuals departed from the Yellow Sea, a migratory stopover location. Our results show species-level differences in migration timing and behavior for these common and widespread species, demonstrating the need to consider their unique temporal and spatial movement ecology when incorporating wild birds into AIV risk modeling and management.
","language":"English","publisher":"Frontiers","doi":"10.3389/fevo.2018.00206","usgsCitation":"Sullivan, J.D., Takekawa, J., Spragens, K.A., Newman, S.H., Xiao, X., Leader, P.J., Smith, B., and Prosser, D.J., 2018, Waterfowl spring migratory behavior and avian influenza transmission risk in the changing landscape of the East Asian-Australasian Flyway: Frontiers in Ecology and Evolution, v. 6, p. 1-14, https://doi.org/10.3389/fevo.2018.00206.","productDescription":"Article 206; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-099333","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":468204,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2018.00206","text":"Publisher Index Page"},{"id":359915,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-04","publicationStatus":"PW","scienceBaseUri":"5c07a062e4b0815414cee779","contributors":{"authors":[{"text":"Sullivan, Jeffery D.","contributorId":202910,"corporation":false,"usgs":false,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":753035,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Takekawa, John Y. 0000-0003-0217-5907","orcid":"https://orcid.org/0000-0003-0217-5907","contributorId":203805,"corporation":false,"usgs":false,"family":"Takekawa","given":"John Y.","affiliations":[{"id":36724,"text":"Audubon California, Richardson Bay Audubon Center and Sanctuary, Tiburon, CA","active":true,"usgs":false}],"preferred":false,"id":753036,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spragens, Kyle A. kspragens@usgs.gov","contributorId":211030,"corporation":false,"usgs":false,"family":"Spragens","given":"Kyle","email":"kspragens@usgs.gov","middleInitial":"A.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":753037,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Newman, Scott H.","contributorId":199129,"corporation":false,"usgs":false,"family":"Newman","given":"Scott","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":753038,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xiao, Xiangming","contributorId":181792,"corporation":false,"usgs":false,"family":"Xiao","given":"Xiangming","email":"","affiliations":[],"preferred":false,"id":753039,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leader, Paul J.","contributorId":211031,"corporation":false,"usgs":false,"family":"Leader","given":"Paul","email":"","middleInitial":"J.","affiliations":[{"id":38173,"text":"AEC Ltd","active":true,"usgs":false}],"preferred":false,"id":753040,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Bena","contributorId":211032,"corporation":false,"usgs":false,"family":"Smith","given":"Bena","email":"","affiliations":[{"id":38174,"text":"WWT Consulting","active":true,"usgs":false}],"preferred":false,"id":753041,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Prosser, Diann J. 0000-0002-5251-1799 dprosser@usgs.gov","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":2389,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","email":"dprosser@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":753034,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70201153,"text":"70201153 - 2018 - North Atlantic midlatitude surface-circulation changes through the Plio-Pleistocene intensification of northern hemisphere glaciation","interactions":[],"lastModifiedDate":"2019-01-28T08:40:19","indexId":"70201153","displayToPublicDate":"2018-12-03T15:51:57","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5790,"text":"Paleoceanography and Paleoclimatology","active":true,"publicationSubtype":{"id":10}},"title":"North Atlantic midlatitude surface-circulation changes through the Plio-Pleistocene intensification of northern hemisphere glaciation","docAbstract":"The North Atlantic Current (NAC) transports warm salty water to high northern latitudes, with important repercussions for ocean circulation and global climate. A southward displacement of the NAC and Subarctic Front, which separate subpolar and subtropical water masses, is widely suggested for the Last Glacial Maximum (LGM) and may have acted as a positive feedback in glacial expansion at this time. However, the role of the NAC during the intensification of Northern Hemisphere glaciation (iNHG) at ~3.5 to 2.5 Ma is less clear. Here we present new records from Integrated Ocean Drilling Program Site U1313 (41°N) spanning ~2.8–2.4 Ma to trace the influence of Subarctic Front waters above this mid‐latitude site. We reconstruct surface and permanent pycnocline