{"pageNumber":"239","pageRowStart":"5950","pageSize":"25","recordCount":40783,"records":[{"id":70220677,"text":"70220677 - 2021 - Modern Mars' geomorphological activity, driven by wind, frost, and gravity","interactions":[],"lastModifiedDate":"2021-05-25T12:46:27.117445","indexId":"70220677","displayToPublicDate":"2021-01-30T07:38:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Modern Mars' geomorphological activity, driven by wind, frost, and gravity","docAbstract":"

Extensive evidence of landform-scale martian geomorphic changes has been acquired in the last decade, and the number and range of examples of surface activity have increased as more high-resolution imagery has been acquired. Within the present-day Mars climate, wind and frost/ice are the dominant drivers, resulting in large avalanches of material down icy, rocky, or sandy slopes; sediment transport leading to many scales of aeolian bedforms and erosion; pits of various forms and patterned ground; and substrate material carved out from under subliming ice slabs. Due to the ability to collect correlated observations of surface activity and new landforms with relevant environmental conditions with spacecraft on or around Mars, studies of martian geomorphologic activity are uniquely positioned to directly test surface-atmosphere interaction and landform formation/evolution models outside of Earth. In this paper, we outline currently observed and interpreted surface activity occurring within the modern Mars environment, and tie this activity to wind, seasonal surface CO2 frost/ice, sublimation of subsurface water ice, and/or gravity drivers. Open questions regarding these processes are outlined, and then measurements needed for answering these questions are identified. In the final sections, we discuss how many of these martian processes and landforms may provide useful analogs for conditions and processes active on other planetary surfaces, with an emphasis on those that stretch the bounds of terrestrial-based models or that lack terrestrial analogs. In these ways, modern Mars presents a natural and powerful comparative planetology base case for studies of Solar System surface processes, beyond or instead of Earth.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2021.107627","usgsCitation":"Diniega, S., Bramson, A.M., Buratti, B.J., Buhler, P., Burr, D., Chojnacki, M., Conway, S.J., Dundas, C.M., Hansen, C.J., McEwen, A.S., Lapotre, M.G., Levy, J.S., McKeown, L., Piqueux, S., Portyankina, G., Swann, C., Titus, T.N., and Widmer, J., 2021, Modern Mars' geomorphological activity, driven by wind, frost, and gravity: Geomorphology, v. 380, 107627, 43 p., https://doi.org/10.1016/j.geomorph.2021.107627.","productDescription":"107627, 43 p.","ipdsId":"IP-121082","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":453646,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://hal.science/hal-03186543","text":"Publisher Index Page"},{"id":385915,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"380","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Diniega, Serina","contributorId":212017,"corporation":false,"usgs":false,"family":"Diniega","given":"Serina","email":"","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":816382,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bramson, Ali M 0000-0003-4903-0916","orcid":"https://orcid.org/0000-0003-4903-0916","contributorId":201618,"corporation":false,"usgs":false,"family":"Bramson","given":"Ali","email":"","middleInitial":"M","affiliations":[{"id":27205,"text":"U. Arizona","active":true,"usgs":false}],"preferred":false,"id":816383,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buratti, Bonnie J.","contributorId":152192,"corporation":false,"usgs":false,"family":"Buratti","given":"Bonnie","email":"","middleInitial":"J.","affiliations":[{"id":18876,"text":"California Institute of Technology, Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":816384,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buhler, Peter","contributorId":258300,"corporation":false,"usgs":false,"family":"Buhler","given":"Peter","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":816385,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burr, Devon M.","contributorId":229491,"corporation":false,"usgs":false,"family":"Burr","given":"Devon M.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":816386,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chojnacki, Matthew","contributorId":201621,"corporation":false,"usgs":false,"family":"Chojnacki","given":"Matthew","affiliations":[{"id":27205,"text":"U. 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This report and associated dataset provide tabular county-level estimates of kilograms of nitrogen and phosphorus generated from two sources: (a) fertilizer from commercial sources and (b) livestock-based manure, for the period 1950 through 2017 for the conterminous United States. Datasets collected during this time span are for intervals of approximately 5 years that coincide with the U.S. Department of Agriculture’s census years. Nutrients from fertilizer for 1950–2012 are exactly those previously described in U.S. Geological Survey (USGS) reports and datasets; however, estimates of nutrient masses from fertilizer applied in 2017 are described here as a new product modeled from 11 predictor variables for 2017 including county-level fertilizer expenditures, land use, and acres of fertilized land. Fertilizer-based estimates are provided for both farm and nonfarm (urban) usage. The estimates of nutrients from manure for all years were generated anew by using methods and formulas described in previous USGS reports but adjusted to account for historical changes in the annual averages of animal weights. The tabular data are available as an accompanying data release.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201153","usgsCitation":"Falcone, J.A., 2021, Estimates of county-level nitrogen and phosphorus from fertilizer and manure from 1950 through 2017 in the conterminous United States: U.S. Geological Survey Open-File Report 2020–1153, 20 p., https://doi.org/10.3133/ofr20201153.","productDescription":"Report: vii, 20 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-117936","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":382790,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1153/coverthb.jpg"},{"id":382791,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1153/ofr20201153.pdf","text":"Report","size":"5.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 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Earth System Processes Division
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U.S. Geological Survey
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Reston, VA 20192

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","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2021-01-29","noUsgsAuthors":false,"publicationDate":"2021-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Falcone, James A. 0000-0001-7202-3592 jfalcone@usgs.gov","orcid":"https://orcid.org/0000-0001-7202-3592","contributorId":614,"corporation":false,"usgs":true,"family":"Falcone","given":"James","email":"jfalcone@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":809379,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70217730,"text":"sir20205132 - 2021 - Characterization of groundwater quality and discharge with emphasis on selenium in an irrigated agricultural drainage near Delta, Colorado, 2017–19","interactions":[],"lastModifiedDate":"2021-08-18T22:10:40.433467","indexId":"sir20205132","displayToPublicDate":"2021-01-29T13:45:00","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":"2020-5132","displayTitle":"Characterization of Groundwater Quality and Discharge with Emphasis on Selenium in an Irrigated Agricultural Drainage near Delta, Colorado, 2017–19","title":"Characterization of groundwater quality and discharge with emphasis on selenium in an irrigated agricultural drainage near Delta, Colorado, 2017–19","docAbstract":"

Selenium is a water-quality constituent of concern for aquatic ecosystems in the lower Gunnison River Basin. Selenium is derived from bedrock of the Mancos Shale and is mobilized and transported to groundwater and surface water by application of irrigation water. Although it is recognized that groundwater contributes an appreciable amount of selenium to surface water, few studies have addressed interactions between the two. The U.S. Geological Survey in cooperation with the Colorado Water Conservation Board conducted a study during 2017–19 to characterize the quality and quantity of groundwater discharging to an agricultural drainage near Delta, Colorado, locally known as Sunflower Drain.

Water quality in the study area is characterized by high dissolved solids with elevated concentrations of selenium and nitrate resulting from dissolution of soluble salts in the Mancos Shale. Selenium concentrations have decreased by 50 percent since the early 2000s, possibly in response to irrigation system improvements. Stable water isotopes indicate streamflow is dominated by canal water during the irrigation season (April to October) and, during the nonirrigation season (November to March), is dominated by groundwater that has undergone some degree of evaporation. Pesticide and pharmaceutical compounds were infrequently detected, and results indicate they were derived from sources outside the study area such that they do not appear to be useful as tracers of groundwater sources. Stable isotopes of nitrate indicate that nitrate originates from the Mancos Shale, and the isotopic composition is enriched by denitrification in the groundwater system. Using a mass-balance approach, estimated groundwater discharge rates to Sunflower Drain ranged from 0.15 to 0.27 cubic feet per second per mile with one losing reach identified. Selenium, sulfate, and nitrate concentrations in groundwater estimated by mass-balance calculations were similar to concentrations measured in the Poly 17 observation well, located in a largely irrigated area in east tributary. One tributary reach had higher concentrations of selenium, sulfate, and nitrate likely reflecting localized inputs of more concentrated groundwater, similar to the concentrations in the Poly 7 observation well, which is downgradient from a residential area in the west tributary.

Three pilot studies were conducted, including fiber optic distributed temperature sensing to detect groundwater discharge zones in the stream channel, a passive seismic technique to estimate depth to bedrock, and use of radon-222 as a geochemical tracer of groundwater discharge. All three techniques show promise as additional approaches for investigating groundwater discharge surface-water systems in irrigated drainage areas on Mancos Shale.

The factors that affect groundwater movement mainly include when and where irrigation water is transported and applied, and the distribution of bedrock of the Mancos Shale and overlying alluvial deposits. The average groundwater recharge rate for the study area was estimated at 8.1 inches per year, based on mass balance calculations from synoptic survey data. Along the western tributary of Sunflower Drain, there was evidence that spills from the East Canal may recharge the groundwater aquifer adjacent to the stream channel. Groundwater movement to the stream channel may be controlled by the topography of the alluvial/bedrock interface or focused along human-made features, such as tile drains and ditches constructed around irrigated fields. On larger scales, the top of bedrock was also important, creating a topographic constriction that caused a zone of groundwater discharge. The groundwater system is complex, and further study could better define the system, possibly through application of a groundwater flow model and more extensive studies using some of the exploratory methods evaluated in this study.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20205132","collaboration":"Prepared in cooperation with Colorado Water Conservation Board","usgsCitation":"Mast, M.A., 2021, Characterization of groundwater quality and discharge with emphasis on selenium in an irrigated agricultural drainage near Delta, Colorado, 2017–19: U.S. Geological Survey Scientific Investigations Report 2020–5132, 34 p., https://doi.org/10.3133/sir20205132.","productDescription":"Report: vi, 34 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119514","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":382809,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LKYX9H","text":"USGS data release","linkHelpText":"Near-surface geophysical data collected in the Sunflower Drain study area near Delta, Colorado, March 2018"},{"id":382805,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5132/coverthb.jpg"},{"id":382806,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5132/sir20205132.pdf","text":"Report","size":"5.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5132"}],"country":"United States","state":"Colorado","city":"Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.21945190429688,\n 38.638327308061875\n ],\n [\n -107.97019958496094,\n 38.638327308061875\n ],\n [\n -107.97019958496094,\n 38.82205601494022\n ],\n [\n -108.21945190429688,\n 38.82205601494022\n ],\n [\n -108.21945190429688,\n 38.638327308061875\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

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

","tableOfContents":"","publishedDate":"2021-01-29","noUsgsAuthors":false,"publicationDate":"2021-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Mast, M. Alisa 0000-0001-6253-8162","orcid":"https://orcid.org/0000-0001-6253-8162","contributorId":211054,"corporation":false,"usgs":true,"family":"Mast","given":"M.","email":"","middleInitial":"Alisa","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809410,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70217743,"text":"ofr20201134 - 2021 - Characterizing fault roughness—Are faults rougher at long or short wavelengths?","interactions":[],"lastModifiedDate":"2021-02-01T15:17:49.001421","indexId":"ofr20201134","displayToPublicDate":"2021-01-29T12:36:55","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":"2020-1134","displayTitle":"Characterizing Fault Roughness—Are Faults Rougher at Long or Short Wavelengths?","title":"Characterizing fault roughness—Are faults rougher at long or short wavelengths?","docAbstract":"

