{"pageNumber":"215","pageRowStart":"5350","pageSize":"25","recordCount":46677,"records":[{"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":70220287,"text":"70220287 - 2021 - Quarterly wildlife mortality report January 2021","interactions":[],"lastModifiedDate":"2023-10-13T13:37:56.075106","indexId":"70220287","displayToPublicDate":"2021-01-29T07:45:42","publicationYear":"2021","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":9359,"text":"Wildlife Disease Association Newsletter","active":true,"publicationSubtype":{"id":30}},"title":"Quarterly wildlife mortality report January 2021","docAbstract":"The USGS National Wildlife Health Center (NWHC) Quarterly Mortality Report provides brief summaries of epizootic mortality and morbidity events by quarter. The write-ups, highlighting epizootic events and other wildlife disease topics of interest, are published in the Wildlife Disease Association quarterly newsletter. A link is provided in this WDA newsletter to the Wildlife Health Information Sharing Partnership event reporting system (WHISPers) so readers can view associated data.","language":"English","publisher":"Wildlife Disease Association","usgsCitation":"Richards, B.J., Bodenstein, B., Grear, D.A., Ip, H., Ballmann, A., Lankton, J.S., and Shearn-Bochsler, V.I., 2021, Quarterly wildlife mortality report January 2021: Wildlife Disease Association Newsletter, p. 15-17.","productDescription":"3 p.","startPage":"15","endPage":"17","ipdsId":"IP-125495","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":385398,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.wildlifedisease.org/PersonifyEbusiness/Resources/Publications/Newsletter/Archive","linkFileType":{"id":5,"text":"html"}},{"id":385413,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Richards, Bryan J. 0000-0001-9955-2523","orcid":"https://orcid.org/0000-0001-9955-2523","contributorId":219535,"corporation":false,"usgs":true,"family":"Richards","given":"Bryan","email":"","middleInitial":"J.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":815013,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bodenstein, Barbara L. 0000-0001-7946-0103 bbodenstein@usgs.gov","orcid":"https://orcid.org/0000-0001-7946-0103","contributorId":189820,"corporation":false,"usgs":true,"family":"Bodenstein","given":"Barbara","email":"bbodenstein@usgs.gov","middleInitial":"L.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":815014,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grear, Daniel A. 0000-0002-5478-1549 dgrear@usgs.gov","orcid":"https://orcid.org/0000-0002-5478-1549","contributorId":189819,"corporation":false,"usgs":true,"family":"Grear","given":"Daniel","email":"dgrear@usgs.gov","middleInitial":"A.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":815015,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ip, Hon S. 0000-0003-4844-7533","orcid":"https://orcid.org/0000-0003-4844-7533","contributorId":126815,"corporation":false,"usgs":true,"family":"Ip","given":"Hon S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":815016,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ballmann, Anne 0000-0002-0380-056X aballmann@usgs.gov","orcid":"https://orcid.org/0000-0002-0380-056X","contributorId":140319,"corporation":false,"usgs":true,"family":"Ballmann","given":"Anne","email":"aballmann@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":815017,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lankton, Julia S. 0000-0002-6843-4388 jlankton@usgs.gov","orcid":"https://orcid.org/0000-0002-6843-4388","contributorId":5888,"corporation":false,"usgs":true,"family":"Lankton","given":"Julia","email":"jlankton@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":815018,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shearn-Bochsler, Valerie I. 0000-0002-5590-6518 vbochsler@usgs.gov","orcid":"https://orcid.org/0000-0002-5590-6518","contributorId":3234,"corporation":false,"usgs":true,"family":"Shearn-Bochsler","given":"Valerie","email":"vbochsler@usgs.gov","middleInitial":"I.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":815019,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"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":70219570,"text":"70219570 - 2021 - Comparison of detection limits estimated using single- and multi-concentration spike-based and blank-based procedures","interactions":[],"lastModifiedDate":"2021-05-27T13:23:08.289537","indexId":"70219570","displayToPublicDate":"2021-01-29T07:04:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3517,"text":"Talanta","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of detection limits estimated using single- and multi-concentration spike-based and blank-based procedures","docAbstract":"