temperatures and seawater δ18O using paired Mg/Ca‐δ18O measurements on the planktic foraminifers Globigerinoides ruber and Globorotalia crassaformis and determine abundances of the subpolar foraminifer Neogloboquadrina atlantica. We find that the first significant glacial incursions of Subarctic Front surface waters above Site U1313 did not occur until ~2.6 Ma. At no time during our study interval was (sub)surface reorganization in the midlatitude North Atlantic analogous to the LGM. Our findings suggest that LGM‐like processes sensu stricto cannot be invoked to explain interglacial‐glacial cycle amplification during iNHG. They also imply that increased glacial productivity at Site U1313 during iNHG was not only driven by southward deflections of the Subarctic Front. We suggest that nutrient injection from cold‐core eddies and enhanced glacial dust delivery may have played additional roles in increasing export productivity in the midlatitude North Atlantic from 2.7 Ma.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018PA003412","usgsCitation":"Bolton, C.T., Bailey, I., Friedrich, O., Tachikawa, K., de Garidel-Thoron, T., Vidal, L., Sonzogni, C., Marino, G., Rohling, E.J., Robinson, M.M., Ermini, M., Koch, M., Cooper, M.J., and Wilson, P.A., 2018, North Atlantic midlatitude surface-circulation changes through the Plio-Pleistocene intensification of northern hemisphere glaciation: Paleoceanography and Paleoclimatology, v. 33, no. 11, p. 1186-1205, https://doi.org/10.1029/2018PA003412.","productDescription":"20 p.","startPage":"1186","endPage":"1205","ipdsId":"IP-097975","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":460797,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018pa003412","text":"Publisher Index Page"},{"id":437661,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OFDVZ6","text":"USGS data release","linkHelpText":"Planktic foraminifer census data for ODP Sites 907, 909 and 911"},{"id":359881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"11","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-09","publicationStatus":"PW","scienceBaseUri":"5c064edfe4b0815414cecb02","contributors":{"authors":[{"text":"Bolton, Clara T.","contributorId":191676,"corporation":false,"usgs":false,"family":"Bolton","given":"Clara","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":752961,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bailey, Ian","contributorId":210997,"corporation":false,"usgs":false,"family":"Bailey","given":"Ian","email":"","affiliations":[{"id":35448,"text":"University of Exeter, UK","active":true,"usgs":false}],"preferred":false,"id":752962,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedrich, Oliver","contributorId":210998,"corporation":false,"usgs":false,"family":"Friedrich","given":"Oliver","email":"","affiliations":[{"id":38165,"text":"Heidelberg University, Germany","active":true,"usgs":false}],"preferred":false,"id":752963,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tachikawa, Kazuyo","contributorId":210999,"corporation":false,"usgs":false,"family":"Tachikawa","given":"Kazuyo","email":"","affiliations":[{"id":38166,"text":"CEREGE, France","active":true,"usgs":false}],"preferred":false,"id":752964,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"de Garidel-Thoron, Thibault","contributorId":211000,"corporation":false,"usgs":false,"family":"de Garidel-Thoron","given":"Thibault","email":"","affiliations":[{"id":38166,"text":"CEREGE, France","active":true,"usgs":false}],"preferred":false,"id":752965,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vidal, Laurence","contributorId":211001,"corporation":false,"usgs":false,"family":"Vidal","given":"Laurence","email":"","affiliations":[{"id":38166,"text":"CEREGE, France","active":true,"usgs":false}],"preferred":false,"id":752966,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sonzogni, Corinne","contributorId":211002,"corporation":false,"usgs":false,"family":"Sonzogni","given":"Corinne","email":"","affiliations":[{"id":38166,"text":"CEREGE, France","active":true,"usgs":false}],"preferred":false,"id":752967,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Marino, Gianluca","contributorId":211003,"corporation":false,"usgs":false,"family":"Marino","given":"Gianluca","email":"","affiliations":[{"id":38167,"text":"The Australian National University, Australia","active":true,"usgs":false}],"preferred":false,"id":752968,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rohling, Eelco J.","contributorId":211004,"corporation":false,"usgs":false,"family":"Rohling","given":"Eelco","email":"","middleInitial":"J.","affiliations":[{"id":38167,"text":"The Australian National University, Australia","active":true,"usgs":false}],"preferred":false,"id":752969,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Robinson, Marci M. 0000-0002-9200-4097 mmrobinson@usgs.gov","orcid":"https://orcid.org/0000-0002-9200-4097","contributorId":2082,"corporation":false,"usgs":true,"family":"Robinson","given":"Marci","email":"mmrobinson@usgs.gov","middleInitial":"M.