{"pageNumber":"184","pageRowStart":"4575","pageSize":"25","recordCount":68801,"records":[{"id":70248735,"text":"70248735 - 2021 - Resilience of terrestrial and aquatic fauna to historical and future wildfire regimes in western North America","interactions":[],"lastModifiedDate":"2023-09-19T12:25:00.745005","indexId":"70248735","displayToPublicDate":"2021-08-30T07:22:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Resilience of terrestrial and aquatic fauna to historical and future wildfire regimes in western North America","docAbstract":"

Wildfires in many western North American forests are becoming more frequent, larger, and severe, with changed seasonal patterns. In response, coniferous forest ecosystems will transition toward dominance by fire-adapted hardwoods, shrubs, meadows, and grasslands, which may benefit some faunal communities, but not others. We describe factors that limit and promote faunal resilience to shifting wildfire regimes for terrestrial and aquatic ecosystems. We highlight the potential value of interspersed nonforest patches to terrestrial wildlife. Similarly, we review watershed thresholds and factors that control the resilience of aquatic ecosystems to wildfire, mediated by thermal changes and chemical, debris, and sediment loadings. We present a 2-dimensional life history framework to describe temporal and spatial life history traits that species use to resist wildfire effects or to recover after wildfire disturbance at a metapopulation scale. The role of fire refuge is explored for metapopulations of species. In aquatic systems, recovery of assemblages postfire may be faster for smaller fires where unburned tributary basins or instream structures provide refuge from debris and sediment flows. We envision that more-frequent, lower-severity fires will favor opportunistic species and that less-frequent high-severity fires will favor better competitors. Along the spatial dimension, we hypothesize that fire regimes that are predictable and generate burned patches in close proximity to refuge will favor species that move to refuges and later recolonize, whereas fire regimes that tend to generate less-severely burned patches may favor species that shelter in place. Looking beyond the trees to forest fauna, we consider mitigation options to enhance resilience and buy time for species facing a no-analog future.

","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8026","usgsCitation":"Jager, H.I., Long, J.W., Malison, R.L., Murphy, B., Rust, A.J., Silva, L., Sollmann, R., Steel, Z.L., Bowen, M., Dunham, J., Ebersole, J.L., and Flitcroft, R.L., 2021, Resilience of terrestrial and aquatic fauna to historical and future wildfire regimes in western North America: Ecology and Evolution, v. 11, no. 18, p. 12259-12284, https://doi.org/10.1002/ece3.8026.","productDescription":"26 p.","startPage":"12259","endPage":"12284","ipdsId":"IP-131638","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":451046,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.8026","text":"Publisher Index Page"},{"id":420946,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"18","noUsgsAuthors":false,"publicationDate":"2021-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Jager, Henriette I.","contributorId":206774,"corporation":false,"usgs":false,"family":"Jager","given":"Henriette","email":"","middleInitial":"I.","affiliations":[{"id":37400,"text":"Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee","active":true,"usgs":false}],"preferred":false,"id":883374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, Jonathan W.","contributorId":329818,"corporation":false,"usgs":false,"family":"Long","given":"Jonathan","email":"","middleInitial":"W.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":883375,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Malison, Rachel L 0000-0001-6803-8230","orcid":"https://orcid.org/0000-0001-6803-8230","contributorId":329819,"corporation":false,"usgs":false,"family":"Malison","given":"Rachel","email":"","middleInitial":"L","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":883376,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murphy, Brendan P.","contributorId":301152,"corporation":false,"usgs":false,"family":"Murphy","given":"Brendan P.","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":883377,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rust, Ashley J.","contributorId":219575,"corporation":false,"usgs":false,"family":"Rust","given":"Ashley","email":"","middleInitial":"J.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":883378,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Silva, Luiz 0000-0002-2329-5601","orcid":"https://orcid.org/0000-0002-2329-5601","contributorId":329820,"corporation":false,"usgs":false,"family":"Silva","given":"Luiz","email":"","affiliations":[{"id":40173,"text":"Charles Sturt University","active":true,"usgs":false}],"preferred":false,"id":883379,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sollmann, Rahel 0000-0002-1607-2039","orcid":"https://orcid.org/0000-0002-1607-2039","contributorId":244998,"corporation":false,"usgs":false,"family":"Sollmann","given":"Rahel","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":883380,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Steel, Zachary L 0000-0002-1659-3141","orcid":"https://orcid.org/0000-0002-1659-3141","contributorId":329821,"corporation":false,"usgs":false,"family":"Steel","given":"Zachary","email":"","middleInitial":"L","affiliations":[{"id":6643,"text":"University of California - Berkeley","active":true,"usgs":false}],"preferred":false,"id":883381,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bowen, Mark D","contributorId":329822,"corporation":false,"usgs":false,"family":"Bowen","given":"Mark D","affiliations":[{"id":78723,"text":"Thomas Gast & Associates Environmental Consultants","active":true,"usgs":false}],"preferred":false,"id":883382,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Dunham, Jason 0000-0002-6268-0633","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":220078,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":883383,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ebersole, Joseph L.","contributorId":146938,"corporation":false,"usgs":false,"family":"Ebersole","given":"Joseph","email":"","middleInitial":"L.","affiliations":[{"id":12657,"text":"EPA NEIC","active":true,"usgs":false}],"preferred":false,"id":883384,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Flitcroft, Rebecca L. 0000-0003-3341-996X","orcid":"https://orcid.org/0000-0003-3341-996X","contributorId":172180,"corporation":false,"usgs":false,"family":"Flitcroft","given":"Rebecca","email":"","middleInitial":"L.","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":883385,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70229529,"text":"70229529 - 2021 - American eel personality and body length influence passage success in an experimental fishway","interactions":[],"lastModifiedDate":"2022-03-11T12:32:47.498359","indexId":"70229529","displayToPublicDate":"2021-08-28T10:51:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"American eel personality and body length influence passage success in an experimental fishway","docAbstract":"
  1. Millions of dams impair watershed connectivity across the globe and have severely affected migratory fish populations. Fishways offer upstream passage opportunities, but artificial selection may be imposed by these structures. Using juvenile American eel Anguilla rostrata as a model species, we consider whether individual differences in behaviour (i.e. personality) and fish size can predict passage success.
  2. We evaluated the expression of bold and exploratory behaviours using open field and emergence assays in the laboratory. Then we assessed the propensity for individuals to volitionally climb through an experimental fishway to understand if personality and fish size could predict climbing success.
  3. We demonstrate personality in juvenile eels, and swimming speed in the open field was negatively associated with climbing propensity. Slower swimmers were up to 60% more likely to use the passage device suggesting that more exploratory eels incurred greater passage success. For successful climbers, climbing time was negatively associated with fish length.
  4. Synthesis and applications. Our results suggest fish may segregate at barriers based on personality and size. Preventing a subset of individuals from accessing upstream habitat is likely to have negative consequences for fish populations and aquatic ecosystems. Selection may be alleviated by increasing passage opportunities, maximizing fishway attraction and avoiding inefficient passage solutions.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.14009","usgsCitation":"Mensinger, M.A., Brehm, A.M., Mortelliti, A., Blomberg, E., and Zydlewski, J.D., 2021, American eel personality and body length influence passage success in an experimental fishway: Journal of Applied Ecology, v. 58, no. 12, p. 2760-2769, https://doi.org/10.1111/1365-2664.14009.","productDescription":"10 p.","startPage":"2760","endPage":"2769","ipdsId":"IP-126473","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":397007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Mensinger, Matthew A.","contributorId":288336,"corporation":false,"usgs":false,"family":"Mensinger","given":"Matthew","email":"","middleInitial":"A.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":837768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brehm, Allison M.","contributorId":288337,"corporation":false,"usgs":false,"family":"Brehm","given":"Allison","email":"","middleInitial":"M.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":837769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mortelliti, Alessio","contributorId":288338,"corporation":false,"usgs":false,"family":"Mortelliti","given":"Alessio","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":837770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blomberg, Erik J.","contributorId":288339,"corporation":false,"usgs":false,"family":"Blomberg","given":"Erik J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":837771,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":837767,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224565,"text":"70224565 - 2021 - Groundwater, biodiversity, and the role of flow system scale","interactions":[],"lastModifiedDate":"2022-01-06T17:22:41.248645","indexId":"70224565","displayToPublicDate":"2021-08-28T07:33:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater, biodiversity, and the role of flow system scale","docAbstract":"

Groundwater-dependent ecosystems and species (GDEs) are found throughout watersheds at locations of groundwater discharge, yet not all GDEs are the same, nor are the groundwater systems supporting them. Groundwater moves along a variety of flow paths of different lengths and with different contributing areas, ranging from shorter local flow paths with low discharge and large seasonal variability to streams, springs and wetlands to longer regional flow paths with potentially larger discharge and low seasonal variability, commonly at low basin elevations. How does this variation in physical hydrology affect the type and distribution of GDEs? Using data on hypsographic position, groundwater-dependent species distributions, groundwater pumping and streamflow from Oregon, USA, we provide a conceptual model and initial supporting evidence demonstrating that spatial variation in groundwater flow path scales, illustrated using basin hypsography, is a driver of non-random distribution of GDEs across watersheds. Further, we posit that the spatial variation in primary stressors to groundwater (e.g. pumping and climate change) will differentially affect GDEs depending on their hypsographic position. Furthermore, because of their use for irrigation and municipal water supply, regional groundwater systems and associated species are more likely to be studied and receive regulatory protection. Our initial data point to a disproportionate focus on larger discharge, lower elevation GDEs, which leads to a bias in our understanding of the full suite of biodiversity associated with groundwater discharge as well as their stressors and potential mechanisms for protection.

","language":"English","publisher":"Wiley","doi":"10.1002/eco.2342","usgsCitation":"Aldous, A.R., and Gannett, M.W., 2021, Groundwater, biodiversity, and the role of flow system scale: Ecohydrology, v. 14, no. 8, e2342, 14 p., https://doi.org/10.1002/eco.2342.","productDescription":"e2342, 14 p.","ipdsId":"IP-117907","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":451049,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eco.2342","text":"Publisher Index Page"},{"id":389865,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Aldous, Allison R 0000-0002-8670-6017","orcid":"https://orcid.org/0000-0002-8670-6017","contributorId":266015,"corporation":false,"usgs":false,"family":"Aldous","given":"Allison","email":"","middleInitial":"R","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":824080,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gannett, Marshall W. 0000-0003-2498-2427 mgannett@usgs.gov","orcid":"https://orcid.org/0000-0003-2498-2427","contributorId":2942,"corporation":false,"usgs":true,"family":"Gannett","given":"Marshall","email":"mgannett@usgs.gov","middleInitial":"W.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824081,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70224265,"text":"70224265 - 2021 - Taxonomic and functional differences between winter and summer crustacean zooplankton communities in lakes across a trophic gradient","interactions":[],"lastModifiedDate":"2021-10-06T16:02:30.076217","indexId":"70224265","displayToPublicDate":"2021-08-28T07:10:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2430,"text":"Journal of Plankton Research","active":true,"publicationSubtype":{"id":10}},"title":"Taxonomic and functional differences between winter and summer crustacean zooplankton communities in lakes across a trophic gradient","docAbstract":"

Despite increasing interest in winter limnology, few studies have examined under-ice zooplankton communities and the factors shaping them in different types of temperate lakes. To better understand drivers of zooplankton community structure in winter and summer, we sampled 13 lakes across a large trophic status gradient for crustacean zooplankton abundance, taxonomic and functional community composition and C/N stable isotopes. Average winter zooplankton densities were one-third of summer densities across the study lakes. Proportionally, cladocerans were more abundant in summer than winter, with the opposite pattern for calanoids and cyclopoids. In green (eutrophic) lakes, zooplankton densities were higher under the ice than in brown (dystrophic) and blue (oligotrophic) lakes, suggesting better conditions for zooplankton in productive lakes during winter. Overall, zooplankton communities were more similar across lakes under the ice than during the open water season. Feeding group classification showed a decrease in herbivore abundance and an increase in predators from summer to winter. C/N stable isotope results suggested higher lipid content in overwintering zooplankton and potentially increased reliance on the microbial loop by winter zooplankton. Our results show substantial variation in the seasonality of zooplankton communities in different lake types and identify some of the factors responsible for this variation.