Changes in fault roughness with scale, “scaling,” is the topic of this report; changes are considered using a general power law relation between some measure of surface height, H, and another of length, L, H=kLn, where k is a constant and n is an exponent that characterizes the scaling. Extensive profile measurements of natural fault surfaces show that the ratio of average surface height to profile length decreases with scale. Average height is defined using the root mean squared height, Rq. For this analysis, fault surfaces are smoother at long wavelengths (have smaller average height to profile length ratios) than they are at shorter wavelengths. These and other statistical properties of natural fault surfaces hold for more than five orders of magnitude, a huge range from tens of micrometers to 10 meters. However, a different roughness metric, the average height (amplitude) that is specifically associated with a wavelength shows the opposite sense of scaling. The ratio of average amplitude to wavelength increases with wavelength. Thus, the same fault surface can be deemed rougher at long wavelength, or smoother, depending on the chosen metric. This apparent contradiction is a curiosity of the statistics of rough surfaces that have scaling exponents that relate profile length to Rq between 0.5 and 1, as most natural faults do. To add context, the implied roughness scaling for reference synthetic surfaces is determined. These span the natural range of scaling exponents and have moderate to strong point to point amplitude correlation. The potential payoff of expanded descriptions of natural fault roughness and of reference surfaces are improved constraints on physical mechanisms that generate and modify roughness during shear.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201134","usgsCitation":"Beeler, N.M., 2021, Characterizing fault roughness—Are faults rougher at long or short wavelengths?: U.S. Geological Survey Open-File Report 2020–1134, 15 p., https://doi.org/10.3133/ofr20201134.","productDescription":"iv, 13 p.","onlineOnly":"Y","ipdsId":"IP-097230","costCenters":[],"links":[{"id":382822,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1134/ofr20201134.pdf","text":"Report","size":"3.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1134"},{"id":382821,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1134/coverthb.jpg"}],"contact":"

Contact Information, Menlo Park, Calif.
Office—Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025

","tableOfContents":"","publishedDate":"2021-01-29","noUsgsAuthors":false,"publicationDate":"2021-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Beeler, Nicholas M. 0000-0002-3397-8481 nbeeler@usgs.gov","orcid":"https://orcid.org/0000-0002-3397-8481","contributorId":2682,"corporation":false,"usgs":true,"family":"Beeler","given":"Nicholas","email":"nbeeler@usgs.gov","middleInitial":"M.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":809439,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70217751,"text":"70217751 - 2021 - Piloting urban ecosystem accounting for the United States","interactions":[],"lastModifiedDate":"2021-02-01T16:27:36.592165","indexId":"70217751","displayToPublicDate":"2021-01-29T10:22:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1477,"text":"Ecosystem Services","active":true,"publicationSubtype":{"id":10}},"title":"Piloting urban ecosystem accounting for the United States","docAbstract":"

In this study, we develop urban ecosystem accounts in the U.S., using the System of Environmental-Economic Accounting Experimental Ecosystem Accounting (SEEA EEA) framework. Most ecosystem accounts focus on regional and national scales, which are appropriate for many ecosystem services. However, ecosystems provide substantial services in cities, improving quality of life and contributing to resiliency for substantial parts of the population. Our models estimate energy savings for indoor cooling resulting from heat mitigated by trees and rainfall intercepted by trees. Both models cover major cities in the contiguous U.S. and report the results through physical supply and use tables for multiple accounting periods (2011 and 2016). Using conservative assumptions, urban trees provide substantial heat mitigation (4,098 and 4,229 GWh, valued at $523 and $539 million in 2011 and 2016, respectively) and rainfall interception (2,422 and 2,627 million m3, valued at $434 and $425 million for 2011 and 2016, respectively). Interannual differences largely reflect variations in weather patterns. Our work shows how Earth observation data can support urban ecosystem accounting. We provide model code within a public repository to facilitate model runs elsewhere, enabling the SEEA EEA and Earth observation user communities to reuse our models and provide feedback for improvement.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoser.2020.101226","usgsCitation":"Heris, M., Bagstad, K.J., Rhodes, C., Troy, A., Middel, A., Hopkins, K.G., and Matuszak, J., 2021, Piloting urban ecosystem accounting for the United States: Ecosystem Services, v. 48, 101226, 18 p., https://doi.org/10.1016/j.ecoser.2020.101226.","productDescription":"101226, 18 p.","ipdsId":"IP-114089","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":453652,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoser.2020.101226","text":"Publisher Index Page"},{"id":436527,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QV182X","text":"USGS data release","linkHelpText":"Data 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0000-0002-4418-5030","orcid":"https://orcid.org/0000-0002-4418-5030","contributorId":248592,"corporation":false,"usgs":false,"family":"Heris","given":"Mehdi","affiliations":[{"id":12652,"text":"University of Colorado-Denver","active":true,"usgs":false}],"preferred":false,"id":809469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":809470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rhodes, Charles 0000-0002-9040-3684","orcid":"https://orcid.org/0000-0002-9040-3684","contributorId":245881,"corporation":false,"usgs":true,"family":"Rhodes","given":"Charles","email":"","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":809471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Troy, Austin","contributorId":139102,"corporation":false,"usgs":false,"family":"Troy","given":"Austin","email":"","affiliations":[{"id":12652,"text":"University of Colorado-Denver","active":true,"usgs":false}],"preferred":false,"id":809472,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Middel, Ariane 0000-0002-1565-095X","orcid":"https://orcid.org/0000-0002-1565-095X","contributorId":248593,"corporation":false,"usgs":false,"family":"Middel","given":"Ariane","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":809473,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hopkins, Kristina G. 0000-0003-1699-9384 khopkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1699-9384","contributorId":195604,"corporation":false,"usgs":true,"family":"Hopkins","given":"Kristina","email":"khopkins@usgs.gov","middleInitial":"G.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809474,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Matuszak, John","contributorId":211869,"corporation":false,"usgs":false,"family":"Matuszak","given":"John","email":"","affiliations":[{"id":38336,"text":"U.S. Department of State","active":true,"usgs":false}],"preferred":false,"id":809475,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223858,"text":"70223858 - 2021 - Implications of aggregating and smoothing daily production data on estimates of the transition time between flow regimes in horizontal hydraulically fractured Bakken oil wells","interactions":[],"lastModifiedDate":"2021-09-10T16:10:38.075312","indexId":"70223858","displayToPublicDate":"2021-01-29T10:04:40","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2701,"text":"Mathematical Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"Implications of aggregating and smoothing daily production data on estimates of the transition time between flow regimes in horizontal hydraulically fractured Bakken oil wells","docAbstract":"

The level to which data are aggregated or smoothed can impact analytical and predictive modeling results. This paper discusses findings regarding such impacts on estimating change points in production flow regimes of horizontal hydraulically fractured shale oil wells producing from the middle member of the Bakken Formation. Change points that signal transitions in flow regimes are important because they subsequently affect estimates of ultimate recovery from wells producing from shale plays. Extending our earlier work, we employ two different statistical approaches, Bacon–Watts Bayesian regression and nonlinear constrained least squares regression, and a designed computational experiment to estimate the time of transition from the transient to the boundary-dominated flow regime for 14 different wells using daily production data rather than aggregated monthly data, as previously considered. The daily data were also smoothed to reduce noise. Computational experiments suggest that both statistical approaches can lead to plausible estimates of the transition point under different data aggregation or smoothing regimes, but that daily data are likely too granular to produce credible estimates. Although the expected value of transition points using smoothed daily data and monthly disaggregated data are generally comparable, the confidence intervals bounding the estimates based on smoothed daily data are generally wider. Our results not only inform the operational practices of oil producers engaged in economic evaluation of their shale resources and additional play development activities, but also the activities of petroleum research groups, government agencies, and financial organizations seeking to improve the trustworthiness of resource projections.

","language":"English","publisher":"Springer Link","doi":"10.1007/s11004-020-09909-7","usgsCitation":"Coburn, T.C., and Attanasi, E., 2021, Implications of aggregating and smoothing daily production data on estimates of the transition time between flow regimes in horizontal hydraulically fractured Bakken oil wells: Mathematical Geosciences, v. 53, p. 1261-1292, https://doi.org/10.1007/s11004-020-09909-7.","productDescription":"32 p.","startPage":"1261","endPage":"1292","ipdsId":"IP-114030","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":389064,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","noUsgsAuthors":false,"publicationDate":"2021-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Coburn, T. C.","contributorId":219832,"corporation":false,"usgs":false,"family":"Coburn","given":"T.","email":"","middleInitial":"C.","affiliations":[{"id":40076,"text":"1 University of Tulsa, School of Energy Economics, Policy and Commerce, USA,","active":true,"usgs":false}],"preferred":false,"id":823008,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Attanasi, Emil D. 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":198728,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil D.","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":823009,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70229002,"text":"70229002 - 2021 - Ensemble species distribution model identifies survey opportunities for at-risk bearded beaksedge (Rhynchospora crinipes) in the southeastern United States","interactions":[],"lastModifiedDate":"2022-02-25T15:39:57.320263","indexId":"70229002","displayToPublicDate":"2021-01-29T09:33:28","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2821,"text":"Natural Areas Journal","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Ensemble species distribution model identifies survey opportunities for at-risk bearded beaksedge (Rhynchospora crinipes) in the southeastern United States","title":"Ensemble species distribution model identifies survey opportunities for at-risk bearded beaksedge (Rhynchospora crinipes) in the southeastern United States","docAbstract":"

Locating additional occurrences of at-risk species can inform assessments of their status and conservation needs (including potential legal protections). The perennial bearded beaksedge (Rhynchospora crinipes) ranges from Mississippi to North Carolina, but known occurrences are limited. Because of the species' apparent rarity, a model to identify areas with suitable habitat conditions for the species will allow conservationists to effectively prioritize and allocate scarce surveying resources. We used known occurrence records, a suite of environmental datasets, and four species distribution modeling techniques (generalized additive, GAM; maximum entropy, MaxEnt; generalized boosted, GBM; and weighted ensemble) to generate maps to inform surveys for R. crinipes. The ensemble approach improved predictive performance (AUC-PR = 0.95) compared to other techniques (individual model AUC-PR ranged from 0.7 to 0.8). We also obtained quantitative insights on the species' habitat relationships, including the likelihood of R. crinipes's presence near Atlantic white cedar (Chamaecyparis thyoides) habitat and floodplains, which is consistent with prior field observations. The ensemble model indicated that 3.6% of the study area could be suitable habitat, but only 0.38% had high suitability. Small stream riparian habitats and Atlantic swamp forests in Alabama, Florida, and Georgia had the highest proportion of suitable areas. Prioritizing surveys in areas with model-indicated high habitat suitability is expected to reveal additional R. crinipes occurrences. We suggest surveying efforts for other at-risk species may benefit from using an ensemble modeling approach to identify and prioritize survey areas and improve ecological knowledge of these species.