Spike- and blank-based procedures were applied to estimate the detection limits (DLs) for example analytes from inorganic and organic methods for water samples to compare with the U.S. Environmental Protection Agency's (EPA) Method Detection Limit (MDL) procedures (revisions 1.11 and 2.0). The multi-concentration spike-based procedures ASTM Within-laboratory Critical Level (DQCALC) and EPA's Lowest Concentration Minimum Reporting Level were compared in one application, with DQCALC further applied to many methods. The blank-based DLs, MDLb99 (99th percentile) or MDLbY (= mean blank concentration + s × t), estimated using large numbers (>100) of blank samples often provide DLs that better approach or achieve the desired ≤1% false positive risk level compared to spike-based DLs. For primarily organic methods that do not provide many uncensored blank results, spike-based DQCALC or MDL rev. 2.0 are needed to simulate the blank distribution and estimate the DL. DQCALC is especially useful for estimating DLs for multi-analyte methods having very different analyte response characteristics. Time series plots of DLs estimated using different procedures reveal that DLs are dependent on the applied procedure, should not be expected to be static over time, and seem best viewed as falling over a range versus being a single value. Use of both blank- and spike-based DL procedures help inform this DL range. Data reporting conventions that censor data at a threshold and report “less than” that threshold concentration as the reporting level have unknown and potentially high false negative risk. The U.S. Geological Survey National Water Quality Laboratory's Laboratory Reporting Level (LRL) convention (applied primarily to organic methods) attempts to simultaneously minimize both the false positive and false negative risk when <LRL is reported and data between DL and the higher LRL are allowed to be reported.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.talanta.2021.122139","usgsCitation":"Foreman, W.T., Williams, T.L., Furlong, E., Hemmerle, D., Stetson, S., Jha, V.K., Noriega, M., Decess, J.A., Reed-Parker, C., and Sandstrom, M.W., 2021, Comparison of detection limits estimated using single- and multi-concentration spike-based and blank-based procedures: Talanta, v. 228, 122139, 15 p., https://doi.org/10.1016/j.talanta.2021.122139.","productDescription":"122139, 15 p.","ipdsId":"IP-121087","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":436530,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MUSPFI","text":"USGS data release","linkHelpText":"Data from USGS National Water Quality Laboratory methods used to calculate and compare detection limits estimated using single- and multi-concentration spike-based and blank-based procedures"},{"id":385078,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"228","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Foreman, William T. 0000-0002-2530-3310 wforeman@usgs.gov","orcid":"https://orcid.org/0000-0002-2530-3310","contributorId":190786,"corporation":false,"usgs":true,"family":"Foreman","given":"William","email":"wforeman@usgs.gov","middleInitial":"T.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":814196,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Teresa Lynne 0000-0002-9507-9350","orcid":"https://orcid.org/0000-0002-9507-9350","contributorId":257407,"corporation":false,"usgs":true,"family":"Williams","given":"Teresa","email":"","middleInitial":"Lynne","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":814197,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Furlong, Edward 0000-0002-7305-4603","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":213730,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":814198,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hemmerle, Dawn 0000-0002-9495-6681","orcid":"https://orcid.org/0000-0002-9495-6681","contributorId":257409,"corporation":false,"usgs":true,"family":"Hemmerle","given":"Dawn","email":"","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":814199,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stetson, Sarah 0000-0002-4930-4748 sstetson@usgs.gov","orcid":"https://orcid.org/0000-0002-4930-4748","contributorId":216528,"corporation":false,"usgs":true,"family":"Stetson","given":"Sarah","email":"sstetson@usgs.gov","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":814200,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jha, Virendra K. 0000-0002-1076-0738 vkjha@usgs.gov","orcid":"https://orcid.org/0000-0002-1076-0738","contributorId":257416,"corporation":false,"usgs":true,"family":"Jha","given":"Virendra","email":"vkjha@usgs.gov","middleInitial":"K.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":814205,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Noriega, Mary C 0000-0002-4426-3553","orcid":"https://orcid.org/0000-0002-4426-3553","contributorId":257413,"corporation":false,"usgs":false,"family":"Noriega","given":"Mary C","affiliations":[{"id":52011,"text":"USGS, National Water Quality Laboratory, retired","active":true,"usgs":false}],"preferred":false,"id":814201,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Decess, Jessica A 0000-0002-4202-3265","orcid":"https://orcid.org/0000-0002-4202-3265","contributorId":257414,"corporation":false,"usgs":false,"family":"Decess","given":"Jessica","email":"","middleInitial":"A","affiliations":[{"id":52014,"text":"Formerly: Cherokee Nation Technology Solutions, Denver, CO; Currently: The Medical Center of Aurora, Aurora, CO","active":true,"usgs":false}],"preferred":false,"id":814202,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Reed-Parker, Carmen 0000-0001-9579-578X","orcid":"https://orcid.org/0000-0001-9579-578X","contributorId":257415,"corporation":false,"usgs":false,"family":"Reed-Parker","given":"Carmen","email":"","affiliations":[{"id":52011,"text":"USGS, National Water Quality Laboratory, retired","active":true,"usgs":false}],"preferred":false,"id":814203,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sandstrom, Mark W. 0000-0003-0006-5675 sandstro@usgs.gov","orcid":"https://orcid.org/0000-0003-0006-5675","contributorId":706,"corporation":false,"usgs":true,"family":"Sandstrom","given":"Mark","email":"sandstro@usgs.gov","middleInitial":"W.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true}],"preferred":true,"id":814204,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70217705,"text":"ds1134 - 2021 - Distribution and abundance of Least Bell's Vireos and Southwestern Willow Flycatchers on the middle San Luis Rey River, San Diego County, southern California—2020 data summary","interactions":[],"lastModifiedDate":"2021-01-29T12:45:13.001068","indexId":"ds1134","displayToPublicDate":"2021-01-28T14:17:37","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1134","displayTitle":"Distribution and Abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) on the Middle San Luis Rey River, San Diego County, Southern California—2020 Data Summary","title":"Distribution and abundance of Least Bell's Vireos and Southwestern Willow Flycatchers on the middle San Luis Rey River, San Diego County, southern California—2020 data summary","docAbstract":"

We surveyed for Least Bell’s Vireos (Vireo bellii pusillus; vireo) and Southwestern Willow Flycatchers (Empidonax traillii extimus; flycatcher) along the San Luis Rey River, between College Boulevard in Oceanside and Interstate 15 in Fallbrook, California (middle San Luis Rey River), in 2020. Surveys were conducted from April 13 to July 13 (vireo) and from May 14 to July 13 (flycatcher). We found 192 vireo territories, at least 150 of which were occupied by pairs. Vireo territories increased by 40 percent from 2019 to 2020 in the portion of the middle San Luis Rey River that burned as a result of a wildfire in 2017. In contrast, vireo territories decreased by 5 percent from 2019 to 2020 in the unburned portion of the middle San Luis Rey River. 

Vireos used six different habitat types in the survey area: (1) willow-cottonwood, (2) mixed willow riparian, (3) riparian scrub, (4) upland scrub, (5) willow-sycamore, and (6) non-native. Forty-nine percent of the vireos were detected in habitat characterized as willow-cottonwood, and 93 percent of the vireos were detected in habitat with greater than 50-percent native plant cover. Of the 17 banded vireos detected in the survey area, 6 were resighted with a full color-band combination. Two other vireos with single (natal) federal bands were recaptured, identified, and color-banded in 2020. Eight vireos with a single dark blue federal band, indicating that they were banded as nestlings on the lower San Luis Rey River (LSLR), could not be recaptured for identification. One vireo with a single gold federal band, indicating that it was banded as a nestling at Marine Corps Base Camp Pendleton (MCBCP), could not be recaptured for identification. The two natal vireos that were recaptured on the middle San Luis Rey River dispersed from 2.6 to 6.2 kilometers (km) from their natal territories. Banded vireos with a known age ranged from 1 to 8 years old. 