","language":"English","publisher":"Oxford Academic","doi":"10.1093/plankt/fbab050","usgsCitation":"Shchapov, K., Wilburn, P., Bramburger, A., Silsbe, G., Olmanson, L., Crawford, C., Litchmann, E., and Ozersky, T., 2021, Taxonomic and functional differences between winter and summer crustacean zooplankton communities in lakes across a trophic gradient: Journal of Plankton Research, v. 43, no. 5, p. 732-750, https://doi.org/10.1093/plankt/fbab050.","productDescription":"19 p.","startPage":"732","endPage":"750","ipdsId":"IP-129395","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":451050,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/41624","text":"External Repository"},{"id":389326,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.41650390625,\n 44.793530904744074\n ],\n [\n -90.615234375,\n 44.793530904744074\n ],\n [\n -90.615234375,\n 48.56024979174329\n ],\n [\n -94.41650390625,\n 48.56024979174329\n ],\n [\n -94.41650390625,\n 44.793530904744074\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-08-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Shchapov, Kirill","contributorId":265794,"corporation":false,"usgs":false,"family":"Shchapov","given":"Kirill","email":"","affiliations":[{"id":18006,"text":"University of Minnesota Duluth","active":true,"usgs":false}],"preferred":false,"id":823401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilburn, P.","contributorId":265795,"corporation":false,"usgs":false,"family":"Wilburn","given":"P.","email":"","affiliations":[{"id":54804,"text":"NASA Ames","active":true,"usgs":false}],"preferred":false,"id":823402,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bramburger, A.","contributorId":265796,"corporation":false,"usgs":false,"family":"Bramburger","given":"A.","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":823403,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Silsbe, G.","contributorId":265798,"corporation":false,"usgs":false,"family":"Silsbe","given":"G.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":823404,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olmanson, L.","contributorId":265799,"corporation":false,"usgs":false,"family":"Olmanson","given":"L.","affiliations":[{"id":33108,"text":"University of Minnesota Twin Cities","active":true,"usgs":false}],"preferred":false,"id":823405,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":823406,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Litchmann, E.","contributorId":265800,"corporation":false,"usgs":false,"family":"Litchmann","given":"E.","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":823407,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ozersky, T.","contributorId":265801,"corporation":false,"usgs":false,"family":"Ozersky","given":"T.","affiliations":[{"id":18006,"text":"University of Minnesota Duluth","active":true,"usgs":false}],"preferred":false,"id":823408,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70223484,"text":"ofr20211077 - 2021 - Water quality, instream habitat, and the distribution of suckers in the upper Lost River watershed of Oregon and California, summer 2018","interactions":[],"lastModifiedDate":"2021-08-30T11:54:50.759707","indexId":"ofr20211077","displayToPublicDate":"2021-08-27T10:32:27","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1077","displayTitle":"Water Quality, Instream Habitat, and the Distribution of Suckers in the Upper Lost River Watershed of Oregon and California, Summer 2018","title":"Water quality, instream habitat, and the distribution of suckers in the upper Lost River watershed of Oregon and California, summer 2018","docAbstract":"

Executive Summary

Endangered Lost River (Deltistes luxatus) and shortnose (Chasmistes brevirostris) suckers primarily use lotic habitats during the spring spawning season in the Upper Klamath Lake watershed. However, summer-time surveys of the upper Lost River watershed in 1972, 1975 and 1989–90 indicated that adults of both endangered species use tributaries of Clear Lake Reservoir (hereafter: Clear Lake) year-round. Adult shortnose suckers have also been documented to use tributaries of Gerber Reservoir year-round. We surveyed the tributaries of Clear Lake and Gerber Reservoir to provide up-to-date information on the timing, distribution, and habitat use within the upper Lost River drainage by these two endangered sucker species.

Contrary to previous studies, this study did not capture any Lost River suckers in the Clear Lake tributaries. Genetics samples from suckers collected during this study were used to verify that no Lost River suckers were captured. At the time of this study, genetics could not identify the differences between shortnose and the non-endangered Klamath largescale suckers (Catostomus snyderi), therefore, morphology was used to separate these two species. Furthermore, the shortnose suckers and the Klamath largescale suckers documented in the upper Lost River drainage are more similar to Klamath largescale suckers than shortnose suckers that exist in the Upper Klamath Lake recovery unit. Therefore, the suckers we documented during our surveys were most likely Klamath largescale suckers.

We captured suckers, age-0 to age-9, in the Clear Lake tributaries within stream pools and flooded meadows behind water retention structures. However, no suckers were collected in small reservoirs sampled upstream of Clear Lake. Suckers were found in habitats with mud and fine substrate at depths of 0.5–3.0 meters, with most captured at 1.0 meter or less. Suckers co-occurred with nonnative species, which were more abundant in our survey than in previous surveys in the tributaries to Clear Lake.

Gerber Reservoir tributaries yielded more suckers per unit effort than Clear Lake tributaries. All suckers captured in the tributaries of Gerber Reservoir were identified as Klamath Largescale suckers. The suckers in tributaries to Gerber Reservoir were collected in similar habitat as those in Clear Lake tributaries and were age-0 to age-6.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211077","collaboration":"Prepared in cooperation with the U.S. Bureau of Reclamation","usgsCitation":"Martin, B.A., Burdick, S.M., Staiger, S.T., and Kelsey, C., 2021, Water quality, instream habitat, and the distribution of suckers in the upper Lost River watershed of Oregon and California, summer 2018: U.S. Geological Survey Open-File Report 2021–1077, 29 p., https://doi.org/10.3133/ofr20211077.","productDescription":"v, 29 p.","onlineOnly":"Y","ipdsId":"IP-122858","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":388609,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1077/coverthb.jpg"},{"id":388610,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1077/ofr20211077.pdf","text":"Report","size":"3.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1077"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Lost River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.27783203125,\n 41.63186741069748\n ],\n [\n -120.73974609374999,\n 41.63186741069748\n ],\n [\n -120.73974609374999,\n 42.66628070564928\n ],\n [\n -122.27783203125,\n 42.66628070564928\n ],\n [\n -122.27783203125,\n 41.63186741069748\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016

","tableOfContents":"","publishedDate":"2021-08-27","noUsgsAuthors":false,"publicationDate":"2021-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Martin, Barbara A. 0000-0002-9415-6377 barbara_ann_martin@usgs.gov","orcid":"https://orcid.org/0000-0002-9415-6377","contributorId":2855,"corporation":false,"usgs":true,"family":"Martin","given":"Barbara","email":"barbara_ann_martin@usgs.gov","middleInitial":"A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":822130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burdick, Summer M. 0000-0002-3480-5793 sburdick@usgs.gov","orcid":"https://orcid.org/0000-0002-3480-5793","contributorId":3448,"corporation":false,"usgs":true,"family":"Burdick","given":"Summer","email":"sburdick@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":822131,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Staiger, Stephen T. 0000-0002-3777-2421 sstaiger@usgs.gov","orcid":"https://orcid.org/0000-0002-3777-2421","contributorId":264884,"corporation":false,"usgs":true,"family":"Staiger","given":"Stephen","email":"sstaiger@usgs.gov","middleInitial":"T.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":822132,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kelsey, Caylen M. 0000-0003-0470-0963 ckelsey@usgs.gov","orcid":"https://orcid.org/0000-0003-0470-0963","contributorId":258179,"corporation":false,"usgs":true,"family":"Kelsey","given":"Caylen","email":"ckelsey@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":822133,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223456,"text":"fs20213047 - 2021 - Michigan and Landsat","interactions":[],"lastModifiedDate":"2023-01-24T11:48:11.924537","indexId":"fs20213047","displayToPublicDate":"2021-08-26T14:49:39","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3047","displayTitle":"Michigan and Landsat","title":"Michigan and Landsat","docAbstract":"

Water means a lot to Michigan, often called the Great Lakes State. The name “Michigan” comes from an Ojibwe word meaning large, or great, water or lake. As the only State touching four of the five Great Lakes—Michigan, Superior, Huron, and Erie—it claims the longest freshwater coastline in the United States.

Yet Michigan is not just about water—forests, agriculture, mines, cities, and even sand dunes stretch across the State’s landscape. Much of what happens on the land does connect in some way with Michigan’s inland and coastal waters. Michigan relies on a healthy environment to support its residents, abundant tourists, and diverse species of wildlife that call the State and its surrounding waters home. From hundreds of miles above, Landsat satellites provide a clearer picture of the connections among land, water, and the people and wildlife that inhabit the State.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213047","usgsCitation":"U.S. Geological Survey, 2021, Michigan and Landsat (ver. 1.1, January 2023): U.S. Geological Survey Fact Sheet 2021–3047, 2 p., https://doi.org/10.3133/fs20213047.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-126134","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":412223,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20213047/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":388556,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3047/coverthb2.jpg"},{"id":412196,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3047/fs20213047.pdf","text":"Report","size":"2.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3047"},{"id":412197,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2021/3047/versionHist.txt","size":"4.01 kB","linkFileType":{"id":2,"text":"txt"}},{"id":412198,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2021/3047/fs20213047.XML"},{"id":412199,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2021/3047/images"}],"country":"United States","state":"Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.923828125,\n 41.77131167976407\n ],\n [\n -83.4521484375,\n 41.73852846935917\n ],\n [\n -82.46337890625,\n 42.66628070564928\n ],\n [\n -82.33154296875,\n 42.956422511073335\n ],\n [\n -82.5732421875,\n 43.83452678223682\n ],\n [\n -82.6611328125,\n 44.040218713142146\n ],\n [\n -82.94677734375,\n 44.22945656830167\n ],\n [\n -83.8037109375,\n 43.89789239125797\n ],\n [\n -83.1884765625,\n 44.55916341529182\n ],\n [\n -83.34228515625,\n 45.413876460821086\n ],\n [\n -84.6826171875,\n 45.82879925192134\n ],\n [\n -85.5615234375,\n 45.82879925192134\n ],\n [\n -86.7041015625,\n 44.55916341529182\n ],\n [\n -86.7041015625,\n 43.644025847699496\n ],\n [\n -86.396484375,\n 42.97250158602597\n ],\n [\n -86.572265625,\n 42.22851735620852\n ],\n [\n -86.923828125,\n 41.77131167976407\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.439453125,\n 46.649436163350245\n ],\n [\n -89.9560546875,\n 46.22545288226939\n ],\n [\n -88.06640625,\n 45.920587344733654\n ],\n [\n -87.8466796875,\n 45.30580259943578\n ],\n [\n -87.7587890625,\n 44.87144275016589\n ],\n [\n -86.6162109375,\n 45.73685954736049\n ],\n [\n -85.3857421875,\n 46.01222384063236\n ],\n [\n -84.8583984375,\n 45.79816953017265\n ],\n [\n -83.8916015625,\n 45.73685954736049\n ],\n [\n -83.4521484375,\n 46.01222384063236\n ],\n [\n -84.19921875,\n 46.46813299215554\n ],\n [\n -84.990234375,\n 46.92025531537451\n ],\n [\n -85.9130859375,\n 46.86019101567027\n ],\n [\n -86.8798828125,\n 46.649436163350245\n ],\n [\n -87.71484375,\n 46.98025235521883\n ],\n [\n -87.802734375,\n 47.487513008956554\n ],\n [\n -88.41796875,\n 47.635783590864854\n ],\n [\n -89.5166015625,\n 47.010225655683485\n ],\n [\n -90.439453125,\n 46.649436163350245\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: August 26, 2021; Version 1.1: January 23, 2023","contact":"

Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-08-26","revisedDate":"2023-01-23","noUsgsAuthors":false,"publicationDate":"2021-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":127955,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":822068,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70223408,"text":"70223408 - 2021 - Negligible evidence for detrimental effects of Leucocytozoon infections among Emperor Geese (Anser canagicus) breeding on the Yukon-Kuskokwim Delta, Alaska","interactions":[],"lastModifiedDate":"2021-08-26T15:50:24.140426","indexId":"70223408","displayToPublicDate":"2021-08-26T10:40:41","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2025,"text":"International Journal for Parasitology: Parasites and Wildlife","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Negligible evidence for detrimental effects of Leucocytozoon infections among Emperor Geese (Anser canagicus) breeding on the Yukon-Kuskokwim Delta, Alaska","title":"Negligible evidence for detrimental effects of Leucocytozoon infections among Emperor Geese (Anser canagicus) breeding on the Yukon-Kuskokwim Delta, Alaska","docAbstract":"