","language":"English","publisher":"The Natural Areas Association","doi":"10.3375/043.041.0108","usgsCitation":"Ramirez-Reyes, C., Street, G., Vilella, F., Jones-Farrand, T., Wiggers, M.S., and Evans, K., 2021, Ensemble species distribution model identifies survey opportunities for at-risk bearded beaksedge (Rhynchospora crinipes) in the southeastern United States: Natural Areas Journal, v. 41, no. 1, p. 55-63, https://doi.org/10.3375/043.041.0108.","productDescription":"9 p.","startPage":"55","endPage":"63","ipdsId":"IP-120003","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":396486,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Georgia, Mississippi, North Carolina, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.978515625,\n 33.54139466898275\n ],\n [\n -89.9560546875,\n 32.69486597787505\n ],\n [\n -91.14257812499999,\n 31.50362930577303\n ],\n [\n -89.47265625,\n 30.259067203213018\n ],\n [\n -87.71484375,\n 29.99300228455108\n ],\n [\n -86.4404296875,\n 30.221101852485987\n ],\n [\n -85.0341796875,\n 29.611670115197377\n ],\n [\n -83.7158203125,\n 29.6880527498568\n ],\n [\n -82.8369140625,\n 28.8831596093235\n ],\n [\n -80.2880859375,\n 27.449790329784214\n ],\n [\n -80.6396484375,\n 29.305561325527698\n ],\n [\n -81.0791015625,\n 31.052933985705163\n ],\n [\n -75.1904296875,\n 35.71083783530009\n ],\n [\n -78.44238281249999,\n 36.491973470593685\n ],\n [\n -82.8369140625,\n 34.70549341022544\n ],\n [\n -84.990234375,\n 33.32134852669881\n ],\n [\n -87.978515625,\n 33.54139466898275\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ramirez-Reyes, C.","contributorId":275333,"corporation":false,"usgs":false,"family":"Ramirez-Reyes","given":"C.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":836101,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Street, G.","contributorId":280202,"corporation":false,"usgs":false,"family":"Street","given":"G.","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":836102,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vilella, Francisco 0000-0003-1552-9989 fvilella@usgs.gov","orcid":"https://orcid.org/0000-0003-1552-9989","contributorId":171363,"corporation":false,"usgs":true,"family":"Vilella","given":"Francisco","email":"fvilella@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":836103,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones-Farrand, T.","contributorId":280203,"corporation":false,"usgs":false,"family":"Jones-Farrand","given":"T.","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":836104,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wiggers, M. S.","contributorId":280204,"corporation":false,"usgs":false,"family":"Wiggers","given":"M.","email":"","middleInitial":"S.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":836105,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Evans, K. O.","contributorId":280205,"corporation":false,"usgs":false,"family":"Evans","given":"K. O.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":836106,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223825,"text":"70223825 - 2021 - Multiple feedbacks due to biotic interactions across trophic levels can lead to persisten novel conditions that hinder restoration","interactions":[],"lastModifiedDate":"2021-09-09T13:00:52.526436","indexId":"70223825","displayToPublicDate":"2021-01-29T08:00:06","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"23","title":"Multiple feedbacks due to biotic interactions across trophic levels can lead to persisten novel conditions that hinder restoration","docAbstract":"Unlike traditional successional theory, Alternate Stable Equilibrium (ASE) theory posits that more than one community state is possible in a single environment, depending on the order that species arrive. ASE theory is often invoked in management situations where initial stressors have been removed, but native-dominated communities are not returning to degraded areas. Fundamental to this theory is the assumption that equilibria are maintained by positive feedbacks between colonizers and their environment. While ASE has been relatively well studied in aquatic ecosystems, more complex terrestrial systems offer multiple challenges, including species interactions across trophic levels that can lead to multiple feedbacks. Here, we discuss ASE theory as it applies to terrestrial, invaded ecosystems, and detail a case study from Hawaii that exemplifies how species interactions can favour the persistence of invaders, and how an understanding of interactions and feedbacks can be used to guide management. Our system includes intact native-dominated mesic forest and areas cleared for pasture, planted with non-native grasses, and later planted with a monoculture of a native nitrogen-fixing tree in an effort to restore forests. We discuss interactions between birds, understorey fruiting native species, understorey non-native grasses, soils and bryophytes in separate feedback mechanisms, and explain our efforts to identify which of these feedbacks is most important to address in a management context. Finally, we suggest that using models can help overcome some of the challenges that terrestrial ecosystems pose when studying ASE.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Plant Invasions: The role of biotic interactions","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CABI","usgsCitation":"Yelenik, S.G., D’Antonio, C.M., Rehm, E.M., and Caldwell, I., 2021, Multiple feedbacks due to biotic interactions across trophic levels can lead to persisten novel conditions that hinder restoration, chap. 23 of Plant Invasions: The role of biotic interactions, p. 402-420.","productDescription":"19 p.","startPage":"402","endPage":"420","ipdsId":"IP-116356","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":388998,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yelenik, Stephanie G. 0000-0002-9011-0769 syelenik@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-0769","contributorId":5251,"corporation":false,"usgs":true,"family":"Yelenik","given":"Stephanie","email":"syelenik@usgs.gov","middleInitial":"G.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":822803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"D’Antonio, Carla M.","contributorId":196690,"corporation":false,"usgs":false,"family":"D’Antonio","given":"Carla","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":822804,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rehm, Evan M","contributorId":216487,"corporation":false,"usgs":false,"family":"Rehm","given":"Evan","email":"","middleInitial":"M","affiliations":[{"id":39457,"text":"University of California at Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":822805,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Caldwell, Iain","contributorId":265492,"corporation":false,"usgs":false,"family":"Caldwell","given":"Iain","email":"","affiliations":[{"id":35992,"text":"James Cook University, Australia","active":true,"usgs":false}],"preferred":false,"id":822806,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70217720,"text":"70217720 - 2021 - Tectonic and magmatic controls on the metallogenesis of porphyry deposits in Alaska","interactions":[],"lastModifiedDate":"2021-02-01T14:12:59.353933","indexId":"70217720","displayToPublicDate":"2021-01-29T07:47:46","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Tectonic and magmatic controls on the metallogenesis of porphyry deposits in Alaska","docAbstract":"Porphyry Cu and Mo deposits and occurrences are found throughout Alaska; they formed episodically during repeated subduction and arc-continent collisions spanning the Silurian to Quaternary. Porphyry systems occur in continental-margin and island arcs, which are broadly grouped into pre-accretionary or post-accretionary arcs. Pre-Mesozoic occurrences formed in continental or island arcs prior to accretion onto the margin of North America, whereas Mesozoic and younger systems formed in arcs that developed after terrane fragments were accreted to the margin of North America. As a result, older porphyry systems are typically in the interior and northern metallogenic belts, whereas the younger porphyry systems are predominantly found in the southern third of the state, closer to the modern continental margin.\nAlaska porphyry formation peaked in the mid-Cretaceous and continued through the Late Cretaceous to Tertiary, in association with continental-margin arcs extending from the eastern interior, into southwest Alaska and along the Alaska Peninsula and Aleutian Islands. Porphyry system formation is not generally recognized in the Early Cretaceous, Triassic, or early Paleozoic – time periods that coincide with continental collisional events or extension. Relatively few pre-accretionary porphyry systems are documented in more deeply exhumed arc segments due to low preservation potential in areas of rapid or repeated bedrock uplift and associated erosion that occurred during later tectonic events.\nSignificant diversity is observed in porphyry occurrences across the state and even within the same region. Occurrences form in association with arc-related intrusions or intrusive complexes, that range in composition from diorite to syenite, but are commonly monzonitic to granitic. Some porphyry occurrences are associated with alkaline intrusive belts that exhibit a stronger crustal contribution to magmatic sources. Intrusions associated with porphyry formation in Alaska are commonly moderately oxidized, however, a distinct group of porphyry systems are associated with more-reduced magmas.\nHydrothermal alteration described at many occurrences exhibits zoning from proximal potassic alteration to typically peripheral and(or) later sericitic alteration, that is flanked by large zones of propylitic alteration. Diversity in alteration is observed where sodic and sodic-calcic alteration is present, commonly in more-enigmatic deposits, such as Island Mountain and Chicken Mountain. Advanced argillic alteration is rare, but present in notable examples, such as the Pebble porphyry Cu(-Au-Mo) deposit. Sulfide mineralization is characterized by pyrite, chalcopyrite, molybdenite, and rare bornite hosted in veinlets, veins and disseminations in wallrocks and causative intrusions. Some porphyry systems contain abundant pyrrhotite and(or) arsenopyrite in the mineral assemblages. Systems that exhibit bornite-bearing assemblages containing abundant molybdenite are commonly arsenic- and gold-poor and tend to be associated with more oxidized arc magmas. In contrast, those systems that are pyrrhotite and(or) arsenopyrite dominant tend to be gold, arsenic, and bismuth bearing, and are commonly associated with more-reduced magmas. \nExploration for porphyry occurrences in Alaska has experienced a resurgence and currently constitutes about 20% of exploration dollars in the state. Many systems lack complete descriptions, and coupled with the cost of exploration, remain incompletely explored. Additional understanding of known occurrences combined with a framework geologic understanding of porphyry-bearing metallogenic belts will likely result in new discoveries in the future.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Porphyry deposits of the northwestern Cordillera of North America: A 25-year update","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Canadian Institute of Mining, Metallurgy and Petroleum","usgsCitation":"Kreiner, D.C., Jones, J.V., Kelley, K.D., and Graham, G.E., 2021, Tectonic and magmatic controls on the metallogenesis of porphyry deposits in Alaska, chap. of Porphyry deposits of the northwestern Cordillera of North America: A 25-year update, v. 57, p. 134-175.","productDescription":"42 p.","startPage":"134","endPage":"175","ipdsId":"IP-113700","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":477,"text":"North Central Climate Science 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III 0000-0002-6602-5935 jvjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6602-5935","contributorId":201245,"corporation":false,"usgs":true,"family":"Jones","given":"James","suffix":"III","email":"jvjones@usgs.gov","middleInitial":"V.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":809367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelley, Karen D. 0000-0002-3232-5809 kdkelley@usgs.gov","orcid":"https://orcid.org/0000-0002-3232-5809","contributorId":179012,"corporation":false,"usgs":true,"family":"Kelley","given":"Karen","email":"kdkelley@usgs.gov","middleInitial":"D.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":809368,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graham, Garth E. 0000-0003-0657-0365 ggraham@usgs.gov","orcid":"https://orcid.org/0000-0003-0657-0365","contributorId":1031,"corporation":false,"usgs":true,"family":"Graham","given":"Garth","email":"ggraham@usgs.gov","middleInitial":"E.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":809369,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70217863,"text":"70217863 - 2021 - Non-native Asian swamp eel, Monopterus albus/javanensis (Zuiew, 1973/Lacepede, 1800), responses to low temperatures","interactions":[],"lastModifiedDate":"2023-07-07T14:11:49.37828","indexId":"70217863","displayToPublicDate":"2021-01-29T07:42:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1651,"text":"Fish Physiology and Biochemistry","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Non-native Asian swamp eel, Monopterus albus/javanensis (Zuiew, 1973/Lacepede, 1800), responses to low temperatures","title":"Non-native Asian swamp eel, Monopterus albus/javanensis (Zuiew, 1973/Lacepede, 1800), responses to low temperatures","docAbstract":"

Asian swamp eel, Monopterus albus/javanensis [Zuiew, 1973/Lacepede 1800], has been established in the southeastern USA since at least 1994, yet little is known about its ability to survive low winter temperatures. We use standard thermal methodologies to quantify low temperature responses and provide a detailed description of swamp eel reactions to cold temperatures. When exposed to chronic temperature decreases of 1.0 °C day−1, swamp eel ceased foraging at 15.0 °C, markedly diminished movements below 11.0 °C, and became incapacitated near 9.6 °C. During critical thermal minima trials, swamp eel exposed to acute temperature drops (0.25 °C min−1) tolerated temperatures as low as 6.2 °C. Swamp eel exhibited a moderate cold acclimation response, gaining 0.23 °C in cold tolerance for every 1 °C drop in acclimation temperature. Progressive time-series critical thermal minimum temperatures (CTmin) estimates for eel acclimated to 20.5 °C followed by an acute temperature decrease to 16.0 °C, revealed that cold acclimation may occur in only 8 days. Fringe populations of swamp eel in their native range periodically experience colder winter temperatures, which may explain the ability of introduced populations to survive winter cold fronts in Florida. Understanding Asian swamp eel acute and chronic thermal limits may be useful in assessing dispersal risk and range expansion in the southeastern USA.