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":752960,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ermini, Magali","contributorId":211005,"corporation":false,"usgs":false,"family":"Ermini","given":"Magali","email":"","affiliations":[{"id":38166,"text":"CEREGE, France","active":true,"usgs":false}],"preferred":false,"id":752970,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Koch, Mirjam","contributorId":211006,"corporation":false,"usgs":false,"family":"Koch","given":"Mirjam","email":"","affiliations":[{"id":38168,"text":"Goethe-Universitat Frankfurt, Germany","active":true,"usgs":false}],"preferred":false,"id":752971,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Cooper, Matthew J.","contributorId":211007,"corporation":false,"usgs":false,"family":"Cooper","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":38169,"text":"University of Southamton, UK","active":true,"usgs":false}],"preferred":false,"id":752972,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Wilson, Paul A.","contributorId":211008,"corporation":false,"usgs":false,"family":"Wilson","given":"Paul","email":"","middleInitial":"A.","affiliations":[{"id":38169,"text":"University of Southamton, UK","active":true,"usgs":false}],"preferred":false,"id":752973,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70200811,"text":"sir20185153 - 2018 - Prioritization framework for ranking riverine ecosystem stressors using example sites from the Tualatin River Basin, Oregon","interactions":[],"lastModifiedDate":"2018-12-04T11:02:38","indexId":"sir20185153","displayToPublicDate":"2018-12-03T12:40:58","publicationYear":"2018","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":"2018-5153","displayTitle":"Prioritization Framework for Ranking Riverine Ecosystem Stressors Using Example Sites from the Tualatin River Basin, Oregon","title":"Prioritization framework for ranking riverine ecosystem stressors using example sites from the Tualatin River Basin, Oregon","docAbstract":"As human populations increase, so does their influence over the environment. Altered terrain, degraded water quality, and threatened or endangered species are all-too-common consequences of a growing anthropogenic influence on the landscape. To help manage these effects, researchers have developed new ways to characterize current environmental conditions and help resource managers seek solutions to bring affected areas back to their best attainable health. Before an ecosystem can be improved, however, its current level of ecological stress must be determined. Characterizing environmental conditions at many sites across a landscape helps managers understand the range of current conditions and prioritize where they might focus restoration and protection efforts.
This report details the development of a prioritization framework to score riverine ecosystem stressors in a watershed based on example sites from the Tualatin River Basin in northwestern Oregon. The framework incorporated the most influential site-specific stressors throughout the basin built on a long history of data collection. These stressors were characterized with 13 metrics that were organized into 4 groups: hydrologic, water quality, physical habitat, and biological. Each stressor metric used readily accessible data and was translated to a score between 0 and 10. The higher the score, the healthier the site. This initial application of the framework used field observations and measurements to rank site conditions at two Tualatin River sites and four Tualatin River tributary sites. Given the versatility of this framework, it easily could be expanded to include more sites or new metrics, if necessary. Because stressors varied by season, all metrics for the tributary sites were scored separately during the wet season (November through April) and dry season (May through October). Water-quality data were available over a prolonged period; therefore, water-quality metrics were assessed by season and by decade (1990–99 compared to 2000–12) to evaluate long-term stressor trends.
Results for the Tualatin River Basin prioritization framework indicated that the urban tributaries demonstrated the greatest stress throughout the year, especially during the dry summer months. Spatially, the upper Tualatin River was healthier than the lower reaches of the river. Water-quality has improved in the last 10 years, mostly due to improvements in the dry period contaminant scores, but challenges remain with high water temperatures and low dissolved-oxygen conditions.