Emperor Geese (Anser canagicus) are iconic waterfowl endemic to Alaska and adjacent areas of northeastern Russia that are considered to be near threatened by the International Union for Conservation. This species has been identified as harboring diverse viruses and parasites which have, at times, been associated with disease in other avian taxa. To better assess if disease represents a vulnerability for Emperor Geese breeding on the Yukon-Kuskokwim Delta, Alaska, we evaluated if haemosporidian parasites were associated with decreased mass or survival among adult female nesting birds captured during 2006–2016. Through molecular analyses, we detected genetically diverse Leucocytozoon, Haemoproteus, and Plasmodium parasites in 28%, 1%, and 1% of 607 blood samples screened in triplicate, respectively. Using regression analysis, we found evidence for a small effect of Leucocytozoon infection on the mass of incubating adult female Emperor Geese. The estimated mass of infected individuals was approximately 43 g (95% CI: 20–67 g), or approximately 2%, less than uninfected birds when captured during the second half of incubation (days 11–25). We did not, however, find support for an effect of Leucocytozoon infection on survival of adult female nesting Emperor Geese using a multi-state hidden Markov framework to analyze mark-resight and recapture data. Using parasite mitochondrial DNA cytochrome b sequences, we identified 23 haplotypes among infected Emperor Geese. Leucocytozoon haplotypes clustered into three phylogenetically supported clades designated as ‘L. simondi clade A’, ‘L. simondi clade B’, and ‘other Leucocytozoon’. We did not find evidence that parasites assigned to any of these clades were associated with differential mass measures among nesting adult female Emperor Geese. Collectively, our results provide negligible evidence for Leucocytozoon parasites as causing detrimental effects to adult female Emperor Geese breeding on the Yukon-Kuskokwim Delta.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijppaw.2021.08.006","usgsCitation":"Ramey, A.M., Bucheit, R., Uher-Koch, B.D., Reed, J., Pacheco, M.A., Escalante, A., and Schmutz, J., 2021, Negligible evidence for detrimental effects of Leucocytozoon infections among Emperor Geese (Anser canagicus) breeding on the Yukon-Kuskokwim Delta, Alaska: International Journal for Parasitology: Parasites and Wildlife, v. 16, p. 103-112, https://doi.org/10.1016/j.ijppaw.2021.08.006.","productDescription":"10 p.","startPage":"103","endPage":"112","ipdsId":"IP-130428","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":451055,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijppaw.2021.08.006","text":"Publisher Index Page"},{"id":436223,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B5JUBW","text":"USGS data release","linkHelpText":"Blood Parasite Infection, Body Mass, and Survival Data from Emperor Geese (Anser canagicus), Yukon-Kuskokwim Delta, Alaska, 2006-2016"},{"id":388545,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon-Kuskokwim Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -166.728515625,\n 60.646262316136976\n ],\n [\n -163.23486328125,\n 60.646262316136976\n ],\n [\n -163.23486328125,\n 63.28800124531419\n ],\n [\n -166.728515625,\n 63.28800124531419\n ],\n [\n -166.728515625,\n 60.646262316136976\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":821973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bucheit, Raymond","contributorId":264772,"corporation":false,"usgs":false,"family":"Bucheit","given":"Raymond","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":821974,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Uher-Koch, Brian D. 0000-0002-1885-0260 buher-koch@usgs.gov","orcid":"https://orcid.org/0000-0002-1885-0260","contributorId":5117,"corporation":false,"usgs":true,"family":"Uher-Koch","given":"Brian","email":"buher-koch@usgs.gov","middleInitial":"D.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":821975,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reed, John 0000-0002-3239-6906","orcid":"https://orcid.org/0000-0002-3239-6906","contributorId":214852,"corporation":false,"usgs":true,"family":"Reed","given":"John","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":821976,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pacheco, M. Andreina","contributorId":264773,"corporation":false,"usgs":false,"family":"Pacheco","given":"M.","email":"","middleInitial":"Andreina","affiliations":[{"id":12547,"text":"Temple University","active":true,"usgs":false}],"preferred":false,"id":821977,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Escalante, Ananias","contributorId":264774,"corporation":false,"usgs":false,"family":"Escalante","given":"Ananias","email":"","affiliations":[{"id":12547,"text":"Temple University","active":true,"usgs":false}],"preferred":false,"id":821978,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schmutz, Joel 0000-0002-6516-0836","orcid":"https://orcid.org/0000-0002-6516-0836","contributorId":264776,"corporation":false,"usgs":false,"family":"Schmutz","given":"Joel","affiliations":[{"id":54549,"text":"retired from USGS Alaska Science Center","active":true,"usgs":false}],"preferred":false,"id":821979,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223403,"text":"sir20215090 - 2021 - Estimates of water use associated with continuous oil and gas development in the Permian Basin, Texas and New Mexico, 2010–19","interactions":[],"lastModifiedDate":"2021-12-14T12:26:17.570498","indexId":"sir20215090","displayToPublicDate":"2021-08-26T10:24:57","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5090","displayTitle":"Estimates of Water Use Associated with Continuous Oil and Gas Development in the Permian Basin, Texas and New Mexico, 2010–19","title":"Estimates of water use associated with continuous oil and gas development in the Permian Basin, Texas and New Mexico, 2010–19","docAbstract":"

In 2015, the U.S. Geological Survey started a topical study to quantify water use in areas of continuous oil and gas (COG) development. The first phase of the study was completed in 2019 and analyzed the Williston Basin. The second phase of the study analyzed the Permian Basin using the same techniques and approaches used for the Williston Basin analysis. The Permian Basin was selected for the second phase of water-use analysis for the following reasons: (1) the basin has the largest undiscovered technically recoverable oil and gas resource in the United States, (2) the basin has a continuous resource in tight shale that primarily produces oil, and (3) the basin is within the contiguous United States. This study used data from 60 counties in Texas and New Mexico with spatial coverage based on the Permian Basin extent defined by the U.S. Energy Information Administration, a representation of the geologically defined Permian Basin.

Data from several sources were used in the analysis of direct, indirect, and ancillary water use associated with COG development in the Permian Basin and are available in an associated data release. Hydraulic fracturing water-use data were used to determine the start of the recent (before 2019) COG development boom in oil production in the Permian Basin in the same way that the data were used for the Williston Basin study. Water-use data were aggregated by county and year, which were the sampling units used in the analysis.

The water-use analysis of the Permian Basin contained three elements: (1) estimates of water use, in million gallons, by county and year; (2) coefficients of water use from regression models, in million gallons per developed oil and gas well; and (3) performance (based on goodness-of-fit metrics) of the regression models in estimating the observed water use.

Coefficients from the linear and quantile regression models of direct, indirect, and ancillary water use in the Permian Basin were produced as aggregate values for the counties and years. The mean estimate of direct water use had a 95-percent confidence interval of 4.13–5.45 million gallons (Mgal) per developed oil and gas well. The coefficient from the linear regression model of indirect water use was 0.111 Mgal per well, with a 95-percent confidence interval of 0.104–0.117 Mgal per well. The mean estimate of ancillary water use in the Permian Basin was 1.09 Mgal per well, with a 95-percent confidence interval of 1.05–1.13 Mgal per well. Model performance was evaluated with goodness-of-fit metrics including coefficient of determination (R2), root mean square error, and the ratio of root mean square error to standard deviation of observations computed from leave-one-out cross validation of the linear and quantile regression models of direct, indirect, and ancillary water use. Model performance for direct water use was acceptable, with an R2 value of 0.91. The model performance of indirect water use was acceptable, with an R2 value of 0.89. Values of R2 for the ancillary water-use categories were at least 0.89.

Annual mean estimates for hydraulic fracturing, cementing, drilling, indirect, and ancillary water use per well for the years 2010–17 were comparable between the Permian and Williston Basins. Hydraulic fracturing water use increased similarly from 2010 to 2015 in the Permian Basin and the Williston Basin, increasing from 0.6 Mgal per well in 2010 to 5.4 Mgal per well in 2015 in the Permian Basin and from 1.4 Mgal per well in 2010 to 4.7 Mgal per well in 2015 in the Williston Basin.

By design, the Permian water-use assessment is a simplification of a complex and continually developing system and therefore has uncertainty and limitations in the interpretation of results. Despite the modeling limitations, the results summarized in the report, when compared to other studies, compare well with water-use estimations. The favorable comparison highlights the transferability of the water-use methodology to other areas of COG development.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215090","programNote":"Water Availability and Use Science Program","usgsCitation":"Valder, J.F., McShane, R.R., Thamke, J.N., McDowell, J.S., Ball, G.P., Houston, N.A., and Galanter, A.E., 2021, Estimates of water use associated with continuous oil and gas development in the Permian Basin, Texas and New Mexico, 2010–19: U.S. Geological Survey Scientific Investigations Report 2021–5090, 27 p., https://doi.org/10.3133/sir20215090.","productDescription":"Report: vii, 27 p.; Data Releases: 3; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-126972","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":388522,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JIOU3V","text":"USGS data release","description":"USGS data release","linkHelpText":"R scripts and results of estimated water use associated with continuous oil and gas development, Permian Basin, United States, 2010–19"},{"id":388521,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CPKRLW","text":"USGS data release","description":"USGS data release","linkHelpText":"Data to estimate water use associated with continuous oil and gas development, Williston Basin, United States, 1980–2017 (ver. 2.0, September 2019)"},{"id":388520,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LAWIPH","text":"USGS data release","description":"USGS data release","linkHelpText":"Data to estimate water use associated with continuous oil and gas development, Permian Basin, United States, 1980–2019"},{"id":391022,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/fs20213053","text":"FS 2021–3053","size":"4.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3053","linkHelpText":"— Estimates of Water Use Associated with Continuous Oil and Gas Development in the Permian Basin, Texas and New Mexico, 2010–19, with Comparisons to the Williston Basin, North Dakota and Montana"},{"id":388523,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":388518,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5090/coverthb.jpg"},{"id":388519,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5090/sir20215090.pdf","text":"Report","size":"2.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5090"}],"country":"United States","state":"New Mexico, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.18359375,\n 34.27083595165\n ],\n [\n -104.8974609375,\n 33.797408767572485\n ],\n [\n -105.0732421875,\n 32.43561304116276\n ],\n [\n -104.8974609375,\n 31.87755764334002\n ],\n [\n -103.447265625,\n 30.334953881988564\n ],\n [\n -102.0849609375,\n 30.259067203213018\n ],\n [\n -100.634765625,\n 29.916852233070173\n ],\n [\n -100.01953125,\n 29.305561325527698\n ],\n [\n -99.0966796875,\n 29.38217507514529\n ],\n [\n -98.701171875,\n 30.41078179084589\n ],\n [\n -99.0966796875,\n 31.353636941500987\n ],\n [\n -99.84374999999999,\n 32.39851580247402\n ],\n [\n -100.8544921875,\n 32.39851580247402\n ],\n [\n -100.986328125,\n 33.100745405144245\n ],\n [\n -101.0302734375,\n 33.65120829920497\n ],\n [\n -101.6455078125,\n 34.52466147177172\n ],\n [\n -103.18359375,\n 34.27083595165\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503

1608 Mountain View Road
Rapid City, SD 57702

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The U.S. Geological Survey, in cooperation with Federal, State, and local agencies, maintains a long-term network of hydrologic monitoring stations in Kansas. This network included 219 real-time streamgages, 12 real-time reservoir-level monitoring stations, and 20 groundwater monitoring stations in water year (WY) 2020. A WY is a 12-month period from October 1 to September 30 and is designated by the calendar year in which it ends. Real-time data are verified by U.S. Geological Survey personnel throughout the year with regular measurements of streamflow, lake levels, and groundwater levels. Hydrologic data collected in real time aid in the understanding of, and decisions made involving, water resources of Kansas. Hydrologic conditions are assessed annually by comparing statistical analyses of current and past WY data for the period of record. The monitoring of hydrologic conditions in Kansas can provide critical information to meet several needs including water-resources management, protection of life and property, reservoir operations, agricultural practices, public supply, ecological assessments, and industrial and recreational purposes.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213045","usgsCitation":"Davis, C., 2021, Hydrologic conditions in Kansas, water year 2020: U.S. Geological Survey Fact Sheet 2021–3045, 6 p., https://doi.org/10.3133/fs20213045.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-125662","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":388515,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS Water Data for the Nation"},{"id":388514,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3045/fs20213045.pdf","text":"Report","size":"4.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 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\"}}]}","contact":"

Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-08-26","noUsgsAuthors":false,"publicationDate":"2021-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Chantelle 0000-0001-6415-7320","orcid":"https://orcid.org/0000-0001-6415-7320","contributorId":225019,"corporation":false,"usgs":true,"family":"Davis","given":"Chantelle","email":"","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":821954,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70224579,"text":"70224579 - 2021 - Marine distribution and foraging habitat highlight potential threats at sea for Endangered Bermuda Petrel Pterodroma cahow","interactions":[],"lastModifiedDate":"2021-09-29T13:45:54.460103","indexId":"70224579","displayToPublicDate":"2021-08-26T08:45:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1497,"text":"Endangered Species Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Marine distribution and foraging habitat highlight potential threats at sea for Endangered Bermuda Petrel Pterodroma cahow","title":"Marine distribution and foraging habitat highlight potential threats at sea for Endangered Bermuda Petrel Pterodroma cahow","docAbstract":"

Marine spatial planning relies on detailed spatial information of marine areas to ensure effective conservation of species. To enhance our understanding of marine habitat use by the highly pelagic Bermuda petrel Pterodroma cahow, we deployed GPS tags on 6 chick-rearing adults in April 2019 and constructed a habitat suitability model using locations classified as foraging to explore functional responses to a selection of marine environmental variables. We defined 15 trips for 5 individuals, ranging from 1-6 trips per bird, that included both short and long foraging excursions indicative of a dual foraging strategy that optimizes chick feeding and self maintenance. The maximum distance birds flew from Bermuda during foraging trips ranged from 61 to 2513 km (total trip lengths: 186-14051 km). Behaviourally deduced foraging habitat was best predicted at shorter distances from the colony, under warmer sea surface temperature, greater sea surface height, and in deeper water compared to transiting locations; our model results indicated that suitable foraging habitat exists beyond the core home range of the population, as far north as the highly productive Gulf Stream frontal system, and within the territorial waters of both the USA and Canada. Our results are crucial to inform management decisions and international conservation efforts by better identifying potential threats encountered at sea by this globally rare seabird and highlighting jurisdictions potentially responsible for mitigating those threats.