","language":"English","publisher":"Springer","doi":"10.1007/s10695-021-00925-w","usgsCitation":"Saylor, R.K., Schofield, P., and Bennett, W., 2021, Non-native Asian swamp eel, Monopterus albus/javanensis (Zuiew, 1973/Lacepede, 1800), responses to low temperatures: Fish Physiology and Biochemistry, v. 47, p. 465-476, https://doi.org/10.1007/s10695-021-00925-w.","productDescription":"12 p.; Data Release","startPage":"465","endPage":"476","ipdsId":"IP-120529","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":383089,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418749,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NFFZTV","text":"Non-native Asian swamp eel, Monopterus albus/javanensis (Zuiew, 1973/Lacepede, 1800), responses to low temperatures","linkFileType":{"id":5,"text":"html"}}],"volume":"47","noUsgsAuthors":false,"publicationDate":"2021-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Saylor, Ryan K.","contributorId":248815,"corporation":false,"usgs":false,"family":"Saylor","given":"Ryan","email":"","middleInitial":"K.","affiliations":[{"id":16703,"text":"University of West Florida","active":true,"usgs":false}],"preferred":false,"id":809965,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schofield, Pam 0000-0002-8752-2797","orcid":"https://orcid.org/0000-0002-8752-2797","contributorId":213749,"corporation":false,"usgs":true,"family":"Schofield","given":"Pam","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":809966,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bennett, Wayne A","contributorId":248816,"corporation":false,"usgs":false,"family":"Bennett","given":"Wayne A","affiliations":[{"id":16703,"text":"University of West Florida","active":true,"usgs":false}],"preferred":false,"id":809967,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70220471,"text":"70220471 - 2021 - Reconstructing population dynamics of a threatened marine mammal using multiple data sets","interactions":[],"lastModifiedDate":"2021-05-14T12:48:58.278944","indexId":"70220471","displayToPublicDate":"2021-01-29T07:38:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Reconstructing population dynamics of a threatened marine mammal using multiple data sets","docAbstract":"

Models of marine mammal population dynamics have been used extensively to predict abundance. A less common application of these models is to reconstruct historical population dynamics, filling in gaps in observation data by integrating information from multiple sources. We developed an integrated population model for the Florida manatee (Trichechus manatus latirostris) to reconstruct its population dynamics in the southwest region of the state over the past 20 years. Our model improved precision of key parameter estimates and permitted inference on poorly known parameters. Population growth was slow (averaging 1.02; 95% credible interval 1.01–1.03) but not steady, and an unusual mortality event in 2013 led to an estimated net loss of 332 (217–466) manatees. Our analyses showed that precise estimates of abundance could be derived from estimates of vital rates and a few input estimates of abundance, which may mean costly surveys to estimate abundance don’t need to be conducted as frequently. Our study also shows that retrospective analyses can be useful to: (1) model the transient dynamics of age distribution; (2) assess and communicate the conservation status of wild populations; and (3) improve our understanding of environmental effects on population dynamics and thus enhance our ability to forecast.

","language":"English","publisher":"Nature","doi":"10.1038/s41598-021-81478-z","usgsCitation":"Hostetler, J., Martin, J., Kosempa, M., Edwards, H., Rood, K., Barton, S., and Runge, M.C., 2021, Reconstructing population dynamics of a threatened marine mammal using multiple data sets: Scientific Reports, v. 11, 2702 , 15 p., https://doi.org/10.1038/s41598-021-81478-z.","productDescription":"2702 , 15 p.","ipdsId":"IP-117972","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":453658,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-021-81478-z","text":"Publisher Index Page"},{"id":436529,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98835OJ","text":"USGS data release","linkHelpText":"Data from: Reconstructing population dynamics of a threatened marine mammal using multiple data sets"},{"id":385637,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Southwest Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.232421875,\n 25.16517336866393\n ],\n [\n -80.419921875,\n 25.16517336866393\n ],\n [\n -80.419921875,\n 28.65203063036226\n ],\n [\n -83.232421875,\n 28.65203063036226\n ],\n [\n -83.232421875,\n 25.16517336866393\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2021-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Hostetler, J. 0000-0003-3669-1758","orcid":"https://orcid.org/0000-0003-3669-1758","contributorId":258049,"corporation":false,"usgs":false,"family":"Hostetler","given":"J.","affiliations":[{"id":35758,"text":"FWC","active":true,"usgs":false}],"preferred":false,"id":815612,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Julien 0000-0002-7375-129X","orcid":"https://orcid.org/0000-0002-7375-129X","contributorId":216734,"corporation":false,"usgs":true,"family":"Martin","given":"Julien","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":815613,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kosempa, M.","contributorId":258050,"corporation":false,"usgs":false,"family":"Kosempa","given":"M.","affiliations":[{"id":35758,"text":"FWC","active":true,"usgs":false}],"preferred":false,"id":815614,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Edwards, H.","contributorId":258052,"corporation":false,"usgs":false,"family":"Edwards","given":"H.","email":"","affiliations":[{"id":35758,"text":"FWC","active":true,"usgs":false}],"preferred":false,"id":815615,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rood, K.","contributorId":258054,"corporation":false,"usgs":false,"family":"Rood","given":"K.","email":"","affiliations":[{"id":35758,"text":"FWC","active":true,"usgs":false}],"preferred":false,"id":815616,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barton, S.","contributorId":258057,"corporation":false,"usgs":false,"family":"Barton","given":"S.","email":"","affiliations":[{"id":52219,"text":"Mote","active":true,"usgs":false}],"preferred":false,"id":815617,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":815618,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223118,"text":"70223118 - 2021 - Knowledge inventory of foundational data products in planetary science","interactions":[],"lastModifiedDate":"2021-08-11T12:27:54.283197","indexId":"70223118","displayToPublicDate":"2021-01-29T07:26:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8607,"text":"The Planetary Science Journal","active":true,"publicationSubtype":{"id":10}},"title":"Knowledge inventory of foundational data products in planetary science","docAbstract":"

Some of the key components of any Planetary Spatial Data Infrastructure (PDSI) are the data products that end-users wish to discover, access, and interrogate. One precursor to the implementation of a PSDI is a knowledge inventory that catalogs what products are available, from which data producers, and at what initially understood data qualities. We present a knowledge inventory of foundational PSDI data products: geodetic coordinate reference frames, elevation or topography, and orthoimages or orthomosaics. Additionally, we catalog the available gravity models that serve as critical data for the assessment of spatial location, spatial accuracy, and ultimately spatial efficacy. We strengthen our previously published definitions of foundational data products to assist in solidifying a common vocabulary that will improve communication about these essential data products.

","language":"English","publisher":"IOP Science","doi":"10.3847/psj/abcb94","usgsCitation":"Laura, J., and Beyer, R.A., 2021, Knowledge inventory of foundational data products in planetary science: The Planetary Science Journal, v. 2, no. 1, 18, 28 p., https://doi.org/10.3847/psj/abcb94.","productDescription":"18, 28 p.","ipdsId":"IP-115047","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":453661,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3847/psj/abcb94","text":"Publisher Index Page"},{"id":387838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Laura, Jason 0000-0002-1377-8159","orcid":"https://orcid.org/0000-0002-1377-8159","contributorId":222124,"corporation":false,"usgs":true,"family":"Laura","given":"Jason","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":821035,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beyer, Ross A.","contributorId":264165,"corporation":false,"usgs":false,"family":"Beyer","given":"Ross","email":"","middleInitial":"A.","affiliations":[{"id":37319,"text":"SETI Institute","active":true,"usgs":false}],"preferred":false,"id":821036,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70217754,"text":"70217754 - 2021 - Integrated hierarchical models to inform management of transitional habitat and the recovery of a habitat specialist","interactions":[],"lastModifiedDate":"2021-02-01T17:12:01.757871","indexId":"70217754","displayToPublicDate":"2021-01-28T11:03:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Integrated hierarchical models to inform management of transitional habitat and the recovery of a habitat specialist","docAbstract":"

Quantifying the contribution of habitat dynamics relative to intrinsic population processes in regulating species persistence remains an ongoing challenge in ecological and applied conservation. Understanding these drivers and their relationship is essential for managing habitat‐dependent species, especially those that specialize in transitional habitats. Limitations in the ability of natural disturbance to mediate transitional habitat dynamics have resulted in a decline in early‐ and mid‐successional vegetation structure and prompted the need for aggressive habitat management to replace natural perturbations and increase habitat structural complexity. We describe a collaborative effort with a group of independent land managers to design an adaptive management program for restoring an imperiled ecosystem and recovering declining populations of an endemic habitat specialist. We developed a set of integrated, hierarchical models to estimate management‐mediated transition rates among vegetation classes in two dominant scrub communities and the species response (local colonization and extinction probabilities) as a function of habitat state. Models were fit using a long‐term data set of habitat and occupancy observations from 361 Florida scrub‐jay territories across two Florida counties. Occupancy model results correspond closely to previous approaches of estimating differential survival and reproductive success associated with habitat conditions, with highest colonization and lowest extinction rates estimated for those habitat states found to have the highest rates of survival and reproduction. In addition to offering an innovative approach for jointly modeling habitat and species population dynamics, the program we describe will also be of interest from a management perspective by providing guidance for developing collaborative, adaptive management frameworks from the ground up. We engaged land managers via workshops to specify objectives and desired state‐variable conditions, identify management alternatives, and elicit consensus opinions on model parameters. Treating expert opinions as pseudo‐observations to define Dirichlet priors allowed us to make use of existing management knowledge. Formal learning was then accumulated by updating transition probability estimates as management activities were implemented over the study period. We believe this adaptive management framework provides a useful approach for increasing our understanding of complex ecological relationships and hope that it will be adopted by others who have interest in informing management and conservation efforts.

","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.3306","usgsCitation":"Eaton, M.J., Breininger, D., Nichols, J.D., Paul, F., McGee, S., Smurl, M., DeMeyer, D., Baker, J., and Zondervan, M.B., 2021, Integrated hierarchical models to inform management of transitional habitat and the recovery of a habitat specialist: Ecosphere, v. 12, no. 1, e03306, 26 p., https://doi.org/10.1002/ecs2.3306.","productDescription":"e03306, 26 p.","ipdsId":"IP-115268","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":488926,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3306","text":"Publisher Index Page"},{"id":382851,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","county":"Brevard County, Indian River County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.1285400390625,\n 27.668934069896217\n ],\n [\n -80.4034423828125,\n 27.668934069896217\n ],\n [\n -80.4034423828125,\n 28.64479960910591\n ],\n [\n -81.1285400390625,\n 28.64479960910591\n ],\n [\n -81.1285400390625,\n 27.668934069896217\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Eaton, Mitchell J. 0000-0001-7324-6333","orcid":"https://orcid.org/0000-0001-7324-6333","contributorId":213526,"corporation":false,"usgs":true,"family":"Eaton","given":"Mitchell","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":809484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Breininger, David","contributorId":248597,"corporation":false,"usgs":false,"family":"Breininger","given":"David","affiliations":[{"id":49958,"text":"NASA Ecology Program","active":true,"usgs":false}],"preferred":false,"id":809485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":200533,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":809486,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paul, F.","contributorId":248598,"corporation":false,"usgs":false,"family":"Paul","given":"F.","affiliations":[],"preferred":false,"id":809487,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McGee, Samantha","contributorId":248609,"corporation":false,"usgs":false,"family":"McGee","given":"Samantha","email":"","affiliations":[],"preferred":false,"id":809522,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smurl, Michelle","contributorId":248610,"corporation":false,"usgs":false,"family":"Smurl","given":"Michelle","email":"","affiliations":[],"preferred":false,"id":809523,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"DeMeyer, David","contributorId":248611,"corporation":false,"usgs":false,"family":"DeMeyer","given":"David","email":"","affiliations":[],"preferred":false,"id":809524,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Baker, Jonny","contributorId":248612,"corporation":false,"usgs":false,"family":"Baker","given":"Jonny","email":"","affiliations":[],"preferred":false,"id":809525,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Zondervan, Maria B.","contributorId":248614,"corporation":false,"usgs":false,"family":"Zondervan","given":"Maria","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":809526,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70218170,"text":"70218170 - 2021 - Joint species distribution models of Everglades wading birds to inform restoration planning","interactions":[],"lastModifiedDate":"2023-07-07T14:08:20.276686","indexId":"70218170","displayToPublicDate":"2021-01-28T10:04:37","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":"Joint species distribution models of Everglades wading birds to inform restoration planning","docAbstract":"

Restoration of the Florida Everglades, a substantial wetland ecosystem within the United States, is one of the largest ongoing restoration projects in the world. Decision-makers and managers within the Everglades ecosystem rely on ecological models forecasting indicator wildlife response to changes in the management of water flows within the system. One such indicator of ecosystem health, the presence of wading bird communities on the landscape, is currently assessed using three species distribution models that assume perfect detection and report output on different scales that are challenging to compare against one another. We sought to use current advancements in species distribution modeling to improve models of Everglades wading bird distribution. Using a joint species distribution model that accounted for imperfect detection, we modeled the presence of nine species of wading bird simultaneously in response to annual hydrologic conditions and landscape characteristics within the Everglades system. Our resulting model improved upon the previous model in three key ways: 1) the model predicts probability of occupancy for the nine species on a scale of 0–1, making the output more intuitive and easily comparable for managers and decision-makers that must consider the responses of several species simultaneously; 2) through joint species modeling, we were able to consider rarer species within the modeling that otherwise are detected in too few numbers to fit as individual models; and 3) the model explicitly allows detection probability of species to be less than 1 which can reduce bias in the site occupancy estimates. These improvements are essential as Everglades restoration continues and managers require models that consider the impacts of water management on key indicator wildlife such as the wading bird community.