One resident flycatcher was observed in the survey area in 2020. The resident flycatcher (male) was detected in a territory of mixed willow habitat with greater than 50-percent native plant cover. He was detected as a single male from May 27 to July 2, 2020, and no evidence of pairing or nesting was observed. The male flycatcher was resighted with a unique color-band combination and had occupied the same territory since 2018.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1134","usgsCitation":"Allen, L.D., and Kus, B.E., 2021, Distribution and abundance of Least Bell's Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) on the middle San Luis Rey River, San Diego County, southern California—2020 data summary: U.S. Geological Survey Data Series 1134, 11 p., https://doi.org/10.3133/ds1134.","productDescription":"iv, 11 p.","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-124769","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":382770,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1134/ds1134.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":382769,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1134/covrthb.jpg"}],"country":"United States","state":"California","county":"San Diego County","otherGeospatial":"Middle San Luis Rey River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.33947753906249,\n 33.02248191961359\n ],\n [\n -115.76293945312499,\n 33.08233672856376\n ],\n [\n -116.35620117187499,\n 33.84760762988741\n ],\n [\n -117.55920410156249,\n 33.394759218577995\n ],\n [\n -117.33947753906249,\n 33.02248191961359\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2021-01-28","noUsgsAuthors":false,"publicationDate":"2021-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Allen, Lisa D. 0000-0002-6147-3165 ldallen@usgs.gov","orcid":"https://orcid.org/0000-0002-6147-3165","contributorId":196789,"corporation":false,"usgs":true,"family":"Allen","given":"Lisa","email":"ldallen@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":809305,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kus, Barbara E. 0000-0002-3679-3044 barbara_kus@usgs.gov","orcid":"https://orcid.org/0000-0002-3679-3044","contributorId":3026,"corporation":false,"usgs":true,"family":"Kus","given":"Barbara E.","email":"barbara_kus@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":809306,"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":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":70219193,"text":"70219193 - 2021 - Channel response to a dam‐removal sediment pulse captured at high‐temporal resolution using routine gage data","interactions":[],"lastModifiedDate":"2021-06-01T17:29:08.413936","indexId":"70219193","displayToPublicDate":"2021-01-28T07:07:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7951,"text":"Earth Surfaces Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Channel response to a dam‐removal sediment pulse captured at high‐temporal resolution using routine gage data","docAbstract":"

In this study, we captured how a river channel responds to a sediment pulse originating from a dam removal using multiple lines of evidence derived from streamflow gages along the Patapsco River, Maryland, USA. Gages captured characteristics of the sediment pulse, including travel times of its leading edge (~7.8 km yr−1) and peak (~2.6 km yr−1) and suggest both translation and increasing dispersion. The pulse also changed local hydraulics and energy conditions, increasing flow velocities and Froude number, due to bed fining, homogenization and/or slope adjustment. Immediately downstream of the dam, recovery to pre‐pulse conditions occurred within the year, but farther downstream recovery was slower, with the tail of the sediment pulse working through the lower river by the end of the study 7 years later.

The patterns and timing of channel change associated with the sediment pulse were not driven by large flow or suspended sediment‐transporting events, with change mostly occurring during lower flows. This suggests pulse mobility was controlled by process‐factors largely independent of high flow.

In contrast, persistent changes occurred to out‐of‐channel flooding dynamics. Stage associated with flooding increased during the arrival of the sediment pulse, 1 to 2 years after dam removal, suggesting persistent sediment deposition at the channel margins and nearby floodplain. This resulted in National Weather Service‐indicated flood stages being attained by 3–43% smaller discharges compared to earlier in the study period.

This study captured a two‐signal response from the sediment pulse: (1) short‐ to medium‐term (weeks to months) translation and dispersion within the channel, resulting in aggradation and recovery of bed elevations and changing local hydraulics; and (2) dispersion and persistent longer‐term (years) effects of sediment deposition on overbank surfaces. This study further demonstrated the utility of US Geological Survey gage data to quantify geomorphic change, increase temporal resolution, and provide insights into trajectories of change over varying spatial and temporal scales.