","language":"English","publisher":"Inter-Research","doi":"10.3354/esr01139","usgsCitation":"Raine, A., Gjerdrum, C., Pratte, I., Madeiros, J., Felis, J.J., and Adams, J., 2021, Marine distribution and foraging habitat highlight potential threats at sea for Endangered Bermuda Petrel Pterodroma cahow: Endangered Species Research, v. 45, p. 337-356, https://doi.org/10.3354/esr01139.","productDescription":"20 p.","startPage":"337","endPage":"356","ipdsId":"IP-124810","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":451059,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01139","text":"Publisher Index Page"},{"id":389951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Bermuda, Canada, United States","otherGeospatial":"Nonsuch Island, Horn Rock","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -63.54492187500001,\n 31.39115752282472\n ],\n [\n -49.7021484375,\n 43.77109381775651\n ],\n [\n -46.8896484375,\n 48.86471476180277\n ],\n [\n -55.06347656249999,\n 45.182036837015886\n ],\n [\n -61.962890625,\n 43.004647127794435\n ],\n [\n -69.345703125,\n 40.613952441166596\n ],\n [\n -72.99316406249999,\n 38.34165619279595\n ],\n [\n -72.99316406249999,\n 34.34343606848294\n ],\n [\n -66.4013671875,\n 30.90222470517144\n ],\n [\n -63.54492187500001,\n 31.39115752282472\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Raine, André F","contributorId":266026,"corporation":false,"usgs":false,"family":"Raine","given":"André F","affiliations":[{"id":54862,"text":"Archipelago Research and Conservation, Kauai, Hawai’i 96716, USA","active":true,"usgs":false}],"preferred":false,"id":824149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gjerdrum, Carina","contributorId":266027,"corporation":false,"usgs":false,"family":"Gjerdrum","given":"Carina","email":"","affiliations":[{"id":54863,"text":"Canadian Wildlife Service, Dartmouth, Nova Scotia B2Y 2N6, Canada","active":true,"usgs":false}],"preferred":false,"id":824150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pratte, Isabeau","contributorId":266028,"corporation":false,"usgs":false,"family":"Pratte","given":"Isabeau","email":"","affiliations":[{"id":54863,"text":"Canadian Wildlife Service, Dartmouth, Nova Scotia B2Y 2N6, Canada","active":true,"usgs":false}],"preferred":false,"id":824151,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Madeiros, Jeremy","contributorId":196171,"corporation":false,"usgs":false,"family":"Madeiros","given":"Jeremy","email":"","affiliations":[],"preferred":false,"id":824152,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Felis, Jonathan J. 0000-0002-0608-8950 jfelis@usgs.gov","orcid":"https://orcid.org/0000-0002-0608-8950","contributorId":4825,"corporation":false,"usgs":true,"family":"Felis","given":"Jonathan","email":"jfelis@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824153,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Adams, Josh 0000-0003-3056-925X","orcid":"https://orcid.org/0000-0003-3056-925X","contributorId":213442,"corporation":false,"usgs":true,"family":"Adams","given":"Josh","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824154,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223424,"text":"70223424 - 2021 - Coalescent methods reconstruct contributions of natural colonization and stocking to origins of Michigan inland Cisco (Coregonus artedi)","interactions":[],"lastModifiedDate":"2022-01-07T15:57:22.646685","indexId":"70223424","displayToPublicDate":"2021-08-25T10:21:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Coalescent methods reconstruct contributions of natural colonization and stocking to origins of Michigan inland Cisco (Coregonus artedi)","title":"Coalescent methods reconstruct contributions of natural colonization and stocking to origins of Michigan inland Cisco (Coregonus artedi)","docAbstract":"

Fish population structure in previously glaciated regions is often influenced by natural colonization processes and human-mediated dispersal, including fish stocking. Endemic populations are of conservation interest because they may contain rare and unique genetic variation. While coregonines are native to certain Michigan inland lakes, some were stocked with fish from Great Lakes sources, calling into question the origin of extant populations. While most stocking targeted lake whitefish (Coregonus clupeaformis), cisco (C. artedi) were also stocked from the Great Lakes to inland waterbodies. We used population genetic data (microsatellite genotypes and mitochondrial (mt)DNA sequences), coalescent modeling, and approximate Bayesian computation to investigate the origins of 12 inland Michigan cisco populations. The spatial distribution of mtDNA haplotypes suggests Michigan is an introgression zone for two ancestral cisco lineages associated with separate glacial refugia. Low levels of genetic diversity and high levels of genetic divergence were observed for populations located well inland of the Great Lakes relative to populations occupying waterbodies near the Great Lakes. Estimates of recent Great Lakes gene flow ranged from 27 to 48% for populations near the Great Lakes shoreline but were substantially lower (under 8%) for populations further inland. Inland lakes with elevated recent gene flow estimates may have been recipients of stocked coregonine fry, including cisco. Low levels of genetic diversity paired with a high likelihood of endemism as indicated by strong genetic divergence and low Great Lakes population inputs suggest the analyzed cisco populations occupying southern Michigan kettle lakes are of elevated conservation interest.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.08.008","usgsCitation":"Homola, J.J., Robinson, J.D., Kanefsky, J., Stott, W., Whelan, G., and Scribner, K.T., 2021, Coalescent methods reconstruct contributions of natural colonization and stocking to origins of Michigan inland Cisco (Coregonus artedi): Journal of Great Lakes Research, v. 47, no. 6, p. 1781-1792, https://doi.org/10.1016/j.jglr.2021.08.008.","productDescription":"12 p.","startPage":"1781","endPage":"1792","ipdsId":"IP-124168","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":388588,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.505859375,\n 41.47566020027821\n ],\n [\n -81.38671875,\n 41.47566020027821\n ],\n [\n -81.38671875,\n 46.830133640447386\n ],\n [\n -88.505859375,\n 46.830133640447386\n ],\n [\n -88.505859375,\n 41.47566020027821\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Homola, Jared J.","contributorId":264547,"corporation":false,"usgs":false,"family":"Homola","given":"Jared","email":"","middleInitial":"J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":822012,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robinson, John D","contributorId":264810,"corporation":false,"usgs":false,"family":"Robinson","given":"John","email":"","middleInitial":"D","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":822013,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kanefsky, Jeannette","contributorId":243198,"corporation":false,"usgs":false,"family":"Kanefsky","given":"Jeannette","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":822014,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stott, Wendylee 0000-0002-5252-4901 wstott@usgs.gov","orcid":"https://orcid.org/0000-0002-5252-4901","contributorId":191249,"corporation":false,"usgs":true,"family":"Stott","given":"Wendylee","email":"wstott@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":822015,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whelan, Gary","contributorId":146115,"corporation":false,"usgs":false,"family":"Whelan","given":"Gary","email":"","affiliations":[{"id":16584,"text":"Fisheries Division, Michigan Department of Natural Resources, P.O. Box 30446, Lansing, MI 48909","active":true,"usgs":false}],"preferred":false,"id":822016,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scribner, Kim T","contributorId":264811,"corporation":false,"usgs":false,"family":"Scribner","given":"Kim","email":"","middleInitial":"T","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":822017,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223450,"text":"70223450 - 2021 - An experimental evaluation of the efficacy of imaging flow cytometry (FlowCam) for detecting invasive Dreissened and Corbiculid bivalve veligers","interactions":[],"lastModifiedDate":"2021-12-10T16:40:28.007302","indexId":"70223450","displayToPublicDate":"2021-08-25T10:16:39","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2592,"text":"Lake and Reservoir Management","active":true,"publicationSubtype":{"id":10}},"title":"An experimental evaluation of the efficacy of imaging flow cytometry (FlowCam) for detecting invasive Dreissened and Corbiculid bivalve veligers","docAbstract":"

Zebra (Dreissena polymorpha) and quagga (D. bugensis) mussels, first introduced from central Asia into the Great Lakes of North America in the late 1980s, have crossed the continental divide and more recently spread across western North America. At the same time, several new technologies have been developed for the early detection of dreissenids, including the FlowCam, a digital imaging-in-flow instrument, intended to detect dreissenid planktonic larvae (veligers). However, the efficacy of this technology has rarely been tested. We experimentally evaluated the FlowCam’s ability to capture identifiable images of quagga mussel veligers under 2 different types of conditions: (i) deionized water, and (ii) Columbia River Basin water (CRBW), including natural sediment and native plankton. We further evaluated the FlowCam’s ability to distinguish between dreissenid veligers and corbiculid veligers (Asian clam, Corbicula fluminea). We interpret our results to indicate that the FlowCam can consistently detect dreissenid veligers across a range of veliger densities. Moreover, the presence of other plankton and detritus only slightly affected dreissenid detection by the FlowCam. However, the orientation of individual bivalve veligers as they were imaged by the FlowCam precluded specific identification of a substantial proportion (24.8%) of veligers as either dreissenid or corbiculid. We suggest that the FlowCam is an important detection tool best utilized as part of a multifaceted approach, including traditional microscopy and possibly environmental DNA.

","language":"English","publisher":"Tayor and Francis Group","doi":"10.1080/10402381.2021.1961176","usgsCitation":"Hassett, W., Zimmerman, J., Rollwagen-Bollens, G., Bollens, S.M., and Counihan, T., 2021, An experimental evaluation of the efficacy of imaging flow cytometry (FlowCam) for detecting invasive Dreissened and Corbiculid bivalve veligers: Lake and Reservoir Management, v. 37, no. 4, p. 406-417, https://doi.org/10.1080/10402381.2021.1961176.","productDescription":"12 p.","startPage":"406","endPage":"417","ipdsId":"IP-073274","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":388587,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Hassett, Whitney","contributorId":190161,"corporation":false,"usgs":false,"family":"Hassett","given":"Whitney","email":"","affiliations":[],"preferred":false,"id":822048,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zimmerman, Julie","contributorId":190163,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Julie","affiliations":[],"preferred":false,"id":822049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rollwagen-Bollens, Gretchen","contributorId":190162,"corporation":false,"usgs":false,"family":"Rollwagen-Bollens","given":"Gretchen","email":"","affiliations":[],"preferred":false,"id":822050,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bollens, Stephen M. 0000-0001-9214-9037","orcid":"https://orcid.org/0000-0001-9214-9037","contributorId":148958,"corporation":false,"usgs":false,"family":"Bollens","given":"Stephen","email":"","middleInitial":"M.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":822051,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Counihan, Timothy D. 0000-0003-4967-6514","orcid":"https://orcid.org/0000-0003-4967-6514","contributorId":207532,"corporation":false,"usgs":true,"family":"Counihan","given":"Timothy D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":822052,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224289,"text":"70224289 - 2021 - Spatiotemporal dynamics of CO2 gas exchange from headwater mountain streams","interactions":[],"lastModifiedDate":"2021-09-20T12:49:53.852729","indexId":"70224289","displayToPublicDate":"2021-08-25T07:43:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal dynamics of CO2 gas exchange from headwater mountain streams","docAbstract":"

Mountain streams play an important role in the global carbon cycle by transporting, metabolizing, and exchanging carbon they receive from the terrestrial environment. The rates at which these processes occur remain highly uncertain because of a paucity of observations and the difficulty of measuring gas exchange rates in steep, turbulent mountain streams. This uncertainty is compounded by large temporal and spatial variability in stream carbon dioxide (CO2) concentrations in mountain environments. In this study, we measured diel, seasonal, and annual variations in CO2 partial pressure (pCO2) in seven headwater streams and a groundwater spring in the Colorado Rocky Mountains to determine how CO2 exchange fluxes (\"urn:x-wiley:21698953:media:jgrg22024:jgrg22024-math-0001\") vary with time, annual precipitation, and landscape characteristics. Our results show that temporal variability in pCO2 and \"urn:x-wiley:21698953:media:jgrg22024:jgrg22024-math-0002\" in mountain streams is large and is strongly influenced by solar radiation, the accumulation and melting of seasonal snowpacks, and interannual variations in precipitation. Spatial variations in pCO2 and \"urn:x-wiley:21698953:media:jgrg22024:jgrg22024-math-0003\" were related to landscape characteristics, with soil organic matter, wetlands, and likely groundwater discharge zones having a positive influence. Periglacial features, such as ice and rock glaciers, had a negative influence on stream pCO2 and \"urn:x-wiley:21698953:media:jgrg22024:jgrg22024-math-0004\". Estimated \"urn:x-wiley:21698953:media:jgrg22024:jgrg22024-math-0005\" from streams in an alpine/subalpine region of Colorado was 3.4 kg C m−2 yr−1 normalized to stream surface area (95% CI: 2.1–5.0 kg C m−2 yr−1), consistent with recent work on CO2 exchange from mountain streams in the Swiss Alps. Our results highlight the importance of mountain streams as substantial contributors in the global carbon cycle.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JG006509","usgsCitation":"Clow, D.W., Striegl, R.G., and Dornblaser, M., 2021, Spatiotemporal dynamics of CO2 gas exchange from headwater mountain streams: Journal of Geophysical Research: Biogeosciences, v. 126, no. 9, e2021JG006509, 18 p., https://doi.org/10.1029/2021JG006509.","productDescription":"e2021JG006509, 18 p.","ipdsId":"IP-118820","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":451070,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021jg006509","text":"Publisher Index Page"},{"id":436226,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S775Y4","text":"USGS data release","linkHelpText":"Continuous water-quality data for selected streams in Rocky Mountain National Park, Colorado, water years 2011-19 (ver. 2.0, January 2022)"},{"id":436225,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RNS5FP","text":"USGS data release","linkHelpText":"Continuous water-quality data for selected streams in Rocky Mountain National Park, Colorado, water years 2011-19"},{"id":389471,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Loch Vale","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.74821472167969,\n 40.16418235037417\n ],\n [\n -105.50102233886719,\n 40.16418235037417\n ],\n [\n -105.50102233886719,\n 40.33660027347341\n ],\n [\n -105.74821472167969,\n 40.33660027347341\n ],\n [\n -105.74821472167969,\n 40.16418235037417\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Striegl, Robert G. 0000-0002-8251-4659 rstriegl@usgs.gov","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":1630,"corporation":false,"usgs":true,"family":"Striegl","given":"Robert","email":"rstriegl@usgs.gov","middleInitial":"G.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":false,"id":823466,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dornblaser, Mark 0000-0002-6298-3757","orcid":"https://orcid.org/0000-0002-6298-3757","contributorId":220741,"corporation":false,"usgs":true,"family":"Dornblaser","given":"Mark","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":823467,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70231659,"text":"70231659 - 2021 - Mapping wetland burned area from Sentinel-2 across the southeastern United States and its contributions relative to Landsat 8 (2016-2019)","interactions":[],"lastModifiedDate":"2022-05-19T12:16:16.833966","indexId":"70231659","displayToPublicDate":"2021-08-25T07:13:36","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5678,"text":"Fire","active":true,"publicationSubtype":{"id":10}},"title":"Mapping wetland burned area from Sentinel-2 across the southeastern United States and its contributions relative to Landsat 8 (2016-2019)","docAbstract":"
Prescribed fires and wildfires are common in wetland ecosystems across the Southeastern United States. However, the wetland burned area has been chronically underestimated across the region due to (1) spectral confusion between open water and burned area, (2) rapid post-fire vegetation regrowth, and (3) high annual precipitation limiting clear-sky satellite observations. We developed a machine learning algorithm specifically for burned area in wetlands, and applied the algorithm to the Sentinel-2 archive (2016–2019) across the Southeastern US (>290,000 km2). Combining Landsat-8 imagery with Sentinel-2 increased the annual clear-sky observation count from 17 to 46 in 2016 and from 16 to 78 in 2019. When validated with WorldView imagery, the Sentinel-2 burned area had a 29% and 30% omission and commission rates of error for burned area, respectively, compared to the US Geological Survey Landsat-8 Burned Area Product (L8 BA), which had a 47% and 8% omission and commission rate of error, respectively. The Sentinel-2 algorithm and the L8 BA mapped burned area within 78% and 60% of wetland fire perimeters (n = 555) compiled from state and federal agencies, respectively. This analysis demonstrated the potential of Sentinel-2 to support efforts to track the burned area, especially across challenging ecosystem types, such as wetlands.
","language":"English","publisher":"MDPI","doi":"10.3390/fire4030052","usgsCitation":"Vanderhoof, M.K., Hawbaker, T., Teske, C., Ku, A., Noble, J., and Picotte, J., 2021, Mapping wetland burned area from Sentinel-2 across the southeastern United States and its contributions relative to Landsat 8 (2016-2019): Fire, v. 4, no. 3, 52, 25 p., https://doi.org/10.3390/fire4030052.","productDescription":"52, 25 p.","ipdsId":"IP-127398","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":451071,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fire4030052","text":"Publisher Index Page"},{"id":436227,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S8SLEM","text":"USGS data release","linkHelpText":"Wetland burned area extent derived from Sentinel-2 across the southeastern U.S. (2016-2019)"},{"id":400801,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Montana, 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\"}}]}","volume":"4","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":843283,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":843284,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teske, Casey","contributorId":224732,"corporation":false,"usgs":false,"family":"Teske","given":"Casey","email":"","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":843285,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ku, Andrea","contributorId":291889,"corporation":false,"usgs":false,"family":"Ku","given":"Andrea","affiliations":[{"id":27232,"text":"Former USGS Student Contractor","active":true,"usgs":false}],"preferred":false,"id":843286,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Noble, Joe","contributorId":257938,"corporation":false,"usgs":false,"family":"Noble","given":"Joe","email":"","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":843287,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Picotte, Joshua J. 0000-0002-4021-4623","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":202800,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":843288,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70249720,"text":"70249720 - 2021 - Exploring environmental factors that drive diel variations in tree water storage using wavelet analysis","interactions":[],"lastModifiedDate":"2023-10-25T11:59:54.29182","indexId":"70249720","displayToPublicDate":"2021-08-25T06:52:34","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7170,"text":"Frontiers in Water","active":true,"publicationSubtype":{"id":10}},"title":"Exploring environmental factors that drive diel variations in tree water storage using wavelet analysis","docAbstract":"