","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0245973","usgsCitation":"D’Acunto, L., Pearlstine, L.G., and Romanach, S., 2021, Joint species distribution models of Everglades wading birds to inform restoration planning: PLoS ONE, v. 16, no. 1, e0245973, 21 p.; Data Release, https://doi.org/10.1371/journal.pone.0245973.","productDescription":"e0245973, 21 p.; Data Release","ipdsId":"IP-119201","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":453665,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0245973","text":"Publisher Index Page"},{"id":383272,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418748,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P934K8T0","text":"EverWaders species distribution model development and output in the Greater Everglades from 2000-2009","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.419921875,\n 25.209911213827688\n ],\n [\n -80.474853515625,\n 25.27450351782018\n ],\n [\n -80.562744140625,\n 25.348990395713393\n ],\n [\n -80.562744140625,\n 25.55730943578312\n ],\n [\n -80.299072265625,\n 26.288490072961164\n ],\n [\n -80.1617431640625,\n 26.426308999847024\n ],\n [\n -80.26611328125,\n 26.711266913515747\n ],\n [\n -80.4473876953125,\n 26.716173757934094\n ],\n [\n -80.5133056640625,\n 26.554136386183785\n ],\n [\n -80.79345703125,\n 26.504988828743404\n ],\n [\n -81.0296630859375,\n 26.455820238459893\n ],\n [\n -81.00082397460938,\n 26.292183791046618\n ],\n [\n -81.47872924804688,\n 26.223215001067558\n ],\n [\n -81.64352416992188,\n 26.172694044887898\n ],\n [\n -81.78085327148438,\n 26.078987535225927\n ],\n [\n -81.74514770507812,\n 25.90617390922084\n ],\n [\n -81.6339111328125,\n 25.808545671771615\n ],\n [\n -81.38259887695312,\n 25.720735134412106\n ],\n [\n -81.24252319335938,\n 25.492868271257127\n ],\n [\n -81.18209838867188,\n 25.370086680063082\n ],\n [\n -81.17660522460938,\n 25.21488107113259\n ],\n [\n -81.03790283203125,\n 25.069429002821355\n ],\n [\n -80.58059692382812,\n 25.14652775303499\n ],\n [\n -80.419921875,\n 25.209911213827688\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"D’Acunto, Laura 0000-0001-6227-0143","orcid":"https://orcid.org/0000-0001-6227-0143","contributorId":215343,"corporation":false,"usgs":true,"family":"D’Acunto","given":"Laura","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":810303,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearlstine, Leonard G.","contributorId":34751,"corporation":false,"usgs":false,"family":"Pearlstine","given":"Leonard","email":"","middleInitial":"G.","affiliations":[{"id":12462,"text":"U.S. Department of the Interior, National Park Service","active":true,"usgs":false}],"preferred":false,"id":810304,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romanach, Stephanie 0000-0003-0271-7825","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":223479,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":810305,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225621,"text":"70225621 - 2021 - The 2018 update of the US National Seismic Hazard Model: Where, why, and how much probabilistic ground motion maps changed","interactions":[],"lastModifiedDate":"2021-10-28T13:21:41.798091","indexId":"70225621","displayToPublicDate":"2021-01-28T08:15:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"The 2018 update of the US National Seismic Hazard Model: Where, why, and how much probabilistic ground motion maps changed","docAbstract":"

The 2018 US Geological Survey National Seismic Hazard Model (NSHM) incorporates new data and updated science to improve the underlying earthquake and ground motion forecasts for the conterminous United States. The NSHM considers many new data and component input models: (1) new earthquakes between 2013 and 2017 and updated earthquake magnitudes for some earlier earthquakes; (2) two updated smoothed seismicity models to forecast earthquake rates; (3) two suites of new central and eastern US (CEUS) ground motion models (GMMs) to translate ground shaking for various earthquake sizes and source-to-site distances considered in the model; (4) two CEUS GMMs for aleatory variability; (5) two CEUS site-effect models that modify ground shaking based on alternative shallow site conditions; (6) more advanced western US (WUS) lithologic and structural information to assess basin site effects for selected urban regions; and (7) a more comprehensive range of outputs (22 periods and 8 site classes) than in previous versions of the NSHMs. Each of these new datasets and models produces changes in the probabilistic ground shaking levels that are spatially and statistically analyzed. Recent earthquakes or changes to some older earthquake magnitudes and locations mostly result in probabilistic ground shaking levels that are similar to previous models, but local changes can reach up to +80% and −60% compared to the 2014 model. Newly developed CEUS models for GMMs, aleatory variability, and site effects cause overall changes up to ±64%. The addition of the WUS basin amplifications causes changes of up to +60% at longer periods for sites overlying deep soft soils. Across the conterminous United States, the hazard changes in the model are mainly caused by new GMMs in the CEUS, by sedimentary basin effects for long periods (≥1 s) in the WUS, and by seismicity changes for short (0.2 s) and long (1 s) periods for both areas.

","language":"English","publisher":"Earthquake Engineering Research Institute","doi":"10.1177/8755293020988016","usgsCitation":"Petersen, M.D., Shumway, A., Powers, P.M., Mueller, C.S., Moschetti, M.P., Frankel, A.D., Rezaeian, S., McNamara, D., Luco, N., Boyd, O.S., Rukstales, K.S., Jaiswal, K.S., Thompson, E.M., Hoover, S., Clayton, B., Field, E.H., and Zeng, Y., 2021, The 2018 update of the US National Seismic Hazard Model: Where, why, and how much probabilistic ground motion maps changed: Earthquake Spectra, v. 37, no. 2, p. 959-987, https://doi.org/10.1177/8755293020988016.","productDescription":"29 p.","startPage":"959","endPage":"987","ipdsId":"IP-123826","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":436531,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PCEE26","text":"USGS data release","linkHelpText":"Data 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field@usgs.gov","orcid":"https://orcid.org/0000-0001-8172-7882","contributorId":52242,"corporation":false,"usgs":true,"family":"Field","given":"Edward","email":"field@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":825973,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Zeng, Yuehua 0000-0003-1161-1264 zeng@usgs.gov","orcid":"https://orcid.org/0000-0003-1161-1264","contributorId":145693,"corporation":false,"usgs":true,"family":"Zeng","given":"Yuehua","email":"zeng@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":825974,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70217819,"text":"70217819 - 2021 - Future regulated flows of the Colorado River in Grand Canyon foretell decreased areal extent of sediment and increases in riparian vegetation","interactions":[],"lastModifiedDate":"2021-02-04T13:58:17.537836","indexId":"70217819","displayToPublicDate":"2021-01-28T07:53:02","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":"Future regulated flows of the Colorado River in Grand Canyon foretell decreased areal extent of sediment and increases in riparian vegetation","docAbstract":"

Sediment transfer, or connectivity, by aeolian processes between channel-proximal and upland deposits in river valleys is important for the maintenance of river corridor biophysical characteristics. In regulated river systems, dams control the magnitude and duration of discharge. Alterations to the flow regime driven by dams that increase the inundation duration of sediment, or which drive the encroachment of vegetation into areas formerly composed of labile sediment and result in channel narrowing, may reduce sediment transfer from near-channel deposits to uplands via aeolian processes. Employing spatial methods developed by Kasprak et al (2018 Prog. Phys. Geogr.), here we use data describing the areal extent of bare (i.e. subaerially exposed and non-vegetated) sediment along 168 km of the Colorado River downstream from Glen Canyon Dam in Grand Canyon, USA, in conjunction with inundation extent modeling to forecast how future flows of this highly regulated river will drive changes in the areal extent of sediment available for aeolian transport. We also compare modern bare sediment area to that which presumably would have existed under pre-dam hydrographs. Over the next two decades, the planned flow regime from Glen Canyon Dam will result in slight decreases in bare sediment area (−1%) on an annual scale. This is in contrast to pre-dam years, when unregulated low flows led to marked increases in bare sediment area as compared to the current discharge regime. Our findings also indicate that ~75% of bare sediment in the study reach is inundated continuously at present, owing to increased baseflows in the post-dam flow regime; consequently, any reductions in flows below modern-day low discharges have the potential to expose large areas of bare sediment. We use vegetation modeling to quantify areas susceptible to vegetation encroachment under future flows, finding that 80% of bare sediment area is suitable for colonization by invasive tamarisk under the current flow regime. Our findings imply that the Colorado River in Grand Canyon, a system marked by widespread erosion of sediment resources and encroachment of riparian vegetation in the post-dam period, is likely to continue to see decreasing bare sediment extent over the coming decades in the absence of direct intervention through flow regime modification or widespread vegetation removal.

","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/abc9e4","usgsCitation":"Kasprak, A., Sankey, J.B., and Butterfield, B.J., 2021, Future regulated flows of the Colorado River in Grand Canyon foretell decreased areal extent of sediment and increases in riparian vegetation: Environmental Research Letters, v. 16, no. 1, 014029, 14 p., https://doi.org/10.1088/1748-9326/abc9e4.","productDescription":"014029, 14 p.","ipdsId":"IP-120844","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":486997,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/abc9e4","text":"Publisher Index Page"},{"id":436532,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P918E2P3","text":"USGS data release","linkHelpText":"Discharge records and sand extents along the Colorado River between Glen Canyon Dam and Phantom Ranch, Arizona"},{"id":382945,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.97241210937499,\n 35.576916524038616\n ],\n [\n -111.434326171875,\n 35.576916524038616\n ],\n [\n -111.434326171875,\n 36.57142382346277\n ],\n [\n -112.97241210937499,\n 36.57142382346277\n ],\n [\n -112.97241210937499,\n 35.576916524038616\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Kasprak, Alan 0000-0001-8184-6128","orcid":"https://orcid.org/0000-0001-8184-6128","contributorId":245742,"corporation":false,"usgs":false,"family":"Kasprak","given":"Alan","affiliations":[{"id":49307,"text":"Current: Utah State University. Former: Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, U.S. Geological Survey, Flagstaff, AZ 86001, USA","active":true,"usgs":false}],"preferred":false,"id":809824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":809825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Butterfield, Bradley J. 0000-0003-0974-9811","orcid":"https://orcid.org/0000-0003-0974-9811","contributorId":167009,"corporation":false,"usgs":false,"family":"Butterfield","given":"Bradley","email":"","middleInitial":"J.","affiliations":[{"id":24591,"text":"Merriam-Powell Center for Environmental Research and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":809826,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70220311,"text":"70220311 - 2021 - The optical river bathymetry toolkit","interactions":[],"lastModifiedDate":"2021-05-04T12:12:56.975607","indexId":"70220311","displayToPublicDate":"2021-01-28T07:10:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"The optical river bathymetry toolkit","docAbstract":"

Spatially distributed information on water depth is essential for many applications in river research and management and, under certain circumstances, can be inferred from remotely sensed data. Although fluvial remote sensing has emerged as a rapidly developing subdiscipline of the riverine sciences, more widespread adoption of these techniques has been hindered by a lack of accessible software. The Optical River Bathymetry Toolkit (ORByT) fills this void by providing a standalone package for mapping water depth from passive optical image data. The ORByT interface enables end users to import images and field‐based depth measurements, create and refine water masks, and perform spectrally based depth retrieval via an Optimal Band Ratio Analysis algorithm. The resulting bathymetric map can be exported as an image file, point cloud, and/or cross section; a thorough accuracy assessment also is incorporated into the workflow. In addition, image‐derived depth estimates can be subtracted from water surface elevations to obtain bed elevations suitable for input to a hydrodynamic model. Potential users of ORByT must bear in mind the inherent limitations of passive optical remote sensing: reliable bathymetry can only be inferred in clear‐flowing, shallow streams; this approach is not appropriate for more turbid, deeper rivers.