","language":"English","publisher":"Wiley","doi":"10.1002/esp.5083","usgsCitation":"Cashman, M.J., Gellis, A.C., Boyd, E.L., Collins, M.J., Anderson, S.W., Mcfarland, B.D., and Ryan, A.M., 2021, Channel response to a dam‐removal sediment pulse captured at high‐temporal resolution using routine gage data: Earth Surfaces Processes and Landforms, v. 46, no. 6, p. 1145-1159, https://doi.org/10.1002/esp.5083.","productDescription":"15 p.","startPage":"1145","endPage":"1159","ipdsId":"IP-113441","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":436533,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9REXNQ9","text":"USGS data release","linkHelpText":"Data for Specific Gage Analysis on the Patapsco River, 2010-2017"},{"id":384751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Patapsco River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.9317626953125,\n 39.38738660316804\n ],\n [\n -76.98257446289062,\n 39.35394512666976\n ],\n [\n -76.88438415527344,\n 39.31198794598777\n ],\n [\n -76.8218994140625,\n 39.29976783250087\n ],\n [\n -76.7999267578125,\n 39.26043647112078\n ],\n [\n -76.75666809082031,\n 39.216295294574024\n ],\n [\n -76.68937683105469,\n 39.21097520599528\n ],\n [\n -76.60697937011719,\n 39.22480659786848\n ],\n [\n -76.9317626953125,\n 39.38738660316804\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Cashman, Matthew J. 0000-0002-6635-4309","orcid":"https://orcid.org/0000-0002-6635-4309","contributorId":203315,"corporation":false,"usgs":true,"family":"Cashman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":813165,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813166,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyd, Eric L. 0000-0002-1473-967X","orcid":"https://orcid.org/0000-0002-1473-967X","contributorId":256743,"corporation":false,"usgs":true,"family":"Boyd","given":"Eric","email":"","middleInitial":"L.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collins, Matthias J. 0000-0003-4238-2038","orcid":"https://orcid.org/0000-0003-4238-2038","contributorId":196365,"corporation":false,"usgs":false,"family":"Collins","given":"Matthias","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":813168,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, Scott W. 0000-0003-1678-5204 swanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-1678-5204","contributorId":196687,"corporation":false,"usgs":true,"family":"Anderson","given":"Scott","email":"swanderson@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813169,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mcfarland, Brett Dare 0000-0002-2941-4966","orcid":"https://orcid.org/0000-0002-2941-4966","contributorId":256744,"corporation":false,"usgs":true,"family":"Mcfarland","given":"Brett","email":"","middleInitial":"Dare","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813170,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ryan, Ashley Mattie 0000-0001-5647-7447","orcid":"https://orcid.org/0000-0001-5647-7447","contributorId":256746,"corporation":false,"usgs":true,"family":"Ryan","given":"Ashley","email":"","middleInitial":"Mattie","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813171,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70217664,"text":"sir20205121 - 2021 - Spring types and contributing aquifers from water-chemistry and multivariate statistical analyses for seeps and springs in Theodore Roosevelt National Park, North Dakota, 2018","interactions":[],"lastModifiedDate":"2021-01-28T01:29:43.632301","indexId":"sir20205121","displayToPublicDate":"2021-01-27T16:00:00","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":"2020-5121","displayTitle":"Spring Types and Contributing Aquifers from Water-Chemistry and Multivariate Statistical Analyses for Seeps and Springs in Theodore Roosevelt National Park, North Dakota, 2018","title":"Spring types and contributing aquifers from water-chemistry and multivariate statistical analyses for seeps and springs in Theodore Roosevelt National Park, North Dakota, 2018","docAbstract":"

Water resources in Theodore Roosevelt National Park, North Dakota, support wildlife, visitors, and staff, and play a vital role in supporting the native ecology of the park. The U.S. Geological Survey, in cooperation with the National Park Service, completed field work in 2018 for a study to address concerns about water availability and possible sources of groundwater contamination for seeps and springs in Theodore Roosevelt National Park. The objective of the study was to improve hydrologic knowledge and determine the water composition of 11 seeps and springs in the park by collecting water-chemistry data at springs, streams, wells, and rain collectors.

Water samples were collected at 26 sites at springs, streams, wells, and rain collectors in the North and South Units of Theodore Roosevelt National Park. Samples in the North Unit were collected at 5 springs, 1 stream, 2 wells, and 1 rain collector. Samples in the South Unit were collected at 6 springs, 2 streams, 8 wells, and 1 rain collector. Samples from springs, streams, and wells were collected in May, July, and September 2018. Samples from rain collectors were collected when enough daily precipitation accumulated in the collectors. Sampled precipitation events during the study period were in May, June, July, August, and September 2018. Physical properties of sampled water—temperature, pH, and specific conductance—were measured in the field. Water samples were analyzed for stable isotopes of oxygen and hydrogen and for chloride concentration. Recharge rates for aquifers supplying springs were determined using precipitation volume and chloride concentrations for a 12-day period before the sample-collection date. Multivariate statistical analysis methods used on water-chemistry data included principal component analysis, cluster analysis, and end-member mixing analysis.

Water composition was used to determine the spring type and contributing aquifers for 11 springs in the North and South Units of Theodore Roosevelt National Park from analyses of water-chemistry data between May and September 2018. In the North Unit, Achenbach Spring was classified as a filtration spring with water from an unconfined part of the upper Fort Union aquifer and infiltration of precipitation. Hagen Spring, Mandal Spring, and Stevens Spring were classified as contact springs supplied by semiconfined parts of the upper Fort Union aquifer. Overlook Spring at one time may have been a natural spring or seep but now is a developed spring that behaves like a flowing artesian well completed in a confined part of the upper Fort Union aquifer. In the South Unit, six springs were classified into two spring types: filtration and contact springs. Boicourt Spring and Sheep Butte Spring were classified as filtration springs that have water supplied by unconfined parts of the upper Fort Union aquifer and infiltrated precipitation. Big Plateau Spring, Lone Tree Spring, Sheep Pasture Spring, and Southeast Corner Spring were classified as contact springs that receive waters from a semiconfined part of the upper Fort Union aquifer.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20205121","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Medler, C.J., and Eldridge, W.G., 2021, Spring types and contributing aquifers from water-chemistry and multivariate statistical analyses for seeps and springs in Theodore Roosevelt National Park, North Dakota, 2018: U.S. Geological Survey Scientific Investigations Report 2020–5121, 48 p., https://doi.org/10.3133/sir20205121.","productDescription":"Report: viii, 48 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-115769","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":382693,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5121/coverthb.jpg"},{"id":382694,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5121/sir20205121.pdf","text":"Report","size":"4.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"sir2020-5121"},{"id":382695,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System database"}],"country":"United States","state":"North Dakota","otherGeospatial":"Theodore Roosevelt National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.63334655761719,\n 46.87990702860922\n ],\n [\n -103.29757690429686,\n 46.87990702860922\n ],\n [\n -103.29757690429686,\n 47.02801434856074\n ],\n [\n -103.63334655761719,\n 47.02801434856074\n ],\n [\n -103.63334655761719,\n 46.87990702860922\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.48983764648438,\n 47.52832925298343\n ],\n [\n -103.216552734375,\n 47.52832925298343\n ],\n [\n -103.216552734375,\n 47.65428791076272\n ],\n [\n -103.48983764648438,\n 47.65428791076272\n ],\n [\n -103.48983764648438,\n 47.52832925298343\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.63677978515625,\n 47.22726254715105\n ],\n [\n -103.60965728759764,\n 47.22726254715105\n ],\n [\n -103.60965728759764,\n 47.250106104326235\n ],\n [\n -103.63677978515625,\n 47.250106104326235\n ],\n [\n -103.63677978515625,\n 47.22726254715105\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