Internal water storage within trees can be a critical reservoir that helps trees overcome both short- and long-duration environmental stresses. We monitored changes in internal tree water storage in a ponderosa pine on daily and seasonal scales using moisture probes, a dendrometer, and time-lapse electrical resistivity imaging (ERI). These data were used to investigate how patterns of in-tree water storage are affected by changes in sapflow rates, soil moisture, and meteorologic factors such as vapor pressure deficit. Measurements of xylem fluid electrical conductivity were constant in the early growing season while inverted sapwood electrical conductivity steadily increased, suggesting that increases in sapwood electrical conductivity did not result from an increase in xylem fluid electrical conductivity. Seasonal increases in stem electrical conductivity corresponded with seasonal increases in trunk diameter, suggesting that increased electrical conductivity may result from new growth. On the daily scale, changes in inverted sapwood electrical conductivity correspond to changes in sapwood moisture. Wavelet analyses indicated that lag times between inverted electrical conductivity and sapflow increased after storm events, suggesting that as soils wetted, reliance on internal water storage decreased, as did the time required to refill daily deficits in internal water storage. We found short time lags between sapflow and inverted electrical conductivity with dry conditions, when ponderosa pine are known to reduce stomatal conductance to avoid xylem cavitation. A decrease in diel amplitudes of inverted sapwood electrical conductivity during dry periods suggest that the ponderosa pine relied on internal water storage to supplement transpiration demands, but as drought conditions progressed, tree water storage contributions to transpiration decreased. Time-lapse ERI- and wavelet-analysis results highlight the important role internal tree water storage plays in supporting transpiration throughout a day and during periods of declining subsurface moisture.

","language":"English","publisher":"Frontiers","doi":"10.3389/frwa.2021.682285","usgsCitation":"Harmon, R., Barnard, H., Day-Lewis, F., Mao, D., and Singha, K., 2021, Exploring environmental factors that drive diel variations in tree water storage using wavelet analysis: Frontiers in Water, v. 3, 682285, 22 p., https://doi.org/10.3389/frwa.2021.682285.","productDescription":"682285, 22 p.","ipdsId":"IP-130437","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":451074,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frwa.2021.682285","text":"Publisher Index Page"},{"id":422091,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Gordon Gulch","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.5213291829561,\n 40.029532063000204\n ],\n [\n -105.5213291829561,\n 39.982722180293365\n ],\n [\n -105.46742751059293,\n 39.982722180293365\n ],\n [\n -105.46742751059293,\n 40.029532063000204\n ],\n [\n -105.5213291829561,\n 40.029532063000204\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","noUsgsAuthors":false,"publicationDate":"2021-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Harmon, Ryan","contributorId":331165,"corporation":false,"usgs":false,"family":"Harmon","given":"Ryan","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":886848,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnard, Holly","contributorId":331166,"corporation":false,"usgs":false,"family":"Barnard","given":"Holly","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":886849,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Day-Lewis, Frederick 0000-0003-3526-886X","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":216359,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":886850,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mao, Deqiang","contributorId":331169,"corporation":false,"usgs":false,"family":"Mao","given":"Deqiang","email":"","affiliations":[{"id":79141,"text":"Shandong University","active":true,"usgs":false}],"preferred":false,"id":886851,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singha, Kamini","contributorId":331170,"corporation":false,"usgs":false,"family":"Singha","given":"Kamini","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":886852,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223331,"text":"sir20215072 - 2021 - Evaluation of actual evapotranspiration rates from the Operational Simplified Surface Energy Balance (SSEBop) model in Florida and parts of Alabama and Georgia, 2000–17","interactions":[],"lastModifiedDate":"2021-08-25T11:39:29.585628","indexId":"sir20215072","displayToPublicDate":"2021-08-24T14:28:01","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5072","displayTitle":"Evaluation of Actual Evapotranspiration Rates from the Operational Simplified Surface Energy Balance (SSEBop) Model in Florida and Parts of Alabama and Georgia, 2000–17","title":"Evaluation of actual evapotranspiration rates from the Operational Simplified Surface Energy Balance (SSEBop) model in Florida and parts of Alabama and Georgia, 2000–17","docAbstract":"

Evapotranspiration (ET) is the water-vapor flux transported from the surface of the Earth into the atmosphere and is the sum of surface water directly evaporated and subsurface water transpired by plants. ET rates are commonly estimated by using potential or reference ET, which might differ from actual ET rates. Actual evapotranspiration (ETa) rates can be estimated by using the Operational Simplified Surface Energy Balance (SSEBop) model. This report evaluates SSEBop ETa rates at the point and basin scales in Florida and parts of Alabama and Georgia for 2000–17. ETa rates computed by using data from 24 micrometeorological stations in Florida are referred to as mETa rates and were used to quantify biases in the SSEBop ETa rates, stratified by generalized land-use type. Bias was computed as mETa minus SSEBop ETa rates for given generalized land-use types, and bias-correction equations were computed by using least-squares regressions. In addition to mETa rates at station locations, annual average ETa rates calculated from the application of a water-balance method to 55 basins in Florida and parts of Alabama and Georgia were used to assess the accuracy of the annual SSEBop ETa rates at the basin scale. Another independent model used to simulate ETa rates was based on monthly reference ET from the statewide daily reference evapotranspiration (ETo) gridded dataset for Florida computed by using Geostationary Operational Environmental Satellite estimates of solar radiation (GOES ETo). ETa at grid points was computed as monthly GOES ETo multiplied by ratios of monthly mETa to GOES ETo, computed at micrometeorological stations and stratified by each generalized land-use type.

The coefficient of determination (R2) between monthly mETa and SSEBop ETa rates for all stations combined improved from 0.37 before bias correction of SSEBop ETa rates to 0.79 after the bias correction stratified by land-use type. For individual land-uses types, R2 varied from 0.59 for the monthly mETa at a station in the land-use type forest to 0.82 for the monthly mETa at stations in the land-use type shallow-water-table pasture. Root-mean-square error (RMSE) was computed as a function of the difference between SSEBop ETa rates and mETa rates. RMSE of monthly SSEBop ETa rates was 1.27 inches per month before the bias corrections improved to 0.73 inch per month after the bias corrections. RMSE for bias-corrected annual SSEBop ETa rates based on micrometeorological stations with complete years of records ranged from 2.01 inches per year (in/yr) for the land-use type of agriculture to 5.73 in/yr for the land-use type of deep water-table pasture, or 4.96 and 21.21 percent errors relative to annual mETa rates, respectively. Bias-corrected annual SSEBop ETa rates were also compared to annual ETa rates computed by using a water-balance method (wbETa) for 55 basins in Florida. Differences in bias-corrected average annual SSEBop ETa rates and average annual wbETa rates for the 55 basins ranged from −3.67 to 5.29 in/yr (−9.24 to 17.36 percent). RMSE when computed as a function of the differences between annual SSEBop ETa rates and wbETa rates decreased, on average, from 4.13 in/yr for the uncorrected bias SSEBop ETa rates to 1.95 in/yr for the bias-corrected SSEBop rates. The average annual bias-corrected SSEBop ETa rates, from all basins, was 36.46 in/yr or 3.41 percent lower than the average annual wbETa rate of 37.79 inches.

Bias in SSEBop ETa rates varies based on time step (monthly versus annual), scale (point, basin, statewide), and land-use type. Applications to hydrologic models should consider bias relative to the inherent error in models. Bias-corrected SSEBop ETa rates could be used as calibration targets in models of hydrologic processes, such as groundwater models. Annual bias in SSEBop ETa introduced to the model calibration is typically below the margin of error associated with typical residuals in model simulations, depending on scale. Surface-water and groundwater-flow models with RMSEs on the order of a few feet could benefit from bias-corrected SSEBop values of ETa.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215072","collaboration":"Prepared in cooperation with Northwest Florida Water Management District, Suwannee River Water Management District, St. Johns River Water Management District, South Florida Water Management District, Southwest Florida Water Management District, and Tampa Bay Water","usgsCitation":"Sepúlveda, N., 2021, Evaluation of actual evapotranspiration rates from the Operational Simplified Surface Energy Balance (SSEBop) model in Florida and parts of Alabama and Georgia, 2000–17: U.S. Geological Survey Scientific Investigations Report 2021–5072, 66 p., https://doi.org/10.3133/sir20215072.","productDescription":"Report: x, 66 p.; Data Release","numberOfPages":"80","onlineOnly":"Y","ipdsId":"IP-112971","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":388346,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5072/coverthb.jpg"},{"id":388349,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5072/images"},{"id":388347,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5072/sir20215072.pdf","text":"Report","size":"12.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5072"},{"id":388348,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99AB3X4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data sets of actual evapotranspiration rates from 2000 to 2017 for basins in Florida and parts of Alabama and Georgia, calculated using the water-balance method, the bias-corrected Operational Simplified Surface Energy Balance (SSEBop) model, and the land-use crop coefficients model"}],"country":"United States","state":"Alabama, Florida, Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.71484375,\n 25.005972656239187\n ],\n [\n -79.98046875,\n 25.005972656239187\n ],\n [\n -79.98046875,\n 31.98944183792288\n ],\n [\n -87.71484375,\n 31.98944183792288\n ],\n [\n -87.71484375,\n 25.005972656239187\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2021-08-24","noUsgsAuthors":false,"publicationDate":"2021-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Sepulveda, Nicasio 0000-0002-6333-1865 nsepul@usgs.gov","orcid":"https://orcid.org/0000-0002-6333-1865","contributorId":1454,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Nicasio","email":"nsepul@usgs.gov","affiliations":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"preferred":true,"id":821783,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70223361,"text":"sir20215080 - 2021 - Estimation of dissolved-solids concentrations using continuous water-quality monitoring and regression models at four sites in the Yuma area, Arizona and California, January 2017 through March 2019","interactions":[],"lastModifiedDate":"2021-08-25T11:44:55.7065","indexId":"sir20215080","displayToPublicDate":"2021-08-24T14:20:10","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5080","displayTitle":"Estimation of Dissolved-Solids Concentrations Using Continuous Water-Quality Monitoring and Regression Models at Four Sites in the Yuma Area, Arizona and California, January 2017 through March 2019","title":"Estimation of dissolved-solids concentrations using continuous water-quality monitoring and regression models at four sites in the Yuma area, Arizona and California, January 2017 through March 2019","docAbstract":"

Multiple linear regression models were developed to estimate dissolved-solids concentrations in water at four sites in the Yuma area between Imperial Dam, Arizona and California and the southerly international boundary with Mexico at San Luis, Arizona. Continuous and discrete water-quality data were collected at gaging stations in the Colorado River upstream from Imperial Dam, Arizona-California, the Colorado River below Cooper wasteway near Yuma, Arizona, the Yuma Main Drain above Arizona–Sonora, Mexico boundary, and the 242 lateral above Main Drain at the Arizona–Sonora boundary. Continuous specific conductance and water temperature data were collected at each site between January 2017 and March 2019. Bi-weekly to monthly dissolved-solids water samples were collected during the same period. Continuous specific conductance data collected at the Colorado River below Cooper wasteway were affected by poorly mixed streamflow during periods when the Pilot Knob Hydro-electric Plant was releasing water to the river. The continuous specific conductance data for the site downstream from Cooper wasteway were corrected using mean specific conductance values computed from cross-section measurements collected during site visits. Continuous specific conductance data were affected by sensor fouling issues at the 242 lateral site, and continued operation at the site would require more frequent visits for cleaning and service to ensure data quality.