","language":"English","publisher":"Wiley","doi":"10.1002/rra.3773","usgsCitation":"Legleiter, C.J., 2021, The optical river bathymetry toolkit: River Research and Applications, v. 4, no. 37, p. 555-568, https://doi.org/10.1002/rra.3773.","productDescription":"14 p.","startPage":"555","endPage":"568","ipdsId":"IP-119553","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":453673,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.3773","text":"Publisher Index Page"},{"id":385444,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"37","noUsgsAuthors":false,"publicationDate":"2021-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","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":815120,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70218778,"text":"70218778 - 2021 - Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty","interactions":[],"lastModifiedDate":"2021-05-13T15:53:26.945858","indexId":"70218778","displayToPublicDate":"2021-01-28T07:10:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty","docAbstract":"
  1. Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision‐making at multiple scales.
  2. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on‐the‐ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal‐explicit, pixel‐level estimates of uncertainty. We evaluated the model with 5,780 on‐the‐ground vegetation plots removed from the training data.
  3. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product.
  4. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel‐level uncertainty. The new product is available on the Rangeland Analysis Platform (https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on‐the‐ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision‐making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.
","language":"English","publisher":"Wiley","doi":"10.1111/2041-210X.13564","usgsCitation":"Allred, B.W., Bestelmeyer, B.T., Boyd, C.S., Brown, C., Davies, K.W., Duniway, M.C., Ellsworth, L.M., Erickson, T.A., Fuhlendorf, S.D., Griffiths, T.V., Jansen, V., Jones, M.O., Karl, J.W., Knight, A.C., Maestas, J.D., Maynard, J.J., McCord, S.E., Naugle, D., Starns, H.D., Twidwell, D., and Uden, D.R., 2021, Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty: Methods in Ecology and Evolution, v. 12, no. 5, p. 841-849, https://doi.org/10.1111/2041-210X.13564.","productDescription":"9 p.","startPage":"841","endPage":"849","ipdsId":"IP-122860","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":453676,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/2041-210x.13564","text":"External Repository"},{"id":384299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-02-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Allred, Brady W.","contributorId":255105,"corporation":false,"usgs":false,"family":"Allred","given":"Brady","email":"","middleInitial":"W.","affiliations":[{"id":51432,"text":"W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, 59812, USA","active":true,"usgs":false}],"preferred":false,"id":811805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bestelmeyer, Brandon T.","contributorId":26180,"corporation":false,"usgs":false,"family":"Bestelmeyer","given":"Brandon","email":"","middleInitial":"T.","affiliations":[{"id":6973,"text":"USDA-ARS Jornada Experimental Range and Jornada Basin LTER, Las Cruces, NM; New Mexico State University, Dept. of Plant and Environmental Sciences, Las Cruces, NM","active":true,"usgs":false}],"preferred":false,"id":811806,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyd, Chad S.","contributorId":255106,"corporation":false,"usgs":false,"family":"Boyd","given":"Chad","email":"","middleInitial":"S.","affiliations":[{"id":51433,"text":"Eastern Oregon Agricultural Research Center, USDA Agricultural Research Service, Burns, OR 97720 USA","active":true,"usgs":false}],"preferred":false,"id":811807,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Christopher","contributorId":255107,"corporation":false,"usgs":false,"family":"Brown","given":"Christopher","affiliations":[{"id":51434,"text":"Google, Inc., Mountain View, CA 94043, USA","active":true,"usgs":false}],"preferred":false,"id":811808,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davies, Kirk W.","contributorId":255108,"corporation":false,"usgs":false,"family":"Davies","given":"Kirk","email":"","middleInitial":"W.","affiliations":[{"id":51433,"text":"Eastern Oregon Agricultural Research Center, USDA Agricultural Research Service, Burns, OR 97720 USA","active":true,"usgs":false}],"preferred":false,"id":811809,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":811810,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ellsworth, Lisa M.","contributorId":255109,"corporation":false,"usgs":false,"family":"Ellsworth","given":"Lisa","email":"","middleInitial":"M.","affiliations":[{"id":51436,"text":"Fisheries and Wildlife Department, Oregon State University, Corvallis, Oregon 97331 USA","active":true,"usgs":false}],"preferred":false,"id":811811,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Erickson, Tyler A.","contributorId":255110,"corporation":false,"usgs":false,"family":"Erickson","given":"Tyler","email":"","middleInitial":"A.","affiliations":[{"id":51434,"text":"Google, Inc., Mountain View, CA 94043, USA","active":true,"usgs":false}],"preferred":false,"id":811812,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fuhlendorf, Samuel D.","contributorId":171488,"corporation":false,"usgs":false,"family":"Fuhlendorf","given":"Samuel","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":811813,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Griffiths, Timothy V.","contributorId":255111,"corporation":false,"usgs":false,"family":"Griffiths","given":"Timothy","email":"","middleInitial":"V.","affiliations":[{"id":51437,"text":"USDA Natural Resources Conservation Service, Landscape Initiatives Team, Bozeman, MT 59715, USA","active":true,"usgs":false}],"preferred":false,"id":811814,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jansen, Vincent","contributorId":255112,"corporation":false,"usgs":false,"family":"Jansen","given":"Vincent","email":"","affiliations":[{"id":51438,"text":"Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, ID 83844, USA","active":true,"usgs":false}],"preferred":false,"id":811815,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Jones, Matthew O.","contributorId":169805,"corporation":false,"usgs":false,"family":"Jones","given":"Matthew","email":"","middleInitial":"O.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":811816,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Karl, Jason W.","contributorId":191703,"corporation":false,"usgs":false,"family":"Karl","given":"Jason","email":"","middleInitial":"W.","affiliations":[{"id":7045,"text":"USDA-ARS Jornada Experimental Range ","active":true,"usgs":false}],"preferred":false,"id":811817,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Knight, Anna C. 0000-0002-9455-2855","orcid":"https://orcid.org/0000-0002-9455-2855","contributorId":255113,"corporation":false,"usgs":true,"family":"Knight","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":811818,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Maestas, Jeremy D.","contributorId":219258,"corporation":false,"usgs":false,"family":"Maestas","given":"Jeremy","email":"","middleInitial":"D.","affiliations":[{"id":39978,"text":"USDA Natural Resources Conservation Service, Redmond, OR","active":true,"usgs":false}],"preferred":false,"id":811819,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Maynard, Jonathan J.","contributorId":216782,"corporation":false,"usgs":false,"family":"Maynard","given":"Jonathan","email":"","middleInitial":"J.","affiliations":[{"id":39514,"text":"USDA-Agricultural Resource Service, Jornada Experimental Range, Las Cruces, NM 88003, USA","active":true,"usgs":false}],"preferred":false,"id":811820,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"McCord, Sarah E.","contributorId":195931,"corporation":false,"usgs":false,"family":"McCord","given":"Sarah","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":811821,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Naugle, David E.","contributorId":255114,"corporation":false,"usgs":false,"family":"Naugle","given":"David E.","affiliations":[{"id":51432,"text":"W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, 59812, USA","active":true,"usgs":false}],"preferred":false,"id":811822,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Starns, Heath D.","contributorId":131091,"corporation":false,"usgs":false,"family":"Starns","given":"Heath","email":"","middleInitial":"D.","affiliations":[{"id":6960,"text":"Department of Biology, Texas State University","active":true,"usgs":false}],"preferred":false,"id":811823,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Twidwell, Dirac","contributorId":187431,"corporation":false,"usgs":false,"family":"Twidwell","given":"Dirac","email":"","affiliations":[],"preferred":false,"id":811824,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Uden, Daniel R.","contributorId":219904,"corporation":false,"usgs":false,"family":"Uden","given":"Daniel","email":"","middleInitial":"R.","affiliations":[{"id":40095,"text":"Nebraska Cooperative Fish and Wildlife Unit, School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE","active":true,"usgs":false}],"preferred":false,"id":811825,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70217663,"text":"sir20205134 - 2021 - Groundwater flow conceptualization of the Pahute Mesa–Oasis Valley Groundwater Basin, Nevada—A synthesis of geologic, hydrologic, hydraulic-property, and tritium data","interactions":[],"lastModifiedDate":"2021-01-28T01:40:20.23064","indexId":"sir20205134","displayToPublicDate":"2021-01-27T12:05:58","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":"2020-5134","displayTitle":"Groundwater Flow Conceptualization of the Pahute Mesa–Oasis Valley Groundwater Basin, Nevada: A Synthesis of Geologic, Hydrologic, Hydraulic-Property, and Tritium Data","title":"Groundwater flow conceptualization of the Pahute Mesa–Oasis Valley Groundwater Basin, Nevada—A synthesis of geologic, hydrologic, hydraulic-property, and tritium data","docAbstract":"

This report provides a groundwater-flow conceptualization that integrates geologic, hydrologic, hydraulic-property, and radionuclide data in the Pahute Mesa–Oasis Valley (PMOV) groundwater basin, southern Nevada. Groundwater flow in the PMOV basin is of interest because 82 underground nuclear tests were detonated, most near or below the water table. A potentiometric map and nine sets of hydrostratigraphic and hydrologic cross sections supplement the conceptualization. 

Potentiometric contours indicate that groundwater in the PMOV basin generally flows south-southwest and discharges at Oasis Valley. Groundwater encounters an alternating sequence of low- and high-transmissivity rocks, referred to as dams and pools, respectively, as it moves from east to west across eastern Pahute Mesa. Flow from all Pahute Mesa nuclear tests is to Oasis Valley and is well-constrained by water-level data. Flow converges along a corridor of high transmissivity between Pahute Mesa and Oasis Valley. 

The location of the lateral PMOV basin boundary is well defined, and this boundary, with a few minor exceptions, represents a no-flow boundary. Some boundary uncertainty exists in the northeastern part of the basin, but potential flow-rate estimates across the northeastern boundary resulting from this uncertainty are small relative to the basin groundwater budget. 

Recharge in the PMOV basin is derived from episodic pulses of modern water and the diffuse percolation of old water (greater than 1,000 years). Episodic recharge is a minor recharge component observed as a rise in groundwater levels that occurs 3 months to 1 year following a wet winter. Minor amounts of episodic recharge through an unsaturated zone in excess of 1,000 feet (ft) requires preferential flow through faults and fractures. The dominant recharge component is slow, steady, diffuse percolation of old water through the unsaturated zone. A large component of old water recharging the groundwater system is consistent with observations of isotopically light deuterium and oxygen 18 compositions in water from wells on Pahute Mesa and central Oasis Valley. About half the recharge in the PMOV basin is derived from the eastern Pahute Mesa area. The remaining recharge is derived primarily from other highland areas including Timber Mountain, Belted and Kawich Ranges, and Black Mountain. 

The PMOV groundwater system is nearly steady state, where recharge is balanced by the 5,900 acre-feet per year of natural discharge at Oasis Valley. This assumption is reasonable because the basin is dominated by steady-state conditions, where long-term changes in groundwater storage are minimal. Total groundwater withdrawals from 1963 to 2018 have amounted to less than 10 percent of annual groundwater discharge and less than 0.2 percent of the basin’s groundwater storage. Therefore, present-day (2020) conditions are considered representative of predevelopment (pre-1950) conditions in nearly all areas of the basin. 