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

1608 Mountain View Road
Rapid City, SD 57702

","tableOfContents":"","publishedDate":"2021-01-27","noUsgsAuthors":false,"publicationDate":"2021-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Medler, Colton J. 0000-0001-6119-5065","orcid":"https://orcid.org/0000-0001-6119-5065","contributorId":201463,"corporation":false,"usgs":true,"family":"Medler","given":"Colton","email":"","middleInitial":"J.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809196,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eldridge, William G. 0000-0002-3562-728X","orcid":"https://orcid.org/0000-0002-3562-728X","contributorId":208529,"corporation":false,"usgs":true,"family":"Eldridge","given":"William","email":"","middleInitial":"G.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809197,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"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":70218711,"text":"70218711 - 2021 - Great expectations: Deconstructing the process pathways underlying beaver-related restoration","interactions":[],"lastModifiedDate":"2021-03-08T14:25:04.534627","indexId":"70218711","displayToPublicDate":"2021-01-27T07:52:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":997,"text":"BioScience","active":true,"publicationSubtype":{"id":10}},"title":"Great expectations: Deconstructing the process pathways underlying beaver-related restoration","docAbstract":"

Beaver-related restoration is a process-based strategy that seeks to address wide-ranging ecological objectives by reestablishing dam building in degraded stream systems. Although the beaver-related restoration has broad appeal, especially in water-limited systems, its effectiveness is not yet well documented. In this article, we present a process-expectation framework that links beaver-related restoration tactics to commonly expected outcomes by identifying the set of process pathways that must occur to achieve those expected outcomes. We explore the contingency implicit within this framework using social and biophysical data from project and research sites. This analysis reveals that outcomes are often predicated on complex process pathways over which humans have limited control. Consequently, expectations often shift through the course of projects, suggesting that a more useful paradigm for evaluating process-based restoration would be to identify relevant processes and to rigorously document how projects do or do not proceed along expected process pathways using both quantitative and qualitative data.

","language":"English","publisher":"Oxford Academic","doi":"10.1093/biosci/biaa165","usgsCitation":"Nash, C., Grant, G., Charnley, S., Dunham, J.B., Gosnell, H., Hausner, M.B., Pilliod, D.S., and Taylor, J.D., 2021, Great expectations: Deconstructing the process pathways underlying beaver-related restoration: BioScience, v. 71, no. 3, p. 249-267, https://doi.org/10.1093/biosci/biaa165.","productDescription":"19 p.","startPage":"249","endPage":"267","ipdsId":"IP-106141","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":453685,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biaa165","text":"Publisher Index Page"},{"id":384223,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"71","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Nash, Caroline","contributorId":204146,"corporation":false,"usgs":false,"family":"Nash","given":"Caroline","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":811497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, Gordon E.","contributorId":30881,"corporation":false,"usgs":false,"family":"Grant","given":"Gordon E.","affiliations":[{"id":12647,"text":"U.S. Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":811498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Charnley, Susan","contributorId":169897,"corporation":false,"usgs":false,"family":"Charnley","given":"Susan","email":"","affiliations":[{"id":25613,"text":"Pacific Northwest Research Station, USDA Forest Service.","active":true,"usgs":false}],"preferred":false,"id":811499,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":811500,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gosnell, Hannah","contributorId":192214,"corporation":false,"usgs":false,"family":"Gosnell","given":"Hannah","email":"","affiliations":[],"preferred":false,"id":811501,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hausner, Mark B.","contributorId":204145,"corporation":false,"usgs":false,"family":"Hausner","given":"Mark","email":"","middleInitial":"B.","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":811502,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pilliod, David S. 0000-0003-4207-3518 dpilliod@usgs.gov","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":149254,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","email":"dpilliod@usgs.gov","middleInitial":"S.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":811503,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Taylor, Jimmy D.","contributorId":140178,"corporation":false,"usgs":false,"family":"Taylor","given":"Jimmy","email":"","middleInitial":"D.","affiliations":[{"id":13402,"text":"USDA APHIS Wildlife Services","active":true,"usgs":false}],"preferred":false,"id":811504,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70224248,"text":"70224248 - 2021 - Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data","interactions":[],"lastModifiedDate":"2021-09-15T12:24:24.631311","indexId":"70224248","displayToPublicDate":"2021-01-27T07:19:49","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":"Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data","docAbstract":"

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.