During the study, instream specific conductance readings ranged from 966 to 3,030 microsiemens per centimeter (μS/cm) at 25 degrees Celsius. Computed dissolved-solids concentrations from discrete samples ranged from 690 to 2,580 milligrams per liter (mg/L). Dissolved-solids concentrations were estimated from regression models using the optimal relation between dissolved solids and environmental factors, such as specific conductance, water temperature, dissolved oxygen, streamflow, and seasonality. Specific conductance was the primary factor at all four sites and explained 87.6 to 94 percent of variation in dissolved solids. Water temperature, as an indicator of seasonality, was determined to be a statistically significant secondary factor at both the Colorado River above Imperial Dam and Colorado River below Cooper wasteway sites explaining an additional 6.9 and 2.1 percent of variation in dissolved solids, respectively. Regression models explained 87.6 to 96.9 percent of the variation in dissolved solids; the root mean square error in the modeled data ranged between about 6 and 27 mg/L.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215080","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Cederberg, J.R., Paretti, N.V., Coes, A.L., Hermosillo, E., Andrade, L., 2021, Estimation of dissolved-solids concentrations using continuous water-quality monitoring and regression models at four sites in the Yuma area, Arizona and California, January 2017 through March 2019: U.S. Geological Survey Scientific Investigations Report 2021–5080, 26 p., https://doi.org/10.3133/sir20215080.","productDescription":"Report: vii, 26 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-111110","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":436228,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SMK908","text":"USGS data release","linkHelpText":"Water-Quality Field Blank and Replicate Sample Data, Instantaneous and Mean Daily Discharge Data, and Dissolved-Solids Concentrations Data Collected in Four Waterways of Southwest Arizona, January 2017-March 2019"},{"id":388445,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/p9SMK908","linkHelpText":"Supplemental streamflow, quality-assurance, and dissolved-solids concentration datasets used for regression model development at four sites in the Yuma area, Arizona and California, January 2017 through March 2019"},{"id":388447,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5080/covrthb.jpg"},{"id":388448,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5080/sir20215080.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Arizona, California","otherGeospatial":"Yuma area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.873046875,\n 32.58384932565662\n ],\n [\n -114.3896484375,\n 32.58384932565662\n ],\n [\n -114.3896484375,\n 32.88881315761995\n ],\n [\n -114.873046875,\n 32.88881315761995\n ],\n [\n -114.873046875,\n 32.58384932565662\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2021-08-24","noUsgsAuthors":false,"publicationDate":"2021-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Cederberg, Jay R. 0000-0001-6649-7353 cederber@usgs.gov","orcid":"https://orcid.org/0000-0001-6649-7353","contributorId":964,"corporation":false,"usgs":true,"family":"Cederberg","given":"Jay","email":"cederber@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821857,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paretti, Nicholas V. 0000-0003-2178-4820 nparetti@usgs.gov","orcid":"https://orcid.org/0000-0003-2178-4820","contributorId":173412,"corporation":false,"usgs":true,"family":"Paretti","given":"Nicholas","email":"nparetti@usgs.gov","middleInitial":"V.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821858,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coes, Alissa L. 0000-0001-6682-5417 alcoes@usgs.gov","orcid":"https://orcid.org/0000-0001-6682-5417","contributorId":4231,"corporation":false,"usgs":true,"family":"Coes","given":"Alissa","email":"alcoes@usgs.gov","middleInitial":"L.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821859,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hermosillo, Edyth 0000-0003-1648-1016 ehermosillo@usgs.gov","orcid":"https://orcid.org/0000-0003-1648-1016","contributorId":175455,"corporation":false,"usgs":true,"family":"Hermosillo","given":"Edyth","email":"ehermosillo@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821860,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andrade, Lucia 0000-0003-3741-1404","orcid":"https://orcid.org/0000-0003-3741-1404","contributorId":264674,"corporation":false,"usgs":true,"family":"Andrade","given":"Lucia","email":"","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821861,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223421,"text":"70223421 - 2021 - Seasonally dynamic nutrient modeling quantifies storage lags and time-varying reactivity across large river basins","interactions":[],"lastModifiedDate":"2021-08-27T15:16:07.4255","indexId":"70223421","displayToPublicDate":"2021-08-24T10:12:21","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Seasonally dynamic nutrient modeling quantifies storage lags and time-varying reactivity across large river basins","docAbstract":"

Nutrients that have gradually accumulated in soils, groundwaters, and river sediments in the United States over the past century can remobilize and increase current downstream loading, obscuring effects of conservation practices aimed at protecting water resources. Drivers of storage accumulation and release of nutrients are poorly understood at the spatial scale of basins to watersheds. Predicting water quality outcomes in large river basins demands modeling storage lags and time varying reactivity that models of mean conditions typically cannot elucidate. We developed a seasonally dynamic approach to large-scale nutrient modeling based on a multiscale framework and nutrient storage lags were quantified for the nearly 190 000 small catchments that feed the rivers across the northeastern United States where catchment mean transit times were found to be around 4.7 (2–10) years for nitrogen and 1.3 (0.7–2) years for phosphorus. Nutrient loads carried in river flow in the current season contained a significant—and sometimes dominant—portion of mass lagged in its release from catchment storage repositories. Our approach of integrating storage releases with seasonally dynamic hydroclimatic drivers sets the stage to assess the accumulated effects of nutrient storage and lagged releases to the river interacting with seasonally varying nutrient reactivity and societal management actions throughout large river basins.

","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ac1af4","usgsCitation":"Schmadel, N., Harvey, J., and Schwarz, G.E., 2021, Seasonally dynamic nutrient modeling quantifies storage lags and time-varying reactivity across large river basins: Environmental Research Letters, v. 16, no. 9, 095004, 11 p., https://doi.org/10.1088/1748-9326/ac1af4.","productDescription":"095004, 11 p.","ipdsId":"IP-126236","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":451077,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ac1af4","text":"Publisher Index Page"},{"id":436229,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NRFWOV","text":"USGS data release","linkHelpText":"Mean seasonal SPARROW model inputs and simulated nitrogen and phosphorus loads for the Northeastern United States 2002 base year"},{"id":388586,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmadel, Noah 0000-0002-2046-1694","orcid":"https://orcid.org/0000-0002-2046-1694","contributorId":219105,"corporation":false,"usgs":true,"family":"Schmadel","given":"Noah","email":"","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":822009,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":822010,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":213621,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory","email":"gschwarz@usgs.gov","middleInitial":"E.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":822011,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225709,"text":"70225709 - 2021 - The Holocene dynamics of Ryder Glacier and ice tongue in north Greenland","interactions":[],"lastModifiedDate":"2021-11-04T13:41:17.401004","indexId":"70225709","displayToPublicDate":"2021-08-24T08:25:19","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3554,"text":"The Cryosphere","active":true,"publicationSubtype":{"id":10}},"title":"The Holocene dynamics of Ryder Glacier and ice tongue in north Greenland","docAbstract":"

The northern sector of the Greenland Ice Sheet is considered to be particularly susceptible to ice mass loss arising from increased glacier discharge in the coming decades. However, the past extent and dynamics of outlet glaciers in this region, and hence their vulnerability to climate change, are poorly documented. In the summer of 2019, the Swedish icebreaker Oden entered the previously unchartered waters of Sherard Osborn Fjord, where Ryder Glacier drains approximately 2 % of Greenland's ice sheet into the Lincoln Sea. Here we reconstruct the Holocene dynamics of Ryder Glacier and its ice tongue by combining radiocarbon dating with sedimentary facies analyses along a 45 km transect of marine sediment cores collected between the modern ice tongue margin and the mouth of the fjord. The results illustrate that Ryder Glacier retreated from a grounded position at the fjord mouth during the Early Holocene (> 10.7±0.4 ka cal BP) and receded more than 120 km to the end of Sherard Osborn Fjord by the Middle Holocene (6.3±0.3 ka cal BP), likely becoming completely land-based. A re-advance of Ryder Glacier occurred in the Late Holocene, becoming marine-based around 3.9±0.4 ka cal BP. An ice tongue, similar in extent to its current position was established in the Late Holocene (between 3.6±0.4 and 2.9±0.4 ka cal BP) and extended to its maximum historical position near the fjord mouth around 0.9±0.3 ka cal BP. Laminated, clast-poor sediments were deposited during the entire retreat and regrowth phases, suggesting the persistence of an ice tongue that only collapsed when the glacier retreated behind a prominent topographic high at the landward end of the fjord. Sherard Osborn Fjord narrows inland, is constrained by steep-sided cliffs, contains a number of bathymetric pinning points that also shield the modern ice tongue and grounding zone from warm Atlantic waters, and has a shallowing inland sub-ice topography. These features are conducive to glacier stability and can explain the persistence of Ryder's ice tongue while the glacier remained marine-based. However, the physiography of the fjord did not halt the dramatic retreat of Ryder Glacier under the relatively mild changes in climate forcing during the Holocene. Presently, Ryder Glacier is grounded more than 40 km seaward of its inferred position during the Middle Holocene, highlighting the potential for substantial retreat in response to ongoing climate change.

","language":"English","publisher":"European Geosciences Union","doi":"10.5194/tc-15-4073-2021","usgsCitation":"O’Regan, M., Cronin, T.M., Reilly, B., Olsen Alstrup, A.K., Gemery, L., Golub, A., Mayer, L.A., Morlighem, M., Moros, M., Munk, O.L., Nilsson, J., Pearce, C., Detlef, H., Stranne, C., Vermassen, F., West, G., and Jakobsson, M., 2021, The Holocene dynamics of Ryder Glacier and ice tongue in north Greenland: The Cryosphere, v. 15, p. 4073-4097, https://doi.org/10.5194/tc-15-4073-2021.","productDescription":"25 p.","startPage":"4073","endPage":"4097","ipdsId":"IP-127375","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":451081,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-15-4073-2021","text":"Publisher Index Page"},{"id":391381,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Greenland","otherGeospatial":"Ryder Glacier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -63.6328125,\n 80.17871349622823\n ],\n [\n -32.34375,\n 80.17871349622823\n ],\n [\n -32.34375,\n 83.57940370073115\n ],\n [\n -63.6328125,\n 83.57940370073115\n ],\n [\n -63.6328125,\n 80.17871349622823\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2021-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"O’Regan, Matt","contributorId":197135,"corporation":false,"usgs":false,"family":"O’Regan","given":"Matt","email":"","affiliations":[{"id":25421,"text":"Department of Geological Sciences, Stockholm University, Sweden","active":true,"usgs":false}],"preferred":false,"id":826358,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cronin, Thomas M. 0000-0002-2643-0979 tcronin@usgs.gov","orcid":"https://orcid.org/0000-0002-2643-0979","contributorId":2579,"corporation":false,"usgs":true,"family":"Cronin","given":"Thomas","email":"tcronin@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":826359,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reilly, Brendan","contributorId":258076,"corporation":false,"usgs":false,"family":"Reilly","given":"Brendan","email":"","affiliations":[],"preferred":false,"id":826360,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olsen Alstrup, Aage K.","contributorId":268312,"corporation":false,"usgs":false,"family":"Olsen 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A.","contributorId":69583,"corporation":false,"usgs":true,"family":"Mayer","given":"Larry","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":826364,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Morlighem, Mathieu","contributorId":141050,"corporation":false,"usgs":false,"family":"Morlighem","given":"Mathieu","email":"","affiliations":[{"id":6976,"text":"University of California, Irvine","active":true,"usgs":false}],"preferred":false,"id":826365,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Moros, Matthias","contributorId":268314,"corporation":false,"usgs":false,"family":"Moros","given":"Matthias","email":"","affiliations":[],"preferred":false,"id":826366,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Munk, Ole L.","contributorId":268315,"corporation":false,"usgs":false,"family":"Munk","given":"Ole","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":826367,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Nilsson, Johan","contributorId":166855,"corporation":false,"usgs":false,"family":"Nilsson","given":"Johan","email":"","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":826368,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pearce, Christof","contributorId":197126,"corporation":false,"usgs":false,"family":"Pearce","given":"Christof","email":"","affiliations":[{"id":25421,"text":"Department of Geological Sciences, Stockholm University, Sweden","active":true,"usgs":false}],"preferred":false,"id":826369,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Detlef, Henrieka","contributorId":268316,"corporation":false,"usgs":false,"family":"Detlef","given":"Henrieka","email":"","affiliations":[],"preferred":false,"id":826370,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Stranne, Christian","contributorId":166862,"corporation":false,"usgs":false,"family":"Stranne","given":"Christian","email":"","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":826371,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Vermassen, Flor","contributorId":268317,"corporation":false,"usgs":false,"family":"Vermassen","given":"Flor","email":"","affiliations":[],"preferred":false,"id":826372,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"West, Gabriel","contributorId":258085,"corporation":false,"usgs":false,"family":"West","given":"Gabriel","email":"","affiliations":[],"preferred":false,"id":826373,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Jakobsson, Martin","contributorId":166854,"corporation":false,"usgs":false,"family":"Jakobsson","given":"Martin","email":"","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":826374,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70224308,"text":"70224308 - 2021 - Drivers of extreme water levels in a large, urban, high-energy coastal estuary – A case study of the San Francisco Bay","interactions":[],"lastModifiedDate":"2021-09-21T12:47:16.692844","indexId":"70224308","displayToPublicDate":"2021-08-24T07:45:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Drivers of extreme water levels in a large, urban, high-energy coastal estuary – A case study of the San Francisco Bay","docAbstract":"