The lower PMOV basin boundary is defined at 4,000 ft below the water table to encompass all underground nuclear tests and tritium plumes. This boundary defines the lower boundary of radionuclide migration. However, nearly all flow and tritium transport occur in the upper 1,600 ft of the saturated zone because a transmissivity-with-depth relation indicates that greater than 90 percent of the transmissivity contributing to groundwater flow occurs within 1,600 ft of the water table. Rocks at deeper depths have low transmissivity because argillic and mineralized alterations plug the fractures. 

Volcanic rocks form the primary aquifers and confining units in the PMOV basin. Volcanic hydrogeologic units (HGUs) and hydrostratigraphic units (HSUs) have transmissivity distributions that span up to eight orders of magnitude with considerable overlap between distributions. Despite the large overlap between units, mean transmissivities of aquifers are one-to-two orders of magnitude greater than the confining units. However, all volcanic-rock HGUs and HSUs are composite units, meaning that they can function spatially as either an aquifer or confining unit.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205134","collaboration":"Prepared in cooperation with the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office, Office of Environmental Management under Interagency Agreement, DE-EM0004969","usgsCitation":"Jackson, T.R., Fenelon, J.M., and Paylor, R.L., 2021, Groundwater flow conceptualization of the Pahute Mesa–Oasis Valley Groundwater Basin, Nevada—A synthesis of geologic, hydrologic, hydraulic-property, and tritium data: U.S. Geological Survey Scientific Investigations Report 2020–5134, 100 p., https://doi.org/10.3133/sir20205134.","productDescription":"Report: viii, 100 p.; 2 Plates: 26.00 x 42.00 inches and 120.01 x 36.00 inches; 7 Appendixes","onlineOnly":"Y","ipdsId":"IP-095406","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":382683,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix2.xlsx","text":"Appendix 2","size":"78 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 2"},{"id":382684,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix3.xlsm","text":"Appendix 3","size":"530 KB xlsm","description":"SIR 2020-5134 Appendix 3"},{"id":382685,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix4.xlsx","text":"Appendix 4","size":"6.1 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 4"},{"id":382681,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_plate02.pdf","text":"Plate 2","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5134 Plate 2"},{"id":382678,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5134/coverthb.jpg"},{"id":382679,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134.pdf","text":"Report","size":"9.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5134"},{"id":382680,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_plate01.pdf","text":"Plate 1","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5134 Plate 1"},{"id":382682,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix1.xlsx","text":"Appendix 1","size":"2.5 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 1"},{"id":382688,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix7.xlsx","text":"Appendix 7","size":"433 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 7"},{"id":382687,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix6.xlsx","text":"Appendix 6","size":"856 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 6"},{"id":382686,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix5.xlsx","text":"Appendix 5","size":"799 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 5"}],"country":"United States","state":"Nevada","otherGeospatial":"Pahute Mesa–Oasis Valley Groundwater Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.00,\n 36.65079252503471\n ],\n [\n -116.00,\n 36.65079252503471\n ],\n [\n -116.00,\n 38.00\n ],\n [\n -117.00,\n 38.00\n ],\n [\n -117.00,\n 36.65079252503471\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 95819

","tableOfContents":"","publishedDate":"2021-01-27","noUsgsAuthors":false,"publicationDate":"2021-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Jackson, Tracie R. 0000-0001-8553-0323 tjackson@usgs.gov","orcid":"https://orcid.org/0000-0001-8553-0323","contributorId":150591,"corporation":false,"usgs":true,"family":"Jackson","given":"Tracie","email":"tjackson@usgs.gov","middleInitial":"R.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":false,"id":809193,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fenelon, Joseph M. 0000-0003-4449-245X jfenelon@usgs.gov","orcid":"https://orcid.org/0000-0003-4449-245X","contributorId":2355,"corporation":false,"usgs":true,"family":"Fenelon","given":"Joseph","email":"jfenelon@usgs.gov","middleInitial":"M.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809194,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paylor, Randall L. 0000-0002-1059-6384","orcid":"https://orcid.org/0000-0002-1059-6384","contributorId":248456,"corporation":false,"usgs":true,"family":"Paylor","given":"Randall","email":"","middleInitial":"L.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":false,"id":809195,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229376,"text":"70229376 - 2021 - Estimating detection and occupancy coefficients for the Pacific Islands coral reef fish species","interactions":[],"lastModifiedDate":"2022-03-04T17:38:48.028579","indexId":"70229376","displayToPublicDate":"2021-01-27T11:23:36","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":6053,"text":"Hawaii Cooperative Studies Unit Technical Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"HCFRU-001","title":"Estimating detection and occupancy coefficients for the Pacific Islands coral reef fish species","docAbstract":"

The data-limited stock assessment models used to monitor the status of coral reef fish species in the Western Pacific region are dependent upon accurate estimates of standing stock biomass generated from underwater visual surveys of reefs. However, the imperfect detection of and variable occupancy of habitat by reef fishes are not currently accounted for in these estimates. Therefore, the objective of this project was to estimate detection and occupancy coefficients for the species listed in the Western Pacific Regional Fishery Management Council’s Fishery Ecosystem Plans by analyzing the Pacific Island Fishery Science Center-Coral Reef Ecosystem Program Reef Fish Dataset. These detection and occupancy coefficients would then be applied to refine standing stock biomass estimates. In general, species with higher detection probabilities and/or lower occupancy rates tended to exhibit the greatest differences in the estimates of standing stock biomass calculated with and without accounting for detection and occupancy. The standing stock biomass of most reef fish species seem to be underestimated when detection and occupancy are not accounted for. However, the standing stock biomass of larger-bodied targeted species, such as jacks, snappers, and groupers, seem to be over-estimated relative to the estimates generated when accounting for occupancy and detection. While there are still issues to resolve regarding how well the current data collection methods meet the underlying assumptions of the detection and occupancy modeling approach, the inclusion of detection and occupancy coefficients seems likely to improve estimates of standing stock biomass of coral reef fish species.

","language":"English","publisher":"Hawaii Cooperative Research Studies Unit","collaboration":"Western Pacific Regional Fishery Management Council","usgsCitation":"Suarez, B., and Grabowski, T.B., 2021, Estimating detection and occupancy coefficients for the Pacific Islands coral reef fish species: Hawaii Cooperative Studies Unit Technical Report HCFRU-001, 22 p.","productDescription":"22 p.","ipdsId":"IP-124358","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":396761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396760,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://dspace.lib.hawaii.edu/handle/10790/5553"}],"country":"Marianas Islands, United States","state":"Hawaii","otherGeospatial":"Pacific Remote Island Area,, Samoa","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Suarez, Bobbie","contributorId":287958,"corporation":false,"usgs":false,"family":"Suarez","given":"Bobbie","email":"","affiliations":[{"id":25429,"text":"UH","active":true,"usgs":false}],"preferred":false,"id":837231,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grabowski, Timothy B. 0000-0001-9763-8948 tgrabowski@usgs.gov","orcid":"https://orcid.org/0000-0001-9763-8948","contributorId":4178,"corporation":false,"usgs":true,"family":"Grabowski","given":"Timothy","email":"tgrabowski@usgs.gov","middleInitial":"B.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837230,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218237,"text":"70218237 - 2021 - Forecasting community reassembly using climate-linked spatio-temporal ecosystem models","interactions":[],"lastModifiedDate":"2021-04-08T14:55:31.493847","indexId":"70218237","displayToPublicDate":"2021-01-27T10:55:28","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting community reassembly using climate-linked spatio-temporal ecosystem models","docAbstract":"

Ecosystems are increasingly impacted by human activities, altering linkages among physical and biological components. Spatial community reassembly occurs when these human impacts modify the spatial overlap between system components, and there is need for practical tools to forecast spatial community reassembly at landscape scales using monitoring data. To illustrate a new approach, we extend a generalization of empirical orthogonal function (EOF) analysis, which involves a spatio‐temporal ecosystem model that approximates coupled physical, biological and human dynamics. We then demonstrate its application to five trophic levels for the eastern Bering Sea by fitting to multiple, spatially unbalanced datasets measuring physical characteristics (temperature measurements and climate‐linked forecasts), primary producers (spring and fall size‐fractionated chlorophyll‐a), secondary producers (copepods), juveniles (age‐0 walleye pollock), adult consumers (five commercially important fishes), human activities (seasonal fishing effort) and mobile predators (seabirds). We identify the spatial niche for each ecosystem component, as well as dominant modes of variability that are highly correlated with a known bottom–up driver of dynamics. We then measure spatial overlap between interacting variables (using Schoener's‐D) and identify that age‐0 pollock have decreased spatial overlap with copepods and increased overlap with adult pollock during warm years, and also that adult pollock have increased overlap with arrowtooth flounder and decreased overlap with catcher–processor fishing effort during these warm years. Given the warming conditions that are projected for the coming decade, the model forecasts increased prey and competitor overlap involving adult pollock (between age‐0 pollock, adult pollock and arrowtooth flounder) and decreased overlap with the copepod forage base and with the catcher–processor fishery during future warming. We recommend that joint species distribution models be extended to incorporate ‘ecological teleconnections' (correlations between distant locations arising from known mechanisms) arising from behavioral adaptation by mobile animals as well as passive advection of nutrients and planktonic juvenile stages.