The biggest challenge with this type of research derived from inconsistencies within the available data. Both spatial and temporal data gaps must be addressed to improve the prioritization. Incorporating both discrete and continuous datasets into the prioritization framework remains a challenge because the datasets have slightly different information and criteria and are not always comparable. Regardless, this report provides guidelines for developing a prioritization framework that ranks the ecological health of sites in a watershed and provides guidance on management actions for improving conditions by targeting factors that greatly affect the health of river ecosystems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185153","collaboration":"Prepared in cooperation with Clean Water Services","usgsCitation":"Sobieszczyk, S., Jones, K.L., Rounds, S.A., Nilsen, E.B., and Morace, J.L., 2018, Prioritization framework for ranking riverine ecosystem stressors using example sites from the Tualatin River Basin, Oregon: U.S. Geological Survey Scientific Investigations Report 2018-5153, 40 p., https://doi.org/10.3133/sir20185153.","productDescription":"vii, 40 p.","onlineOnly":"Y","ipdsId":"IP-060830","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":359875,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5153/sir20185153.pdf","text":"Report","size":"3.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5153"},{"id":359874,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5153/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Tualatin River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.5,\n 45.3\n ],\n [\n -122.5,\n 45.3\n ],\n [\n -122.5,\n 45.75\n ],\n [\n -123.5,\n 45.75\n ],\n [\n -123.5,\n 45.3\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
In 2017, the Lake Superior fish community was sampled with daytime bottom trawls at 76 nearshore and 36 offshore stations. Spring nearshore and summer offshore water temperatures in 2017 were similar to slightly cooler than the 1991-2017 average. In the nearshore zone, a total of 28,902 individual fish from 27 species or morphotypes were collected. The number of species collected at each station ranged from 0 to 13, with a mean of 5.5 and median of 5. Lakewide nearshore mean biomass was 3.8 kg/ha which was below the long-term average of 8.7 kg/ha and the median lakewide biomass was 1.8 kg/ha. which was similar to the long-term average median value of 1.9 kg/ha. Lake Whitefish, Rainbow Smelt, Bloater, Longnose Sucker, and lean Lake Trout were the species with the highest lakewide average biomass. In the offshore zone, a total of 16,674 individuals from 13 species were collected lakewide. The average and median observed species richness at each station was 3.8 and 4 species, respectively, and ranged from 2 to 6 species. Deepwater Sculpin, Kiyi, and siscowet Lake Trout made up 99% of the total number of individuals and biomass collected in offshore waters. Mean and median lakewide biomass for all species in 2017 was 6.8 kg/ha and 6.6 kg/ha, respectively. This was similar to the long-term mean of 6.9 kg/ha and greater than that observed in 2014-2016. Nearshore average larval Coregonus densities in 2017 were greater than observed in any previous year; whereas offshore larval Coregonus densities were much less than observed in previous years.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Complied reports to the Great Lakes Fishery Commission of the annual bottom trawl and acoustics surveys, 2017","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Vinson, M., Evrard, L.M., Gorman, O.T., and Yule, D., 2018, Status and Trends in the Lake Superior Fish Community, 2017, chap. of Complied reports to the Great Lakes Fishery Commission of the annual bottom trawl and acoustics surveys, 2017, p. 1-11.","productDescription":"11 p.","startPage":"1","endPage":"11","ipdsId":"IP-095602","costCenters":[{"id":324,"text":"Great Lakes Science 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levrard@usgs.gov","orcid":"https://orcid.org/0000-0001-8582-5818","contributorId":2720,"corporation":false,"usgs":true,"family":"Evrard","given":"Lori","email":"levrard@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":765632,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gorman, Owen T. 0000-0003-0451-110X otgorman@usgs.gov","orcid":"https://orcid.org/0000-0003-0451-110X","contributorId":2888,"corporation":false,"usgs":true,"family":"Gorman","given":"Owen","email":"otgorman@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":765633,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yule, Daniel 0000-0002-0117-5115 dyule@usgs.gov","orcid":"https://orcid.org/0000-0002-0117-5115","contributorId":139532,"corporation":false,"usgs":true,"family":"Yule","given":"Daniel","email":"dyule@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":765634,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70200001,"text":"sir20185132 - 2018 - Flood-inundation maps for the Salamonie River at Portland, Indiana","interactions":[],"lastModifiedDate":"2018-12-03T14:43:43","indexId":"sir20185132","displayToPublicDate":"2018-12-03T09:55:34","publicationYear":"2018","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":"2018-5132","displayTitle":"Flood-Inundation Maps for the Salamonie River at Portland, Indiana","title":"Flood-inundation maps for the Salamonie River at Portland, Indiana","docAbstract":"Digital flood-inundation maps for a 6.5-mile reach of the Salamonie River at Portland, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Salamonie River at Portland, Ind. (station 03324200). Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System web interface at https://doi.org/10.5066/F7P55KJN or from the National Weather Service Advanced Hydrologic Prediction Service (site PORI3) at https:/water.weather.gov/ahps/.
Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated using the current (2018) stage-discharge relation at the Salamonie River at Portland, Ind., streamgage.
The hydraulic model then was used to compute nine water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 10.7 ft or near bankfull to 18.7 ft, which equals the highest point on the streamgage rating curve. The simulated water-surface profiles then were combined with a geographic information system digital elevation model derived from light detection and ranging data having a 0.49-ft root mean square error and 4.9-ft horizontal resolution resampled to a 10-ft grid to delineate the area flooded at each stage. The availability of these maps, along with information regarding current stage from the USGS streamgage, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185132","collaboration":"Prepared in cooperation with the Indiana Department of Transportation","usgsCitation":"Strauch, K.R., 2018, Flood-inundation maps for the Salamonie River at Portland, Indiana: U.S. Geological Survey Scientific Investigations Report 2018–5132, 9 p., https://doi.org/10.3133/sir20185132.","productDescription":"Report: vi, 9 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-089966","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":359800,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VM4BJD","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Flood-inundation geospatial datasets for the Salamonie River at Portland, Indiana"},{"id":359798,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5132/coverthb.jpg"},{"id":359799,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5132/sir20185132.pdf","text":"Report","size":"996 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5132"}],"country":"United States","state":"Indiana","city":"Portland","otherGeospatial":"Salamonie River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.0587272644043,\n 40.38813537489036\n ],\n [\n -84.93925094604492,\n 40.38813537489036\n ],\n [\n -84.93925094604492,\n 40.44877593183776\n ],\n [\n -85.0587272644043,\n 40.44877593183776\n ],\n [\n -85.0587272644043,\n 40.38813537489036\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio Kentucky Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278
A multitude of biologically active pharmaceuticals contaminate surface waters globally, yet their presence in aquatic food webs remain largely unknown. Here, we show that over 60 pharmaceutical compounds can be detected in aquatic invertebrates and riparian spiders in six streams near Melbourne, Australia. Similar concentrations in aquatic invertebrate larvae and riparian predators suggest direct trophic transfer via emerging adult insects to riparian predators that consume them. As representative vertebrate predators feeding on aquatic invertebrates, platypus and brown trout could consume some drug classes such as antidepressants at as much as one-half of a recommended therapeutic dose for humans based on their estimated prey consumption rates, yet the consequences for fish and wildlife of this chronic exposure are unknown. Overall, this work highlights the potential exposure of aquatic and riparian biota to a diverse array of pharmaceuticals, resulting in exposures to some drugs that are comparable to human dosages.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-018-06822-w","usgsCitation":"Richmond, E.K., Rosi, E.J., Walters, D., Fikk, J., Hamilton, S.K., Brodin, T., Sundelin, A., and Grace, M.R., 2018, A diverse suite of pharmaceuticals contaminates stream and riparian food webs: Nature Communications, v. 9, p. 1-9, https://doi.org/10.1038/s41467-018-06822-w.","productDescription":"Article number 4491; 9 p.","startPage":"1","endPage":"9","ipdsId":"IP-097197","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":468209,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-018-06822-w","text":"Publisher Index Page"},{"id":361064,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Richmond, Erinn K.","contributorId":212849,"corporation":false,"usgs":false,"family":"Richmond","given":"Erinn","email":"","middleInitial":"K.","affiliations":[{"id":27278,"text":"Monash University","active":true,"usgs":false}],"preferred":false,"id":756737,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosi, Emma