Reliable and long-term hindcast data of water levels are essential in quantifying return period and values of extreme water levels. In order to inform design decisions on a local flood control district level, process-based numerical modeling has proven an essential tool to provide the needed temporal and spatial coverage for different extreme value analysis methods. To determine the importance of different physical processes to the extreme water levels we developed a process-based numerical model (Delft3D Flexible Mesh) and applied it to simulate a large, urban, high-energy coastal estuary (the San Francisco Bay). The unstructured grid with 1D/2DH model elements, allows for efficient model simulations and therefore it was possible to simulate over 70 years between 1950 and 2019. Results show significant skill in reproducing observations for the entire modeled time period with an average root-mean-square error of 8.0 cm. A process-based modeling approach allows for the explicit in- and exclusion of different physical processes to quantify their importance to the extremes. For the 100-year still water level (SWL), tide (70%) and non-tidal residual (NTR) (25%) explain the majority of the simulated high water levels in the Bay relative to Mean Higher High Water (MHHW). However, closer to the Delta, local fluvial inflow increases in importance. For longer return periods, the importance of tide decreases and the importance of remote NTRs and fluvial inflow increases.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2021.103984","usgsCitation":"Nederhoff, C.M., Saleh, R., Tehranirad, B., Herdman, L.M., Erikson, L.H., Barnard, P.L., and Van der Wegen, M., 2021, Drivers of extreme water levels in a large, urban, high-energy coastal estuary – A case study of the San Francisco Bay: Coastal Engineering, v. 170, 103984, 12 p., https://doi.org/10.1016/j.coastaleng.2021.103984.","productDescription":"103984, 12 p.","ipdsId":"IP-126090","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":451086,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2021.103984","text":"Publisher Index Page"},{"id":436231,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WWB9V4","text":"USGS data release","linkHelpText":"Hydrodynamic model of the San Francisco Bay and Delta, California"},{"id":389536,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.837890625,\n 36.91476428895589\n ],\n [\n -120.73974609374999,\n 36.91476428895589\n ],\n [\n -120.73974609374999,\n 38.66835610151506\n ],\n [\n -123.837890625,\n 38.66835610151506\n ],\n [\n -123.837890625,\n 36.91476428895589\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"170","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nederhoff, Cornelis M. 0000-0003-0552-3428","orcid":"https://orcid.org/0000-0003-0552-3428","contributorId":265889,"corporation":false,"usgs":false,"family":"Nederhoff","given":"Cornelis","email":"","middleInitial":"M.","affiliations":[{"id":33886,"text":"Deltares USA","active":true,"usgs":false}],"preferred":true,"id":823670,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saleh, Rohin","contributorId":265891,"corporation":false,"usgs":false,"family":"Saleh","given":"Rohin","email":"","affiliations":[{"id":54818,"text":"Alameda Flood Control District","active":true,"usgs":false}],"preferred":false,"id":823676,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tehranirad, Babak 0000-0002-1634-9165","orcid":"https://orcid.org/0000-0002-1634-9165","contributorId":265890,"corporation":false,"usgs":true,"family":"Tehranirad","given":"Babak","email":"","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":823671,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herdman, Liv M. 0000-0002-5444-6441 lherdman@usgs.gov","orcid":"https://orcid.org/0000-0002-5444-6441","contributorId":149964,"corporation":false,"usgs":true,"family":"Herdman","given":"Liv","email":"lherdman@usgs.gov","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823672,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":823673,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":140982,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick","email":"pbarnard@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":823674,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Van der Wegen, Mick","contributorId":191095,"corporation":false,"usgs":false,"family":"Van der Wegen","given":"Mick","email":"","affiliations":[],"preferred":false,"id":823675,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223323,"text":"70223323 - 2021 - Novel microbiome dominated by Arcobacter during anoxic excurrent flow from an ocean blue hole in Andros Island, The Bahamas","interactions":[],"lastModifiedDate":"2021-08-24T11:57:41.742546","indexId":"70223323","displayToPublicDate":"2021-08-23T15:18:36","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Novel microbiome dominated by Arcobacter during anoxic excurrent flow from an ocean blue hole in Andros Island, The Bahamas","docAbstract":"

Andros Island, The Bahamas, composed of porous carbonate rock, has about 175 inland blue holes and over 50 known submerged ocean caves along its eastern barrier reef. These ocean blue holes can have both vertical and horizontal zones that penetrate under the island. Tidal forces drive water flow in and out of these caves. King Kong Cavern has a vertical collapse zone and a deep penetration under Andros Island that emits sulfidic, anoxic water and masses of thin, mucoid filaments ranging to meters in length and off-white turbid water during ebb flow. Our objective was to determine the microbial composition of this mucoid material and the unconsolidated water column turbidity based on the concept that they represent unique lithoautotrophic microbial material swept from the cave into the surrounding ocean. Bacterial DNA extracted from these filaments and surrounding turbid water was characterized using PCR that targeted a portion of the 16S rRNA gene. The genus Arcobacter dominated both the filaments and the water column above the cave entrance. Arcobacter nitrofigilis and Arcobacter sp. UDC415 in the mucoid filaments accounted for as much as 80% of mapped DNA reads. In the water column Arcobacter comprised from 65% to over 85% of the reads in the depth region from about 18 m to 34 m. Bacterial species diversity was much higher in surface water and in water deeper than 36 m than in the intermediate zone. Community composition indicates that ebb flow from the cavern influences the entire water column at least to within 6 m of the surface and perhaps the near surface as well.