","language":"English","publisher":"Wiley","doi":"10.1111/ecog.05471","usgsCitation":"Thorson, J., Arimitsu, M.L., Barnett, L., Cheng, W., Eisner, L., Haynie, A., Hermann, A., Holsman, K., Kimmel, D., Lomas, M., Richar, J., and Siddon, E., 2021, Forecasting community reassembly using climate-linked spatio-temporal ecosystem models: Ecography, v. 44, no. 4, p. 612-625, https://doi.org/10.1111/ecog.05471.","productDescription":"14 p.","startPage":"612","endPage":"625","ipdsId":"IP-119434","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":453681,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.05471","text":"Publisher Index Page"},{"id":383367,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Thorson, James","contributorId":251785,"corporation":false,"usgs":false,"family":"Thorson","given":"James","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":810579,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arimitsu, Mayumi L. 0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":810580,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnett, Lewis","contributorId":251786,"corporation":false,"usgs":false,"family":"Barnett","given":"Lewis","affiliations":[{"id":50398,"text":"JISAO","active":true,"usgs":false}],"preferred":false,"id":810581,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cheng, Wei","contributorId":251787,"corporation":false,"usgs":false,"family":"Cheng","given":"Wei","email":"","affiliations":[{"id":50399,"text":"JISAO, NOAA","active":true,"usgs":false}],"preferred":false,"id":810582,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eisner, Lisa","contributorId":251788,"corporation":false,"usgs":false,"family":"Eisner","given":"Lisa","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":810583,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Haynie, Alan","contributorId":251789,"corporation":false,"usgs":false,"family":"Haynie","given":"Alan","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":810584,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hermann, Albert","contributorId":251790,"corporation":false,"usgs":false,"family":"Hermann","given":"Albert","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":810585,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Holsman, Kirsten","contributorId":251791,"corporation":false,"usgs":false,"family":"Holsman","given":"Kirsten","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":810586,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kimmel, David","contributorId":251792,"corporation":false,"usgs":false,"family":"Kimmel","given":"David","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":810587,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lomas, Michael","contributorId":251793,"corporation":false,"usgs":false,"family":"Lomas","given":"Michael","affiliations":[{"id":50400,"text":"Bigelow Lab for Ocean Sciences","active":true,"usgs":false}],"preferred":false,"id":810588,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Richar, Jon","contributorId":251794,"corporation":false,"usgs":false,"family":"Richar","given":"Jon","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":810589,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Siddon, Elizabeth","contributorId":251795,"corporation":false,"usgs":false,"family":"Siddon","given":"Elizabeth","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":810590,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70226209,"text":"70226209 - 2021 - Assessment of flood forecast products for a coupled tributary-Coastal model","interactions":[],"lastModifiedDate":"2021-11-17T13:49:27.905474","indexId":"70226209","displayToPublicDate":"2021-01-27T07:46:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of flood forecast products for a coupled tributary-Coastal model","docAbstract":"
Compound flooding, resulting from a combination of riverine and coastal processes, is a complex but important hazard to resolve along urbanized shorelines in the vicinity of river mouths. However, inland flooding models rarely consider oceanographic conditions, and vice versa for coastal flood models. Here, we describe the development of an operational, integrated coastal-watershed flooding model to address this issue of compound flooding in a highly urbanized estuarine environment, San Francisco Bay (CA, USA), where the surrounding communities are susceptible to flooding along the bay shoreline and inland rivers and creeks that drain to the bay. The integrated tributary-coastal forecast model (Hydro-Coastal Storm Modeling System, or Hydro-CoSMoS) was developed to provide water managers and other users with flood forecast information beyond what is currently available. Results presented here are focused on the interaction of the Napa River watershed and the San Pablo Bay at the northern end of San Francisco Bay. This paper describes the modeling setup, the scenario used in a tabletop exercise (TTE), and the assessment of the various flood forecast information products. Hydro-CoSMoS successfully demonstrated the capability to provide watershed and coastal flood information at scales and locations where no such information is currently available and was also successful in showing how tributary flows could be used to inform the coastal storm model during a flooding scenario. The TTE provided valuable feedback on how to guide continued model development and to inform what model outputs and formats are most useful to end-users. 
","language":"English","publisher":"MDPI","doi":"10.3390/w13030312","usgsCitation":"Cifelli, R., Johnson, L.E., Kim, J., Coleman, T., Pratt, G., Herdman, L.M., Martyr-Koller, R.C., Finzi-Hart, J., Erikson, L.H., Barnard, P.L., and Anderson, M., 2021, Assessment of flood forecast products for a coupled tributary-Coastal model: Water, v. 3, no. 13, 312, 21 p., https://doi.org/10.3390/w13030312.","productDescription":"312, 21 p.","ipdsId":"IP-125274","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453688,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13030312","text":"Publisher Index Page"},{"id":391794,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Pablo Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.794189453125,\n 37.89219554724437\n ],\n [\n -121.904296875,\n 37.89219554724437\n ],\n [\n -121.904296875,\n 38.65119833229951\n ],\n [\n -122.794189453125,\n 38.65119833229951\n ],\n [\n -122.794189453125,\n 37.89219554724437\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","issue":"13","noUsgsAuthors":false,"publicationDate":"2021-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Cifelli, Robert","contributorId":268882,"corporation":false,"usgs":false,"family":"Cifelli","given":"Robert","email":"","affiliations":[{"id":55708,"text":"NOAA Physical Sciences Laboratory","active":true,"usgs":false}],"preferred":false,"id":826880,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Lynn E.","contributorId":268883,"corporation":false,"usgs":false,"family":"Johnson","given":"Lynn","email":"","middleInitial":"E.","affiliations":[{"id":55708,"text":"NOAA Physical Sciences Laboratory","active":true,"usgs":false}],"preferred":false,"id":826881,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kim, Jungho 0000-0001-5596-1974","orcid":"https://orcid.org/0000-0001-5596-1974","contributorId":268884,"corporation":false,"usgs":false,"family":"Kim","given":"Jungho","email":"","affiliations":[],"preferred":false,"id":826882,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coleman, Tim","contributorId":213545,"corporation":false,"usgs":false,"family":"Coleman","given":"Tim","email":"","affiliations":[],"preferred":false,"id":826883,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pratt, Greg","contributorId":268885,"corporation":false,"usgs":false,"family":"Pratt","given":"Greg","email":"","affiliations":[{"id":55709,"text":"NOAA Global Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":826884,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":826885,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Martyr-Koller, Rosanne C. 0000-0002-0506-667X","orcid":"https://orcid.org/0000-0002-0506-667X","contributorId":260505,"corporation":false,"usgs":false,"family":"Martyr-Koller","given":"Rosanne","email":"","middleInitial":"C.","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":826886,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Finzi-Hart, Juliette 0000-0003-3179-2699","orcid":"https://orcid.org/0000-0003-3179-2699","contributorId":268886,"corporation":false,"usgs":false,"family":"Finzi-Hart","given":"Juliette","email":"","affiliations":[{"id":37487,"text":"formerly USGS","active":true,"usgs":false}],"preferred":false,"id":826887,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"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":826888,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"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":826889,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Anderson, Michael","contributorId":148971,"corporation":false,"usgs":false,"family":"Anderson","given":"Michael","affiliations":[],"preferred":false,"id":826890,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70224248,"text":"70224248 - 2021 - Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow 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Stream water temperature (Ts) is a variable of critical importance for aquatic ecosystem health. Ts is strongly affected by groundwater-surface water interactions which can be learned from streamflow records, but previously such information was challenging to effectively absorb with process-based models due to parameter equifinality. Based on the long short-term memory (LSTM) deep learning architecture, we developed a basin-centric lumped daily mean Ts model, which was trained over 118 data-rich basins with no major dams in the conterminous United States, and showed strong results. At a national scale, we obtained a median root-mean-square error of 0.69°C, Nash–Sutcliffe model efficiency coefficient of 0.985, and correlation of 0.994, which are marked improvements over previous values reported in literature. The addition of streamflow observations as a model input strongly elevated the performance of this model. In the absence of measured streamflow, we showed that a two-stage model could be used, where simulated streamflow from a pre-trained LSTM model (Qsim) still benefited the Ts model even though no new information was brought directly into the inputs of the Ts model. The model indirectly used information learned from streamflow observations provided during the training of Qsim, potentially to improve internal representation of physically meaningful variables. Our results indicate that strong relationships exist between basin-averaged forcing variables, catchment attributes, and Ts that can be simulated by a single model trained by data on the continental scale.

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Farshid","contributorId":265775,"corporation":false,"usgs":false,"family":"Rahmani","given":"Farshid","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":823345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lawson, Kathryn","contributorId":265776,"corporation":false,"usgs":false,"family":"Lawson","given":"Kathryn","affiliations":[{"id":54792,"text":"Civil and Environmental Engineering, Pennsylvania State University, University Park, PA","active":true,"usgs":false}],"preferred":false,"id":823346,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ouyang, Wenyu","contributorId":265777,"corporation":false,"usgs":false,"family":"Ouyang","given":"Wenyu","email":"","affiliations":[{"id":54793,"text":"School of Hydraulic Engineering, Dalian University of Technology, Dalian, China","active":true,"usgs":false}],"preferred":false,"id":823347,"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":823348,"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":823349,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shen, Chaopeng","contributorId":152465,"corporation":false,"usgs":false,"family":"Shen","given":"Chaopeng","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":823350,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70217701,"text":"70217701 - 2021 - Simulating hydrologic effects of wildfire on a small sub-alpine watershed in New Mexico, U.S.","interactions":[],"lastModifiedDate":"2023-04-10T22:11:19.232976","indexId":"70217701","displayToPublicDate":"2021-01-27T07:16:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3627,"text":"Transactions of the American Society of Agricultural and Biological Engineers","active":true,"publicationSubtype":{"id":10}},"title":"Simulating hydrologic effects of wildfire on a small sub-alpine watershed in New Mexico, U.S.","docAbstract":"

Streamflow records available before and after wildfire in a small, mixed conifer, sub-alpine monsoonal dominated watershed in New Mexico provided a unique opportunity to calibrate a watershed model (PRMS) for pre- and postfire conditions. The calibrated model was then used to simulate the hydrologic effects of fire. Simulated postfire surface runoff averaged 14.7 times greater than prefire for the 29-year simulation period. The relationship between precipitation and streamflow changed dramatically after wildfire, largely from a decreased influence of antecedent soil moisture (ASM) and increased influence of canopy factors (less interception) and soil factors (greater hydrophobicity, less infiltration) in controlling surface runoff. For higher ASM, simulated pre- and postfire streamflow was similarly variable. However, for moderate and lower ASM, soil water storage was too low to contribute baseflow for either prefire or postfire conditions, and thus postfire streamflow maintained a linear, surface runoff-dominated response to precipitation, whereas prefire streamflow showed little response. Postfire streamflow efficiency increased with ASM from a mean of 0.02 at the lowest ASM to 0.30 at the highest ASM, whereas prefire conditions showed no sensitivity to ASM at low to moderate ASM. Postfire streamflow increased (2.1 times greater median flow than prefire), particularly from increased surface runoff (14.7 times greater), which occurred across all ASM conditions. As a result, streamflow shifted from baseflow-dominated to surface runoff-dominated after wildfire. This result indicates that substantial increases in runoff efficiency (20% or more of precipitation volume) can occur across a range of ASM postfire, which may have severe consequences for flooding. This result also indicates that monitoring of soil moisture would enhance raingauge networks for early flood warning.

","language":"English","publisher":"American Society of Agricultural and Biological Engineers","doi":"10.13031/trans.13938","usgsCitation":"Moeser, C.D., and Douglas-Mankin, K.R., 2021, Simulating hydrologic effects of wildfire on a small sub-alpine watershed in New Mexico, U.S.: Transactions of the American Society of Agricultural and Biological Engineers, v. 64, no. 1, p. 137-150, https://doi.org/10.13031/trans.13938.","productDescription":"14 p.","startPage":"137","endPage":"150","ipdsId":"IP-101142","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":382749,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New 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David 0000-0003-0154-9110","orcid":"https://orcid.org/0000-0003-0154-9110","contributorId":214563,"corporation":false,"usgs":true,"family":"Moeser","given":"C.","email":"","middleInitial":"David","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809287,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":203927,"corporation":false,"usgs":true,"family":"Douglas-Mankin","given":"Kyle","email":"","middleInitial":"R.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809288,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70217875,"text":"70217875 - 2021 - Changing climate drives future streamflow declines and challenges in meeting water demand across the southwestern United States","interactions":[],"lastModifiedDate":"2021-02-09T13:17:48.692204","indexId":"70217875","displayToPublicDate":"2021-01-27T07:13:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5836,"text":"Journal of Hydrology X","onlineIssn":"2589-9155","active":true,"publicationSubtype":{"id":10}},"title":"Changing climate drives future streamflow declines and challenges in meeting water demand across the southwestern United States","docAbstract":"

Society and the environment in the arid southwestern United States depend on reliable water availability, yet current water use outpaces supply. Water demand is projected to grow in the future and climate change is expected to reduce supply. To adapt, water managers need robust estimates of future regional water supply to support management decisions. To address this need, we estimate future streamflow in seven water resource regions in the southwestern U.S. using a new SPAtially Referenced Regressions On Watershed attributes (SPARROW) streamflow model. We present streamflow projections corresponding to input data from seven climate models and two greenhouse gas Representative Concentration Pathways (RCP4.5 and 8.5) for three, thirty-year intervals centered on the 2030s, 2050s, and 2080s, and for a historical thirty year interval centered on the 1990s. Across water resource regions, about half of the RCP4.5 models (51%) and two thirds of the RCP8.5 models (67%) indicate decreases in streamflow in the 2080s relative to the historical period. Models project maximum decreases in streamflow of 36–80% in all water resource regions for all periods and RCPs relative to historical streamflow, and maximum streamflow decreases of up to 20–45% in the 2080s at sites along the Colorado River used for measuring compliance with interstate and international water agreements. Headwaters are projected to experience the greatest declines, with substantial downstream implications. Among these estimates, the streamflows from models forced with RCP8.5 tend to be lower than those forced with RCP4.5. Not all climate models, times, and RCPs project widespread streamflow declines. The most ubiquitous streamflow increases are projected to occur in the 2030s under RCP4.5. Later time periods and enhanced greenhouse gas forcings indicate smaller regions of streamflow increase and lower accumulated streamflows, suggesting that limiting or reducing greenhouse gas concentrations could support future water availability. Although some possible streamflow increases are promising, the modest and spatially limited increases in streamflow projected for later time periods are still unlikely to be sufficient to meet the projected water demand. These results inform the likelihood of future water agreement compliance, and support developing strategies to balance water supply and demand.

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