J.","contributorId":201758,"corporation":false,"usgs":false,"family":"Rosi","given":"Emma","email":"","middleInitial":"J.","affiliations":[{"id":36248,"text":"Cary Institute of Ecosystem Studies","active":true,"usgs":false}],"preferred":false,"id":756738,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walters, David M. 0000-0002-4237-2158 waltersd@usgs.gov","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":4444,"corporation":false,"usgs":true,"family":"Walters","given":"David M.","email":"waltersd@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":756736,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fikk, Jerker","contributorId":212850,"corporation":false,"usgs":false,"family":"Fikk","given":"Jerker","email":"","affiliations":[{"id":24847,"text":"Umea University","active":true,"usgs":false}],"preferred":false,"id":756739,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hamilton, Stephen K.","contributorId":143690,"corporation":false,"usgs":false,"family":"Hamilton","given":"Stephen","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":756740,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brodin, Tomas","contributorId":212851,"corporation":false,"usgs":false,"family":"Brodin","given":"Tomas","email":"","affiliations":[{"id":24847,"text":"Umea University","active":true,"usgs":false}],"preferred":false,"id":756741,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sundelin, Anna","contributorId":212852,"corporation":false,"usgs":false,"family":"Sundelin","given":"Anna","email":"","affiliations":[{"id":38691,"text":"Umea Univ.","active":true,"usgs":false}],"preferred":false,"id":756742,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Grace, Michael R.","contributorId":201756,"corporation":false,"usgs":false,"family":"Grace","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":36247,"text":"MONASH U","active":true,"usgs":false}],"preferred":false,"id":756743,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70201740,"text":"70201740 - 2018 - How or when samples are collected affects measured arsenic concentration in new drinking water wells","interactions":[],"lastModifiedDate":"2019-01-28T14:57:43","indexId":"70201740","displayToPublicDate":"2018-12-01T14:57:36","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"How or when samples are collected affects measured arsenic concentration in new drinking water wells","docAbstract":"Naturally occurring arsenic can adversely affect water quality in geologically diverse aquifers throughout the world. Chronic exposure to arsenic via drinking water is a human health concern due to risks for certain cancers, skin abnormalities, peripheral neuropathy, and other negative health effects. Statewide in Minnesota, USA, 11% of samples from new drinking water wells have arsenic concentrations exceeding 10 μg/L; in certain counties more than 35% of tested samples exceed 10 μg/L arsenic. Since 2008, Minnesota well code has required testing water from new wells for arsenic. Sample collection protocols are not specified in the well code, so among 180 well drillers there is variability in sampling methods, including sample collection point and sample collection timing. This study examines the effect of arsenic sample collection protocols on the variability of measured arsenic concentrations in water from new domestic water supply wells. Study wells were drilled between 2014 and 2016 in three regions of Minnesota that commonly have elevated arsenic concentrations in groundwater. Variability in measured arsenic concentration at a well was reduced when samples were (1) filtered, (2) collected from household plumbing instead of from the drill rig pump, or (3) collected several months after well construction (instead of within 4 weeks of well installation). Particulates and fine aquifer sediments entrained in groundwater samples, or other artifacts of drilling disturbance, can cause undesirable variability in measurements. Establishing regulatory protocols requiring sample filtration and/or collection from household plumbing could improve the reliability of information provided to well owners and to secondary data users.