","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0256305","usgsCitation":"Iwanowicz, D.D., Jonas, R.B., Schill, W.B., and Marano-Briggs, K., 2021, Novel microbiome dominated by Arcobacter during anoxic excurrent flow from an ocean blue hole in Andros Island, The Bahamas: PLoS ONE, v. 16, no. 8, e0256305, 16 p., https://doi.org/10.1371/journal.pone.0256305.","productDescription":"e0256305, 16 p.","ipdsId":"IP-126590","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":451097,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0256305","text":"Publisher Index Page"},{"id":388384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"The Bahamas","otherGeospatial":"Andros Island, King Kong Cavern","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.93701171875,\n 24.455900450790526\n ],\n [\n -77.52777099609375,\n 24.455900450790526\n ],\n [\n -77.52777099609375,\n 24.857780406707583\n ],\n [\n -77.93701171875,\n 24.857780406707583\n ],\n [\n -77.93701171875,\n 24.455900450790526\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-08-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Iwanowicz, Deborah D. 0000-0002-9613-8594 diwanowicz@usgs.gov","orcid":"https://orcid.org/0000-0002-9613-8594","contributorId":2253,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Deborah","email":"diwanowicz@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":821735,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jonas, Robert B","contributorId":264606,"corporation":false,"usgs":false,"family":"Jonas","given":"Robert","email":"","middleInitial":"B","affiliations":[{"id":12909,"text":"George Mason University","active":true,"usgs":false}],"preferred":false,"id":821736,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schill, William B. 0000-0002-9217-984X wschill@usgs.gov","orcid":"https://orcid.org/0000-0002-9217-984X","contributorId":2736,"corporation":false,"usgs":true,"family":"Schill","given":"William","email":"wschill@usgs.gov","middleInitial":"B.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":821737,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marano-Briggs, Kay kmbriggs@usgs.gov","contributorId":40316,"corporation":false,"usgs":true,"family":"Marano-Briggs","given":"Kay","email":"kmbriggs@usgs.gov","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":false,"id":821786,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230044,"text":"70230044 - 2021 - Identifying the ecological and management implications of mangrove migration in the northern Gulf of Mexico","interactions":[],"lastModifiedDate":"2022-03-28T14:43:35.819413","indexId":"70230044","displayToPublicDate":"2021-08-23T09:38:29","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":10527,"text":"Final Project Report","active":true,"publicationSubtype":{"id":4}},"title":"Identifying the ecological and management implications of mangrove migration in the northern Gulf of Mexico","docAbstract":"Climate change is transforming ecosystems and affecting ecosystem goods and services. Along the Gulf of Mexico and Atlantic coasts of the southeastern United States, the frequency and intensity of extreme freeze events greatly influences whether coastal wetlands are dominated by freeze-sensitive woody plants (mangrove forests) or freeze-tolerant grass-like plants (salt marshes). In response to warming winters, mangroves have been expanding and displacing salt marshes at varying degrees of severity in parts of north Florida, Louisiana, and Texas. As winter warming accelerates, mangrove range expansion is expected to increasingly modify wetland ecosystem structure and function. Because there are differences in the ecological and societal benefits that salt marshes and mangroves provide, coastal environmental managers are challenged to anticipate effects of mangrove expansion on critical wetland ecosystem services, including those related to carbon sequestration, wildlife habitat, storm protection, erosion reduction, water purification, fisheries support, and recreation. This project produced information that is relevant to scientists and coastal resource managers working within the transition zone between mangrove forests and salt marshes. The two primary products are: (1) an investigation that leverages data and information from a community-curated data network called the Mangrove Migration Network to refine temperature thresholds for mangrove range expansion in a warming climate; and (2) a review article that examines current understanding of the effects of mangrove range expansion and displacement of salt marshes on wetland ecosystem services, including those related to carbon sequestration, wildlife habitat, storm protection, erosion reduction, water purification, fisheries support, and recreation.","language":"English","publisher":"Southeast Climate Adaptation Science Center (SECASC)","usgsCitation":"Osland, M., 2021, Identifying the ecological and management implications of mangrove migration in the northern Gulf of Mexico: Final Project Report, 26 p.","productDescription":"26 p.","ipdsId":"IP-132784","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":397706,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397659,"type":{"id":15,"text":"Index Page"},"url":"https://secasc.ncsu.edu/science/mangrove-migration/"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.123046875,\n 25.64152637306577\n ],\n [\n -81.123046875,\n 25.720735134412106\n ],\n [\n -81.123046875,\n 25.720735134412106\n ],\n [\n -81.123046875,\n 25.64152637306577\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.771484375,\n 25.48295117535531\n ],\n [\n -82.177734375,\n 27.137368359795584\n ],\n [\n -82.353515625,\n 28.536274512989916\n ],\n [\n -83.671875,\n 30.44867367928756\n ],\n [\n -84.990234375,\n 29.916852233070173\n ],\n [\n -86.1328125,\n 30.44867367928756\n ],\n [\n -88.59374999999999,\n 30.44867367928756\n ],\n [\n -90.52734374999999,\n 30.29701788337205\n ],\n [\n -89.82421875,\n 29.458731185355344\n ],\n [\n -90.439453125,\n 29.458731185355344\n ],\n [\n -91.7578125,\n 29.916852233070173\n ],\n [\n -92.548828125,\n 29.6880527498568\n ],\n [\n -94.658203125,\n 29.84064389983441\n ],\n [\n -96.328125,\n 28.844673680771795\n ],\n [\n -97.470703125,\n 27.761329874505233\n ],\n [\n -97.998046875,\n 26.745610382199022\n ],\n [\n -97.3828125,\n 25.64152637306577\n ],\n [\n -96.591796875,\n 27.527758206861886\n ],\n [\n -94.04296874999999,\n 28.69058765425071\n ],\n [\n -91.40625,\n 29.22889003019423\n ],\n [\n -89.82421875,\n 28.536274512989916\n ],\n [\n -88.59374999999999,\n 28.304380682962783\n ],\n [\n -88.76953125,\n 29.611670115197377\n ],\n [\n -84.55078125,\n 29.305561325527698\n ],\n [\n -83.14453125,\n 27.059125784374068\n ],\n [\n -81.82617187499999,\n 25.005972656239187\n ],\n [\n -80.771484375,\n 25.48295117535531\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":222814,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":838877,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70247905,"text":"70247905 - 2021 - Physics-guided recurrent graph model for predicting flow and temperature in river networks","interactions":[],"lastModifiedDate":"2023-08-23T11:51:11.142054","indexId":"70247905","displayToPublicDate":"2021-08-23T06:48:52","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Physics-guided recurrent graph model for predicting flow and temperature in river networks","docAbstract":"
This paper proposes a physics-guided machine learning approach that combines machine learning models and physics-based models to improve the prediction of water flow and temperature in river networks. We first build a recurrent graph network model to capture the interactions among multiple segments in the river network. Then we transfer knowledge from physics-based models to guide the learning of the machine learning model. We also propose a new loss function that balances the performance over different river segments. We demonstrate the effectiveness of the proposed method in predicting temperature and streamflow in a subset of the Delaware River Basin. In particular, the proposed method has brought a 33%/14% accuracy improvement over the state-of-the-art physics-based model and 24%/14% over traditional machine learning models (e.g., LSTM) in temperature/streamflow prediction using very sparse (0.1%) training data. The proposed method has also been shown to produce better performance when generalized to different seasons or river segments with different streamflow ranges.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Society for Industrial and Applied Mathematics","doi":"10.1137/1.9781611976700.69","usgsCitation":"Jia, X., Zwart, J.A., Sadler, J.M., Appling, A.P., Oliver, S.K., Markstrom, S.L., Willard, J., Xu, S., Steinbach, M., Read, J., and Kumar, V., 2021, Physics-guided recurrent graph model for predicting flow and temperature in river networks, in Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), p. 612-620, https://doi.org/10.1137/1.9781611976700.69.","productDescription":"7 p.","startPage":"612","endPage":"620","ipdsId":"IP-119777","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":451105,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1137/1.9781611976700.69","text":"Publisher Index Page"},{"id":420064,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-04-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":880945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":880946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sadler, Jeffrey Michael 0000-0001-8776-4844","orcid":"https://orcid.org/0000-0001-8776-4844","contributorId":260092,"corporation":false,"usgs":true,"family":"Sadler","given":"Jeffrey","email":"","middleInitial":"Michael","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":880947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":880948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":880949,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Markstrom, Steven L. 0000-0001-7630-9547 markstro@usgs.gov","orcid":"https://orcid.org/0000-0001-7630-9547","contributorId":146553,"corporation":false,"usgs":true,"family":"Markstrom","given":"Steven","email":"markstro@usgs.gov","middleInitial":"L.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":880950,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":880951,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Xu, Shaoming","contributorId":328661,"corporation":false,"usgs":false,"family":"Xu","given":"Shaoming","email":"","affiliations":[],"preferred":false,"id":880955,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Steinbach, Michael","contributorId":237811,"corporation":false,"usgs":false,"family":"Steinbach","given":"Michael","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":880952,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":880953,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":880954,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70223417,"text":"70223417 - 2021 - Wetland selection by female Ring-Necked Ducks (Aythya collaris) in the Southern Atlantic Flyway","interactions":[],"lastModifiedDate":"2021-08-26T16:52:23.862348","indexId":"70223417","displayToPublicDate":"2021-08-21T11:48:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Wetland selection by female Ring-Necked Ducks (Aythya collaris) in the Southern Atlantic Flyway","title":"Wetland selection by female Ring-Necked Ducks (Aythya collaris) in the Southern Atlantic Flyway","docAbstract":"On the wintering grounds, wetland selection by waterfowl is influenced by spatiotemporal resource distribution. The ring-necked duck (Aythya collaris) winters in the southeastern United States where a disproportionate amount of Atlantic Flyway ring-necked duck harvest occurs. We quantified female ring-necked duck selection for wetland characteristics during and after the 2017-2018 and 2018-2019 waterfowl hunting seasons using discrete choice modeling under a Bayesian framework. Relative probability of selection was primarily influenced by characteristics at the local wetland scale. Relative probability of selection was higher for flooded agriculture and vegetated wetlands than open water and was positively influenced by wetland area during the winter. After the hunting season, the relative probability of selection decreased for flooded agriculture but increased for vegetated wetlands, and the effect of wetland area decreased in magnitude. We attribute changes in selection during and after the hunting season to dietary shifts related to migratory preparation, resource depletion, and reproductive pairing. Understanding the wetland characteristics that wintering waterfowl select, and the spatial scale at which selection occurs, is important for informing effective wetland management and waterfowl harvest practices.","language":"English","publisher":"Springer","doi":"10.1007/s13157-021-01485-8","usgsCitation":"Mezebish, T.D., Chandler, R., Olsen, G.H., Goodman, M., Rohwer, F., and Meng, N.J., 2021, Wetland selection by female Ring-Necked Ducks (Aythya collaris) in the Southern Atlantic Flyway: Wetlands, v. 41, 84, 13 p., https://doi.org/10.1007/s13157-021-01485-8.","productDescription":"84, 13 p.","ipdsId":"IP-122109","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":388555,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Georgia","otherGeospatial":"Southern Atlantic Flyway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.3695068359375,\n 29.578234494739206\n ],\n [\n -82.001953125,\n 29.578234494739206\n ],\n [\n -82.001953125,\n 31.956823015897207\n ],\n [\n -84.3695068359375,\n 31.956823015897207\n ],\n [\n -84.3695068359375,\n 29.578234494739206\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","noUsgsAuthors":false,"publicationDate":"2021-08-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Mezebish, Tori D.","contributorId":239496,"corporation":false,"usgs":false,"family":"Mezebish","given":"Tori","email":"","middleInitial":"D.","affiliations":[{"id":27618,"text":"University of Georgia, Warnell School of Forestry and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":822001,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chandler, Richard B.","contributorId":251714,"corporation":false,"usgs":false,"family":"Chandler","given":"Richard B.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":822002,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olsen, Glenn H. 0000-0002-7188-6203","orcid":"https://orcid.org/0000-0002-7188-6203","contributorId":238130,"corporation":false,"usgs":true,"family":"Olsen","given":"Glenn","email":"","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":822003,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goodman, Michele","contributorId":239497,"corporation":false,"usgs":false,"family":"Goodman","given":"Michele","email":"","affiliations":[{"id":47893,"text":"Elmwood Park Zoo, Norristown, Pennyslvania","active":true,"usgs":false}],"preferred":false,"id":822004,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rohwer, Frank C.","contributorId":239498,"corporation":false,"usgs":false,"family":"Rohwer","given":"Frank C.","affiliations":[{"id":47894,"text":"Delta Waterfowl, Bismark North Dakota","active":true,"usgs":false}],"preferred":false,"id":822005,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meng, Nicholas J.","contributorId":264806,"corporation":false,"usgs":false,"family":"Meng","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":54559,"text":"Warnell School of Forestry and Natural Resources, University of Georgia,","active":true,"usgs":false}],"preferred":false,"id":822006,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223235,"text":"sir20215066 - 2021 - Assessment of diel cycling in nutrients and trace elements in the Eagle River Basin, 2017–18","interactions":[],"lastModifiedDate":"2021-08-23T13:33:24.30876","indexId":"sir20215066","displayToPublicDate":"2021-08-20T14:10:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5066","displayTitle":"Assessment of Diel Cycling in Nutrients and Trace Elements in the Eagle River Basin, 2017–18","title":"Assessment of diel cycling in nutrients and trace elements in the Eagle River Basin, 2017–18","docAbstract":"

Diel cycles are known to occur in all types of waters, and increasing studies indicate routine water samples may not provide an accurate snapshot in concentrations of trace elements and nutrients. Diel behavior in neutral to alkaline pH ranges is independent of streamflow variability and concentration. Extensive historical U.S. Geological Survey (USGS) water-quality data have been collected in the Eagle River Basin during daylight hours, which is defined as the period of time between one-half hour prior to sunrise and one-half hour after sunset. However, no USGS data have been collected throughout the nighttime, defined as the time between one-half hour after sunset and one-half hour prior to sunrise, making the evaluation of diel cycles impossible. To assess the importance of diel cycling within the Eagle River Basin, the USGS, in cooperation with Eagle River Watershed Council, developed a study to assess the mechanisms, patterns, and magnitude of change during the diel cycle for selected constituents. Water-quality monitors at five USGS streamgage sites (09065500, Gore Creek at Upper Station, near Minturn, Colorado, 09063000, Eagle River at Red Cliff, Colorado, 09064600, Eagle River near Minturn, Colorado, 09066325, Gore Creek above Red Sandstone Creek at Vail, Colorado, and 394220106431500, Eagle River below Milk Creek near Wolcott, Colorado) were deployed in 2017 to evaluate the water-quality field parameters and to determine if water conditions were favorable for the diel cycling of nutrients and trace elements. Based on the evaluation of water-quality parameters, three of the five sites were sampled for nutrient and trace-element concentrations in 2018 to confirm the presence and magnitude of diel cycling. Historical data were also analyzed to assess the effect of time of day on measured nutrient and trace-element concentrations. An assessment of the effect of land use on diel cycling was also investigated.

Measurable nutrients displayed a diel cycle at all three sites with the largest percentage change at the most downstream site (394220106431500), located on the Eagle River. More notable diel cycles at this site include filtered nitrate plus nitrite, which varied 179 percent, with concentrations from 0.24 to 0.67 milligrams per liter (mg/L) and filtered orthophosphate, which varied 71 percent, with concentrations from 0.07 to 0.12 mg/L. Filtered nitrate plus nitrite at site 09066325 varied 57 percent, ranging from 0.14 to 0.22 mg/L. Maximum concentrations occurred prior to noon, decreased through the afternoon (between noon and sunset), and increased during the night (between sunset and sunrise). That pattern is consistent with nutrient uptake in response to daytime (between sunrise and sunset) photosynthesis along with biologically driven denitrification and nitrification cycles. Nutrient concentrations at sites 09064600 and 09066325 were generally low and below laboratory reporting limits, which is the smallest measured concentration that nutrients could be measured by a given analytical method.

Trace-element concentrations were detectable at all sites with the largest percentage change at the most downstream site (394220106431500) and exhibited diel concentration variation from 11.6 to 284 percent. Appreciable diel cycles included filtered copper (0.98–1.40 micrograms per liter [µg/L], 42.9 percent), filtered zinc (less than [<] 4.00–5.50 µg/L, greater than [>] 37.5 percent), total manganese (9.70–19.5 µg/L, 101 percent), and total arsenic (0.30–0.40 µg/L, 33.3 percent). The largest percentage change in concentration was filtered manganese (2.84–10.9 µg/L, 284 percent). Diel cycles at site 09064600 ranged from 9.1 to 64.5 percent across the trace elements measured. Dissolved trace elements with appreciable diel cycles during the sampling period include filtered cadmium (0.09–0.12 µg/L, 33.3 percent), filtered copper (0.99–1.40 µg/L, 41.4 percent), and total arsenic (0.20–0.30 µg/L, 50 percent). The largest percentage change was filtered zinc (38.3–63.0 µg/L, 65 percent). Trace-element concentrations at site 09066325 were below laboratory reporting limits for many parameters, and no diel cycle could be assessed for these parameters. However, total recoverable iron, filtered barium, filtered manganese, and filtered selenium exhibited changes in concentrations of <10.0–19.4 µg/L (>94 percent), 115–121 µg/L (5 percent), 1.44–1.72 µg/L (19.4 percent), and 0.25–0.28 µg/L (12 percent), respectively. At sites 09064600 and 394220106431500, maximum trace-element concentrations occurred during nighttime with some variation regarding the timing of the peak. The exceptions to this were filtered copper, total arsenic, and filtered selenium, which had maximum concentrations around noon or as the sun disappeared below the horizon. The timing of minimum concentrations occurred in the afternoon for many trace elements, with filtered copper, total arsenic, and filtered selenium having minimum concentrations in the morning or just prior to the appearance of the sun.

Analysis of historical data also showed evidence of diel cycling. Historical samples collected from July through October were used to identify diel cycling in base-flow conditions. The resulting diel pattern in the median concentration for filtered manganese, filtered zinc at water-quality site 09064600, and filtered manganese and filtered nitrate plus nitrite at water-quality site 39422016431500 were consistent with the diel pattern in the September 2018 samples, and indicate time of day can bias sampling results even during daylight hours.

Diel cycling in the Eagle River Basin appears to be driven primarily by instream, biological processes. However, land use, particularly human effects downstream from urban areas, mining, and agriculture, may affect these processes. At some locations, diel variations in nutrient and trace-element concentrations are small enough to be of low concern. At other locations, however, variations in concentrations up to 284 percent in the data collected for this study and 214 percent in base-flow historical data, indicate daytime-only sampling, particularly in late afternoon, can underestimate daily average nutrient and trace-element concentrations. When feasible, the potential of diel cycling warrants consideration in sample design to account for the potential of diel cycles, or at a minimum, be recognized as a component of the river dynamic and the potential consequences that diel cycles may have in data interpretation and river management decisions.

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Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225

","tableOfContents":"","publishedDate":"2021-08-20","noUsgsAuthors":false,"publicationDate":"2021-08-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Richards, Rodney J. 0000-0003-3953-984X","orcid":"https://orcid.org/0000-0003-3953-984X","contributorId":202708,"corporation":false,"usgs":true,"family":"Richards","given":"Rodney J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821486,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Henneberg, Mark F. 0000-0002-6991-1211 mfhenneb@usgs.gov","orcid":"https://orcid.org/0000-0002-6991-1211","contributorId":187481,"corporation":false,"usgs":true,"family":"Henneberg","given":"Mark","email":"mfhenneb@usgs.gov","middleInitial":"F.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821487,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}