","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/abd501","usgsCitation":"Rahmani, F., Lawson, K., Ouyang, W., Appling, A.P., Oliver, S.K., and Shen, C., 2021, Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: Environmental Research Letters, v. 16, no. 2, 024025, 11 p., https://doi.org/10.1088/1748-9326/abd501.","productDescription":"024025, 11 p.","ipdsId":"IP-121983","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":453692,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/abd501","text":"Publisher Index Page"},{"id":436536,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97CGHZH","text":"USGS data release","linkHelpText":"Exploring the exceptional performance of a deep 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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":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.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.hydroa.2021.100074","usgsCitation":"Miller, O.L., Putman, A.L., Alder, J.R., Miller, M., Jones, D.K., and Wise, D., 2021, Changing climate drives future streamflow declines and challenges in meeting water demand across the southwestern United States: Journal of Hydrology X, v. 11, 100074, 16 p., https://doi.org/10.1016/j.hydroa.2021.100074.","productDescription":"100074, 16 p.","ipdsId":"IP-118339","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":453696,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hydroa.2021.100074","text":"Publisher Index Page"},{"id":436540,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J0J1I5","text":"USGS data release","linkHelpText":"Streamflow projections for southwestern United States (1975-2099)"},{"id":436539,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J0J1I5","text":"USGS data release","linkHelpText":"Streamflow projections for southwestern United States (1975-2099)"},{"id":436538,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J0J1I5","text":"USGS data release","linkHelpText":"Streamflow projections for southwestern United States (1975-2099)"},{"id":436537,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J0J1I5","text":"USGS data release","linkHelpText":"Streamflow projections for southwestern United States (1975-2099)"},{"id":383146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado Idaho, Nevada, New Mexico, Texas, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.3388671875,\n 26.194876675795218\n ],\n [\n -97.2509765625,\n 27.761329874505233\n ],\n [\n -94.7900390625,\n 29.19053283229458\n ],\n [\n -94.921875,\n 31.316101383495624\n ],\n [\n -98.0859375,\n 33.65120829920497\n ],\n [\n -100.7666015625,\n 35.28150065789119\n ],\n [\n -103.35937499999999,\n 37.23032838760387\n ],\n [\n -105.5126953125,\n 39.774769485295465\n ],\n [\n -106.74316406249999,\n 40.94671366508002\n ],\n [\n -107.9736328125,\n 41.934976500546604\n ],\n [\n -111.26953125,\n 42.90816007196054\n ],\n [\n -113.7744140625,\n 43.03677585761058\n ],\n [\n -116.89453125,\n 43.16512263158296\n ],\n [\n -119.4873046875,\n 44.465151013519616\n ],\n [\n -120.89355468749999,\n 44.213709909702054\n ],\n [\n -120.9814453125,\n 43.42100882994726\n ],\n [\n -120.62988281249999,\n 42.13082130188811\n ],\n [\n -120.498046875,\n 40.1452892956766\n ],\n [\n -119.7509765625,\n 38.09998264736481\n ],\n [\n -118.125,\n 35.60371874069731\n ],\n [\n -115.8837890625,\n 33.8339199536547\n ],\n [\n -115.400390625,\n 33.063924198120645\n ],\n [\n -113.73046875,\n 32.287132632616384\n ],\n [\n -108.5009765625,\n 31.240985378021307\n ],\n [\n -106.74316406249999,\n 31.353636941500987\n ],\n [\n -103.4033203125,\n 28.729130483430154\n ],\n [\n -101.4697265625,\n 29.267232865200878\n ],\n [\n -99.755859375,\n 26.588527147308614\n ],\n [\n -98.2177734375,\n 26.115985925333536\n ],\n [\n -97.6025390625,\n 25.799891182088334\n ],\n [\n -97.7783203125,\n 26.23430203240673\n ],\n [\n -97.3388671875,\n 26.194876675795218\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Olivia L. 0000-0002-8846-7048","orcid":"https://orcid.org/0000-0002-8846-7048","contributorId":216556,"corporation":false,"usgs":true,"family":"Miller","given":"Olivia","email":"","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Putman, Annie L. 0000-0002-9424-1707","orcid":"https://orcid.org/0000-0002-9424-1707","contributorId":225134,"corporation":false,"usgs":true,"family":"Putman","given":"Annie","email":"","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810007,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alder, Jay R. 0000-0003-2378-2853 jalder@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-2853","contributorId":5118,"corporation":false,"usgs":true,"family":"Alder","given":"Jay","email":"jalder@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - 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2021 - Sediment mobility and river corridor assessment for a 140-kilometer segment of the main-stem Klamath River below Iron Gate Dam, California","interactions":[],"lastModifiedDate":"2022-03-15T19:59:13.766549","indexId":"ofr20201141","displayToPublicDate":"2021-01-26T15:46:01","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":"2020-1141","displayTitle":"Sediment Mobility and River Corridor Assessment for a 140-Kilometer Segment of the Main-Stem Klamath River Below Iron Gate Dam, California","title":"Sediment mobility and river corridor assessment for a 140-kilometer segment of the main-stem Klamath River below Iron Gate Dam, California","docAbstract":"

This river corridor assessment documents sediment mobility and river response to flood disturbance along a 140-kilometer segment of the main-stem Klamath River below Iron Gate Dam, California. Field and remote sensing methods were used to assess fundamental indicators of active sediment transport and river response to a combination of natural runoff events and reservoir releases during the study period from 2005 to 2019. Discharge measurements at two gaged sites and bed-material samples at two ungaged sites provided direct and indirect evidence of mobile bed conditions, scour and fill, and surface flushing of fine sediment. Available remote-sensing datasets collected in 2005, 2009, 2010, and 2016 were used to determine sediment storage, flood inundation boundaries, and provide indirect evidence of flood-induced scour. These datasets validate channel-maintenance flows defined by Shea and others (2016). During the study period, flows greater than or equal to 6,030 cubic feet per second mobilized the substrate, caused localized scour, and flushed fine sediment from bar surfaces. Flows greater than or equal to 10,400 cubic feet per second stripped vegetation from bars and floodplains and produced deeper scour. Flood disturbance within the study reach is produced by the combined effect of natural flows and reservoir releases, which resulted in mobile bed conditions during the study period. Periodic scour and substrate disturbance are considered by the U.S. Fish and Wildlife Service to be integral for managing disease-induced mortality of juvenile and adult salmonids. Substrate conditions conducive to parasites that host infectious diseases, particularly Ceratonova shasta, occur periodically. Additional studies are required to determine whether disease prevalence can be mitigated by well-timed reservoir releases. Study results are useful for interpreting linkages among physical and biological processes and for evaluating the effectiveness of flow management targeted to improve river bed conditions for endangered salmonid populations.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201141","collaboration":"Water Availability and Use Science Program
Prepared in cooperation with the U.S. Fish and Wildlife Service and the National Fish and Wildlife Foundation","usgsCitation":"Curtis, J., Poitras, T., Bond, S., and Byrd, K., 2021, Sediment mobility and river corridor assessment for a 140-kilometer segment of the main-stem Klamath River below Iron Gate Dam, California: U.S. Geological Survey Open-File Report 2020–1141, 38 p., https://doi.org/10.3133/ofr20201141.","productDescription":"Report: viii, 38 p.; 2 Data Releases; Related Work","onlineOnly":"Y","ipdsId":"IP-120782","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":397134,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1141/ofr20201141_table1.1.csv","text":"Table 1.1","size":"5 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"— Discharge measurements with an accuracy rating of fair (±8%) or better for the Klamath River below Iron Gate Dam CA (USGS 11516530) gaging station for water years 2016 to 2019."},{"id":382633,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS web interface","description":"U.S. Geological Survey, 2020, National Water Information System: U.S. Geological Survey web interface, https://doi.org/10.5066/F7P55KJN.","linkHelpText":"— U.S. Geological Survey, 2020, National Water Information System"},{"id":382632,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q2FZK2","text":"USGS data release","description":"Poitras, T.B., and Byrd, K.K, Curtis, J.A., and Bond, S., 2020, Sediment mobility and river corridor assessment for a 140-km segment of the mainstem Klamath River below Iron Gate Dam, CA – vegetation mapping 2005, 2009, 2016: U.S. Geological Survey data release, https://doi.org/10.5066/P9Q2FZK2.","linkHelpText":"Sediment mobility and river corridor assessment for a 140-km segment of 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Director, California Water Science Center
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6000 J Street, Placer Hall
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The Chesapeake Bay Program maintains an extensive nontidal monitoring network, measuring nitrogen and phosphorus (nutrients) at more than 100 locations on rivers and streams in the watershed. Data from these locations are used by United States Geological Survey to assess the ecosystem’s response to nutrient-reduction efforts. This fact sheet summarizes recent trends in nitrogen and phosphorus in nontidal tributaries and identifies some of the complex factors that affect local water quality, and ultimately, the Chesapeake Bay.