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12643","usgsCitation":"Erickson, M., Malenda, H.F., and Berquist, E.C., 2018, How or when samples are collected affects measured arsenic concentration in new drinking water wells: Groundwater, v. 56, no. 6, p. 921-933, https://doi.org/10.1111/gwat.12643.","productDescription":"13 p.","startPage":"921","endPage":"933","ipdsId":"IP-090483","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":468212,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.12643","text":"Publisher Index Page"},{"id":437663,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7736PVK","text":"USGS data release","linkHelpText":"Arsenic and field parameter determinations for newly constructed wells in the central, northwest, and northeast regions in Minnesota"},{"id":360765,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":360748,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/abs/10.1111/gwat.12643"}],"volume":"56","issue":"6","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-06","publicationStatus":"PW","scienceBaseUri":"5c5022c5e4b0708288f7e817","contributors":{"authors":[{"text":"Erickson, Melinda L. 0000-0002-1117-2866 merickso@usgs.gov","orcid":"https://orcid.org/0000-0002-1117-2866","contributorId":3671,"corporation":false,"usgs":true,"family":"Erickson","given":"Melinda L.","email":"merickso@usgs.gov","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":755126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Malenda, Helen F. 0000-0003-4143-6460","orcid":"https://orcid.org/0000-0003-4143-6460","contributorId":211885,"corporation":false,"usgs":false,"family":"Malenda","given":"Helen","email":"","middleInitial":"F.","affiliations":[{"id":38341,"text":"Colorodo School of Mines","active":true,"usgs":false}],"preferred":true,"id":755127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Berquist, Emily C.","contributorId":202174,"corporation":false,"usgs":false,"family":"Berquist","given":"Emily","email":"","middleInitial":"C.","affiliations":[{"id":36357,"text":"Minnesota Department of Health","active":true,"usgs":false}],"preferred":false,"id":755128,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202557,"text":"70202557 - 2018 - Event-response ellipses: A method to quantify and compare the role of dynamic storage at the catchment scale in snowmelt-dominated systems","interactions":[],"lastModifiedDate":"2019-03-11T14:53:01","indexId":"70202557","displayToPublicDate":"2018-12-01T14:52:55","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Event-response ellipses: A method to quantify and compare the role of dynamic storage at the catchment scale in snowmelt-dominated systems","docAbstract":"A method for quantifying the role of dynamic storage as a physical buffer between snowmelt and streamflow at the catchment scale is introduced in this paper. The method describes a quantitative relation between hydrologic events (e.g., snowmelt) and responses (e.g., streamflow) by generating event-response ellipses that can be used to (a) characterize and compare catchment-scale dynamic storage processes, and (b) assess the closure of the water balance. Event-response ellipses allow for the role of dynamic, short-term storage to be quantified and compared between seasons and between catchments. This method is presented as an idealization of the system: a time series of a snowmelt event as a portion of a sinusoidal wave function. The event function is then related to a response function, which is the original event function modified mathematically through phase and magnitude shifts to represent the streamflow response. The direct relation of these two functions creates an event-response ellipse with measurable characteristics (e.g., eccentricity, angle). The ellipse characteristics integrate the timing and magnitude difference between the hydrologic event and response to quantify physical buffering through dynamic storage. Next, method is applied to eleven snowmelt seasons in two well-instrumented headwater snowmelt-dominated catchments with known differences in storage capacities. Results show the time-period average daily values produce different event-response ellipse characteristics for the two catchments. Event-response ellipses were also generated for individual snowmelt seasons; however, these annual applications of the method show more scatter relative to the time period averaged values. The event-response ellipse method provides a method to compare and evaluate the connectivity between snowmelt and streamflow as well as assumptions of water balance.