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Chesapeake Bay Activities
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228

Contact Pubs Warehouse

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Studies","active":true,"usgs":false}],"preferred":false,"id":808961,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Swanson, Sky","contributorId":248357,"corporation":false,"usgs":false,"family":"Swanson","given":"Sky","email":"","affiliations":[{"id":38802,"text":"University of Maryland Center for Environmental Studies","active":true,"usgs":false}],"preferred":false,"id":808962,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Walsh, Brianne","contributorId":248358,"corporation":false,"usgs":false,"family":"Walsh","given":"Brianne","affiliations":[{"id":38802,"text":"University of Maryland Center for Environmental Studies","active":true,"usgs":false}],"preferred":false,"id":808963,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hawkey, Jane","contributorId":248359,"corporation":false,"usgs":false,"family":"Hawkey","given":"Jane","email":"","affiliations":[{"id":38802,"text":"University of Maryland Center for Environmental 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quality of aquifers overlying the Oxnard Oil Field, Ventura County, California","docAbstract":"

Groundwater samples collected from irrigation, monitoring, and municipal supply wells near the Oxnard Oil Field were analyzed for chemical and isotopic tracers to evaluate if thermogenic gas or water from hydrocarbon-bearing formations have mixed with surrounding groundwater. New and historical data show no evidence of water from hydrocarbon-bearing formations in groundwater overlying the field. However, thermogenic gas mixed with microbial methane was detected in 5 wells at concentrations ranging from 0.011–9.1 mg/L. The presence of these gases at concentrations <10 mg/L do not indicate degraded water quality posing a known health risk. Analysis of carbon isotopes (δ13C-CH4) and hydrogen isotopes (δ2H-CH4) of methane and ratios of methane to heavier hydrocarbon gases were used to differentiate sources of methane between a) microbial, b) thermogenic or c) mixed sources. Results indicate that microbial-sourced methane is widespread in the study area, and concentrations overlap with those from thermogenic sources. The highest concentrations of thermogenic gas were observed in proximity to relatively high density of oil wells, large injection volumes of water disposal and cyclic steam, shallow oil development, and hydrocarbon shows in sediments overlying the producing oil reservoirs. Depths of water wells containing thermogenic gas were within approximately 200 m of the top of the Vaca Tar Sand production zone (approximately 600 m below land surface). Due to the limited sampling density, the source and pathways of thermogenic gas detected in groundwater could not be conclusively determined. Thermogenic gas detected in the absence of co-occurring water from hydrocarbon-bearing formations may result from natural gas migration over geologic time from the Vaca Tar Sand or deeper formations, hydrocarbon shows in sediments overlying producing zones, and/or gas leaking from oil-field infrastructure. Denser sampling of groundwater, potential end-members, and pressure monitoring could help better distinguish pathways of thermogenic gases.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.144822","usgsCitation":"Rosecrans, C.Z., Landon, M.K., Ransom, K.M., Gillespie, J., Kulongoski, J.T., Stephens, M.J., Hunt, A.G., Shimabukuro, D.H., and Davis, T., 2021, Groundwater quality of aquifers overlying the Oxnard Oil Field, Ventura County, California: Science of the Total Environment, v. 771, 144822, 17 p., https://doi.org/10.1016/j.scitotenv.2020.144822.","productDescription":"144822, 17 p.","ipdsId":"IP-116164","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":453714,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.144822","text":"Publisher Index Page"},{"id":436547,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UG4BP3","text":"USGS data release","linkHelpText":"Fluid levels in the Oxnard Oil Field, Ventura County, California"},{"id":436546,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99EHQ8H","text":"USGS data release","linkHelpText":"Water chemistry data for samples collected at groundwater sites near the Oxnard oil field, June 2017-August 2017, Ventura County, California"},{"id":436545,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P913QDD5","text":"USGS data release","linkHelpText":"Mud logs from the Oxnard Oil Field, Ventura County, California"},{"id":385294,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Oxnard","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.410400390625,\n 34.025347738147936\n ],\n [\n -118.641357421875,\n 34.025347738147936\n ],\n [\n 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kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814670,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stephens, Michael J. 0000-0001-8995-9928","orcid":"https://orcid.org/0000-0001-8995-9928","contributorId":205895,"corporation":false,"usgs":true,"family":"Stephens","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814676,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hunt, Andrew G. 0000-0002-3810-8610 ahunt@usgs.gov","orcid":"https://orcid.org/0000-0002-3810-8610","contributorId":1582,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew","email":"ahunt@usgs.gov","middleInitial":"G.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":814677,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shimabukuro, David H. 0000-0002-6106-5284","orcid":"https://orcid.org/0000-0002-6106-5284","contributorId":208209,"corporation":false,"usgs":false,"family":"Shimabukuro","given":"David","email":"","middleInitial":"H.","affiliations":[{"id":37762,"text":"California State University, Sacramento","active":true,"usgs":false}],"preferred":false,"id":814678,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Davis, Tracy 0000-0003-0253-6661 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earthquakes","docAbstract":"

The largest slip in great megathrust earthquakes often occurs in the 10–30 km depth range, yet seismic imaging of the material properties in this region has proven difficult. We utilize a dense onshore-offshore passive seismic dataset from the southernmost Cascadia subduction zone where seismicity in the mantle of the subducted Gorda Plate produces S-to-P and P-to-S conversions generated within a few km of the plate interface. These conversions typically occur in the 10–20 km depth range at either the top or bottom of a ∼5 km thick layer with a high Vp/Vs that we infer to be primarily the subducted crust. We use their arrival times and amplitudes to infer the location of the top and bottom of the subducted crust as well as the velocity contrasts across these discontinuities. Comparing with both the Slab1.0 and the updated Slab2 interface models, the Slab2 model is generally consistent with the converted phases, while the Slab1.0 model is 1–2 km deeper in the 2–20 km depth range and ∼6–8 km too deep in the 10–20 km depth range between 40.25°N and 40.4°N. Comparing the amplitudes of the converted phases to synthetics for simplified velocity structures, the amplitude of the converted phases requires models containing a ∼5 km thick zone with at least a ∼10%–20% reduction in S wave velocity. Thus, the plate boundary is likely contained within or at the top of this low velocity zone, which potentially indicates a significant porosity and fluid content within the seismogenic zone.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JB019962","usgsCitation":"Gong, J., and McGuire, J., 2021, Constraints on the geometry of the subducted Gorda Plate with converted phases generated by local earthquakes: JGR Solid Earth, v. 126, no. 2, e2020JB019962, 23 p., https://doi.org/10.1029/2020JB019962.","productDescription":"e2020JB019962, 23 p.","ipdsId":"IP-114309","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":453718,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2020jb019962","text":"External Repository"},{"id":419501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Gorda Plate","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.5,\n 41\n ],\n [\n -125.5,\n 40\n ],\n [\n -124,\n 40\n ],\n [\n -124,\n 41\n ],\n [\n -125.5,\n 41\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"126","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-02-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Gong, Jianhua","contributorId":317847,"corporation":false,"usgs":false,"family":"Gong","given":"Jianhua","email":"","affiliations":[{"id":34004,"text":"Scripps Institute of Oceanography","active":true,"usgs":false}],"preferred":false,"id":879445,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, Jeffrey J. 0000-0001-9235-2166","orcid":"https://orcid.org/0000-0001-9235-2166","contributorId":219786,"corporation":false,"usgs":true,"family":"McGuire","given":"Jeffrey J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":879446,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70217697,"text":"70217697 - 2021 - Quantifying nuisance ground motion thresholds for induced earthquakes","interactions":[],"lastModifiedDate":"2021-04-22T18:04:20.342226","indexId":"70217697","displayToPublicDate":"2021-01-25T07:40:41","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":"Quantifying nuisance ground motion thresholds for induced earthquakes","docAbstract":"

Hazards from induced earthquakes are a growing concern with a need for effective management. One aspect of that concern is the “nuisance” from unexpected ground motions, which have the potential to cause public alarm and discontent. In this article, we borrow earthquake engineering concepts to quantify the chance of building damage states and adapt them to quantify felt thresholds for induced earthquakes in the Central and Eastern United States. We compare binary data of felt or not-felt reports from the “Did You Feel It” database with ShakeMap ground motion intensity measures (IM) for ∼360 earthquakes. We use a Monte Carlo logistic regression to discern the likelihood of perceiving various degrees of felt intensity, given a particular IM. These best-fit nuisance functions are reported in this article and are readily transferable. Of the shaking types considered, we find that peak ground velocity tends to be the best predictor of a felt earthquake. We also find that felt thresholds tended to decrease with increasing earthquake magnitude, after M ∼3.9. We interpret this effect as related to the duration of the event, where events smaller than M 3.9 are perceived as “impulsive” to the human senses. Improved quantification of the nuisance from induced earthquake ground motions could be utilized in management of the public perception of their causal operations. Although aimed at anthropogenic earthquakes, thresholds we derive could be useful in other realms, such as establishing best practices and protocols for earthquake early warning.

","language":"English","publisher":"Sage Publications","doi":"10.1177/8755293020988025","usgsCitation":"Schultz, R., Quitoriano, V., Wald, D.J., and Beroza, G.C., 2021, Quantifying nuisance ground motion thresholds for induced earthquakes: Earthquake Spectra, v. 37, no. 2, p. 789-802, https://doi.org/10.1177/8755293020988025.","productDescription":"14 p.","startPage":"789","endPage":"802","ipdsId":"IP-118511","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":382753,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-01-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Schultz, Ryan","contributorId":241702,"corporation":false,"usgs":false,"family":"Schultz","given":"Ryan","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":809279,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quitoriano, Vince 0000-0003-4157-1101 vinceq@usgs.gov","orcid":"https://orcid.org/0000-0003-4157-1101","contributorId":2582,"corporation":false,"usgs":true,"family":"Quitoriano","given":"Vince","email":"vinceq@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":809280,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":809281,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beroza, Gregory C.","contributorId":191201,"corporation":false,"usgs":false,"family":"Beroza","given":"Gregory","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":809282,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}