","language":"English","publisher":"MDPI","doi":"10.3390/w10121824","usgsCitation":"Driscoll, J.M., Meixner, T., Molotch, N.P., Ferre, T.P., Williams, M.W., and Sickman, J.O., 2018, Event-response ellipses: A method to quantify and compare the role of dynamic storage at the catchment scale in snowmelt-dominated systems: Water, v. 10, no. 12, p. 1-17, https://doi.org/10.3390/w10121824.","productDescription":"Article 1824; 17 p.","startPage":"1","endPage":"17","ipdsId":"IP-096914","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":468213,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w10121824","text":"Publisher Index Page"},{"id":361982,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"12","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Driscoll, Jessica M. 0000-0003-3097-9603 jdriscoll@usgs.gov","orcid":"https://orcid.org/0000-0003-3097-9603","contributorId":167585,"corporation":false,"usgs":true,"family":"Driscoll","given":"Jessica","email":"jdriscoll@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759101,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meixner, Thomas","contributorId":22653,"corporation":false,"usgs":false,"family":"Meixner","given":"Thomas","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":759102,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Molotch, Noah P. 0000-0003-4733-8060","orcid":"https://orcid.org/0000-0003-4733-8060","contributorId":203466,"corporation":false,"usgs":false,"family":"Molotch","given":"Noah","email":"","middleInitial":"P.","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":759103,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ferre, Ty P. A.","contributorId":214081,"corporation":false,"usgs":false,"family":"Ferre","given":"Ty","email":"","middleInitial":"P. A.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":759104,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williams, Mark W.","contributorId":214082,"corporation":false,"usgs":false,"family":"Williams","given":"Mark","email":"","middleInitial":"W.","affiliations":[{"id":38977,"text":"University of Colorado at Boulder","active":true,"usgs":false}],"preferred":false,"id":759105,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sickman, James O.","contributorId":214083,"corporation":false,"usgs":false,"family":"Sickman","given":"James","email":"","middleInitial":"O.","affiliations":[{"id":38978,"text":"University of California at Riverside","active":true,"usgs":false}],"preferred":false,"id":759106,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70201688,"text":"70201688 - 2018 - Hourly analyses of the large storms and atmospheric rivers that provide most of California's precipitation in only 10 to 100 hours per year","interactions":[],"lastModifiedDate":"2018-12-21T13:29:21","indexId":"70201688","displayToPublicDate":"2018-12-01T13:29:16","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Hourly analyses of the large storms and atmospheric rivers that provide most of California's precipitation in only 10 to 100 hours per year","docAbstract":"California is regularly impacted by floods and droughts, primarily as a result of too many or too few atmospheric rivers (ARs). This study analyzes a two-decade-long hourly precipitation dataset from 176 California weather stations and a 3-hourly AR chronology to report variations in rainfall events across California and their association with ARs. On average, 10-40 and 60-120 hours of rainfall in southern and northern California, respectively, are responsible for more than half of annual rainfall accumulations. Approximately 10-30% of annual precipitation at locations across the state is from only one large storm. On average, northern California receives 25-45 rainfall events annually (40-50% of which are AR-related). These events typically have longer durations and higher event-precipitation totals than those in southern California. Northern California also receives more AR landfalls with longer durations and stronger Integrated Vapor Transport (IVT). On average, ARs contribute 79%, 76%, and 68% of extreme-rainfall accumulations (i.e., top 5% events annually) in the north coast, northern Sierra, and Transverse Ranges of southern California, respectively.
The San Francisco Bay Area terrain gap in the California Coast Range allows more AR water vapor to reach inland over the Delta and Sacramento Valley, and thus, influences precipitation in the Delta’s catchment. This is particularly important for extreme precipitation in the northern Sierra Nevada, including river basins above Oroville Dam and Shasta Dam.
This study highlights differences between rainfall and AR characteristics in coastal versus inland northern California, differences that largely determine the regional geography of flood risks and water-reliability. These analyses support water resource, flood, levee, wetland, and ecosystem management within the catchment of the San Francisco estuary system by describing regional characteristics of ARs and their influence on rainfall on an hourly timescale.
","language":"English","publisher":"University of California","doi":"10.15447/sfews.2018v16iss4art1","usgsCitation":"Lamjiri, M.A., Dettinger, M.D., Ralph, F.M., Oakley, N.S., and Rutz, J.J., 2018, Hourly analyses of the large storms and atmospheric rivers that provide most of California's precipitation in only 10 to 100 hours per year: San Francisco Estuary and Watershed Science, v. 16, no. 4, p. 1-17, https://doi.org/10.15447/sfews.2018v16iss4art1.","productDescription":"Article 1; 17 p.","startPage":"1","endPage":"17","ipdsId":"IP-095473","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":468214,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2018v16iss4art1","text":"Publisher Index Page"},{"id":360681,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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